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Theory of Computing Report

Friday, October 16

Predictive Models Are Never Clean

from Ben Recht
Revisiting Breiman’s Two Cultures paper and what we want from our forecasting models.

Hi there, argmin readers! Today’s post is a live blog of Class 12 of my graduate seminar “Forecasting: A Critical Retrospective.” The syllabus and list of past posts are here. It might also be a subtweet of yesterday’s AI discourse?

Twenty-five years ago, Leo Breiman wrote a prophetic warning to his statistical brethren:

“There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.”

Breiman saw that prediction was at the heart of most of his consulting projects. Rather than being asked to determine causation, he was asked to make forecasts. His list of examples is instructive:

  • Predicting next-day ozone levels.

  • Using mass spectra to identify halogen-containing compounds.

  • Predicting the class of a ship from high altitude radar returns.

  • Using sonar returns to predict the class of a submarine.

  • Identity of hand-sent Morse Code.

  • Toxicity of chemicals.

  • Online prediction of the cause of a freeway traffic breakdown.

  • Speech recognition.

  • The sources of delay in criminal trials in state court systems.

If you asked anyone today how they’d solve these tasks, they’d say “collect data and fit a neural net.” You had to be there at the time, but this was quite controversial in 2001. Indeed, even using a decision tree was controversial. Many people thought models built with expert knowledge, with meaningful parameters fit to data, would be far superior for prediction. Moreover, you’d be able to interpret your predictions because you had a mechanistic understanding of reality encoded your predictive stochastic data model.

Given what’s happened since, I don’t think anyone needs convincing that neural nets will work better. Indeed, the reaction in class yesterday confirmed that the zeitgeist has shifted 180 degrees. Of course you’d rather use a neural network than a complicated model. For all of those tasks, everyone would say “use a transformer or do in-context learning and forget about it.” Autoregression has won out by brute force.

Now, given my long history as a machine learning researcher, I don’t think you’ll be surprised to find that I agree with Breiman and struggle to justify stochastic models. However, I think I can capture the motivation of modelers: people want more from their models than predictions.

People like mathematical models because we think that understanding how something works from top to bottom lets us better predict how it will behave in the future. A mechanistic understanding not only lets us play out likely futures, but lets us imagine what might happen under a variety of different adversarial scenarios. These models assure us that we understand the present well, because we can point at all of the subcomponents and build a bottom-up description of macroscale behavior.

It’s so weird how our forecasting culture turns this around: IF we can make good predictions, THEN we understand. You present your Brier Score first. Only if it tops the prediction-market leaderboard are people impressed enough to go read your 25,000-word description of how you prognosticate. Accepting this logical inversion—that accurate predictions indicate understanding—is why our rational brethren venerate superforecasters. But it’s clear that even the most committed forecast bros don’t like this, because they also obsess about mechanistic interpretability.

But when we seek explanations, all we have is the data from which our predictions are made. Data-driven modeling is implicit, leaning on the tacit knowledge of what makes good data and what makes bad data. Almost everything you want to interpret lies in the data and your optimization objective. The compressed storage that are the parameters of your nonparametric model hold this information too, but reading the tea leaves of the weights is like trying to understand a book by looking at the bits in its zip file.

In a wholly data-driven world, better understanding can only come from getting new data. We can either wait for the future to roll in, or we can act to get that data now. That is, to rapidly understand the world you forecast, you have to be able to do things. You need to intervene and act to rule out explanations. This is experimentation.

Forecasts alone are not experimentation. Indeed, a recurring theme in this class is how passive forecasting is. Focusing so hard on prediction makes us forget we can take actions to change the future.

Some think that predictions can shape the world. By warning people of doom, perhaps someone out there will take action. And yet, my impression is that the most dedicated forecasters think their best available action is to place bets. Or write open letters. Abandoning their agency makes their prophecies self-fulfilling.

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By Ben Recht

TCS for All FOCS 2026 Travel Scholarship Application

from CS Theory Events
October 15-22, 2026 Online sigact.org/tcsforall/focs-2026-events/travel-scholarship-for-focs-2026/ Submission deadline: October 22, 2026 TCS4All travel scholarship application (short deadline)

By shacharlovett

October 15-22, 2026 Online https://sigact.org/tcsforall/focs-2026-events/travel-scholarship-for-focs-2026/ Submission deadline: October 22, 2026 TCS4All travel scholarship application (short deadline)

By shacharlovett

TR26-256 | Polynomial Block Lengths Are Necessary and Sufficient to Beat Insertion Sort | Nikolai Chukhin, Alexander Kulikov, Maksim Levitskii, Ivan Mihajlin

from ECCC Papers
Insertion sort is a basic algorithm for sorting permutations: it repeatedly cuts out one element and inserts it at its place among the elements sorted so far. It sorts every permutation of length $n$ with at most $n-1$ such moves, and some permutations cannot be sorted with fewer moves (Heath and Vergara, 1998). A move that cuts out a block of any length is called a transposition, and the number of transpositions that suffices to sort every permutation lies between $n/2$ and $2n/3$ for $n\geq 9$ (Bafna and Pevzner, 1998; Eriksson, Eriksson, Karlander, Svensson, and Wästlund, 2001). We ask how long the cut blocks must be to beat insertion sort by a constant factor. We show that cut blocks of polynomial length are necessary and sufficient. If the cut blocks have length $n^{o(1)}$, then some permutations need $n-o(n)$ moves, whereas for any $\alpha > 0$, $(1 - \varepsilon_{\alpha}) n$ moves with cut blocks of length $n^{\alpha}$ suffice for all large enough $n$. The lower bound follows from a new upper bound on the number of~permutations at a given distance from the identity. The upper bound follows from a recursive reduction of the exponent, starting from the known bound for unrestricted transpositions.
Insertion sort is a basic algorithm for sorting permutations: it repeatedly cuts out one element and inserts it at its place among the elements sorted so far. It sorts every permutation of length $n$ with at most $n-1$ such moves, and some permutations cannot be sorted with fewer moves (Heath and Vergara, 1998). A move that cuts out a block of any length is called a transposition, and the number of transpositions that suffices to sort every permutation lies between $n/2$ and $2n/3$ for $n\geq 9$ (Bafna and Pevzner, 1998; Eriksson, Eriksson, Karlander, Svensson, and Wästlund, 2001). We ask how long the cut blocks must be to beat insertion sort by a constant factor. We show that cut blocks of polynomial length are necessary and sufficient. If the cut blocks have length $n^{o(1)}$, then some permutations need $n-o(n)$ moves, whereas for any $\alpha > 0$, $(1 - \varepsilon_{\alpha}) n$ moves with cut blocks of length $n^{\alpha}$ suffice for all large enough $n$. The lower bound follows from a new upper bound on the number of~permutations at a given distance from the identity. The upper bound follows from a recursive reduction of the exponent, starting from the known bound for unrestricted transpositions.

TR26-255 | Unconditional Quantum PAC Learning Advantage in the Large Error Regime | Igor Carboni Oliveira

from ECCC Papers
We establish an unconditional super-polynomial separation between quantum and classical PAC learning of Boolean functions under the uniform distribution in the large error regime. For every advantage $\lambda(n) = n^{-\omega(1)}$, we construct a concept class that a polynomial-time quantum learner learns to agreement $1/2+\lambda(n)$ with success probability at least $1-1/n^k$. The learner uses only quantum random examples and outputs a polynomial-size deterministic classical circuit. In contrast, no polynomial-time classical learner achieves agreement $1/2 + \lambda(n)/n^k$ with success probability greater than $1/n^k$, even with membership queries. To achieve this, we develop a nearly-optimal quantum learner that uses quantum random examples to produce a hypothesis achieving a prescribed correlation with an arbitrary Boolean function. The learner generalizes Fourier sampling from parity hypotheses to quadratic hypotheses. While standard Fourier sampling is insensitive to the sign of the correlation, we use interference to bias sampling toward positively correlated hypotheses. This is combined with a careful analysis of the resulting distribution to boost both the learner's success probability and its agreement with the target function. We believe that the resulting quantum learning algorithm might be of independent interest. Finally, although a robust quantum PAC learning advantage holds unconditionally in the large error regime, we note that an analogous separation between quantum and classical learning in the small error regime would imply that NP is not contained in BPP.
We establish an unconditional super-polynomial separation between quantum and classical PAC learning of Boolean functions under the uniform distribution in the large error regime. For every advantage $\lambda(n) = n^{-\omega(1)}$, we construct a concept class that a polynomial-time quantum learner learns to agreement $1/2+\lambda(n)$ with success probability at least $1-1/n^k$. The learner uses only quantum random examples and outputs a polynomial-size deterministic classical circuit. In contrast, no polynomial-time classical learner achieves agreement $1/2 + \lambda(n)/n^k$ with success probability greater than $1/n^k$, even with membership queries. To achieve this, we develop a nearly-optimal quantum learner that uses quantum random examples to produce a hypothesis achieving a prescribed correlation with an arbitrary Boolean function. The learner generalizes Fourier sampling from parity hypotheses to quadratic hypotheses. While standard Fourier sampling is insensitive to the sign of the correlation, we use interference to bias sampling toward positively correlated hypotheses. This is combined with a careful analysis of the resulting distribution to boost both the learner's success probability and its agreement with the target function. We believe that the resulting quantum learning algorithm might be of independent interest. Finally, although a robust quantum PAC learning advantage holds unconditionally in the large error regime, we note that an analogous separation between quantum and classical learning in the small error regime would imply that NP is not contained in BPP.

Parallel Repetition for Entangled Games via Conditional Relative Entropy

from arXiv: Computational Complexity

Authors: Rahul Jain

We give a proof of the cubic parallel-repetition bound for finite two-player entangled games using quantum conditional relative entropy. For a game with entangled value $1-\varepsilon<1$ and answer alphabets $\mathcal A,\mathcal B$, its $n$-fold repeated value is at most \[ \exp[-Ω(\varepsilon^3n/L)],\qquad L:=\max\{1,\log(|\mathcal A||\mathcal B|)\}, \] for every $n\ge1$. This recovers Song's cubic rate. We also prove a threshold bound: the probability of winning a fraction at least $1-\varepsilon+δ$ is at most $\exp[-Ω(δ^5n/(\varepsilon^2L))]$ for $0<δ\le\varepsilon$. Our information charge is the decrease in relative entropy when a question reference is discarded. Our comparison states preserve the original conditional question law. A chain rule bounds the coordinate charges using the cost of conditioning on checked wins and retaining checked answers. Our main lemma converts small charges into nearby pure-state descriptions and local corrections, each depending only on one player's question. Together with Song's sampler, these enable approximate local preparation of the conditioned workspace.

Authors: Rahul Jain

We give a proof of the cubic parallel-repetition bound for finite two-player entangled games using quantum conditional relative entropy. For a game with entangled value $1-\varepsilon<1$ and answer alphabets $\mathcal A,\mathcal B$, its $n$-fold repeated value is at most \[ \exp[-Ω(\varepsilon^3n/L)],\qquad L:=\max\{1,\log(|\mathcal A||\mathcal B|)\}, \] for every $n\ge1$. This recovers Song's cubic rate. We also prove a threshold bound: the probability of winning a fraction at least $1-\varepsilon+δ$ is at most $\exp[-Ω(δ^5n/(\varepsilon^2L))]$ for $0<δ\le\varepsilon$. Our information charge is the decrease in relative entropy when a question reference is discarded. Our comparison states preserve the original conditional question law. A chain rule bounds the coordinate charges using the cost of conditioning on checked wins and retaining checked answers. Our main lemma converts small charges into nearby pure-state descriptions and local corrections, each depending only on one player's question. Together with Song's sampler, these enable approximate local preparation of the conditioned workspace.

Metric Dimension is NP-complete on Graphs of Treewidth Two

from arXiv: Computational Complexity

Authors: Tristan Benoit

The metric dimension of a connected graph is the least number of vertices whose distance vectors distinguish all vertices. We show that deciding whether the metric dimension of a graph is at most a given integer is NP-complete on graphs of treewidth at most two. As the problem is polynomial on trees, the graphs of treewidth one, this settles the treewidth threshold of its hardness, which was known to lie between two and twenty-four. The reduction is from satisfiability of formulas in which every literal occurs at most twice. To such a formula with $n$ clauses and $r$ variables it associates a graph $G_\varphi$ of treewidth at most two with $O((n+r)^2)$ vertices, whose metric dimension is $2(n+r)+5$ if the formula is satisfiable and $2(n+r)+6$ otherwise.

Authors: Tristan Benoit

The metric dimension of a connected graph is the least number of vertices whose distance vectors distinguish all vertices. We show that deciding whether the metric dimension of a graph is at most a given integer is NP-complete on graphs of treewidth at most two. As the problem is polynomial on trees, the graphs of treewidth one, this settles the treewidth threshold of its hardness, which was known to lie between two and twenty-four. The reduction is from satisfiability of formulas in which every literal occurs at most twice. To such a formula with $n$ clauses and $r$ variables it associates a graph $G_\varphi$ of treewidth at most two with $O((n+r)^2)$ vertices, whose metric dimension is $2(n+r)+5$ if the formula is satisfiable and $2(n+r)+6$ otherwise.

On Knowledge Compilation Languages for QBFs

from arXiv: Computational Complexity

Authors: Sravanthi Chede, Vaibhav Krishan, Anil Shukla

In this work, we design knowledge compilation languages for quantified Boolean formulas (QBFs), with a particular focus on model counting. We introduce various languages in increasing order of their power of representation, starting from a simple tree-based structure, and extending it to DAGs for a more efficient representation. We study the power and limitations of each language. We also show that QBF queries such as model counting and MaxQBF are easy to handle in all these languages. These languages naturally lead to various static proof systems for the #QBF problem, the model-counting version of the QBF problem. We compare these systems with one another and with a recently introduced line-based #QBF-system called Q-MICE (Chede, Chew, Krishan and Shukla, SAT `26). We show that a minor extension of the Q-MICE system efficiently simulates all these static proof systems, and is in fact exponentially stronger than each of them. We also initiate the implementation of a solver based on these languages. The solver compiles a representation of an input QBF in the proposed tree-like language, which is subsequently used for model counting. This is the first knowledge compilation based solver for #QBF which can handle QBFs with doubly exponential model counts as well. Experimentally, we compare its performance with the state-of-the-art #QBF-solver d4-QBF which works well for QBFs with small model counts.

Authors: Sravanthi Chede, Vaibhav Krishan, Anil Shukla

In this work, we design knowledge compilation languages for quantified Boolean formulas (QBFs), with a particular focus on model counting. We introduce various languages in increasing order of their power of representation, starting from a simple tree-based structure, and extending it to DAGs for a more efficient representation. We study the power and limitations of each language. We also show that QBF queries such as model counting and MaxQBF are easy to handle in all these languages. These languages naturally lead to various static proof systems for the #QBF problem, the model-counting version of the QBF problem. We compare these systems with one another and with a recently introduced line-based #QBF-system called Q-MICE (Chede, Chew, Krishan and Shukla, SAT `26). We show that a minor extension of the Q-MICE system efficiently simulates all these static proof systems, and is in fact exponentially stronger than each of them. We also initiate the implementation of a solver based on these languages. The solver compiles a representation of an input QBF in the proposed tree-like language, which is subsequently used for model counting. This is the first knowledge compilation based solver for #QBF which can handle QBFs with doubly exponential model counts as well. Experimentally, we compare its performance with the state-of-the-art #QBF-solver d4-QBF which works well for QBFs with small model counts.

An Optimal Gap-Majority Lemma for Bounded Rounds Communication

from arXiv: Computational Complexity

Authors: Pachara Sawettamalya

Recent work of Sawettamalya and Yu (FOCS 2026) initiated the study of \emph{gap-majority} composition in randomized communication. They showed that if computing a $\{\pm 1\}$-valued function $f$ in $r$ rounds requires $I$ bits of information, then distinguishing $\sum_{i=1}^n f(X_i,Y_i)\geq 0.01\sqrt{n}$ from $\sum_{i=1}^n f(X_i,Y_i)\leq -0.01\sqrt{n}$ in $r$ rounds requires $n \cdot (I-O(\log n))$ bits of information. They also conjectured that the $O(\log n)$-additive ``loss'' can be further reduced to $O(1)$. In this note, we record that a minor adaptation to their approach combining with a finer analysis achieves the conjectured $O(1)$ loss, establishing a gap-majority lemma for bounded-round communication that is optimal up to constant factors.

Authors: Pachara Sawettamalya

Recent work of Sawettamalya and Yu (FOCS 2026) initiated the study of \emph{gap-majority} composition in randomized communication. They showed that if computing a $\{\pm 1\}$-valued function $f$ in $r$ rounds requires $I$ bits of information, then distinguishing $\sum_{i=1}^n f(X_i,Y_i)\geq 0.01\sqrt{n}$ from $\sum_{i=1}^n f(X_i,Y_i)\leq -0.01\sqrt{n}$ in $r$ rounds requires $n \cdot (I-O(\log n))$ bits of information. They also conjectured that the $O(\log n)$-additive ``loss'' can be further reduced to $O(1)$. In this note, we record that a minor adaptation to their approach combining with a finer analysis achieves the conjectured $O(1)$ loss, establishing a gap-majority lemma for bounded-round communication that is optimal up to constant factors.

Smoothed analysis of policy iteration and value iteration for deterministic discounted and mean-payoff games played on any graph

from arXiv: Computational Complexity

Authors: Bruno Loff, Mateusz Skomra

Our understanding of turn-based stochastic games is reminiscent of our understanding of linear programming in the 1970s. Instead of the simplex method, we have two families of algorithms - policy iteration and value iteration - which work well in practice but are known to run in exponential time on some input instances, and we still lack an algorithm that runs in polynomial time on all instances. A recent result by Loff and Skomra showed that, for deterministic discounted and mean-payoff games played on an ergodic graph, a variant of policy iteration runs in smoothed polynomial time. This offered an explanation of why policy iteration performs well in practice, analogous to Spielman and Teng's explanation for the simplex method. In this paper, we drop the ergodicity assumption: we show that their policy iteration algorithms run in smoothed polynomial time for any game graph. We do this by defining a condition number that naturally generalizes theirs, showing that sufficiently-perturbed games are well-conditioned with high probability, and showing that their algorithms are efficient on well-conditioned instances. Although the outline is similar, our proof requires a substantially new approach, based on log-Lipschitz distributions which recently appeared in work on the smoothed analysis of the simplex method. For our second contribution, we propose three new value iteration algorithms for solving deterministic mean-payoff games (also on any graph). All three are based on iterative methods for finding fixed points of non-expansive operators, which we show can be applied to the Shapley operator. The first two are simple applications of Krasnoselskii-Mann iteration and Halpern iteration. The third is quadratically faster but more complicated: it runs Zwick-Paterson and Halpern iterations in parallel. We prove that all three algorithms are correct and run in smoothed polynomial time.

Authors: Bruno Loff, Mateusz Skomra

Our understanding of turn-based stochastic games is reminiscent of our understanding of linear programming in the 1970s. Instead of the simplex method, we have two families of algorithms - policy iteration and value iteration - which work well in practice but are known to run in exponential time on some input instances, and we still lack an algorithm that runs in polynomial time on all instances. A recent result by Loff and Skomra showed that, for deterministic discounted and mean-payoff games played on an ergodic graph, a variant of policy iteration runs in smoothed polynomial time. This offered an explanation of why policy iteration performs well in practice, analogous to Spielman and Teng's explanation for the simplex method. In this paper, we drop the ergodicity assumption: we show that their policy iteration algorithms run in smoothed polynomial time for any game graph. We do this by defining a condition number that naturally generalizes theirs, showing that sufficiently-perturbed games are well-conditioned with high probability, and showing that their algorithms are efficient on well-conditioned instances. Although the outline is similar, our proof requires a substantially new approach, based on log-Lipschitz distributions which recently appeared in work on the smoothed analysis of the simplex method. For our second contribution, we propose three new value iteration algorithms for solving deterministic mean-payoff games (also on any graph). All three are based on iterative methods for finding fixed points of non-expansive operators, which we show can be applied to the Shapley operator. The first two are simple applications of Krasnoselskii-Mann iteration and Halpern iteration. The third is quadratically faster but more complicated: it runs Zwick-Paterson and Halpern iterations in parallel. We prove that all three algorithms are correct and run in smoothed polynomial time.

Discrete Temporal CSPs et al

from arXiv: Computational Complexity

Authors: Lukas Pieper

We revisit the complexity classification of constraint satisfaction problems (CSPs) for first-order reducts of $(\mathbb{Z},<)$, established by Bodirsky, Martin, and Mottet in 2018. Following their approach, we give a self-contained proof of this classification, correcting several errors in the argument. We further show that, with the exception of cases arising from finite-domain CSPs, all tractable cases are solved by a well-known uniform algorithm, eliminating the need for the collection of ad-hoc algorithms used in the original paper. A central component of the proof allows the analysis to be restricted to first-order reducts of $(\mathbb{Z},<)$ that contain the successor relation. We substantially simplify this reduction and generalize the underlying construction. This generalization allows us to apply the same technique to first-order reducts of the triangular lattice graph and of infinite regular trees, yielding a complete complexity classification for these classes of structures, in particular a $\mathsf{P}$ versus $\mathsf{NP}$-complete dichotomy.

Authors: Lukas Pieper

We revisit the complexity classification of constraint satisfaction problems (CSPs) for first-order reducts of $(\mathbb{Z},<)$, established by Bodirsky, Martin, and Mottet in 2018. Following their approach, we give a self-contained proof of this classification, correcting several errors in the argument. We further show that, with the exception of cases arising from finite-domain CSPs, all tractable cases are solved by a well-known uniform algorithm, eliminating the need for the collection of ad-hoc algorithms used in the original paper. A central component of the proof allows the analysis to be restricted to first-order reducts of $(\mathbb{Z},<)$ that contain the successor relation. We substantially simplify this reduction and generalize the underlying construction. This generalization allows us to apply the same technique to first-order reducts of the triangular lattice graph and of infinite regular trees, yielding a complete complexity classification for these classes of structures, in particular a $\mathsf{P}$ versus $\mathsf{NP}$-complete dichotomy.

Strong NP-Hardness of Rank-2 Nonnegative Matrix Factorization

from arXiv: Computational Complexity

Authors: Arnaud Vandaele

Nonnegative matrix factorization (NMF) approximates a nonnegative matrix by the product of two nonnegative matrices with a prescribed inner dimension~$r$. The existence of an exact NMF can be decided in polynomial time for every fixed~$r$, and a best rank-$1$ NMF in the Frobenius norm can be obtained from a truncated singular value decomposition. We prove that computing a best rank-$2$ NMF in the Frobenius norm is strongly NP-hard. Our proof uses a polynomial-time reduction from \textsc{Clique} that produces symmetric positive-definite, completely positive matrices with polynomially bounded integer entries. The same reduction also establishes strong NP-hardness of rank-$2$ symmetric NMF.

Authors: Arnaud Vandaele

Nonnegative matrix factorization (NMF) approximates a nonnegative matrix by the product of two nonnegative matrices with a prescribed inner dimension~$r$. The existence of an exact NMF can be decided in polynomial time for every fixed~$r$, and a best rank-$1$ NMF in the Frobenius norm can be obtained from a truncated singular value decomposition. We prove that computing a best rank-$2$ NMF in the Frobenius norm is strongly NP-hard. Our proof uses a polynomial-time reduction from \textsc{Clique} that produces symmetric positive-definite, completely positive matrices with polynomially bounded integer entries. The same reduction also establishes strong NP-hardness of rank-$2$ symmetric NMF.

The Six-Dimensional Tarski Problem is $Ω(\log^3 n)$-Hard

from arXiv: Computational Complexity

Authors: Kristóf Zólomy

We study the deterministic query complexity of finding a fixed point of a monotone function $f:[n]^d\to[n]^d$, where querying $x$ returns $f(x)$. For fixed $d\ge2$, earlier work established a lower bound of $Ω((\log n)^2)$ and an upper bound of $O_d((\log n)^{\lceil(d+2)/3\rceil})$ for the query complexity. Very recently, several papers obtained improved lower bounds using AI-assisted proofs. In this paper, we further improve these lower bounds to $\text{Tarski}(n,d)=Ω_d((\log n)^{2+\lfloor\log_3(d/2)\rfloor})$. In particular, we obtain the optimal $Θ((\log n)^3)$ deterministic query complexity in six dimensions.

Authors: Kristóf Zólomy

We study the deterministic query complexity of finding a fixed point of a monotone function $f:[n]^d\to[n]^d$, where querying $x$ returns $f(x)$. For fixed $d\ge2$, earlier work established a lower bound of $Ω((\log n)^2)$ and an upper bound of $O_d((\log n)^{\lceil(d+2)/3\rceil})$ for the query complexity. Very recently, several papers obtained improved lower bounds using AI-assisted proofs. In this paper, we further improve these lower bounds to $\text{Tarski}(n,d)=Ω_d((\log n)^{2+\lfloor\log_3(d/2)\rfloor})$. In particular, we obtain the optimal $Θ((\log n)^3)$ deterministic query complexity in six dimensions.

Spherical Convex Hull Membership: Complexity and Performance of Geometric and Accelerated Methods

from arXiv: Computational Geometry

Authors: Bahman Kalantari

We study the problem of testing membership of the origin in the convex hull of $n$ unit vectors in $\mathbb{R}^m$, seeking a convex combination of norm at most $\varepsilon$ or a strict separating hyperplane. The Spherical Triangle Algorithm (STA) uses nearest-point updates on pivot segments. We first establish a conditional $O(1/\varepsilon)$ iteration bound from accumulated geometric progress, allowing deeper pivots to compensate for shallow ones. A three-point example shows that ordinary STA can require $Θ(\varepsilon^{-2})$ iterations. We show that the same conditional bound holds for STA with Wolfe minimum-norm correction (STA--WMC), provided the averaged geometric progress condition holds for the STA steps taken within this corrected method. We then combine STA, WMC, and the Nemirovski weighted-pivot method (NWP) in a hybrid with unconditional bounds of $O(\sqrt{\log n}/\varepsilon)$ matrix--vector products with the input matrix or its transpose and $O(mn\sqrt{\log n}/\varepsilon)$ arithmetic operations, including corrections. We also give a self-contained derivation of the $O(\sqrt{\log n}/\varepsilon)$ membership iteration bound for entropic Nesterov acceleration. Computationally, we compare five methods---ordinary STA, STA--WMC, the hybrid, standalone NWP, and entropic Nesterov acceleration---on convex-hull queries derived from 14 public benchmark datasets and on 204 controlled synthetic inputs. STA--WMC completes all cases in both comparisons, and the hybrid completes all public-data cases. STA performs well on favorable interior and separation instances; Nesterov acceleration is faster than the STA-based methods on the highest-dimensional random membership tests. The experiments show complementary strengths and distinguish practical performance from worst-case guarantees.

Authors: Bahman Kalantari

We study the problem of testing membership of the origin in the convex hull of $n$ unit vectors in $\mathbb{R}^m$, seeking a convex combination of norm at most $\varepsilon$ or a strict separating hyperplane. The Spherical Triangle Algorithm (STA) uses nearest-point updates on pivot segments. We first establish a conditional $O(1/\varepsilon)$ iteration bound from accumulated geometric progress, allowing deeper pivots to compensate for shallow ones. A three-point example shows that ordinary STA can require $Θ(\varepsilon^{-2})$ iterations. We show that the same conditional bound holds for STA with Wolfe minimum-norm correction (STA--WMC), provided the averaged geometric progress condition holds for the STA steps taken within this corrected method. We then combine STA, WMC, and the Nemirovski weighted-pivot method (NWP) in a hybrid with unconditional bounds of $O(\sqrt{\log n}/\varepsilon)$ matrix--vector products with the input matrix or its transpose and $O(mn\sqrt{\log n}/\varepsilon)$ arithmetic operations, including corrections. We also give a self-contained derivation of the $O(\sqrt{\log n}/\varepsilon)$ membership iteration bound for entropic Nesterov acceleration. Computationally, we compare five methods---ordinary STA, STA--WMC, the hybrid, standalone NWP, and entropic Nesterov acceleration---on convex-hull queries derived from 14 public benchmark datasets and on 204 controlled synthetic inputs. STA--WMC completes all cases in both comparisons, and the hybrid completes all public-data cases. STA performs well on favorable interior and separation instances; Nesterov acceleration is faster than the STA-based methods on the highest-dimensional random membership tests. The experiments show complementary strengths and distinguish practical performance from worst-case guarantees.

VoroDuce: Massively Parallel 3D Voronoi Simplification with Volume-Aware Edge Collapse

from arXiv: Computational Geometry

Authors: Felix Windisch, Lukas Radl, Thomas Köhler, Shrisudhan Govindarajan, Marcel Pur, Andrea Tagliasacchi, Markus Steinberger

3D Voronoi diagrams provide a powerful mathematical framework for representing volumetric functions and geometry with applications to Materials Science, Biology, Simulation and Astrophysics. However, accurately resolving fine structures often requires millions of cells, imposing severe computational and memory constraints. Traditional mesh decimation or point cloud simplification techniques fail to account for the effects re-meshing has on neighbouring cells. In this work, we formalize the Voronoi reduction problem and propose a greedy edge-collapsing algorithm on the dual Delaunay tetrahedralisation that accounts for changes in the local geometry. Further, we demonstrate a novel volumetric error approximation technique that models convex cell intersections as truncated prismatoids, circumventing computationally prohibitive exact volume calculations and enabling massive GPU parallelization. Evaluations across Synthetic, Medicine and Material Science data demonstrate that VoroDuce decimates models with up to a million primitives, successfully compressing the representation while preserving the underlying data better than naive decimation approaches.

Authors: Felix Windisch, Lukas Radl, Thomas Köhler, Shrisudhan Govindarajan, Marcel Pur, Andrea Tagliasacchi, Markus Steinberger

3D Voronoi diagrams provide a powerful mathematical framework for representing volumetric functions and geometry with applications to Materials Science, Biology, Simulation and Astrophysics. However, accurately resolving fine structures often requires millions of cells, imposing severe computational and memory constraints. Traditional mesh decimation or point cloud simplification techniques fail to account for the effects re-meshing has on neighbouring cells. In this work, we formalize the Voronoi reduction problem and propose a greedy edge-collapsing algorithm on the dual Delaunay tetrahedralisation that accounts for changes in the local geometry. Further, we demonstrate a novel volumetric error approximation technique that models convex cell intersections as truncated prismatoids, circumventing computationally prohibitive exact volume calculations and enabling massive GPU parallelization. Evaluations across Synthetic, Medicine and Material Science data demonstrate that VoroDuce decimates models with up to a million primitives, successfully compressing the representation while preserving the underlying data better than naive decimation approaches.

Geometric Hashing in MPC with Applications to Clustering and Stochastic Embeddings

from arXiv: Data Structures and Algorithms

Authors: Sujoy Bhore, Arnold Filter, Eunjin Oh, Hyeonjun Shin, Himanshi Singh

In this paper we study consistent hashing (a.k.a. sparse partitions) for doubling metrics in the Massively Parallel Computation (MPC) model. A $(Γ,Λ)$-hash with diameter bound $Δ$ partitions a metric space into clusters of diameter at most $Δ$ such that every ball of radius $Δ/Γ$ intersects at most $Λ$ clusters in expectation. Consistent hashing has emerged as a central tool in geometric MPC algorithms. However, so far it has been solely used in Euclidean spaces, where it can be pre-computed. On the other hand, in cases where the metric space is received as input and unknown in advance, no previous parallel construction was known. We present the first fully scalable MPC algorithm over machines with local space $n^σ$, that given an $n$-point metric space with doubling dimension $d\leq O(σ\log n)$, samples a randomized $(Γ,2^{O(d/Γ)})$-hash in $O(d/σ^3)$ rounds. There is a dichotomy: if the doubling dimension is $d=Ω(σ\cdot\log n)$, a polynomial number of rounds is required to compute such a hash. We conclude by showing that this MPC primitive yields $O(d/σ^3)$-round fully scalable MPC algorithms for a broad class of fundamental geometric problems in doubling metrics, including stochastic tree embeddings with $O(\log n)$ expected distortion, constant factor approximation for facility location, bicriteria $(O(ε^{-2}),1+ε)$-approximation for $k$-median and $k$-means.

Authors: Sujoy Bhore, Arnold Filter, Eunjin Oh, Hyeonjun Shin, Himanshi Singh

In this paper we study consistent hashing (a.k.a. sparse partitions) for doubling metrics in the Massively Parallel Computation (MPC) model. A $(Γ,Λ)$-hash with diameter bound $Δ$ partitions a metric space into clusters of diameter at most $Δ$ such that every ball of radius $Δ/Γ$ intersects at most $Λ$ clusters in expectation. Consistent hashing has emerged as a central tool in geometric MPC algorithms. However, so far it has been solely used in Euclidean spaces, where it can be pre-computed. On the other hand, in cases where the metric space is received as input and unknown in advance, no previous parallel construction was known. We present the first fully scalable MPC algorithm over machines with local space $n^σ$, that given an $n$-point metric space with doubling dimension $d\leq O(σ\log n)$, samples a randomized $(Γ,2^{O(d/Γ)})$-hash in $O(d/σ^3)$ rounds. There is a dichotomy: if the doubling dimension is $d=Ω(σ\cdot\log n)$, a polynomial number of rounds is required to compute such a hash. We conclude by showing that this MPC primitive yields $O(d/σ^3)$-round fully scalable MPC algorithms for a broad class of fundamental geometric problems in doubling metrics, including stochastic tree embeddings with $O(\log n)$ expected distortion, constant factor approximation for facility location, bicriteria $(O(ε^{-2}),1+ε)$-approximation for $k$-median and $k$-means.

Incremental Exact Vertex Connectivity

from arXiv: Data Structures and Algorithms

Authors: Felix Adena, Monika Henzinger, Christoph Kern, Daniel Rutschmann

We study the problem of maintaining the vertex connectivity $κ$ of a graph under edge insertions. For undirected graphs, our algorithm uses $O(κ^4 \log^2 n)$ amortized time per edge insertion. After each edge insertion, the algorithm outputs the current value of $κ$, plus a set of $κ$ vertices whose removal disconnects the graph. Our algorithm is Monte-Carlo randomized and works with a randomness-adaptive adversary. The algorithm also works on directed graphs against an oblivious adversary, which is equivalent to an output-adaptive adversary if we only have to output the current value of $κ$. Previously, algorithms for maintaining the vertex connectivity with $o(n)$ time per edge insertion were only known for $κ\le 2$, and need $\tilde{O}(\log^2 n)$ and $O(\sqrt{n})$ time per edge insertion for $κ= 1,2$, respectively. Moreover, for $κ> 4$, no algorithm faster than recomputation from scratch was known. We complement our algorithm with a conditional lower bound: Under the OMv conjecture, any insertions-only algorithm that outputs the current value of $κ$ after every insertion needs $Ω(n^{1-\varepsilon})$ amortized time per edge insertion for $κ= Θ(n)$. Previous conditional lower bounds for this problem only apply to worst case running time, or algorithms that support both edge insertions and edge deletions.

Authors: Felix Adena, Monika Henzinger, Christoph Kern, Daniel Rutschmann

We study the problem of maintaining the vertex connectivity $κ$ of a graph under edge insertions. For undirected graphs, our algorithm uses $O(κ^4 \log^2 n)$ amortized time per edge insertion. After each edge insertion, the algorithm outputs the current value of $κ$, plus a set of $κ$ vertices whose removal disconnects the graph. Our algorithm is Monte-Carlo randomized and works with a randomness-adaptive adversary. The algorithm also works on directed graphs against an oblivious adversary, which is equivalent to an output-adaptive adversary if we only have to output the current value of $κ$. Previously, algorithms for maintaining the vertex connectivity with $o(n)$ time per edge insertion were only known for $κ\le 2$, and need $\tilde{O}(\log^2 n)$ and $O(\sqrt{n})$ time per edge insertion for $κ= 1,2$, respectively. Moreover, for $κ> 4$, no algorithm faster than recomputation from scratch was known. We complement our algorithm with a conditional lower bound: Under the OMv conjecture, any insertions-only algorithm that outputs the current value of $κ$ after every insertion needs $Ω(n^{1-\varepsilon})$ amortized time per edge insertion for $κ= Θ(n)$. Previous conditional lower bounds for this problem only apply to worst case running time, or algorithms that support both edge insertions and edge deletions.

Decoding Failure Biases IBLT Count Measurements: A Gaussian-Selection Law at the Peeling Threshold

from arXiv: Data Structures and Algorithms

Authors: Min Wu

In a self-sizing set-reconciliation protocol, a small first-round Invertible Bloom Lookup Table (IBLT) sketch attempts peeling decoding; after failure, the protocol estimates the difference count $d$ from the count energy and sizes the second round from a lower quantile of the measurement. This quantile must be computed conditionally on failure: the measurement and peeling success are determined by the same random mapping, so conditioning can shift the measurement law. In the ideal peeling model, inside the critical window where failure has nondegenerate probability, the standardized energy and a statistic of the unsigned mapping that governs the peeling outcome (the precritical score) have a joint Gaussian limit with correlation $η_kρ^2$, $ρ$ being the sign imbalance and $η_k$ a closed-form constant. The measurement conditional on failure is thus a bivariate normal truncated to a half-plane, which we call the Gaussian-selection law. Since $η_k$ changes sign between $k=4$ and $k=5$, under one-sided imbalance the unconditional threshold undercovers exactly when $k\ge5$ and is conservative for $k=3,4$. The key tool is a quenched CLT for a signed quadratic form on a random overlap graph; under asymptotically balanced signs, conditioning on any unsigned mapping event that is permutation-invariant and has probability bounded away from zero leaves the standard normal limit unchanged. After failure the protocol observes the total sign; inverting the selection law yields an upper confidence bound for $d$, asymptotically conservative in the critical window and, when $d/\sqrt M\to\infty$, along the whole load axis. The experiments use analytic constants, none fitted; the unconditional threshold carries a selection error that does not vanish with scale, while the corrected selection-law threshold matches per-configuration empirical quantiles.

Authors: Min Wu

In a self-sizing set-reconciliation protocol, a small first-round Invertible Bloom Lookup Table (IBLT) sketch attempts peeling decoding; after failure, the protocol estimates the difference count $d$ from the count energy and sizes the second round from a lower quantile of the measurement. This quantile must be computed conditionally on failure: the measurement and peeling success are determined by the same random mapping, so conditioning can shift the measurement law. In the ideal peeling model, inside the critical window where failure has nondegenerate probability, the standardized energy and a statistic of the unsigned mapping that governs the peeling outcome (the precritical score) have a joint Gaussian limit with correlation $η_kρ^2$, $ρ$ being the sign imbalance and $η_k$ a closed-form constant. The measurement conditional on failure is thus a bivariate normal truncated to a half-plane, which we call the Gaussian-selection law. Since $η_k$ changes sign between $k=4$ and $k=5$, under one-sided imbalance the unconditional threshold undercovers exactly when $k\ge5$ and is conservative for $k=3,4$. The key tool is a quenched CLT for a signed quadratic form on a random overlap graph; under asymptotically balanced signs, conditioning on any unsigned mapping event that is permutation-invariant and has probability bounded away from zero leaves the standard normal limit unchanged. After failure the protocol observes the total sign; inverting the selection law yields an upper confidence bound for $d$, asymptotically conservative in the critical window and, when $d/\sqrt M\to\infty$, along the whole load axis. The experiments use analytic constants, none fitted; the unconditional threshold carries a selection error that does not vanish with scale, while the corrected selection-law threshold matches per-configuration empirical quantiles.

Testing Connectedness and Edge Connectedness

from arXiv: Data Structures and Algorithms

Authors: Diptaksho Palit, Ramesh Krishnan S. Pallavoor, Sofya Raskhodnikova, Nithin Varma

We revisit the classical problem of testing connectedness of graphs represented in the adjacency lists model and obtain optimal bounds. Specifically, we show that $\varepsilon$-testing connectedness of graphs with maximum degree at most $d$ requires $Ω(\frac{1}{\varepsilon} \log \frac{1}{\varepsilon d})$ queries (when $\varepsilon \le \frac{1}{4d^5}$). Our result implies that the connectedness tester of Levi, Pallavoor, Raskhodnikova, and Varma ({\em ACM Trans.\ Comput.\ Theory, 2022}) is optimal. We also study the problem of testing $k$-edge-connectedness of both directed and undirected graphs of average degree $\overline{d}$ in the adjacency lists model and design a tester with query complexity $O(\frac{k}{\varepsilon} \log \frac{1}{\varepsilon \overline{d}})$, improving upon the bounds by Forster, Nanongkai, Saranurak, Yang, and Yingchareonthawornchai (SODA 2020). Furthermore, we complement our upper bound by showing that every one-sided error $k$-edge-connectedness tester must make $Ω(k/\varepsilon)$ queries.

Authors: Diptaksho Palit, Ramesh Krishnan S. Pallavoor, Sofya Raskhodnikova, Nithin Varma

We revisit the classical problem of testing connectedness of graphs represented in the adjacency lists model and obtain optimal bounds. Specifically, we show that $\varepsilon$-testing connectedness of graphs with maximum degree at most $d$ requires $Ω(\frac{1}{\varepsilon} \log \frac{1}{\varepsilon d})$ queries (when $\varepsilon \le \frac{1}{4d^5}$). Our result implies that the connectedness tester of Levi, Pallavoor, Raskhodnikova, and Varma ({\em ACM Trans.\ Comput.\ Theory, 2022}) is optimal. We also study the problem of testing $k$-edge-connectedness of both directed and undirected graphs of average degree $\overline{d}$ in the adjacency lists model and design a tester with query complexity $O(\frac{k}{\varepsilon} \log \frac{1}{\varepsilon \overline{d}})$, improving upon the bounds by Forster, Nanongkai, Saranurak, Yang, and Yingchareonthawornchai (SODA 2020). Furthermore, we complement our upper bound by showing that every one-sided error $k$-edge-connectedness tester must make $Ω(k/\varepsilon)$ queries.

A General Framework for Matching with Delays

from arXiv: Data Structures and Algorithms

Authors: Sara Ahmadian, Shuchi Chawla, Ravi Kumar, Manish Purohit, Shirley Zhang

We present a simple deterministic $O(\log n)$-competitive algorithm for the online minimum-cost perfect matching with delays (MPMD) problem on arbitrary, unknown metric spaces, where $n$ is the (unknown) number of requests. This improves upon the recent $O(\log^5 n)$-competitive algorithm of Dufay and Wattenhofer (SODA 2026) and nearly matches the $Ω(\log n / \log \log n)$ randomized lower bound of Ashlagi et al. (APPROX 2017). Our algorithmic framework rests on two ingredients: an online moat-growing process that grows active dual variables at a deliberately slowed constant rate $α< 1$ to control temporal asymmetry, and a delayed eligibility rule that matches requests in the same dual component only after their waiting time is proportional to their time-expanded distance. The framework extends naturally to arbitrary concave delay functions, maintaining an $O(\log n)$ competitive ratio, and to $k$-way matching with delays, yielding an $O(k \log n)$-competitive deterministic algorithm.

Authors: Sara Ahmadian, Shuchi Chawla, Ravi Kumar, Manish Purohit, Shirley Zhang

We present a simple deterministic $O(\log n)$-competitive algorithm for the online minimum-cost perfect matching with delays (MPMD) problem on arbitrary, unknown metric spaces, where $n$ is the (unknown) number of requests. This improves upon the recent $O(\log^5 n)$-competitive algorithm of Dufay and Wattenhofer (SODA 2026) and nearly matches the $Ω(\log n / \log \log n)$ randomized lower bound of Ashlagi et al. (APPROX 2017). Our algorithmic framework rests on two ingredients: an online moat-growing process that grows active dual variables at a deliberately slowed constant rate $α< 1$ to control temporal asymmetry, and a delayed eligibility rule that matches requests in the same dual component only after their waiting time is proportional to their time-expanded distance. The framework extends naturally to arbitrary concave delay functions, maintaining an $O(\log n)$ competitive ratio, and to $k$-way matching with delays, yielding an $O(k \log n)$-competitive deterministic algorithm.

A 0.4584-approximation for DR-submodular maximization over down-closed convex sets

from arXiv: Data Structures and Algorithms

Authors: Donglei Du

We obtain a $0.4584$-approximation for nonnegative, non-monotone, $L$-smooth DR-submodular maximization over compact convex down-closed subsets of the unit cube, improving the $4/9\approx0.4444$ ratio of Aggarwal's projection-based algorithm~\cite{aggarwal2026geometry}. A recovery map combining exponential and linear-fractional branches admits a cube-wide affine upper bound. The resulting continuous-time Frank--Wolfe-type algorithm with randomized output and its randomized discrete-time implementation both satisfy $\E F(\widehat x)\ge(0.4584-\varepsilon)\OPT-δLD^2$; the implementation uses polynomially many cube-wide value/full-gradient queries and ordinary linear maximization calls, where $D$ is the feasible diameter and $\varepsilon,δ>0$ are accuracy parameters. Our guarantee and Aggarwal's hardness bound~\cite{aggarwal2026geometry} place the optimal approximation ratio for cube-wide value/full-gradient queries and linear maximization, with additive error $δLD^2$, in $[0.4584,0.4705)$. Standard multilinear sampling and pipage rounding imply an expected $(0.4584-\varepsilon)$-approximation for nonnegative submodular set-function maximization under a matroid constraint \cite[Lemma B.3]{vondrak2013symmetry}.

Authors: Donglei Du

We obtain a $0.4584$-approximation for nonnegative, non-monotone, $L$-smooth DR-submodular maximization over compact convex down-closed subsets of the unit cube, improving the $4/9\approx0.4444$ ratio of Aggarwal's projection-based algorithm~\cite{aggarwal2026geometry}. A recovery map combining exponential and linear-fractional branches admits a cube-wide affine upper bound. The resulting continuous-time Frank--Wolfe-type algorithm with randomized output and its randomized discrete-time implementation both satisfy $\E F(\widehat x)\ge(0.4584-\varepsilon)\OPT-δLD^2$; the implementation uses polynomially many cube-wide value/full-gradient queries and ordinary linear maximization calls, where $D$ is the feasible diameter and $\varepsilon,δ>0$ are accuracy parameters. Our guarantee and Aggarwal's hardness bound~\cite{aggarwal2026geometry} place the optimal approximation ratio for cube-wide value/full-gradient queries and linear maximization, with additive error $δLD^2$, in $[0.4584,0.4705)$. Standard multilinear sampling and pipage rounding imply an expected $(0.4584-\varepsilon)$-approximation for nonnegative submodular set-function maximization under a matroid constraint \cite[Lemma B.3]{vondrak2013symmetry}.

Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy

from arXiv: Data Structures and Algorithms

Authors: Mohammad Afzali, Hassan Ashtiani, Sivan Sabato

We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials. This gives the first sample-efficient LDP learners for several distribution classes, including mixtures of log-concave distributions, such as Gaussian mixture models (GMMs). These learners also provide agnostic guarantees and are computationally efficient. We also prove a lower bound for learning GMMs under LDP, improving on the standard pure-DP packing lower bounds of Bun et al. [BKSW19]. To obtain our general learning framework, we develop an efficient non-interactive LDP algorithm for synthetic data generation over large domains, which may be of independent interest. We then use it together with a deterministic adaptation of the non-private piecewise polynomial learner of Acharya et al. [ADLS17].

Authors: Mohammad Afzali, Hassan Ashtiani, Sivan Sabato

We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polynomials. This gives the first sample-efficient LDP learners for several distribution classes, including mixtures of log-concave distributions, such as Gaussian mixture models (GMMs). These learners also provide agnostic guarantees and are computationally efficient. We also prove a lower bound for learning GMMs under LDP, improving on the standard pure-DP packing lower bounds of Bun et al. [BKSW19]. To obtain our general learning framework, we develop an efficient non-interactive LDP algorithm for synthetic data generation over large domains, which may be of independent interest. We then use it together with a deterministic adaptation of the non-private piecewise polynomial learner of Acharya et al. [ADLS17].

The Komlós Problem in Matrix-Multiplication Time

from arXiv: Data Structures and Algorithms

Authors: Emile Anand, Peter Chen

We give a randomized algorithm that, for an $m\times n$ matrix whose columns have Euclidean norm at most one, finds a signing with discrepancy below $99$ in expected $\widetilde O(L_{\mathrm{in}}+n^ω)$ bit operations, where $L_{\mathrm{in}}$ is the input encoding length and $ω$ is the exponent of matrix multiplication. The previous fastest algorithm with a constant discrepancy bound, due to Li, is deterministic and uses $O(mn+n^{ω+2}\log^3 n)$ arithmetic operations. We build on the spectral signing framework of Guo, Fang, and Lu and of Li, which rounds a single fractional coloring while one spectral potential controls every row. Li's potential tracks a top eigenvalue, and the gap below that eigenvalue, which can be of order $1/n$, limits the length of every step. We replace it with a resolvent potential that is smooth without any gap condition, and we draw step signs at random, so that the potential only needs to be concave while progress is measured separately. Our key insight is to reuse a single subspace of directions throughout a batch of steps, thereby using only one spectral computation, so that the more expensive linear algebraic operations are paid for only polylogarithmically often at each scale. Within a batch, we sample directions from a Gaussian completion matrix whose inverse we maintain under rank-one row replacements. Finally, we use a finite-precision analysis to prove stability of the algorithm and attain the bit-complexity bound.

Authors: Emile Anand, Peter Chen

We give a randomized algorithm that, for an $m\times n$ matrix whose columns have Euclidean norm at most one, finds a signing with discrepancy below $99$ in expected $\widetilde O(L_{\mathrm{in}}+n^ω)$ bit operations, where $L_{\mathrm{in}}$ is the input encoding length and $ω$ is the exponent of matrix multiplication. The previous fastest algorithm with a constant discrepancy bound, due to Li, is deterministic and uses $O(mn+n^{ω+2}\log^3 n)$ arithmetic operations. We build on the spectral signing framework of Guo, Fang, and Lu and of Li, which rounds a single fractional coloring while one spectral potential controls every row. Li's potential tracks a top eigenvalue, and the gap below that eigenvalue, which can be of order $1/n$, limits the length of every step. We replace it with a resolvent potential that is smooth without any gap condition, and we draw step signs at random, so that the potential only needs to be concave while progress is measured separately. Our key insight is to reuse a single subspace of directions throughout a batch of steps, thereby using only one spectral computation, so that the more expensive linear algebraic operations are paid for only polylogarithmically often at each scale. Within a batch, we sample directions from a Gaussian completion matrix whose inverse we maintain under rank-one row replacements. Finally, we use a finite-precision analysis to prove stability of the algorithm and attain the bit-complexity bound.

Near-Linear Preprocessing for Range Weighted Longest Increasing Subsequence Queries

from arXiv: Data Structures and Algorithms

Authors: Keisuke Korogi

We give a deterministic data structure for range weighted longest increasing subsequence (LIS) queries on $n$ values with positive weights. Preprocessing takes $O(n\log^2 n)$ time and space. Value queries take $O(\log n)$ time, and an optimal subsequence of $k$ elements can be reported in $O(k + \log n)$ time. This resolves an open question on truly subquadratic preprocessing with polylogarithmic query time. The key is a single-crossing property of envelopes for value-separated endpoint sets, enabling $O(\log n)$-time persistent merges. The bounds assume constant-time exact addition and comparison of weights.

Authors: Keisuke Korogi

We give a deterministic data structure for range weighted longest increasing subsequence (LIS) queries on $n$ values with positive weights. Preprocessing takes $O(n\log^2 n)$ time and space. Value queries take $O(\log n)$ time, and an optimal subsequence of $k$ elements can be reported in $O(k + \log n)$ time. This resolves an open question on truly subquadratic preprocessing with polylogarithmic query time. The key is a single-crossing property of envelopes for value-separated endpoint sets, enabling $O(\log n)$-time persistent merges. The bounds assume constant-time exact addition and comparison of weights.

Tolerant Testers for Subgraph-Freeness

from arXiv: Data Structures and Algorithms

Authors: Reut Levi, Jonathan Meiri

In this paper we study the problem of tolerantly testing the property of being $H$-free (which also implies distance approximation from being $H$-free). In the general-graphs model, we show that tolerant $K_k$-freeness testing can be achieved with query complexity that is polynomial in the arboricity of the input graph $G$, $arb(G)$, and independent of the size of $G$ (for graphs in which the average degree is $Ω(1))$. Specifically for triangles, our algorithm distinguishes graphs which are $ε$-close to being triangle-free from graphs that are $3ε(1+η)$-far from being triangle-free with expected query complexity which is $\tilde{O}(arb^3(G))$ (for constant $η$ and $ε$). For general $k$-cliques our algorithm distinguishes graphs which are $ε$-close to being $K_k$-free from graphs which are ${k \choose 2}ε(1+η)$-far from being $K_k$-free with expected query complexity which is polynomial in $k$, $1/ε$, $1/η$ and $arb(G)$. We then generalize our result and provide a similar result for any motif $H$ which is $2$-connected and has radius $1$. This includes for example the wheel graph. Finally, we show that our tester can be applied to the bounded-degree model for tolerantly testing $H$-freeness for any motif $H$. The query complexity of the algorithm is polynomial in the degree bound, $d$, improving the previous state-of-the-art by Marko and Ron (TALG 2009) that obtained quasi-polynomial query complexity in $d$.

Authors: Reut Levi, Jonathan Meiri

In this paper we study the problem of tolerantly testing the property of being $H$-free (which also implies distance approximation from being $H$-free). In the general-graphs model, we show that tolerant $K_k$-freeness testing can be achieved with query complexity that is polynomial in the arboricity of the input graph $G$, $arb(G)$, and independent of the size of $G$ (for graphs in which the average degree is $Ω(1))$. Specifically for triangles, our algorithm distinguishes graphs which are $ε$-close to being triangle-free from graphs that are $3ε(1+η)$-far from being triangle-free with expected query complexity which is $\tilde{O}(arb^3(G))$ (for constant $η$ and $ε$). For general $k$-cliques our algorithm distinguishes graphs which are $ε$-close to being $K_k$-free from graphs which are ${k \choose 2}ε(1+η)$-far from being $K_k$-free with expected query complexity which is polynomial in $k$, $1/ε$, $1/η$ and $arb(G)$. We then generalize our result and provide a similar result for any motif $H$ which is $2$-connected and has radius $1$. This includes for example the wheel graph. Finally, we show that our tester can be applied to the bounded-degree model for tolerantly testing $H$-freeness for any motif $H$. The query complexity of the algorithm is polynomial in the degree bound, $d$, improving the previous state-of-the-art by Marko and Ron (TALG 2009) that obtained quasi-polynomial query complexity in $d$.

Eccentricity Shortest Path: the structural parameter landscape

from arXiv: Data Structures and Algorithms

Authors: Anand Babu, Ashwin Jacob, Hitendra Kumar, R. B. Sandeep

The Eccentricity Shortest Path problem asks, given a connected graph and a nonnegative integer $R$, whether some shortest path is within distance $R$ of every vertex. We study its structural parameterized complexity. We give fixed-parameter algorithms for vertex-deletion distance to cographs, vertex-deletion distance to interval graphs, and bandwidth. The interval-deletion result answers a question of Bhyravarapu et al. (IWOCA 2023). We obtain XP algorithms for vertex-deletion distance to chordal graphs, clique-width, and rank-width, without requiring expressions or decompositions for the latter two. Consequently, the problem is polynomial-time solvable for every fixed treewidth, answering a question of Chakraborty et al. (2026). We also derive fixed-parameter tractability for treedepth and XP membership for diameter from earlier algorithms. We prove $\mathsf{W}[3]$-hardness for cutwidth, and hence pathwidth and treewidth, even on graphs of maximum degree at most five; $\mathsf{W}[2]$-hardness for vertex clique-cover number; and NP-hardness on graphs of tree-independence number at most two. For maximum leaf number $\ell$, we obtain an $O(\ell^4)$-bit compression and a polynomial kernel. Finally, unless $\mathsf{NP}\subseteq\mathrm{coNP}/\mathrm{poly}$, we exclude polynomial kernels for bandwidth, feedback edge number, vertex-deletion distance to a clique, and vertex cover number. The last bound also excludes polynomial kernels for vertex-deletion distance to proper interval graphs.

Authors: Anand Babu, Ashwin Jacob, Hitendra Kumar, R. B. Sandeep

The Eccentricity Shortest Path problem asks, given a connected graph and a nonnegative integer $R$, whether some shortest path is within distance $R$ of every vertex. We study its structural parameterized complexity. We give fixed-parameter algorithms for vertex-deletion distance to cographs, vertex-deletion distance to interval graphs, and bandwidth. The interval-deletion result answers a question of Bhyravarapu et al. (IWOCA 2023). We obtain XP algorithms for vertex-deletion distance to chordal graphs, clique-width, and rank-width, without requiring expressions or decompositions for the latter two. Consequently, the problem is polynomial-time solvable for every fixed treewidth, answering a question of Chakraborty et al. (2026). We also derive fixed-parameter tractability for treedepth and XP membership for diameter from earlier algorithms. We prove $\mathsf{W}[3]$-hardness for cutwidth, and hence pathwidth and treewidth, even on graphs of maximum degree at most five; $\mathsf{W}[2]$-hardness for vertex clique-cover number; and NP-hardness on graphs of tree-independence number at most two. For maximum leaf number $\ell$, we obtain an $O(\ell^4)$-bit compression and a polynomial kernel. Finally, unless $\mathsf{NP}\subseteq\mathrm{coNP}/\mathrm{poly}$, we exclude polynomial kernels for bandwidth, feedback edge number, vertex-deletion distance to a clique, and vertex cover number. The last bound also excludes polynomial kernels for vertex-deletion distance to proper interval graphs.

Subset Sum in Almost-Linear Time

from arXiv: Data Structures and Algorithms

Authors: Ce Jin, Xiao Mao

We give a deterministic algorithm for finding a subset of the input integers $1\le a_1,\ldots,a_n \le Δ$ that sums to the target $t$ in $\mathcal{O}(n+Δ^{1+o(1)})$ time. This improves upon the previous $\widetilde{\mathcal{O}}(n + Δ^{1.5})$-time algorithm by Chen, Lian, Mao, and Zhang (SODA 2024), and achieves almost tight dependence on $Δ$ assuming SETH. We use an inverse theorem for subset sums due to Conlon, Fox, and Pham (2023), and develop a quotient variant of economic cap covering from the polytope approximation literature.

Authors: Ce Jin, Xiao Mao

We give a deterministic algorithm for finding a subset of the input integers $1\le a_1,\ldots,a_n \le Δ$ that sums to the target $t$ in $\mathcal{O}(n+Δ^{1+o(1)})$ time. This improves upon the previous $\widetilde{\mathcal{O}}(n + Δ^{1.5})$-time algorithm by Chen, Lian, Mao, and Zhang (SODA 2024), and achieves almost tight dependence on $Δ$ assuming SETH. We use an inverse theorem for subset sums due to Conlon, Fox, and Pham (2023), and develop a quotient variant of economic cap covering from the polytope approximation literature.

Parameterized Complexity of Isometric Path Cover: Treewidth and Diameter

from arXiv: Data Structures and Algorithms

Authors: Dibyayan Chakraborty, Oscar Defrain, Florent Foucaud, Mathieu Mari, Prafullkumar Tale

We study the fixed-parameter tractability of the \textsc{Isometric Path Cover} problem where, given an integer $k$ and a simple, undirected, and unweighted graph $G$, the objective is to decide whether $G$ admits a set of $k$ isometric paths (i.e., shortest paths between their endpoints) whose union is the entire vertex set of $G$. We show that \textsc{Isometric Path Cover} is NP-hard on graphs of pathwidth (and thus treewidth) at most 287. This is an interesting contrast with the closely related problems \textsc{Isometric Path Partition} and \textsc{Path Cover}, which are both known to be XP for treewidth, see [Chakraborty et al., WG 2026] and [Foucaud et al., TCS 2026]. On the positive side, we show, by a suitable dynamic programming scheme, that \textsc{Isometric Path Cover} admits an XP algorithm w.r.t.~the parameter combination of treewidth and diameter, running in time $n^{\diam^{O(\tw)}}$ where $\diam$, $\tw$, and $n$ denote the diameter, the treewidth, and the number of vertices of the input graph, respectively. However, we also prove that \textsc{Isometric Path Cover} becomes W[1]-hard for pathwidth on graphs of diameter~7, showing that an XP algorithm is the best one can hope for w.r.t.~this parameterization, under standard complexity theory assumptions.

Authors: Dibyayan Chakraborty, Oscar Defrain, Florent Foucaud, Mathieu Mari, Prafullkumar Tale

We study the fixed-parameter tractability of the \textsc{Isometric Path Cover} problem where, given an integer $k$ and a simple, undirected, and unweighted graph $G$, the objective is to decide whether $G$ admits a set of $k$ isometric paths (i.e., shortest paths between their endpoints) whose union is the entire vertex set of $G$. We show that \textsc{Isometric Path Cover} is NP-hard on graphs of pathwidth (and thus treewidth) at most 287. This is an interesting contrast with the closely related problems \textsc{Isometric Path Partition} and \textsc{Path Cover}, which are both known to be XP for treewidth, see [Chakraborty et al., WG 2026] and [Foucaud et al., TCS 2026]. On the positive side, we show, by a suitable dynamic programming scheme, that \textsc{Isometric Path Cover} admits an XP algorithm w.r.t.~the parameter combination of treewidth and diameter, running in time $n^{\diam^{O(\tw)}}$ where $\diam$, $\tw$, and $n$ denote the diameter, the treewidth, and the number of vertices of the input graph, respectively. However, we also prove that \textsc{Isometric Path Cover} becomes W[1]-hard for pathwidth on graphs of diameter~7, showing that an XP algorithm is the best one can hope for w.r.t.~this parameterization, under standard complexity theory assumptions.

Geometric Graph Representations via Compact SAT Encodings

from arXiv: Data Structures and Algorithms

Authors: L. Sunil Chandran, Rishikesh Gajjala, Suraj Kumar Sahoo, Aatman Supkar

We present compact SAT encodings and certifying exact solvers for boxicity and cubicity. The boxicity of a graph is the minimum dimension in which it is the intersection graph of axis-aligned boxes; cubicity is defined analogously using unit cubes. Grid-based formulations face a geometric discretization bottleneck, growing rapidly with the target dimension. We avoid this bottleneck by encoding the total order of interval endpoints in each coordinate. For an $n$-vertex graph and target dimension $d$, our boxicity encoding uses $O(n^2d)$ variables and $O(n^3d)$ clauses, both linear in $d$. Requiring the left- and right-endpoint orders to agree yields a cubicity encoding of the same asymptotic size. Satisfying models are decoded into independently checked geometric representations, while unsatisfiable instances used as lower-bound evidence carry mechanically verified DRAT certificates. In a controlled evaluation with 186 trials per encoding on standard and structured graph families, the order encoding solves 96.8% of trials, compared with 71.0% for the known grid formulation, and reduces mean PAR-2 time from 178.21 to 21.70 seconds. The pipeline also certifies exact values for several named graphs. We turn one such value into a reusable obstruction by proving that every Kneser graph $K(n,k)$ with $k\ge3$ and $n\ge2k+1$ contains an induced Desargues graph. The resulting lower bound, together with an explicit three-dimensional representation of $K(7,3)$, establishes $\operatorname{boxicity}(K(7,3))=3$. This disproves the conjecture of Caoduro and Sebo that $\operatorname{boxicity}(K(n,k))=n-k$ for all $k\ge3$ and $n\ge2k+1$.

Authors: L. Sunil Chandran, Rishikesh Gajjala, Suraj Kumar Sahoo, Aatman Supkar

We present compact SAT encodings and certifying exact solvers for boxicity and cubicity. The boxicity of a graph is the minimum dimension in which it is the intersection graph of axis-aligned boxes; cubicity is defined analogously using unit cubes. Grid-based formulations face a geometric discretization bottleneck, growing rapidly with the target dimension. We avoid this bottleneck by encoding the total order of interval endpoints in each coordinate. For an $n$-vertex graph and target dimension $d$, our boxicity encoding uses $O(n^2d)$ variables and $O(n^3d)$ clauses, both linear in $d$. Requiring the left- and right-endpoint orders to agree yields a cubicity encoding of the same asymptotic size. Satisfying models are decoded into independently checked geometric representations, while unsatisfiable instances used as lower-bound evidence carry mechanically verified DRAT certificates. In a controlled evaluation with 186 trials per encoding on standard and structured graph families, the order encoding solves 96.8% of trials, compared with 71.0% for the known grid formulation, and reduces mean PAR-2 time from 178.21 to 21.70 seconds. The pipeline also certifies exact values for several named graphs. We turn one such value into a reusable obstruction by proving that every Kneser graph $K(n,k)$ with $k\ge3$ and $n\ge2k+1$ contains an induced Desargues graph. The resulting lower bound, together with an explicit three-dimensional representation of $K(7,3)$, establishes $\operatorname{boxicity}(K(7,3))=3$. This disproves the conjecture of Caoduro and Sebo that $\operatorname{boxicity}(K(n,k))=n-k$ for all $k\ge3$ and $n\ge2k+1$.

Restless Guards: Robust Guarding of Polyominoes

from arXiv: Data Structures and Algorithms

Authors: Linda Kleist, Ramin Kosfeld, Christian Rieck

The art gallery problem is a fundamental problem whose practical applications have inspired numerous variants. In this work, we investigate a setting in which full coverage of the gallery shall be ensured despite locational uncertainty of the guards. We study this problem for polyominoes under rectangular visibility, where a $k$-restless guard assigned to a cell may move to any cell within distance at most $k$. While all polyominoes are guardable for $k\in\{0, 1\}$, the same is not true for all $k\geq 2$. However, for a given polyomino $P$ and integer $k$, we present an algorithm that identifies a $k$-restless guard set of $P$ in polynomial time, or decides that no such set exists. For the cases $k\in\{0,1\}$, where $k$-restless guard sets always exist, we provide tight worst-case bounds on the number of $k$-restless guards that are always sufficient and sometimes necessary to guard a hole-free polyomino with $n$ vertices or $m$ cells. Moreover, we show that computing a minimum guard set is NP-hard. Complementing the hardness, we provide polynomial-time algorithms for solving the problem optimally for histograms, and leverage this to obtain a $20k$-approximation for polyominoes without holes.

Authors: Linda Kleist, Ramin Kosfeld, Christian Rieck

The art gallery problem is a fundamental problem whose practical applications have inspired numerous variants. In this work, we investigate a setting in which full coverage of the gallery shall be ensured despite locational uncertainty of the guards. We study this problem for polyominoes under rectangular visibility, where a $k$-restless guard assigned to a cell may move to any cell within distance at most $k$. While all polyominoes are guardable for $k\in\{0, 1\}$, the same is not true for all $k\geq 2$. However, for a given polyomino $P$ and integer $k$, we present an algorithm that identifies a $k$-restless guard set of $P$ in polynomial time, or decides that no such set exists. For the cases $k\in\{0,1\}$, where $k$-restless guard sets always exist, we provide tight worst-case bounds on the number of $k$-restless guards that are always sufficient and sometimes necessary to guard a hole-free polyomino with $n$ vertices or $m$ cells. Moreover, we show that computing a minimum guard set is NP-hard. Complementing the hardness, we provide polynomial-time algorithms for solving the problem optimally for histograms, and leverage this to obtain a $20k$-approximation for polyominoes without holes.

Near-Optimal Parallel $M$-Matrix Scaling

from arXiv: Data Structures and Algorithms

Authors: Chih-Hung Liu, Aaron Sidford, Ta-Wei Tu

In this paper we consider the problem of computing a positive diagonal scaling of a $d \times d$ symmetric $M$-matrix so that all row and column sums are $1$. We provide a poly-logarithmic iteration algorithm that solves this problem to high accuracy, where the cost of each iteration is dominated by the cost of solving a Laplacian system. This improves upon the recent $\tilde{O}(d^{1/3})$ iteration bound of [Vladu STOC 2025] and yields a randomized algorithm for the problem that runs in near-linear time and poly-logarithmic parallel depth.

Authors: Chih-Hung Liu, Aaron Sidford, Ta-Wei Tu

In this paper we consider the problem of computing a positive diagonal scaling of a $d \times d$ symmetric $M$-matrix so that all row and column sums are $1$. We provide a poly-logarithmic iteration algorithm that solves this problem to high accuracy, where the cost of each iteration is dominated by the cost of solving a Laplacian system. This improves upon the recent $\tilde{O}(d^{1/3})$ iteration bound of [Vladu STOC 2025] and yields a randomized algorithm for the problem that runs in near-linear time and poly-logarithmic parallel depth.

Subgraph Counting with Local Node Differential Privacy

from arXiv: Data Structures and Algorithms

Authors: Sofya Raskhodnikova, Adam Smith, Connor Wagaman, Anatoly Zavyalov

We study the problem of approximately counting subgraphs under local node differential privacy (LNDP), a recently introduced model that enforces strong privacy guarantees in analyses of distributed social networks. We design a noninteractive $(\varepsilon,0)$-LNDP algorithm that, given any public $k$-node template graph $H$, counts occurrences of $H$ in a sensitive undirected $n$-node graph with additive error $O(n^{k-\frac{1}{2}}/\varepsilon^{k-1})$, for any constant $k$. We complement this algorithm with a lower bound showing that every noninteractive $(\varepsilon,δ)$-LNDP algorithm for counting copies of any $k$-node template graph with maximum degree greater than $1$ must have error $Ω(n^{k - \frac{1}{2}})$. Thus, our algorithm achieves optimal error for counting constant-size subgraphs, up to $\mathrm{poly}(1/\varepsilon)$ factors. Our results substantially expand the types of network statistics known to be approximable under LNDP: prior work considered only functions of the degree distribution. In addition, our lower bound yields the first known $\mathrm{poly}(n)$ gap between central and local node differential privacy when accuracy is required for all input graphs. Previous separations either only involved $\mathrm{poly}(1/\varepsilon)$ factors or only applied to promise problems.

Authors: Sofya Raskhodnikova, Adam Smith, Connor Wagaman, Anatoly Zavyalov

We study the problem of approximately counting subgraphs under local node differential privacy (LNDP), a recently introduced model that enforces strong privacy guarantees in analyses of distributed social networks. We design a noninteractive $(\varepsilon,0)$-LNDP algorithm that, given any public $k$-node template graph $H$, counts occurrences of $H$ in a sensitive undirected $n$-node graph with additive error $O(n^{k-\frac{1}{2}}/\varepsilon^{k-1})$, for any constant $k$. We complement this algorithm with a lower bound showing that every noninteractive $(\varepsilon,δ)$-LNDP algorithm for counting copies of any $k$-node template graph with maximum degree greater than $1$ must have error $Ω(n^{k - \frac{1}{2}})$. Thus, our algorithm achieves optimal error for counting constant-size subgraphs, up to $\mathrm{poly}(1/\varepsilon)$ factors. Our results substantially expand the types of network statistics known to be approximable under LNDP: prior work considered only functions of the degree distribution. In addition, our lower bound yields the first known $\mathrm{poly}(n)$ gap between central and local node differential privacy when accuracy is required for all input graphs. Previous separations either only involved $\mathrm{poly}(1/\varepsilon)$ factors or only applied to promise problems.

Tree Edit Distance: Near-Linear-Time Approximate, Sublinear, and Dynamic

from arXiv: Data Structures and Algorithms

Authors: Bingbing Hu, Tomasz Kociumaka, Jakob Nogler, Barna Saha

Tree edit distance (TED), the analog of string edit distance (ED) for hierarchical data, is the minimum number of node insertions, deletions, and substitutions transforming one labeled rooted ordered tree into another. We narrow polynomial gaps between TED and ED in near-linear-time approximation, sublinear algorithms, and dynamic algorithms. We give an $n^{o(1)}$-approximation in $O(n)$ time and, for every $0<δ<1$, a $(\log n)^{O(1/δ)}$-approximation in $O(n^{1+δ})$ time. This improves the previous near-linear-time $O(\sqrt n)$-approximation [Boroujeni et al., STOC'19] and brings TED to parity with ED in this regime up to polylogarithmic factors. With query access to the balanced-parentheses (Euler-tour) representation of the input trees, we give the first sublinear-time TED algorithm: for every $τ\in[1,n]$, it computes a $τn^{o(1)}$-approximation of $1+\mathrm{ted}$ in $O(n/τ)$ time. For dynamic TED, we give an exact algorithm with amortized update time $\tilde{O}(k^7)$ and a $(1+\varepsilon)$-approximation with amortized update time $\tilde{O}_{\varepsilon}(k)$, where $k$ caps the distance that must be reported. The exact algorithm is the first to beat recomputation in a nontrivial parameter regime. The approximation improves prior dynamic guarantees [Das et al., FOCS'25] and cannot be significantly improved in any parameter regime without a breakthrough for static $(1+\varepsilon)$-approximation of ED. All results use dynamic programming on a new hierarchical tree decomposition, analogous to partitioning strings into blocks of geometrically decreasing sizes. It combines structural properties previously achieved only in isolation, providing a framework for transferring algorithmic ideas from ED to TED, with additional ingredients addressing the obstacles in each computational setting.

Authors: Bingbing Hu, Tomasz Kociumaka, Jakob Nogler, Barna Saha

Tree edit distance (TED), the analog of string edit distance (ED) for hierarchical data, is the minimum number of node insertions, deletions, and substitutions transforming one labeled rooted ordered tree into another. We narrow polynomial gaps between TED and ED in near-linear-time approximation, sublinear algorithms, and dynamic algorithms. We give an $n^{o(1)}$-approximation in $O(n)$ time and, for every $0<δ<1$, a $(\log n)^{O(1/δ)}$-approximation in $O(n^{1+δ})$ time. This improves the previous near-linear-time $O(\sqrt n)$-approximation [Boroujeni et al., STOC'19] and brings TED to parity with ED in this regime up to polylogarithmic factors. With query access to the balanced-parentheses (Euler-tour) representation of the input trees, we give the first sublinear-time TED algorithm: for every $τ\in[1,n]$, it computes a $τn^{o(1)}$-approximation of $1+\mathrm{ted}$ in $O(n/τ)$ time. For dynamic TED, we give an exact algorithm with amortized update time $\tilde{O}(k^7)$ and a $(1+\varepsilon)$-approximation with amortized update time $\tilde{O}_{\varepsilon}(k)$, where $k$ caps the distance that must be reported. The exact algorithm is the first to beat recomputation in a nontrivial parameter regime. The approximation improves prior dynamic guarantees [Das et al., FOCS'25] and cannot be significantly improved in any parameter regime without a breakthrough for static $(1+\varepsilon)$-approximation of ED. All results use dynamic programming on a new hierarchical tree decomposition, analogous to partitioning strings into blocks of geometrically decreasing sizes. It combines structural properties previously achieved only in isolation, providing a framework for transferring algorithmic ideas from ED to TED, with additional ingredients addressing the obstacles in each computational setting.

Per-State Signatures and Mean Time-to-Failure: An Algorithm for Systems With Simultaneous Failures

from arXiv: Data Structures and Algorithms

Authors: Guido Lagos, Simón Ávila

We present an algorithm that computes the structural signature and the mean time-to-failure (MTTF) of a monotone binary system at every one of its states, rather than only at the state in which all components work. It makes no assumption on the structure function and evaluates it exactly once per state, as many times as needed for the signature of the complete system alone, so computing the MTTF everywhere costs only a factor polynomial in the number of components over computing it once. As a by-product, it identifies all minimal path sets. We assume exchangeable component failure times whose survivor count is a continuous-time Markov chain. This setting admits simultaneous failures, as in the Lévy-frailty Marshall-Olkin, binomial failure rate and lethal-shock models, where the simplifications available for single failures do not apply. We use the per-state MTTF to define threshold repair policies. We show that the per-state signature is a strictly finer decision criterion than the MTTF: signature dominance at a common failure count implies MTTF dominance, but not conversely. Under the Lévy-frailty Marshall-Olkin model the MTTF is monotone in the state, which yields the long-run cost rate of every threshold policy in a single pass. Experiments on 22 benchmark networks with up to 30 components and 31 failure models show that the best MTTF-threshold policy has a median excess cost of 0.36% over the optimal state-based policy, and that it is never worse than the best policy that repairs after a fixed number of failures.

Authors: Guido Lagos, Simón Ávila

We present an algorithm that computes the structural signature and the mean time-to-failure (MTTF) of a monotone binary system at every one of its states, rather than only at the state in which all components work. It makes no assumption on the structure function and evaluates it exactly once per state, as many times as needed for the signature of the complete system alone, so computing the MTTF everywhere costs only a factor polynomial in the number of components over computing it once. As a by-product, it identifies all minimal path sets. We assume exchangeable component failure times whose survivor count is a continuous-time Markov chain. This setting admits simultaneous failures, as in the Lévy-frailty Marshall-Olkin, binomial failure rate and lethal-shock models, where the simplifications available for single failures do not apply. We use the per-state MTTF to define threshold repair policies. We show that the per-state signature is a strictly finer decision criterion than the MTTF: signature dominance at a common failure count implies MTTF dominance, but not conversely. Under the Lévy-frailty Marshall-Olkin model the MTTF is monotone in the state, which yields the long-run cost rate of every threshold policy in a single pass. Experiments on 22 benchmark networks with up to 30 components and 31 failure models show that the best MTTF-threshold policy has a median excess cost of 0.36% over the optimal state-based policy, and that it is never worse than the best policy that repairs after a fixed number of failures.

Asteroidal Triples and Friends in (Likely Necessary) Matrix Multiplication Time

from arXiv: Data Structures and Algorithms

Authors: Jakob Nogler, Agnès Totschnig, Virginia Vassilevska Williams

In a seminal paper, Kratsch and Spinrad [SODA 2003 and SIAM Journal on Computing 2006] studied several important problems in structural graph theory that anticipated two trends in fine-grained complexity, both from the upper- and lower-bound side: * From the upper-bound side, for each of these problems the authors give an intermediate running time strictly between $n^ω$ and $n^3$, where $ω<2.371177$ is the matrix multiplication exponent. When optimized, this running time can be made $\tilde{\mathcal{O}}(n^{(3+ω)/2})$. By now, many more problems can be solved in this running time: directed unweighted or node-weighted APSP, a variety of matrix products such as max-min and dominance, RNA Folding and many more. For some of these, the community has also developed tools suggesting that this intermediate running time is necessary. * From the lower-bound side, the authors give lower bounds of $n^{ω-o(1)}$, conditioned on three natural hardness hypotheses on the complexity of quantified variants of triangle detection that account for different types of hardness of such problems. Despite this, it has remained open for over 20 years which of these two pieces of evidence reflects the truth for the problems that Kratsch and Spinrad study: whether the intermediate running time is indeed necessary and stronger lower bounds hold, or whether the hardness evidence of Kratsch and Spinrad is essentially tight. In this paper, we resolve this question and show that the latter is the case: all problems admit an $\tilde{\mathcal{O}}(n^ω)$ time algorithm. As an additional contribution, we make partial algorithmic progress on further problems in structural graph theory of intermediate complexity, improving over previous work [Chiu and Lu, STACS 2022 and Information and Computation 2024][Spinrad, Discrete Applied Math 2004].

Authors: Jakob Nogler, Agnès Totschnig, Virginia Vassilevska Williams

In a seminal paper, Kratsch and Spinrad [SODA 2003 and SIAM Journal on Computing 2006] studied several important problems in structural graph theory that anticipated two trends in fine-grained complexity, both from the upper- and lower-bound side: * From the upper-bound side, for each of these problems the authors give an intermediate running time strictly between $n^ω$ and $n^3$, where $ω<2.371177$ is the matrix multiplication exponent. When optimized, this running time can be made $\tilde{\mathcal{O}}(n^{(3+ω)/2})$. By now, many more problems can be solved in this running time: directed unweighted or node-weighted APSP, a variety of matrix products such as max-min and dominance, RNA Folding and many more. For some of these, the community has also developed tools suggesting that this intermediate running time is necessary. * From the lower-bound side, the authors give lower bounds of $n^{ω-o(1)}$, conditioned on three natural hardness hypotheses on the complexity of quantified variants of triangle detection that account for different types of hardness of such problems. Despite this, it has remained open for over 20 years which of these two pieces of evidence reflects the truth for the problems that Kratsch and Spinrad study: whether the intermediate running time is indeed necessary and stronger lower bounds hold, or whether the hardness evidence of Kratsch and Spinrad is essentially tight. In this paper, we resolve this question and show that the latter is the case: all problems admit an $\tilde{\mathcal{O}}(n^ω)$ time algorithm. As an additional contribution, we make partial algorithmic progress on further problems in structural graph theory of intermediate complexity, improving over previous work [Chiu and Lu, STACS 2022 and Information and Computation 2024][Spinrad, Discrete Applied Math 2004].

Preprocessed 3SUM: From Reals to Integers

from arXiv: Data Structures and Algorithms

Authors: Nick Fischer, Jonas Schmidt

The Preprocessed 3SUM problem asks to preprocess a set $A$ of $n$ numbers so that we can quickly decide whether a query subset $A' \subseteq A$ contains a solution $a+b=c$. The best known query bounds differ substantially between integer and real inputs. For integers, a series of advances with near-quadratic preprocessing reduced the query time from $\tilde O(n^{13/7})$ [Chan, Lewenstein; STOC '15] to $\tilde O(n^{11/6})$ [Chan, Vassilevska W., Xu; STOC '23], and finally to $\tilde O(n^{3/2})$ [Kasliwal, Polak, Sharma; SOSA '25]. For reals, only the first of these bounds has been achieved, by adapting the corresponding integer algorithm [Fischer; SODA '25]. Is this gap inherent to the more limited tools available for real inputs? We give a randomized real-to-integer reduction with $\tilde O(n^2)$ preprocessing overhead and no asymptotic increase in query time. This yields $\tilde O(n^2)$ preprocessing and $\tilde O(n^{3/2})$ query time for real inputs, closing the gap. We show how to extend our result to the setting where one set is unknown at preprocessing time. Our reduction is conceptually simple, and relies on an interesting technical toolkit around old and new tools from sparse linear algebra. More broadly, it suggests a route to real-to-integer reductions for other additive problems.

Authors: Nick Fischer, Jonas Schmidt

The Preprocessed 3SUM problem asks to preprocess a set $A$ of $n$ numbers so that we can quickly decide whether a query subset $A' \subseteq A$ contains a solution $a+b=c$. The best known query bounds differ substantially between integer and real inputs. For integers, a series of advances with near-quadratic preprocessing reduced the query time from $\tilde O(n^{13/7})$ [Chan, Lewenstein; STOC '15] to $\tilde O(n^{11/6})$ [Chan, Vassilevska W., Xu; STOC '23], and finally to $\tilde O(n^{3/2})$ [Kasliwal, Polak, Sharma; SOSA '25]. For reals, only the first of these bounds has been achieved, by adapting the corresponding integer algorithm [Fischer; SODA '25]. Is this gap inherent to the more limited tools available for real inputs? We give a randomized real-to-integer reduction with $\tilde O(n^2)$ preprocessing overhead and no asymptotic increase in query time. This yields $\tilde O(n^2)$ preprocessing and $\tilde O(n^{3/2})$ query time for real inputs, closing the gap. We show how to extend our result to the setting where one set is unknown at preprocessing time. Our reduction is conceptually simple, and relies on an interesting technical toolkit around old and new tools from sparse linear algebra. More broadly, it suggests a route to real-to-integer reductions for other additive problems.

Thursday, October 15

Linkage

from David Eppstein
arXiv imposes a new limit of two submissions per submitter per month (\(\mathbb{M}\)).

By David Eppstein

Complexity and Lean

from Computational Complexity

In the OpenAI Math Drop some papers have Lean proofs, others don't, including L = RL = BPL. Why is it hard to formalize results on complexity classes?

Let's consider an example, Lautemann's proof that BPP is in \(\Sigma^p_2\). It's a beautiful proof because it comes down to a single formula. I gave the full proof in a 2025 post, but to remind you, here is the setup.

Let \(B\) be in BPP, accepted by a probabilistic polynomial-time Turing machine \(M\) with error bounded by \(2^{-n}\). Let \(A(x,r)\) be true iff \(M(x)\) using random tape \(r\) accepts. \(A(x,r)\) is deterministically polynomial-time computable. We can assume \(|r|=|x|^k\) for some \(k\).

Here is the \(\Sigma^p_2\) expression for input \(x\) of length \(n\). All quantification is over binary strings of length \(n^k\). Let \(\oplus\) be bitwise parity (XOR) and \(\vee\) be logical OR.

\[\exists z_1,\ldots,z_{n^k}\ \forall y\ \ A(x,z_1\oplus y) \vee \cdots \vee A(x,z_{n^k}\oplus y)\]

The hard part of the proof, besides coming up with this formula itself, is the probabilistic argument to show you always get the right answer.

So this seems simple enough to give a full Lean proof. And it is not too hard (for Claude) to get Lean to formalize the probabilistic argument, though even there Lean makes you deal with details we wave away on paper. When \(x\not\in B\) the argument needs \(n^k2^{-n}<1\), which fails for small \(n\), so short inputs need separate handling. But you also need to show the following:

Let \(A(x,r)\) be polynomial-time computable. Then given \(x\), \(y\) and the \(z_i\) as inputs, you can compute \(A(x,z_1\oplus y) \vee \cdots \vee A(x,z_{n^k}\oplus y)\) in polynomial time, where \(u\oplus v\) is bitwise parity.

There is nothing challenging about this statement. It is so painfully obviously true that I didn't even bring up this point in the 2025 post. But proving you can do this in polynomial time on a Turing machine, even though the only operations are bitwise parity, ORs and composition, is just very complicated to formalize in Lean.

Some suggest that using Cobham's characterization might be easier. But see below for what you need to do for bitwise parity alone. Imagine how complicated it would all get for something like Toda's theorem.

Update: Since I first drafted this post, OpenAI dropped a claimed Lean proof of L = RL = BPL, so maybe ignore this whole post?

Cobham defined his class as the smallest set of functions on the natural numbers (viewed as binary strings, with \(|x|\) the length of \(x\) and \(|0|=0\)) that contains the constant zero, the projections, the two successor functions \(s_0(x)=2x\) and \(s_1(x)=2x+1\), and the smash function \(x\#y=2^{|x|\cdot|y|}\), and that is closed under composition and bounded recursion on notation. The second operation defines a new function \(f\) from functions \(g\), \(h_0\), \(h_1\) in the class by \[\begin{aligned} f(0,\vec{y})&=g(\vec{y})\\ f(s_i(x),\vec{y})&=h_i(x,\vec{y},f(x,\vec{y})) \end{aligned}\] for \(i\in\{0,1\}\) and \(s_i(x)\neq 0\), provided that \(f(x,\vec{y})\leq k(x,\vec{y})\) for some \(k\) already in the class. The recursion runs over the bits of \(x\), so it takes only \(|x|\) steps. The smash function gives the outputs polynomial growth in their lengths, and the bound \(k\) keeps that growth from compounding over the recursion. Cobham's theorem says that this class is exactly the set of functions computable in polynomial time.

As an example, consider bitwise XOR, \(f(x,y)=x\oplus y\). The difficulty is that bounded recursion on notation peels bits off only one argument, while XOR has to move through \(x\) and \(y\) together. The fix is to recurse on a long enough counter \(T\) and extract the bits of \(x\) and \(y\) by shifting. We first need four helper functions, each defined by a single bounded recursion on notation: \[\begin{aligned} \mathrm{par}(0)=\mathrm{par}(s_0(x))&=0, & \mathrm{par}(s_1(x))&=1\\ \mathrm{half}(0)&=0, & \mathrm{half}(s_i(x))&=x\\ \mathrm{msp}(0,x)&=x, & \mathrm{msp}(s_i(w),x)&=\mathrm{half}(\mathrm{msp}(w,x))\\ C(0,u,v)=C(s_0(a),u,v)&=u, & C(s_1(a),u,v)&=v \end{aligned}\] with bounds \(1\), \(x\), \(x\) and \(s_1(u)\#s_1(v)\) respectively. Here \(\mathrm{msp}(w,x)=\lfloor x/2^{|w|}\rfloor\), so bit \(j\) of \(x\) is \(\mathrm{par}(\mathrm{msp}(w,x))\) for any \(w\) with \(|w|=j\). \(C\) is a conditional on the low bit of \(a\). Composition then gives the XOR of the low bits of \(a\) and \(c\), \(\beta(a,c)=C(a,\mathrm{par}(c),C(c,1,0))\), and appending the low bit of \(b\) to \(z\), \(\mathrm{app}(b,z)=C(b,s_0(z),s_1(z))\).

Now we recurse on \(w\) with \(T\), \(x\) and \(y\) as parameters, building the answer from the top bit down. Let \(m_i=\mathrm{msp}(s_i(w),T)\), which has length \(|T|-|w|-1\), the position of the next bit to append, and let \(b_i=\beta(\mathrm{msp}(m_i,x),\mathrm{msp}(m_i,y))\) be that bit of \(x\oplus y\). Then \[\begin{aligned} G(0,T,x,y)&=0\\ G(s_i(w),T,x,y)&=\mathrm{app}(b_i,G(w,T,x,y)) \end{aligned}\] By induction, \(G(w,T,x,y)\) is \(x\oplus y\) shifted right by \(|T|-|w|\) bits, so it is bounded by \(s_1(x)\#s_1(y)\). Finally we set \[f(x,y)=G(T,T,x,y)\quad\text{where } T=s_1(x)\#s_1(y).\] The smash gives \(|T|=(|x|+1)(|y|+1)+1\geq\max(|x|,|y|)\), so the recursion starts above the high bits of both arguments, where every bit is zero. \(T\) is much longer than it needs to be, but that costs only polynomially many extra steps.

By Lance Fortnow

In the OpenAI Math Drop some papers have Lean proofs, others don't, including L = RL = BPL. Why is it hard to formalize results on complexity classes?

Let's consider an example, Lautemann's proof that BPP is in \(\Sigma^p_2\). It's a beautiful proof because it comes down to a single formula. I gave the full proof in a 2025 post, but to remind you, here is the setup.

Let \(B\) be in BPP, accepted by a probabilistic polynomial-time Turing machine \(M\) with error bounded by \(2^{-n}\). Let \(A(x,r)\) be true iff \(M(x)\) using random tape \(r\) accepts. \(A(x,r)\) is deterministically polynomial-time computable. We can assume \(|r|=|x|^k\) for some \(k\).

Here is the \(\Sigma^p_2\) expression for input \(x\) of length \(n\). All quantification is over binary strings of length \(n^k\). Let \(\oplus\) be bitwise parity (XOR) and \(\vee\) be logical OR.

\[\exists z_1,\ldots,z_{n^k}\ \forall y\ \ A(x,z_1\oplus y) \vee \cdots \vee A(x,z_{n^k}\oplus y)\]

The hard part of the proof, besides coming up with this formula itself, is the probabilistic argument to show you always get the right answer.

So this seems simple enough to give a full Lean proof. And it is not too hard (for Claude) to get Lean to formalize the probabilistic argument, though even there Lean makes you deal with details we wave away on paper. When \(x\not\in B\) the argument needs \(n^k2^{-n}<1\), which fails for small \(n\), so short inputs need separate handling. But you also need to show the following:

Let \(A(x,r)\) be polynomial-time computable. Then given \(x\), \(y\) and the \(z_i\) as inputs, you can compute \(A(x,z_1\oplus y) \vee \cdots \vee A(x,z_{n^k}\oplus y)\) in polynomial time, where \(u\oplus v\) is bitwise parity.

There is nothing challenging about this statement. It is so painfully obviously true that I didn't even bring up this point in the 2025 post. But proving you can do this in polynomial time on a Turing machine, even though the only operations are bitwise parity, ORs and composition, is just very complicated to formalize in Lean.

Some suggest that using Cobham's characterization might be easier. But see below for what you need to do for bitwise parity alone. Imagine how complicated it would all get for something like Toda's theorem.

Update: Since I first drafted this post, OpenAI dropped a claimed Lean proof of L = RL = BPL, so maybe ignore this whole post?

Cobham defined his class as the smallest set of functions on the natural numbers (viewed as binary strings, with \(|x|\) the length of \(x\) and \(|0|=0\)) that contains the constant zero, the projections, the two successor functions \(s_0(x)=2x\) and \(s_1(x)=2x+1\), and the smash function \(x\#y=2^{|x|\cdot|y|}\), and that is closed under composition and bounded recursion on notation. The second operation defines a new function \(f\) from functions \(g\), \(h_0\), \(h_1\) in the class by \[\begin{aligned} f(0,\vec{y})&=g(\vec{y})\\ f(s_i(x),\vec{y})&=h_i(x,\vec{y},f(x,\vec{y})) \end{aligned}\] for \(i\in\{0,1\}\) and \(s_i(x)\neq 0\), provided that \(f(x,\vec{y})\leq k(x,\vec{y})\) for some \(k\) already in the class. The recursion runs over the bits of \(x\), so it takes only \(|x|\) steps. The smash function gives the outputs polynomial growth in their lengths, and the bound \(k\) keeps that growth from compounding over the recursion. Cobham's theorem says that this class is exactly the set of functions computable in polynomial time.

As an example, consider bitwise XOR, \(f(x,y)=x\oplus y\). The difficulty is that bounded recursion on notation peels bits off only one argument, while XOR has to move through \(x\) and \(y\) together. The fix is to recurse on a long enough counter \(T\) and extract the bits of \(x\) and \(y\) by shifting. We first need four helper functions, each defined by a single bounded recursion on notation: \[\begin{aligned} \mathrm{par}(0)=\mathrm{par}(s_0(x))&=0, & \mathrm{par}(s_1(x))&=1\\ \mathrm{half}(0)&=0, & \mathrm{half}(s_i(x))&=x\\ \mathrm{msp}(0,x)&=x, & \mathrm{msp}(s_i(w),x)&=\mathrm{half}(\mathrm{msp}(w,x))\\ C(0,u,v)=C(s_0(a),u,v)&=u, & C(s_1(a),u,v)&=v \end{aligned}\] with bounds \(1\), \(x\), \(x\) and \(s_1(u)\#s_1(v)\) respectively. Here \(\mathrm{msp}(w,x)=\lfloor x/2^{|w|}\rfloor\), so bit \(j\) of \(x\) is \(\mathrm{par}(\mathrm{msp}(w,x))\) for any \(w\) with \(|w|=j\). \(C\) is a conditional on the low bit of \(a\). Composition then gives the XOR of the low bits of \(a\) and \(c\), \(\beta(a,c)=C(a,\mathrm{par}(c),C(c,1,0))\), and appending the low bit of \(b\) to \(z\), \(\mathrm{app}(b,z)=C(b,s_0(z),s_1(z))\).

Now we recurse on \(w\) with \(T\), \(x\) and \(y\) as parameters, building the answer from the top bit down. Let \(m_i=\mathrm{msp}(s_i(w),T)\), which has length \(|T|-|w|-1\), the position of the next bit to append, and let \(b_i=\beta(\mathrm{msp}(m_i,x),\mathrm{msp}(m_i,y))\) be that bit of \(x\oplus y\). Then \[\begin{aligned} G(0,T,x,y)&=0\\ G(s_i(w),T,x,y)&=\mathrm{app}(b_i,G(w,T,x,y)) \end{aligned}\] By induction, \(G(w,T,x,y)\) is \(x\oplus y\) shifted right by \(|T|-|w|\) bits, so it is bounded by \(s_1(x)\#s_1(y)\). Finally we set \[f(x,y)=G(T,T,x,y)\quad\text{where } T=s_1(x)\#s_1(y).\] The smash gives \(|T|=(|x|+1)(|y|+1)+1\geq\max(|x|,|y|)\), so the recursion starts above the high bits of both arguments, where every bit is zero. \(T\) is much longer than it needs to be, but that costs only polynomially many extra steps.

By Lance Fortnow

TR26-254 | Parameterized Uniformity Testing in the Huge Object Model | Clément Canonne, Sayantan Sen, Joy Qiping Yang

from ECCC Papers
We continue the study of distribution testing in the Huge Object Model (Goldreich and Ron, 2023), focusing on the flagship question of \emph{parameterized uniformity testing}: given access to data in $\{0,1\}^n$, is its underlying probability distribution uniform on a small subset of size $m$? The focus here is on both the number of data points required (sample complexity) and the number of bits from these data points one needs to observe (query complexity). While previous work fully characterized both of these in the ``tiny support'' regime $m \ll n$, very little was understood in the ``medium-to-large'' support one ($n \leq m \ll 2^n$). Our work remedies this state of affairs, establishing the existence of a sample-query complexity tradeoff and showing that (in contrast to the tiny-support regime) adaptive algorithms can significantly outperform non-adaptive ones.
We continue the study of distribution testing in the Huge Object Model (Goldreich and Ron, 2023), focusing on the flagship question of \emph{parameterized uniformity testing}: given access to data in $\{0,1\}^n$, is its underlying probability distribution uniform on a small subset of size $m$? The focus here is on both the number of data points required (sample complexity) and the number of bits from these data points one needs to observe (query complexity). While previous work fully characterized both of these in the ``tiny support'' regime $m \ll n$, very little was understood in the ``medium-to-large'' support one ($n \leq m \ll 2^n$). Our work remedies this state of affairs, establishing the existence of a sample-query complexity tradeoff and showing that (in contrast to the tiny-support regime) adaptive algorithms can significantly outperform non-adaptive ones.

Full AFLs Closed under Nested Iterated $k$-Variate Substitution and Depth-k Storage Automata

from arXiv: Computational Complexity

Authors: Tomoyuki Yamakami

Greibach and Ginsburg (1969) studied abstract families of languages or AFLs. A full AFL is a class of languages closed under the following operations: homomorphism, inverse homomorphism, intersection with regular languages, union, concatenation, and Kleene star. Greibach (1970) used a restricted substitution, called nested iterated substitution, to introduce the notion of super AFLs, which are full AFLs that are further closed under nested iterated substitution. The smallest super AFL was shown to be the class of all context-free languages. From this fact, we focus on Greibach's limited, univariable application of nested iterated substitution and further generalize it into k-variate. This introduces the notion of nested iterated $k$-variate substitution whose definition does not rely on "machines". Recently, a "two-tape" version of Hibbard's (1967) rewriting systems, which were later reformulated by Pighizzini and Pisoni (2014) as k-limited automata with "single" input/work tapes, has been studied under the name of depth-k storage automata. This work unearths a few characteristics of the languages recognized by those automata under a restriction known as frozen blank sensitivity in terms of full AFLs and nested iterated k-variate substitution. This will promote a further abstract treatment of those languages.

Authors: Tomoyuki Yamakami

Greibach and Ginsburg (1969) studied abstract families of languages or AFLs. A full AFL is a class of languages closed under the following operations: homomorphism, inverse homomorphism, intersection with regular languages, union, concatenation, and Kleene star. Greibach (1970) used a restricted substitution, called nested iterated substitution, to introduce the notion of super AFLs, which are full AFLs that are further closed under nested iterated substitution. The smallest super AFL was shown to be the class of all context-free languages. From this fact, we focus on Greibach's limited, univariable application of nested iterated substitution and further generalize it into k-variate. This introduces the notion of nested iterated $k$-variate substitution whose definition does not rely on "machines". Recently, a "two-tape" version of Hibbard's (1967) rewriting systems, which were later reformulated by Pighizzini and Pisoni (2014) as k-limited automata with "single" input/work tapes, has been studied under the name of depth-k storage automata. This work unearths a few characteristics of the languages recognized by those automata under a restriction known as frozen blank sensitivity in terms of full AFLs and nested iterated k-variate substitution. This will promote a further abstract treatment of those languages.

Semi-Linearity and Generalized Stratified Semi-Linearity beyond Context-Free Languages

from arXiv: Computational Complexity

Authors: Tomoyuki Yamakami

Parikh's famous theorem relates to the semi-linearity of context-free languages. We seek for expanding the scope of the semi-linear property from context-free languages to languages beyond them. Of those languages, this work focuses on the languages that are particularly recognized by one-way nondeterministic depth-k storage automata under a natural restriction of frozen blank sensitivity. Those languages (which are called $k$-storable languages) have been already proven to satisfy a natural generalization of the well-known pumping lemmas for regular and context-free languages. The semi-linearity of k-storable languages generalizes Parikh's theorem on the semi-linearity of context-free languages. As for bounded languages, Ginsburg and Spanier's well-known theorem characterizes context-free languages in terms of stratified semi-linearity. This notion is further generalized and is used to characterize the k-storable languages as well. Such a generalization is interesting in its own right. This helps us take an abstract treatment of k-storable languages without relying on "machines". Our results strengthen the belief that those languages are "natural" generalizations of context-free languages.

Authors: Tomoyuki Yamakami

Parikh's famous theorem relates to the semi-linearity of context-free languages. We seek for expanding the scope of the semi-linear property from context-free languages to languages beyond them. Of those languages, this work focuses on the languages that are particularly recognized by one-way nondeterministic depth-k storage automata under a natural restriction of frozen blank sensitivity. Those languages (which are called $k$-storable languages) have been already proven to satisfy a natural generalization of the well-known pumping lemmas for regular and context-free languages. The semi-linearity of k-storable languages generalizes Parikh's theorem on the semi-linearity of context-free languages. As for bounded languages, Ginsburg and Spanier's well-known theorem characterizes context-free languages in terms of stratified semi-linearity. This notion is further generalized and is used to characterize the k-storable languages as well. Such a generalization is interesting in its own right. This helps us take an abstract treatment of k-storable languages without relying on "machines". Our results strengthen the belief that those languages are "natural" generalizations of context-free languages.

The Two-Level Poset Game is PSPACE-Complete

from arXiv: Computational Complexity

Authors: Kengo Hashimoto

Poset games form a fundamental class of combinatorial games that includes classical games such as Nim and Chomp. Determining the outcome of a poset game is known to be PSPACE-complete even for posets of height at most three. However, the exact computational complexity of Two-Level Poset Game, played on posets of height at most two, has remained an open problem for over a decade. We resolve this problem by proving that Two-Level Poset Game is PSPACE-complete via a polynomial-time reduction from Avoid True on positive CNF formulas.

Authors: Kengo Hashimoto

Poset games form a fundamental class of combinatorial games that includes classical games such as Nim and Chomp. Determining the outcome of a poset game is known to be PSPACE-complete even for posets of height at most three. However, the exact computational complexity of Two-Level Poset Game, played on posets of height at most two, has remained an open problem for over a decade. We resolve this problem by proving that Two-Level Poset Game is PSPACE-complete via a polynomial-time reduction from Avoid True on positive CNF formulas.

Generalized Fibonacci gates are Frobenius algebras: a proof of Liu's conjecture

from arXiv: Computational Complexity

Authors: Aaron Sterling

Liu introduced generalized Fibonacci gates on domains of size 3 and 4, proved that Holant problems built from them are tractable by a cut-and-merge algorithm, and conjectured that Fibonacci gates with matching polynomial-time algorithms exist on all higher domains. We extend the definition uniformly to every domain size $k$ and show that a family of domain-$k$ generalized Fibonacci gates is the same thing as a $k$-dimensional commutative Frobenius (equivalently, Artinian Gorenstein) algebra $A$ equipped with an orthonormal basis containing $1$. Under this dictionary Liu's parameter constraints are exactly associativity of $A$, a gate is an element $w\in A$, merging two gates is multiplication in $A$, and the Holant value of a connected graph $G$ is $τ\bigl(h^{\,|E|-|V|+1}\prod_v w_v\bigr)$ for a fixed ``handle'' element $h\in A$. This proves Liu's conjecture for every $k$, with an algorithm whose cost is polynomial in $k$ as well as in the graph. We also give a closure criterion for linear recurrences of any depth (a balance condition implies closure, and is necessary when the unit is a combination of the colours), and we map the landscape: semisimple algebras are exactly Liu's orthogonal frames, smoothable algebras with nilpotents are collided frames, and families whose algebra is not smoothable (``frameless'' families) exist exactly when $k\ge 14$. Finally we show that every non-semisimple part of a family is invisible to the Holant on connected graphs with two or more independent cycles. This note states the results and sketches the proofs; the proofs of the main theorems are short and are given essentially in full.

Authors: Aaron Sterling

Liu introduced generalized Fibonacci gates on domains of size 3 and 4, proved that Holant problems built from them are tractable by a cut-and-merge algorithm, and conjectured that Fibonacci gates with matching polynomial-time algorithms exist on all higher domains. We extend the definition uniformly to every domain size $k$ and show that a family of domain-$k$ generalized Fibonacci gates is the same thing as a $k$-dimensional commutative Frobenius (equivalently, Artinian Gorenstein) algebra $A$ equipped with an orthonormal basis containing $1$. Under this dictionary Liu's parameter constraints are exactly associativity of $A$, a gate is an element $w\in A$, merging two gates is multiplication in $A$, and the Holant value of a connected graph $G$ is $τ\bigl(h^{\,|E|-|V|+1}\prod_v w_v\bigr)$ for a fixed ``handle'' element $h\in A$. This proves Liu's conjecture for every $k$, with an algorithm whose cost is polynomial in $k$ as well as in the graph. We also give a closure criterion for linear recurrences of any depth (a balance condition implies closure, and is necessary when the unit is a combination of the colours), and we map the landscape: semisimple algebras are exactly Liu's orthogonal frames, smoothable algebras with nilpotents are collided frames, and families whose algebra is not smoothable (``frameless'' families) exist exactly when $k\ge 14$. Finally we show that every non-semisimple part of a family is invisible to the Holant on connected graphs with two or more independent cycles. This note states the results and sketches the proofs; the proofs of the main theorems are short and are given essentially in full.

A Simple Algorithm for Counting Solutions to Integer Programs

from arXiv: Computational Complexity

Authors: Daniel Dadush, Anita Dürr, Thekla Hamm, Jonas Schmidt, Karol Węgrzycki

We consider the problem of counting solutions to integer linear programming problems of the form $\{x \in \mathbb{Z}^n_{\geq 0} : Ax = b\}$ where $A \in \{-Δ,\ldots,Δ\}^{m \times n}$ and $b \in \mathbb{Z}^m$. We present an algorithm that counts solutions with $(nΔ)^{m + o(1)}\! \cdot \log\lVert b\rVert$ arithmetic operations. This simplifies the algorithm of Gribanov and Malyshev, who considered the problem for the more general setting in which $A$ is $Δ$-modular.

Authors: Daniel Dadush, Anita Dürr, Thekla Hamm, Jonas Schmidt, Karol Węgrzycki

We consider the problem of counting solutions to integer linear programming problems of the form $\{x \in \mathbb{Z}^n_{\geq 0} : Ax = b\}$ where $A \in \{-Δ,\ldots,Δ\}^{m \times n}$ and $b \in \mathbb{Z}^m$. We present an algorithm that counts solutions with $(nΔ)^{m + o(1)}\! \cdot \log\lVert b\rVert$ arithmetic operations. This simplifies the algorithm of Gribanov and Malyshev, who considered the problem for the more general setting in which $A$ is $Δ$-modular.

Majority and Torus Polynomials: Bounded-Lift Lower Bounds and Explicit Constructions

from arXiv: Computational Complexity

Authors: Saint Wesonga

Bhrushundi et al. (2019) introduced torus polynomial approximation as an approach to separating Majority from $ACC^0$. In this model, approximation is measured modulo integers. Krishan and Vishwanathan (2026) developed a duality framework for lower bounds and posed five open problems. We resolve two of these, concerning integer lifts and explicit polynomial approximations. An integer lift records the integer shifts that turn a torus approximation into an ordinary real approximation. We show that a degree-d torus polynomial approximating Majority with error at most 1/8 and lift contained in an interval of width $B$ must satisfy $d(B + 1) = Ω(n)$. This rules out low-degree approximating polynomials with lifts in {-1, 0, 1}, answering their Open Problem 2. We also answer Open Problem 5 by giving, for every Boolean function on $n \geq 1$ variables, an explicit torus polynomial approximation of degree at most $n - 1$. Its error is optimal: zero when the accepting set has even cardinality and $2^{-n-1}$ otherwise. The results presented were discovered and formalized in Lean using GPT-6 Astra.

Authors: Saint Wesonga

Bhrushundi et al. (2019) introduced torus polynomial approximation as an approach to separating Majority from $ACC^0$. In this model, approximation is measured modulo integers. Krishan and Vishwanathan (2026) developed a duality framework for lower bounds and posed five open problems. We resolve two of these, concerning integer lifts and explicit polynomial approximations. An integer lift records the integer shifts that turn a torus approximation into an ordinary real approximation. We show that a degree-d torus polynomial approximating Majority with error at most 1/8 and lift contained in an interval of width $B$ must satisfy $d(B + 1) = Ω(n)$. This rules out low-degree approximating polynomials with lifts in {-1, 0, 1}, answering their Open Problem 2. We also answer Open Problem 5 by giving, for every Boolean function on $n \geq 1$ variables, an explicit torus polynomial approximation of degree at most $n - 1$. Its error is optimal: zero when the accepting set has even cardinality and $2^{-n-1}$ otherwise. The results presented were discovered and formalized in Lean using GPT-6 Astra.

The Gap Between Average-Case and Worst-Case Complexity

from arXiv: Computational Complexity

Authors: Halley Goldberg, Jinqiao Hu, Zhenjian Lu, Jingyi Lyu, Igor C. Oliveira

Understanding the average-case complexity of $\mathsf{NP}$ is a central goal in complexity theory and cryptography. A necessary milestone is to understand the corresponding average-case versus worst-case question for the polynomial hierarchy ($\mathsf{PH}$). In a celebrated result, Hirahara [Hir21] proved that if every distributional problem in $\mathsf{PH}$ is easy on average, then every language in $\mathsf{PH}$ admits worst-case algorithms running in time $2^{O(n/\log n)}$. Remarkably, although the statement of Hirahara's theorem makes no reference to meta-complexity, all known proofs appear to use time-bounded Kolmogorov complexity and its associated meta-computational problems in an essential way. We show that the reliance on techniques and perspectives from meta-complexity is not simply a coincidence. Building on previous work of Hirahara [Hir20] and on a recent result of Hu, Lu, and Oliveira [HLO25], we prove that relating the average-case and worst-case complexities of $\mathsf{PH}$ is in fact equivalent to a purely meta-computational implication: efficient algorithms for a standard gap version of $\mathsf{MINpKT}$ with $\mathsf{PH}$ oracle access imply efficient algorithms for a mild gap version. Crucially, in these promise problems the standard gap separates a YES instance with time bound $t$ from a NO instance with time bound $\mathsf{poly}(t)$, whereas the mild gap keeps the YES threshold $t$ and moves the NO threshold to $t+\mathsf{poly}(n)$. Together with prior work, this equivalence points to the study of gap reductions as a potentially promising route toward understanding the relation between average-case and worst-case complexity. We make progress by proving that the gap in running time can be eliminated in the average-case setting. Finally, we prove that in the worst-case setting any efficient reduction between the two problems must use nonrelativizing techniques.

Authors: Halley Goldberg, Jinqiao Hu, Zhenjian Lu, Jingyi Lyu, Igor C. Oliveira

Understanding the average-case complexity of $\mathsf{NP}$ is a central goal in complexity theory and cryptography. A necessary milestone is to understand the corresponding average-case versus worst-case question for the polynomial hierarchy ($\mathsf{PH}$). In a celebrated result, Hirahara [Hir21] proved that if every distributional problem in $\mathsf{PH}$ is easy on average, then every language in $\mathsf{PH}$ admits worst-case algorithms running in time $2^{O(n/\log n)}$. Remarkably, although the statement of Hirahara's theorem makes no reference to meta-complexity, all known proofs appear to use time-bounded Kolmogorov complexity and its associated meta-computational problems in an essential way. We show that the reliance on techniques and perspectives from meta-complexity is not simply a coincidence. Building on previous work of Hirahara [Hir20] and on a recent result of Hu, Lu, and Oliveira [HLO25], we prove that relating the average-case and worst-case complexities of $\mathsf{PH}$ is in fact equivalent to a purely meta-computational implication: efficient algorithms for a standard gap version of $\mathsf{MINpKT}$ with $\mathsf{PH}$ oracle access imply efficient algorithms for a mild gap version. Crucially, in these promise problems the standard gap separates a YES instance with time bound $t$ from a NO instance with time bound $\mathsf{poly}(t)$, whereas the mild gap keeps the YES threshold $t$ and moves the NO threshold to $t+\mathsf{poly}(n)$. Together with prior work, this equivalence points to the study of gap reductions as a potentially promising route toward understanding the relation between average-case and worst-case complexity. We make progress by proving that the gap in running time can be eliminated in the average-case setting. Finally, we prove that in the worst-case setting any efficient reduction between the two problems must use nonrelativizing techniques.

On Complexity of Gaussian Sampling

from arXiv: Computational Complexity

Authors: Huanjian Zhou, Taiji Suzuki

We study the number of exact matrix-vector queries needed to sample a centered $d$-dimensional Gaussian with condition number at most $κ$ to total variation error $\varepsilon$. The best known algorithm uses $O(\min\{d,\sqrtκ\log(\frac{d}{\varepsilon})\})$ queries, while the known lower bounds do not grow as $\varepsilon\to0$ and match this bound only for $κ\le d^{1/5-δ}$ at constant accuracy. We introduce a construction based on a hidden determinant sign and prove a lower bound of $Ω\!\left(\min\left\{d,\sqrtκ\log\left(1+\frac{d}{\sqrtκ\,\varepsilon}\right)\right\}\right)$ uniformly for all $d\ge1$, $κ\ge4$, and $0<\varepsilon\le\tfrac{1}{16}$. This bound matches the known upper bound up to constant factors whenever $κ\le d^{2-δ}$ for fixed $δ\in(0,2)$, or $\varepsilon\le d^{-c}$ for fixed $c>0$. We also extend a recent fixed-spectrum construction with a hidden eigenbasis from $κ=2$ to $2\leκ\le d^{1-δ}$, for any fixed $δ\in(0,1)$. This gives an $Ω_δ(\sqrtκ\log d)$ lower bound at constant accuracy that holds even when the spectrum of the precision matrix is known to the algorithm.

Authors: Huanjian Zhou, Taiji Suzuki

We study the number of exact matrix-vector queries needed to sample a centered $d$-dimensional Gaussian with condition number at most $κ$ to total variation error $\varepsilon$. The best known algorithm uses $O(\min\{d,\sqrtκ\log(\frac{d}{\varepsilon})\})$ queries, while the known lower bounds do not grow as $\varepsilon\to0$ and match this bound only for $κ\le d^{1/5-δ}$ at constant accuracy. We introduce a construction based on a hidden determinant sign and prove a lower bound of $Ω\!\left(\min\left\{d,\sqrtκ\log\left(1+\frac{d}{\sqrtκ\,\varepsilon}\right)\right\}\right)$ uniformly for all $d\ge1$, $κ\ge4$, and $0<\varepsilon\le\tfrac{1}{16}$. This bound matches the known upper bound up to constant factors whenever $κ\le d^{2-δ}$ for fixed $δ\in(0,2)$, or $\varepsilon\le d^{-c}$ for fixed $c>0$. We also extend a recent fixed-spectrum construction with a hidden eigenbasis from $κ=2$ to $2\leκ\le d^{1-δ}$, for any fixed $δ\in(0,1)$. This gives an $Ω_δ(\sqrtκ\log d)$ lower bound at constant accuracy that holds even when the spectrum of the precision matrix is known to the algorithm.

On the Complexity of Linear Programs with Parametric Constraint Matrices

from arXiv: Computational Complexity

Authors: Laurie Boveroux, Margarida Carvalho, Andrea Lodi, Quentin Louveaux

While sensitivity analysis for linear programs with parametric right- hand side vectors is well established, cases involving parametric constraint matrices remain less explored. To address this gap, we investigate linear programs where the constraint matrix depends linearly on a scalar parameter. Our goal is to compute the minimum and maximum optimal values of these parametric linear programs within a fixed parameter range. This corresponds to solving two bilevel programs. We show that both decision versions of these optimization problems are NP-complete by proving both NP-hardness and membership in NP. We additionally identify three sufficient conditions on the parametric matrix under which the bilevel programs are no longer NP-hard. We introduce a reformulation for both problems, together with a Farkas-based certificate for the lower-level feasibility condition, and compare the execution time for solving them on standard benchmark instances. The experiments highlight a practical dichotomy. Although both bilevel optimizations are NP-hard, computing the parameter value corresponding to the minimum of the linear program is fast, whereas computing the value corresponding to the maximum is not. We relate this asymmetry to the parametric equality constraints, whose free dual variables leave the relaxation of the maximum reformulation unbounded.

Authors: Laurie Boveroux, Margarida Carvalho, Andrea Lodi, Quentin Louveaux

While sensitivity analysis for linear programs with parametric right- hand side vectors is well established, cases involving parametric constraint matrices remain less explored. To address this gap, we investigate linear programs where the constraint matrix depends linearly on a scalar parameter. Our goal is to compute the minimum and maximum optimal values of these parametric linear programs within a fixed parameter range. This corresponds to solving two bilevel programs. We show that both decision versions of these optimization problems are NP-complete by proving both NP-hardness and membership in NP. We additionally identify three sufficient conditions on the parametric matrix under which the bilevel programs are no longer NP-hard. We introduce a reformulation for both problems, together with a Farkas-based certificate for the lower-level feasibility condition, and compare the execution time for solving them on standard benchmark instances. The experiments highlight a practical dichotomy. Although both bilevel optimizations are NP-hard, computing the parameter value corresponding to the minimum of the linear program is fast, whereas computing the value corresponding to the maximum is not. We relate this asymmetry to the parametric equality constraints, whose free dual variables leave the relaxation of the maximum reformulation unbounded.

Asymmetry and Complexity of Nondeterministic Computations

from arXiv: Computational Complexity

Authors: Halley Goldberg, Jinqiao Hu, Zhenjian Lu, Jingyi Lyu, Igor C. Oliveira

Symmetry of Information (SoI) [ZL70] is a fundamental theorem of classical Kolmogorov complexity whose status for time-bounded computation is closely related to central open problems in complexity theory and cryptography. In a recent development, [HMO26] showed that SoI fails for $rKt$ complexity, namely in the setting of randomized computation. By contrast, many basic properties of Kolmogorov complexity are known to remain valid in the presence of nondeterminism [HLO26], suggesting that SoI may provide a fruitful structural principle for exploring the relative power of randomness and nondeterminism in computation. Our results reveal that the validity of SoI for nondeterministic computation, namely for $nKt$ complexity, is a subtle question connected to several frontiers. We show that symmetry of information holds on average for randomized extensions of $nKt$, while its worst case validity would imply breakthrough complexity separations. Although the standard cryptographic argument for asymmetry does not extend to the nondeterministic setting, we provide evidence that information remains asymmetric in nondeterministic computations. Among other results, we show that SoI would imply a collapse of exponential time classes to low levels of the polynomial hierarchy. Then, building on the work of [Hir22b, GK22, KK26, HMO26], we develop a general framework connecting symmetry of information, meta-complexity, and explicit constructions in the nondeterministic setting. More precisely, we prove that suitable formulations of these three notions are quantitatively equivalent. We apply different aspects of this correspondence to make progress on complexity lower bounds and on the failure of SoI for nondeterministic computation. These consequences are unconditional and provide some of the strongest known lower bounds in meta-complexity.

Authors: Halley Goldberg, Jinqiao Hu, Zhenjian Lu, Jingyi Lyu, Igor C. Oliveira

Symmetry of Information (SoI) [ZL70] is a fundamental theorem of classical Kolmogorov complexity whose status for time-bounded computation is closely related to central open problems in complexity theory and cryptography. In a recent development, [HMO26] showed that SoI fails for $rKt$ complexity, namely in the setting of randomized computation. By contrast, many basic properties of Kolmogorov complexity are known to remain valid in the presence of nondeterminism [HLO26], suggesting that SoI may provide a fruitful structural principle for exploring the relative power of randomness and nondeterminism in computation. Our results reveal that the validity of SoI for nondeterministic computation, namely for $nKt$ complexity, is a subtle question connected to several frontiers. We show that symmetry of information holds on average for randomized extensions of $nKt$, while its worst case validity would imply breakthrough complexity separations. Although the standard cryptographic argument for asymmetry does not extend to the nondeterministic setting, we provide evidence that information remains asymmetric in nondeterministic computations. Among other results, we show that SoI would imply a collapse of exponential time classes to low levels of the polynomial hierarchy. Then, building on the work of [Hir22b, GK22, KK26, HMO26], we develop a general framework connecting symmetry of information, meta-complexity, and explicit constructions in the nondeterministic setting. More precisely, we prove that suitable formulations of these three notions are quantitatively equivalent. We apply different aspects of this correspondence to make progress on complexity lower bounds and on the failure of SoI for nondeterministic computation. These consequences are unconditional and provide some of the strongest known lower bounds in meta-complexity.

Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space

from arXiv: Computational Geometry

Authors: Ditbul Ban, Younghun Roh, Julian Shun, Vihan Lakshman

Quantifying the diversity of a set of objects is a fundamental problem in machine learning and statistics. Mironov and Prokhorenkova (2025) recently showed that three axioms-monotonicity, uniqueness, and continuity-are crucial for a reliable diversity measure and that none of the standard metrics in the literature satisfy all three. They also constructed two measures that do satisfy the axioms, but both are NP-hard to compute, leaving open the question of whether efficient axiomatic diversity measures can exist. We study a Euclidean-restricted version of this question. We introduce Geometric Kernel Diversity (GKD), which associates a radial kernel solid with each point and measures the volume of their union. We show that canonical GKD satisfies all three Mironov-Prokhorenkova axioms on Euclidean-realizable inputs. For canonical GKD, we give exact polynomial-time algorithms in dimensions $d \le 3$. In arbitrary dimensions, we give a randomized algorithm that returns a $(1 \pm \varepsilon)$-approximation with probability at least $1-δ$ in time $O \left(\frac{nd}{\varepsilon^2}\log\frac{2}δ\right)$ for $n$ points. The axiomatic guarantee applies to exact canonical GKD; the randomized algorithm approximates this deterministic quantity. We further show that the two diversity measures of Mironov and Prokhorenkova (2025) remain NP-hard on Euclidean-realizable inputs and, unless $\mathrm{NP} = \mathrm{RP}$, admit no FPRAS even on this restricted domain.

Authors: Ditbul Ban, Younghun Roh, Julian Shun, Vihan Lakshman

Quantifying the diversity of a set of objects is a fundamental problem in machine learning and statistics. Mironov and Prokhorenkova (2025) recently showed that three axioms-monotonicity, uniqueness, and continuity-are crucial for a reliable diversity measure and that none of the standard metrics in the literature satisfy all three. They also constructed two measures that do satisfy the axioms, but both are NP-hard to compute, leaving open the question of whether efficient axiomatic diversity measures can exist. We study a Euclidean-restricted version of this question. We introduce Geometric Kernel Diversity (GKD), which associates a radial kernel solid with each point and measures the volume of their union. We show that canonical GKD satisfies all three Mironov-Prokhorenkova axioms on Euclidean-realizable inputs. For canonical GKD, we give exact polynomial-time algorithms in dimensions $d \le 3$. In arbitrary dimensions, we give a randomized algorithm that returns a $(1 \pm \varepsilon)$-approximation with probability at least $1-δ$ in time $O \left(\frac{nd}{\varepsilon^2}\log\frac{2}δ\right)$ for $n$ points. The axiomatic guarantee applies to exact canonical GKD; the randomized algorithm approximates this deterministic quantity. We further show that the two diversity measures of Mironov and Prokhorenkova (2025) remain NP-hard on Euclidean-realizable inputs and, unless $\mathrm{NP} = \mathrm{RP}$, admit no FPRAS even on this restricted domain.

Arithmetic Visibility in Polygons: Curved Sight in the Art Gallery

from arXiv: Computational Geometry

Authors: Malik Barrett

In the classical art gallery problem, a guard sees a target when the segment joining them lies entirely within the polygon. We explore what changes when sight may bend along regular arcs on absolutely irreducible curves, but only those defined by primitive integer polynomials of total degree at most \(d\) and coefficient height at most \(H\). We call such curves admissible sight carriers. Arcs can dramatically improve sight. In a one-sided comb, no straight-line guard sees selected targets in two different teeth, yet a single central guard sees a chosen target in every tooth along parabolic carriers. Sight can also fail in two different ways. Geometrically, the polygon may block every sight arc of the allowed degree between two points. For instance, in an alternating comb, the number of guards needed for a row of targets spanning the comb grows linearly with the number of sides at each fixed degree, even for carriers with arbitrary real coefficients. Arithmetically, no admissible carrier may pass through the points at all. Even in the unit square, at fixed \(d\) and \(H\), the proportion of rational targets that must guard themselves tends to one as the height bound grows. An algebraic target may lie on no rational curve of degree at most \(d\), however large \(H\) is. Both failures can be overcome for finite target sets by allowing the degree and coefficient height to grow. We show that every finite set of algebraic targets in a rational polygon becomes visible from any rational interior guard, with explicit bounds. We also construct, for every \(d\), arbitrarily large Galois orbits in the unit square, all of projective height \(4^{d+1}\), whose points must each guard themselves at degree \(d\) for every \(H\). Yet at degree \(d+1\) and coefficient height one, a single guard sees the whole orbit along \(Z(Y-X^{d+1})\).

Authors: Malik Barrett

In the classical art gallery problem, a guard sees a target when the segment joining them lies entirely within the polygon. We explore what changes when sight may bend along regular arcs on absolutely irreducible curves, but only those defined by primitive integer polynomials of total degree at most \(d\) and coefficient height at most \(H\). We call such curves admissible sight carriers. Arcs can dramatically improve sight. In a one-sided comb, no straight-line guard sees selected targets in two different teeth, yet a single central guard sees a chosen target in every tooth along parabolic carriers. Sight can also fail in two different ways. Geometrically, the polygon may block every sight arc of the allowed degree between two points. For instance, in an alternating comb, the number of guards needed for a row of targets spanning the comb grows linearly with the number of sides at each fixed degree, even for carriers with arbitrary real coefficients. Arithmetically, no admissible carrier may pass through the points at all. Even in the unit square, at fixed \(d\) and \(H\), the proportion of rational targets that must guard themselves tends to one as the height bound grows. An algebraic target may lie on no rational curve of degree at most \(d\), however large \(H\) is. Both failures can be overcome for finite target sets by allowing the degree and coefficient height to grow. We show that every finite set of algebraic targets in a rational polygon becomes visible from any rational interior guard, with explicit bounds. We also construct, for every \(d\), arbitrarily large Galois orbits in the unit square, all of projective height \(4^{d+1}\), whose points must each guard themselves at degree \(d\) for every \(H\). Yet at degree \(d+1\) and coefficient height one, a single guard sees the whole orbit along \(Z(Y-X^{d+1})\).

Structural identification in general crystalline systems via Voronoi cell topology

from arXiv: Computational Geometry

Authors: Emanuel A. Lazar, Chris H. Rycroft

Widely used methods for identifying crystal structure in atomistic simulations are limited in practice to a handful of simple structures, leaving many important crystalline systems without automated classification techniques. Voronoi topology analysis offers a route to classifying arbitrary crystalline and defect environments, but realizing this potential first requires enumerating the Voronoi cell topologies that can arise under thermal perturbation of a structure of interest. We present an algorithm that deterministically enumerates all Voronoi cell topologies arising from generic infinitesimal perturbations of an arbitrary crystal structure, producing structural classifiers that extend automated identification to general crystalline systems. We demonstrate the approach on several structures of general interest.

Authors: Emanuel A. Lazar, Chris H. Rycroft

Widely used methods for identifying crystal structure in atomistic simulations are limited in practice to a handful of simple structures, leaving many important crystalline systems without automated classification techniques. Voronoi topology analysis offers a route to classifying arbitrary crystalline and defect environments, but realizing this potential first requires enumerating the Voronoi cell topologies that can arise under thermal perturbation of a structure of interest. We present an algorithm that deterministically enumerates all Voronoi cell topologies arising from generic infinitesimal perturbations of an arbitrary crystal structure, producing structural classifiers that extend automated identification to general crystalline systems. We demonstrate the approach on several structures of general interest.

Towards Learning-Guided Discovery of Extremal Geometric Constructions: A PatternBoost Study

from arXiv: Computational Geometry

Authors: Deepak Ajwani, Paula Carroll, Rishikesh Gajjala, Ananta Manoranjan, Ryan O'Connor, Rajiv Raman, Saurabh Ray, Aimen Taha

Extremal constructions play a central role in the design and analysis of algorithms. Integrality-gap instances expose the limitations of relaxations, while hard configurations reveal structural sources of computational difficulty. Such constructions, however, are still typically discovered through a slow, manual process. This paper investigates how machine learning can accelerate their discovery to support algorithm design and analysis. We build on PatternBoost, a framework alternating local search with generation from a trained transformer, using its Axplorer implementation to investigate three problems: integrality gaps for maximum independent set of rectangles, integrality gaps for stabbing unit squares with axis-parallel lines, and hard instances for recursive guillotine cutting. By exploring candidate representations, local search operations, and surrogate models used to rank candidates before exact verification, we carefully adapt the framework to these combinatorial geometry problems.While naive representations and search strategies fail to find non-trivial instances, our tailored adaptation discovers numerous non-trivial constructions. For stabbing unit squares, the method reaches a gap of $20/13\approx 1.54$, improving on the literature's best known lower bound of $3/2$. Purely automated search converges rapidly and then plateaus, prompting the question of what is required to push the bound higher still. We find that initial instance diversity plays an important role: with modest expert input via a structured staircase construction, the system reaches an improved integrality gap of $14/9\approx 1.555$. These results demonstrate that machine learning and domain expertise can be combined to discover new extremal constructions that advance algorithmic lower bounds.

Authors: Deepak Ajwani, Paula Carroll, Rishikesh Gajjala, Ananta Manoranjan, Ryan O'Connor, Rajiv Raman, Saurabh Ray, Aimen Taha

Extremal constructions play a central role in the design and analysis of algorithms. Integrality-gap instances expose the limitations of relaxations, while hard configurations reveal structural sources of computational difficulty. Such constructions, however, are still typically discovered through a slow, manual process. This paper investigates how machine learning can accelerate their discovery to support algorithm design and analysis. We build on PatternBoost, a framework alternating local search with generation from a trained transformer, using its Axplorer implementation to investigate three problems: integrality gaps for maximum independent set of rectangles, integrality gaps for stabbing unit squares with axis-parallel lines, and hard instances for recursive guillotine cutting. By exploring candidate representations, local search operations, and surrogate models used to rank candidates before exact verification, we carefully adapt the framework to these combinatorial geometry problems.While naive representations and search strategies fail to find non-trivial instances, our tailored adaptation discovers numerous non-trivial constructions. For stabbing unit squares, the method reaches a gap of $20/13\approx 1.54$, improving on the literature's best known lower bound of $3/2$. Purely automated search converges rapidly and then plateaus, prompting the question of what is required to push the bound higher still. We find that initial instance diversity plays an important role: with modest expert input via a structured staircase construction, the system reaches an improved integrality gap of $14/9\approx 1.555$. These results demonstrate that machine learning and domain expertise can be combined to discover new extremal constructions that advance algorithmic lower bounds.

Output-linear time enumeration of potential maximal cliques

from arXiv: Data Structures and Algorithms

Authors: Pierre Bergé, Vincent Limouzy, Nicolas Schivre

First defined by Bouchitté and Todinca, Potential Maximal Cliques (PMC) are a well studied notion in graph theory due to its numerous applications. The PMCs of an undirected graph $G$ are defined as the maximal cliques of the minimal triangulations of $G$. Their number can be exponential in the size of the graph. PMCs were crucial in the design of a polynomial-time algorithm which solves Maximum Weight Independent Set on $P_6$-free graphs (Grzesik, Klimošová, Pilipczuk, Pilipczuk, 2020). They also play a central role in the proposal of a polynomial-time algorithm to solve Largest Induced Subgraph of treewidth $\le t$ (Fomin, Todinca, Villanger, 2013). Last but not least, certainly one of the main application of PMCs is the existence of an exact dynamic-programming algorithm to determine the treewidth of a graph (Fomin, Kratsch, Todinca, Villanger, 2008) which uses its list of PMCs as an input. In fact, the enumeration of PMCs stands as the bottleneck for the computation of treewidth with this method: the PMC-enumeration step takes output-quadratic time (Bouchitté, Todinca, 2002) while the dynamic-programming one takes a time linear in the number of PMCs. This two-step procedure was proven to be among the most performing approaches in practice to determine treewidth. Answering a longstanding open question, we propose an algorithm to enumerate PMCs in output-linear time, {\em i.e.} a running time linear in the number of PMCs. This offers nice perspectives not only from the theoretical viewpoint - better running time for the PMC-based methods determining treewidth, for Minimum Fill-In and Largest Induced Subgraph of treewidth $\le t$ - but also from the practical side, with the possibility to propose top-performance implementation.

Authors: Pierre Bergé, Vincent Limouzy, Nicolas Schivre

First defined by Bouchitté and Todinca, Potential Maximal Cliques (PMC) are a well studied notion in graph theory due to its numerous applications. The PMCs of an undirected graph $G$ are defined as the maximal cliques of the minimal triangulations of $G$. Their number can be exponential in the size of the graph. PMCs were crucial in the design of a polynomial-time algorithm which solves Maximum Weight Independent Set on $P_6$-free graphs (Grzesik, Klimošová, Pilipczuk, Pilipczuk, 2020). They also play a central role in the proposal of a polynomial-time algorithm to solve Largest Induced Subgraph of treewidth $\le t$ (Fomin, Todinca, Villanger, 2013). Last but not least, certainly one of the main application of PMCs is the existence of an exact dynamic-programming algorithm to determine the treewidth of a graph (Fomin, Kratsch, Todinca, Villanger, 2008) which uses its list of PMCs as an input. In fact, the enumeration of PMCs stands as the bottleneck for the computation of treewidth with this method: the PMC-enumeration step takes output-quadratic time (Bouchitté, Todinca, 2002) while the dynamic-programming one takes a time linear in the number of PMCs. This two-step procedure was proven to be among the most performing approaches in practice to determine treewidth. Answering a longstanding open question, we propose an algorithm to enumerate PMCs in output-linear time, {\em i.e.} a running time linear in the number of PMCs. This offers nice perspectives not only from the theoretical viewpoint - better running time for the PMC-based methods determining treewidth, for Minimum Fill-In and Largest Induced Subgraph of treewidth $\le t$ - but also from the practical side, with the possibility to propose top-performance implementation.

Exact Cut Complexity of Equal-Length Proportional Cake Cutting

from arXiv: Data Structures and Algorithms

Authors: Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin Sanpui

We study proportional cake cutting under an equal-length constraint, where each of $n$ agents must receive a bundle of length exactly $1/n$ that is worth at least $1/n$ to that agent. Our main result determines the exact worst-case number of cuts needed to guarantee such an allocation, namely $2n-2$ for every $n$. This improves on the previously known quadratic upper bound. The proof combines the constrained necklace-splitting theorem with a recursive assignment argument based on Hall's theorem. The same bound extends to the stronger requirement of envy-freeness when $n$ is a prime power, and an $O(n^{1.525})$ upper bound holds for arbitrary $n$. We then ask whether such allocations can be computed. We first show that no deterministic protocol in the Robertson--Webb model terminates after finitely many queries on every instance and always returns an exact equal-length proportional allocation, even for two agents with identical valuations. For every $\varepsilon>0$, however, $O(n^2/\varepsilon)$ queries suffice to produce an allocation in which every agent receives a bundle of length exactly $1/n$ and value at least $1/n-\varepsilon$, again using at most $2n-2$ cuts. Finally, we augment the Robertson--Webb model with two-knife queries. We show that recursive balanced halving attains the optimal bound of $2n-2$ cuts when $n$ is a power of two. For arbitrary $n$, we give a procedure using $O(n\log n)$ cuts.

Authors: Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin Sanpui

We study proportional cake cutting under an equal-length constraint, where each of $n$ agents must receive a bundle of length exactly $1/n$ that is worth at least $1/n$ to that agent. Our main result determines the exact worst-case number of cuts needed to guarantee such an allocation, namely $2n-2$ for every $n$. This improves on the previously known quadratic upper bound. The proof combines the constrained necklace-splitting theorem with a recursive assignment argument based on Hall's theorem. The same bound extends to the stronger requirement of envy-freeness when $n$ is a prime power, and an $O(n^{1.525})$ upper bound holds for arbitrary $n$. We then ask whether such allocations can be computed. We first show that no deterministic protocol in the Robertson--Webb model terminates after finitely many queries on every instance and always returns an exact equal-length proportional allocation, even for two agents with identical valuations. For every $\varepsilon>0$, however, $O(n^2/\varepsilon)$ queries suffice to produce an allocation in which every agent receives a bundle of length exactly $1/n$ and value at least $1/n-\varepsilon$, again using at most $2n-2$ cuts. Finally, we augment the Robertson--Webb model with two-knife queries. We show that recursive balanced halving attains the optimal bound of $2n-2$ cuts when $n$ is a power of two. For arbitrary $n$, we give a procedure using $O(n\log n)$ cuts.

Dynamic 3-Coloring of Bipartite Graphs with Optimal Locality

from arXiv: Data Structures and Algorithms

Authors: Yann Bourreau, Filippo Casagrande, Tim Göttlicher, Ananth Narayanan Karunattillam

We give a dynamic graph algorithm which maintains a 3-coloring in bipartite graphs under edge insertions such that the coloring only changes within a radius of $O(\log n)$ around an inserted edge. In the context of locality theory in distributed graph algorithms, this gives an upper bound on the locality under the dynamic-LOCAL model. Our bound on the locality is tight, as the problem admits a matching $Ω(\log n)$ lower bound in the stronger online-LOCAL model, by a result of Chang et al. [PODC 2024]. Thus, our result closes the gap between dynamic and online locality of this problem.

Authors: Yann Bourreau, Filippo Casagrande, Tim Göttlicher, Ananth Narayanan Karunattillam

We give a dynamic graph algorithm which maintains a 3-coloring in bipartite graphs under edge insertions such that the coloring only changes within a radius of $O(\log n)$ around an inserted edge. In the context of locality theory in distributed graph algorithms, this gives an upper bound on the locality under the dynamic-LOCAL model. Our bound on the locality is tight, as the problem admits a matching $Ω(\log n)$ lower bound in the stronger online-LOCAL model, by a result of Chang et al. [PODC 2024]. Thus, our result closes the gap between dynamic and online locality of this problem.

Revisiting Non-Adaptive Set Membership Schemes: Removing the Logarithmic Factor

from arXiv: Data Structures and Algorithms

Authors: Mirza Galib Anwarul Husain Baig, Aman Kumar, Pawan Kumar Saw, Vikash Tripathi

We study non-adaptive bit-probe schemes for static set membership. Each query reads a fixed number of bits and answers by strict majority. We revisit the random bipartite-graph construction of Garg and Radhakrishnan [STACS, 2017]. The main observation is that a slightly stronger expansion property already bounds the number of exceptional elements. This makes a separate probabilistic estimate for exceptional elements unnecessary. A detailed union-bound calculation then gives, for every fixed $t\ge5$, a logarithm-free existence bound of $O_t(m^{1/c}n^{1-1/c})$, where $c=\lfloor(t-1)/2\rfloor$. For odd $t$, this removes the logarithmic factor from the general upper bound of Garg and Radhakrishnan [STACS, 2017].

Authors: Mirza Galib Anwarul Husain Baig, Aman Kumar, Pawan Kumar Saw, Vikash Tripathi

We study non-adaptive bit-probe schemes for static set membership. Each query reads a fixed number of bits and answers by strict majority. We revisit the random bipartite-graph construction of Garg and Radhakrishnan [STACS, 2017]. The main observation is that a slightly stronger expansion property already bounds the number of exceptional elements. This makes a separate probabilistic estimate for exceptional elements unnecessary. A detailed union-bound calculation then gives, for every fixed $t\ge5$, a logarithm-free existence bound of $O_t(m^{1/c}n^{1-1/c})$, where $c=\lfloor(t-1)/2\rfloor$. For odd $t$, this removes the logarithmic factor from the general upper bound of Garg and Radhakrishnan [STACS, 2017].

Approximation Algorithms for Emergency Vertex Cover with Few Stations and Fault-Tolerant Coverage

from arXiv: Data Structures and Algorithms

Authors: Quanlong Niu, Riheng Jia

The Emergency Vertex Cover problem (Em-VC) assigns budgets to stations at graph vertices to cover every edge while minimizing the total budget. The cost of covering an edge from a station is the shortest-path distance from the station to the nearer endpoint plus the edge length. We study how restricting the number of stations affects the cost of ordinary and fault-tolerant coverage. In ordinary Em-VC, we prove that the optimal solution using at most two stations costs at most $5/4$ times the unrestricted optimum, improving the known $4/3$ guarantee for a single station. A recurrence extends this bound to larger station limits, giving approximation factors that converge to $\sqrt{5}-1$. Budget-threshold enumeration computes the restricted optimum in polynomial time for every fixed station limit. Under $k$-fold coverage, every edge must be covered by at least $k$ distinct stations, ensuring coverage after any $k-1$ station failures. We prove that selecting the $k$ vertices with the smallest full-coverage costs yields a $2$-approximation relative to the unrestricted optimum for $k$-fold coverage. When $k=2$, we combine upper bounds derived from the structure of the covering balls to improve the approximation guarantee to $5/3$.

Authors: Quanlong Niu, Riheng Jia

The Emergency Vertex Cover problem (Em-VC) assigns budgets to stations at graph vertices to cover every edge while minimizing the total budget. The cost of covering an edge from a station is the shortest-path distance from the station to the nearer endpoint plus the edge length. We study how restricting the number of stations affects the cost of ordinary and fault-tolerant coverage. In ordinary Em-VC, we prove that the optimal solution using at most two stations costs at most $5/4$ times the unrestricted optimum, improving the known $4/3$ guarantee for a single station. A recurrence extends this bound to larger station limits, giving approximation factors that converge to $\sqrt{5}-1$. Budget-threshold enumeration computes the restricted optimum in polynomial time for every fixed station limit. Under $k$-fold coverage, every edge must be covered by at least $k$ distinct stations, ensuring coverage after any $k-1$ station failures. We prove that selecting the $k$ vertices with the smallest full-coverage costs yields a $2$-approximation relative to the unrestricted optimum for $k$-fold coverage. When $k=2$, we combine upper bounds derived from the structure of the covering balls to improve the approximation guarantee to $5/3$.

Learning-Augmented Streaming Algorithms for Approximating Boolean Max-CSPs

from arXiv: Data Structures and Algorithms

Authors: Yinhao Dong, Pan Peng, Ali Vakilian, Santhoshini Velusamy, Xiaoyang Xu

We study learning-augmented streaming algorithms for estimating the value of Boolean Maximum Constraint Satisfaction Problems (Max-CSPs). Specifically, we consider streaming algorithms equipped with an $\varepsilon$-accurate oracle: for each variable, the oracle outputs a label in $\{-1,1\}$ that agrees with a fixed optimal assignment with probability $1/2+\varepsilon$, and disagrees otherwise. Prior work by Dong, Peng, and Vakilian [ITCS 2025] showed that, for the special case of Max-Cut, such an oracle enables a $(1/2+Ω(\varepsilon^2))$-approximation using $\operatorname{poly}(1/\varepsilon)$ words of space in insertion-only streams, and $\operatorname{poly}(1/\varepsilon,\log n)$ words of space in dynamic streams. We substantially generalize and strengthen their result, provided that a highly accurate estimate of $\varepsilon$ is available. For any Boolean Max-$2$CSP and any $η>0$ independent of $\varepsilon$, we obtain, with high constant probability, a single-pass $(1-η)$-approximation using $\operatorname{poly}(1/\varepsilon,1/η)$ words of space in insertion-only streams, and $\operatorname{poly}(1/\varepsilon,1/η,\log n)$ words of space in dynamic streams, where $n$ is the number of variables. We further extend our techniques to general Boolean Max-$k$CSPs for $k\ge 3$, with slightly worse space complexity.

Authors: Yinhao Dong, Pan Peng, Ali Vakilian, Santhoshini Velusamy, Xiaoyang Xu

We study learning-augmented streaming algorithms for estimating the value of Boolean Maximum Constraint Satisfaction Problems (Max-CSPs). Specifically, we consider streaming algorithms equipped with an $\varepsilon$-accurate oracle: for each variable, the oracle outputs a label in $\{-1,1\}$ that agrees with a fixed optimal assignment with probability $1/2+\varepsilon$, and disagrees otherwise. Prior work by Dong, Peng, and Vakilian [ITCS 2025] showed that, for the special case of Max-Cut, such an oracle enables a $(1/2+Ω(\varepsilon^2))$-approximation using $\operatorname{poly}(1/\varepsilon)$ words of space in insertion-only streams, and $\operatorname{poly}(1/\varepsilon,\log n)$ words of space in dynamic streams. We substantially generalize and strengthen their result, provided that a highly accurate estimate of $\varepsilon$ is available. For any Boolean Max-$2$CSP and any $η>0$ independent of $\varepsilon$, we obtain, with high constant probability, a single-pass $(1-η)$-approximation using $\operatorname{poly}(1/\varepsilon,1/η)$ words of space in insertion-only streams, and $\operatorname{poly}(1/\varepsilon,1/η,\log n)$ words of space in dynamic streams, where $n$ is the number of variables. We further extend our techniques to general Boolean Max-$k$CSPs for $k\ge 3$, with slightly worse space complexity.

With Leverage, the Gambler is Not the One Who Gets Ruined

from arXiv: Data Structures and Algorithms

Authors: William Kuszmaul

In this note we explore the following simple question: How does the classic \emph{gambler's ruin} phenomenon change if we allow the gambler \emph{financial leverage}? We show that, with a moderate amount of leverage, it is possible for an (idealized) gambler to sustain seemingly improbable profits over a remarkably long time horizon without ever going bankrupt, \emph{even} if the gambler makes only fair bets. With a leverage ratio of $L = 10$ (less than what Lehman Brothers Holdings had from 1995--2008), the probability of multiplying one's money by $1000\times$ without going bankrupt is roughly $50\%$. More generally, given $L$-fold leverage, and given the goal of multiplying one's money by a factor of $r > 1$, the optimal gambling strategy has probability roughly $r^{-1/L}$ of success. The results in this note are designed as part of the course development for the CMU randomized algorithms course.

Authors: William Kuszmaul

In this note we explore the following simple question: How does the classic \emph{gambler's ruin} phenomenon change if we allow the gambler \emph{financial leverage}? We show that, with a moderate amount of leverage, it is possible for an (idealized) gambler to sustain seemingly improbable profits over a remarkably long time horizon without ever going bankrupt, \emph{even} if the gambler makes only fair bets. With a leverage ratio of $L = 10$ (less than what Lehman Brothers Holdings had from 1995--2008), the probability of multiplying one's money by $1000\times$ without going bankrupt is roughly $50\%$. More generally, given $L$-fold leverage, and given the goal of multiplying one's money by a factor of $r > 1$, the optimal gambling strategy has probability roughly $r^{-1/L}$ of success. The results in this note are designed as part of the course development for the CMU randomized algorithms course.

Online Tokenization with Optimal Delay in Linear Time

from arXiv: Data Structures and Algorithms

Authors: Mina Dalirrooyfard, Christopher Ye

Tokenization is an essential preprocessing step in almost all large language models (LLMs). One of the most popular frameworks is BPE tokenization, which consists of special token segmentation, pretokenization, and byte-pair encoding (BPE). However, existing algorithms are sequential, with each stage only processing complete output from the previous stage. In order to reduce latency and memory footprints, recent works (Jiang and Gong, ICML 2026) (Li, Yang and Mamouras, ASPLOS 2026) (Mamouras, Li and Yang, PLDI 2026) have initiated the study of online algorithms for subroutines such as pretokenization and BPE with a goal of streaming input to output as the input arrives online. However, the search for efficient online tokenization remains open. In this work, we formalize and resolve online tokenization. We introduce the notion of delay for online tokenization which characterizes the extent to which the algorithm output lags behind the (possibly computationally intractable) optimum. We give a near-linear time algorithm with optimal delay (up to constant factors). We complement this algorithm with a conditional lower bound: unless $\mathbf{P} = \mathbf{NP}$, there is no polynomial time algorithm that beats the delay of our algorithm by more than a constant factor. We implement a practical variant for tokenizers from OpenAI's tiktoken and HuggingFace's tokenizers. We obtain a $7.9\times$-$34.8\times$ latency advantage (measured as time to first token) over tiktoken (as well as the recent gigatoken tokenizer) and our implementation maintains approximately constant memory usage independent of input size. Our implementation also achieves a $2.4\times$-$7.0\times$ improvement in throughput over tiktoken and $15.8\times$-$38.1\times$ over tokenizers.

Authors: Mina Dalirrooyfard, Christopher Ye

Tokenization is an essential preprocessing step in almost all large language models (LLMs). One of the most popular frameworks is BPE tokenization, which consists of special token segmentation, pretokenization, and byte-pair encoding (BPE). However, existing algorithms are sequential, with each stage only processing complete output from the previous stage. In order to reduce latency and memory footprints, recent works (Jiang and Gong, ICML 2026) (Li, Yang and Mamouras, ASPLOS 2026) (Mamouras, Li and Yang, PLDI 2026) have initiated the study of online algorithms for subroutines such as pretokenization and BPE with a goal of streaming input to output as the input arrives online. However, the search for efficient online tokenization remains open. In this work, we formalize and resolve online tokenization. We introduce the notion of delay for online tokenization which characterizes the extent to which the algorithm output lags behind the (possibly computationally intractable) optimum. We give a near-linear time algorithm with optimal delay (up to constant factors). We complement this algorithm with a conditional lower bound: unless $\mathbf{P} = \mathbf{NP}$, there is no polynomial time algorithm that beats the delay of our algorithm by more than a constant factor. We implement a practical variant for tokenizers from OpenAI's tiktoken and HuggingFace's tokenizers. We obtain a $7.9\times$-$34.8\times$ latency advantage (measured as time to first token) over tiktoken (as well as the recent gigatoken tokenizer) and our implementation maintains approximately constant memory usage independent of input size. Our implementation also achieves a $2.4\times$-$7.0\times$ improvement in throughput over tiktoken and $15.8\times$-$38.1\times$ over tokenizers.

Finding non-bisimplicial and triad-free minimal separators

from arXiv: Data Structures and Algorithms

Authors: Maria Chudnovsky, Ilya Maier

A $u\hbox{-}v$ separator in $G$ is a set $S\subseteq V(G)\setminus\{u,v\}$ such that there is no $u\hbox{-}v$ path in $G\setminus S$. A minimal separator in $G$ is a set $S$ such that for some $u,v\in V(G)\setminus S$, $S$ is a $u\hbox{-}v$ separator in $G$ but no proper subset of $S$ is. A set $S$ is bisimplicial if it is the union of at most two cliques. Recognizing graphs in which every minimal separator is the union of at most $k$ cliques is polynomial-time solvable for $k=1$ and NP-hard for every fixed $k\geq 3$. We settle the remaining case $k=2$ by giving a polynomial-time algorithm to decide whether every minimal separator in a graph is bisimplicial. A set $S$ is triad-free if its independence number $α(S)$ is at most two. Given a graph $G$ and two vertices $u,v$, the problem of finding a $u\hbox{-}v$ separator $S$ with $α(S)\leq k$ had the same complexity gap: it was known to be polynomial-time solvable for $k=1$ and NP-hard for every fixed $k\geq 3$. We resolve the remaining case $k=2$ by giving a polynomial-time algorithm that finds a triad-free $u\hbox{-}v$ separator or determines that none exists.

Authors: Maria Chudnovsky, Ilya Maier

A $u\hbox{-}v$ separator in $G$ is a set $S\subseteq V(G)\setminus\{u,v\}$ such that there is no $u\hbox{-}v$ path in $G\setminus S$. A minimal separator in $G$ is a set $S$ such that for some $u,v\in V(G)\setminus S$, $S$ is a $u\hbox{-}v$ separator in $G$ but no proper subset of $S$ is. A set $S$ is bisimplicial if it is the union of at most two cliques. Recognizing graphs in which every minimal separator is the union of at most $k$ cliques is polynomial-time solvable for $k=1$ and NP-hard for every fixed $k\geq 3$. We settle the remaining case $k=2$ by giving a polynomial-time algorithm to decide whether every minimal separator in a graph is bisimplicial. A set $S$ is triad-free if its independence number $α(S)$ is at most two. Given a graph $G$ and two vertices $u,v$, the problem of finding a $u\hbox{-}v$ separator $S$ with $α(S)\leq k$ had the same complexity gap: it was known to be polynomial-time solvable for $k=1$ and NP-hard for every fixed $k\geq 3$. We resolve the remaining case $k=2$ by giving a polynomial-time algorithm that finds a triad-free $u\hbox{-}v$ separator or determines that none exists.

Undirected Network Coding and Faster External-Memory Integer Sorting

from arXiv: Data Structures and Algorithms

Authors: Baochun Li, Zongpeng Li, Sirui Liu

Network coding allows intermediate nodes to combine messages, whereas routing only forwards them. The Li-Li conjecture asserts that network coding and fractional routing have the same capacity region in undirected multiple-unicast networks. We give a counterexample to this conjecture and apply it to external-memory integer sorting. Since Aggarwal and Vitter posed the question in 1988, whether integer sorting can beat the external-memory comparison bound has remained a central and extensively studied open problem. We give a deterministic sorting algorithm: for $N$ integers of $Θ(\log N)$ bits, a memory holding $M$ integers, and block transfers of $B$ integers, where $2B\le M\le N$, the number of block transfers is $O\left(\frac{N}{B}\left[1+\frac{\log N}{(\log(M/B))^{1-\varepsilon}(\log M)^\varepsilon}\right]\right)$, where $\varepsilon>0$ is an absolute constant. In a range of parameter regimes, this bound is asymptotically smaller than the classical comparison-sorting bound. We also use Lean 4 to verify the sorting algorithm's correctness and complexity bounds, and the strict advantage of coding over routing.

Authors: Baochun Li, Zongpeng Li, Sirui Liu

Network coding allows intermediate nodes to combine messages, whereas routing only forwards them. The Li-Li conjecture asserts that network coding and fractional routing have the same capacity region in undirected multiple-unicast networks. We give a counterexample to this conjecture and apply it to external-memory integer sorting. Since Aggarwal and Vitter posed the question in 1988, whether integer sorting can beat the external-memory comparison bound has remained a central and extensively studied open problem. We give a deterministic sorting algorithm: for $N$ integers of $Θ(\log N)$ bits, a memory holding $M$ integers, and block transfers of $B$ integers, where $2B\le M\le N$, the number of block transfers is $O\left(\frac{N}{B}\left[1+\frac{\log N}{(\log(M/B))^{1-\varepsilon}(\log M)^\varepsilon}\right]\right)$, where $\varepsilon>0$ is an absolute constant. In a range of parameter regimes, this bound is asymptotically smaller than the classical comparison-sorting bound. We also use Lean 4 to verify the sorting algorithm's correctness and complexity bounds, and the strict advantage of coding over routing.

FPTAS for Polygonal Multi-Robot Motion Planning

from arXiv: Data Structures and Algorithms

Authors: Sayan Bandyapadhyay, William Lochet, Saket Saurabh, Jie Xue

We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain $\mathcal{W}$ with $n$ vertices and $k$ robots $R_1,\dots,R_k$ translating in $\mathcal{W}$ each of which is represented by a polygon of constant complexity. Given a source configuration $\mathbf{s}$ and a target configuration $\mathbf{t}$ of the robots in $\mathcal{W}$, the goal of the problem is to find a collision-free motion plan from $\mathbf{s}$ to $\mathbf{t}$ that is shortest with respect to some cost function. We present the first fully polynomial-time approximation schemes (FPTAS) for motion planning with any fixed number $k$ of robots, under the min-sum objective, the min-max objective, and the min-makespan objective. Here, the min-sum variant aims to minimize the total traveling distance of all robots, the min-max variant aims to minimize the maximum traveling distance of a single robot, and the min-makespan variant aims to minimize the total time cost under the assumption that all robots move with a maximum speed $1$. The algorithm directly generalizes to the problem in any fixed dimension $d$, with a polytope environment and polytope robots. Our result settles an open question of Agarwal, Halperin, Sharir, Steiger [SODA'24] and Agarwal, Holmgren, Steiger [SODA'26], who gave an FPTAS for $k=2$ unit-square robots and a bicriteria-approximation algorithm for a fixed number $k$ of unit-square or unit-disk robots, respectively. Compared to the previous results, our algorithm is substantially simpler.

Authors: Sayan Bandyapadhyay, William Lochet, Saket Saurabh, Jie Xue

We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain $\mathcal{W}$ with $n$ vertices and $k$ robots $R_1,\dots,R_k$ translating in $\mathcal{W}$ each of which is represented by a polygon of constant complexity. Given a source configuration $\mathbf{s}$ and a target configuration $\mathbf{t}$ of the robots in $\mathcal{W}$, the goal of the problem is to find a collision-free motion plan from $\mathbf{s}$ to $\mathbf{t}$ that is shortest with respect to some cost function. We present the first fully polynomial-time approximation schemes (FPTAS) for motion planning with any fixed number $k$ of robots, under the min-sum objective, the min-max objective, and the min-makespan objective. Here, the min-sum variant aims to minimize the total traveling distance of all robots, the min-max variant aims to minimize the maximum traveling distance of a single robot, and the min-makespan variant aims to minimize the total time cost under the assumption that all robots move with a maximum speed $1$. The algorithm directly generalizes to the problem in any fixed dimension $d$, with a polytope environment and polytope robots. Our result settles an open question of Agarwal, Halperin, Sharir, Steiger [SODA'24] and Agarwal, Holmgren, Steiger [SODA'26], who gave an FPTAS for $k=2$ unit-square robots and a bicriteria-approximation algorithm for a fixed number $k$ of unit-square or unit-disk robots, respectively. Compared to the previous results, our algorithm is substantially simpler.

Claw-Free and Line Graph Edge Deletion on Block Graphs

from arXiv: Data Structures and Algorithms

Authors: Rajat Adak

Edge deletion problems ask for the fewest edges whose deletion gives a graph with a specified property. We study two such problems on block graphs. First making the graph claw-free, and second making it a line graph. Since every line graph is claw-free, the second problem is at least as difficult as the first. We show that on block graphs the two optimum values are always equal, and both are attained by the line graph of a forest. The main step is a completion argument, in which starting from any claw-free solution, we put back edges to get some nice structure inside each block still maintaining the claw-free property. This reduces both edge-deletion problems to the same problem of selecting vertices within the blocks of the input graph. We solve it by dynamic programming in $O(n+m)$ time, which also produces an optimal deletion set and a forest whose line graph is what remains after the completion argument. We also extend the method to $K_{1,r+1}$-free edge deletion for every fixed $r$.

Authors: Rajat Adak

Edge deletion problems ask for the fewest edges whose deletion gives a graph with a specified property. We study two such problems on block graphs. First making the graph claw-free, and second making it a line graph. Since every line graph is claw-free, the second problem is at least as difficult as the first. We show that on block graphs the two optimum values are always equal, and both are attained by the line graph of a forest. The main step is a completion argument, in which starting from any claw-free solution, we put back edges to get some nice structure inside each block still maintaining the claw-free property. This reduces both edge-deletion problems to the same problem of selecting vertices within the blocks of the input graph. We solve it by dynamic programming in $O(n+m)$ time, which also produces an optimal deletion set and a forest whose line graph is what remains after the completion argument. We also extend the method to $K_{1,r+1}$-free edge deletion for every fixed $r$.

Bounded-Density Vertex Deletion Beyond Vertex Cover

from arXiv: Data Structures and Algorithms

Authors: Adrian Miclăuş, Alexandru Popa, Andrei Popa

We study \textsc{Bounded-Density Vertex Deletion} (BDVD), where one asks whether at most \(k\) vertices can be deleted so that every remaining subgraph has density at most a rational input threshold \(τ_ρ\). We address the twin-cover and distance-to-cluster cases left open by Raupach, Breitkopf, Herrmann, and Nichterlein (MFCS 2026). We prove fixed-parameter tractability parameterized solely by the twin-cover number, for unrestricted input density. For distance to cluster, we establish fixed-parameter tractability parameterized by \(\operatorname{dc}(G)+\lfloor2τ_ρ+1\rfloor\), and complement this with \(\mathsf{W[1]}\)-hardness parameterized by \(\operatorname{dc}(G)\) alone when the target density is part of the input, even for integral targets. Thus distance to cluster alone is fixed-parameter tractable throughout \(τ_ρ<1\), while it is insufficient for unrestricted input density. Finally, we settle the combined parameter treewidth plus solution size, an open problem posed by Bazgan, Nichterlein, and Vazquez Alferez (JCSS 2025). We prove that BDVD is \(\mathsf{W[1]}\)-hard parameterized by \(\operatorname{td}(G)+k\), and hence also by \(\operatorname{tw}(G)+k\), already for targets strictly between \(1\) and \(2\).

Authors: Adrian Miclăuş, Alexandru Popa, Andrei Popa

We study \textsc{Bounded-Density Vertex Deletion} (BDVD), where one asks whether at most \(k\) vertices can be deleted so that every remaining subgraph has density at most a rational input threshold \(τ_ρ\). We address the twin-cover and distance-to-cluster cases left open by Raupach, Breitkopf, Herrmann, and Nichterlein (MFCS 2026). We prove fixed-parameter tractability parameterized solely by the twin-cover number, for unrestricted input density. For distance to cluster, we establish fixed-parameter tractability parameterized by \(\operatorname{dc}(G)+\lfloor2τ_ρ+1\rfloor\), and complement this with \(\mathsf{W[1]}\)-hardness parameterized by \(\operatorname{dc}(G)\) alone when the target density is part of the input, even for integral targets. Thus distance to cluster alone is fixed-parameter tractable throughout \(τ_ρ<1\), while it is insufficient for unrestricted input density. Finally, we settle the combined parameter treewidth plus solution size, an open problem posed by Bazgan, Nichterlein, and Vazquez Alferez (JCSS 2025). We prove that BDVD is \(\mathsf{W[1]}\)-hard parameterized by \(\operatorname{td}(G)+k\), and hence also by \(\operatorname{tw}(G)+k\), already for targets strictly between \(1\) and \(2\).

Hypothesis Selection via Weighted Medians

from arXiv: Data Structures and Algorithms

Authors: Liang-Yu Zou

Given $n$ distributions (hypotheses) $\mathcal H=\{H_1,\ldots,H_n\}$ and samples from an unknown target distribution $P$, proper hypothesis selection seeks a distribution whose total variation distance to $P$ is close to the best achievable within the known distributions. We give an algorithm that, using $O(\varepsilon^{-2}\log(n/δ))$ target samples, running in $O(n\varepsilon^{-2}\log(n/δ))$ time, with probability at least $1-δ$, returns $H_{\hat i}$ satisfying $d_\mathrm{TV}(P,H_{\hat i})\leq 3\min_{i\in[n]}d_\mathrm{TV}(P,H_i)+\varepsilon.$ The algorithm achieves the optimal approximation factor and sample complexity. The running time improves the dependence on the failure probability from polynomial to logarithmic in the state-of-the-art bound $\tilde{O}(n\varepsilon^{-2}δ^{-1})$ of Aamand, Aliakbarpour, Chen, and Silwal (COLT 2026), thereby answering their Question~2.

Authors: Liang-Yu Zou

Given $n$ distributions (hypotheses) $\mathcal H=\{H_1,\ldots,H_n\}$ and samples from an unknown target distribution $P$, proper hypothesis selection seeks a distribution whose total variation distance to $P$ is close to the best achievable within the known distributions. We give an algorithm that, using $O(\varepsilon^{-2}\log(n/δ))$ target samples, running in $O(n\varepsilon^{-2}\log(n/δ))$ time, with probability at least $1-δ$, returns $H_{\hat i}$ satisfying $d_\mathrm{TV}(P,H_{\hat i})\leq 3\min_{i\in[n]}d_\mathrm{TV}(P,H_i)+\varepsilon.$ The algorithm achieves the optimal approximation factor and sample complexity. The running time improves the dependence on the failure probability from polynomial to logarithmic in the state-of-the-art bound $\tilde{O}(n\varepsilon^{-2}δ^{-1})$ of Aamand, Aliakbarpour, Chen, and Silwal (COLT 2026), thereby answering their Question~2.

Simple, Faster (min, +) Matrix Multiplication and APSP

from arXiv: Data Structures and Algorithms

Authors: Barna Saha, Christopher Ye

In this note, we provide a deterministic algorithm that computes the $(\min, +)$-Matrix Product $C = A * B$ of two $N \times N$ matrices $A$ and $B$ with polynomially bounded integer entries in $\tilde{O}(N^{2.9964})$ time, improving upon the exponent of $O(N^{2.9995})$ time obtained in the recent breakthrough by Anthropic (Alman and Vassilevska Williams, 2026). Our algorithm uses a phased approach first proposed by Bringmann, Grandoni, Saha, and Vassilevska Williams in 2016. The phased approach produces an approximation $\tilde{C}$ of $C$ in truly subcubic time such that the number of remaining triples $(i,k,j): A_{i,k}+B_{k,j} < \tilde{C}(i,j)$ is truly subcubic. This provided strong evidence that a truly subcubic $(\min, +)$-Matrix Product and therefore, a truly subcubic algorithm for All Pairs Shortest Paths (APSP) may exist. However, to find the remaining triples in subcubic time, a sparse certificate is needed whose construction was not known. We use the thin matrix product theorem of the recent breakthrough to provide such a certificate. This gives a simple modular subcubic algorithm for APSP.

Authors: Barna Saha, Christopher Ye

In this note, we provide a deterministic algorithm that computes the $(\min, +)$-Matrix Product $C = A * B$ of two $N \times N$ matrices $A$ and $B$ with polynomially bounded integer entries in $\tilde{O}(N^{2.9964})$ time, improving upon the exponent of $O(N^{2.9995})$ time obtained in the recent breakthrough by Anthropic (Alman and Vassilevska Williams, 2026). Our algorithm uses a phased approach first proposed by Bringmann, Grandoni, Saha, and Vassilevska Williams in 2016. The phased approach produces an approximation $\tilde{C}$ of $C$ in truly subcubic time such that the number of remaining triples $(i,k,j): A_{i,k}+B_{k,j} < \tilde{C}(i,j)$ is truly subcubic. This provided strong evidence that a truly subcubic $(\min, +)$-Matrix Product and therefore, a truly subcubic algorithm for All Pairs Shortest Paths (APSP) may exist. However, to find the remaining triples in subcubic time, a sparse certificate is needed whose construction was not known. We use the thin matrix product theorem of the recent breakthrough to provide such a certificate. This gives a simple modular subcubic algorithm for APSP.

Subexponential Parameterized Approximation for Group-Fair Euclidean $k$-Center

from arXiv: Data Structures and Algorithms

Authors: Rudranarayan Kar, Diya Roy, Abhishek Sahu

Clustering is a basic tool for grouping data into meaningful classes, but in many applications the resulting clusters must also respect the fairness conditions among different groups. We study this direction for the group-fair Euclidean $k$-center problem in fixed dimension, where the input points are partitioned into disjoint color classes, and every cluster must satisfy given lower and upper bounds on the fraction of each color. We give a separator-based recursive approximation algorithm for the continuous-center setting. The main idea is to first reduce the continuous search space to a finite candidate center set and then use the geometric sphere separators to break the instance into smaller subproblems. Our algorithm gives a bicriteria approximation: the clustering radius is within a factor of $(1+ε)$ of the optimum exactly fair solution, while the fairness bounds are relaxed only by a multiplicative factor. For fixed dimension, number of colors, and approximation parameters, the running time is polynomial in input size and subexponential in $k$. A recent IJCAI-ECAI2026 result gives a $(1+ε)$-approximation for group-fair $k$-center in bounded doubling metrics with running time $(k/ε)^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$. In the fixed-dimensional Euclidean setting considered here, we improve the dependence on $k$ to subexponential, while allowing continuous centers. The fairness guarantees of the two results are different: the previous work allows an additive fairness violation, whereas our algorithm uses a controlled multiplicative relaxation.

Authors: Rudranarayan Kar, Diya Roy, Abhishek Sahu

Clustering is a basic tool for grouping data into meaningful classes, but in many applications the resulting clusters must also respect the fairness conditions among different groups. We study this direction for the group-fair Euclidean $k$-center problem in fixed dimension, where the input points are partitioned into disjoint color classes, and every cluster must satisfy given lower and upper bounds on the fraction of each color. We give a separator-based recursive approximation algorithm for the continuous-center setting. The main idea is to first reduce the continuous search space to a finite candidate center set and then use the geometric sphere separators to break the instance into smaller subproblems. Our algorithm gives a bicriteria approximation: the clustering radius is within a factor of $(1+ε)$ of the optimum exactly fair solution, while the fairness bounds are relaxed only by a multiplicative factor. For fixed dimension, number of colors, and approximation parameters, the running time is polynomial in input size and subexponential in $k$. A recent IJCAI-ECAI2026 result gives a $(1+ε)$-approximation for group-fair $k$-center in bounded doubling metrics with running time $(k/ε)^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$. In the fixed-dimensional Euclidean setting considered here, we improve the dependence on $k$ to subexponential, while allowing continuous centers. The fairness guarantees of the two results are different: the previous work allows an additive fairness violation, whereas our algorithm uses a controlled multiplicative relaxation.

An Analytic 0.48-Approximation for Non-Monotone k-Submodular Maximization under a Matroid Constraint

from arXiv: Data Structures and Algorithms

Authors: Zizong Wang, Yibo Wang

We give a randomized polynomial-time $0.48$-approximation for maximizing a nonnegative non-monotone $k$-submodular function under a matroid constraint, for every $k\ge2$. The algorithm combines a top-two label rule with a nested-chain potential and maximum-weight matroid bases. Its analysis reduces the approximation guarantee to a one-dimensional time allocation: a uniform local lower bound leads to a linear differential equation, concavity closes the remaining interval, and two elementary budgets complete the proof. A strict saving in the time budget absorbs sampling and discretization errors, yielding the exact $0.48$ guarantee in the value-oracle model.

Authors: Zizong Wang, Yibo Wang

We give a randomized polynomial-time $0.48$-approximation for maximizing a nonnegative non-monotone $k$-submodular function under a matroid constraint, for every $k\ge2$. The algorithm combines a top-two label rule with a nested-chain potential and maximum-weight matroid bases. Its analysis reduces the approximation guarantee to a one-dimensional time allocation: a uniform local lower bound leads to a linear differential equation, concavity closes the remaining interval, and two elementary budgets complete the proof. A strict saving in the time budget absorbs sampling and discretization errors, yielding the exact $0.48$ guarantee in the value-oracle model.

Optimal Online Stochastic Matching

from arXiv: Data Structures and Algorithms

Authors: Paul Duetting, Michal Feldman, Mathieu Molina

We resolve the problem of unweighted online stochastic matching with a known arrival law by establishing its optimal competitive ratio. For Poisson arrivals, we achieve $1-e/e^e\approx0.8206$, matching the upper bound of Chierichetti, Giacchini, Panconesi, and Vattani [ICALP 2025]. For exactly $n$ i.i.d.~arrivals, we obtain the same guarantee up to an additive $O(n^{-1/2})$ loss. Our key technical contribution is an exact correspondence between Poisson matching and exponential scheduling, preserving both online performance and the expected offline optimum. To prove the latter, we show that the distribution of the maximum of a random supermodular set function is determined by its joint distributions along chains of nested sets, a result of independent interest. This reduces the analysis to chains, where classical Poisson--exponential duality applies. The offline equivalence extends to arbitrary nonnegative edge weights. We then design a posterior allocation policy for exponential scheduling and use the correspondence to transfer its optimal competitive guarantee to online matching. Additionally, we show that the optimal competitive ratio under an unknown arrival law is strictly lower than $1-e/e^e$, establishing a strict separation between the known and unknown arrival models.

Authors: Paul Duetting, Michal Feldman, Mathieu Molina

We resolve the problem of unweighted online stochastic matching with a known arrival law by establishing its optimal competitive ratio. For Poisson arrivals, we achieve $1-e/e^e\approx0.8206$, matching the upper bound of Chierichetti, Giacchini, Panconesi, and Vattani [ICALP 2025]. For exactly $n$ i.i.d.~arrivals, we obtain the same guarantee up to an additive $O(n^{-1/2})$ loss. Our key technical contribution is an exact correspondence between Poisson matching and exponential scheduling, preserving both online performance and the expected offline optimum. To prove the latter, we show that the distribution of the maximum of a random supermodular set function is determined by its joint distributions along chains of nested sets, a result of independent interest. This reduces the analysis to chains, where classical Poisson--exponential duality applies. The offline equivalence extends to arbitrary nonnegative edge weights. We then design a posterior allocation policy for exponential scheduling and use the correspondence to transfer its optimal competitive guarantee to online matching. Additionally, we show that the optimal competitive ratio under an unknown arrival law is strictly lower than $1-e/e^e$, establishing a strict separation between the known and unknown arrival models.

Wednesday, October 14

TCS+ talk: Wednesday, October 21 — Rachel Zhang, UC Berkeley

from TCS+ Seminar Series
The next TCS+ talk will take place this coming Wednesday, October 21st at 1:00 PM Eastern Time (10:00 AM Pacific Time, 19:00 Central European Time, 17:00 UTC). Rachel Zhang from UC Berkeley will speak about “Linear Time Encodable Quantum Error-Correcting Codes” (abstract below). You can reserve a spot as an individual or a group to […]

The next TCS+ talk will take place this coming Wednesday, October 21st at 1:00 PM Eastern Time (10:00 AM Pacific Time, 19:00 Central European Time, 17:00 UTC). Rachel Zhang from UC Berkeley will speak about “Linear Time Encodable Quantum Error-Correcting Codes” (abstract below).

You can reserve a spot as an individual or a group to join us live by signing up on the online form. Registration is not required to attend the interactive talk, and the link will be posted on the website the day prior to the talk; however, by registering in the form, you will receive a reminder, along with the link. (The recorded talk will also be posted on our website afterwards) As usual, for more information about the TCS+ online seminar series and the upcoming talks, or to suggest a possible topic or speaker, please see the website.

Abstract: In this talk, I will describe a construction of quantum CSS codes with rate-distance tradeoff approaching the CSS GV bound, which can be encoded by a quantum circuit of logarithmic depth and a linear number of gates. Our construction is heavily inspired by a work of Brehm and Resch and can be viewed as a quantum analogue of classical multiple accumulate codes. The encoder has a particularly simple structure, consisting of a single constant depth outer quantum circuit followed by several classical accumulation and inverse-accumulation layers.

By plustcs

Postdoc positions for IAS special year at Institute for Advanced Study (apply by December 1, 2026)

from CCI: jobs
The Institute for Advanced Study is holding a special year in Expansion and Computation in 2027/2028. We are seeking PhD holders with interest in theoretical aspects of computer science and extensive background in relevant areas in mathematics. Website: www.ias.edu/math/events/special-year-expansion-and-computation Email: dinuri@gmail.com, dminzer@mit.edu

The Institute for Advanced Study is holding a special year in Expansion and Computation in 2027/2028. We are seeking PhD holders with interest in theoretical aspects of computer science and extensive background in relevant areas in mathematics.

Website: https://www.ias.edu/math/events/special-year-expansion-and-computation
Email: dinuri@gmail.com, dminzer@mit.edu

By shacharlovett

TR26-253 | The Gap Between Average-Case and Worst-Case Complexity | Halley Goldberg, Jinqiao Hu, Zhenjian Lu, Jingyi Lyu, Igor Oliveira

from ECCC Papers
Understanding the average-case complexity of $\mathrm{NP}$ is a central goal in complexity theory and cryptography. A necessary milestone is to understand the corresponding average-case versus worst-case question for the polynomial hierarchy ($\mathrm{PH}$). In a celebrated result, Hirahara [Hir21] proved that if every distributional problem in $\mathrm{PH}$ is easy on average, then every language in $\mathrm{PH}$ admits worst-case algorithms running in time $2^{O(n/\log n)}$. Remarkably, although the statement of Hirahara's theorem makes no reference to meta-complexity, all known proofs appear to use time-bounded Kolmogorov complexity and its associated meta-computational problems in an essential way. We show that the reliance on techniques and perspectives from meta-complexity is not simply a coincidence. Building on previous work of Hirahara [Hir20] and on a recent result of Hu, Lu, and Oliveira [HLO25], we prove that relating the average-case and worst-case complexities of $\mathrm{PH}$ is in fact equivalent to a purely meta-computational implication: efficient algorithms for a standard gap version of $\mathrm{MINpKT}$ with $\mathrm{PH}$ oracle access imply efficient algorithms for a mild gap version. Crucially, in these promise problems the standard gap separates a YES instance with time bound $t$ from a NO instance with time bound $\mathrm{poly}(t)$, whereas the mild gap keeps the YES threshold $t$ and moves the NO threshold to $t+\mathrm{poly}(n)$. Together with prior work, this equivalence points to the study of gap reductions as a potentially promising route toward understanding the relation between average-case and worst-case complexity. We make progress by proving that the gap in running time can be eliminated in the average-case setting. Finally, we prove that in the worst-case setting any efficient reduction between the two computational problems must use nonrelativizing techniques.
Understanding the average-case complexity of $\mathrm{NP}$ is a central goal in complexity theory and cryptography. A necessary milestone is to understand the corresponding average-case versus worst-case question for the polynomial hierarchy ($\mathrm{PH}$). In a celebrated result, Hirahara [Hir21] proved that if every distributional problem in $\mathrm{PH}$ is easy on average, then every language in $\mathrm{PH}$ admits worst-case algorithms running in time $2^{O(n/\log n)}$. Remarkably, although the statement of Hirahara's theorem makes no reference to meta-complexity, all known proofs appear to use time-bounded Kolmogorov complexity and its associated meta-computational problems in an essential way. We show that the reliance on techniques and perspectives from meta-complexity is not simply a coincidence. Building on previous work of Hirahara [Hir20] and on a recent result of Hu, Lu, and Oliveira [HLO25], we prove that relating the average-case and worst-case complexities of $\mathrm{PH}$ is in fact equivalent to a purely meta-computational implication: efficient algorithms for a standard gap version of $\mathrm{MINpKT}$ with $\mathrm{PH}$ oracle access imply efficient algorithms for a mild gap version. Crucially, in these promise problems the standard gap separates a YES instance with time bound $t$ from a NO instance with time bound $\mathrm{poly}(t)$, whereas the mild gap keeps the YES threshold $t$ and moves the NO threshold to $t+\mathrm{poly}(n)$. Together with prior work, this equivalence points to the study of gap reductions as a potentially promising route toward understanding the relation between average-case and worst-case complexity. We make progress by proving that the gap in running time can be eliminated in the average-case setting. Finally, we prove that in the worst-case setting any efficient reduction between the two computational problems must use nonrelativizing techniques.

TR26-252 | Unconditional Separation between LL and CLL | Shubham Bhardwaj

from ECCC Papers
Catalytic computation involves the study of Turing machines that are provided an additional catalytic tape (with arbitrary content) that the machine may use while ensuring that the tape contents are restored at the end of the computation. Although it is known that catalytic log-space machines are at least as powerful as non-deterministic log-space (and more), we do not have unconditional asymptotic separations. In this work, we prove an unconditional separation between a deterministic space complexity class and its catalytic counterpart. More precisely, at the doubly logarithmic scale, we show that catalytic Turing machines can provably compute more languages than standard deterministic doubly-logarithmic space machines. The separation is witnessed by the unary language $\mathrm{POW2} = \{1^n : n \text{ is a power of two}\}$. We construct a catalytic Turing machine that uses $O(\log \log n)$ bits of space and $O(\log n)$ bits of catalytic tape to decide $\mathrm{POW2}$, whereas any standard deterministic Turing machine requires $\Omega(\log n)$ space to decide it.
Catalytic computation involves the study of Turing machines that are provided an additional catalytic tape (with arbitrary content) that the machine may use while ensuring that the tape contents are restored at the end of the computation. Although it is known that catalytic log-space machines are at least as powerful as non-deterministic log-space (and more), we do not have unconditional asymptotic separations. In this work, we prove an unconditional separation between a deterministic space complexity class and its catalytic counterpart. More precisely, at the doubly logarithmic scale, we show that catalytic Turing machines can provably compute more languages than standard deterministic doubly-logarithmic space machines. The separation is witnessed by the unary language $\mathrm{POW2} = \{1^n : n \text{ is a power of two}\}$. We construct a catalytic Turing machine that uses $O(\log \log n)$ bits of space and $O(\log n)$ bits of catalytic tape to decide $\mathrm{POW2}$, whereas any standard deterministic Turing machine requires $\Omega(\log n)$ space to decide it.

TR26-251 | Pseudorandom Bits for Low-Degree Modular Polynomials | Chin Ho Lee, Shachar Lovett, Tejas Nareddy, Emanuele Viola

from ECCC Papers
We revisit the problem of constructing explicit pseudorandom generators which output $n$ bits that look random to $n$-variate degree-$d$ polynomials modulo $m$. We give new generators with seed length $2^{O(dm^2)} \log(n/\varepsilon)$, which is optimal for any fixed $m$ and $d$. Along the way, we prove new Fourier growth bounds for polynomial phase functions on the Boolean cube. Our results simplify previous constructions, improve their parameters, and extend them to composite moduli. The previous best seed length for odd prime $m$ had a tower dependence on the degree (Lovett, Mukhopadhyay, and Shpilka; FOCS 2010); and for general composite $m$ was at least $2^{\sqrt{\log n}}$ even for degree $2$ (Luby, Veli?kovi?, and Wigderson; ISTCS 1993). As an application, we obtain generators with optimal seed length for branching programs over nilpotent groups, extending recent work (Lee and Viola; CCC 2025). We also obtain new results on the so-called coin problem for polynomials modulo composites.
We revisit the problem of constructing explicit pseudorandom generators which output $n$ bits that look random to $n$-variate degree-$d$ polynomials modulo $m$. We give new generators with seed length $2^{O(dm^2)} \log(n/\varepsilon)$, which is optimal for any fixed $m$ and $d$. Along the way, we prove new Fourier growth bounds for polynomial phase functions on the Boolean cube. Our results simplify previous constructions, improve their parameters, and extend them to composite moduli. The previous best seed length for odd prime $m$ had a tower dependence on the degree (Lovett, Mukhopadhyay, and Shpilka; FOCS 2010); and for general composite $m$ was at least $2^{\sqrt{\log n}}$ even for degree $2$ (Luby, Veli?kovi?, and Wigderson; ISTCS 1993). As an application, we obtain generators with optimal seed length for branching programs over nilpotent groups, extending recent work (Lee and Viola; CCC 2025). We also obtain new results on the so-called coin problem for polynomials modulo composites.

On Collusion-Free Teaching in Planar Graphs

from arXiv: Computational Complexity

Authors: Khazaliya Liana, Kuske Clemens

Non-clashing teaching minimizes teaching complexity among models satisfying the Goldman--Mathias collusion-avoidance criterion. While the Vapnik--Chervonenkis dimension (VCdim) measures the complexity of learning from samples, non-clashing teaching dimension (NCTD) measures the number of examples required when a teacher can select them. Whether NCTD is always bounded above by VCdim remains a fundamental open question in learning theory. In this paper, we investigate this question for the concept classes of closed neighborhoods in planar graphs. We prove a tight upper bound of two on their NCTD, thereby establishing the conjectured inequality for this family. For the positive variant NCTD+, in which teaching sets contain only positive examples, we obtain a tight upper bound of four. Finally, we show that deciding whether either NCTD+ or NCTD is at most one is NP-complete, even for concept classes of closed neighborhoods in planar graphs. In particular, despite the upper bound of two on NCTD for such classes, distinguishing between NCTD values of one and two remains NP-hard.

Authors: Khazaliya Liana, Kuske Clemens

Non-clashing teaching minimizes teaching complexity among models satisfying the Goldman--Mathias collusion-avoidance criterion. While the Vapnik--Chervonenkis dimension (VCdim) measures the complexity of learning from samples, non-clashing teaching dimension (NCTD) measures the number of examples required when a teacher can select them. Whether NCTD is always bounded above by VCdim remains a fundamental open question in learning theory. In this paper, we investigate this question for the concept classes of closed neighborhoods in planar graphs. We prove a tight upper bound of two on their NCTD, thereby establishing the conjectured inequality for this family. For the positive variant NCTD+, in which teaching sets contain only positive examples, we obtain a tight upper bound of four. Finally, we show that deciding whether either NCTD+ or NCTD is at most one is NP-complete, even for concept classes of closed neighborhoods in planar graphs. In particular, despite the upper bound of two on NCTD for such classes, distinguishing between NCTD values of one and two remains NP-hard.

CLS Hardness of 2D Min-Max Optimization with Product Constraints

from arXiv: Computational Complexity

Authors: Alkis Kalavasis, Panagiotis Patsilinakos, Stratis Skoulakis, Manolis Zampetakis

We study the complexity of computing approximate first-order Nash equilibria in min-max optimization problems with one dimensional min and max players and product constraints. For black-box access to the objective, we prove that any algorithm requires $Ω(1/\sqrtε)$ queries to find an $ε$-first-order Nash point. In the white-box setting, we establish CLS-hardness by reducing from End-of-Potential-Line. These results show that even the simplest min-max setting with one dimensional min and max players is at least as hard as CLS and therefore does not admit polynomial-time algorithms unless $\mathsf{CLS}\subseteq \mathsf{P}$.

Authors: Alkis Kalavasis, Panagiotis Patsilinakos, Stratis Skoulakis, Manolis Zampetakis

We study the complexity of computing approximate first-order Nash equilibria in min-max optimization problems with one dimensional min and max players and product constraints. For black-box access to the objective, we prove that any algorithm requires $Ω(1/\sqrtε)$ queries to find an $ε$-first-order Nash point. In the white-box setting, we establish CLS-hardness by reducing from End-of-Potential-Line. These results show that even the simplest min-max setting with one dimensional min and max players is at least as hard as CLS and therefore does not admit polynomial-time algorithms unless $\mathsf{CLS}\subseteq \mathsf{P}$.

$T$-efficient unitary synthesis via block-sparse Hamiltonian simulation

from arXiv: Computational Complexity

Authors: Diyi Liu, Shuchen Zhu, Wibe Albert de Jong, Di Fang, David Williams-Young, Chao Yang

We construct Clifford+$T$ circuits implementing arbitrary $n$-qubit unitaries with worst-case $T$-count $\widetilde{O}(N^{8/7})$ at constant operator-norm error, where $N=2^n$, improving the previous exponent $5/4$. The construction uses $\widetilde{O}(N^{6/7})$ clean ancillas. The main idea is to reduce the data addressed by each query to the block-encoding circuit for a sparse piece while controlling the additional simulation work needed to combine the pieces. We simulate a Hermitian logarithm of the target by flattening its traceless part with a two-layer random similarity transformation, randomly partitioning the transformed generator into block-sparse Hermitian pieces, and combining their evolutions using qDRIFT. For a target operator-norm error $ε\in(0,1/2)$, let $L=\log_2(N/ε)$, the qubit count plus the requested bits of accuracy. By successively correcting the approximation error, we obtain a circuit using $\widetilde{O}(N^{8/7}L^{3/7}+NL)$ $T$ gates and $\widetilde{O}(N)$ clean ancillas whenever $L\le N$. We also prove that, at constant precision, the exponent $8/7$ is optimal within a sequential block-SELECT model.

Authors: Diyi Liu, Shuchen Zhu, Wibe Albert de Jong, Di Fang, David Williams-Young, Chao Yang

We construct Clifford+$T$ circuits implementing arbitrary $n$-qubit unitaries with worst-case $T$-count $\widetilde{O}(N^{8/7})$ at constant operator-norm error, where $N=2^n$, improving the previous exponent $5/4$. The construction uses $\widetilde{O}(N^{6/7})$ clean ancillas. The main idea is to reduce the data addressed by each query to the block-encoding circuit for a sparse piece while controlling the additional simulation work needed to combine the pieces. We simulate a Hermitian logarithm of the target by flattening its traceless part with a two-layer random similarity transformation, randomly partitioning the transformed generator into block-sparse Hermitian pieces, and combining their evolutions using qDRIFT. For a target operator-norm error $ε\in(0,1/2)$, let $L=\log_2(N/ε)$, the qubit count plus the requested bits of accuracy. By successively correcting the approximation error, we obtain a circuit using $\widetilde{O}(N^{8/7}L^{3/7}+NL)$ $T$ gates and $\widetilde{O}(N)$ clean ancillas whenever $L\le N$. We also prove that, at constant precision, the exponent $8/7$ is optimal within a sequential block-SELECT model.

PSPACE-completeness of the Hunters and Rabbit game

from arXiv: Computational Complexity

Authors: Walid Ben-Ameur, Alessandro Maddaloni

In the Hunters and Rabbit game, hunters shoot at a set of vertices at each round, while an invisible rabbit must move along an edge. We prove that deciding whether a given number of hunters can guarantee capture is PSPACE-complete, even on simple bipartite graphs. The reduction uses intersection nonemptiness for inverse finite automata: one common word must put all automata in accepting states simultaneously. A clique acting as a safe haven and two families of guards force every winning strategy to simulate common transitions. Existing transformations remove the restriction on the rabbit's initial positions and yield PSPACE-completeness for the directed game with one hunter, even on oriented graphs with positive minimum outdegree. Adjusting the guard sizes also proves PSPACE-completeness of the firefighting game, in which the rabbit may remain at its current vertex.

Authors: Walid Ben-Ameur, Alessandro Maddaloni

In the Hunters and Rabbit game, hunters shoot at a set of vertices at each round, while an invisible rabbit must move along an edge. We prove that deciding whether a given number of hunters can guarantee capture is PSPACE-complete, even on simple bipartite graphs. The reduction uses intersection nonemptiness for inverse finite automata: one common word must put all automata in accepting states simultaneously. A clique acting as a safe haven and two families of guards force every winning strategy to simulate common transitions. Existing transformations remove the restriction on the rabbit's initial positions and yield PSPACE-completeness for the directed game with one hunter, even on oriented graphs with positive minimum outdegree. Adjusting the guard sizes also proves PSPACE-completeness of the firefighting game, in which the rabbit may remain at its current vertex.

Path Length Existence Problems in Bounded-Treewidth Probabilistic Graphs

from arXiv: Computational Complexity

Authors: Antoine Amarilli, Mikaël Monet, Harshul Sagar

We consider the problem of computing the probability of existence of a source-to-target path or walk satisfying a given length constraint in an input probabilistic graph. The length constraints we consider can require the length to be at least $\ell$, at most $\ell$, or exactly $\ell$, for $\ell$ a target length given as input. The probabilistic graphs that we consider are directed graphs where each edge is annotated with an independent probability of existence. As this task is generally intractable (#P-hard), we aim for algorithms applying to input graphs that are assumed to be of bounded treewidth. We classify the complexity landscape of these problems: - We give an XP algorithm parameterised by treewidth to compute the probability that an input probabilistic graph contains a source-to-target path/walk of length $\geq \ell$, and do the same for a path/walk of length $\leq \ell$. - We show that the four problems mentioned in the last item are W[1]-hard parameterised by pathwidth (hence not FPT under the standard assumption that FPT$\neq$W[1]). This is in contrast to the case of non-probabilistic graphs, where the corresponding problems were known to be FPT parameterised by treewidth; and to the connectivity problem (and more generally MSO evaluation) on probabilistic graphs, where the same was known. - Surprisingly, we show that it is #P-hard to compute the probability that an input graph contains a source-to-target path/walk of length exactly $\ell$, even on graphs of pathwidth $2$. By contrast, all our problems are tractable on graphs of treewidth $1$. - Last, we investigate the complexity of our problems when the edges can carry weights. This causes all our problems to become #P-hard already on pathwidth-2 graphs, and causes specifically the ``exactly $\ell$'' walk problem to be #P-hard even on pathwidth-1 graphs.

Authors: Antoine Amarilli, Mikaël Monet, Harshul Sagar

We consider the problem of computing the probability of existence of a source-to-target path or walk satisfying a given length constraint in an input probabilistic graph. The length constraints we consider can require the length to be at least $\ell$, at most $\ell$, or exactly $\ell$, for $\ell$ a target length given as input. The probabilistic graphs that we consider are directed graphs where each edge is annotated with an independent probability of existence. As this task is generally intractable (#P-hard), we aim for algorithms applying to input graphs that are assumed to be of bounded treewidth. We classify the complexity landscape of these problems: - We give an XP algorithm parameterised by treewidth to compute the probability that an input probabilistic graph contains a source-to-target path/walk of length $\geq \ell$, and do the same for a path/walk of length $\leq \ell$. - We show that the four problems mentioned in the last item are W[1]-hard parameterised by pathwidth (hence not FPT under the standard assumption that FPT$\neq$W[1]). This is in contrast to the case of non-probabilistic graphs, where the corresponding problems were known to be FPT parameterised by treewidth; and to the connectivity problem (and more generally MSO evaluation) on probabilistic graphs, where the same was known. - Surprisingly, we show that it is #P-hard to compute the probability that an input graph contains a source-to-target path/walk of length exactly $\ell$, even on graphs of pathwidth $2$. By contrast, all our problems are tractable on graphs of treewidth $1$. - Last, we investigate the complexity of our problems when the edges can carry weights. This causes all our problems to become #P-hard already on pathwidth-2 graphs, and causes specifically the ``exactly $\ell$'' walk problem to be #P-hard even on pathwidth-1 graphs.

Compositional Information Geometry of Probabilistic Programs

from arXiv: Computational Geometry

Authors: Jacek Karwowski, Frank Nielsen, Sam Staton

We study the compositional, intensional information geometry of probabilistic programs. For a first-order probabilistic programming language with finite random variables, we construct a contravariant lax monoidal functor that, for a program of type $X \to Y$, pulls back a (degenerate) Riemannian metric on $Y$ to $X$. It specialises to the Fisher metric in the case of standard statistical models and to ordinary pullback in the case of deterministic programs. We further justify this construction in two ways. First, we show that this functor arises as a pullback of geometry from a universal space of programs over a type $Y$ with a fixed trace space. Second, we show that it is a special case of a more general pullback construction over weighted measures. Our compositional geometry depends on the presentation of the program, and we study how it relates to the (non-compositional) geometry of the realised measures by establishing a three-level inequality relating our score-velocity metric for the program, the program's corresponding output-conditioned metric, and the standard Wasserstein-Fisher-Rao metric of the realised family. Finally, we show how the theory can be extended to accommodate continuous random variables using diffeological quasi-Borel spaces. We also discuss several concrete directions for future work.

Authors: Jacek Karwowski, Frank Nielsen, Sam Staton

We study the compositional, intensional information geometry of probabilistic programs. For a first-order probabilistic programming language with finite random variables, we construct a contravariant lax monoidal functor that, for a program of type $X \to Y$, pulls back a (degenerate) Riemannian metric on $Y$ to $X$. It specialises to the Fisher metric in the case of standard statistical models and to ordinary pullback in the case of deterministic programs. We further justify this construction in two ways. First, we show that this functor arises as a pullback of geometry from a universal space of programs over a type $Y$ with a fixed trace space. Second, we show that it is a special case of a more general pullback construction over weighted measures. Our compositional geometry depends on the presentation of the program, and we study how it relates to the (non-compositional) geometry of the realised measures by establishing a three-level inequality relating our score-velocity metric for the program, the program's corresponding output-conditioned metric, and the standard Wasserstein-Fisher-Rao metric of the realised family. Finally, we show how the theory can be extended to accommodate continuous random variables using diffeological quasi-Borel spaces. We also discuss several concrete directions for future work.

Optimal Planar $k$ Nearest Neighbors Queries for Additively Weighted Euclidean Distances and Convex Metrics

from arXiv: Computational Geometry

Authors: Chih-Hung Liu

Given a set $P$ of $n$ point sites in the plane, the $k$ nearest neighbors problem is to build a data structure that, for a query point $v$ and a query integer $k$, reports the $k$ nearest point sites of $v$ in $P$ efficiently. For the Euclidean metric, after four decades of efforts, an optimal data structure has been developed with $O(n)$ space, $O(\log n+k)$ query time and $O(n\log n)$ preprocessing time [Mat92b, Cha00, AC09, CT16]. In contrast, for a family of distance functions, the state-of-the-art data structure [AES99, KMR+20, Liu22] attains $O(n\log\log n)$ space, $O(\log n+k)$ query time and expected $O(n\log n)$ preprocessing time. An open question is the existence of an optimal data structure with $O(n)$ space and $O(\log n + k)$ query time for distance functions more general than the Euclidean metric. Such an optimal data structure also remains unknown for non-point sites in the Euclidean metric, even for disk sites or, more generally, additively weighted Euclidean distances. We develop an optimal $k$-nearest-neighbors data structure for additively weighted Euclidean distances with $O(n)$ space, $O(\log n + k)$ query time and expected $O(n\log n)$ preprocessing time. Since this data structure can be adjusted to disk sites, it serves as an initial result toward optimality for non-point sites. We also adapt our data structure to point sites under a family of convex metrics, including commonly used $L_p$ metrics and uniform orientation metrics. The data structure can be further extended to distance functions that satisfy certain axioms. Thus, we demonstrate the existence of optimal $k$-nearest-neighbors data structures for distance functions more general than point sites in the Euclidean metric. Our main technical advance is to generalize Matoušek's shallow partition theorem for hyperplanes [Mat92b] to these settings, while achieving a logarithmic crossing number.

Authors: Chih-Hung Liu

Given a set $P$ of $n$ point sites in the plane, the $k$ nearest neighbors problem is to build a data structure that, for a query point $v$ and a query integer $k$, reports the $k$ nearest point sites of $v$ in $P$ efficiently. For the Euclidean metric, after four decades of efforts, an optimal data structure has been developed with $O(n)$ space, $O(\log n+k)$ query time and $O(n\log n)$ preprocessing time [Mat92b, Cha00, AC09, CT16]. In contrast, for a family of distance functions, the state-of-the-art data structure [AES99, KMR+20, Liu22] attains $O(n\log\log n)$ space, $O(\log n+k)$ query time and expected $O(n\log n)$ preprocessing time. An open question is the existence of an optimal data structure with $O(n)$ space and $O(\log n + k)$ query time for distance functions more general than the Euclidean metric. Such an optimal data structure also remains unknown for non-point sites in the Euclidean metric, even for disk sites or, more generally, additively weighted Euclidean distances. We develop an optimal $k$-nearest-neighbors data structure for additively weighted Euclidean distances with $O(n)$ space, $O(\log n + k)$ query time and expected $O(n\log n)$ preprocessing time. Since this data structure can be adjusted to disk sites, it serves as an initial result toward optimality for non-point sites. We also adapt our data structure to point sites under a family of convex metrics, including commonly used $L_p$ metrics and uniform orientation metrics. The data structure can be further extended to distance functions that satisfy certain axioms. Thus, we demonstrate the existence of optimal $k$-nearest-neighbors data structures for distance functions more general than point sites in the Euclidean metric. Our main technical advance is to generalize Matoušek's shallow partition theorem for hyperplanes [Mat92b] to these settings, while achieving a logarithmic crossing number.

Bipartite Matching with Few Rounds of Demand Queries

from arXiv: Data Structures and Algorithms

Authors: Shravan Mehra, Sagnik Mukhopadhyay, Vihan Shah

We study the randomised and deterministic demand-query complexity of approximate bipartite matching with few query rounds. The demand-query model, introduced in [Blumrosen and Nisan SICOMP 2009; Nisan SODA 2021], is inspired by combinatorial auctions and welfare maximisation, where an oracle returns the first neighbouring vertex (item) of a vertex u (agent) according to a given permutation pi (preference ordering of the agent over the items). For randomised algorithms, our main result gives a trade-off between the number of rounds k, the per-round query budget q=n^{1+δ}, and the achievable approximation ratio. Let 1 <= k <= log n, and -1 <= delta <= 1/k. Every k-round randomised algorithm using O(q) demand queries per round has a worst-case expected-size approximation factor of Omega(n^{1/2k-delta/2}/k). Conversely, we give a randomised algorithm achieving approximation factor O(n^{(1-k*delta)/(k+1)}/k) in expectation. For deterministic algorithms, for k*q >= n, we show that the optimal deterministic approximation factor is ~Theta(n^2/(q*k^2)), thereby improving the previous lower bound of [Nisan SODA 2021] by a polynomial factor over a wide range of parameters. In particular, for q=Theta(n), we improve the deterministic round-complexity lower bound for constant-factor approximation from ~Omega(n^{1/3}) to ~Omega(sqrt n). Our algorithms produce matching and vertex-cover certificates; the deterministic trade-off also extends to minimum vertex cover and maximum fractional matching. We extend the randomised upper bounds to the weaker Demand-OR model [Nisan 2021]. We prove a non-adaptive simulation of uniform random-neighbour queries using known techniques, avoiding the O(log n)-round penalty of the trivial binary-search simulation. Consequently, our upper bounds yield the same k-round approximation guarantees using O(q * log^2 n) Demand-OR queries per round.

Authors: Shravan Mehra, Sagnik Mukhopadhyay, Vihan Shah

We study the randomised and deterministic demand-query complexity of approximate bipartite matching with few query rounds. The demand-query model, introduced in [Blumrosen and Nisan SICOMP 2009; Nisan SODA 2021], is inspired by combinatorial auctions and welfare maximisation, where an oracle returns the first neighbouring vertex (item) of a vertex u (agent) according to a given permutation pi (preference ordering of the agent over the items). For randomised algorithms, our main result gives a trade-off between the number of rounds k, the per-round query budget q=n^{1+δ}, and the achievable approximation ratio. Let 1 <= k <= log n, and -1 <= delta <= 1/k. Every k-round randomised algorithm using O(q) demand queries per round has a worst-case expected-size approximation factor of Omega(n^{1/2k-delta/2}/k). Conversely, we give a randomised algorithm achieving approximation factor O(n^{(1-k*delta)/(k+1)}/k) in expectation. For deterministic algorithms, for k*q >= n, we show that the optimal deterministic approximation factor is ~Theta(n^2/(q*k^2)), thereby improving the previous lower bound of [Nisan SODA 2021] by a polynomial factor over a wide range of parameters. In particular, for q=Theta(n), we improve the deterministic round-complexity lower bound for constant-factor approximation from ~Omega(n^{1/3}) to ~Omega(sqrt n). Our algorithms produce matching and vertex-cover certificates; the deterministic trade-off also extends to minimum vertex cover and maximum fractional matching. We extend the randomised upper bounds to the weaker Demand-OR model [Nisan 2021]. We prove a non-adaptive simulation of uniform random-neighbour queries using known techniques, avoiding the O(log n)-round penalty of the trivial binary-search simulation. Consequently, our upper bounds yield the same k-round approximation guarantees using O(q * log^2 n) Demand-OR queries per round.

Exploring Minimal Blocking Sets for Kernelization Lower Bounds for $\mathcal{F}$-Minor Deletion

from arXiv: Data Structures and Algorithms

Authors: Ahmed Ghazy, Jakob Greilhuber, Tim A. Hartmann

For Vertex Cover, a minimal blocking set of a graph $G$ is an inclusion-wise minimal vertex set contained in no minimum vertex cover of $G$. Bougeret et al. [SIDMA 2022] showed that, for all minor-closed graph classes $\mathcal{G}$, Vertex Cover has a polynomial kernel parameterized by the distance to $\mathcal{G}$ if and only if $\mathcal{G}$ has bounded minimal blocking sets. In this work, we explore how far this connection between minimal blocking sets and kernelization extends to $\mathcal{F}$-Minor Deletion parameterized by the deletion distance to $\mathcal{G}$. A minimal $\mathcal{F}$-blocking set of $G$ is a minimal vertex set not contained in any optimal $\mathcal{F}$-Minor Deletion solution of $G$. For connected families $\mathcal{F}$, we show that if some graph class $\mathcal{G}$ closed under disjoint union has unbounded minimal $\mathcal{F}$-blocking sets, then $\mathcal{F}$-Minor Deletion parameterized by the deletion distance to $\mathcal{G}$ has no polynomial kernel. This generalizes a result of Hols et al. [SIDMA 2022] for Vertex Cover. We then show that this criterion does not capture all kernelization lower bounds for $\mathcal{F}$-Minor Deletion, even for minor-closed graph classes. Motivated by the work of Dekker and Jansen [Discrete Appl. Math. 2024], we study the more general notion of extended $\mathcal{F}$-blocking sets and the kernelization lower bounds they yield. For every fixed integer $η\geq 1$, Pw-$η$ Deletion is the problem of deleting at most $k$ vertices to reduce the pathwidth to at most $η$. We show that it has no polynomial kernel parameterized by the distance to graphs with pathwidth at most $η+1$. Moreover, for connected families $\mathcal{F}$, we show that $\mathcal{F}$-Minor Deletion parameterized by the distance to graphs with pathwidth at most $\max_{F\in\mathcal{F}} pw(F)+1$ has no polynomial kernel.

Authors: Ahmed Ghazy, Jakob Greilhuber, Tim A. Hartmann

For Vertex Cover, a minimal blocking set of a graph $G$ is an inclusion-wise minimal vertex set contained in no minimum vertex cover of $G$. Bougeret et al. [SIDMA 2022] showed that, for all minor-closed graph classes $\mathcal{G}$, Vertex Cover has a polynomial kernel parameterized by the distance to $\mathcal{G}$ if and only if $\mathcal{G}$ has bounded minimal blocking sets. In this work, we explore how far this connection between minimal blocking sets and kernelization extends to $\mathcal{F}$-Minor Deletion parameterized by the deletion distance to $\mathcal{G}$. A minimal $\mathcal{F}$-blocking set of $G$ is a minimal vertex set not contained in any optimal $\mathcal{F}$-Minor Deletion solution of $G$. For connected families $\mathcal{F}$, we show that if some graph class $\mathcal{G}$ closed under disjoint union has unbounded minimal $\mathcal{F}$-blocking sets, then $\mathcal{F}$-Minor Deletion parameterized by the deletion distance to $\mathcal{G}$ has no polynomial kernel. This generalizes a result of Hols et al. [SIDMA 2022] for Vertex Cover. We then show that this criterion does not capture all kernelization lower bounds for $\mathcal{F}$-Minor Deletion, even for minor-closed graph classes. Motivated by the work of Dekker and Jansen [Discrete Appl. Math. 2024], we study the more general notion of extended $\mathcal{F}$-blocking sets and the kernelization lower bounds they yield. For every fixed integer $η\geq 1$, Pw-$η$ Deletion is the problem of deleting at most $k$ vertices to reduce the pathwidth to at most $η$. We show that it has no polynomial kernel parameterized by the distance to graphs with pathwidth at most $η+1$. Moreover, for connected families $\mathcal{F}$, we show that $\mathcal{F}$-Minor Deletion parameterized by the distance to graphs with pathwidth at most $\max_{F\in\mathcal{F}} pw(F)+1$ has no polynomial kernel.

Solving SAT via Treewidth Backdoors in Linear Time: Beyond Sparsity

from arXiv: Data Structures and Algorithms

Authors: Jan Dreier, Robert Ganian, Marlene Gründel, Roohani Sharma, Simon Wietheger

The Boolean Satisfiability (SAT) problem is a remarkably expressive computational problem, serving both as a cornerstone of complexity theory and as a unifying language for a wide range of search and reasoning tasks. While $\mathrm{NP}$-complete in general, SAT is well known to be linear-time solvable on instances with bounded treewidth. This motivates the search for "hidden" tree-like structures in formulas---for instance by detecting so-called weak $t$-treewidth backdoors. Such backdoors are sets of $k$ variables that can be assigned in such a way that the remaining instance has treewidth at most $t$ and is satisfiable whenever the original instance is. Ever since Gaspers and Szeider [ICALP 2012] showed that finding a weak $t$-treewidth backdoor for a SAT formula is $\mathrm{W}[2]$-hard with respect to $k$, tractability results have been restricted to structurally sparse formulas---most notably, a linear-time algorithm for $d$-SAT (bounded clause-length SAT) by Fomin, Lokshtanov, Misra, Ramanujan, and Saurabh [SODA 2015] and a non-uniform polynomial-time algorithm when the incidence graph of the input formula belongs to a nowhere-dense graph class (or more generally is $K_{d,d}$-free) by Lokshtanov, Panolan, and Ramanujan [ICALP 2022]. In this paper, we push the tractability boundaries of finding weak $t$-treewidth backdoors beyond sparsity and into the regime of "well-behaved" dense formulas. Aiming towards relaxing structural input restrictions from nowhere-dense to edge-stable graph classes, as featured in the theory of beyond sparsity for FO model checking, or to bounded VC-dimension set systems, we identify signed semi-ladder-freeness as the correct dense structural restriction that establishes the boundary between tractability and intractability along this line.

Authors: Jan Dreier, Robert Ganian, Marlene Gründel, Roohani Sharma, Simon Wietheger

The Boolean Satisfiability (SAT) problem is a remarkably expressive computational problem, serving both as a cornerstone of complexity theory and as a unifying language for a wide range of search and reasoning tasks. While $\mathrm{NP}$-complete in general, SAT is well known to be linear-time solvable on instances with bounded treewidth. This motivates the search for "hidden" tree-like structures in formulas---for instance by detecting so-called weak $t$-treewidth backdoors. Such backdoors are sets of $k$ variables that can be assigned in such a way that the remaining instance has treewidth at most $t$ and is satisfiable whenever the original instance is. Ever since Gaspers and Szeider [ICALP 2012] showed that finding a weak $t$-treewidth backdoor for a SAT formula is $\mathrm{W}[2]$-hard with respect to $k$, tractability results have been restricted to structurally sparse formulas---most notably, a linear-time algorithm for $d$-SAT (bounded clause-length SAT) by Fomin, Lokshtanov, Misra, Ramanujan, and Saurabh [SODA 2015] and a non-uniform polynomial-time algorithm when the incidence graph of the input formula belongs to a nowhere-dense graph class (or more generally is $K_{d,d}$-free) by Lokshtanov, Panolan, and Ramanujan [ICALP 2022]. In this paper, we push the tractability boundaries of finding weak $t$-treewidth backdoors beyond sparsity and into the regime of "well-behaved" dense formulas. Aiming towards relaxing structural input restrictions from nowhere-dense to edge-stable graph classes, as featured in the theory of beyond sparsity for FO model checking, or to bounded VC-dimension set systems, we identify signed semi-ladder-freeness as the correct dense structural restriction that establishes the boundary between tractability and intractability along this line.

Hitting Minors: Stronger Lower Bounds

from arXiv: Data Structures and Algorithms

Authors: Jongseo Lee, Roohani Sharma, Sebastian Wiederrecht

For a fixed finite collection of graphs $\mathcal{F}$, the $\mathcal{F}$-Minor-Free Deletion problem asks, given a graph $G$ and an integer $k$, whether there is a set $S\subseteq V(G)$ with $|S|\leq k$ such that $G-S$ contains no graph of $\mathcal{F}$ as a minor. This generalizes Vertex Cover, Feedback Vertex Set, and Vertex Planarization, among many others. Baste, Sau, and Thilikos [JCSS 2020, SICOMP 2023] showed that, assuming the Exponential Time Hypothesis (ETH), there are collections $\mathcal{F}$ for which the problem cannot be solved in time $2^{o(\mathrm{tw}\log\mathrm{tw})}n^{O(1)}$ on $n$-vertex graphs of treewidth $\mathrm{tw}$, and gave a matching algorithm. We ask whether this super-exponential dependence persists under more restrictive structural parameters. First, we strengthen their lower bound. Assuming ETH, for every fixed $h\geq 6$, $\{P_h\}$-Minor-Free Deletion, where $P_h$ is the path on $h$ vertices, cannot be solved in time $2^{o(t\log t)}n^{O(1)}$, where $t=\mathrm{fvs}+\mathrm{td}$ is the sum of the feedback vertex set number and treedepth of the input graph. These two parameters are incomparable, but both individually upper bound the treewidth. Together with the $2^{O(\mathrm{tw}\log\mathrm{tw})}n^{O(1)}$-time algorithm of Baste, Sau, and Thilikos [SICOMP 2023], our lower bound is tight. In contrast, we show that asymptotically faster algorithms are possible when parameterized by the vertex cover number $\mathrm{vc}$. Specifically, for any finite family $\mathcal{F}$, $\mathcal{F}$-Minor-Free Deletion can be solved in time $2^{O(\mathrm{vc})}n^{O(1)}$. Taken together, our results show that the boundary between single-exponential and super-exponential complexity for this problem lies between the vertex cover number and the feedback vertex set number (plus treedepth).

Authors: Jongseo Lee, Roohani Sharma, Sebastian Wiederrecht

For a fixed finite collection of graphs $\mathcal{F}$, the $\mathcal{F}$-Minor-Free Deletion problem asks, given a graph $G$ and an integer $k$, whether there is a set $S\subseteq V(G)$ with $|S|\leq k$ such that $G-S$ contains no graph of $\mathcal{F}$ as a minor. This generalizes Vertex Cover, Feedback Vertex Set, and Vertex Planarization, among many others. Baste, Sau, and Thilikos [JCSS 2020, SICOMP 2023] showed that, assuming the Exponential Time Hypothesis (ETH), there are collections $\mathcal{F}$ for which the problem cannot be solved in time $2^{o(\mathrm{tw}\log\mathrm{tw})}n^{O(1)}$ on $n$-vertex graphs of treewidth $\mathrm{tw}$, and gave a matching algorithm. We ask whether this super-exponential dependence persists under more restrictive structural parameters. First, we strengthen their lower bound. Assuming ETH, for every fixed $h\geq 6$, $\{P_h\}$-Minor-Free Deletion, where $P_h$ is the path on $h$ vertices, cannot be solved in time $2^{o(t\log t)}n^{O(1)}$, where $t=\mathrm{fvs}+\mathrm{td}$ is the sum of the feedback vertex set number and treedepth of the input graph. These two parameters are incomparable, but both individually upper bound the treewidth. Together with the $2^{O(\mathrm{tw}\log\mathrm{tw})}n^{O(1)}$-time algorithm of Baste, Sau, and Thilikos [SICOMP 2023], our lower bound is tight. In contrast, we show that asymptotically faster algorithms are possible when parameterized by the vertex cover number $\mathrm{vc}$. Specifically, for any finite family $\mathcal{F}$, $\mathcal{F}$-Minor-Free Deletion can be solved in time $2^{O(\mathrm{vc})}n^{O(1)}$. Taken together, our results show that the boundary between single-exponential and super-exponential complexity for this problem lies between the vertex cover number and the feedback vertex set number (plus treedepth).

Nearly Optimal Quantum Sample Complexity of Learning Fourier-Sparse Boolean Functions

from arXiv: Data Structures and Algorithms

Authors: Zekun Ye

We study the number of uniform quantum examples needed to exactly learn a Boolean function with at most $k$ nonzero Fourier coefficients. We give a learner with near-optimal $\widetilde{O}(k)$ sample complexity independent of the input length $n$. The learner uses only separate measurements of individual examples. This improves the previous $O(k^{3/2}\log^2 k)$ upper bound (Arunachalam, Chakraborty, Lee, Paraashar, and de Wolf, 2021). The main ingredient is a candidate set formed by symmetric differences of subsets from independent groups of Fourier samples. A multilinear majority polynomial combines unbiased random approximations into an accurate witness whose Fourier support lies in this set. Granularity then forces the set to contain the entire Fourier support, including subsets never directly sampled. For comparison, we derive the tight classical sample complexity $Θ(nk)$ by combining known sparse-recovery and lower-bound results.

Authors: Zekun Ye

We study the number of uniform quantum examples needed to exactly learn a Boolean function with at most $k$ nonzero Fourier coefficients. We give a learner with near-optimal $\widetilde{O}(k)$ sample complexity independent of the input length $n$. The learner uses only separate measurements of individual examples. This improves the previous $O(k^{3/2}\log^2 k)$ upper bound (Arunachalam, Chakraborty, Lee, Paraashar, and de Wolf, 2021). The main ingredient is a candidate set formed by symmetric differences of subsets from independent groups of Fourier samples. A multilinear majority polynomial combines unbiased random approximations into an accurate witness whose Fourier support lies in this set. Granularity then forces the set to contain the entire Fourier support, including subsets never directly sampled. For comparison, we derive the tight classical sample complexity $Θ(nk)$ by combining known sparse-recovery and lower-bound results.

Query Complexity of Densest Sub(hyper)graph Problems

from arXiv: Data Structures and Algorithms

Authors: Sinan G. Aksoy, Eli Friedman, Nate Veldt, Stephen J. Young

Rubinstein et al. introduce the study of connectivity and minimum cut problems in the cut query model, where an algorithm can only access the edges of a graph through CUT queries. In this work, we examine the query complexity of densest subgraph problems. Our first contribution is to generalize the uniform sparsification approach for Densest Subgraph and other heavy subgraph problems to weighted graphs, achieving the following results. >A $(1+\varepsilon)$-approximation for weighted Densest Subgraph in $O(n\log^2 n/\varepsilon^2)$ queries, roughly matching the $Ω(n/\log n)$ lower bound we prove for randomized algorithms. >A $(1+\varepsilon)$-approximation for weighted Max Cut in $O(n\log^2 n/\varepsilon^2)$ queries. This improves upon the bound of $O(n\log^3 n(\log n+1/\varepsilon^2))$ queries achieved by Plevrakis et al., making significant progress towards their lower bound of $Ω(n/\log n)$ queries. In contrast, we rule out approximation schemes with truly subquadratic query complexity for Densest $k$-Subgraph and settle its exact query complexity in both the unweighted and deterministic settings. For hypergraphs, we show CUT queries alone cannot achieve $o(\sqrt{n})$-approximation for Densest Subhypergraph or $o(n)$-approximation for Densest $k$-Subhypergraph, regardless of the number of queries. Augmenting CUT queries with the EDGE oracle of Chen et al., we overcome this barrier for Densest Subhypergraph and obtain a $(1+\varepsilon)$-approximation on rank-$r$ hypergraphs using $O(nr\log n/\varepsilon^2)$ queries. We also extend our Densest $k$-Subgraph hardness results to hypergraphs and prove exponential query lower bounds for exact Densest $k$-Subhypergraph. Lastly, confirming a conjecture of Chakrabarty and Liao, we show that simulating an ADD query using CUT and IS-EDGE$^?$ queries requires exponentially-many queries.

Authors: Sinan G. Aksoy, Eli Friedman, Nate Veldt, Stephen J. Young

Rubinstein et al. introduce the study of connectivity and minimum cut problems in the cut query model, where an algorithm can only access the edges of a graph through CUT queries. In this work, we examine the query complexity of densest subgraph problems. Our first contribution is to generalize the uniform sparsification approach for Densest Subgraph and other heavy subgraph problems to weighted graphs, achieving the following results. >A $(1+\varepsilon)$-approximation for weighted Densest Subgraph in $O(n\log^2 n/\varepsilon^2)$ queries, roughly matching the $Ω(n/\log n)$ lower bound we prove for randomized algorithms. >A $(1+\varepsilon)$-approximation for weighted Max Cut in $O(n\log^2 n/\varepsilon^2)$ queries. This improves upon the bound of $O(n\log^3 n(\log n+1/\varepsilon^2))$ queries achieved by Plevrakis et al., making significant progress towards their lower bound of $Ω(n/\log n)$ queries. In contrast, we rule out approximation schemes with truly subquadratic query complexity for Densest $k$-Subgraph and settle its exact query complexity in both the unweighted and deterministic settings. For hypergraphs, we show CUT queries alone cannot achieve $o(\sqrt{n})$-approximation for Densest Subhypergraph or $o(n)$-approximation for Densest $k$-Subhypergraph, regardless of the number of queries. Augmenting CUT queries with the EDGE oracle of Chen et al., we overcome this barrier for Densest Subhypergraph and obtain a $(1+\varepsilon)$-approximation on rank-$r$ hypergraphs using $O(nr\log n/\varepsilon^2)$ queries. We also extend our Densest $k$-Subgraph hardness results to hypergraphs and prove exponential query lower bounds for exact Densest $k$-Subhypergraph. Lastly, confirming a conjecture of Chakrabarty and Liao, we show that simulating an ADD query using CUT and IS-EDGE$^?$ queries requires exponentially-many queries.

On the Parameterized Complexity of Capacitated Stack and Queue Layouts with Colored Edges

from arXiv: Data Structures and Algorithms

Authors: Thomas Depian, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nöllenburg

Stack and queue layouts are two extensively studied and complementary linear representations of graphs. A stack (queue) layout $\langle \prec, σ\rangle$ of a graph $G$ consists of a total ordering $\prec$ of its vertices and of a partitioning $σ$ of its edges into pages, such that no two independent edges on the same page cross (nest). We propose a generalized model with colored edges (one color per edge) and capacitated pages. Namely, the input graph is equipped with colored edges and a prescribed budget for each page-color pair $(p,c)$ - this budget determines the maximum number of edges of color $c$ that page $p$ can accept. This model is motivated by fair allocation scenarios where budgets can be used to control the share of monochromatic edges on each page. Unfortunately, it is known that deciding whether a stack (queue) layout exists is NP-hard already with two (one) pages. Thus, we focus on the fixed-order setting - where the linear order $\prec$ is part of the input - for which an ordinary 2-page stack or $\ell$-page queue layout can be computed in polynomial time. Despite the restrictive setting, we show that both capacitated problems are NP-complete already for two pages. Furthermore, we study the complexity of the problems when parameterized by the number of pages, colors, and/or the structure of the edge conflict graph with respect to the fixed linear order $\prec$. Our analysis paints a detailed complexity landscape and uncovers a surprisingly intricate interplay between the considered parameters.

Authors: Thomas Depian, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nöllenburg

Stack and queue layouts are two extensively studied and complementary linear representations of graphs. A stack (queue) layout $\langle \prec, σ\rangle$ of a graph $G$ consists of a total ordering $\prec$ of its vertices and of a partitioning $σ$ of its edges into pages, such that no two independent edges on the same page cross (nest). We propose a generalized model with colored edges (one color per edge) and capacitated pages. Namely, the input graph is equipped with colored edges and a prescribed budget for each page-color pair $(p,c)$ - this budget determines the maximum number of edges of color $c$ that page $p$ can accept. This model is motivated by fair allocation scenarios where budgets can be used to control the share of monochromatic edges on each page. Unfortunately, it is known that deciding whether a stack (queue) layout exists is NP-hard already with two (one) pages. Thus, we focus on the fixed-order setting - where the linear order $\prec$ is part of the input - for which an ordinary 2-page stack or $\ell$-page queue layout can be computed in polynomial time. Despite the restrictive setting, we show that both capacitated problems are NP-complete already for two pages. Furthermore, we study the complexity of the problems when parameterized by the number of pages, colors, and/or the structure of the edge conflict graph with respect to the fixed linear order $\prec$. Our analysis paints a detailed complexity landscape and uncovers a surprisingly intricate interplay between the considered parameters.

Settling the Approximability of Submodular Facility Location

from arXiv: Data Structures and Algorithms

Authors: Adam N. Elmachtoub, Bento Natura, Meike Neuwohner

In submodular facility location (SFL), introduced by Svitkina and Tardos (SODA 2006), clients $\cal C$ in a metric space are assigned to facilities $\cal F$. A facility serving a set $S \subseteq \cal C$ of clients incurs cost $g(S)$, where $g$ is a monotone submodular function common to all facilities. The best known approximation factor for SFL is $O(\log\log|\cal C|)$, due to Abbasi, Adamczyk, Bosch-Calvo, Byrka, Grandoni, Sornat, and Tinguely (ICALP 2024), improving on the $O(\log|\cal C|)$-approximation of Svitkina and Tardos. We show that this is best possible up to a constant factor in the value-oracle model by showing that there exists no algorithm with only polynomially many queries to $g$ with approximation ratio $o(\log\log|\cal C|)$. This bound holds for algorithms with unlimited computation time, and when $g$ is restricted to matroid rank functions. We also show that the natural linear programming relaxation has an integrality gap of $Θ(\log\log|\cal C|)$. Our lower bound follows by constructing an extension of Vondrák's symmetry-gap approach (FOCS 2009) for matroid rank functions.

Authors: Adam N. Elmachtoub, Bento Natura, Meike Neuwohner

In submodular facility location (SFL), introduced by Svitkina and Tardos (SODA 2006), clients $\cal C$ in a metric space are assigned to facilities $\cal F$. A facility serving a set $S \subseteq \cal C$ of clients incurs cost $g(S)$, where $g$ is a monotone submodular function common to all facilities. The best known approximation factor for SFL is $O(\log\log|\cal C|)$, due to Abbasi, Adamczyk, Bosch-Calvo, Byrka, Grandoni, Sornat, and Tinguely (ICALP 2024), improving on the $O(\log|\cal C|)$-approximation of Svitkina and Tardos. We show that this is best possible up to a constant factor in the value-oracle model by showing that there exists no algorithm with only polynomially many queries to $g$ with approximation ratio $o(\log\log|\cal C|)$. This bound holds for algorithms with unlimited computation time, and when $g$ is restricted to matroid rank functions. We also show that the natural linear programming relaxation has an integrality gap of $Θ(\log\log|\cal C|)$. Our lower bound follows by constructing an extension of Vondrák's symmetry-gap approach (FOCS 2009) for matroid rank functions.

Two Holes Make a Difference: Deterministic Paging with Delay and Deadlines

from arXiv: Data Structures and Algorithms

Authors: Darya Melnyk, Linda Thelen, Yuyi Wang

In this paper, we study online paging with delay or deadlines. In online paging with delay, an online algorithm manages a cache of size $k$ and needs to serve incoming requests by adding the requested pages to the cache. In addition, it has the option to delay serving incoming requests at a cost that depends on the delay. We propose a new online algorithm, which combines a threshold rule with first-in, first-out (FIFO) replacement, and show that it is $(2k+2)$-competitive for cache size $k$. This result corrects a previously published claim that the competitive ratio is exactly $2k+1$ regardless of the number of holes (pages outside the cache). However, in the case of one hole, we show that the FIFO rule with a different threshold is indeed $(2k+1)$-competitive, giving a tight result. In the case of deadlines, the same analysis gives a deadline-triggered algorithm that is $(k+1)$-competitive in general and $k$-competitive if there is only one hole. Moreover, we show that the second hole makes paging with deadlines strictly harder than classical paging, where the competitive ratio is independent of the number of holes. For paging with deadlines, we prove a deterministic lower bound of $k+\tfrac12$, already for two holes. We show that, as a consequence, no threshold-triggered algorithm can achieve a competitive ratio less than $2k+\frac32$ in general.

Authors: Darya Melnyk, Linda Thelen, Yuyi Wang

In this paper, we study online paging with delay or deadlines. In online paging with delay, an online algorithm manages a cache of size $k$ and needs to serve incoming requests by adding the requested pages to the cache. In addition, it has the option to delay serving incoming requests at a cost that depends on the delay. We propose a new online algorithm, which combines a threshold rule with first-in, first-out (FIFO) replacement, and show that it is $(2k+2)$-competitive for cache size $k$. This result corrects a previously published claim that the competitive ratio is exactly $2k+1$ regardless of the number of holes (pages outside the cache). However, in the case of one hole, we show that the FIFO rule with a different threshold is indeed $(2k+1)$-competitive, giving a tight result. In the case of deadlines, the same analysis gives a deadline-triggered algorithm that is $(k+1)$-competitive in general and $k$-competitive if there is only one hole. Moreover, we show that the second hole makes paging with deadlines strictly harder than classical paging, where the competitive ratio is independent of the number of holes. For paging with deadlines, we prove a deterministic lower bound of $k+\tfrac12$, already for two holes. We show that, as a consequence, no threshold-triggered algorithm can achieve a competitive ratio less than $2k+\frac32$ in general.

Selecting the 7th Smallest of 16 Elements Takes Exactly 28 Comparisons

from arXiv: Data Structures and Algorithms

Authors: Jonathan Cremer

Let $V_i(n)$ denote the minimum worst-case number of comparisons needed to find the $i$-th smallest of $n$ distinct elements. We construct algorithms showing $V_7(16)\le28$ and $V_8(16)\le29$, improving the previously reported upper bounds of 30 and 31. Together with known lower bounds, these results establish $V_7(16)=28$ and leave $V_8(16)\in\{28,29\}$. For 17 elements, we establish $V_5(17)=27$ and improve the upper bounds for ranks 6 through 9 to 29, 30, 31, and 31, respectively. Every new upper bound has an explicit decision-graph certificate verified by a static checker developed independently of the search. We also derive a lower bound for selection from a partially ordered state by combining the Fussenegger-Gabow weight with its order-reversed counterpart. All new upper bounds and this lower bound are formally verified in Lean 4. For $V_8(16)$, we show that a lower bound based solely on the total number of decision-tree leaves cannot establish $V_8(16)\ge29$. The algorithms were found using fixed-budget search, information-guided comparison ordering, and proof-number search. Proof-number search was decisive in finding the 28-comparison algorithm; in our experiments, refuting a comparison budget remained substantially more expensive than finding an algorithm within it.

Authors: Jonathan Cremer

Let $V_i(n)$ denote the minimum worst-case number of comparisons needed to find the $i$-th smallest of $n$ distinct elements. We construct algorithms showing $V_7(16)\le28$ and $V_8(16)\le29$, improving the previously reported upper bounds of 30 and 31. Together with known lower bounds, these results establish $V_7(16)=28$ and leave $V_8(16)\in\{28,29\}$. For 17 elements, we establish $V_5(17)=27$ and improve the upper bounds for ranks 6 through 9 to 29, 30, 31, and 31, respectively. Every new upper bound has an explicit decision-graph certificate verified by a static checker developed independently of the search. We also derive a lower bound for selection from a partially ordered state by combining the Fussenegger-Gabow weight with its order-reversed counterpart. All new upper bounds and this lower bound are formally verified in Lean 4. For $V_8(16)$, we show that a lower bound based solely on the total number of decision-tree leaves cannot establish $V_8(16)\ge29$. The algorithms were found using fixed-budget search, information-guided comparison ordering, and proof-number search. Proof-number search was decisive in finding the 28-comparison algorithm; in our experiments, refuting a comparison budget remained substantially more expensive than finding an algorithm within it.

Optimal Support Size Testing

from arXiv: Data Structures and Algorithms

Authors: Qisheng Wang

We present an optimal tester for support size that determines whether an unknown probability distribution is supported on $n$ elements or $\varepsilon$-far away (in total variation distance) with sample complexity $Θ(\frac{n}{\varepsilon\log(n)})$, improving the prior best $O(\frac{n}{\varepsilon\log(n)} \log(\min\{\frac{1}{\varepsilon}, n\}))$ due to Ferreira Pinto Jr. and Harms (STOC 2025). As a generalization, we provide a pretty good lower bound on support size.

Authors: Qisheng Wang

We present an optimal tester for support size that determines whether an unknown probability distribution is supported on $n$ elements or $\varepsilon$-far away (in total variation distance) with sample complexity $Θ(\frac{n}{\varepsilon\log(n)})$, improving the prior best $O(\frac{n}{\varepsilon\log(n)} \log(\min\{\frac{1}{\varepsilon}, n\}))$ due to Ferreira Pinto Jr. and Harms (STOC 2025). As a generalization, we provide a pretty good lower bound on support size.

Semi-Streaming Estimation of the Held--Karp Bound and Graph-Metric TSP Cost

from arXiv: Data Structures and Algorithms

Authors: Sharareh Alipour

The Held--Karp bound is a linear-programming lower bound for the metric traveling salesperson problem (TSP). We approximate this bound in the multi-pass insertion-only streaming model for metrics induced by connected positive-weight graphs. For an $n$-vertex, $m$-edge graph and $0<\eps\le1/2$, the algorithm returns a $(1+\eps)$ upper approximation with high probability, using $\wtO(\eps^{-4})$ passes, $\wtO(n\eps^{-2})$ words, and $\wtO(m\eps^{-4})$ arithmetic operations per pass. We replace the changing edge-weight vector by a short history of multiplicative-weights (MWU) updates. After initialization, updates repeat until a column is removed. We store each repeated update once, with its repetition count. The record length depends on the number of columns, not updates; the argument applies to any fixed nonnegative matrix. For Held--Karp, tree-path queries recover edge weights from these records. Separate search and update thresholds allow fresh sparsifiers at successive search stages. Controlled rounding gives a bounded-precision implementation. The estimate yields a $(3/2+\eps)$ graph-metric TSP value estimate and a $(7/5+\eps)$ estimate when the input graph is unweighted. Conversely, in weighted graph streams, every one-pass $(1+η)$ upper approximation to Held--Karp, for fixed $0\leη<1$, requires quadratic space. The algorithms estimate values; they do not construct tours within these bounds.

Authors: Sharareh Alipour

The Held--Karp bound is a linear-programming lower bound for the metric traveling salesperson problem (TSP). We approximate this bound in the multi-pass insertion-only streaming model for metrics induced by connected positive-weight graphs. For an $n$-vertex, $m$-edge graph and $0<\eps\le1/2$, the algorithm returns a $(1+\eps)$ upper approximation with high probability, using $\wtO(\eps^{-4})$ passes, $\wtO(n\eps^{-2})$ words, and $\wtO(m\eps^{-4})$ arithmetic operations per pass. We replace the changing edge-weight vector by a short history of multiplicative-weights (MWU) updates. After initialization, updates repeat until a column is removed. We store each repeated update once, with its repetition count. The record length depends on the number of columns, not updates; the argument applies to any fixed nonnegative matrix. For Held--Karp, tree-path queries recover edge weights from these records. Separate search and update thresholds allow fresh sparsifiers at successive search stages. Controlled rounding gives a bounded-precision implementation. The estimate yields a $(3/2+\eps)$ graph-metric TSP value estimate and a $(7/5+\eps)$ estimate when the input graph is unweighted. Conversely, in weighted graph streams, every one-pass $(1+η)$ upper approximation to Held--Karp, for fixed $0\leη<1$, requires quadratic space. The algorithms estimate values; they do not construct tours within these bounds.

Sublinear-Time and Semi-Streaming Algorithms for Path Cover and TSP

from arXiv: Data Structures and Algorithms

Authors: Sharareh Alipour, Ali Nowzari

We give improved sublinear-time and semi-streaming approximation algorithms for maximum path cover, $(1,2)$-TSP, and Max-TSP. For each fixed $\eps>0$, our distance-query algorithm estimates the optimum cost of an $n$-vertex $(1,2)$-TSP instance from above within factor $2-\cstar+\eps=1.413184\ldots+\eps$, with high probability, in $n^{2-Ω_\eps(1)}$ time, where $\cstar=2(\sec1-\tan1)$. This improves the $3/2+\eps$ factor of direct matching estimation in this subquadratic regime~\cite{BhattacharyaKissSaranurak2023} and of the faster near-linear estimator~\cite{BehnezhadEtAl2024}. A single random vertex order defines an implicit bipartite graph whose matchings project to path covers. An alternating-permutation analysis shows that its expected maximum matching size is at least $\cstar$ times the optimum path-cover size. On long paths, even the best of polynomially many uniform orders retains only a $\cstar+o(1)$ fraction with high probability. In semi-streaming, we construct deterministic $(5/7-\eps)$-approximate unweighted path covers and $(9/7+\eps+5/(7n))$-approximate $(1,2)$-TSP tours, improving the respective $2/3-\eps$ and $4/3+\eps+1/n$ guarantees in~\cite{AlipourFarokhnejadMoemke2025}. For nonnegative weights, we obtain $2/3-\eps$ for path cover and Max-TSP, improving the earlier asymptotic $7/12-\eps$ Max-TSP bound. These algorithms use $\poly(1/\eps)$ passes independent of $n$ and $O(n\log(n/\eps)\poly(1/\eps))$ words. We compute two approximate matchings separated by edge deletion. A cycle-compensation lemma accounts for the weight removed when breaking their cycles. For unweighted graphs, a third approximate matching on a simple auxiliary graph with at most $2n$ vertices chooses component connections; a Tutte--Berge count proves the stronger bound.

Authors: Sharareh Alipour, Ali Nowzari

We give improved sublinear-time and semi-streaming approximation algorithms for maximum path cover, $(1,2)$-TSP, and Max-TSP. For each fixed $\eps>0$, our distance-query algorithm estimates the optimum cost of an $n$-vertex $(1,2)$-TSP instance from above within factor $2-\cstar+\eps=1.413184\ldots+\eps$, with high probability, in $n^{2-Ω_\eps(1)}$ time, where $\cstar=2(\sec1-\tan1)$. This improves the $3/2+\eps$ factor of direct matching estimation in this subquadratic regime~\cite{BhattacharyaKissSaranurak2023} and of the faster near-linear estimator~\cite{BehnezhadEtAl2024}. A single random vertex order defines an implicit bipartite graph whose matchings project to path covers. An alternating-permutation analysis shows that its expected maximum matching size is at least $\cstar$ times the optimum path-cover size. On long paths, even the best of polynomially many uniform orders retains only a $\cstar+o(1)$ fraction with high probability. In semi-streaming, we construct deterministic $(5/7-\eps)$-approximate unweighted path covers and $(9/7+\eps+5/(7n))$-approximate $(1,2)$-TSP tours, improving the respective $2/3-\eps$ and $4/3+\eps+1/n$ guarantees in~\cite{AlipourFarokhnejadMoemke2025}. For nonnegative weights, we obtain $2/3-\eps$ for path cover and Max-TSP, improving the earlier asymptotic $7/12-\eps$ Max-TSP bound. These algorithms use $\poly(1/\eps)$ passes independent of $n$ and $O(n\log(n/\eps)\poly(1/\eps))$ words. We compute two approximate matchings separated by edge deletion. A cycle-compensation lemma accounts for the weight removed when breaking their cycles. For unweighted graphs, a third approximate matching on a simple auxiliary graph with at most $2n$ vertices chooses component connections; a Tutte--Berge count proves the stronger bound.

A Randomized Approximation Algorithm for Doubly Free-Boundary Macdonald Processes

from arXiv: Data Structures and Algorithms

Authors: Zhongyang Li

We construct an approximate sampler for finite doubly free-boundary Macdonald rail-yard processes with Macdonald, boundary, and edge parameters in $(0,1)$. The construction allows arbitrary finite sequences of additions and removals of horizontal or vertical strips and all four boundary types. It reverses a finite commutation--reflection history from a partition sequence whose entries are equal, with the common partition weighted by both boundaries. We derive local reconstruction kernels from the skew Cauchy, Littlewood, and branching identities. Bulk and boundary updates combine integer increments with conditional shape sampling at a prescribed size; same-sign swaps use finite conditional resampling. For $K$ reflection periods and an initial partition-size cutoff $M$, the algorithm terminates almost surely and samples the resulting approximating law exactly. We prove total variation convergence to the target process as $K,M\to\infty$ and give explicit bounds for both truncation errors, decaying geometrically in the cutoffs for fixed input parameters. Under fixed parameter bounds, we also bound the expected operation count of direct enumeration in a unit-cost model. We further construct an exact rejection sampler for the initial partition law without a size cutoff or evaluation of its normalizing constant. Using this sampler removes the size-truncation error, leaving only the error from the finite reflection history.

Authors: Zhongyang Li

We construct an approximate sampler for finite doubly free-boundary Macdonald rail-yard processes with Macdonald, boundary, and edge parameters in $(0,1)$. The construction allows arbitrary finite sequences of additions and removals of horizontal or vertical strips and all four boundary types. It reverses a finite commutation--reflection history from a partition sequence whose entries are equal, with the common partition weighted by both boundaries. We derive local reconstruction kernels from the skew Cauchy, Littlewood, and branching identities. Bulk and boundary updates combine integer increments with conditional shape sampling at a prescribed size; same-sign swaps use finite conditional resampling. For $K$ reflection periods and an initial partition-size cutoff $M$, the algorithm terminates almost surely and samples the resulting approximating law exactly. We prove total variation convergence to the target process as $K,M\to\infty$ and give explicit bounds for both truncation errors, decaying geometrically in the cutoffs for fixed input parameters. Under fixed parameter bounds, we also bound the expected operation count of direct enumeration in a unit-cost model. We further construct an exact rejection sampler for the initial partition law without a size cutoff or evaluation of its normalizing constant. Using this sampler removes the size-truncation error, leaving only the error from the finite reflection history.

Subgaussian Vector Balancing by an Ising Model

from arXiv: Data Structures and Algorithms

Authors: Heng Guo, Daniel A. Spielman, Xinyuan Zhang

We show that the guaranties of the Gram-Schmidt Walk Algorithm of Bansal, Dadush, Garg, and Lovett can be achieved by an Ising model. Given vectors $v_1, \ldots, v_n$ of norm less than 1, we give a closed form for a centrally symmetric distribution on a vector $σ\in \{\pm 1 \}^n$ so that the signed sum $\sum_j σ(j) v_j$ is $1$-subgaussian. We show that the Glauber dynamics for this model mix rapidly provided that the norms of the input vectors are bounded below 1 by an inverse-polynomial. The proofs employ the theory of Lorentzian polynomials, and we use these techniques to show fast mixing bounds for other spin systems.

Authors: Heng Guo, Daniel A. Spielman, Xinyuan Zhang

We show that the guaranties of the Gram-Schmidt Walk Algorithm of Bansal, Dadush, Garg, and Lovett can be achieved by an Ising model. Given vectors $v_1, \ldots, v_n$ of norm less than 1, we give a closed form for a centrally symmetric distribution on a vector $σ\in \{\pm 1 \}^n$ so that the signed sum $\sum_j σ(j) v_j$ is $1$-subgaussian. We show that the Glauber dynamics for this model mix rapidly provided that the norms of the input vectors are bounded below 1 by an inverse-polynomial. The proofs employ the theory of Lorentzian polynomials, and we use these techniques to show fast mixing bounds for other spin systems.

Almost Optimal Relative Error Streaming Quantiles with Full Mergeability

from arXiv: Data Structures and Algorithms

Authors: Tomáš Domes, Pavel Veselý

Quantile summaries provide a scalable way to estimate the distribution of individual attributes in large datasets that are often distributed across multiple machines or generated by sensor networks. The state-of-the-art summaries with \emph{relative error guarantees} of $\pm \varepsilon\cdot \mathrm{rank}$, offering increasingly higher accuracy towards the distribution's tails, are ReqSketch (Cormode et al., JACM 2023) and the recent streaming algorithm by Gribelyuk et al. (SODA 2025). For inputs of size $N$ and probability $δ$ of exceeding the error bound, ReqSketch achieves space of $O(\varepsilon^{-1}\cdot \log^{1.5}(\varepsilon N)\cdot \sqrt{\log δ^{-1}})$ memory words, and offers \emph{full mergeability}, allowing distributed or parallel processing of datasets. In contrast, the latter algorithm exceeds the lower bound of $Ω(\varepsilon^{-1}\cdot \log(\varepsilon N)\cdot \log\logδ^{-1})$ from (Cormode \& Veselý, PODS 2020) only by a factor of $O(\log\varepsilon^{-1}\cdot (\log \varepsilon^{-1} + \log\log N)\cdot\log^2\logδ^{-1})$, but it lacks the mergeability property, is substantially more involved than ReqSketch, and has unclear practical performance. Both of these data structures consist of relative compactors from ReqSketch that subsample the stream in a structured way and work well in the streaming setting but are difficult to analyze under mergeability. We design a new relative-error quantile summary that achieves full mergeability and also improves the state-of-the-art streaming space bound to $$O(\varepsilon^{-1}\cdot \log(\varepsilon N) \cdot\log \log δ^{-1} \cdot (\log \varepsilon^{-1} + \log \log(\varepsilon N) + \log \log δ^{-1})),$$ which is substantially closer to the lower bound. At the same time, our data structure is simpler than the algorithm from (Gribelyuk et al., SODA 2025) and therefore more suitable for implementation.

Authors: Tomáš Domes, Pavel Veselý

Quantile summaries provide a scalable way to estimate the distribution of individual attributes in large datasets that are often distributed across multiple machines or generated by sensor networks. The state-of-the-art summaries with \emph{relative error guarantees} of $\pm \varepsilon\cdot \mathrm{rank}$, offering increasingly higher accuracy towards the distribution's tails, are ReqSketch (Cormode et al., JACM 2023) and the recent streaming algorithm by Gribelyuk et al. (SODA 2025). For inputs of size $N$ and probability $δ$ of exceeding the error bound, ReqSketch achieves space of $O(\varepsilon^{-1}\cdot \log^{1.5}(\varepsilon N)\cdot \sqrt{\log δ^{-1}})$ memory words, and offers \emph{full mergeability}, allowing distributed or parallel processing of datasets. In contrast, the latter algorithm exceeds the lower bound of $Ω(\varepsilon^{-1}\cdot \log(\varepsilon N)\cdot \log\logδ^{-1})$ from (Cormode \& Veselý, PODS 2020) only by a factor of $O(\log\varepsilon^{-1}\cdot (\log \varepsilon^{-1} + \log\log N)\cdot\log^2\logδ^{-1})$, but it lacks the mergeability property, is substantially more involved than ReqSketch, and has unclear practical performance. Both of these data structures consist of relative compactors from ReqSketch that subsample the stream in a structured way and work well in the streaming setting but are difficult to analyze under mergeability. We design a new relative-error quantile summary that achieves full mergeability and also improves the state-of-the-art streaming space bound to $$O(\varepsilon^{-1}\cdot \log(\varepsilon N) \cdot\log \log δ^{-1} \cdot (\log \varepsilon^{-1} + \log \log(\varepsilon N) + \log \log δ^{-1})),$$ which is substantially closer to the lower bound. At the same time, our data structure is simpler than the algorithm from (Gribelyuk et al., SODA 2025) and therefore more suitable for implementation.

A Deterministic Near-Linear Time Approximation Algorithm for Restricted Shortest Paths in Directed Graphs

from arXiv: Data Structures and Algorithms

Authors: Vikrant Ashvinkumar

In the restricted shortest paths problem, we are given an $n$-vertex $m$-edge directed graph $G$ whose edges are assigned two non-negative weights (or resources) called lengths and costs, a source vertex $s$, and a cost threshold $C$. The goal is to find, for each target vertex $t$, the length of the shortest $(s,t)$-path whose total cost is at most $C$. Ashvinkumar, Bernstein, and Karczmarz (SODA 2025) gave the first randomized bicriteria $(1+\varepsilon,1+\varepsilon)$-approximate algorithm on directed graphs running in sub-$mn$ time, answering a problem posed by Bernstein (SODA 2012). We substantially improve this on two fronts, supplying a deterministic bicriteria $(1+\varepsilon,1+\varepsilon)$-approximate algorithm running in near-linear time. Our approach straightforwardly generalizes to more than two resources. For $k$ resources, a deterministic $(1+\varepsilon_1,\ldots,1+\varepsilon_k)$-approximate algorithm running in near-linear time is possible, with $\varepsilon_1=\ldots=\varepsilon_k=\varepsilon$.

Authors: Vikrant Ashvinkumar

In the restricted shortest paths problem, we are given an $n$-vertex $m$-edge directed graph $G$ whose edges are assigned two non-negative weights (or resources) called lengths and costs, a source vertex $s$, and a cost threshold $C$. The goal is to find, for each target vertex $t$, the length of the shortest $(s,t)$-path whose total cost is at most $C$. Ashvinkumar, Bernstein, and Karczmarz (SODA 2025) gave the first randomized bicriteria $(1+\varepsilon,1+\varepsilon)$-approximate algorithm on directed graphs running in sub-$mn$ time, answering a problem posed by Bernstein (SODA 2012). We substantially improve this on two fronts, supplying a deterministic bicriteria $(1+\varepsilon,1+\varepsilon)$-approximate algorithm running in near-linear time. Our approach straightforwardly generalizes to more than two resources. For $k$ resources, a deterministic $(1+\varepsilon_1,\ldots,1+\varepsilon_k)$-approximate algorithm running in near-linear time is possible, with $\varepsilon_1=\ldots=\varepsilon_k=\varepsilon$.

A Practical Combinatorial Interior Point Method for Minimum-Cost Flow

from arXiv: Data Structures and Algorithms

Authors: Emil Schätzle, Simon Meierhans, Rasmus Kyng, Maximilian Probst Gutenberg

In a recent breakthrough, Chen, Kyng, Liu, Peng, Probst Gutenberg, and Sachdeva (arXiv:2203.00671) designed the first almost-linear-time algorithm for minimum-cost flow. Their approach uses an Interior Point Method (IPM) to achieve complexity $m^{1+o(1)}\log U \log C$. However, the $m^{1+o(1)}$ factor hides intricate recursive data structures, large constants and multiple log factors. In this paper, we give an implementation following the core ideas of their IPM with practical data structures and empirically obtained decision rules. We compare our solver with the cost scaling and network simplex solvers of LEMON on the instance families of the DIMACS Flow Challenge 2.0 core benchmark. Our implementation scales competitively on five of twelve families and better on two. On grid_wide we observe close to linear scaling: $m^{1.04}$ against $m^{1.48}$ for LEMON. We read these results as first evidence that the almost-linear-time framework can be relevant in practice, discuss our insights gained during the implementation process, and motivate where future work should focus to bridge the theory-practice gap.

Authors: Emil Schätzle, Simon Meierhans, Rasmus Kyng, Maximilian Probst Gutenberg

In a recent breakthrough, Chen, Kyng, Liu, Peng, Probst Gutenberg, and Sachdeva (arXiv:2203.00671) designed the first almost-linear-time algorithm for minimum-cost flow. Their approach uses an Interior Point Method (IPM) to achieve complexity $m^{1+o(1)}\log U \log C$. However, the $m^{1+o(1)}$ factor hides intricate recursive data structures, large constants and multiple log factors. In this paper, we give an implementation following the core ideas of their IPM with practical data structures and empirically obtained decision rules. We compare our solver with the cost scaling and network simplex solvers of LEMON on the instance families of the DIMACS Flow Challenge 2.0 core benchmark. Our implementation scales competitively on five of twelve families and better on two. On grid_wide we observe close to linear scaling: $m^{1.04}$ against $m^{1.48}$ for LEMON. We read these results as first evidence that the almost-linear-time framework can be relevant in practice, discuss our insights gained during the implementation process, and motivate where future work should focus to bridge the theory-practice gap.

The Complexity of Hamiltonian Cycles and Paths with Prescribed Walk Lengths

from arXiv: Data Structures and Algorithms

Authors: Hitendra Kumar, Mathieu Mari, R. B. Sandeep

Fix a set $S$ of positive integers. An $S$-Hamiltonian cycle is a cyclic ordering of all vertices of a graph such that each consecutive pair is joined by a walk whose length belongs to $S$. We study the complexity of deciding whether an input graph admits such an ordering, together with the corresponding path variants with prescribed or unrestricted endpoints. We complete the complexity classification for undirected graphs. In particular, we prove NP-completeness for every finite nonempty set of odd lengths, resolving an open problem posed by Amarilli, Lombardo, and Monet (WG 2026). For digraphs, all three problems are NP-complete for every fixed finite nonempty $S$ and admit linear-time algorithms when $S$ is cofinite. For other fixed infinite sets $S$, we establish NP-complete and undecidable cases, with undecidability occurring even when $S$ is recursive.

Authors: Hitendra Kumar, Mathieu Mari, R. B. Sandeep

Fix a set $S$ of positive integers. An $S$-Hamiltonian cycle is a cyclic ordering of all vertices of a graph such that each consecutive pair is joined by a walk whose length belongs to $S$. We study the complexity of deciding whether an input graph admits such an ordering, together with the corresponding path variants with prescribed or unrestricted endpoints. We complete the complexity classification for undirected graphs. In particular, we prove NP-completeness for every finite nonempty set of odd lengths, resolving an open problem posed by Amarilli, Lombardo, and Monet (WG 2026). For digraphs, all three problems are NP-complete for every fixed finite nonempty $S$ and admit linear-time algorithms when $S$ is cofinite. For other fixed infinite sets $S$, we establish NP-complete and undecidable cases, with undecidability occurring even when $S$ is recursive.

No Truly Sublogarithmic-Round Algorithm for Distributed MIS in the LOCAL Model

from arXiv: Data Structures and Algorithms

Authors: Seri Khoury, Aaron Schild

We prove that $o(\frac{\log n}{\log\log n})$-round randomized LOCAL algorithms find a maximal independent set (MIS) with subconstant probability on some input. This shows that the 40-year-old $O(\log n)$-round randomized CONGEST algorithm of Luby and Alon, Babai, and Itai is nearly optimal. Our lower bound improves upon the $Ω(\sqrt{\log n})$-round lower bound of Khoury and Schild and nearly closes the 20+ year-old gap between the $O(\log n)$-round algorithm and the $Ω(\sqrt{\frac{\log n}{\log\log n}})$-round lower bound of Kuhn, Moscibroda, and Wattenhofer. To close this gap, one must show a lower bound for a graph that is not locally treelike. We do this by starting with a $D$-regular, $Θ(\log_D n)$-girth graph, with $D = Θ(\log n)$. Crucially, this is not our final graph. We obtain our hard instance by \emph{replacing} each vertex of this treelike graph with a clique on $n^c$ vertices for some constant $c \in (0,1)$. We then replace each edge with a bipartite Erdős--Rényi graph between the corresponding cliques, with each edge present independently with probability $1/2$. We analyze arbitrary LOCAL algorithms on our non-treelike hard instance with a novel round elimination approach. We describe algorithms that output a set of vertices in this graph using two metrics: their \emph{occupancy probability} and their \emph{conflict probability}. Our round elimination framework takes an $r$-round algorithm and produces an $(r-1)$-round algorithm where occupancy and conflict probabilities collectively slightly degrade, but individually can significantly improve or degrade. This round elimination result is obtained by exploiting the lack of large biclique complements in dense bipartite Erdős--Rényi graphs, which in turn allows us to bound the entropy of the output distribution for any independent set algorithm.

Authors: Seri Khoury, Aaron Schild

We prove that $o(\frac{\log n}{\log\log n})$-round randomized LOCAL algorithms find a maximal independent set (MIS) with subconstant probability on some input. This shows that the 40-year-old $O(\log n)$-round randomized CONGEST algorithm of Luby and Alon, Babai, and Itai is nearly optimal. Our lower bound improves upon the $Ω(\sqrt{\log n})$-round lower bound of Khoury and Schild and nearly closes the 20+ year-old gap between the $O(\log n)$-round algorithm and the $Ω(\sqrt{\frac{\log n}{\log\log n}})$-round lower bound of Kuhn, Moscibroda, and Wattenhofer. To close this gap, one must show a lower bound for a graph that is not locally treelike. We do this by starting with a $D$-regular, $Θ(\log_D n)$-girth graph, with $D = Θ(\log n)$. Crucially, this is not our final graph. We obtain our hard instance by \emph{replacing} each vertex of this treelike graph with a clique on $n^c$ vertices for some constant $c \in (0,1)$. We then replace each edge with a bipartite Erdős--Rényi graph between the corresponding cliques, with each edge present independently with probability $1/2$. We analyze arbitrary LOCAL algorithms on our non-treelike hard instance with a novel round elimination approach. We describe algorithms that output a set of vertices in this graph using two metrics: their \emph{occupancy probability} and their \emph{conflict probability}. Our round elimination framework takes an $r$-round algorithm and produces an $(r-1)$-round algorithm where occupancy and conflict probabilities collectively slightly degrade, but individually can significantly improve or degrade. This round elimination result is obtained by exploiting the lack of large biclique complements in dense bipartite Erdős--Rényi graphs, which in turn allows us to bound the entropy of the output distribution for any independent set algorithm.

A Better-than-2 Approximation for k-Median Part I: The Power of General Bids

from arXiv: Data Structures and Algorithms

Authors: Rares-Darius Buhai, Vincent Cohen-Addad, Henry Fleischmann, Anupam Gupta, Antoni Jubés Monforte, Madhusudhan Reddy Pittu, Miltiadis Stouras, Ola Svensson, Lukas Vogl

The Greedy algorithm of Jain, Mahdian, Markakis, Saberi, and Vazirani was the best Lagrangian-multiplier-preserving (LMP) approximation algorithm for facility location, and thus the engine behind the best $k$-median guarantees, for over two decades. Its $2$-LMP guarantee was only recently broken by a small amount ($(2-η)$-LMP where $η\approx 2.25 \cdot 10^{-7}$) through local-search post-processing. That analysis relies on the structure of worst-case instances, and it appears difficult to obtain substantial further improvements along those lines. We take a different approach and change the algorithm itself: we introduce general bidding functions, replacing the classical linear bid with a richer family of rules and prove a polynomial-time $1.95$-LMP approximation, a large improvement over the long-standing factor of~$2$. We further show that these generalized bidding rules can be made smooth (log-adaptive), building on the recent framework developed for the classical Greedy algorithm by Cohen-Addad, Grandoni, Lee, Schwiegelshohn, and Svensson. This yields a polynomial-time $1.95$-approximation that opens $k+O_\varepsilon(\log n)$ centers. Combined with a $(1.89+\varepsilon)$-approximation algorithm for stable instances in our companion work \cite{stableinstances}, we achieve a better-than-2 approximation for the $k$-median problem in polynomial-time.

Authors: Rares-Darius Buhai, Vincent Cohen-Addad, Henry Fleischmann, Anupam Gupta, Antoni Jubés Monforte, Madhusudhan Reddy Pittu, Miltiadis Stouras, Ola Svensson, Lukas Vogl

The Greedy algorithm of Jain, Mahdian, Markakis, Saberi, and Vazirani was the best Lagrangian-multiplier-preserving (LMP) approximation algorithm for facility location, and thus the engine behind the best $k$-median guarantees, for over two decades. Its $2$-LMP guarantee was only recently broken by a small amount ($(2-η)$-LMP where $η\approx 2.25 \cdot 10^{-7}$) through local-search post-processing. That analysis relies on the structure of worst-case instances, and it appears difficult to obtain substantial further improvements along those lines. We take a different approach and change the algorithm itself: we introduce general bidding functions, replacing the classical linear bid with a richer family of rules and prove a polynomial-time $1.95$-LMP approximation, a large improvement over the long-standing factor of~$2$. We further show that these generalized bidding rules can be made smooth (log-adaptive), building on the recent framework developed for the classical Greedy algorithm by Cohen-Addad, Grandoni, Lee, Schwiegelshohn, and Svensson. This yields a polynomial-time $1.95$-approximation that opens $k+O_\varepsilon(\log n)$ centers. Combined with a $(1.89+\varepsilon)$-approximation algorithm for stable instances in our companion work \cite{stableinstances}, we achieve a better-than-2 approximation for the $k$-median problem in polynomial-time.

Inclusion-wise extremal knapsack problems are (mostly) hard to approximate

from arXiv: Data Structures and Algorithms

Authors: Fabio Ciccarelli, Fabio Furini, Ulrich Pferschy, Elena Rener

We study two inclusion-wise extremal variants of the 0-1 Knapsack Problem (KP). Given $n$ items with integer weights and nonnegative costs or profits, the Minimum-Cost Maximal Knapsack Packing Problem seeks a minimum-cost packing for capacity $c$ to which no item can be added, while the Maximum-Profit Minimal Knapsack Cover Problem seeks a maximum-profit cover for threshold $d$ from which no item can be removed. Both problems are weakly $\mathrm{NP}$-hard and pseudo-polynomially solvable, as is the classical KP, but their approximability is fundamentally different: unless $\mathrm{P}=\mathrm{NP}$, with positive values neither problem admits a polynomial-time $2^{p(n)}$-approximation for any polynomial $p$; allowing zero values rules out every multiplicative factor. Even with unit costs for packing and unit profits for covering, neither admits an $n^{1-\varepsilon}$-approximation for any fixed $\varepsilon>0$, whereas a trivial $n$-approximation exists. We identify restrictions that restore approximability. If every item weighs at least a fixed fraction of the capacity, respectively of the threshold, both problems can be solved exactly in polynomial time. For the cover problem, if each weight $w_i$ satisfies $w_i-1\geδ(d-1)$, we give an approximation scheme for arbitrary profits whose running time is polynomial in $n$, $1/\varepsilon$, and $1/δ$. If values equal weights, a fully polynomial-time approximation scheme (FPTAS) was already known for the packing problem; we give an FPTAS for the cover problem, improving on the previously known factor $2$. If value-to-weight ratios differ by at most a factor $κ$, we obtain a $(1+\varepsilon)κ$-approximation for both problems.

Authors: Fabio Ciccarelli, Fabio Furini, Ulrich Pferschy, Elena Rener

We study two inclusion-wise extremal variants of the 0-1 Knapsack Problem (KP). Given $n$ items with integer weights and nonnegative costs or profits, the Minimum-Cost Maximal Knapsack Packing Problem seeks a minimum-cost packing for capacity $c$ to which no item can be added, while the Maximum-Profit Minimal Knapsack Cover Problem seeks a maximum-profit cover for threshold $d$ from which no item can be removed. Both problems are weakly $\mathrm{NP}$-hard and pseudo-polynomially solvable, as is the classical KP, but their approximability is fundamentally different: unless $\mathrm{P}=\mathrm{NP}$, with positive values neither problem admits a polynomial-time $2^{p(n)}$-approximation for any polynomial $p$; allowing zero values rules out every multiplicative factor. Even with unit costs for packing and unit profits for covering, neither admits an $n^{1-\varepsilon}$-approximation for any fixed $\varepsilon>0$, whereas a trivial $n$-approximation exists. We identify restrictions that restore approximability. If every item weighs at least a fixed fraction of the capacity, respectively of the threshold, both problems can be solved exactly in polynomial time. For the cover problem, if each weight $w_i$ satisfies $w_i-1\geδ(d-1)$, we give an approximation scheme for arbitrary profits whose running time is polynomial in $n$, $1/\varepsilon$, and $1/δ$. If values equal weights, a fully polynomial-time approximation scheme (FPTAS) was already known for the packing problem; we give an FPTAS for the cover problem, improving on the previously known factor $2$. If value-to-weight ratios differ by at most a factor $κ$, we obtain a $(1+\varepsilon)κ$-approximation for both problems.

Exact power indices for plurality-voting ensembles

from arXiv: Data Structures and Algorithms

Authors: Ilie Sarpe, Theofanis Georgakopoulos, Aristides Gionis

Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random. Despite the simple aggregation rule, attributing the impact of each base classifier to the ensemble's predictions is far from straightforward. That is, a single base classifier's impact depends on how it interacts with the votes of all other classifiers in the ensemble, for example, in resolving ties, and not only on its own predictions. We address the attribution problem using established tools from cooperative game theory, studying the Banzhaf and Shapley power indices for new games modeling plurality voting and random tie-breaking, for binary and multiclass classification settings. Computing power indices is, in general, intractable. Surprisingly, our methods, collectively named ENPOWER, enable the exact computation of Banzhaf and Shapley values in linear time for the binary classification setting and in low-degree polynomial time for the multiclass setting. The algorithms in ENPOWER rely on new combinatorial formulations of the Banzhaf and Shapley indices, yielding closed-form solutions for the binary case and ordinary generating function representations for the multiclass case. We validate empirically the methods in ENPOWER demonstrating their high applicability, providing attribution values not captured by existing methods. We apply the attribution of ENPOWER to: ensemble pruning and prompt-based ensembling of vision-language models.

Authors: Ilie Sarpe, Theofanis Georgakopoulos, Aristides Gionis

Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random. Despite the simple aggregation rule, attributing the impact of each base classifier to the ensemble's predictions is far from straightforward. That is, a single base classifier's impact depends on how it interacts with the votes of all other classifiers in the ensemble, for example, in resolving ties, and not only on its own predictions. We address the attribution problem using established tools from cooperative game theory, studying the Banzhaf and Shapley power indices for new games modeling plurality voting and random tie-breaking, for binary and multiclass classification settings. Computing power indices is, in general, intractable. Surprisingly, our methods, collectively named ENPOWER, enable the exact computation of Banzhaf and Shapley values in linear time for the binary classification setting and in low-degree polynomial time for the multiclass setting. The algorithms in ENPOWER rely on new combinatorial formulations of the Banzhaf and Shapley indices, yielding closed-form solutions for the binary case and ordinary generating function representations for the multiclass case. We validate empirically the methods in ENPOWER demonstrating their high applicability, providing attribution values not captured by existing methods. We apply the attribution of ENPOWER to: ensemble pruning and prompt-based ensembling of vision-language models.

Mining Correlated Motifs with Wildcards in PPI Networks: Exact and Optimal

from arXiv: Data Structures and Algorithms

Authors: Lucas P. Oliveira, Ben Bals, Aalt D. J. van Dijk, Solon P. Pissis

A protein-protein interaction (PPI) network is a graph whose nodes are protein sequences and whose edges represent physical interactions between proteins. Correlated Motif Mining (CMM) seeks pairs of sequence motifs whose co-occurrence in interacting proteins reveals binding sites and interaction mechanisms. Formally, in the $(\ell,d)$-CMM problem, we are given a graph $G=(V,E)$, with $V\subseteqΣ^*$ for an alphabet $Σ$, and integers $0\leq d<\ell$, and we seek a pair $\{X,Y\}$ of motifs, each of length $\ell$ and containing $d$ wildcards, that maximizes a support measure $f(G,\{X,Y\})$; e.g., the count $f_E$ of edges $\{u,v\}\in E$ such that $X$ matches a substring of $u$ and $Y$ matches a substring of $v$, or vice versa. Although existing approaches have produced biologically meaningful results, they are either inefficient or heuristic-based, as the underlying optimization problem is NP-hard. Here, we revisit $(\ell,d)$-CMM. For $d=0$, we give an exact $\mathcal{O}(N^2)$-time and $\mathcal{O}(N)$-space algorithm, where $N$ is the total length of the node strings, and prove that this time is conditionally optimal under the Strong Exponential-Time Hypothesis. For any $d>0$, we give an exact $\mathcal{O}\!\left(\binom{\ell}{d}^2N^2\right)$-time and $\mathcal{O}(N)$-space algorithm, and observe that the problem is W[1]-hard parameterized by $d$. The latter algorithm improves the state of the art by exponentially large factors in both time and space. We further demonstrate experimentally that our algorithms are practical on real-world PPI networks and find significantly higher-scoring motif pairs than the state of the art. Moreover, we show that our algorithms find many top-scoring motif pairs that the state of the art misses entirely. Taken together, our results suggest that exact CMM with wildcards is more tractable in practice than previously assumed.

Authors: Lucas P. Oliveira, Ben Bals, Aalt D. J. van Dijk, Solon P. Pissis

A protein-protein interaction (PPI) network is a graph whose nodes are protein sequences and whose edges represent physical interactions between proteins. Correlated Motif Mining (CMM) seeks pairs of sequence motifs whose co-occurrence in interacting proteins reveals binding sites and interaction mechanisms. Formally, in the $(\ell,d)$-CMM problem, we are given a graph $G=(V,E)$, with $V\subseteqΣ^*$ for an alphabet $Σ$, and integers $0\leq d<\ell$, and we seek a pair $\{X,Y\}$ of motifs, each of length $\ell$ and containing $d$ wildcards, that maximizes a support measure $f(G,\{X,Y\})$; e.g., the count $f_E$ of edges $\{u,v\}\in E$ such that $X$ matches a substring of $u$ and $Y$ matches a substring of $v$, or vice versa. Although existing approaches have produced biologically meaningful results, they are either inefficient or heuristic-based, as the underlying optimization problem is NP-hard. Here, we revisit $(\ell,d)$-CMM. For $d=0$, we give an exact $\mathcal{O}(N^2)$-time and $\mathcal{O}(N)$-space algorithm, where $N$ is the total length of the node strings, and prove that this time is conditionally optimal under the Strong Exponential-Time Hypothesis. For any $d>0$, we give an exact $\mathcal{O}\!\left(\binom{\ell}{d}^2N^2\right)$-time and $\mathcal{O}(N)$-space algorithm, and observe that the problem is W[1]-hard parameterized by $d$. The latter algorithm improves the state of the art by exponentially large factors in both time and space. We further demonstrate experimentally that our algorithms are practical on real-world PPI networks and find significantly higher-scoring motif pairs than the state of the art. Moreover, we show that our algorithms find many top-scoring motif pairs that the state of the art misses entirely. Taken together, our results suggest that exact CMM with wildcards is more tractable in practice than previously assumed.

Jumbled Simon's Equivalence and Embeddings

from arXiv: Data Structures and Algorithms

Authors: Pamela Fleischmann, Annika Huch, Sungmin Kim, Tore Koß, Emil Wiards

\emph{Jumbled scattered factors} are a generalisation of scattered factors (or subsequences) where a jumbled scattered factor is obtained from another word by deleting and rearranging letters. For limiting the number of rearrangements, we distinguish $\ell$-jumbled scattered factors where $\ell$ limits the number of letters that may occur rearranged. For instance, $\mathtt{car}$, $\mathtt{start}$, $\mathtt{rat}, \mathtt{cat}$ are jumbled scattered factors of $\mathtt{abstract}$ even though $\mathtt{car}$, $\mathtt{start}$ and $\mathtt{cat}$ are no scattered factors and each need one jumble. An example for a jumbled scattered factor with $2$ jumbles is $\mathtt{tatar}$. In this work, we investigate two problems on jumbled scattered factors from an algorithmic perspective. First, we give algorithms for testing jumbled Simon's equivalence where two words are equivalent if they share the same $\ell$-jumbled scattered factors up to length $k$. Second, we investigate how to count the possible occurrences of a jumbled scattered factor -- the jumbled binomial coefficient. After defining the jumbled binomial coefficient, we study jumbled embeddings and factorisations of such as well.

Authors: Pamela Fleischmann, Annika Huch, Sungmin Kim, Tore Koß, Emil Wiards

\emph{Jumbled scattered factors} are a generalisation of scattered factors (or subsequences) where a jumbled scattered factor is obtained from another word by deleting and rearranging letters. For limiting the number of rearrangements, we distinguish $\ell$-jumbled scattered factors where $\ell$ limits the number of letters that may occur rearranged. For instance, $\mathtt{car}$, $\mathtt{start}$, $\mathtt{rat}, \mathtt{cat}$ are jumbled scattered factors of $\mathtt{abstract}$ even though $\mathtt{car}$, $\mathtt{start}$ and $\mathtt{cat}$ are no scattered factors and each need one jumble. An example for a jumbled scattered factor with $2$ jumbles is $\mathtt{tatar}$. In this work, we investigate two problems on jumbled scattered factors from an algorithmic perspective. First, we give algorithms for testing jumbled Simon's equivalence where two words are equivalent if they share the same $\ell$-jumbled scattered factors up to length $k$. Second, we investigate how to count the possible occurrences of a jumbled scattered factor -- the jumbled binomial coefficient. After defining the jumbled binomial coefficient, we study jumbled embeddings and factorisations of such as well.

A Matroid Generalization of Weighted Popular Matching: Characterization and Algorithm

from arXiv: Data Structures and Algorithms

Authors: Yuni Iwamasa, Yusuke Kobayashi, Ryoga Mahara, Kenjiro Takazawa

We study a problem that unifies two major generalizations of popular matchings in the one-sided preference model. In a bipartite graph in which each vertex, or applicant, on one side has preferences over the adjacent vertices, a matching is called popular if it does not lose a head-to-head election against any other matching. Since the seminal work of Abraham, Irving, Kavitha, and Mehlhorn, who gave the first polynomial-time algorithm for finding a popular matching, numerous generalizations of the popular matching problem have been investigated. Two prominent directions are weighted popular matchings, in which each applicant is assigned a voting weight, and popularity problems under matroid constraints. The former admits a polynomial-time algorithm due to Mestre, while the most general form of the latter is the popular common independent set problem, for which a polynomial-time algorithm is given by Kavitha, Makino, Schlotter, and Yokoi. In this paper, we introduce the weighted popular common independent set problem, which simultaneously generalizes weighted popular matchings and popular common independent sets. Our main result is the first polynomial-time algorithm for this problem. A key ingredient of the algorithm is a solution-independent characterization of weighted popular common independent sets, which we establish in this paper. As consequences of our algorithmic result, we obtain polynomial-time algorithms for several previously unresolved problems, including weighted popular matchings with matroid constraints and weighted popular branchings.

Authors: Yuni Iwamasa, Yusuke Kobayashi, Ryoga Mahara, Kenjiro Takazawa

We study a problem that unifies two major generalizations of popular matchings in the one-sided preference model. In a bipartite graph in which each vertex, or applicant, on one side has preferences over the adjacent vertices, a matching is called popular if it does not lose a head-to-head election against any other matching. Since the seminal work of Abraham, Irving, Kavitha, and Mehlhorn, who gave the first polynomial-time algorithm for finding a popular matching, numerous generalizations of the popular matching problem have been investigated. Two prominent directions are weighted popular matchings, in which each applicant is assigned a voting weight, and popularity problems under matroid constraints. The former admits a polynomial-time algorithm due to Mestre, while the most general form of the latter is the popular common independent set problem, for which a polynomial-time algorithm is given by Kavitha, Makino, Schlotter, and Yokoi. In this paper, we introduce the weighted popular common independent set problem, which simultaneously generalizes weighted popular matchings and popular common independent sets. Our main result is the first polynomial-time algorithm for this problem. A key ingredient of the algorithm is a solution-independent characterization of weighted popular common independent sets, which we establish in this paper. As consequences of our algorithmic result, we obtain polynomial-time algorithms for several previously unresolved problems, including weighted popular matchings with matroid constraints and weighted popular branchings.

Sampling from the Sherrington-Kirkpatrick Model up to Criticality

from arXiv: Data Structures and Algorithms

Authors: Hang Du, Brice Huang, Sidhanth Mohanty, Amit Rajaraman

We prove that the simulated annealing algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature $β< 1$ to an arbitrary inverse polynomial accuracy in total variation.

Authors: Hang Du, Brice Huang, Sidhanth Mohanty, Amit Rajaraman

We prove that the simulated annealing algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature $β< 1$ to an arbitrary inverse polynomial accuracy in total variation.

Fully Dynamic Random-Walk Quantities on Undirected Expanders and $κ$-Uniform Directed Graphs

from arXiv: Data Structures and Algorithms

Authors: Richard Peng, Sushant Sachdeva, Junzhao Yang, Yibin Zhao

We study fully dynamic data structures for random-walk quantities on undirected expanders and fast-mixing directed graphs. For an unweighted graph whose normalized Laplacian has spectral gap at least $γ$, we give a $(1\pm\varepsilon)$ effective-resistance data structure with expected update time $O(γ^{-4}\varepsilon^{-2}\log n\log^4(n/\varepsilon))$ and expected $O(1)$ query time. For directed graphs, somewhat surprisingly, the OMv conjecture precludes a fast data structure based on rapid mixing conditions alone. To address this, we introduce the notion of $κ$-uniform directed graphs, which requires the stationary distribution to $κ$-approximate the uniform distribution, and show that in this setting, random walk based data structures with $\text{poly}(κ, \log{n}, \varepsilon^{-1})$ update/query times are possible.

Authors: Richard Peng, Sushant Sachdeva, Junzhao Yang, Yibin Zhao

We study fully dynamic data structures for random-walk quantities on undirected expanders and fast-mixing directed graphs. For an unweighted graph whose normalized Laplacian has spectral gap at least $γ$, we give a $(1\pm\varepsilon)$ effective-resistance data structure with expected update time $O(γ^{-4}\varepsilon^{-2}\log n\log^4(n/\varepsilon))$ and expected $O(1)$ query time. For directed graphs, somewhat surprisingly, the OMv conjecture precludes a fast data structure based on rapid mixing conditions alone. To address this, we introduce the notion of $κ$-uniform directed graphs, which requires the stationary distribution to $κ$-approximate the uniform distribution, and show that in this setting, random walk based data structures with $\text{poly}(κ, \log{n}, \varepsilon^{-1})$ update/query times are possible.

Certifying Capacity and Gaps of the Rank-Dependent Gain-Sharing Framework for Ranking

from arXiv: Data Structures and Algorithms

Authors: Pan Xu

The randomized primal-dual framework of Devanur, Jain, and Kleinberg (SODA 2013) splits each unit matching gain between endpoint dual variables according to the matched offline rank. The gain-sharing function affects the certificate but not Ranking's decisions. Keeping the splitting rule and expected edgewise feasibility criterion fixed, we define certifying capacity as the strongest uniform guarantee obtainable by functional optimization over a prescribed function class. Its shortfall from Ranking's actual competitiveness is the certifying gap. We study unweighted online bipartite matching under adversarial arrivals on graphs with online degree at most $d$ and on $d$-regular graphs. The optimized protocol is tight on both degree-one classes and in the unrestricted setting, but has a strictly positive gap on both degree-two classes, even over all measurable nonnegative complementary splits. In both degree-two settings, we determine Ranking's exact competitiveness and the exact certifying capacity over nondecreasing gain-sharing functions. For general $d$, we optimize over nondecreasing, right-continuous functions $f:[0,1]\to[0,1]$ with $f(1)=1$, using their probability-measure representation. Both the certifying capacity and actual competitiveness converge to $1-1/e$ in both instance families as $d\to\infty$. The resulting gap is at most $1/(2ed)+O(d^{-2})$ for online degree at most $d$; on $d$-regular graphs, it vanishes but is at least $(1/(2e)-o(1))/d$. These results distinguish function-class restrictions from limitations of the fixed protocol and motivate tighter randomized primal-dual frameworks.

Authors: Pan Xu

The randomized primal-dual framework of Devanur, Jain, and Kleinberg (SODA 2013) splits each unit matching gain between endpoint dual variables according to the matched offline rank. The gain-sharing function affects the certificate but not Ranking's decisions. Keeping the splitting rule and expected edgewise feasibility criterion fixed, we define certifying capacity as the strongest uniform guarantee obtainable by functional optimization over a prescribed function class. Its shortfall from Ranking's actual competitiveness is the certifying gap. We study unweighted online bipartite matching under adversarial arrivals on graphs with online degree at most $d$ and on $d$-regular graphs. The optimized protocol is tight on both degree-one classes and in the unrestricted setting, but has a strictly positive gap on both degree-two classes, even over all measurable nonnegative complementary splits. In both degree-two settings, we determine Ranking's exact competitiveness and the exact certifying capacity over nondecreasing gain-sharing functions. For general $d$, we optimize over nondecreasing, right-continuous functions $f:[0,1]\to[0,1]$ with $f(1)=1$, using their probability-measure representation. Both the certifying capacity and actual competitiveness converge to $1-1/e$ in both instance families as $d\to\infty$. The resulting gap is at most $1/(2ed)+O(d^{-2})$ for online degree at most $d$; on $d$-regular graphs, it vanishes but is at least $(1/(2e)-o(1))/d$. These results distinguish function-class restrictions from limitations of the fixed protocol and motivate tighter randomized primal-dual frameworks.

Improved Combinatorial Auctions with and without Samples

from arXiv: Data Structures and Algorithms

Authors: Aviad Rubinstein, Sahil Singla

We give new mechanisms for combinatorial auctions in the offline, secretary, and prophet-with-samples models. Specifically, we obtain the following guarantees for social welfare maximization with $m$ items and $n$ XOS buyers: * In the prophet-with-samples version, buyers arrive in adversarial order and draw their values from independent distributions. We give a universally truthful, polynomial-time $(2+\eps)$-approximation mechanism using $O(\log\!\log m)$ samples from each distribution. The approximation factor is tight even for a single item and without the incentive constraints, due to the classic prophet inequality. Compared to the previous state-of-the-art with the same guarantees [Dütting--Kesselheim--Lucier--Reiffenhäuser--Singla (FOCS 2024)], our running time is exponentially faster, and our sample complexity is doubly-exponentially lower, resolving two explicit open problems by Dütting et al. * Using the same techniques, we also obtain improved mechanisms in the secretary and offline versions, where the values are worst-case, and buyers are processed in either random order (secretary) or without any timing restrictions (offline). After a long line of works, the state of the art in both models is an $O((\log\!\log m)^2)$-approximation due to [Assadi--Kesselheim--Singla (SODA 2021)], who explicitly ask whether it can be improved to $O(\log\!\log m)$. We indeed obtain an $O(\log\!\log m)$-approximate universally truthful mechanism for the secretary and offline models.

Authors: Aviad Rubinstein, Sahil Singla

We give new mechanisms for combinatorial auctions in the offline, secretary, and prophet-with-samples models. Specifically, we obtain the following guarantees for social welfare maximization with $m$ items and $n$ XOS buyers: * In the prophet-with-samples version, buyers arrive in adversarial order and draw their values from independent distributions. We give a universally truthful, polynomial-time $(2+\eps)$-approximation mechanism using $O(\log\!\log m)$ samples from each distribution. The approximation factor is tight even for a single item and without the incentive constraints, due to the classic prophet inequality. Compared to the previous state-of-the-art with the same guarantees [Dütting--Kesselheim--Lucier--Reiffenhäuser--Singla (FOCS 2024)], our running time is exponentially faster, and our sample complexity is doubly-exponentially lower, resolving two explicit open problems by Dütting et al. * Using the same techniques, we also obtain improved mechanisms in the secretary and offline versions, where the values are worst-case, and buyers are processed in either random order (secretary) or without any timing restrictions (offline). After a long line of works, the state of the art in both models is an $O((\log\!\log m)^2)$-approximation due to [Assadi--Kesselheim--Singla (SODA 2021)], who explicitly ask whether it can be improved to $O(\log\!\log m)$. We indeed obtain an $O(\log\!\log m)$-approximate universally truthful mechanism for the secretary and offline models.

Thin matrix products up to D <= N^0.172 and lossless reductions for 3SUM, Exact Triangle and APSP, with 3SUM over abelian groups and a dichotomy for coordinatewise problems

from arXiv: Data Structures and Algorithms

Authors: Ruhollah Majdoddin

Alman and Vassilevska Williams (arXiv:2610.06783) gave the first polynomial improvements over the textbook algorithms for 3SUM and APSP: deterministic algorithms with exponents 1.9992 for 3SUM, 2.9983 for Exact Triangle and 2.99942 for APSP. Their algorithm has two steps: a matrix theorem, computing N^2/sqrt(D) prescribed entries of the product of an N x D and a D x N integer matrix in time N^2/D^gamma, gamma > 0, when D <= N^eps, for every eps < 0.1204; and reductions from the three problems to the lopsided sparse triangle problem. We improve both. With Schönhage's five-term partial-product tensor, the matrix theorem holds for every eps < 0.172. The preprint's reductions keep a quarter of the matrix theorem's saving for 3SUM and a half for Exact Triangle and APSP; ours keep all of it, by calling the counting version of the triangle problem adaptively and building composite moduli one prime at a time. Together this gives deterministic 3SUM in n^{1.9945+o(1)}, and Exact Triangle, (min,+)-product and APSP in n^{2.9945+o(1)}. We also improve on the preprint's proof sketch for 3XOR and solve it deterministically in O(n^{1.99451}). Which other problems on three lists does the method make truly subquadratic? It solves 3SUM over F_p^d for every fixed prime p with 3XOR's exponents, and over every finite abelian group of exponent p^2, such as Z_4^d, in O(n^{1.99726}). For coordinatewise problems with a fixed relation P on every coordinate triple, a certified exhaustive computation shows that every Boolean P, and every functional P over three letters, either is the solution set of an equation over Z, F_2 or F_3, and the problem is then truly subquadratic, or has a gadget for Orthogonal Vectors, and the problem is then hard under the Orthogonal Vectors hypothesis. Over four letters, just four functional relations, up to symmetry, satisfy neither condition.

Authors: Ruhollah Majdoddin

Alman and Vassilevska Williams (arXiv:2610.06783) gave the first polynomial improvements over the textbook algorithms for 3SUM and APSP: deterministic algorithms with exponents 1.9992 for 3SUM, 2.9983 for Exact Triangle and 2.99942 for APSP. Their algorithm has two steps: a matrix theorem, computing N^2/sqrt(D) prescribed entries of the product of an N x D and a D x N integer matrix in time N^2/D^gamma, gamma > 0, when D <= N^eps, for every eps < 0.1204; and reductions from the three problems to the lopsided sparse triangle problem. We improve both. With Schönhage's five-term partial-product tensor, the matrix theorem holds for every eps < 0.172. The preprint's reductions keep a quarter of the matrix theorem's saving for 3SUM and a half for Exact Triangle and APSP; ours keep all of it, by calling the counting version of the triangle problem adaptively and building composite moduli one prime at a time. Together this gives deterministic 3SUM in n^{1.9945+o(1)}, and Exact Triangle, (min,+)-product and APSP in n^{2.9945+o(1)}. We also improve on the preprint's proof sketch for 3XOR and solve it deterministically in O(n^{1.99451}). Which other problems on three lists does the method make truly subquadratic? It solves 3SUM over F_p^d for every fixed prime p with 3XOR's exponents, and over every finite abelian group of exponent p^2, such as Z_4^d, in O(n^{1.99726}). For coordinatewise problems with a fixed relation P on every coordinate triple, a certified exhaustive computation shows that every Boolean P, and every functional P over three letters, either is the solution set of an equation over Z, F_2 or F_3, and the problem is then truly subquadratic, or has a gadget for Orthogonal Vectors, and the problem is then hard under the Orthogonal Vectors hypothesis. Over four letters, just four functional relations, up to symmetry, satisfy neither condition.

Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback

from arXiv: Data Structures and Algorithms

Authors: Tao Hu, Rohan Ghuge

Stochastic optimization models sequential decision-making under uncertainty, typically assuming known input distributions. When these distributions are unknown, \citet{agarwal2026semibandit} give an online-to-offline reduction for monotone stochastic optimization under semi-bandit feedback, where only probed variables are observed. Its expected regret relative to the offline guarantee is $\widetilde O(n\sqrt T)$, where $n$ is the number of variables and $T$ the horizon. For optimal stopping and Pandora's box, \citet{liu2025improved} achieve $\widetilde O(\sqrt{nT})$ using problem-specific algorithms. \emph{Can a general online-to-offline reduction achieve this improved rate?} We give such a reduction for strongly monotone stochastic optimization, answering the corresponding open question. Strong monotonicity is defined relative to the chosen offline algorithm. Our oracle-efficient reduction achieves $\widetilde O(\sqrt{nT})$ expected regret while preserving approximation guarantees for oracles satisfying the same condition. It combines Bernstein-type optimistic distribution estimates with a Hellinger stability bound for adaptive policy transcripts, aggregating squared marginal errors weighted by the executed policy's probing probabilities. The rate is optimal up to logarithmic factors for $T\ge n$, even under full feedback. Under monotonicity and uniformly bounded coordinate-wise variation, the same algorithm achieves $\widetilde O(n^{3/4}\sqrt T)$ expected approximation regret, improving the previous linear dependence on $n$. The exponent $3/4$ is sharp for its ordinary regret over instances and exact oracle choices. Our framework applies to prophet inequality, Pandora's box, best-of-$K$ selection, stochastic knapsack, stochastic orienteering, and stochastic submodular maximization.

Authors: Tao Hu, Rohan Ghuge

Stochastic optimization models sequential decision-making under uncertainty, typically assuming known input distributions. When these distributions are unknown, \citet{agarwal2026semibandit} give an online-to-offline reduction for monotone stochastic optimization under semi-bandit feedback, where only probed variables are observed. Its expected regret relative to the offline guarantee is $\widetilde O(n\sqrt T)$, where $n$ is the number of variables and $T$ the horizon. For optimal stopping and Pandora's box, \citet{liu2025improved} achieve $\widetilde O(\sqrt{nT})$ using problem-specific algorithms. \emph{Can a general online-to-offline reduction achieve this improved rate?} We give such a reduction for strongly monotone stochastic optimization, answering the corresponding open question. Strong monotonicity is defined relative to the chosen offline algorithm. Our oracle-efficient reduction achieves $\widetilde O(\sqrt{nT})$ expected regret while preserving approximation guarantees for oracles satisfying the same condition. It combines Bernstein-type optimistic distribution estimates with a Hellinger stability bound for adaptive policy transcripts, aggregating squared marginal errors weighted by the executed policy's probing probabilities. The rate is optimal up to logarithmic factors for $T\ge n$, even under full feedback. Under monotonicity and uniformly bounded coordinate-wise variation, the same algorithm achieves $\widetilde O(n^{3/4}\sqrt T)$ expected approximation regret, improving the previous linear dependence on $n$. The exponent $3/4$ is sharp for its ordinary regret over instances and exact oracle choices. Our framework applies to prophet inequality, Pandora's box, best-of-$K$ selection, stochastic knapsack, stochastic orienteering, and stochastic submodular maximization.

Greedy Region Growing: Improved Approximations for Cutwidth, Pathwidth, Flow-Expander Decomposition, and More

from arXiv: Data Structures and Algorithms

Authors: Aditya Anand

We give a new rounding technique for spreading-metric linear programs, called Greedy Region Growing, which adapts the classical technique of region growing to min-max graph layout problems and flow-expander decomposition. Using this technique, we obtain state-of-the-art approximation algorithms for several fundamental problems. (1) An $O(\log n)$-approximation for Cutwidth and Pathwidth, improving upon the previous bound of $O(\log n\exp(O(\sqrt{\log\log n})))$ due to Bansal, Katzelnick, and Schwartz (FOCS 2024). This matches the $Ω(\log n)$ integrality gap of the standard LP relaxations. (2) An $O(\log n)$-approximation for their directed analogues, Minimum Storage and Minimum Register Sufficiency on directed acyclic graphs, improving the previous bound of $O(\log^{3/2} n)$. (3) A $φ$-flow-expander decomposition that cuts at most an $O(φ\log n\log\log n)$ fraction of the edges. This improves the $O(φ\log n\exp(O(\sqrt{\log\log n})))$ bound of Bansal, Jambulapati, and Saranurak (ICALP 2026), and is within an $O(\log\log n)$ factor of the graph-theoretic lower bound. The vertex version of our algorithm also gives an $O(\log n)$-approximation for Treedepth. In particular, our results answer several open problems of Bansal, Katzelnick, and Schwartz (FOCS 2024). Technically, we also solve the conceptual question raised by their work: our technique shows how to adapt Seymour recursion to min-max objectives, and constructs $O(1)$-weak local partitions in spreading metrics.

Authors: Aditya Anand

We give a new rounding technique for spreading-metric linear programs, called Greedy Region Growing, which adapts the classical technique of region growing to min-max graph layout problems and flow-expander decomposition. Using this technique, we obtain state-of-the-art approximation algorithms for several fundamental problems. (1) An $O(\log n)$-approximation for Cutwidth and Pathwidth, improving upon the previous bound of $O(\log n\exp(O(\sqrt{\log\log n})))$ due to Bansal, Katzelnick, and Schwartz (FOCS 2024). This matches the $Ω(\log n)$ integrality gap of the standard LP relaxations. (2) An $O(\log n)$-approximation for their directed analogues, Minimum Storage and Minimum Register Sufficiency on directed acyclic graphs, improving the previous bound of $O(\log^{3/2} n)$. (3) A $φ$-flow-expander decomposition that cuts at most an $O(φ\log n\log\log n)$ fraction of the edges. This improves the $O(φ\log n\exp(O(\sqrt{\log\log n})))$ bound of Bansal, Jambulapati, and Saranurak (ICALP 2026), and is within an $O(\log\log n)$ factor of the graph-theoretic lower bound. The vertex version of our algorithm also gives an $O(\log n)$-approximation for Treedepth. In particular, our results answer several open problems of Bansal, Katzelnick, and Schwartz (FOCS 2024). Technically, we also solve the conceptual question raised by their work: our technique shows how to adapt Seymour recursion to min-max objectives, and constructs $O(1)$-weak local partitions in spreading metrics.

Tuesday, October 13

The Most Outrageous Conjecture in the World

from Gil Kalai
Donald Knuth, Enrico Bombieri, and Julia Robinson. Ten years ago I asked a question on MathOverflow about The most outrageous (or ridiculous) conjectures in mathematics. It led to a nice list of many outrageous conjectures. Here I would like to … Continue reading →

Donald Knuth, Enrico Bombieri, and Julia Robinson.

Ten years ago I asked a question on MathOverflow about The most outrageous (or ridiculous) conjectures in mathematics. It led to a nice list of many outrageous conjectures. Here I would like to present what I regard as a serious contender for the most outrageous (ridiculous) conjecture. It is originated from the following  MathOverflow quetion I asked in 2013  Infinitely many primes, and Mobius randomness in sparse sets, and it is closely related to some aspects of polymath4 (2009).

The most outragous conjecture (informal form): There is an inherent difficulty to prove infinitude of primes,  and related number theoretic results (like Möbius randomness) for explicit, sparse sets of integers.

The terms “sparse,” “explicit,” and “inherent difficulty” are negotiable and we will give them the most outrageous interpretations.

By “sparse sets” we mean sets of integers that has less than n^{0.4} elements smaller than n, for every n. For example, (i) integers with more than 90% ones in their binary expansion and (ii) integers of the form n=a^7+b^3, where a,b are integers. (iii) Unions of intervals of the form [n,n+t], where, say n is a power of 2;  here indeed the best known unconditional results is when t=n^{0.535..}. RH allows t=\sqrt n \log n and it looks that for this question the n^{1/2} is a viable barrier.

Several number theorists that I talked with do not regard the \sqrt n as a barrier for general problems and expect that for some problems (like items (i) and (ii)), current techniques could be pushed for density below \sqrt n.

Let me move now to a precise formulation. Before that, let me thank Lior Silberman for helpful comments and corrections and Andrew Granville and several other colleagues for helpful discussions over the years.

The most outrageous conjecture

We say that a sequence A=a_1,a_2,\dots ,a_n,\dots  of integers is explicit if there is a deterministic polynomial time algorithm in \log (a_n) to compute a_n. For example, the sequences n^2+1, n^3+2 and  2^n+1 are explicit. The sequence of primes p_n is for all we know not explicit.

The Most Outrageous Conjecture (MOC):

Let A=a_1,a_2,\dots,a_n,\dots be an explicit  set of integers. Suppose that for some constant C,

|A \cap [m]| \le C m^{0.4}, then:

(1) It cannot be proved in ZFC that A contains infinitely many primes.

(2)  Möbius randomness for A cannot be proved in ZFC.

(3) The assertion of the PNT for A cannot be proved in ZFC.

Short further explanation

A priory, there could be three possibilities for the assertion that A contains infinitely many primes: It could be proved in ZFC; it could be disproved in ZFC and it could be undecidable in ZFC. The conjecture asserts that the first possibility cannot occur for explicit sparse sequences. ZFC is the standard set of axioms for mathematics.

Möbius randomness asserts that \displaystyle \sum _{k=1}^N\mu (k) a_k=o(N). The assertion of the Prime Number Theorem (PNT) is that the number of primes among a_1,a_2,\dots, a_n behaves like C  n\sum 1/\log (a_n) [corrected]. (This is a stronger assertion compared to infinitude of primes.)

Examples:

a) a_n=n^3+2.

(In contrast, for the elements of the sequence a_n=n^3+1 we have n^3+1=(n+1)(n^2-n+1), so all elements are composite except the first one.)

b) All integers with at least 90% ‘ones’ in their binary expansion.

c) All integers of the form a^3+b^7, where a,b are integers.

d) The first 2^n integers greater or equal than 10^n.  (Of course, it is  a standard conjecture that there are even infinitely many primes among the first n^3 integers greater or equal than 10^n.)

Claims b) – d), conjectured here to be undecidable, are not that far from established theorems:

b’) MOC asserts that proving PNT and MR for integers with more than 90% ones in their binary expansion is undecidable. Eric Naslund proved Möbius randomness and infinitude of primes for the set of integers with at lease twice as many ones as zeros in their binary expansion. In fact, he proved these assertions when the ratio of ones is at least 0.7375. Naslund relied on works of  Baker, Harman, and Pintz. Earlier works by  Drmota, Mauduit, and Rivat.  Bourgain proved these assertions for some smaller ratio C>1/2.

c’) MOC asserts that PNT and MR for the set of all integers of the form a^3+b^7, where a,b are integers are undecidable. However, there is a result by Friedlander and Iwaniec that there are infinitely many primes of the form a^2+b^4. There is also a subsequent result by Heath-Brown for all integers of the form a^3+2b^3.

d’) As we already mentioned, for interval of the form [n,n+t], the best known absolute results are when t=n^{0.535..}. RH allows t=\sqrt n \log n and, in this case,  it looks that n^{1/2} is a recognized barrier.

As far as I can speculate, the MOC holds even if  the validity of GRH is assumed.

Computational complexity

MOC has also outrageous consequences in computational complexity. It implies that strong forms of the derandomization conjecture are undecidable. (I am comfortable with these conclusions but you can avoid these consequences by adopting a stricter form of “explicit”.)

A weak form

The main challenge is, of course, for number theorists to disprove MOC.  This challenge may not be so hard because we have a large variety of sets to choose from and a large variety of number theoretic methods to choose from. On the other hand, proving MOC goes beyond known techniques for proving undecidability. It would be very interesting (but still beyond reach in present methods) to prove MOC just for a specific (non-artificial) sequence. Lior Silberman pointed out we can use Godel’s theorem to artificially create a sequence of integers for which, say, the infinitude of primes is undecidable.

Further Discussion
  1. The conjecture can be regarded as a variation with a twist of an outrageous conjecture (or intuition) of Knuth discussed for example here: Knuth’s intuition that Goldbach might be unprovable.  The twist is that refuting Knuth’s intuition would require proving Goldbach conjecture. Here number theorists can choose between a variety of sets and a variety of  proof techniques and they can tailor the explicit set for this purpose. Similar assertions about undecidability of strong number theoretic conjectures were raised by several other researchers including Enrico Bombieri.  In his interesting paper that we discussed in this post, the number theorist Andrew Granville regards such conjectures as manifesting human hubris.
  2.  As we already mentioned MOC has some consequences for computational complexity. It implies that strong forms of the derandomization conjecture are undecidable. These consequences can be avoided by pausing positing a stricter form of the term “explicit” that would still include all number theoretical statements that occurs “in nature”. (Our notion of “explicit”  adds to the courageousness of the conjecture, but it will probably not make it easier to disprove.)
  3. This conjecture (and the previous comment) is related to the interesting 2009 discussion in polymath4: a polymath project for finding a deterministic polynomial time algorithm (in logn) to find a prime number with n digits.
  4.  As mentioned above, the question, in more moderate terms is raised in my MO question: Infinitely many primes, and Mobius randomness in sparse sets. The question is related to other MO questions (such as Why so difficult to prove infinitely many restricted primes? ). I first raised MOC in my lecture at Gowers’ 60th birthday conference (Here are somewhat edited slides, and here is the recording.)
  5. There is a result by Noam Elkies about the infinitude of supersingular primes. Those primes are conjectured to be of density n^{1/2} among the first n numbers but provably it is only known that they are of density less or equal n^{3/4}. I don’t know if there are Elkies-like results that can lead (provably or conjecturally) to sparser sets of primes.
  6. There are results about primes and Möbius randomness in short and even very short intervals. Kaisa Matomäki and Maksym Radziwiłł  wrote in 2015, a breakthrough paper Multiplicative functions in short intervals  and initially I thought it can lead to a refutation of MOC. However, as far as I can see these startling results do not violate MOC because they are statistical: they talk about the expected behavior over many short intervals.
  7. The philosophical point of view of MOC is that as far as ZFC is concerned the primes are “really” random.
  8. The four problems in Landau’s famous list of number theory conjectures, are (barely) above the barrier of MOC.
  9. I did not try to use AI to find a counterexample.
Are the primes random?

There is a the famous 1936 Cramér model for predicting the behavior of primes based on a probabilistic model and his model led to the famous Cramér conjecture that asserts that p_n+1-p_n=O(\log n)^2. (It is undecidable by MOC.) MOC asserts that when it comes to ZFC the axioms are too weak to give very precise information about primes so with respect to various different models of ZFC the primes do behave as “random” objects.

Cramér model gives good coarse predictions but for precise predictions it is believed to be just a good starting point. The paper An uncertainty principle for arithmetic sequences by Andrew Granville and Kannan Soundararajan  established an uncertainty principle which more-or-less shows that there is no formulation of Cramér type heuristics that can give completely correct predictions!

Why undecidability?

Beside undecidability, there are various mathematical ways to express the idea that proving  certain mathematical statements is inherently beyond reach.

a) The proof is huge, there is no way a human (or AI) will be able to find and present such a proof.

b) There is a short proof but finding it is computationally intractable. There is no way a human (or AI) will find such a proof.

I am not aware mathematical methods for proving (a) or (b) for specific mathematical statements. For showing independence from ZFC (or other axioms) there are at least some available methods.

Of course it is quite possible that there is no inherent barrier at density \sqrt n whatsoever, and that to pass this barrier require more effort using known techniques or perhaps some new ideas.

Let me also mention that David Kazhdan referred to the question “why is mathematics possible” (in view of Gödel-type and NP-hard type obstacles) in a lovely 1999 essay Reflection on the development of mathematics in the twentieth century .

As posed here the conjecture also implies that various claims in computational complexity are undecidable. (And this is closely related to the themes we discussed in polymath 4.) Readers who do not like this feature can replace “explicit” by a more modest definition that would still include all number theoretical statement that occurs “in nature”. But I actually like this feature and it implies that proving also several (albeit very strong) computational complexity claims are undecidable. (This adds to the outrageousness of the conjecture, but it will probably not make it easier to disprove.)

Equations in primes

There are far reaching theorems and conjectures regarding the number of solutions of equations in primes.  The Hardy-Littlewood conjecture (and several generalizations) provides asymptotic estimates for the number of prime solutions for n linear equations in m variables. For such questions there seem to be a barrier in the available mathematical methods between m linear equations in n variables (for the primes) when n=m+1 and when n>m+1; in the later case the Hardy-Littlewood conjecture (even in a greater generality) was settled by Green, Tao, and Ziegler.

By Gil Kalai

Podcasts about the Topic

from Scott Aaronson
“The Topic” being, of course, the Mathocalypse, except sometimes with AI alignment and even quantum computing and complexity theory and other stuff thrown in. With Ryan Peterman: Notably, near the end of the above podcast, you can watch my live reaction to the news that an Anthropic model found algorithms for the 3SUM and the […]

“The Topic” being, of course, the Mathocalypse, except sometimes with AI alignment and even quantum computing and complexity theory and other stuff thrown in.

With Ryan Peterman:

Notably, near the end of the above podcast, you can watch my live reaction to the news that an Anthropic model found algorithms for the 3SUM and the All-Pairs-Shortest-Path problems beating records that stood for generations—news that hadn’t yet reached me when I woke up and logged in. (If you don’t feel like watching, my reaction is basically: “uh, wow … if I hadn’t been so overstuffed with delicacies right now from history’s biggest math breakthrough buffet, I’d surely be giving this one the individual amazement that it deserves…”)

And here’s with “Modern CTO”, on a similar set of topics:

Enjoy, and thanks to the hosts of both podcasts!

By Scott

Will literature survive?

from Emanuele Viola
It would have been much harder for AI to simulate the features that make us animals rather than humans. Once mankind decided to confine expression to symbols, once the symbol of cattle was first carved, its fate was sealed, though it must have been hard to foresee back then. These days I am thinking what […]

It would have been much harder for AI to simulate the features that make us animals rather than humans. Once mankind decided to confine expression to symbols, once the symbol of cattle was first carved, its fate was sealed, though it must have been hard to foresee back then. These days I am thinking what “life” can be left in various forms of “symbolic expression.”

Computer code: This is the easiest prey for AI, since the most important feature of code is that it runs; only the select few who maintain it care about how it’s written, consumers don’t.

Math: This is strangely in-between computer code and the next, literature. Perhaps the most significant bit is still that the math “runs,” i.e., that proofs are correct. However, consumers of math actually read the code. I remain mildly shocked that AI can solve millennium problems as if they were tic-tac-toe, but still can’t quite write a paper that I enjoy reading.

Literature: This doesn’t need to run, it’s all about the way the code is written. Can AI produce literature that I will enjoy reading before bed, like a Nabokov tale? If you only know Nabokov from Lolita, read his tales instead — by the way he used to say that Literature and Math share the same “combinatorial delight.” At the moment of this writing, the answer seems that AI can’t. It would be most interesting if this task remains hard for AI, that no matter how hard it reads all that’s out there it still can’t reshuffle it in a way that’s both new and stirs us inside the way great literature does. (Literature was perhaps the biggest passion of my parents and they succeeded in transmitting it to me despite all my computer stuff.)

Music: This is the field I know least about, but it’s another excellent example, especially because we can restrict ourselves to pure score-writing, which seems more comparable to the above.

Graphical art: This would be another one, but it’s somewhat less formalized than the other ones. (This isn’t necessarily a problem for AI, but it is for me as I prefer to think of expression within a very simple symbolic domain, as is the case for the above ones.)

Even more on a personal note, after about two months on the west coast it seems I have finally converged on a nice hiking loop of about an hour that I can enjoy every day. It enters a natural area and climbs up to a stunning view of the bay. There’s even a bench. I mourn the math problems I will not be pondering during it, but perhaps there will be something else, like all the mega-exciting new results, or this blog post.

By Manu

Optimal Clock Synchronization When There Are No Failures or Drift

from Decentralized Thoughts
Synchronized clocks help servers coordinate. For example, servers with synchronized clocks following the same schedule can switch leaders at nearly the same time. A natural approach is for servers to exchange their current clock values and take the average. But messages take time to arrive. By the time a server receives another server’s clock value, that clock has already advanced. If the delay were known, the receiver could account for...

By Ittai Abraham, Jennifer Welch

Synchronized clocks help servers coordinate. For example, servers with synchronized clocks following the same schedule can switch leaders at nearly the same time. A natural approach is for servers to exchange their current clock values and take the average. But messages take time to arrive. By the time a server receives another server’s clock value, that clock has already advanced. If the delay were known, the receiver could account for...

By Ittai Abraham, Jennifer Welch

AI Fellow Positions The Center for Artificial Intelligence and Natural Sciences at Korea Institute for Advanced Study(KIAS) (apply by November 30, 2026)

from CCI: jobs
The Center for AI and Natural Sciences invites applications from PhD holders in mathematics, theoretical computer science, or related fields. We seek candidates with strong research potential in mathematics and/or algorithms. The position offers an annual salary of 77,880,000 KRW plus a research grant. The initial 2-year term may be renewed once. – CV, publication […]

The Center for AI and Natural Sciences invites applications from PhD holders in mathematics, theoretical computer science, or related fields. We seek candidates with strong research potential in mathematics and/or algorithms. The position offers an annual salary of 77,880,000 KRW plus a research grant. The initial 2-year term may be renewed once.

– CV, publication list, research plan, 3 letters

Website: https://jobs.kias.re.kr/ext/rec/rec_1001.do?ANNC_NO=REC202608210001
Email: aikias@kias.re.kr

By shacharlovett

Block Sensitivity and Certificate Complexity of Rotation Symmetric Boolean Functions

from arXiv: Computational Complexity

Authors: Yuan Li, Jing Zhang

We study block sensitivity and certificate complexity of rotation symmetric Boolean functions, with particular emphasis on monomial rotation symmetric Boolean functions. We first show that every odd-degree monomial rotation symmetric Boolean function on $n$ variables satisfies $s=bs=C=n$. We then determine sensitivity, block sensitivity, and certificate complexity for every quadratic monomial rotation symmetric Boolean function. For the quadratic function $Q_{n,k}$ generated by $x_0x_k$, we prove, outside the short-orbit case, that \[ C(Q_{n,k})= \begin{cases} n,&4\mid n/\gcd(n,k),\\ n-\gcd(n,k),&4\nmid n/\gcd(n,k). \end{cases} \] Moreover, \(bs(Q_{n,k})=C(Q_{n,k})\) for every quadratic MRS function, and outside the short-orbit case, strict separation \(s

Authors: Yuan Li, Jing Zhang

We study block sensitivity and certificate complexity of rotation symmetric Boolean functions, with particular emphasis on monomial rotation symmetric Boolean functions. We first show that every odd-degree monomial rotation symmetric Boolean function on $n$ variables satisfies $s=bs=C=n$. We then determine sensitivity, block sensitivity, and certificate complexity for every quadratic monomial rotation symmetric Boolean function. For the quadratic function $Q_{n,k}$ generated by $x_0x_k$, we prove, outside the short-orbit case, that \[ C(Q_{n,k})= \begin{cases} n,&4\mid n/\gcd(n,k),\\ n-\gcd(n,k),&4\nmid n/\gcd(n,k). \end{cases} \] Moreover, \(bs(Q_{n,k})=C(Q_{n,k})\) for every quadratic MRS function, and outside the short-orbit case, strict separation \(s

Sensitivity of Rotation Symmetric Boolean Functions via Binary Necklaces

from arXiv: Computational Complexity

Authors: Yuan Li, Jing Zhang

Rotation symmetric Boolean functions are Boolean functions invariant under cyclic permutation of their variables and can be represented by binary necklaces. We express local sensitivity as a weighted bichromatic degree in the necklace quotient and derive an exact formula for the multiplicities between neighboring necklaces. For an odd prime number of variables, we obtain explicit sensitivity formulas and show that multiplicity corrections occur only on an asymptotically negligible family of necklaces. For monomial rotation symmetric Boolean functions, we prove that every odd-degree function has maximum sensitivity, determine the sensitivity of every quadratic function, and determine the sensitivity of the consecutive quartic family. We also determine exactly the sensitivity of cyclic run-threshold functions for arbitrary length and threshold.

Authors: Yuan Li, Jing Zhang

Rotation symmetric Boolean functions are Boolean functions invariant under cyclic permutation of their variables and can be represented by binary necklaces. We express local sensitivity as a weighted bichromatic degree in the necklace quotient and derive an exact formula for the multiplicities between neighboring necklaces. For an odd prime number of variables, we obtain explicit sensitivity formulas and show that multiplicity corrections occur only on an asymptotically negligible family of necklaces. For monomial rotation symmetric Boolean functions, we prove that every odd-degree function has maximum sensitivity, determine the sensitivity of every quadratic function, and determine the sensitivity of the consecutive quartic family. We also determine exactly the sensitivity of cyclic run-threshold functions for arbitrary length and threshold.

Graph Distortion Distances, Gromov-Hausdorff and the Hardness of Gromov-Wasserstein

from arXiv: Computational Complexity

Authors: Jens Breitung, Timo Gervens, Julius Pörzgen

We study the computational complexity of graph distortion distances, a family of graph metrics measuring how vertex distances differ between two graphs. Formally, these distances are defined in terms of standard matrix norms on the difference of distance matrices under an optimal vertex alignment. The resulting metrics have natural interpretations, corresponding to three ways of measuring the distortion of shortest-path distances: the maximum distortion among all vertex pairs (entrywise maximum norm), global aggregation (entrywise $p$-norms), and local aggregation (operator $p$-norms) of distortions. For maximum distortion, we show that approximation below a factor of two is NP-hard even for highly restricted trees. Due to the close connection to the Gromov-Hausdorff distance, we obtain a similar hardness result for the latter. In both cases, we show essentially optimal additive inapproximability. On the algorithmic side, we give a subcubic-time algorithm for optimal embeddings of connected graphs into paths, based on a structural decomposition along a diametral path. Additionally, we give a simple approximation algorithm when one input graph has a "relatively path-like" metric structure, i.e. has bounded pathlength. For local and global distortion, we derive various hardness results even for restricted inputs. Making use of a close connection between the global distortion distances and Gromov-Wasserstein distances, we give the first formal proof of NP-hardness of the latter, which has been claimed in the literature many times but has, to the best of our knowledge, never been correctly proven.

Authors: Jens Breitung, Timo Gervens, Julius Pörzgen

We study the computational complexity of graph distortion distances, a family of graph metrics measuring how vertex distances differ between two graphs. Formally, these distances are defined in terms of standard matrix norms on the difference of distance matrices under an optimal vertex alignment. The resulting metrics have natural interpretations, corresponding to three ways of measuring the distortion of shortest-path distances: the maximum distortion among all vertex pairs (entrywise maximum norm), global aggregation (entrywise $p$-norms), and local aggregation (operator $p$-norms) of distortions. For maximum distortion, we show that approximation below a factor of two is NP-hard even for highly restricted trees. Due to the close connection to the Gromov-Hausdorff distance, we obtain a similar hardness result for the latter. In both cases, we show essentially optimal additive inapproximability. On the algorithmic side, we give a subcubic-time algorithm for optimal embeddings of connected graphs into paths, based on a structural decomposition along a diametral path. Additionally, we give a simple approximation algorithm when one input graph has a "relatively path-like" metric structure, i.e. has bounded pathlength. For local and global distortion, we derive various hardness results even for restricted inputs. Making use of a close connection between the global distortion distances and Gromov-Wasserstein distances, we give the first formal proof of NP-hardness of the latter, which has been claimed in the literature many times but has, to the best of our knowledge, never been correctly proven.

Period Piece: NEXPTIME-complete problems on digraphs via Boolean network dynamics

from arXiv: Computational Complexity

Authors: Florian Bridoux, Maximilien Gadouleau, David C. Kutner, Adrien Richard

The central object in this work is the Boolean network, a function $f:\{0,1\}^V \to \{0,1\}^V$. Often identified with $f$ is its dynamics $D_f$, the digraph with exactly $\{x \to y : f(x)=y\}$ as its arcs. The interaction graph of $f$ is the digraph $G$ on $V$ which can be thought of as representing the dependencies of the components of $f$ on one another. On the other hand, model-checking is a major field of study in graph algorithms and complexity. We consider problems at the intersection of these paradigms: for a given property $φ$, we say that a digraph $G$ is $φ$-dynamic if $G$ is the interaction graph of some Boolean network $f$ such that $D_f \models φ$. The corresponding decision problem $φ$-DYNAMIC asks whether $G$ is $φ$-dynamic. Problems of this form are well-studied in the Boolean networks community. For example, it is a well-known result that, letting $φ$ be 'No fixed points' (i.e. $\nexists x:x\to x$), $φ$-DYNAMIC is equivalent to the structural digraph problem NON-ACYCLIC, which is solvable in linear time. We establish, for the first time, properties $φ$ for which $φ$-DYNAMIC is computationally hard. These include the 'Unique fixed point' property $\exists!x:x\to x$, and a range of properties concerning the periodic points of $f$ (i.e. vertices of $D_f$ which lie on a cycle). More widely, we exhibit an infinite family of natural properties $φ$ such that $φ$-DYNAMIC is $\mathrm{NEXPTIME}$-complete, which we call PERIOD PIECE. As a special case, these include all properties of the form 'Unique cycle of length $k$' and 'Exactly $k$ fixed points' for any $k \ge 1$. On the other hand, we show that if the input digraph has bounded in-degree, then for those same problems $φ$-DYNAMIC collapses down to $\mathrm{NP}^{\mathrm{NP}}$, the second level of the polynomial hierarchy, though it remains $\mathrm{NP}^{\mathrm{NP}}$-complete.

Authors: Florian Bridoux, Maximilien Gadouleau, David C. Kutner, Adrien Richard

The central object in this work is the Boolean network, a function $f:\{0,1\}^V \to \{0,1\}^V$. Often identified with $f$ is its dynamics $D_f$, the digraph with exactly $\{x \to y : f(x)=y\}$ as its arcs. The interaction graph of $f$ is the digraph $G$ on $V$ which can be thought of as representing the dependencies of the components of $f$ on one another. On the other hand, model-checking is a major field of study in graph algorithms and complexity. We consider problems at the intersection of these paradigms: for a given property $φ$, we say that a digraph $G$ is $φ$-dynamic if $G$ is the interaction graph of some Boolean network $f$ such that $D_f \models φ$. The corresponding decision problem $φ$-DYNAMIC asks whether $G$ is $φ$-dynamic. Problems of this form are well-studied in the Boolean networks community. For example, it is a well-known result that, letting $φ$ be 'No fixed points' (i.e. $\nexists x:x\to x$), $φ$-DYNAMIC is equivalent to the structural digraph problem NON-ACYCLIC, which is solvable in linear time. We establish, for the first time, properties $φ$ for which $φ$-DYNAMIC is computationally hard. These include the 'Unique fixed point' property $\exists!x:x\to x$, and a range of properties concerning the periodic points of $f$ (i.e. vertices of $D_f$ which lie on a cycle). More widely, we exhibit an infinite family of natural properties $φ$ such that $φ$-DYNAMIC is $\mathrm{NEXPTIME}$-complete, which we call PERIOD PIECE. As a special case, these include all properties of the form 'Unique cycle of length $k$' and 'Exactly $k$ fixed points' for any $k \ge 1$. On the other hand, we show that if the input digraph has bounded in-degree, then for those same problems $φ$-DYNAMIC collapses down to $\mathrm{NP}^{\mathrm{NP}}$, the second level of the polynomial hierarchy, though it remains $\mathrm{NP}^{\mathrm{NP}}$-complete.

Learning, Compression, and Meta-Complexity

from arXiv: Computational Complexity

Authors: Halley Goldberg, Mikito Nanashima, Igor C. Oliveira

The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine learning and learning theory. Here we focus on settings in which that intuition can be made mathematically precise, surveying formal connections among learning algorithms, data compression, and computational complexity theory, with an emphasis on recent progress and current frontiers. We describe how techniques from complexity theory can guide the design of both supervised and unsupervised learning algorithms with provable guarantees, and conversely, how learning algorithms can yield new insights and results in complexity theory. A central theme is the role of data compression as a bridge between learning and complexity, especially through an area called meta-complexity, which studies the computational difficulty of compression problems and time-bounded variants of Kolmogorov complexity.

Authors: Halley Goldberg, Mikito Nanashima, Igor C. Oliveira

The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine learning and learning theory. Here we focus on settings in which that intuition can be made mathematically precise, surveying formal connections among learning algorithms, data compression, and computational complexity theory, with an emphasis on recent progress and current frontiers. We describe how techniques from complexity theory can guide the design of both supervised and unsupervised learning algorithms with provable guarantees, and conversely, how learning algorithms can yield new insights and results in complexity theory. A central theme is the role of data compression as a bridge between learning and complexity, especially through an area called meta-complexity, which studies the computational difficulty of compression problems and time-bounded variants of Kolmogorov complexity.

NP-hardness of additive approximation for generalized Turán problems

from arXiv: Computational Complexity

Authors: Yihan Chen, Jialin He, Xihe Li, Jie Ma, Tianying Xie

For a fixed graph $T$ and a fixed nonempty family $\mathcal{F}$ of graphs, let $\operatorname{ex}(G, T, \mathcal{F})$ denote the maximum number of copies of $T$ in an $\mathcal{F}$-free subgraph of a graph $G$. Alon and Shikhelman~[Algorithmica, 2022] conjectured that if $e(T)> 0$ and no member of $\mathcal{F}$ is a subgraph of a blow-up of $T$, then for every $\varepsilon>0$, approximating $\operatorname{ex}(G, T, \mathcal{F})$ within an additive error $n^{v(T)-\varepsilon}$ is NP-hard for an input graph $G$ on $n$ vertices. The previously established general cases for this conjecture include $\operatorname{ex}(G, K_2, \mathcal{F})$ by Alon, Shapira and Sudakov~[Ann. of Math., 2009], and $\operatorname{ex}(G, K_m, K_k)$ by Alon and Shikhelman. In this paper, we confirm this conjecture for every bipartite graph $T$ and every clique $T$. Our reductions use iterative leaf additions, symmetric rooted extensions and random transversals to encode the maximum number of edges in a $(k-1)$-colorable subgraph of $G$, where $k=\min_{F\in \mathcal{F}}χ(F)$.

Authors: Yihan Chen, Jialin He, Xihe Li, Jie Ma, Tianying Xie

For a fixed graph $T$ and a fixed nonempty family $\mathcal{F}$ of graphs, let $\operatorname{ex}(G, T, \mathcal{F})$ denote the maximum number of copies of $T$ in an $\mathcal{F}$-free subgraph of a graph $G$. Alon and Shikhelman~[Algorithmica, 2022] conjectured that if $e(T)> 0$ and no member of $\mathcal{F}$ is a subgraph of a blow-up of $T$, then for every $\varepsilon>0$, approximating $\operatorname{ex}(G, T, \mathcal{F})$ within an additive error $n^{v(T)-\varepsilon}$ is NP-hard for an input graph $G$ on $n$ vertices. The previously established general cases for this conjecture include $\operatorname{ex}(G, K_2, \mathcal{F})$ by Alon, Shapira and Sudakov~[Ann. of Math., 2009], and $\operatorname{ex}(G, K_m, K_k)$ by Alon and Shikhelman. In this paper, we confirm this conjecture for every bipartite graph $T$ and every clique $T$. Our reductions use iterative leaf additions, symmetric rooted extensions and random transversals to encode the maximum number of edges in a $(k-1)$-colorable subgraph of $G$, where $k=\min_{F\in \mathcal{F}}χ(F)$.

Two Linear Programs are Better than One

from arXiv: Computational Complexity

Authors: Alexander E. Black, Christoph Hertrich, Stefan Kober, Georg Loho

The extension complexity $\operatorname{xc}(P)$ of a polytope $P$ is the minimum number of facets of a polytope that projects onto it. Motivated by the complexity of neural networks, Hertrich and Loho introduced the virtual extension complexity $\operatorname{vxc}(P)$ of a polytope $P$, which is the minimum of $\operatorname{xc}(Q) + \operatorname{xc}(R)$ among all polytopes $Q$ and $R$ such that $P+Q=R$. We show the following results pertaining to virtual extension complexity. First, we resolve the core open problem on the topic by showing the virtual extension complexity can be exponentially smaller than extension complexity. Second, we show there are matroid base polytopes with exponential virtual extension complexity by adapting a semi-algebraic counting argument by Hrubeš that extends the counting arguments of Rothvoss. Finally, we show that the maximum virtual extension complexity of a polygon with $n$ vertices is $Θ(\sqrt{n})$. This resolves an analogue of a question on extension complexity of Fiorini, Rothvoss, and Tiwary in 2011 in the case of virtual extension complexity.

Authors: Alexander E. Black, Christoph Hertrich, Stefan Kober, Georg Loho

The extension complexity $\operatorname{xc}(P)$ of a polytope $P$ is the minimum number of facets of a polytope that projects onto it. Motivated by the complexity of neural networks, Hertrich and Loho introduced the virtual extension complexity $\operatorname{vxc}(P)$ of a polytope $P$, which is the minimum of $\operatorname{xc}(Q) + \operatorname{xc}(R)$ among all polytopes $Q$ and $R$ such that $P+Q=R$. We show the following results pertaining to virtual extension complexity. First, we resolve the core open problem on the topic by showing the virtual extension complexity can be exponentially smaller than extension complexity. Second, we show there are matroid base polytopes with exponential virtual extension complexity by adapting a semi-algebraic counting argument by Hrubeš that extends the counting arguments of Rothvoss. Finally, we show that the maximum virtual extension complexity of a polygon with $n$ vertices is $Θ(\sqrt{n})$. This resolves an analogue of a question on extension complexity of Fiorini, Rothvoss, and Tiwary in 2011 in the case of virtual extension complexity.

Superpolynomial Lists for Reed-Solomon Codes over Binary Extension Fields

from arXiv: Computational Complexity

Authors: Minshen Zhu

Recent work shows that ordinary Reed--Solomon codes over prime fields admit polynomial-size lists up to list-decoding capacity on every evaluation set. We show a contrasting phenomenon over binary extension fields. For every fixed rate $00$ such that, for all sufficiently large $h$, the full-field Reed--Solomon code over $\mathbb{F}_{2^h}$ has a received word with $n^{Ω_R(\log n)}$ codewords within relative distance $1-R-\varepsilon_R$. To our knowledge, these are the first superpolynomial ordinary Reed--Solomon list-size lower bounds at fixed positive rate and a fixed positive agreement gap above the rate. Thus capacity-approaching polynomial list size for Reed--Solomon codes is not uniform over the underlying field, and the obstruction occurs at every fixed rate. As a puncturing consequence, the same holds for primitive Reed--Solomon codes; the obstruction is also robust under controlled dense puncturing. The proof begins with an exact correspondence that turns certain binary Bose--Chaudhuri--Hocquenghem weight-enumerator coefficients into sizes of single Reed--Solomon lists. A classical binary weight family gives the initial quarter-rate construction, while a quadratic-form enumeration supplies its superpolynomial multiplicity. A common-kernel quotient/descent argument and common-root padding then extend the construction to all rates. We also determine the optimized agreement envelope achieved by the construction.

Authors: Minshen Zhu

Recent work shows that ordinary Reed--Solomon codes over prime fields admit polynomial-size lists up to list-decoding capacity on every evaluation set. We show a contrasting phenomenon over binary extension fields. For every fixed rate $00$ such that, for all sufficiently large $h$, the full-field Reed--Solomon code over $\mathbb{F}_{2^h}$ has a received word with $n^{Ω_R(\log n)}$ codewords within relative distance $1-R-\varepsilon_R$. To our knowledge, these are the first superpolynomial ordinary Reed--Solomon list-size lower bounds at fixed positive rate and a fixed positive agreement gap above the rate. Thus capacity-approaching polynomial list size for Reed--Solomon codes is not uniform over the underlying field, and the obstruction occurs at every fixed rate. As a puncturing consequence, the same holds for primitive Reed--Solomon codes; the obstruction is also robust under controlled dense puncturing. The proof begins with an exact correspondence that turns certain binary Bose--Chaudhuri--Hocquenghem weight-enumerator coefficients into sizes of single Reed--Solomon lists. A classical binary weight family gives the initial quarter-rate construction, while a quadratic-form enumeration supplies its superpolynomial multiplicity. A common-kernel quotient/descent argument and common-root padding then extend the construction to all rates. We also determine the optimized agreement envelope achieved by the construction.

A simplified proof of polynomial improvements for 3SUM and APSP

from arXiv: Computational Complexity

Authors: Sankeerth Rao Karingula, Shachar Lovett

We give a simplified proof of the polynomial improvements for integer 3SUM and all-pairs shortest paths (APSP) established by Alman and Vassilevska Williams, with weaker exponents. The central ingredient is selected-entry matrix multiplication: computing a prescribed set of entries of a product of dense rectangular matrices. Starting from Schönhage's inner/outer-product identity, we construct a multiplication rule that shares intermediate products among many outputs. We apply this rule recursively and bound the work by counting the requested entries at each level. Applied to incidence matrices, the algorithm solves Offline Set Disjointness over a small universe. Known deterministic reductions then yield truly subquadratic integer 3SUM and truly subcubic APSP for polynomially bounded integer inputs.

Authors: Sankeerth Rao Karingula, Shachar Lovett

We give a simplified proof of the polynomial improvements for integer 3SUM and all-pairs shortest paths (APSP) established by Alman and Vassilevska Williams, with weaker exponents. The central ingredient is selected-entry matrix multiplication: computing a prescribed set of entries of a product of dense rectangular matrices. Starting from Schönhage's inner/outer-product identity, we construct a multiplication rule that shares intermediate products among many outputs. We apply this rule recursively and bound the work by counting the requested entries at each level. Applied to incidence matrices, the algorithm solves Offline Set Disjointness over a small universe. Known deterministic reductions then yield truly subquadratic integer 3SUM and truly subcubic APSP for polynomially bounded integer inputs.

Saving the Queen is hard: Hive is EXPTIME-complete

from arXiv: Computational Complexity

Authors: Francesco Carboni, Daniele Muscillo

Hive is a boardless two-player strategy game in which the pieces form the playing surface. We prove that deciding whether White can force a win in single-Queen $n$-Hive, the generalization with exactly one Queen Bee per player, is EXPTIME-complete, even when all pieces are already placed. Our hardness proof reduces from unbounded two-player Constraint Logic: Grasshoppers encode edge orientations, while the One Hive rule enforces the vertex constraints. We obtain the EXPTIME upper bound by considering positions up to translation. The result answers the single-Queen question raised by Andel and Rin. Keeping the same inventory and movement rules, it also gives EXPTIME-completeness for multi-Queen variants. To the best of our knowledge, this is the first use of unbounded two-player Constraint Logic to prove EXPTIME-completeness for a natural problem.

Authors: Francesco Carboni, Daniele Muscillo

Hive is a boardless two-player strategy game in which the pieces form the playing surface. We prove that deciding whether White can force a win in single-Queen $n$-Hive, the generalization with exactly one Queen Bee per player, is EXPTIME-complete, even when all pieces are already placed. Our hardness proof reduces from unbounded two-player Constraint Logic: Grasshoppers encode edge orientations, while the One Hive rule enforces the vertex constraints. We obtain the EXPTIME upper bound by considering positions up to translation. The result answers the single-Queen question raised by Andel and Rin. Keeping the same inventory and movement rules, it also gives EXPTIME-completeness for multi-Queen variants. To the best of our knowledge, this is the first use of unbounded two-player Constraint Logic to prove EXPTIME-completeness for a natural problem.

Unconditional Separation between LL and CLL

from arXiv: Computational Complexity

Authors: Shubham Bhardwaj

Catalytic computation involves the study of Turing machines that are provided an additional catalytic tape (with arbitrary content) that the machine may use while ensuring that the tape contents are restored at the end of the computation. Although it is known that catalytic log-space machines are at least as powerful as non-deterministic log-space (and more), we do not have unconditional asymptotic separations. In this work, we prove an unconditional separation between a deterministic space complexity class and its catalytic counterpart. More precisely, at the doubly logarithmic scale, we show that catalytic Turing machines can provably compute more languages than standard deterministic doubly-logarithmic space machines. The separation is witnessed by the unary language $\mathrm{POW2} = \{1^n : n \text{ is a power of two}\}$. We construct a catalytic Turing machine that uses $O(\log \log n)$ bits of space and $O(\log n)$ bits of catalytic tape to decide $\mathrm{POW2}$, whereas any standard deterministic Turing machine requires $Ω(\log n)$ space to decide it.

Authors: Shubham Bhardwaj

Catalytic computation involves the study of Turing machines that are provided an additional catalytic tape (with arbitrary content) that the machine may use while ensuring that the tape contents are restored at the end of the computation. Although it is known that catalytic log-space machines are at least as powerful as non-deterministic log-space (and more), we do not have unconditional asymptotic separations. In this work, we prove an unconditional separation between a deterministic space complexity class and its catalytic counterpart. More precisely, at the doubly logarithmic scale, we show that catalytic Turing machines can provably compute more languages than standard deterministic doubly-logarithmic space machines. The separation is witnessed by the unary language $\mathrm{POW2} = \{1^n : n \text{ is a power of two}\}$. We construct a catalytic Turing machine that uses $O(\log \log n)$ bits of space and $O(\log n)$ bits of catalytic tape to decide $\mathrm{POW2}$, whereas any standard deterministic Turing machine requires $Ω(\log n)$ space to decide it.

Lasso Games

from arXiv: Computational Complexity

Authors: Krzysztof Żyndul

We study games on finite graphs with labelled or weighted edges in which plays end as soon as the first loop is formed. The resulting play has a lasso shape: a path (head) followed by a cycle (loop). We consider two semantics for determining the winner: the loop semantics, where only the loop matters, and the lasso semantics, where the entire play is taken into account. We show that adopting the lasso semantics may have a dramatic impact on both the complexity of the game and the memory required to play optimally. Mean-payoff games with lasso semantics are no longer positionally determined, and the threshold problem – deciding whether the value of the game is at least 0 – is PSPACE-complete. In contrast, lasso parity games are equivalent to weak parity games, remain positionally determined and are solvable in polynomial time. Furthermore, we show that if a positionally determined winning objective is closed under cyclic permutation and both the objective and its complement are closed under concatenation, then it is equivalent to the parity objective. Finally, we consider mod-k games, where the winner depends on the length of the path modulo k >= 2. We show that these games, in both semantics, are PSPACE-complete. Alongside computational complexity, we analyze the memory required to play optimally. We establish that for any game positionally determined in loop semantics, memory of size 2^n is sufficient in lasso semantics. We complement this by showing exponential memory lower bounds for lasso mean-payoff and mod-k games.

Authors: Krzysztof Żyndul

We study games on finite graphs with labelled or weighted edges in which plays end as soon as the first loop is formed. The resulting play has a lasso shape: a path (head) followed by a cycle (loop). We consider two semantics for determining the winner: the loop semantics, where only the loop matters, and the lasso semantics, where the entire play is taken into account. We show that adopting the lasso semantics may have a dramatic impact on both the complexity of the game and the memory required to play optimally. Mean-payoff games with lasso semantics are no longer positionally determined, and the threshold problem – deciding whether the value of the game is at least 0 – is PSPACE-complete. In contrast, lasso parity games are equivalent to weak parity games, remain positionally determined and are solvable in polynomial time. Furthermore, we show that if a positionally determined winning objective is closed under cyclic permutation and both the objective and its complement are closed under concatenation, then it is equivalent to the parity objective. Finally, we consider mod-k games, where the winner depends on the length of the path modulo k >= 2. We show that these games, in both semantics, are PSPACE-complete. Alongside computational complexity, we analyze the memory required to play optimally. We establish that for any game positionally determined in loop semantics, memory of size 2^n is sufficient in lasso semantics. We complement this by showing exponential memory lower bounds for lasso mean-payoff and mod-k games.

Execution-Time Opacity in (Controllable) Timed Automata is Hard

from arXiv: Computational Complexity

Authors: Sarah Dépernet, Engel Lefaucheux

Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as the total duration of a run. Timed automata (TAs) are an extension of finite-state automata with a set of clocks evolving at the same rate and that can be tested or reset. Hence, this formalism is able to represent systems involving timing constraints reacting to the passage of time. Some of the transitions in a timed automaton may be controlled, i.e., enabled or disabled, to limit the runs that are allowed. Execution-time opacity (ET-opacity) aims at studying timing information leakage through the analysis of timed automata. More precisely, the ET-opacity problem consists in deciding whether an attacker can deduce whether some secret locations were visited by a run, solely from the run's duration. When control is allowed, the goal becomes to find a strategy that blocks the runs leaking information. This problem has been shown decidable for TAs, with and without control. However, these existing results only provide upper bounds on the complexity of the ET-opacity problem, sometimes largely above the tight bound. Our main contribution is to provide tight lower bounds for those two results. Moreover, for controllable TAs, two variants of ET-opacity (depending on whether detecting if a run did not visit a secret location represents an information leakage or not, and named full and weak ET-opacity respectively) had been separately studied; we prove here that these two notions are inter-reducible.

Authors: Sarah Dépernet, Engel Lefaucheux

Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as the total duration of a run. Timed automata (TAs) are an extension of finite-state automata with a set of clocks evolving at the same rate and that can be tested or reset. Hence, this formalism is able to represent systems involving timing constraints reacting to the passage of time. Some of the transitions in a timed automaton may be controlled, i.e., enabled or disabled, to limit the runs that are allowed. Execution-time opacity (ET-opacity) aims at studying timing information leakage through the analysis of timed automata. More precisely, the ET-opacity problem consists in deciding whether an attacker can deduce whether some secret locations were visited by a run, solely from the run's duration. When control is allowed, the goal becomes to find a strategy that blocks the runs leaking information. This problem has been shown decidable for TAs, with and without control. However, these existing results only provide upper bounds on the complexity of the ET-opacity problem, sometimes largely above the tight bound. Our main contribution is to provide tight lower bounds for those two results. Moreover, for controllable TAs, two variants of ET-opacity (depending on whether detecting if a run did not visit a secret location represents an information leakage or not, and named full and weak ET-opacity respectively) had been separately studied; we prove here that these two notions are inter-reducible.

Capturing Bisimulation-Invariant Exponential-Space Complexity Classes

from arXiv: Computational Complexity

Authors: Florian Bruse, David Kronenberger, Martin Lange

Otto's Theorem characterises the bisimulation-invariant PTIME queries over graphs as exactly those that can be formulated in the polyadic mu-calculus. This extends to a characterisation of the full bisimulation-invariant exponential-time hierarchy, using a higher-order extension of the polyadic mu-calculus, called PHFL, which is naturally stratified by type order. The bisimulation-invariant fragment of k-EXPTIME is captured by the fragment of PHFL whose formulas have type order at most k. This is obtained using a generalisation of the Immerman-Vardi Theorem by Freire and Martins. Here we provide such a characterisation of the exponential-space hierarchy. We show that the bisimulation-invariant fragment of k-EXPSPACE is captured by the fragment of PHFL of type order k+1 whose formulas are tail-recursive. Tail recursion limits the interplay between fixpoints and higher-order functions so that model checking based on fixpoint iteration can be made more space efficient in a way comparable to tail recursion in programming languages. The capturing result makes use of a generalisation of Vardi's characterisation of PSPACE as first-order logic with partial fixpoints, namely that k-EXPSPACE is captured by higher-order logic of order k+1 with partial fixpoints.

Authors: Florian Bruse, David Kronenberger, Martin Lange

Otto's Theorem characterises the bisimulation-invariant PTIME queries over graphs as exactly those that can be formulated in the polyadic mu-calculus. This extends to a characterisation of the full bisimulation-invariant exponential-time hierarchy, using a higher-order extension of the polyadic mu-calculus, called PHFL, which is naturally stratified by type order. The bisimulation-invariant fragment of k-EXPTIME is captured by the fragment of PHFL whose formulas have type order at most k. This is obtained using a generalisation of the Immerman-Vardi Theorem by Freire and Martins. Here we provide such a characterisation of the exponential-space hierarchy. We show that the bisimulation-invariant fragment of k-EXPSPACE is captured by the fragment of PHFL of type order k+1 whose formulas are tail-recursive. Tail recursion limits the interplay between fixpoints and higher-order functions so that model checking based on fixpoint iteration can be made more space efficient in a way comparable to tail recursion in programming languages. The capturing result makes use of a generalisation of Vardi's characterisation of PSPACE as first-order logic with partial fixpoints, namely that k-EXPSPACE is captured by higher-order logic of order k+1 with partial fixpoints.

Constant-Loss Round-Preserving Direct Sums for Information Complexity

from arXiv: Computational Complexity

Authors: Haoran Wang

Strong direct-sum theorems relate the information needed to solve many independent instances of a communication problem to the information needed for one instance. For Gap-Majority composition, an optimal information bound was previously known, but preserving the number of communication rounds incurred an additional logarithmic loss. We prove a round-preserving Gap-Majority lemma with constant additive information loss, independent of both the number of instances and the number of rounds. More generally, we establish a variance direct-sum theorem for additive square-integrable observables over product distributions. For protocols with a bounded number of terminal answer values, the extracted single-coordinate protocols preserve the round budget, and their average information cost is at most the original information cost divided by the number of coordinates, plus a term depending only on the answer alphabet and the allowed variance loss. The proof uses centered counterfactual conditional means and a global variance amortization argument. Rather than encoding absolute conditional means for every possible terminal answer, we encode the information not already determined by the common view. The total squared innovation is controlled by a telescoping variance potential, yielding an information loss independent of the number of coordinates. We also derive same-round mean-square estimation bounds for arbitrary terminal outputs.

Authors: Haoran Wang

Strong direct-sum theorems relate the information needed to solve many independent instances of a communication problem to the information needed for one instance. For Gap-Majority composition, an optimal information bound was previously known, but preserving the number of communication rounds incurred an additional logarithmic loss. We prove a round-preserving Gap-Majority lemma with constant additive information loss, independent of both the number of instances and the number of rounds. More generally, we establish a variance direct-sum theorem for additive square-integrable observables over product distributions. For protocols with a bounded number of terminal answer values, the extracted single-coordinate protocols preserve the round budget, and their average information cost is at most the original information cost divided by the number of coordinates, plus a term depending only on the answer alphabet and the allowed variance loss. The proof uses centered counterfactual conditional means and a global variance amortization argument. Rather than encoding absolute conditional means for every possible terminal answer, we encode the information not already determined by the common view. The total squared innovation is controlled by a telescoping variance potential, yielding an information loss independent of the number of coordinates. We also derive same-round mean-square estimation bounds for arbitrary terminal outputs.

Coarse-Graining Recursive Expert Computation: Continuation-Relative Interfaces and Failure Modes

from arXiv: Computational Complexity

Authors: Shihua Yu, Yanzhi Wang, Tianchi Chen

Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance. We present a continuation-relative evaluation framework that assesses replacements through the computation that follows them, using downstream task quality and total execution cost as joint criteria. The framework distinguishes boundary representations, summaries of skipped computation, executed operations, and downstream loss. It makes interfaces and permitted deployment information explicit, combines fine-path references with directly trained controls using the same shortened architecture, and accounts for selection overhead. An exact loss decomposition separates replacement effects at a fixed downstream action from effects of changing that action. A finite construction shows how retained information can reduce the stipulated cost of zero-error prediction under fixed information and controller restrictions. Experiments in two frozen pretrained language models show that representation fidelity and task loss can favor different fitting objectives. In a prespecified comparison on a 1.7-billion-parameter model, next-token-loss training lowers in-distribution negative log-likelihood by 0.1901 nats per scored token compared with reconstructing the skipped blocks' output, using the same optimizer and update count. This ranking reverses in all nine paired comparisons under distribution shift. In a recursive expert prototype, the jointly adapted candidate lowers task error relative to frozen-bank and router-only baselines, but trails directly trained controls, and the tested input-only selectors fail the joint quality-cost criteria. Together, the protocol and studies provide explicit criteria and diagnostics for distinguishing useful computational replacement from local approximation gains within specified interfaces and evaluation settings.

Authors: Shihua Yu, Yanzhi Wang, Tianchi Chen

Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance. We present a continuation-relative evaluation framework that assesses replacements through the computation that follows them, using downstream task quality and total execution cost as joint criteria. The framework distinguishes boundary representations, summaries of skipped computation, executed operations, and downstream loss. It makes interfaces and permitted deployment information explicit, combines fine-path references with directly trained controls using the same shortened architecture, and accounts for selection overhead. An exact loss decomposition separates replacement effects at a fixed downstream action from effects of changing that action. A finite construction shows how retained information can reduce the stipulated cost of zero-error prediction under fixed information and controller restrictions. Experiments in two frozen pretrained language models show that representation fidelity and task loss can favor different fitting objectives. In a prespecified comparison on a 1.7-billion-parameter model, next-token-loss training lowers in-distribution negative log-likelihood by 0.1901 nats per scored token compared with reconstructing the skipped blocks' output, using the same optimizer and update count. This ranking reverses in all nine paired comparisons under distribution shift. In a recursive expert prototype, the jointly adapted candidate lowers task error relative to frozen-bank and router-only baselines, but trails directly trained controls, and the tested input-only selectors fail the joint quality-cost criteria. Together, the protocol and studies provide explicit criteria and diagnostics for distinguishing useful computational replacement from local approximation gains within specified interfaces and evaluation settings.

Polynomial Separation of a Shared Tensor Leg and Matrix Multiplication in Arbitrary Characteristic

from arXiv: Computational Complexity

Authors: Houxi Lü

We improve the shared-leg separation construction in Proposition 3.1 of OpenAI, *An Upper Bound of $9/4$ for the Matrix Multiplication Exponent* (October 2, 2026). If $M$ tensor blocks have a common first space and independent matched sectors on their other two spaces, then $M+n-1$ copies of their matched sum degenerate to the full direct sum of the blocks, each tensored with an $n$-dimensional dot product. For $n=M$, this reduces the number of source copies from $5M$ to $2M-1$. The construction uses polynomial interpolation and truncated local substitution, with no roots of unity or factorial denominators. It works over every field containing enough distinct elements. We verify that the remaining spectral and discrete-growth arguments of the cited preprint are independent of characteristic. Over an algebraic closure, the replacement therefore gives $ω\leq9/4$ in every characteristic.

Authors: Houxi Lü

We improve the shared-leg separation construction in Proposition 3.1 of OpenAI, *An Upper Bound of $9/4$ for the Matrix Multiplication Exponent* (October 2, 2026). If $M$ tensor blocks have a common first space and independent matched sectors on their other two spaces, then $M+n-1$ copies of their matched sum degenerate to the full direct sum of the blocks, each tensored with an $n$-dimensional dot product. For $n=M$, this reduces the number of source copies from $5M$ to $2M-1$. The construction uses polynomial interpolation and truncated local substitution, with no roots of unity or factorial denominators. It works over every field containing enough distinct elements. We verify that the remaining spectral and discrete-growth arguments of the cited preprint are independent of characteristic. Over an algebraic closure, the replacement therefore gives $ω\leq9/4$ in every characteristic.

A Factor-Two Lower Bound for Complementing Two-Way Deterministic Finite Automata

from arXiv: Computational Complexity

Authors: Sudais Arif

For every $n\ge22$, we construct a language over a fixed three-letter alphabet whose two-way deterministic state complexity is $n$ and whose complement has state complexity $2n$. Acceptance is at the left endmarker, and infinite computations reject. The lower bound holds for arbitrary deterministic recognizers. The proof uses the group of transformations induced by the witness to constrain the behavior of a smaller recognizer, then derives a contradiction from a permutation query problem. A matching halting complement follows from a $2n$-state construction for sources whose destination states determine the direction of each transition. Splitting states by incoming direction gives a $2n+2b$ upper bound for general sources, where $b$ counts states entered in both directions. In the acceptance-anywhere model of Geffert, Mereghetti, and Pighizzini, the same family has exact complexities $N$ and $2N-1$ for every $N\ge23$.

Authors: Sudais Arif

For every $n\ge22$, we construct a language over a fixed three-letter alphabet whose two-way deterministic state complexity is $n$ and whose complement has state complexity $2n$. Acceptance is at the left endmarker, and infinite computations reject. The lower bound holds for arbitrary deterministic recognizers. The proof uses the group of transformations induced by the witness to constrain the behavior of a smaller recognizer, then derives a contradiction from a permutation query problem. A matching halting complement follows from a $2n$-state construction for sources whose destination states determine the direction of each transition. Splitting states by incoming direction gives a $2n+2b$ upper bound for general sources, where $b$ counts states entered in both directions. In the acceptance-anywhere model of Geffert, Mereghetti, and Pighizzini, the same family has exact complexities $N$ and $2N-1$ for every $N\ge23$.

The Rhythm of the Tiles: Narrow and Low Temperature Computation and Simulation in Synchronous Tile Self-Assembly

from arXiv: Computational Geometry

Authors: Florent Becker, Phillip Drake, Benjamin Durbin, Nicolas Ollinger, Brent Orlina, Matthew J. Patitz

In this paper, we prove that the synchronicity provided by the syncTAM can be leveraged to perform two additional feats impossible in the asynchronous aTAM. The aTAM has long been known to be equivalent to finite automata in the one-dimensional case. For our first set of results, we prove that this still holds when a finite, fixed amount of space is available in the second dimension. Yet, surprisingly, in the synchronous setting, a small, fixed, width is enough to simulate a Turing Machine. This holds even in the non-cooperative setting. For our second set of main results we prove that there exists a single set of syncTAM tiles that can be used to simulate any syncTAM system simply by using those tiles to create a scaled version of the seed structure of the original system and then letting the tiles from that set self-assemble from the scaled seed structure. While it has been shown (FOCS 2012) that such a tile set also exists in the aTAM for simulating all aTAM systems, that "intrinsically universal" (IU) tile set requires cooperativity (and is impossible without cooperativity as shown in SODA 2014). We prove there exists an IU tile set in the syncTAM that works without cooperativity. Furthermore, there exists a tile set that is IU for the class of directed systems (i.e. systems which each have a single terminal assembly) while remaining directed during simulation. This was shown to be impossible in the aTAM (FOCS 2016).

Authors: Florent Becker, Phillip Drake, Benjamin Durbin, Nicolas Ollinger, Brent Orlina, Matthew J. Patitz

In this paper, we prove that the synchronicity provided by the syncTAM can be leveraged to perform two additional feats impossible in the asynchronous aTAM. The aTAM has long been known to be equivalent to finite automata in the one-dimensional case. For our first set of results, we prove that this still holds when a finite, fixed amount of space is available in the second dimension. Yet, surprisingly, in the synchronous setting, a small, fixed, width is enough to simulate a Turing Machine. This holds even in the non-cooperative setting. For our second set of main results we prove that there exists a single set of syncTAM tiles that can be used to simulate any syncTAM system simply by using those tiles to create a scaled version of the seed structure of the original system and then letting the tiles from that set self-assemble from the scaled seed structure. While it has been shown (FOCS 2012) that such a tile set also exists in the aTAM for simulating all aTAM systems, that "intrinsically universal" (IU) tile set requires cooperativity (and is impossible without cooperativity as shown in SODA 2014). We prove there exists an IU tile set in the syncTAM that works without cooperativity. Furthermore, there exists a tile set that is IU for the class of directed systems (i.e. systems which each have a single terminal assembly) while remaining directed during simulation. This was shown to be impossible in the aTAM (FOCS 2016).

Hypergeometric Bridges for Multidimensional Grid Visibility

from arXiv: Computational Geometry

Authors: Ibrahim Ibrahim, Wilm Decré

Established interpolation-based methods efficiently compute visibility from one source to every grid cell using values computed closer to the source. Interpolation yields fractional values even on binary grids of free or fully blocking cells. Two-dimensional path-counting theory interprets these values as fractions of unobstructed grid paths, but three-dimensional methods use different predecessor structures. Interpreting these values consistently across dimensions and on nonbinary grids remains unresolved. We provide a unified mathematical account that gives every visibility value a precise meaning in any dimension. We establish when grid refinement yields eventual agreement with continuous visibility, implement parallel solvers, and compare four three-dimensional methods in accuracy and throughput. We define cell transmission: zero blocks visibility, one preserves it, and intermediate values attenuate it proportionally. We prove each computed value equals the uniform finite-path average of transmission products over finitely many source-to-cell grid paths. It is the fraction of clear paths on binary grids and the mean path transmission on nonbinary grids. For a source--target query, the fractions of paths passing through cells at each step form a hypergeometric distribution in 2D, which we call the hypergeometric bridge. On the last step into the target, these probabilities equal the interpolation weights. Higher-dimensional interpolation weights arise from products of hypergeometric bridges. For any fixed dimension, the full-field algorithm remains linear in grid size. Exact path enumeration reproduces every checked visibility value. NumPy, OpenMP, and CUDA agree numerically, and our OpenMP solver achieves the highest throughput in the four-method CPU comparison at every tested resolution.

Authors: Ibrahim Ibrahim, Wilm Decré

Established interpolation-based methods efficiently compute visibility from one source to every grid cell using values computed closer to the source. Interpolation yields fractional values even on binary grids of free or fully blocking cells. Two-dimensional path-counting theory interprets these values as fractions of unobstructed grid paths, but three-dimensional methods use different predecessor structures. Interpreting these values consistently across dimensions and on nonbinary grids remains unresolved. We provide a unified mathematical account that gives every visibility value a precise meaning in any dimension. We establish when grid refinement yields eventual agreement with continuous visibility, implement parallel solvers, and compare four three-dimensional methods in accuracy and throughput. We define cell transmission: zero blocks visibility, one preserves it, and intermediate values attenuate it proportionally. We prove each computed value equals the uniform finite-path average of transmission products over finitely many source-to-cell grid paths. It is the fraction of clear paths on binary grids and the mean path transmission on nonbinary grids. For a source--target query, the fractions of paths passing through cells at each step form a hypergeometric distribution in 2D, which we call the hypergeometric bridge. On the last step into the target, these probabilities equal the interpolation weights. Higher-dimensional interpolation weights arise from products of hypergeometric bridges. For any fixed dimension, the full-field algorithm remains linear in grid size. Exact path enumeration reproduces every checked visibility value. NumPy, OpenMP, and CUDA agree numerically, and our OpenMP solver achieves the highest throughput in the four-method CPU comparison at every tested resolution.

Shaving Two Logs for Discrete and Continuous Fréchet Distance

from arXiv: Computational Geometry

Authors: Timothy M Chan, Isaac M Hair

We revisit the classical computational geometry problem of computing the exact (discrete or continuous) Fréchet distance between two given polygonal curves with $n$ vertices in a constant dimension $d$. 1. We present an $O((n^2/\log^2n)\log^2\log n)$-time deterministic algorithm for the discrete Fréchet distance problem, improving the previous $O((n^2/\log n)\log\log n)$ result by Agarwal, Ben Avraham, Kaplan, and Sharir [SODA'13]. 2. We present an $O((n^2/\log^2n)\log^2\log n)$-time deterministic algorithm for the decision version of the continuous Fréchet distance problem, improving the previous $O((n^2/\log n)\log^2\log n)$ result by Buchin, Buchin, Meulemans, and Mulzer [SODA'14] and the recent $O(n^2/\log^{1+μ}n)$ randomized result by Cheng and Huang [SODA'25] for a small unspecified constant $μ>0$ (which converges to 0 as $d$ increases). 3. Finally, we present an $O((n^2/\log^2 n)\log^{O(1)}\log n)$-time Las Vegas randomized algorithm for the continuous Fréchet distance problem, improving the previous $O(n^2\log^2\log n)$ randomized result by Buchin, Buchin, Meulemans, and Mulzer [SODA'14] and the recent $O(n^2/\log^μn)$-time randomized result claimed by Cheng and Huang [SODA'25]. Since these problems have near-quadratic OV-based conditional lower bounds and shaving of two log factors is the best that is known for similar, simpler problems such as longest common subsequence and edit distance, our results reach the natural limit of the current state of the art (up to $\log\log$ factors).

Authors: Timothy M Chan, Isaac M Hair

We revisit the classical computational geometry problem of computing the exact (discrete or continuous) Fréchet distance between two given polygonal curves with $n$ vertices in a constant dimension $d$. 1. We present an $O((n^2/\log^2n)\log^2\log n)$-time deterministic algorithm for the discrete Fréchet distance problem, improving the previous $O((n^2/\log n)\log\log n)$ result by Agarwal, Ben Avraham, Kaplan, and Sharir [SODA'13]. 2. We present an $O((n^2/\log^2n)\log^2\log n)$-time deterministic algorithm for the decision version of the continuous Fréchet distance problem, improving the previous $O((n^2/\log n)\log^2\log n)$ result by Buchin, Buchin, Meulemans, and Mulzer [SODA'14] and the recent $O(n^2/\log^{1+μ}n)$ randomized result by Cheng and Huang [SODA'25] for a small unspecified constant $μ>0$ (which converges to 0 as $d$ increases). 3. Finally, we present an $O((n^2/\log^2 n)\log^{O(1)}\log n)$-time Las Vegas randomized algorithm for the continuous Fréchet distance problem, improving the previous $O(n^2\log^2\log n)$ randomized result by Buchin, Buchin, Meulemans, and Mulzer [SODA'14] and the recent $O(n^2/\log^μn)$-time randomized result claimed by Cheng and Huang [SODA'25]. Since these problems have near-quadratic OV-based conditional lower bounds and shaving of two log factors is the best that is known for similar, simpler problems such as longest common subsequence and edit distance, our results reach the natural limit of the current state of the art (up to $\log\log$ factors).

Ellipsoids from Tangents

from arXiv: Computational Geometry

Authors: Efrat Dahan, Michael Werman

This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tangents to the ellipsoid together with their contact points, rather than on points alone. A tangent contributes d independent equations where a point contributes one, so each hypothesis needs fewer samples, which in turn reduces the number of iterations required to recover every ellipsoid. The problem is posed as sets of linear equations whose solutions -- the ellipsoid parameters -- are recovered robustly by clustering in parameter space. The algorithm works in all dimensions and outperforms the state of the art in 2D. The approach applies in general to the robust solution of linear equations, and so has utility beyond ellipsoid fitting.

Authors: Efrat Dahan, Michael Werman

This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tangents to the ellipsoid together with their contact points, rather than on points alone. A tangent contributes d independent equations where a point contributes one, so each hypothesis needs fewer samples, which in turn reduces the number of iterations required to recover every ellipsoid. The problem is posed as sets of linear equations whose solutions -- the ellipsoid parameters -- are recovered robustly by clustering in parameter space. The algorithm works in all dimensions and outperforms the state of the art in 2D. The approach applies in general to the robust solution of linear equations, and so has utility beyond ellipsoid fitting.

Graph determination problem from knowledge of its subgraphs

from arXiv: Data Structures and Algorithms

Authors: Coralie Zens, Dominique Barth, Dimitri Watel, Marc-Antoine Weisser

In the context of graph reconstruction studies, this paper addresses the problem defined as follows: given a sample of vertex-coloured graphs, each associated with a number, does there exist a connected graph containing exactly the given number of induced subgraphs isomorphic to each graph in the sample, such that every edge of the solution graph is included in at least one of these subgraphs. This paper first shows that this problem is $Σ$______-complete. The associated optimisation problem is to determine the minimum number of vertices in a solution graph, if one exists. For instances consisting of two paths, we prove NP-completeness in general and give a polynomial-time dynamic programming algorithm when one path is monochromatic.

Authors: Coralie Zens, Dominique Barth, Dimitri Watel, Marc-Antoine Weisser

In the context of graph reconstruction studies, this paper addresses the problem defined as follows: given a sample of vertex-coloured graphs, each associated with a number, does there exist a connected graph containing exactly the given number of induced subgraphs isomorphic to each graph in the sample, such that every edge of the solution graph is included in at least one of these subgraphs. This paper first shows that this problem is $Σ$______-complete. The associated optimisation problem is to determine the minimum number of vertices in a solution graph, if one exists. For instances consisting of two paths, we prove NP-completeness in general and give a polynomial-time dynamic programming algorithm when one path is monochromatic.

Bootstrapping Classical and Quantum Query Lower Bounds for Finding Tarski Fixed Points

from arXiv: Data Structures and Algorithms

Authors: Tongyang Li, Weiran Ma, Ziyi Yang, Xingyu Zhao

The Tarski fixed-point problem asks for a fixed point of a monotone map on a $k$-dimensional grid lattice of width $n$ and has applications in game theory, economics, and verification. Substantial gaps remain between the known upper and lower bounds on its quantum query complexity. We prove two hardness bootstrapping theorems for quantum query lower bounds. Starting from a hard instance family whose instances have pair-wise incomparable unique fixed points, and a lower bound established by the nonnegative adversary method, we bootstrap this lower bound by (i) a factor of $Ω(t)$ when the dimension is multiplied by $t$ and the width remains unchanged, and (ii) a factor of $Ω(\log m)$ when the width is multiplied by $m$ and the dimension is doubled. Applying these theorems, we obtain three consequences: (i) an $Ω(k\log^2 n)$ lower bound for $k\geq 4$, improving previously best-known classical and quantum lower bounds; (ii) for every fixed constant $α>0$, there exists a constant $δ(α)>0$ such that $Q_ε(\text{Tarski}(n,k))=Ω(kn^{δ(α)})$ whenever $k>n^α$, and in particular, an $Ω(kn^{0.0072})$ lower bound for $k>n^{2/203}$, the first lower bound with simultaneous linear dependence on the dimension and polynomial dependence on the width; and (iii) a superpolynomial lower bound of $(k/\log k)^{Ω(\log k)}$ for exponentially wide grids, where $n=2^{Θ(k)}$. The last result rules out a route to polynomial-time algorithms for simple stochastic games and ARRIVAL through generic polynomial-query black-box Tarski solvers.

Authors: Tongyang Li, Weiran Ma, Ziyi Yang, Xingyu Zhao

The Tarski fixed-point problem asks for a fixed point of a monotone map on a $k$-dimensional grid lattice of width $n$ and has applications in game theory, economics, and verification. Substantial gaps remain between the known upper and lower bounds on its quantum query complexity. We prove two hardness bootstrapping theorems for quantum query lower bounds. Starting from a hard instance family whose instances have pair-wise incomparable unique fixed points, and a lower bound established by the nonnegative adversary method, we bootstrap this lower bound by (i) a factor of $Ω(t)$ when the dimension is multiplied by $t$ and the width remains unchanged, and (ii) a factor of $Ω(\log m)$ when the width is multiplied by $m$ and the dimension is doubled. Applying these theorems, we obtain three consequences: (i) an $Ω(k\log^2 n)$ lower bound for $k\geq 4$, improving previously best-known classical and quantum lower bounds; (ii) for every fixed constant $α>0$, there exists a constant $δ(α)>0$ such that $Q_ε(\text{Tarski}(n,k))=Ω(kn^{δ(α)})$ whenever $k>n^α$, and in particular, an $Ω(kn^{0.0072})$ lower bound for $k>n^{2/203}$, the first lower bound with simultaneous linear dependence on the dimension and polynomial dependence on the width; and (iii) a superpolynomial lower bound of $(k/\log k)^{Ω(\log k)}$ for exponentially wide grids, where $n=2^{Θ(k)}$. The last result rules out a route to polynomial-time algorithms for simple stochastic games and ARRIVAL through generic polynomial-query black-box Tarski solvers.

A Fixed Parameter Sublinear Time Approximation Scheme for Metric Min-Sum Clustering

from arXiv: Data Structures and Algorithms

Authors: Karthik C. S., Euiwoong Lee, Yuval Rabani, Chris Schwiegelshohn, Samson Zhou

Metric $k$-minsum clustering is a simple, natural, appealing density-based clustering criterion whose computational complexity remains poorly understood, despite the progress achieved fornother clustering criteria in the past three decades. In this paper we give an efficient approximation scheme for this problem that runs in sublinear time for mildly increasing $k$, and thus is fixed parameter tractable with respect to the parameter $k$. We complement this result by showing that similar algorithms are unlikely to exist for the closely related problems $k$-median and balanced $k$-median, assuming popular fine-grained complexity conjectures. Specifically, we show that no sublinear time algorithm with small approximation factors can exist even for $2$-median and balanced $2$-median. It was previously known that no efficient PTAS can exist for $k$-median and our results show a strong and perhaps surprising separation between the $k$-minsum clustering problem and $k$-median even in the sublinear regime. Our upper bounds improve qualitatively upon decades old results. Our lower bounds establish the first use of the Distributed PCP framework to clustering problems.

Authors: Karthik C. S., Euiwoong Lee, Yuval Rabani, Chris Schwiegelshohn, Samson Zhou

Metric $k$-minsum clustering is a simple, natural, appealing density-based clustering criterion whose computational complexity remains poorly understood, despite the progress achieved fornother clustering criteria in the past three decades. In this paper we give an efficient approximation scheme for this problem that runs in sublinear time for mildly increasing $k$, and thus is fixed parameter tractable with respect to the parameter $k$. We complement this result by showing that similar algorithms are unlikely to exist for the closely related problems $k$-median and balanced $k$-median, assuming popular fine-grained complexity conjectures. Specifically, we show that no sublinear time algorithm with small approximation factors can exist even for $2$-median and balanced $2$-median. It was previously known that no efficient PTAS can exist for $k$-median and our results show a strong and perhaps surprising separation between the $k$-minsum clustering problem and $k$-median even in the sublinear regime. Our upper bounds improve qualitatively upon decades old results. Our lower bounds establish the first use of the Distributed PCP framework to clustering problems.

Bipartite Carrier Compression and Machine-Certified Bounds in Schönhage Trilinear Multiplication Tensors

from arXiv: Data Structures and Algorithms

Authors: Volkan Dağlı, Zerrin Dağlı, Dağhan Dağlı

Under the conditional framework breaking the longstanding O(n log n) integer multiplication barrier (originating from OpenAI Preprint #109), the asymptotic time complexity exponent T(n) = O(n (log n)^{1-κ}) is governed by bounding the second moment E[X^2] < 1 of recursive branching processes over trilinear tensor carrier graphs. In this work, we present a constructive combinatorial optimization over the alternating carrier bipartite matching graphs of the Schönhage trilinear tensor decomposition at dimensions h=23 and h=25. Through an alternating augmenting path search, we uncover a non-trivial cluster-unlocking cascade that eliminates 185 carrier roles (95 roles in h=23 and 90 roles in h=25), reducing the global physical wire width by 377,890 wires from the reviewed baseline W = 177,284,805 down to W = 176,906,915. Evaluated over the exact 10^{-14} rational grid, this wire reduction certifies an improved second-moment coefficient AB = 5,164,059 / 125,000,000,000 and establishes the sharpened conditional exponent κ= 826,215,307 / 20,000,000,000,000 \approx 4.131076535 \times 10^{-5}, strictly surpassing the preceding record on this architecture (κ\approx 4.123864 \times 10^{-5}). All 47 polynomial constraints and 7 rational security margins are audited in exact Q-arithmetic with zero floating-point error, and the fundamental wire reduction identity is formally verified in the Lean 4 interactive theorem prover without unproven axioms.

Authors: Volkan Dağlı, Zerrin Dağlı, Dağhan Dağlı

Under the conditional framework breaking the longstanding O(n log n) integer multiplication barrier (originating from OpenAI Preprint #109), the asymptotic time complexity exponent T(n) = O(n (log n)^{1-κ}) is governed by bounding the second moment E[X^2] < 1 of recursive branching processes over trilinear tensor carrier graphs. In this work, we present a constructive combinatorial optimization over the alternating carrier bipartite matching graphs of the Schönhage trilinear tensor decomposition at dimensions h=23 and h=25. Through an alternating augmenting path search, we uncover a non-trivial cluster-unlocking cascade that eliminates 185 carrier roles (95 roles in h=23 and 90 roles in h=25), reducing the global physical wire width by 377,890 wires from the reviewed baseline W = 177,284,805 down to W = 176,906,915. Evaluated over the exact 10^{-14} rational grid, this wire reduction certifies an improved second-moment coefficient AB = 5,164,059 / 125,000,000,000 and establishes the sharpened conditional exponent κ= 826,215,307 / 20,000,000,000,000 \approx 4.131076535 \times 10^{-5}, strictly surpassing the preceding record on this architecture (κ\approx 4.123864 \times 10^{-5}). All 47 polynomial constraints and 7 rational security margins are audited in exact Q-arithmetic with zero floating-point error, and the fundamental wire reduction identity is formally verified in the Lean 4 interactive theorem prover without unproven axioms.

A Better-than-$2$ Approximation for $k$-Median Part II: Stable Instances

from arXiv: Data Structures and Algorithms

Authors: Rares-Darius Buhai, Vincent Cohen-Addad, Henry Fleischmann, Anupam Gupta, Antoni Jubés Monforte, Madhusudhan Reddy Pittu, Miltiadis Stouras, Ola Svensson, Lukas Vogl

We consider the $k$-median problem on general metric spaces, subject to the condition that the instances are $β$-stable (i.e., the optimal solution using $k-1$ centers has a cost at least $(1+β)$ times that of the optimal solution with $k$ centers). We give a $(1.89+ε)$-approximation algorithm for such instances running in time $\exp(1/(β\times \mathrm{poly}(ε)))\cdot \mathrm{poly}_ε(n)$. In particular, for $β=\mathrm{poly}(ε)/\log n$ and $ε$ a tiny constant, this is a polynomial-time algorithm. This improves on previous work showing a $(2 + ε)$-approximation algorithm with similar running time [CGL+25]. Combined with the pseudo-approximation algorithm in our companion work, this results in a better-than-$2$ approximation algorithm for general $k$-median instances. Our main technical contribution is a novel nuanced analysis of local search that allows us to isolate a subset of clients on which we lose essentially nothing compared to the optimal solution, while losing an approximation factor of $3$ on the rest of the clients. We complement this with an algorithm based on sampling and partial enumeration, achieving a better-than-$2$ approximation on the rest of the clients. Trading off these two algorithms yields our final approximation.

Authors: Rares-Darius Buhai, Vincent Cohen-Addad, Henry Fleischmann, Anupam Gupta, Antoni Jubés Monforte, Madhusudhan Reddy Pittu, Miltiadis Stouras, Ola Svensson, Lukas Vogl

We consider the $k$-median problem on general metric spaces, subject to the condition that the instances are $β$-stable (i.e., the optimal solution using $k-1$ centers has a cost at least $(1+β)$ times that of the optimal solution with $k$ centers). We give a $(1.89+ε)$-approximation algorithm for such instances running in time $\exp(1/(β\times \mathrm{poly}(ε)))\cdot \mathrm{poly}_ε(n)$. In particular, for $β=\mathrm{poly}(ε)/\log n$ and $ε$ a tiny constant, this is a polynomial-time algorithm. This improves on previous work showing a $(2 + ε)$-approximation algorithm with similar running time [CGL+25]. Combined with the pseudo-approximation algorithm in our companion work, this results in a better-than-$2$ approximation algorithm for general $k$-median instances. Our main technical contribution is a novel nuanced analysis of local search that allows us to isolate a subset of clients on which we lose essentially nothing compared to the optimal solution, while losing an approximation factor of $3$ on the rest of the clients. We complement this with an algorithm based on sampling and partial enumeration, achieving a better-than-$2$ approximation on the rest of the clients. Trading off these two algorithms yields our final approximation.

Parameterized algorithms for representing graphs as intersections of threshold graphs

from arXiv: Data Structures and Algorithms

Authors: L. Sunil Chandran, Suraj Kumar Sahoo

A graph is called a threshold graph if there is an assignment of real valued weights to its vertices such that two vertices have an edge between them if and only if the sum of the weights of the two vertices is above a certain `threshold value'. We will denote the class of all threshold graphs as $TH$. For a graph $G=(V,E)$, if for some integer $t\geq 0$, there are $t$ many threshold graphs $\{G_i=(V,E_i)\}_{i\in [t]}$ such that we have $E=E_1\cap E_2\cap \cdots \cap E_t$, then we say that the collection of threshold graphs $\{G_i=(V,E_i)\}_{i\in [t]}$ forms a $TH$-representation of $G$ of size $t$. \emph{The intersection dimension of $G$ with respect to threshold graphs} (denoted as $dim_{TH}(G)$) is the smallest non-negative integer $t$ such that there is a $TH$-representation of $G$ of size $t$. It is known that for a graph $G$ and integer $t>0$, the problem of deciding whether $dim_{TH}(G)\leq t$ is NP-complete even for $t=3$. In this paper, we study this problem in the framework of parameterized algorithms. We consider the vertex cover number and the cluster vertex deletion number as our parameters. We give a FPT algorithm for determining an optimal $TH$-representation of a graph when parameterized by its vertex cover number. Our main result is an efficient parameterized approximation scheme for this problem when parameterized by the cluster vertex deletion number of $G$.

Authors: L. Sunil Chandran, Suraj Kumar Sahoo

A graph is called a threshold graph if there is an assignment of real valued weights to its vertices such that two vertices have an edge between them if and only if the sum of the weights of the two vertices is above a certain `threshold value'. We will denote the class of all threshold graphs as $TH$. For a graph $G=(V,E)$, if for some integer $t\geq 0$, there are $t$ many threshold graphs $\{G_i=(V,E_i)\}_{i\in [t]}$ such that we have $E=E_1\cap E_2\cap \cdots \cap E_t$, then we say that the collection of threshold graphs $\{G_i=(V,E_i)\}_{i\in [t]}$ forms a $TH$-representation of $G$ of size $t$. \emph{The intersection dimension of $G$ with respect to threshold graphs} (denoted as $dim_{TH}(G)$) is the smallest non-negative integer $t$ such that there is a $TH$-representation of $G$ of size $t$. It is known that for a graph $G$ and integer $t>0$, the problem of deciding whether $dim_{TH}(G)\leq t$ is NP-complete even for $t=3$. In this paper, we study this problem in the framework of parameterized algorithms. We consider the vertex cover number and the cluster vertex deletion number as our parameters. We give a FPT algorithm for determining an optimal $TH$-representation of a graph when parameterized by its vertex cover number. Our main result is an efficient parameterized approximation scheme for this problem when parameterized by the cluster vertex deletion number of $G$.

LOCAL LLL in $\operatorname{poly}(\log\log n)$ Rounds

from arXiv: Data Structures and Algorithms

Authors: Maxime Flin, Mohsen Ghaffari, Magnús M. Halldórsson, Yannic Maus, Alexandre Nolin

We provide a $\operatorname{poly}(\log\log n)$-round randomized LOCAL algorithm for the constructive Lovász Local Lemma with polynomial criterion $pd^{c} < 1$, for a sufficiently large constant $c$. The best previous general round complexity was $\widetilde O(\log n)$. The key ingredient is a local reduction that amplifies the LLL probability exponent quadratically, while increasing the dependency radius only by a constant factor.

Authors: Maxime Flin, Mohsen Ghaffari, Magnús M. Halldórsson, Yannic Maus, Alexandre Nolin

We provide a $\operatorname{poly}(\log\log n)$-round randomized LOCAL algorithm for the constructive Lovász Local Lemma with polynomial criterion $pd^{c} < 1$, for a sufficiently large constant $c$. The best previous general round complexity was $\widetilde O(\log n)$. The key ingredient is a local reduction that amplifies the LLL probability exponent quadratically, while increasing the dependency radius only by a constant factor.

Can Rényi Accountants Beat Lossless Accountants for DP-SGD?

from arXiv: Data Structures and Algorithms

Authors: Jinho Bok

Recent frameworks in differential privacy (DP), such as privacy loss distributions and $f$-DP, provide lossless representations of the privacy leakage of mechanisms. These approaches are often considered to yield tighter $(\varepsilon, δ)$-DP guarantees than those obtained from relaxed notions such as Rényi DP. But is that always the case? We study this question for differentially private stochastic gradient descent (DP-SGD) with fixed-size batches under replace adjacency. Our key observation is that in this setting, privacy accounting based on Rényi DP can use a pair of worst-case distributions (i.e., a dominating pair) that differs from the one used in the lossless frameworks. In particular, we characterize the worst-case distributions as two-dimensional Gaussian mixtures and derive an improved Rényi DP bound for DP-SGD. Numerical and asymptotic comparisons show that across various hyperparameter settings, a Rényi DP accountant based on the new bound can yield better privacy guarantees than those obtained from the lossless frameworks.

Authors: Jinho Bok

Recent frameworks in differential privacy (DP), such as privacy loss distributions and $f$-DP, provide lossless representations of the privacy leakage of mechanisms. These approaches are often considered to yield tighter $(\varepsilon, δ)$-DP guarantees than those obtained from relaxed notions such as Rényi DP. But is that always the case? We study this question for differentially private stochastic gradient descent (DP-SGD) with fixed-size batches under replace adjacency. Our key observation is that in this setting, privacy accounting based on Rényi DP can use a pair of worst-case distributions (i.e., a dominating pair) that differs from the one used in the lossless frameworks. In particular, we characterize the worst-case distributions as two-dimensional Gaussian mixtures and derive an improved Rényi DP bound for DP-SGD. Numerical and asymptotic comparisons show that across various hyperparameter settings, a Rényi DP accountant based on the new bound can yield better privacy guarantees than those obtained from the lossless frameworks.

Exponential Local Search in Bounded-Occurrence MAX-CSP

from arXiv: Data Structures and Algorithms

Authors: Sophia Heimann, Hung P. Hoang, Stefan Hougardy

We study the number of improving steps that local search may require for $(2,2,r)$-MCA (Maximum Constraint Assignment), a bounded-occurrence version of MAX-CSP with valued constraints in which every constraint involves at most two variables, every variable occurs in at most two constraints, and every variable takes one of $r$ values. We consider the flip neighborhood, where two assignments are neighbors if they differ in the value of exactly one variable; moving to such a neighbor is called a flip. Local search repeatedly performs improving flips, that is, flips that strictly increase the objective value, until a local optimum is reached. Our main result is that $(2,2,5)$-MCA has the all-exp property: there is an infinite family of instances with initial assignments for which every sequence of improving flips ending in a local optimum has length exponential in the number of variables. Thus, on these instances, local search requires exponentially many steps regardless of which improving flip is chosen at each step. This is the first all-exp result for $(2,2,r)$-MCA for any value of $r$, and our result extends to every $r\ge 5$. In contrast, for $(2,2,2)$-MCA every sequence of improving flips has length at most quadratic in the number of variables~\cite{Kaz2020,KCJ2020}. We give a short, direct proof of this result.

Authors: Sophia Heimann, Hung P. Hoang, Stefan Hougardy

We study the number of improving steps that local search may require for $(2,2,r)$-MCA (Maximum Constraint Assignment), a bounded-occurrence version of MAX-CSP with valued constraints in which every constraint involves at most two variables, every variable occurs in at most two constraints, and every variable takes one of $r$ values. We consider the flip neighborhood, where two assignments are neighbors if they differ in the value of exactly one variable; moving to such a neighbor is called a flip. Local search repeatedly performs improving flips, that is, flips that strictly increase the objective value, until a local optimum is reached. Our main result is that $(2,2,5)$-MCA has the all-exp property: there is an infinite family of instances with initial assignments for which every sequence of improving flips ending in a local optimum has length exponential in the number of variables. Thus, on these instances, local search requires exponentially many steps regardless of which improving flip is chosen at each step. This is the first all-exp result for $(2,2,r)$-MCA for any value of $r$, and our result extends to every $r\ge 5$. In contrast, for $(2,2,2)$-MCA every sequence of improving flips has length at most quadratic in the number of variables~\cite{Kaz2020,KCJ2020}. We give a short, direct proof of this result.

Approximation Algorithms for Graphic Multi-Depot TSP and Graphic Multi-Path TSP

from arXiv: Data Structures and Algorithms

Authors: Niklas Dahlmeier, Raoul Luqué, Philipp Pabst, Laura Vargas Koch

We give improved approximation algorithms for two graphic TSP variants. First, we consider Multi-Depot TSP on graphic instances. In this variant, we are given an undirected graph with a set of depot vertices, and we are tasked with finding an Eulerian multi-edge set that connects each non-depot vertex to at least one depot. For this problem, Deppert, Kaul and Mnich (ESA 2023) presented a 3/2-approximation algorithm with running time exponential in the number of depots. We improve this result to a fully polynomial 3/2-approximation. Our algorithm is a variant of the classic Christofides-Serdyukov algorithm with an improved and simple analysis. On subcubic instances, we improve the approximation guarantee to 4/3 using the technique of removable pairings introduced by Mömke and Svensson (J. ACM 2016). We use a removable pairing to construct a set of disjoint cycles that can be completed to a cheap feasible solution. Second, we consider the Graphic Multi-Path TSP, which was previously considered by Alimi, Dahlmeier, Mömke, Pabst and Vargas Koch (SOSA 2026) who give a 2-approximation for this problem. Additionally, the authors present a reduction that shows that any better-than-two approximation for Graphic Multi-Depot TSP immediately leads to an improvement for Graphic Multi-Path TSP. We give an improved algorithm and analysis that yield an approximation guarantee of 1.9304.

Authors: Niklas Dahlmeier, Raoul Luqué, Philipp Pabst, Laura Vargas Koch

We give improved approximation algorithms for two graphic TSP variants. First, we consider Multi-Depot TSP on graphic instances. In this variant, we are given an undirected graph with a set of depot vertices, and we are tasked with finding an Eulerian multi-edge set that connects each non-depot vertex to at least one depot. For this problem, Deppert, Kaul and Mnich (ESA 2023) presented a 3/2-approximation algorithm with running time exponential in the number of depots. We improve this result to a fully polynomial 3/2-approximation. Our algorithm is a variant of the classic Christofides-Serdyukov algorithm with an improved and simple analysis. On subcubic instances, we improve the approximation guarantee to 4/3 using the technique of removable pairings introduced by Mömke and Svensson (J. ACM 2016). We use a removable pairing to construct a set of disjoint cycles that can be completed to a cheap feasible solution. Second, we consider the Graphic Multi-Path TSP, which was previously considered by Alimi, Dahlmeier, Mömke, Pabst and Vargas Koch (SOSA 2026) who give a 2-approximation for this problem. Additionally, the authors present a reduction that shows that any better-than-two approximation for Graphic Multi-Depot TSP immediately leads to an improvement for Graphic Multi-Path TSP. We give an improved algorithm and analysis that yield an approximation guarantee of 1.9304.

Tractability of Hard Digraph Cut Problems on Eulerian Instances: Treewidth Reduction and Reducing CMSO to Unbreakable Eulerian Instances

from arXiv: Data Structures and Algorithms

Authors: Esther Galby, Roohani Sharma, Bart van der Steenhoven

Eulerian digraphs retain the directional structure of general digraphs while exhibiting a symmetry of cuts reminiscent of undirected graphs. We show that this symmetry extends beyond individual cuts to two fundamental techniques in parameterized algorithms for undirected cut problems: treewidth reduction and recursive understanding and randomized contractions. Both of these techniques have proved useful for dealing with undirected graph cut problems as each of them reduces the problem at hand on general undirected instances to ones that have bounded treewidth or that are highly connected, respectively. In this work we prove an edge-cut analogue of the treewidth-reduction theorem of Marx, O'Sullivan, and Razgon [ACM TALG 2013], and an Eulerian analogue of the reduction of CMSO model checking to unbreakable instances due to Lokshtanov, Ramanujan, Saurabh, and Zehavi [ICALP 2018]. Both these results allow us to reduce several problems on general Eulerian digraphs to Eulerian digraphs whose underlying undirected graph has bounded treewidth, or to Eulerian instances that are highly connected, thus bringing to Eulerian digraphs two fundamental tools from the undirected parameterized cut toolkit for which no counterparts are known on arbitrary digraphs. Beyond their structural significance, these results have substantial algorithmic consequences: they yield FPT algorithms for several directed cut problems that are W[1]-hard in general. We show that Directed Multicut and Directed Edge Odd Cycle Transversal are FPT on Eulerian digraphs when parameterized by the solution size and the number of terminal pairs, and just the solution size, respectively. This is in sharp contrast to general digraphs, where the first problem is W[1]-hard even for 4 terminal pairs, and the second is W[1]-hard in general. We also obtain FPT algorithms for Directed Edge Multiway Cut-Uncut and Directed Steiner Cut.

Authors: Esther Galby, Roohani Sharma, Bart van der Steenhoven

Eulerian digraphs retain the directional structure of general digraphs while exhibiting a symmetry of cuts reminiscent of undirected graphs. We show that this symmetry extends beyond individual cuts to two fundamental techniques in parameterized algorithms for undirected cut problems: treewidth reduction and recursive understanding and randomized contractions. Both of these techniques have proved useful for dealing with undirected graph cut problems as each of them reduces the problem at hand on general undirected instances to ones that have bounded treewidth or that are highly connected, respectively. In this work we prove an edge-cut analogue of the treewidth-reduction theorem of Marx, O'Sullivan, and Razgon [ACM TALG 2013], and an Eulerian analogue of the reduction of CMSO model checking to unbreakable instances due to Lokshtanov, Ramanujan, Saurabh, and Zehavi [ICALP 2018]. Both these results allow us to reduce several problems on general Eulerian digraphs to Eulerian digraphs whose underlying undirected graph has bounded treewidth, or to Eulerian instances that are highly connected, thus bringing to Eulerian digraphs two fundamental tools from the undirected parameterized cut toolkit for which no counterparts are known on arbitrary digraphs. Beyond their structural significance, these results have substantial algorithmic consequences: they yield FPT algorithms for several directed cut problems that are W[1]-hard in general. We show that Directed Multicut and Directed Edge Odd Cycle Transversal are FPT on Eulerian digraphs when parameterized by the solution size and the number of terminal pairs, and just the solution size, respectively. This is in sharp contrast to general digraphs, where the first problem is W[1]-hard even for 4 terminal pairs, and the second is W[1]-hard in general. We also obtain FPT algorithms for Directed Edge Multiway Cut-Uncut and Directed Steiner Cut.

The Communication Complexity of the Coin Problem

from arXiv: Data Structures and Algorithms

Authors: Adithya Diddapur, Pachara Sawettamalya

We study the two-player communication complexity of the \textsf{Coin} problem. Here, two players, denoted Alice and Bob, are given up to $n$ individual i.i.d. random coins, and asked to output whether there are more heads or tails. An immediate approach is for Alice to send Bob her exact numbers of heads and tails, yielding a deterministic protocol with one-way communication cost $O(\log n)$ bits. Indeed, this is provably optimal in the related multi-player and streaming settings [Braverman, Garg, Woodruff, FOCS'20]. Our contribution is to show that this is not the right answer in the one-way two-player setting. Instead, we prove a $Ω(\log\log n)$ bit lower bound for the constant error-rate regime, and a $Ω(\log(1/δ))$ bit lower bound for arbitrarily small error rates, $δ\geq n^{-0.1}$. We also complement these with a tight one-way protocol using $O(\log\log n + \log(1/δ))$ bits of communication. Unlike the previous works, our lower bounds avoid intricate direct information-theoretic arguments. Instead, our $Ω(\log(1/δ))$ bound is via a standard fooling-set style argument, whilst our $Ω(\log\log n)$ bound is via a novel yet simple reduction from the \textsf{Gap-Greater-Than} problem. This reduction is our main technical contribution, and can be seen as loosely inspired by the \emph{Robust Communication Complexity} arguments of [Chakrabarti, Cormode, McGregor, STOC'08]. Finally, we also show that our constant-error lower bound and protocol can be adapted to the multi-round setting: up to polynomial factors in $r$, the $r$-round communication complexity of the coin problem is $Θ\left((\log\log n)^{1/r}\right)$ bits.

Authors: Adithya Diddapur, Pachara Sawettamalya

We study the two-player communication complexity of the \textsf{Coin} problem. Here, two players, denoted Alice and Bob, are given up to $n$ individual i.i.d. random coins, and asked to output whether there are more heads or tails. An immediate approach is for Alice to send Bob her exact numbers of heads and tails, yielding a deterministic protocol with one-way communication cost $O(\log n)$ bits. Indeed, this is provably optimal in the related multi-player and streaming settings [Braverman, Garg, Woodruff, FOCS'20]. Our contribution is to show that this is not the right answer in the one-way two-player setting. Instead, we prove a $Ω(\log\log n)$ bit lower bound for the constant error-rate regime, and a $Ω(\log(1/δ))$ bit lower bound for arbitrarily small error rates, $δ\geq n^{-0.1}$. We also complement these with a tight one-way protocol using $O(\log\log n + \log(1/δ))$ bits of communication. Unlike the previous works, our lower bounds avoid intricate direct information-theoretic arguments. Instead, our $Ω(\log(1/δ))$ bound is via a standard fooling-set style argument, whilst our $Ω(\log\log n)$ bound is via a novel yet simple reduction from the \textsf{Gap-Greater-Than} problem. This reduction is our main technical contribution, and can be seen as loosely inspired by the \emph{Robust Communication Complexity} arguments of [Chakrabarti, Cormode, McGregor, STOC'08]. Finally, we also show that our constant-error lower bound and protocol can be adapted to the multi-round setting: up to polynomial factors in $r$, the $r$-round communication complexity of the coin problem is $Θ\left((\log\log n)^{1/r}\right)$ bits.

Tight Approximations for Fixed-Order Vehicle Routing with Stochastic Demands

from arXiv: Data Structures and Algorithms

Authors: Jingyang Zhao, Mingyu Xiao

We study the vehicle routing problem (VRP) with stochastic demands, where each client has an independent demand with a known distribution, and its realized demand is observed only on the first visit. We consider a priori and restocking policies that fix the client order before observing demands and serve each client's entire demand before first visiting the next. Gupta, Nagarajan, and Ravi (Oper. Res., 2012) developed randomized a priori $(α+1)$-approximation and $(α+2)$-approximation algorithms for splittable and unsplittable deliveries, respectively, where $α$ is the approximation ratio of a metric traveling salesman problem (TSP) algorithm. We improve these approximation ratios to $2$ and $3$ by using their replenishment rules with a TSP tour constructed via matroid parity. We also obtain deterministic a priori $(2+\varepsilon)$-approximation and $(3+\varepsilon)$-approximation algorithms by compressing the distributions of cumulative demands to select an initial stock in polynomial time. All guarantees are relative to the general adaptive optimum. We then establish matching lower bounds of $2$ and $3$ that hold even for the more general class of randomized restocking policies. Finally, we apply our matroid-parity construction to improve approximation ratios for several VRP variants with deterministic demands.

Authors: Jingyang Zhao, Mingyu Xiao

We study the vehicle routing problem (VRP) with stochastic demands, where each client has an independent demand with a known distribution, and its realized demand is observed only on the first visit. We consider a priori and restocking policies that fix the client order before observing demands and serve each client's entire demand before first visiting the next. Gupta, Nagarajan, and Ravi (Oper. Res., 2012) developed randomized a priori $(α+1)$-approximation and $(α+2)$-approximation algorithms for splittable and unsplittable deliveries, respectively, where $α$ is the approximation ratio of a metric traveling salesman problem (TSP) algorithm. We improve these approximation ratios to $2$ and $3$ by using their replenishment rules with a TSP tour constructed via matroid parity. We also obtain deterministic a priori $(2+\varepsilon)$-approximation and $(3+\varepsilon)$-approximation algorithms by compressing the distributions of cumulative demands to select an initial stock in polynomial time. All guarantees are relative to the general adaptive optimum. We then establish matching lower bounds of $2$ and $3$ that hold even for the more general class of randomized restocking policies. Finally, we apply our matroid-parity construction to improve approximation ratios for several VRP variants with deterministic demands.

Improved Massively Parallel Shortest Path Computation

from arXiv: Data Structures and Algorithms

Authors: Yi-Jun Chang, Shaun Quek

We present new algorithms for approximate shortest path computation in weighted undirected graphs within the Massively Parallel Computation (MPC) model. Specifically, we construct an $a$-approximate Single-Source Shortest Path (SSSP) oracle: Given a query consisting of a source vertex $u \in V$, the oracle returns $a$-approximate distances $δ(u,v)$ for all $v \in V$. Fix an $n$-vertex weighted undirected graph. We consider two regimes of the MPC model: the near-linear regime, where each machine has $\tildeΘ(n)$ memory, and the heterogeneous regime, where all machines have $Θ(n^γ)$ memory for some constant $γ\in (0,1]$, except for a single machine with $\tildeΘ(n)$ memory. We develop randomised algorithms that construct $O(1)$-approximate SSSP oracles in $O(\log\log n)$ rounds in the near-linear regime and in $O(\log^2\log n)$ rounds in the heterogeneous regime. These oracles answer distance queries in $O(1)$ rounds. Our results improve upon the recent work of Dory and Matar (SPAA 2024) which, for the same total memory, requires $\left(\log\log n\right)^{2+O(1/γ)}$ rounds and applies only to unweighted graphs in the heterogeneous setting. We say that $\tilde N_k(u)$ is the approximate $k$-nearest neighbourhood of a vertex $u$ if there exists an approximate distance estimate $δ$ such that $\tilde N_k(u)$ consists of the $k$ vertices closest to $u$ according to $δ$, breaking ties by vertex ID. The main technical ingredient is a new method for efficiently computing approximate $n^{O(γ)}$-nearest neighbourhoods in the heterogeneous MPC model in $O(\log^2\log n)$ rounds.

Authors: Yi-Jun Chang, Shaun Quek

We present new algorithms for approximate shortest path computation in weighted undirected graphs within the Massively Parallel Computation (MPC) model. Specifically, we construct an $a$-approximate Single-Source Shortest Path (SSSP) oracle: Given a query consisting of a source vertex $u \in V$, the oracle returns $a$-approximate distances $δ(u,v)$ for all $v \in V$. Fix an $n$-vertex weighted undirected graph. We consider two regimes of the MPC model: the near-linear regime, where each machine has $\tildeΘ(n)$ memory, and the heterogeneous regime, where all machines have $Θ(n^γ)$ memory for some constant $γ\in (0,1]$, except for a single machine with $\tildeΘ(n)$ memory. We develop randomised algorithms that construct $O(1)$-approximate SSSP oracles in $O(\log\log n)$ rounds in the near-linear regime and in $O(\log^2\log n)$ rounds in the heterogeneous regime. These oracles answer distance queries in $O(1)$ rounds. Our results improve upon the recent work of Dory and Matar (SPAA 2024) which, for the same total memory, requires $\left(\log\log n\right)^{2+O(1/γ)}$ rounds and applies only to unweighted graphs in the heterogeneous setting. We say that $\tilde N_k(u)$ is the approximate $k$-nearest neighbourhood of a vertex $u$ if there exists an approximate distance estimate $δ$ such that $\tilde N_k(u)$ consists of the $k$ vertices closest to $u$ according to $δ$, breaking ties by vertex ID. The main technical ingredient is a new method for efficiently computing approximate $n^{O(γ)}$-nearest neighbourhoods in the heterogeneous MPC model in $O(\log^2\log n)$ rounds.

A Deterministic $0.401$-Approximation for Non-monotone Submodular Maximization under Matroid Constraints

from arXiv: Data Structures and Algorithms

Authors: Shengminjie Chen, Kaifeng Lin, Xiaoming Sun, Jialin Zhang

Submodular maximization arises in a wide range of combinatorial optimization problems and has been extensively studied from the perspective of approximation algorithms. For maximizing nonnegative, non-monotone submodular functions subject to a matroid constraint, randomized algorithms achieve a $0.401$-approximation, whereas obtaining comparable deterministic guarantees remains challenging. In this paper, we present a deterministic $0.401$-approximation algorithm in the exact value and independence oracle model, improving upon the previous deterministic guarantee of $(0.385-\varepsilon)$ \cite{ChenGLSZ2026}. Our algorithm builds on the extended multilinear extension and provides a deterministic implementation of the recursive guided framework underlying the randomized $0.401$-approximation algorithm. The main technical ingredient is a direction oracle supported on at most two partitioned matroid bases. This oracle simultaneously satisfies the growth condition required by the extended-multilinear update and an approximate stationarity condition needed to construct the recursive guides. The resulting finite-support representation enables exact evaluation of the relevant expectations and marginal values without random sampling. The algorithm uses polynomially many value- and independence-oracle queries.

Authors: Shengminjie Chen, Kaifeng Lin, Xiaoming Sun, Jialin Zhang

Submodular maximization arises in a wide range of combinatorial optimization problems and has been extensively studied from the perspective of approximation algorithms. For maximizing nonnegative, non-monotone submodular functions subject to a matroid constraint, randomized algorithms achieve a $0.401$-approximation, whereas obtaining comparable deterministic guarantees remains challenging. In this paper, we present a deterministic $0.401$-approximation algorithm in the exact value and independence oracle model, improving upon the previous deterministic guarantee of $(0.385-\varepsilon)$ \cite{ChenGLSZ2026}. Our algorithm builds on the extended multilinear extension and provides a deterministic implementation of the recursive guided framework underlying the randomized $0.401$-approximation algorithm. The main technical ingredient is a direction oracle supported on at most two partitioned matroid bases. This oracle simultaneously satisfies the growth condition required by the extended-multilinear update and an approximate stationarity condition needed to construct the recursive guides. The resulting finite-support representation enables exact evaluation of the relevant expectations and marginal values without random sampling. The algorithm uses polynomially many value- and independence-oracle queries.

The Online Steinitz Problem

from arXiv: Data Structures and Algorithms

Authors: Matteo Russo, Jiaye Wei

Inspired by Steinitz's celebrated result (1913) and recent online vector-balancing results of Kulkarni et al. (2024) and Aden-Ali (2026), we introduce the online Steinitz problem: $T$ vectors in $\mathbb R^d$, each of $\ell_2$-norm at most $1$ and with total sum zero, arrive one at a time and must be assigned immediately and irrevocably to unused positions of an array. The goal is to keep every prefix sum of the resulting order small. We first obtain deterministic prefix-discrepancy bounds of $O(\log T)$ for $d=1$ and $O(\sqrt T)$ for $d\ge2$. Our main contribution is a general reduction that converts deterministic and randomized online vector-balancing algorithms, which assign signs to arriving vectors, into online ordering algorithms. Combined with recent vector-balancing results, this yields a randomized algorithm with prefix discrepancy $O(\log T+\sqrt{d\log T})$ and running time $O(dT\log T)$. The guarantee holds with high probability against an oblivious adversary. The reduction also gives bounds for sparse vectors and extends to inputs in arbitrary compact convex bodies. Finally, we prove matching lower bounds in both the deterministic and randomized cases.

Authors: Matteo Russo, Jiaye Wei

Inspired by Steinitz's celebrated result (1913) and recent online vector-balancing results of Kulkarni et al. (2024) and Aden-Ali (2026), we introduce the online Steinitz problem: $T$ vectors in $\mathbb R^d$, each of $\ell_2$-norm at most $1$ and with total sum zero, arrive one at a time and must be assigned immediately and irrevocably to unused positions of an array. The goal is to keep every prefix sum of the resulting order small. We first obtain deterministic prefix-discrepancy bounds of $O(\log T)$ for $d=1$ and $O(\sqrt T)$ for $d\ge2$. Our main contribution is a general reduction that converts deterministic and randomized online vector-balancing algorithms, which assign signs to arriving vectors, into online ordering algorithms. Combined with recent vector-balancing results, this yields a randomized algorithm with prefix discrepancy $O(\log T+\sqrt{d\log T})$ and running time $O(dT\log T)$. The guarantee holds with high probability against an oblivious adversary. The reduction also gives bounds for sparse vectors and extends to inputs in arbitrary compact convex bodies. Finally, we prove matching lower bounds in both the deterministic and randomized cases.

Averaged Annealed Markov Chains Achieve the Algorithmic Threshold for Tensor PCA, Part I: Glauber Dynamics

from arXiv: Data Structures and Algorithms

Authors: Aniket Das, Tselil Schramm

In the tensor principal components analysis (tensor PCA) problem, we are asked to recover an unknown vector in $\{\pm1\}^N$ from a noisy observation of its $k$-th tensor power, for a fixed $k\ge3$. Tensor PCA exhibits a well-studied information-computation gap: global algorithms such as spectral methods and sum-of-squares work above an algorithmic threshold, below which we know matching lower bounds (against these and some other restricted models of computation). In contrast, local algorithms such as Markov chains were only known to succeed from a random initialization at a polynomially larger signal-to-noise ratio, and were believed to be inferior to global algorithms for tensor PCA. We show that Glauber dynamics, when supplemented with annealing and ergodic averaging, achieves polynomial-time strong recovery above the algorithmic threshold from a uniformly random initialization, demonstrating that local Markov chains can succeed all the way down to the algorithmic threshold from an uninformative cold start.

Authors: Aniket Das, Tselil Schramm

In the tensor principal components analysis (tensor PCA) problem, we are asked to recover an unknown vector in $\{\pm1\}^N$ from a noisy observation of its $k$-th tensor power, for a fixed $k\ge3$. Tensor PCA exhibits a well-studied information-computation gap: global algorithms such as spectral methods and sum-of-squares work above an algorithmic threshold, below which we know matching lower bounds (against these and some other restricted models of computation). In contrast, local algorithms such as Markov chains were only known to succeed from a random initialization at a polynomially larger signal-to-noise ratio, and were believed to be inferior to global algorithms for tensor PCA. We show that Glauber dynamics, when supplemented with annealing and ergodic averaging, achieves polynomial-time strong recovery above the algorithmic threshold from a uniformly random initialization, demonstrating that local Markov chains can succeed all the way down to the algorithmic threshold from an uninformative cold start.

Graph Matching in Random Geometric Graphs I: Efficient Algorithms

from arXiv: Data Structures and Algorithms

Authors: Dong Huang, Ankang Jiao, Shuangping Li, Zhangsong Li

We study the recovery of the hidden vertex correspondence between two correlated dense random geometric graphs. Each graph is generated from $n$ latent unit vectors in $\mathbb R^d$; the vectors $u_i^A$ and $u_i^B$ of corresponding vertices satisfy $\langle u_i^A,u_i^B\rangle=ρ$, and the vertex labels of the second graph are permuted by an unknown permutation $π$. For every fixed correlation $ρ\in(0,1]$ and every edge density bounded away from zero and one, we give a randomized polynomial time algorithm that recovers $π$ exactly with probability tending to one whenever $d\ge(\log n)^L$, where $L$ depends only on the correlation and the density bounds. The algorithm assigns to each vertex a signature formed by counting decorated paths with a constant number of seeds. The number of such seeded templates grows exponentially with the path length, with an arbitrarily large constant base, so the resulting statistics amplify an arbitrarily small constant correlation. We control the dependencies created by the latent geometry in two ways: when $d$ is larger than $n/(\log n)^{L_0}$ for a suitable constant $L_0$, by comparing local moments with those of correlated Erdős--Rényi graphs; for smaller $d$, by spectral estimation of the latent geometry combined with a harmonic correction. We also provide low-degree evidence for a tradeoff between the correlation and the computational cost.

Authors: Dong Huang, Ankang Jiao, Shuangping Li, Zhangsong Li

We study the recovery of the hidden vertex correspondence between two correlated dense random geometric graphs. Each graph is generated from $n$ latent unit vectors in $\mathbb R^d$; the vectors $u_i^A$ and $u_i^B$ of corresponding vertices satisfy $\langle u_i^A,u_i^B\rangle=ρ$, and the vertex labels of the second graph are permuted by an unknown permutation $π$. For every fixed correlation $ρ\in(0,1]$ and every edge density bounded away from zero and one, we give a randomized polynomial time algorithm that recovers $π$ exactly with probability tending to one whenever $d\ge(\log n)^L$, where $L$ depends only on the correlation and the density bounds. The algorithm assigns to each vertex a signature formed by counting decorated paths with a constant number of seeds. The number of such seeded templates grows exponentially with the path length, with an arbitrarily large constant base, so the resulting statistics amplify an arbitrarily small constant correlation. We control the dependencies created by the latent geometry in two ways: when $d$ is larger than $n/(\log n)^{L_0}$ for a suitable constant $L_0$, by comparing local moments with those of correlated Erdős--Rényi graphs; for smaller $d$, by spectral estimation of the latent geometry combined with a harmonic correction. We also provide low-degree evidence for a tradeoff between the correlation and the computational cost.

Dynamic Maximal Independent Set using Predictions

from arXiv: Data Structures and Algorithms

Authors: Shahbaz Khan, Anshul Kumar

We present the first dynamic maximal independent set (MIS) of a graph with $n$ vertices and up to $m$ edges under edge updates in the prediction model, where a predicted update sequence is available for offline preprocessing before the real updates arrive online. We consider the incremental (insertion-only), decremental (deletions-only), and fully dynamic (insertions and deletions) models. Without predictions, deterministic algorithms need worst-case update time of $O(n)$ for decremental and $O(m)$ for incremental and fully dynamic settings. Bernstein et al.~[SODA26] proved under the combinatorial BMM conjecture, combinatorial incremental algorithms against an adaptive adversary require $Ω(n^{1-o(1)})$ amortized update time. We present deterministic incremental, decremental, and fully dynamic algorithms whose update times depend on the prediction error. This error is measured by the number $η_e$ of mispredicted updates, and by the number $η_v$ and total degree $η_v^*$ of vertices whose MIS membership was mispredicted. With $O(mn)$ preprocessing, our decremental and incremental algorithms achieve worst-case update times $O(η_v)$ and $O(η_e+η_v^*)$, respectively. Our fully dynamic algorithm achieves $O(η_e+η_v^*)$ worst-case update time with $O(un)$ preprocessing for $u$ updates. We also give space-efficient variants using $O(m)$ space for the decremental and $O(m\sqrt{m})$ for the incremental setting, at the cost of a logarithmic overhead in worst-case update time. Our algorithms satisfy consistency, robustness, and smooth degradation, the required properties of algorithms with predictions. They can also utilize the general error correction framework of Khan et al.~[Arxiv26], relaxing the error measures from first point of difference to prefix matching (for partially dynamic) and set edit distance (for fully dynamic), maintaining the same bounds.

Authors: Shahbaz Khan, Anshul Kumar

We present the first dynamic maximal independent set (MIS) of a graph with $n$ vertices and up to $m$ edges under edge updates in the prediction model, where a predicted update sequence is available for offline preprocessing before the real updates arrive online. We consider the incremental (insertion-only), decremental (deletions-only), and fully dynamic (insertions and deletions) models. Without predictions, deterministic algorithms need worst-case update time of $O(n)$ for decremental and $O(m)$ for incremental and fully dynamic settings. Bernstein et al.~[SODA26] proved under the combinatorial BMM conjecture, combinatorial incremental algorithms against an adaptive adversary require $Ω(n^{1-o(1)})$ amortized update time. We present deterministic incremental, decremental, and fully dynamic algorithms whose update times depend on the prediction error. This error is measured by the number $η_e$ of mispredicted updates, and by the number $η_v$ and total degree $η_v^*$ of vertices whose MIS membership was mispredicted. With $O(mn)$ preprocessing, our decremental and incremental algorithms achieve worst-case update times $O(η_v)$ and $O(η_e+η_v^*)$, respectively. Our fully dynamic algorithm achieves $O(η_e+η_v^*)$ worst-case update time with $O(un)$ preprocessing for $u$ updates. We also give space-efficient variants using $O(m)$ space for the decremental and $O(m\sqrt{m})$ for the incremental setting, at the cost of a logarithmic overhead in worst-case update time. Our algorithms satisfy consistency, robustness, and smooth degradation, the required properties of algorithms with predictions. They can also utilize the general error correction framework of Khan et al.~[Arxiv26], relaxing the error measures from first point of difference to prefix matching (for partially dynamic) and set edit distance (for fully dynamic), maintaining the same bounds.

Distributed $(Δ+1)$-Coloring in $O(\sqrtΔ+\log^* n)$ Rounds

from arXiv: Data Structures and Algorithms

Authors: Marc Fuchs, Fabian Kuhn

We give a deterministic distributed algorithm for $(Δ+1)$-coloring an $n$-node graph of maximum degree $Δ$ in $O(\sqrtΔ+\log^* n)$ rounds in both the LOCAL and CONGEST models. More generally, $(°+1)$-list coloring with lists from a color space of size $C\geqΔ+1$ can be solved in $O(\sqrtΔ\log(1+C/Δ)+\log^* n)$ rounds; in CONGEST, we assume $C\leq n^{O(1)}$. The algorithm combines Barenboim's polynomial coloring procedure with arbdefective decompositions, degree reduction, and a short family of hash functions. Using the polynomial procedure in the degree-reduction framework already gives an $O(\sqrtΔ\logΔ+\log^* n)$-round algorithm when $C=O(Δ)$. Hashing adapts the color space to the current degree and removes the logarithmic factor. We prove the existence of the hash family by the probabilistic method and compute it by deterministic exhaustive search, using unrestricted local computation. We also give an explicit variant based on lossless expanders that avoids this search and achieves $O(\sqrtΔ+\log^* n)$ rounds for $C=O(Δ)$.

Authors: Marc Fuchs, Fabian Kuhn

We give a deterministic distributed algorithm for $(Δ+1)$-coloring an $n$-node graph of maximum degree $Δ$ in $O(\sqrtΔ+\log^* n)$ rounds in both the LOCAL and CONGEST models. More generally, $(°+1)$-list coloring with lists from a color space of size $C\geqΔ+1$ can be solved in $O(\sqrtΔ\log(1+C/Δ)+\log^* n)$ rounds; in CONGEST, we assume $C\leq n^{O(1)}$. The algorithm combines Barenboim's polynomial coloring procedure with arbdefective decompositions, degree reduction, and a short family of hash functions. Using the polynomial procedure in the degree-reduction framework already gives an $O(\sqrtΔ\logΔ+\log^* n)$-round algorithm when $C=O(Δ)$. Hashing adapts the color space to the current degree and removes the logarithmic factor. We prove the existence of the hash family by the probabilistic method and compute it by deterministic exhaustive search, using unrestricted local computation. We also give an explicit variant based on lossless expanders that avoids this search and achieves $O(\sqrtΔ+\log^* n)$ rounds for $C=O(Δ)$.

Ramsey Meets Simultaneous Planarity: Vertex Integrity and Pathwidth

from arXiv: Data Structures and Algorithms

Authors: Thomas Depian, Simon D. Fink, Alexander Firbas, Robert Ganian, Matthias Pfretzschner, Ignaz Rutter

We study the following simultaneous embedding problem: Given k graphs on the same vertex set with the same pairwise intersection, do they admit planar drawings that coincide on their shared graph? We establish fixed-parameter tractability with respect to the vertex integrity of the union graph (that is, the union of the input graphs) plus k, resolving an open question and generalizing the known algorithm parameterized by vertex cover number plus k. Our main technical contribution on the way towards this result is the extension of the recently introduced Ramsey pruning technique beyond ordering problems. Finally, we complement our tractability result with a lower bound that establishes NP-hardness when both k and the pathwidth of the union graph are bounded by constants, ruling out a generalization of our main result to path- or treewidth.

Authors: Thomas Depian, Simon D. Fink, Alexander Firbas, Robert Ganian, Matthias Pfretzschner, Ignaz Rutter

We study the following simultaneous embedding problem: Given k graphs on the same vertex set with the same pairwise intersection, do they admit planar drawings that coincide on their shared graph? We establish fixed-parameter tractability with respect to the vertex integrity of the union graph (that is, the union of the input graphs) plus k, resolving an open question and generalizing the known algorithm parameterized by vertex cover number plus k. Our main technical contribution on the way towards this result is the extension of the recently introduced Ramsey pruning technique beyond ordering problems. Finally, we complement our tractability result with a lower bound that establishes NP-hardness when both k and the pathwidth of the union graph are bounded by constants, ruling out a generalization of our main result to path- or treewidth.

Fast Quantized Johnson$-$Lindenstrauss Transforms

from arXiv: Data Structures and Algorithms

Authors: Angelos Korakitis, Vasileios Nakos

Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent example is Quantized Johnson$-$Lindenstrauss (QJL), which compresses KV caches using 1-bit asymmetric quantization while approximately preserving the inner products required by the attention mechanism. Recent theoretical and practical work aims at replacing the dense Gaussian projections used by the aforementioned transforms with more structured embeddings $-$ such as compositions of randomized Hadamard transforms $-$ while retaining the same embedding dimension. In this paper, we introduce two constructions based on a composition of randomized Hadamard transforms, albeit with a Gaussian rather than Rademacher diagonal in the second stage. The first adds Gaussian dithering to smooth the quantizer, while the second inserts a random permutation to regularize the covariance structure of the resulting Gaussian measurements. We prove embedding-dimension and running-time guarantees analogous to those of Fast JL in the non-quantized setting. The first construction achieves $m=O(\varepsilon^{-2}\log^2(d/δ))$, while the second achieves $m=O(\varepsilon^{-2}\log(d/δ))$ under a restriction on $m$. Both constructions can be applied in $O(d\log d)$ time.

Authors: Angelos Korakitis, Vasileios Nakos

Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent example is Quantized Johnson$-$Lindenstrauss (QJL), which compresses KV caches using 1-bit asymmetric quantization while approximately preserving the inner products required by the attention mechanism. Recent theoretical and practical work aims at replacing the dense Gaussian projections used by the aforementioned transforms with more structured embeddings $-$ such as compositions of randomized Hadamard transforms $-$ while retaining the same embedding dimension. In this paper, we introduce two constructions based on a composition of randomized Hadamard transforms, albeit with a Gaussian rather than Rademacher diagonal in the second stage. The first adds Gaussian dithering to smooth the quantizer, while the second inserts a random permutation to regularize the covariance structure of the resulting Gaussian measurements. We prove embedding-dimension and running-time guarantees analogous to those of Fast JL in the non-quantized setting. The first construction achieves $m=O(\varepsilon^{-2}\log^2(d/δ))$, while the second achieves $m=O(\varepsilon^{-2}\log(d/δ))$ under a restriction on $m$. Both constructions can be applied in $O(d\log d)$ time.

Hashrope: A Persistent Data Structure for Mechanisms of LLM Context Management

from arXiv: Data Structures and Algorithms

Authors: Muntaser Syed, Ameerah Alsulami, Marius Silaghi

Serving stacks for large language models manage context through four recurring mechanisms: incremental edits to a growing context, branching and snapshotting for tree search, identification of reusable prefixes for KV-cache reuse, and encoding of repeated segments. Each mechanism is served today by a different specialist structure. We show that all four reduce to a single persistent weight-balanced rope whose every node carries a polynomial hash of its subtree over a Mersenne prime, so that the structure supports logarithmic edits and logarithmic verified content-identity queries at the same time. We validate each mechanism with pre-registered, replicated experiments, then compare against a deployed specialist per mechanism: the Ropey editing library, a faithful PagedAttention copy-on-write block table, and the vendored SGLang radix matcher. On workloads that interleave edits with identity queries, Hashrope is net-superior to Ropey by a factor that grows without bound (369.7x at one million characters). Against the radix matcher on a live vLLM stack serving Qwen2.5-7B-Instruct-1M on four A100 GPUs, Hashrope identifies a one-million-token shared prefix exactly and 1.20x faster, at a context length the dense engine cannot even prefill.

Authors: Muntaser Syed, Ameerah Alsulami, Marius Silaghi

Serving stacks for large language models manage context through four recurring mechanisms: incremental edits to a growing context, branching and snapshotting for tree search, identification of reusable prefixes for KV-cache reuse, and encoding of repeated segments. Each mechanism is served today by a different specialist structure. We show that all four reduce to a single persistent weight-balanced rope whose every node carries a polynomial hash of its subtree over a Mersenne prime, so that the structure supports logarithmic edits and logarithmic verified content-identity queries at the same time. We validate each mechanism with pre-registered, replicated experiments, then compare against a deployed specialist per mechanism: the Ropey editing library, a faithful PagedAttention copy-on-write block table, and the vendored SGLang radix matcher. On workloads that interleave edits with identity queries, Hashrope is net-superior to Ropey by a factor that grows without bound (369.7x at one million characters). Against the radix matcher on a live vLLM stack serving Qwen2.5-7B-Instruct-1M on four A100 GPUs, Hashrope identifies a one-million-token shared prefix exactly and 1.20x faster, at a context length the dense engine cannot even prefill.

Approximation algorithms via discrepancy with hard constraints

from arXiv: Data Structures and Algorithms

Authors: Mikhail Cherniavskii, Andrey Kupavskii, Stanislav Moiseev, Alexander Shekhovtsov

In this paper, we present a discrepancy-based approach to cost-preserving rounding under hard constraints. Starting from a feasible fractional solution, our rounding procedure preserves the designated hard constraints, never increases the linear objective, and bounds the error in the remaining constraints by the discrepancy of a suitable column-difference matrix. Our second discrepancy-theoretic result bounds the discrepancy of a matrix in terms of a novel column parameter, called mountain distance. This notion captures ordered structural features of a matrix which are invisible to the classical Beck--Fiala and Komlós column-norm parameters. We show a variety of applications of these two results. In particular, we obtain new results for min-cost unsplittable flows, bounded-turn routing in grids and tori, rainbow prefix hypergraph decomposition, and $2$-multicast scheduling.

Authors: Mikhail Cherniavskii, Andrey Kupavskii, Stanislav Moiseev, Alexander Shekhovtsov

In this paper, we present a discrepancy-based approach to cost-preserving rounding under hard constraints. Starting from a feasible fractional solution, our rounding procedure preserves the designated hard constraints, never increases the linear objective, and bounds the error in the remaining constraints by the discrepancy of a suitable column-difference matrix. Our second discrepancy-theoretic result bounds the discrepancy of a matrix in terms of a novel column parameter, called mountain distance. This notion captures ordered structural features of a matrix which are invisible to the classical Beck--Fiala and Komlós column-norm parameters. We show a variety of applications of these two results. In particular, we obtain new results for min-cost unsplittable flows, bounded-turn routing in grids and tori, rainbow prefix hypergraph decomposition, and $2$-multicast scheduling.

Improved Approximations for Coflow Scheduling via Iterated Rounding and Stable Matchings

from arXiv: Data Structures and Algorithms

Authors: Mikhail Cherniavskii, Arseni Fedorovich, Mikhail Koshelev, Andrey Kupavskii, Stanislav Moiseev, Fedor Ozhegov, Alexander Shekhovtsov

We study Coflow Scheduling, introduced by Chowdhury, Zhong, and Stoica (2014). We give deterministic polynomial-time algorithms achieving a $2.77$-approximation without release dates and a $3.17$-approximation with integer release dates. These improve the $3.42$ and $4.36$ guarantees of Rohwedder and Schnaars (2025). Our approach combines two ideas: (1) a novel iterated rounding scheme, and (2) Galvin's list edge coloring method, which is based on the Gale--Shapley stable matching algorithm.

Authors: Mikhail Cherniavskii, Arseni Fedorovich, Mikhail Koshelev, Andrey Kupavskii, Stanislav Moiseev, Fedor Ozhegov, Alexander Shekhovtsov

We study Coflow Scheduling, introduced by Chowdhury, Zhong, and Stoica (2014). We give deterministic polynomial-time algorithms achieving a $2.77$-approximation without release dates and a $3.17$-approximation with integer release dates. These improve the $3.42$ and $4.36$ guarantees of Rohwedder and Schnaars (2025). Our approach combines two ideas: (1) a novel iterated rounding scheme, and (2) Galvin's list edge coloring method, which is based on the Gale--Shapley stable matching algorithm.

Online Submodular Welfare Maximization: Computation is not the Bottleneck

from arXiv: Data Structures and Algorithms

Authors: Rajan Udwani, David Wajc

We revisit the online submodular welfare maximization problem (OSW), a vast generalization of numerous well-studied online allocation problems, including online bipartite matching, display ads and AdWords. A natural greedy OSW algorithm achieves a competitive ratio of $1/2$ [LLN06], and $1-1/e$ under i.i.d. arrivals [KPV13]. Both bounds are tight for polynomial-time algorithms unless NP=RP [KPV13]. The information-theoretic limits for OSW, in contrast, remained open. This prompted researchers to ask for tighter upper bounds [Mehta13] or conjecture the existence of better-competitive (slow) algorithms [Huang23, LTX+23, CHS24, Huang2024, HTW24}. We resolve Mehta's question and refute the subsequent conjectures, proving that the greedy algorithm is in fact tight for both adversarial and i.i.d. arrivals, regardless of computation time. Seemingly contradicting recent $(1-1/e)$-competitive fractional algorithms for OSW [HJP+24,Patton26], we show that this impossibility even holds for fractional algorithms, provided their outputs correspond to marginals that are roundable in hindsight. Conceptually, our proofs can be seen as identifying that a bottleneck for OSW, even under (deterministic) adversarial arrivals, is the inherently random notion of correlation gap [ADSY12].

Authors: Rajan Udwani, David Wajc

We revisit the online submodular welfare maximization problem (OSW), a vast generalization of numerous well-studied online allocation problems, including online bipartite matching, display ads and AdWords. A natural greedy OSW algorithm achieves a competitive ratio of $1/2$ [LLN06], and $1-1/e$ under i.i.d. arrivals [KPV13]. Both bounds are tight for polynomial-time algorithms unless NP=RP [KPV13]. The information-theoretic limits for OSW, in contrast, remained open. This prompted researchers to ask for tighter upper bounds [Mehta13] or conjecture the existence of better-competitive (slow) algorithms [Huang23, LTX+23, CHS24, Huang2024, HTW24}. We resolve Mehta's question and refute the subsequent conjectures, proving that the greedy algorithm is in fact tight for both adversarial and i.i.d. arrivals, regardless of computation time. Seemingly contradicting recent $(1-1/e)$-competitive fractional algorithms for OSW [HJP+24,Patton26], we show that this impossibility even holds for fractional algorithms, provided their outputs correspond to marginals that are roundable in hindsight. Conceptually, our proofs can be seen as identifying that a bottleneck for OSW, even under (deterministic) adversarial arrivals, is the inherently random notion of correlation gap [ADSY12].

Stochastic Weighted Caching with Per-Request Predictions

from arXiv: Data Structures and Algorithms

Authors: Shuchi Chawla, Dimitris Christou

Both the unweighted and weighted variants of caching are cornerstone problems in competitive analysis, with tight randomized competitive ratios of $Θ(\log k)$, where $k$ is the cache size. We study weighted caching with per-request predictions, where each page request is accompanied by a prediction of the next arrival time of the same page. For unweighted caching, such perfect predictions suffice to implement Belady's offline optimal rule. For weighted caching, however, previous work shows that a logarithmic lower bound persists even with perfect predictions on adversarial request sequences. Stochastic assumptions alone are also insufficient: the classical caching lower bounds already apply to uniform independent requests. In this work, we ask whether combining these two sources of additional information can overcome this barrier, and answer this question affirmatively. Specifically, we develop an efficient, deterministic cache eviction policy that achieves a competitive ratio of $15.75$ using perfect per-request predictions when page requests are drawn independently from an arbitrary, known static distribution in every step, and page costs are arbitrary. Our algorithm's performance degrades gracefully under erroneous predictions: we prove a bound of $O(\min\{1+E,\log k\})$ on the competitive ratio, where $E$ aggregates each page's average prediction error, and may be unknown to the algorithm.

Authors: Shuchi Chawla, Dimitris Christou

Both the unweighted and weighted variants of caching are cornerstone problems in competitive analysis, with tight randomized competitive ratios of $Θ(\log k)$, where $k$ is the cache size. We study weighted caching with per-request predictions, where each page request is accompanied by a prediction of the next arrival time of the same page. For unweighted caching, such perfect predictions suffice to implement Belady's offline optimal rule. For weighted caching, however, previous work shows that a logarithmic lower bound persists even with perfect predictions on adversarial request sequences. Stochastic assumptions alone are also insufficient: the classical caching lower bounds already apply to uniform independent requests. In this work, we ask whether combining these two sources of additional information can overcome this barrier, and answer this question affirmatively. Specifically, we develop an efficient, deterministic cache eviction policy that achieves a competitive ratio of $15.75$ using perfect per-request predictions when page requests are drawn independently from an arbitrary, known static distribution in every step, and page costs are arbitrary. Our algorithm's performance degrades gracefully under erroneous predictions: we prove a bound of $O(\min\{1+E,\log k\})$ on the competitive ratio, where $E$ aggregates each page's average prediction error, and may be unknown to the algorithm.

QPTAS for Max Weight Independent Set in Graphs with Strongly Edge-Sublinear Separators

from arXiv: Data Structures and Algorithms

Authors: Édouard Bonnet, Yeonsu Chang

A hereditary graph class has strongly edge-sublinear separators if, for some constant $0 \leqslant b < 1$, every graph in the class with $m$ edges admits a balanced separator of size $\mathcal O(m^b)$. We show that Max Weight Independent Set (MWIS) admits a deterministic quasipolynomial-time approximation scheme (QPTAS) on every such class. In particular, this applies to graphs excluding any fixed graph as an induced minor, by a separator theorem of Korhonen and Lokshtanov [SODA 2024]. Our theorem generalizes the QPTASes for axis-parallel rectangles [Adamaszek and Wiese, FOCS 2013] and polygons [Adamaszek, Har-Peled, and Wiese, JACM 2019], or equivalently for packing connected subgraphs of a planar graph [Pilipczuk, van Leeuwen, and Wiese, Algorithmica 2020]. It also generalizes the QPTAS for MWIS on hereditary classes with strongly sublinear separators measured in vertices [Dvořák, EJC 2018; Dvořák and Lahiri, ESA 2021], as well as the QPTASes for graphs excluding particular induced minors [Chudnovsky et al., arXiv 2025; Bonnet et al., SWAT 2026; Czyżewska et al., arXiv 2026].

Authors: Édouard Bonnet, Yeonsu Chang

A hereditary graph class has strongly edge-sublinear separators if, for some constant $0 \leqslant b < 1$, every graph in the class with $m$ edges admits a balanced separator of size $\mathcal O(m^b)$. We show that Max Weight Independent Set (MWIS) admits a deterministic quasipolynomial-time approximation scheme (QPTAS) on every such class. In particular, this applies to graphs excluding any fixed graph as an induced minor, by a separator theorem of Korhonen and Lokshtanov [SODA 2024]. Our theorem generalizes the QPTASes for axis-parallel rectangles [Adamaszek and Wiese, FOCS 2013] and polygons [Adamaszek, Har-Peled, and Wiese, JACM 2019], or equivalently for packing connected subgraphs of a planar graph [Pilipczuk, van Leeuwen, and Wiese, Algorithmica 2020]. It also generalizes the QPTAS for MWIS on hereditary classes with strongly sublinear separators measured in vertices [Dvořák, EJC 2018; Dvořák and Lahiri, ESA 2021], as well as the QPTASes for graphs excluding particular induced minors [Chudnovsky et al., arXiv 2025; Bonnet et al., SWAT 2026; Czyżewska et al., arXiv 2026].

Online Batching with Lookahead: Adversarial Limits and Stochastic Gains

from arXiv: Data Structures and Algorithms

Authors: Dimitris Christou, Jiayi Chen, Nihal Sharma, Aditya Akella, Shuchi Chawla, Sanjay Shakkottai

Modern inference systems rely on batching to exploit parallel execution and amortize serial execution overheads, but batching creates a fundamental online scheduling tradeoff: serving requests immediately reduces request queuing delay, while waiting can form larger batches that better utilize parallel hardware and improve serving efficiency. We study this tradeoff in a single-server online batching model with bounded batch capacity, fixed batch processing time, and the objective of minimizing total flow time. Our focus is on the value of finite lookahead into future arrivals over a purely online setting for scheduling. We show that the benefits of lookahead depend on the arrival model. In the adversarial setting, finite perfect lookahead has inherent limitations: for any bounded lookahead window, every online algorithm remains separated from the offline optimum by a constant factor. Thus, worst-case arrival sequences can prevent perfect knowledge of future events from fully overcoming online uncertainty. In contrast, under Poisson arrivals in the stable regime, finite lookahead is much more powerful. Although the best distribution-aware online algorithm has a nonzero asymptotic gap from the offline optimum, lookahead closes this gap exponentially fast in the window length. We prove matching upper and lower bounds up to constants in the exponent. Together, these results give a tight characterization of when lookahead can close the online--offline gap, incorporating insights from the competitive analysis of online algorithms into queueing-theoretic steady-state bounds. We also study imperfect predictions, showing that the benefit of lookahead degrades gracefully with prediction error. Simulations on synthetic arrivals and experiments on real request traces support the theory: modest lookahead substantially reduces flow time and brings online batching close to the offline benchmark.

Authors: Dimitris Christou, Jiayi Chen, Nihal Sharma, Aditya Akella, Shuchi Chawla, Sanjay Shakkottai

Modern inference systems rely on batching to exploit parallel execution and amortize serial execution overheads, but batching creates a fundamental online scheduling tradeoff: serving requests immediately reduces request queuing delay, while waiting can form larger batches that better utilize parallel hardware and improve serving efficiency. We study this tradeoff in a single-server online batching model with bounded batch capacity, fixed batch processing time, and the objective of minimizing total flow time. Our focus is on the value of finite lookahead into future arrivals over a purely online setting for scheduling. We show that the benefits of lookahead depend on the arrival model. In the adversarial setting, finite perfect lookahead has inherent limitations: for any bounded lookahead window, every online algorithm remains separated from the offline optimum by a constant factor. Thus, worst-case arrival sequences can prevent perfect knowledge of future events from fully overcoming online uncertainty. In contrast, under Poisson arrivals in the stable regime, finite lookahead is much more powerful. Although the best distribution-aware online algorithm has a nonzero asymptotic gap from the offline optimum, lookahead closes this gap exponentially fast in the window length. We prove matching upper and lower bounds up to constants in the exponent. Together, these results give a tight characterization of when lookahead can close the online--offline gap, incorporating insights from the competitive analysis of online algorithms into queueing-theoretic steady-state bounds. We also study imperfect predictions, showing that the benefit of lookahead degrades gracefully with prediction error. Simulations on synthetic arrivals and experiments on real request traces support the theory: modest lookahead substantially reduces flow time and brings online batching close to the offline benchmark.

Sample-Optimal Multi-Group Learning with an ERM Oracle

from arXiv: Data Structures and Algorithms

Authors: Princewill Okoroafor

In multi-group learning, a single predictor must perform nearly as well as the best hypothesis in a class on every group in a rich, possibly infinite and overlapping, collection of subpopulations. The known algorithms with the optimal sample complexity keep track of every pair of a group and a hypothesis, which is impossible for the large classes of interest, and the known oracle-based algorithms have a worse rate. We ask whether the optimal rate can be attained by a learner that accesses the classes only through an empirical risk minimization (ERM) oracle. We give an algorithm that uses $\tilde{O}((d_G+d_H)/\varepsilon^2)$ samples and polynomially many calls to a weighted ERM oracle over pairs of a group and a hypothesis, where $d_G$ and $d_H$ are the VC dimensions of the group and hypothesis classes and $\varepsilon$ bounds the excess risk on every group, weighted by its probability. We also show a matching lower bound of $Ω((d_G+d_H)/\varepsilon^2)$ samples for every learner even with unlimited computation, so oracle efficiency costs no additional samples. We also consider a natural special case in which every input belongs to at most $k$ groups and these groups can be listed. In this case, an ERM oracle for the hypothesis class alone suffices, with $\tilde{O}((d_H+\log k)/\varepsilon^2)$ samples in the agnostic setting and $\tilde{O}((d_H+\log k)/\varepsilon)$ samples when every group has a hypothesis that is correct on it.

Authors: Princewill Okoroafor

In multi-group learning, a single predictor must perform nearly as well as the best hypothesis in a class on every group in a rich, possibly infinite and overlapping, collection of subpopulations. The known algorithms with the optimal sample complexity keep track of every pair of a group and a hypothesis, which is impossible for the large classes of interest, and the known oracle-based algorithms have a worse rate. We ask whether the optimal rate can be attained by a learner that accesses the classes only through an empirical risk minimization (ERM) oracle. We give an algorithm that uses $\tilde{O}((d_G+d_H)/\varepsilon^2)$ samples and polynomially many calls to a weighted ERM oracle over pairs of a group and a hypothesis, where $d_G$ and $d_H$ are the VC dimensions of the group and hypothesis classes and $\varepsilon$ bounds the excess risk on every group, weighted by its probability. We also show a matching lower bound of $Ω((d_G+d_H)/\varepsilon^2)$ samples for every learner even with unlimited computation, so oracle efficiency costs no additional samples. We also consider a natural special case in which every input belongs to at most $k$ groups and these groups can be listed. In this case, an ERM oracle for the hypothesis class alone suffices, with $\tilde{O}((d_H+\log k)/\varepsilon^2)$ samples in the agnostic setting and $\tilde{O}((d_H+\log k)/\varepsilon)$ samples when every group has a hypothesis that is correct on it.

Social Learning of Ranking: Getting the Top Right despite Local Herding

from arXiv: Data Structures and Algorithms

Authors: Shahrzad Haddadan, Jie Gao

In sequential learning, a group of agents sequentially receives a noisy private signal and observes the predictions of prior agents to estimate a hidden ground truth. While extensively studied for binary signals, we formalize and analyze sequential learning when the hidden ground truth is an ordering of $n$ items, and both private signals and inferences involve pairwise comparisons. A key focus in sequential learning is herding, where agents disregard their private signals to follow the crowd. We show that, while local herding in our model may lead agents to make incorrect pairwise inferences, the global hidden order can still be approximated. Specifically, by aggregating all pairwise comparison outputs, the highest-ranked items can be retrieved. We investigate top-$k$ retrieval across three scenarios: (1) passive aggregation, where information is gathered from the social learning process without intervention; (2) strategic selection, where agents choose their pairwise comparisons dynamically; and (3) aggregation with stubborn nodes, where a very small set of stubborn agents, who base their inferences solely on their private signals, join the mechanism. With query complexity as the number of agents needed for a high-probability estimation, we propose top-$k$ retrieval algorithms with query complexities sublinear in $O(n^2)$, the total number of pairs.

Authors: Shahrzad Haddadan, Jie Gao

In sequential learning, a group of agents sequentially receives a noisy private signal and observes the predictions of prior agents to estimate a hidden ground truth. While extensively studied for binary signals, we formalize and analyze sequential learning when the hidden ground truth is an ordering of $n$ items, and both private signals and inferences involve pairwise comparisons. A key focus in sequential learning is herding, where agents disregard their private signals to follow the crowd. We show that, while local herding in our model may lead agents to make incorrect pairwise inferences, the global hidden order can still be approximated. Specifically, by aggregating all pairwise comparison outputs, the highest-ranked items can be retrieved. We investigate top-$k$ retrieval across three scenarios: (1) passive aggregation, where information is gathered from the social learning process without intervention; (2) strategic selection, where agents choose their pairwise comparisons dynamically; and (3) aggregation with stubborn nodes, where a very small set of stubborn agents, who base their inferences solely on their private signals, join the mechanism. With query complexity as the number of agents needed for a high-probability estimation, we propose top-$k$ retrieval algorithms with query complexities sublinear in $O(n^2)$, the total number of pairs.

Adaptive Sorting, also in the BST Model

from arXiv: Data Structures and Algorithms

Authors: Ivor van der Hoog, Eva Rotenberg, Daniel Rutschmann

Sorting is one of the most basic algorithmic problems. Adaptive sorting algorithms exploit features that make an input easier to sort, often captured by entropy measures. Many such measures have been studied, raising two natural questions: how do they relate to one another, and are there algorithms that are adaptive to them? We complete a hierarchy of relations between previously studied entropy measures and show that the classical measure Reg is a source of this hierarchy. We prove that an existing data structure yields a Reg-adaptive sorting algorithm, thereby achieving the adaptive bounds of every measure in the hierarchy. Following McIlroy's observation that Reg has a mathematically inconvenient definition, we study a slightly weaker but simpler measure, reg. For inputs without duplicates, we show that O(n + log reg) = O(n + log Reg), so this simplification preserves the adaptive bound. Finally, we do algorithms design and introduce SproutSort, a reg-adaptive algorithm in the offline binary search tree (BST) model. SproutSort achieves an access cost of O(n + log reg), making it the most adaptive algorithm in this model to date. We also introduce SkeletonSort, a simple randomised pointer-machine algorithm that sorts distinct keys in expected O(n + log reg) time, offering a considerably simpler implementation of a sorting algorithm this high in the adaptive hierarchy.

Authors: Ivor van der Hoog, Eva Rotenberg, Daniel Rutschmann

Sorting is one of the most basic algorithmic problems. Adaptive sorting algorithms exploit features that make an input easier to sort, often captured by entropy measures. Many such measures have been studied, raising two natural questions: how do they relate to one another, and are there algorithms that are adaptive to them? We complete a hierarchy of relations between previously studied entropy measures and show that the classical measure Reg is a source of this hierarchy. We prove that an existing data structure yields a Reg-adaptive sorting algorithm, thereby achieving the adaptive bounds of every measure in the hierarchy. Following McIlroy's observation that Reg has a mathematically inconvenient definition, we study a slightly weaker but simpler measure, reg. For inputs without duplicates, we show that O(n + log reg) = O(n + log Reg), so this simplification preserves the adaptive bound. Finally, we do algorithms design and introduce SproutSort, a reg-adaptive algorithm in the offline binary search tree (BST) model. SproutSort achieves an access cost of O(n + log reg), making it the most adaptive algorithm in this model to date. We also introduce SkeletonSort, a simple randomised pointer-machine algorithm that sorts distinct keys in expected O(n + log reg) time, offering a considerably simpler implementation of a sorting algorithm this high in the adaptive hierarchy.

Exact and Asymptotic Analysis of Greedy Online Facility Assignment on Cycle Metrics

from arXiv: Data Structures and Algorithms

Authors: Md. Tanzeem Rahat, Md. Rawha Siddiqi Riad, Md. Manzurul Hasan

We identify the free-facility gaps of greedy online assignment on a cycle with the discrete additive coalescent and use this connection to determine the exact expected assignment cost. The cycle has circumference $n$, with one unit-capacity facility at each integer point and $n$ independent uniform arrivals. Each request is assigned immediately to a nearest free facility. The conditional one-step cost is the sum of squared gap lengths divided by $4n$. A uniform cyclic-order argument identifies the unordered gap process with additive coalescence. The classical Cayley-tree representation then reduces the accumulated cost to a two-vertex distance functional, yielding \[ G_n=\frac n4\sum_{d=0}^{n-1}\prod_{j=1}^{d}\left(1-\frac jn\right) \sim\frac14\sqrt{\frac\pi2}\,n^{3/2}. \] The formula requires a linear number of scalar recurrence steps to evaluate. We also obtain an asymptotic upper bound of $π\sqrt3/2$ on the ratio of expected greedy cost to expected offline optimum under this arrival model. An exact rational Bellman calculation shows that greedy is optimal for three facilities but exceeds the optimal online expected cost by $1/512$ for four facilities. Independent exact recurrences and finite tree enumeration provide reproducible checks of the analysis.

Authors: Md. Tanzeem Rahat, Md. Rawha Siddiqi Riad, Md. Manzurul Hasan

We identify the free-facility gaps of greedy online assignment on a cycle with the discrete additive coalescent and use this connection to determine the exact expected assignment cost. The cycle has circumference $n$, with one unit-capacity facility at each integer point and $n$ independent uniform arrivals. Each request is assigned immediately to a nearest free facility. The conditional one-step cost is the sum of squared gap lengths divided by $4n$. A uniform cyclic-order argument identifies the unordered gap process with additive coalescence. The classical Cayley-tree representation then reduces the accumulated cost to a two-vertex distance functional, yielding \[ G_n=\frac n4\sum_{d=0}^{n-1}\prod_{j=1}^{d}\left(1-\frac jn\right) \sim\frac14\sqrt{\frac\pi2}\,n^{3/2}. \] The formula requires a linear number of scalar recurrence steps to evaluate. We also obtain an asymptotic upper bound of $π\sqrt3/2$ on the ratio of expected greedy cost to expected offline optimum under this arrival model. An exact rational Bellman calculation shows that greedy is optimal for three facilities but exceeds the optimal online expected cost by $1/512$ for four facilities. Independent exact recurrences and finite tree enumeration provide reproducible checks of the analysis.

Adaptive Robust Confidence Sets in High Dimensions

from arXiv: Data Structures and Algorithms

Authors: Ilias Diakonikolas, Chao Gao, Daniel M. Kane, Yuetian Luo

We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown. We show that an information-theoretically optimal adaptive confidence set can be constructed by simultaneously quantifying uncertainty across all one-dimensional projections. The resulting confidence set achieves the optimal diameter and reveals a substantial adaptation cost compared with the minimax estimation error. Although statistically optimal, this construction is computationally infeasible. We establish a Statistical Query (SQ) lower bound showing that achieving diameter $O(m^{-1/2})$ requires either exponentially many queries or query tolerance $d^{-Ω(m)}$. Thus, in contrast to robust mean estimation, adaptation to the unknown contamination proportion incurs both a statistical cost and a computational barrier within the SQ framework. Finally, we give a low-degree moment-matching algorithm that constructs an adaptive confidence set using $d^{O(m)}$ samples and time. Its diameter bound contains an additional $O(m^{-1/2})$ term beyond the information-theoretic rate, matching the dependence on $m$ in the SQ lower bound. Together, these results characterize the statistical limits of adaptive robust confidence sets under Huber contamination and quantify a computational barrier within the SQ framework.

Authors: Ilias Diakonikolas, Chao Gao, Daniel M. Kane, Yuetian Luo

We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown. We show that an information-theoretically optimal adaptive confidence set can be constructed by simultaneously quantifying uncertainty across all one-dimensional projections. The resulting confidence set achieves the optimal diameter and reveals a substantial adaptation cost compared with the minimax estimation error. Although statistically optimal, this construction is computationally infeasible. We establish a Statistical Query (SQ) lower bound showing that achieving diameter $O(m^{-1/2})$ requires either exponentially many queries or query tolerance $d^{-Ω(m)}$. Thus, in contrast to robust mean estimation, adaptation to the unknown contamination proportion incurs both a statistical cost and a computational barrier within the SQ framework. Finally, we give a low-degree moment-matching algorithm that constructs an adaptive confidence set using $d^{O(m)}$ samples and time. Its diameter bound contains an additional $O(m^{-1/2})$ term beyond the information-theoretic rate, matching the dependence on $m$ in the SQ lower bound. Together, these results characterize the statistical limits of adaptive robust confidence sets under Huber contamination and quantify a computational barrier within the SQ framework.

DualColl: Throughput-Optimal Bandwidth-Bound Collective Communications via Fractional Tree Packing

from arXiv: Data Structures and Algorithms

Authors: Namir Ballan, Reuven Cohen

Large-scale distributed training increasingly makes collective communication bandwidth-bound. In modern GPU clusters, communication libraries such as NCCL implement collectives including AllReduce over heterogeneous interconnects such as NVLink, InfiniBand, and Ethernet, making efficient use of available network bandwidth a central systems challenge. ForestColl showed that collective communication on such fabrics can be formulated using fractional tree packing; its scalable polynomial-time construction is developed for AllGather, with AllReduce obtained by composing AllGather and ReduceScatter. We present DualColl, which targets bandwidth-bound AllReduce directly. Under the tree-based AllReduce model, DualColl applies a Garg--Könemann-style multiplicative-weights algorithm and, for any $0<ε<1$, computes a feasible schedule whose throughput is at least $(1-ε)^2$ of the optimal AllReduce throughput. Its key component is an exact minimum-cost collective-tree oracle that projects physical-link dual prices onto a logical graph of participating ranks, reducing each iteration to shortest-path computations followed by a minimum spanning tree. We further extend the framework to local equal-next-hop packet-sprayed routing and to networks without explicit per-tree bandwidth control. For the latter, we introduce a tree-coupled max-min bandwidth-sharing model and show that tree duplication approximates an explicit fractional allocation with a provable accuracy-complexity tradeoff. Finally, we extend the fractional-packing framework to additional collective operations and multiple concurrent collective groups.

Authors: Namir Ballan, Reuven Cohen

Large-scale distributed training increasingly makes collective communication bandwidth-bound. In modern GPU clusters, communication libraries such as NCCL implement collectives including AllReduce over heterogeneous interconnects such as NVLink, InfiniBand, and Ethernet, making efficient use of available network bandwidth a central systems challenge. ForestColl showed that collective communication on such fabrics can be formulated using fractional tree packing; its scalable polynomial-time construction is developed for AllGather, with AllReduce obtained by composing AllGather and ReduceScatter. We present DualColl, which targets bandwidth-bound AllReduce directly. Under the tree-based AllReduce model, DualColl applies a Garg--Könemann-style multiplicative-weights algorithm and, for any $0<ε<1$, computes a feasible schedule whose throughput is at least $(1-ε)^2$ of the optimal AllReduce throughput. Its key component is an exact minimum-cost collective-tree oracle that projects physical-link dual prices onto a logical graph of participating ranks, reducing each iteration to shortest-path computations followed by a minimum spanning tree. We further extend the framework to local equal-next-hop packet-sprayed routing and to networks without explicit per-tree bandwidth control. For the latter, we introduce a tree-coupled max-min bandwidth-sharing model and show that tree duplication approximates an explicit fractional allocation with a provable accuracy-complexity tradeoff. Finally, we extend the fractional-packing framework to additional collective operations and multiple concurrent collective groups.

Metric Planarity Testing is Hard

from arXiv: Data Structures and Algorithms

Authors: Anubhav Dhar, Debajyoti Kar, Sándor Kisfaludi-Bak, Hung Le, Bartłomiej Lewandowski, Karol Węgrzycki

A key milestone in algorithmic graph theory is Hopcroft and Tarjan's 1974 linear-time algorithm for graph planarity testing. At a workshop in 2002, Linial and Rabinovich asked for either a polynomial-time algorithm or an NP-hardness proof for metric planarity testing: given a metric space $(X,d_X)$, determine whether there exists an edge-weighted planar graph $G=(V,E,w)$ with $X\subseteq V$ that realizes the distances in $X$, meaning that $d_G(x,y)=d_X(x,y)$ for every $x,y\in X$. This question arose in the context of research on metric embeddings and their applications in computer science since the mid-1990s. Over the past few decades, polynomial-time algorithms have been developed for several special cases. In this work, we show that metric planarity testing is NP-hard, in sharp contrast to graph planarity testing. Our reduction yields a stronger result: under the Exponential Time Hypothesis (ETH), no $2^{o(n)}$-time algorithm for metric planarity testing exists, where $n$ is the number of points in the metric. To complement our ETH lower bound, we give two algorithmic results: (i) an algorithm for computing a metric planar embedding in $2^{\tilde{O}(n^2)}$ time and (ii) a $(1+ε)$-approximation algorithm in $2^{\tilde{O}(n)}$ time for any fixed $ε\in(0,1)$. The second algorithm produces a planar graph that preserves the input metric up to a factor of $(1+ε)$.

Authors: Anubhav Dhar, Debajyoti Kar, Sándor Kisfaludi-Bak, Hung Le, Bartłomiej Lewandowski, Karol Węgrzycki

A key milestone in algorithmic graph theory is Hopcroft and Tarjan's 1974 linear-time algorithm for graph planarity testing. At a workshop in 2002, Linial and Rabinovich asked for either a polynomial-time algorithm or an NP-hardness proof for metric planarity testing: given a metric space $(X,d_X)$, determine whether there exists an edge-weighted planar graph $G=(V,E,w)$ with $X\subseteq V$ that realizes the distances in $X$, meaning that $d_G(x,y)=d_X(x,y)$ for every $x,y\in X$. This question arose in the context of research on metric embeddings and their applications in computer science since the mid-1990s. Over the past few decades, polynomial-time algorithms have been developed for several special cases. In this work, we show that metric planarity testing is NP-hard, in sharp contrast to graph planarity testing. Our reduction yields a stronger result: under the Exponential Time Hypothesis (ETH), no $2^{o(n)}$-time algorithm for metric planarity testing exists, where $n$ is the number of points in the metric. To complement our ETH lower bound, we give two algorithmic results: (i) an algorithm for computing a metric planar embedding in $2^{\tilde{O}(n^2)}$ time and (ii) a $(1+ε)$-approximation algorithm in $2^{\tilde{O}(n)}$ time for any fixed $ε\in(0,1)$. The second algorithm produces a planar graph that preserves the input metric up to a factor of $(1+ε)$.

Almost Lossless Transfer for Cuttable Distributed Problems

from arXiv: Data Structures and Algorithms

Authors: Wojciech Wawrzyniak

We study constant-round LOCAL approximation for additive local vertex-subset minimization problems. Given a $k$-uniform $α$-approximation on a hereditary source class closed under disjoint union, Bonamy, Das, Gavoille, Picavet, Suomela, and Wesolek obtained ratio $α(d+1)+1$ on locally nice target classes of asymptotic dimension $d$. We prove two transfers with ratios $α+1+η$ and $α+η$ for every $η>0$. These bounds require the stronger local niceness assumption at a larger scale that depends on $η$. The first algorithm keeps their repair construction and increases the radius used to detect errors. We average over partitions of the graph to bound the retained cost without the factor $d+1$. These partitions are used only in the analysis. The second algorithm tests several deletion widths around the errors. In each component of the repair zone, it chooses a smallest union of retained choices and an exact repair. Averaging over the widths bounds the cost counted twice and yields ratio $α+η$. Both transfers are deterministic and computable, use arbitrary distinct numerical identifiers, and need no knowledge of the graph size. Their outputs also satisfy local uniformity guarantees. For 2-domination on graphs of bounded Euler genus, the same planar source gives ratio $19$ through the earlier transfer and $6+η$ through ours. For minimum dominating set, our transfer gives ratio $7+\varepsilon$ on graphs formed by attaching a clique of arbitrary size at one vertex of a planar graph, using the source algorithm of Amiri and Siebertz. This class satisfies our target assumptions and allows nonempty error sets. Its Euler genus is unbounded, so their guarantee for graphs of bounded Euler genus does not cover the whole class.

Authors: Wojciech Wawrzyniak

We study constant-round LOCAL approximation for additive local vertex-subset minimization problems. Given a $k$-uniform $α$-approximation on a hereditary source class closed under disjoint union, Bonamy, Das, Gavoille, Picavet, Suomela, and Wesolek obtained ratio $α(d+1)+1$ on locally nice target classes of asymptotic dimension $d$. We prove two transfers with ratios $α+1+η$ and $α+η$ for every $η>0$. These bounds require the stronger local niceness assumption at a larger scale that depends on $η$. The first algorithm keeps their repair construction and increases the radius used to detect errors. We average over partitions of the graph to bound the retained cost without the factor $d+1$. These partitions are used only in the analysis. The second algorithm tests several deletion widths around the errors. In each component of the repair zone, it chooses a smallest union of retained choices and an exact repair. Averaging over the widths bounds the cost counted twice and yields ratio $α+η$. Both transfers are deterministic and computable, use arbitrary distinct numerical identifiers, and need no knowledge of the graph size. Their outputs also satisfy local uniformity guarantees. For 2-domination on graphs of bounded Euler genus, the same planar source gives ratio $19$ through the earlier transfer and $6+η$ through ours. For minimum dominating set, our transfer gives ratio $7+\varepsilon$ on graphs formed by attaching a clique of arbitrary size at one vertex of a planar graph, using the source algorithm of Amiri and Siebertz. This class satisfies our target assumptions and allows nonempty error sets. Its Euler genus is unbounded, so their guarantee for graphs of bounded Euler genus does not cover the whole class.

Approximation Algorithms for Dual-Memory Request Scheduling in Disaggregated LLM Inference

from arXiv: Data Structures and Algorithms

Authors: Xin Wang, Hong Shen, Hui Tian, Ye Tao

Disaggregated LLM inference executes the prefill and decode phases on separate resource pools, reducing interference between the two phases. However, this architecture introduces a coupled scheduling problem because a request that completes prefill retains its prompt KV cache in prefill memory until it is admitted to the decode pool. Prefill and decode admission decisions therefore jointly determine memory occupancy and request completion times. To the best of our knowledge, this problem lacks a formal formulation, and no approximation guarantees are known for request scheduling in disaggregated LLM inference. We formalize this setting as the dual-memory request scheduling (DMRS) problem, whose objective is to minimize the sum of request completion times, and prove that it is strongly NP-hard. We develop two deterministic polynomial-time approximation algorithms. The first applies to instances satisfying $α<1$ and $β<1$ and achieves an approximation ratio of $8+\frac{4}{1-α}+\frac{8}{1-β}$, where $α$ and $β$ are the maximum normalized prefill and peak decode memory demands among all requests, respectively. In particular, it yields a $32$-approximation when $α,β\leq 1/2$. The second algorithm applies to all feasible instances and achieves a uniform $36$-approximation. Finally, evaluations on two H100 GPUs using trace-derived offline workloads show that the proposed algorithms reduce the sum of request completion times relative to multiple vLLM deployment baselines.

Authors: Xin Wang, Hong Shen, Hui Tian, Ye Tao

Disaggregated LLM inference executes the prefill and decode phases on separate resource pools, reducing interference between the two phases. However, this architecture introduces a coupled scheduling problem because a request that completes prefill retains its prompt KV cache in prefill memory until it is admitted to the decode pool. Prefill and decode admission decisions therefore jointly determine memory occupancy and request completion times. To the best of our knowledge, this problem lacks a formal formulation, and no approximation guarantees are known for request scheduling in disaggregated LLM inference. We formalize this setting as the dual-memory request scheduling (DMRS) problem, whose objective is to minimize the sum of request completion times, and prove that it is strongly NP-hard. We develop two deterministic polynomial-time approximation algorithms. The first applies to instances satisfying $α<1$ and $β<1$ and achieves an approximation ratio of $8+\frac{4}{1-α}+\frac{8}{1-β}$, where $α$ and $β$ are the maximum normalized prefill and peak decode memory demands among all requests, respectively. In particular, it yields a $32$-approximation when $α,β\leq 1/2$. The second algorithm applies to all feasible instances and achieves a uniform $36$-approximation. Finally, evaluations on two H100 GPUs using trace-derived offline workloads show that the proposed algorithms reduce the sum of request completion times relative to multiple vLLM deployment baselines.

FirstFit coloring process of random bipartite graphs and an online coloring lower bound in the random order model

from arXiv: Data Structures and Algorithms

Authors: Yaqiao Li, Xin Li

FirstFit colors each arriving vertex with the smallest positive integer absent from its previously colored neighborhood. We prove that this rule can require $n^{Ω(1)}$ many colors on certain bipartite graph of $n$ vertices even when the vertices arrive in a uniformly random order. This refutes the conjecture of Bosek, Gutowski, Lasoń, and Przybyło [BGLP24] that FirstFit uses a polylogarithmic expected number of colors on every bipartite graph in the random order model. We obtain the lower bound by analyzing the FirstFit coloring process on random bipartite graphs, where vertices arrive to either side with equal probability, and with an independent edge density $n^{-x}$ for some $0 < x < 1$. Our result can also be viewed as a qualitative strengthening of Kučera [Kuč91], who showed that, for every $ε> 0$, there are graphs with chromatic number at most $n^ε$ on which FirstFit uses $Ω(n/\log n)$ colors in expectation under a uniformly random order. Our result shows that such poor behavior of FirstFit already occurs for $χ(G) = 2$.

Authors: Yaqiao Li, Xin Li

FirstFit colors each arriving vertex with the smallest positive integer absent from its previously colored neighborhood. We prove that this rule can require $n^{Ω(1)}$ many colors on certain bipartite graph of $n$ vertices even when the vertices arrive in a uniformly random order. This refutes the conjecture of Bosek, Gutowski, Lasoń, and Przybyło [BGLP24] that FirstFit uses a polylogarithmic expected number of colors on every bipartite graph in the random order model. We obtain the lower bound by analyzing the FirstFit coloring process on random bipartite graphs, where vertices arrive to either side with equal probability, and with an independent edge density $n^{-x}$ for some $0 < x < 1$. Our result can also be viewed as a qualitative strengthening of Kučera [Kuč91], who showed that, for every $ε> 0$, there are graphs with chromatic number at most $n^ε$ on which FirstFit uses $Ω(n/\log n)$ colors in expectation under a uniformly random order. Our result shows that such poor behavior of FirstFit already occurs for $χ(G) = 2$.

Optimal High Probability Bounds for Random Order Online Sorting

from arXiv: Data Structures and Algorithms

Authors: Chenglin Fan, Mingyu Kang

In random order online sorting, a fixed multiset of $n$ real numbers arrives in a uniformly random order. Each value must be placed immediately and irrevocably into an empty position of an array with exactly $n$ positions. The objective is to minimize the sum of the absolute differences between consecutive entries. If the entire input were known in advance, sorting the values would be optimal and would have cost equal to the range of the input. We prove that the optimal worst case competitive ratio in this model is $Θ(\log n)$ with high probability. In particular, we give a single online algorithm that achieves an $O(\log n)$ competitive ratio with high probability for every fixed input, and we show that every randomized online algorithm has some fixed input on which an $Ω(\log n)$ ratio is unavoidable with high probability. The same upper bound also holds for independent samples from an arbitrary unknown distribution on the real line. The algorithm uses the random arrival order to learn the relative positions of future values from the values seen so far. It maintains a hierarchy of small regions of the input order and allows an arriving value to use nearby space when its preferred region is full. This flexibility avoids the need to reserve large amounts of space for unlikely local fluctuations. The analysis shows that large displacements become rapidly less likely as their scale grows, leading to the $O(\log n)$ high probability bound. A matching lower bound shows that no randomized online algorithm can improve the logarithmic dependence in the worst case.

Authors: Chenglin Fan, Mingyu Kang

In random order online sorting, a fixed multiset of $n$ real numbers arrives in a uniformly random order. Each value must be placed immediately and irrevocably into an empty position of an array with exactly $n$ positions. The objective is to minimize the sum of the absolute differences between consecutive entries. If the entire input were known in advance, sorting the values would be optimal and would have cost equal to the range of the input. We prove that the optimal worst case competitive ratio in this model is $Θ(\log n)$ with high probability. In particular, we give a single online algorithm that achieves an $O(\log n)$ competitive ratio with high probability for every fixed input, and we show that every randomized online algorithm has some fixed input on which an $Ω(\log n)$ ratio is unavoidable with high probability. The same upper bound also holds for independent samples from an arbitrary unknown distribution on the real line. The algorithm uses the random arrival order to learn the relative positions of future values from the values seen so far. It maintains a hierarchy of small regions of the input order and allows an arriving value to use nearby space when its preferred region is full. This flexibility avoids the need to reserve large amounts of space for unlikely local fluctuations. The analysis shows that large displacements become rapidly less likely as their scale grows, leading to the $O(\log n)$ high probability bound. A matching lower bound shows that no randomized online algorithm can improve the logarithmic dependence in the worst case.

Solving Restricted Versions of #SAT Faster than $2^m$: Algorithms and Lower Bounds

from arXiv: Data Structures and Algorithms

Authors: Sanjay Jain, Junqiang Peng, Frank Stephan, Mingyu Xiao

Under the Strong Exponential Time Hypothesis (SETH), for any constant $\varepsilon > 0$, the #SAT problem (counting the number of satisfying assignments of a CNF formula) cannot be solved in $\mathcal{O}^*((2-\varepsilon)^n)$ or $\mathcal{O}^*((2-\varepsilon)^m)$ time, where $n$ is the number of variables and $m$ is the number of clauses. On the other hand, simple exhaustive search and the standard inclusion-exclusion give $\mathcal{O}^*(2^n)$-time and $\mathcal{O}^*(2^m)$-time algorithms, respectively. A natural line of research is to beat these SETH-based barriers for restricted versions, such as #$k$-SAT (where each clause contains at most $k$ literals) or #$d$-occ-SAT (where each variable appears in at most $d$ clauses). It is known that the $2^n$-barrier can be broken when $k$ or $d$ is any constant; yet, whether the $2^m$-barrier can also be broken in these cases remains unknown. We present two simple algorithms that overcome the $2^m$-barrier. First, for #$k$-SAT, we give an $\mathcal{O}^*(2^{m(1-1/O(k))})$-time algorithm, thereby breaking the $2^m$-barrier for any constant $k$. Second, for #$2$-occ-SAT (which also includes the problem of counting the number of edge covers in a graph as a special case), we provide an algorithm with runtime $\mathcal{O}^*(2^{mω/3})$, where $ω< 2.3714$ is the matrix multiplication exponent. Furthermore, we demonstrate that both algorithms generalize to the weighted counting setting while preserving the same runtime bounds. On the hardness side, we present a reduction from #Max-$d$-SAT to #$d$-occ-SAT, showing that for any $d\ge 3$, #$d$-occ-SAT admits no $\mathcal{O}^*((2-\varepsilon)^m)$-time algorithm for any constant $\varepsilon>0$, even on positive formulas, under the hypothesis that #Max-3-SAT on $n$ variables admits no $\mathcal{O}^*((2-\varepsilon)^n)$-time algorithm for any such $\varepsilon$.

Authors: Sanjay Jain, Junqiang Peng, Frank Stephan, Mingyu Xiao

Under the Strong Exponential Time Hypothesis (SETH), for any constant $\varepsilon > 0$, the #SAT problem (counting the number of satisfying assignments of a CNF formula) cannot be solved in $\mathcal{O}^*((2-\varepsilon)^n)$ or $\mathcal{O}^*((2-\varepsilon)^m)$ time, where $n$ is the number of variables and $m$ is the number of clauses. On the other hand, simple exhaustive search and the standard inclusion-exclusion give $\mathcal{O}^*(2^n)$-time and $\mathcal{O}^*(2^m)$-time algorithms, respectively. A natural line of research is to beat these SETH-based barriers for restricted versions, such as #$k$-SAT (where each clause contains at most $k$ literals) or #$d$-occ-SAT (where each variable appears in at most $d$ clauses). It is known that the $2^n$-barrier can be broken when $k$ or $d$ is any constant; yet, whether the $2^m$-barrier can also be broken in these cases remains unknown. We present two simple algorithms that overcome the $2^m$-barrier. First, for #$k$-SAT, we give an $\mathcal{O}^*(2^{m(1-1/O(k))})$-time algorithm, thereby breaking the $2^m$-barrier for any constant $k$. Second, for #$2$-occ-SAT (which also includes the problem of counting the number of edge covers in a graph as a special case), we provide an algorithm with runtime $\mathcal{O}^*(2^{mω/3})$, where $ω< 2.3714$ is the matrix multiplication exponent. Furthermore, we demonstrate that both algorithms generalize to the weighted counting setting while preserving the same runtime bounds. On the hardness side, we present a reduction from #Max-$d$-SAT to #$d$-occ-SAT, showing that for any $d\ge 3$, #$d$-occ-SAT admits no $\mathcal{O}^*((2-\varepsilon)^m)$-time algorithm for any constant $\varepsilon>0$, even on positive formulas, under the hypothesis that #Max-3-SAT on $n$ variables admits no $\mathcal{O}^*((2-\varepsilon)^n)$-time algorithm for any such $\varepsilon$.

Cascade: GPU-Accelerated Out-of-Core Suffix Array Construction for LLM Corpora

from arXiv: Data Structures and Algorithms

Authors: Jamshed Khan, Prashant Pandey

Suffix arrays over LLM (pre)training corpora underpin deduplication, contamination testing, n-gram statistics, and training-data attribution, and every new corpus or tokenizer needs a new one. GPU constructors are fast but bounded by device memory, and out-of-core pipelines run at CPU speed, reaching trillions of tokens by building sharded indexes. Reach is bounded not by the size of the data but by what must stay resident, and in a partitioned construction that is the coupling between partitions. We present Cascade, which requires none of it to stay resident. Splitter K-grams sampled from the corpus fix every partition's range of the final suffix array before any sorting, so the GPU sorts and ranks partitions independently and never merges them. The partitions are still coupled: every round of the method needs ranks that other partitions hold, classically kept in a global rank array (the inverse suffix array) that must stay resident. We instead send each rank to the one partition that needs it, and maintain an invariant ensuring ranks are only ever compared within one partition, so no global rank array is needed. The text and a compact partition map, the only structures read at random, then stay in host memory by choice rather than necessity, one at a time when memory is short; every other structure becomes a stream that can be placed across device memory, host memory, and disk as it is written. On a single GPU node, Cascade builds exact suffix arrays of up to 238B tokens, 2.4-6.8x faster than infini-gram's pipeline and 5.9-10.9x faster than libsais.

Authors: Jamshed Khan, Prashant Pandey

Suffix arrays over LLM (pre)training corpora underpin deduplication, contamination testing, n-gram statistics, and training-data attribution, and every new corpus or tokenizer needs a new one. GPU constructors are fast but bounded by device memory, and out-of-core pipelines run at CPU speed, reaching trillions of tokens by building sharded indexes. Reach is bounded not by the size of the data but by what must stay resident, and in a partitioned construction that is the coupling between partitions. We present Cascade, which requires none of it to stay resident. Splitter K-grams sampled from the corpus fix every partition's range of the final suffix array before any sorting, so the GPU sorts and ranks partitions independently and never merges them. The partitions are still coupled: every round of the method needs ranks that other partitions hold, classically kept in a global rank array (the inverse suffix array) that must stay resident. We instead send each rank to the one partition that needs it, and maintain an invariant ensuring ranks are only ever compared within one partition, so no global rank array is needed. The text and a compact partition map, the only structures read at random, then stay in host memory by choice rather than necessity, one at a time when memory is short; every other structure becomes a stream that can be placed across device memory, host memory, and disk as it is written. On a single GPU node, Cascade builds exact suffix arrays of up to 238B tokens, 2.4-6.8x faster than infini-gram's pipeline and 5.9-10.9x faster than libsais.

A Fast Greedy Algorithm with a 0.45 Approximation Guarantee for the Airplane Refueling Problem

from arXiv: Data Structures and Algorithms

Authors: Oleg Lomachenko, Arnold Enikeev

We study a natural greedy algorithm for the Airplane Refueling Problem (ARP), based on a well-known priority rule. We show that this algorithm achieves an approximation guarantee of $9/20$, noticeably improving the previously known $1/12$ guarantee for a fast algorithm. We also show that the same priority rule can be implemented in $O(n\log n)$ time and $O(n)$ space using a dynamic convex-hull data structure. These results provide a worst-case approximation guarantee for an existing greedy priority rule and establish an efficient implementation. In the real-RAM model, the running-time bound is independent of the magnitude of the processing times. The resulting method is also suitable as a fast primal routine inside more complex exact and approximation algorithms for large instances. These results narrow the gap between theoretically strong but complex approximation schemes and lightweight algorithms that can be used directly in computational pipelines. The algorithm combines a simple selection rule with a provable performance bound, making it a useful baseline for further algorithmic studies of ARP.

Authors: Oleg Lomachenko, Arnold Enikeev

We study a natural greedy algorithm for the Airplane Refueling Problem (ARP), based on a well-known priority rule. We show that this algorithm achieves an approximation guarantee of $9/20$, noticeably improving the previously known $1/12$ guarantee for a fast algorithm. We also show that the same priority rule can be implemented in $O(n\log n)$ time and $O(n)$ space using a dynamic convex-hull data structure. These results provide a worst-case approximation guarantee for an existing greedy priority rule and establish an efficient implementation. In the real-RAM model, the running-time bound is independent of the magnitude of the processing times. The resulting method is also suitable as a fast primal routine inside more complex exact and approximation algorithms for large instances. These results narrow the gap between theoretically strong but complex approximation schemes and lightweight algorithms that can be used directly in computational pipelines. The algorithm combines a simple selection rule with a provable performance bound, making it a useful baseline for further algorithmic studies of ARP.

nanoflann: A Header-Only KD-Tree Library for Exact Nearest-Neighbor Search on Compile-Time Product Manifolds

from arXiv: Data Structures and Algorithms

Authors: José Luis Blanco-Claraco

Nearest-neighbor (NN) search underlies countless robotics algorithms, but many state spaces are not Euclidean: orientations live on $SO(2)$ or $SO(3)$ and poses on $SE(2)$ or $SE(3)$, and a Euclidean KD-tree on their raw coordinates returns wrong neighbors. We present a header-only, dependency-free C++ KD-tree library for exact NN search on user-defined product manifolds declared at compile time: the space is composed from base manifolds ($\mathbb{R}^n$, $SO(2)$, $SO(3)$, $S^n$; products $SE(2)$, $SE(3)$, tori) with a single type alias, and the library synthesizes a metric whose pruning is provably admissible, wrap-aware for circular coordinates and double-cover-aware for unit quaternions. Experiments confirm exactness (recall $1.0$ vs an exhaustive scan under the same product metric), where the Euclidean-on-raw shortcut collapses to recall $\approx 0.5^{m}$ for $m$ rotation blocks, with up to three orders of magnitude speedup over brute force. We further contribute an incremental, self-balancing single-tree index for points under heavy churn (e.g. sliding-window LiDAR maps) or $SE(3)$ trajectories: bounded memory and faster updates than prior work, with an optional asynchronous tail-latency-bounding mode. The implementation is open source: github.com/jlblancoc/nanoflann

Authors: José Luis Blanco-Claraco

Nearest-neighbor (NN) search underlies countless robotics algorithms, but many state spaces are not Euclidean: orientations live on $SO(2)$ or $SO(3)$ and poses on $SE(2)$ or $SE(3)$, and a Euclidean KD-tree on their raw coordinates returns wrong neighbors. We present a header-only, dependency-free C++ KD-tree library for exact NN search on user-defined product manifolds declared at compile time: the space is composed from base manifolds ($\mathbb{R}^n$, $SO(2)$, $SO(3)$, $S^n$; products $SE(2)$, $SE(3)$, tori) with a single type alias, and the library synthesizes a metric whose pruning is provably admissible, wrap-aware for circular coordinates and double-cover-aware for unit quaternions. Experiments confirm exactness (recall $1.0$ vs an exhaustive scan under the same product metric), where the Euclidean-on-raw shortcut collapses to recall $\approx 0.5^{m}$ for $m$ rotation blocks, with up to three orders of magnitude speedup over brute force. We further contribute an incremental, self-balancing single-tree index for points under heavy churn (e.g. sliding-window LiDAR maps) or $SE(3)$ trajectories: bounded memory and faster updates than prior work, with an optional asynchronous tail-latency-bounding mode. The implementation is open source: https://github.com/jlblancoc/nanoflann

Random Fourier Features in Near-Linear Time

from arXiv: Data Structures and Algorithms

Authors: Sepideh Mahabadi, Sandeep Silwal, Tal Wagner

Random Fourier features approximate a shift-invariant kernel in $\mathbb{R}^d$, such as the Gaussian kernel $\exp(-\tfrac12\|x-y\|_2^2)$, using independent samples from its spectral measure to construct an unbiased estimator for the kernel value with subgaussian concentration. However, generating $m$ such features requires $O(md)$ time. A long line of work on structured random features has accelerated this computation for the Gaussian kernel and other radial kernels, albeit with weaker concentration guarantees. We close this gap by showing a random feature map that can be computed in near-linear time $O((d+m)\log d)$ and yields near-subgaussian concentration. We obtain it through a new and tighter analysis of the Fastfood feature map of Le, Sarlós and Smola (ICML 2013), using Brascamp-Lieb inequalities and the Schoenberg representation of radial kernels.

Authors: Sepideh Mahabadi, Sandeep Silwal, Tal Wagner

Random Fourier features approximate a shift-invariant kernel in $\mathbb{R}^d$, such as the Gaussian kernel $\exp(-\tfrac12\|x-y\|_2^2)$, using independent samples from its spectral measure to construct an unbiased estimator for the kernel value with subgaussian concentration. However, generating $m$ such features requires $O(md)$ time. A long line of work on structured random features has accelerated this computation for the Gaussian kernel and other radial kernels, albeit with weaker concentration guarantees. We close this gap by showing a random feature map that can be computed in near-linear time $O((d+m)\log d)$ and yields near-subgaussian concentration. We obtain it through a new and tighter analysis of the Fastfood feature map of Le, Sarlós and Smola (ICML 2013), using Brascamp-Lieb inequalities and the Schoenberg representation of radial kernels.

The Matching Number Characterizes the Query Complexity of Edge Counting and Degree Moment Estimation

from arXiv: Data Structures and Algorithms

Authors: Chenglin Fan, Wonseok Shin

Consider an unknown graph $G = (V,E)$ with vertex degrees $d_v$, accessed only by sampling uniformly random edges and querying the degrees of their endpoints. We study the query complexity of estimating the number of edges $m$ and the degree moments $M_s = \sum_{v \in V} (d_v)^s$ for fixed integers $s \ge 2$. In this model, [Beretta and Tětek, TALG 2024] estimate $m$ within relative error $\varepsilon$ using $O(\log n/\varepsilon^2+\sqrt n/\varepsilon)$ expected queries without knowing the number of vertices $n$, and [Bhattacharya and Pradhan, CIAC 2025] estimate $M_s$ using $O(n^{1-1/s}/\varepsilon^2)$ queries when $n$ is given. We show that the query complexity of both problems is governed by the matching number $ν$ of $G$ (the maximum number of pairwise disjoint edges) rather than the number of vertices $n$, and that the dependence on $\varepsilon$ for degree moments can be polynomially improved. Our algorithms rest on a simple observation: the endpoints of a maximum matching cover every edge, so a random endpoint of a random edge falls on this set of $2ν$ vertices with probability at least $1/2$. For every $\varepsilon\in(0,1]$, we give advice-free algorithms that estimate $m$ and $M_s$ within relative error $\varepsilon$ with probability at least $2/3$, without knowledge of $n$, $ν$, or other graph parameters. Specifically, our algorithms use $O(\min\{ν/\varepsilon, 1/\varepsilon^2+\sqrtν/\varepsilon\})$ expected queries for $m$ and $O(\min\{ν^{1-1/s}/\varepsilon^{2/s}, ν^{1-1/(2s-1)}/\varepsilon^{2/(2s-1)}\})$ for $M_s$. Finally, our lower bounds show that for every $k \ge 2$ and sufficiently small $\varepsilon$, these query complexities are optimal up to constant factors over graphs with matching number at most $k$.

Authors: Chenglin Fan, Wonseok Shin

Consider an unknown graph $G = (V,E)$ with vertex degrees $d_v$, accessed only by sampling uniformly random edges and querying the degrees of their endpoints. We study the query complexity of estimating the number of edges $m$ and the degree moments $M_s = \sum_{v \in V} (d_v)^s$ for fixed integers $s \ge 2$. In this model, [Beretta and Tětek, TALG 2024] estimate $m$ within relative error $\varepsilon$ using $O(\log n/\varepsilon^2+\sqrt n/\varepsilon)$ expected queries without knowing the number of vertices $n$, and [Bhattacharya and Pradhan, CIAC 2025] estimate $M_s$ using $O(n^{1-1/s}/\varepsilon^2)$ queries when $n$ is given. We show that the query complexity of both problems is governed by the matching number $ν$ of $G$ (the maximum number of pairwise disjoint edges) rather than the number of vertices $n$, and that the dependence on $\varepsilon$ for degree moments can be polynomially improved. Our algorithms rest on a simple observation: the endpoints of a maximum matching cover every edge, so a random endpoint of a random edge falls on this set of $2ν$ vertices with probability at least $1/2$. For every $\varepsilon\in(0,1]$, we give advice-free algorithms that estimate $m$ and $M_s$ within relative error $\varepsilon$ with probability at least $2/3$, without knowledge of $n$, $ν$, or other graph parameters. Specifically, our algorithms use $O(\min\{ν/\varepsilon, 1/\varepsilon^2+\sqrtν/\varepsilon\})$ expected queries for $m$ and $O(\min\{ν^{1-1/s}/\varepsilon^{2/s}, ν^{1-1/(2s-1)}/\varepsilon^{2/(2s-1)}\})$ for $M_s$. Finally, our lower bounds show that for every $k \ge 2$ and sufficiently small $\varepsilon$, these query complexities are optimal up to constant factors over graphs with matching number at most $k$.

Faster Deterministic Approximation for Counting Knapsack

from arXiv: Data Structures and Algorithms

Authors: Ce Jin, Jaewoo Park, Barna Saha

Given $n$ nonnegative integer weights $w_1, \ldots, w_n$ and an integer knapsack capacity $T$, the $\#$Knapsack problem asks to count the number of subsets $I \subseteq \{1, 2, \dots, n\}$ such that $\sum_{i \in I} w_i \le T$. A long line of research has been devoted to designing faster $(1 \pm \varepsilon)$-approximation algorithms for this \#P-hard problem. Until very recently, the best-known time complexity was $\tilde{O}\bigl(n^{2.5}/\mathrm{poly}(\varepsilon)\bigr)$, achieved by the randomized algorithm of [Dyer, STOC 2003] and the deterministic algorithm of [Gawrychowski, Markin, and Weimann, ICALP 2018]. The recent work by [Feng and Jin, SODA 2025] improved the randomized complexity to $\tilde{O}\bigl(n^{1.5}/\mathrm{poly}(\varepsilon)\bigr)$, leaving open the question whether this $n^{2.5}$ barrier could also be broken by a \emph{deterministic} algorithm. In this work, we answer this question in the affirmative by presenting a deterministic $(1 + \varepsilon)$-approximation algorithm for \#Knapsack in $\tilde{O}\bigl(n^{2.4}/\mathrm{poly}(\varepsilon)\bigr)$ time. Our key technical innovation is to deterministically simulate the balancing technique, which was previously used in the literature of knapsack-type problems via random partitions and seemed to require randomization.

Authors: Ce Jin, Jaewoo Park, Barna Saha

Given $n$ nonnegative integer weights $w_1, \ldots, w_n$ and an integer knapsack capacity $T$, the $\#$Knapsack problem asks to count the number of subsets $I \subseteq \{1, 2, \dots, n\}$ such that $\sum_{i \in I} w_i \le T$. A long line of research has been devoted to designing faster $(1 \pm \varepsilon)$-approximation algorithms for this \#P-hard problem. Until very recently, the best-known time complexity was $\tilde{O}\bigl(n^{2.5}/\mathrm{poly}(\varepsilon)\bigr)$, achieved by the randomized algorithm of [Dyer, STOC 2003] and the deterministic algorithm of [Gawrychowski, Markin, and Weimann, ICALP 2018]. The recent work by [Feng and Jin, SODA 2025] improved the randomized complexity to $\tilde{O}\bigl(n^{1.5}/\mathrm{poly}(\varepsilon)\bigr)$, leaving open the question whether this $n^{2.5}$ barrier could also be broken by a \emph{deterministic} algorithm. In this work, we answer this question in the affirmative by presenting a deterministic $(1 + \varepsilon)$-approximation algorithm for \#Knapsack in $\tilde{O}\bigl(n^{2.4}/\mathrm{poly}(\varepsilon)\bigr)$ time. Our key technical innovation is to deterministically simulate the balancing technique, which was previously used in the literature of knapsack-type problems via random partitions and seemed to require randomization.

The Sample Complexity of Multicalibration with an ERM Oracle

from arXiv: Data Structures and Algorithms

Authors: Princewill Okoroafor

A predictor is multicalibrated with respect to a class of subpopulations (formally, of functions on the inputs, called auditors) if, for every subpopulation in the class and every value the predictor outputs, the average outcome among the members of that subpopulation who receive that value is close to the value itself. For finite classes, about $\varepsilon^{-3}$ samples are necessary and sufficient to learn a predictor with multicalibration error $\varepsilon$, but the algorithms that attain this rate enumerate the class, which is impossible for the large classes of interest. We ask how many samples suffice when the learner accesses the class only through an empirical risk minimization (ERM) oracle. We give an algorithm that uses $\tilde{O}(d/\varepsilon^{3})$ samples and polynomially many ERM calls, where $d$ is the VC dimension of the class. Its dependence on $\varepsilon$ is optimal for all learners. When $d$ is at least about $1/\varepsilon$, every learner that can only access the class through polynomially many ERM calls needs $\tildeΩ(d/\varepsilon^{3})$ samples. Learners with unlimited computation need only $\tilde{O}(\varepsilon^{-3}+d\varepsilon^{-2})$ samples, so the extra factor $1/\varepsilon$ on the dimension term is the price of oracle efficiency. For swap multicalibration, which allows a different function from the class for each predicted value, $\tildeΘ(d/\varepsilon^{3})$ samples are necessary and sufficient. For estimating the error of a given predictor, the ordinary notion is the harder one: the swap error can be estimated with an ERM oracle, whereas the ordinary error is NP-hard to approximate even for classes with linear-time ERM. Consequently, there are classes for which it is computationally easier to learn a multicalibrated predictor than to compute the multicalibration error of a given predictor.

Authors: Princewill Okoroafor

A predictor is multicalibrated with respect to a class of subpopulations (formally, of functions on the inputs, called auditors) if, for every subpopulation in the class and every value the predictor outputs, the average outcome among the members of that subpopulation who receive that value is close to the value itself. For finite classes, about $\varepsilon^{-3}$ samples are necessary and sufficient to learn a predictor with multicalibration error $\varepsilon$, but the algorithms that attain this rate enumerate the class, which is impossible for the large classes of interest. We ask how many samples suffice when the learner accesses the class only through an empirical risk minimization (ERM) oracle. We give an algorithm that uses $\tilde{O}(d/\varepsilon^{3})$ samples and polynomially many ERM calls, where $d$ is the VC dimension of the class. Its dependence on $\varepsilon$ is optimal for all learners. When $d$ is at least about $1/\varepsilon$, every learner that can only access the class through polynomially many ERM calls needs $\tildeΩ(d/\varepsilon^{3})$ samples. Learners with unlimited computation need only $\tilde{O}(\varepsilon^{-3}+d\varepsilon^{-2})$ samples, so the extra factor $1/\varepsilon$ on the dimension term is the price of oracle efficiency. For swap multicalibration, which allows a different function from the class for each predicted value, $\tildeΘ(d/\varepsilon^{3})$ samples are necessary and sufficient. For estimating the error of a given predictor, the ordinary notion is the harder one: the swap error can be estimated with an ERM oracle, whereas the ordinary error is NP-hard to approximate even for classes with linear-time ERM. Consequently, there are classes for which it is computationally easier to learn a multicalibrated predictor than to compute the multicalibration error of a given predictor.

Assortment Optimization under the Mallows Model: Independent-Utility Choice Equivalence and Its Algorithmic Implications

from arXiv: Data Structures and Algorithms

Authors: Danny Segev

The Mallows model is a well-established construct in preference modeling, describing random rankings through their Kendall-Tau distance from a reference permutation. Its compact parameterization and interpretable treatment of preference uncertainty have made it influential across statistics, machine learning, probability, and revenue management. In the latter domain, recent literature has studied the embedding of Mallows-driven choice into assortment optimization. Early approximation schemes in this context require stylized structural simplifications or restrictive parametric assumptions. In the absence of these levers, a highly involved quasi-polynomial-time approximation scheme has subsequently been proposed, leaving wide open the question of whether near-optimal assortments can be computed in true polynomial time. The main contribution of this focused technical paper resides in closing these gaps by uncovering a striking independent-utility structure within Mallows choices, and turning this representation into a polynomial-time approximation scheme. Our central structural characterization explicitly constructs a single collection of mutually independent utilities, reproducing Mallows choice probabilities simultaneously for all offered sets. This exact choice equivalence connects pending Mallows-related challenges to the recent approximation framework of Bowers et al. (FOCS 2025) for assortment optimization under independent random utilities. We make this connection algorithmically feasible through a finite-support reduction that produces distributions of polynomial size while controlling revenue loss uniformly over all assortments. Fused together, these ingredients lead to an $O(n^{\tilde{O}(1/ε^2)})$-time approximation scheme for assortment optimization under the Mallows model, while allowing arbitrary product prices, concentration parameters, and modal rankings.

Authors: Danny Segev

The Mallows model is a well-established construct in preference modeling, describing random rankings through their Kendall-Tau distance from a reference permutation. Its compact parameterization and interpretable treatment of preference uncertainty have made it influential across statistics, machine learning, probability, and revenue management. In the latter domain, recent literature has studied the embedding of Mallows-driven choice into assortment optimization. Early approximation schemes in this context require stylized structural simplifications or restrictive parametric assumptions. In the absence of these levers, a highly involved quasi-polynomial-time approximation scheme has subsequently been proposed, leaving wide open the question of whether near-optimal assortments can be computed in true polynomial time. The main contribution of this focused technical paper resides in closing these gaps by uncovering a striking independent-utility structure within Mallows choices, and turning this representation into a polynomial-time approximation scheme. Our central structural characterization explicitly constructs a single collection of mutually independent utilities, reproducing Mallows choice probabilities simultaneously for all offered sets. This exact choice equivalence connects pending Mallows-related challenges to the recent approximation framework of Bowers et al. (FOCS 2025) for assortment optimization under independent random utilities. We make this connection algorithmically feasible through a finite-support reduction that produces distributions of polynomial size while controlling revenue loss uniformly over all assortments. Fused together, these ingredients lead to an $O(n^{\tilde{O}(1/ε^2)})$-time approximation scheme for assortment optimization under the Mallows model, while allowing arbitrary product prices, concentration parameters, and modal rankings.

Many Scores Force Many Reads: An Optimality Proof for Score of Parentheses

from arXiv: Data Structures and Algorithms

Authors: Andrew Au

LeetCode Problem 856, Score of Parentheses, admits a one-pass algorithm using $O(n)$ time and $O(1)$ auxiliary registers. Its correctness follows from a local interpretation: every occurrence of () contributes a power of two determined by its nesting depth. The space bound is immediately optimal in the usual programming-challenge model, but the linear-time bound is less obvious. In particular, a direct adversary that forces an algorithm to inspect every character is difficult to maintain under the promise that the input is balanced. We instead prove a counting lower bound. For every $k$, we construct $2^k$ balanced parenthesis strings of the same length $4k+2$, all having distinct scores. The construction encodes each bit using one of two operations, changing a current score $v$ to either $2v+1$ or $2v+2$. Consequently, any deterministic character-query decision tree computing the score must have depth at least $k$. A two-character padding argument extends the result to every even input length, proving an $Ω(n)$ worst-case time lower bound. The one-pass algorithm is therefore asymptotically optimal in the unit-cost RAM model.

Authors: Andrew Au

LeetCode Problem 856, Score of Parentheses, admits a one-pass algorithm using $O(n)$ time and $O(1)$ auxiliary registers. Its correctness follows from a local interpretation: every occurrence of () contributes a power of two determined by its nesting depth. The space bound is immediately optimal in the usual programming-challenge model, but the linear-time bound is less obvious. In particular, a direct adversary that forces an algorithm to inspect every character is difficult to maintain under the promise that the input is balanced. We instead prove a counting lower bound. For every $k$, we construct $2^k$ balanced parenthesis strings of the same length $4k+2$, all having distinct scores. The construction encodes each bit using one of two operations, changing a current score $v$ to either $2v+1$ or $2v+2$. Consequently, any deterministic character-query decision tree computing the score must have depth at least $k$. A two-character padding argument extends the result to every even input length, proving an $Ω(n)$ worst-case time lower bound. The one-pass algorithm is therefore asymptotically optimal in the unit-cost RAM model.

Monday, October 12

Faster Fourier Transforms

from Ben Recht
The OpenAI algorithm for the FFT is less a breakthrough and more a malicious exploit.

Hi there, argmin readers! Today’s post looks at part of OpenAI’s math repository that claims to compute faster Fourier transforms. It’s more technical than usual, but I tried to add some tutorial material for people who need a refresher course on signal processing or computational complexity.

The Fast Fourier Transform is one of the most important innovations in digital signal processing and one of my favorite algorithms. The FFT is a fast way to extract the frequency content of signals, letting you rapidly analyze, filter, and manipulate frequencies across different bands of the spectrum.

The Fourier transform computes the energy in each frequency band through a simple dot product.1 Let x denote your input, a length n array of samples, and f a frequency. Then the amount of frequency content in x at f can be computed by the formula:

For digital signals, a bit of math shows you only need to compute n of these dot products, one at each multiple of the frequency 2𝜋/n, to understand all of the frequency content. Naively, this requires n multiplications and additions for each frequency. Hence, computing the entire Fourier transform appears to require n2 arithmetic operations.

The fast Fourier transform is a clever recursive procedure devised by James Cooley and John Tukey in 1965.2 The main idea is that a length-n Fourier transform can be computed by shuffling the input vector, computing two Fourier transforms of length n/2 on the shuffled components, and then recombining the outputs using atomic arithmetic operations. The shuffles and recombinations take a linear number of arithmetic operations. Repeating this procedure iteratively, namely, log2(n) times, computes the full discrete Fourier transform. With the bookkeeping, the FFT computes a length-n Fourier transform in fewer than 5 n log2 n arithmetic operations.

To make this acceleration concrete, when n is 1,000, n^2 is 1,000,000 and 5 * n log n is around 50,000, a 20-fold speedup. When n is 1,000,000, the speedup is 10,000-fold. These speedups can be the difference between a transform being computable or not given the constraints of a signal processing application.

Since the invention of the FFT, people have looked for even faster Fourier transforms. One question is just how fast you can go. It seems like you probably need at least n operations to compute the FFT, as it takes n steps just to write it down. The FFT is 5*log2(n) times slower than that reasonable speed limit, and that 5*log2(n) factor can be big. It’s 200 when n is a trillion. Can you do something clever to reduce that prefactor of 200?

Well, lo and behold, in the massive paper dump OpenAI took on the mathematics world last week, they claim to have a faster Fourier transform. The main claim from their discriminatively named “main.pdf” is

I was intrigued about what was going on here. The log(n) term, in theory, was a teeny tiny bit smaller than in the standard FFT.

Did I read this paper? Of course not. No one at OpenAI read this paper either, so why should I? I did what every other mathematician in the world does with AI slop mathematics: I fed it back into the slop machine and ran an information retrieval session with a chatbot interface to extract salient facts. Anthropomorphically speaking: I asked ChatGPT what it thinks about its own output. This is how we do mathematical research now. What a world.

Unfortunately, I did not find a faster Fourier transform. Instead, I found the rot that develops inside applied mathematical theory when formalism becomes so detached from reality that a “breakthrough” is nothing more than an artifact of mathematicians not being able to properly specify the rules of their games in natural language.

First, “big-O” notation has been so abused that it has become meaningless. Let’s review the definition, which impressed me as an undergrad. An algorithm has running time T(n) = O(F(n)) if there exists an integer N and a constant C such that

That is, the number of steps the algorithm actually takes is upper bounded by a constant times some convenient function F(n) as long as n is large. F(n) is usually something familiar and interpretable, like a polynomial or exponential function. This notation says that we only care about what happens when the size of the computation is large. We care about the asymptotic growth rate of the computation time. All constants are equivalent.

Big-O notation is useful for shortcuts in algorithmic accounting, but it also allows for remarkable logical laziness. If we want a practically faster FFT, we want an algorithm that requires fewer than 50n operations for reasonably sized n. But O(n) does not mean 50 n. It could mean 1080 n. This would be considered a “linear time” algorithm. An algorithm that requires n + 1080 is also considered a linear time algorithm in the big-O regime. An algorithm that only has linear-time guarantees when n is greater than 1080 is also considered linear time. 1080 is approximately equal to the number of atoms in the known universe. That is why Richard Lipton and Ken Regan call algorithms with unfathomably large hidden constants galactic algorithms.

The OpenAI FFT algorithm is definitely galactic. The estimated values for C were 10100 when N=1012. So even when n=10100, the venerable Cooley-Tukey-Gauss FFT would be 1099 times faster.

So why is this new FFT result interesting? Well, note that in big-O notation, it is technically asymptotically faster than the Cooley-Tukey algorithm because

I mean, sure. But given the constants, the algorithm runs faster only when n is impossibly large. Roughly, you need

whatever the hell that number means.

Lipton and Regan argue galactic algorithms are useful because they spur research. Once you show a running time is asymptotically possible, you can spend your time finding an algorithm that practically achieves this provable running time. Maybe you can send your AI agent swarms after it. You do you. Maybe you can get a practical algorithm out, but I don’t know of any legitimate examples where this happened. I’m particularly doubtful here, because the model of “computation” OpenAI uses bears no resemblance to an actual computer.

“The model uses exact complex arithmetic.” That means it assumes you can store and multiply complex numbers at arbitrary precision. Indeed, the algorithm requires you to multiply complex numbers whose magnitudes grow superpolynomially with n. If you stored these numbers as bits, the memory requirement would effectively be exponential in the input length. It assumes you can multiply such arbitrarily large complex numbers together in a single operation. We do not have computers that can do this. So even if this theorem in main.pdf is “true” (no one knows, and I don’t care), it’s not actually solving the problem we set out to solve when looking for a faster FFT.

For people familiar with theoretical analyses of the FFT, that you need an absurd model of computation to break the FFT speed limit isn’t surprising. In 1973, Jacques Morgenstern proved that you couldn’t get a Fourier transform faster than ½ n log n if you required that complex coefficients used in the computation had bounded magnitude. That should have been the end of the story! But it wasn’t. People said Morgenstern’s restricted coefficient model was too restrictive. Forty years later, Nir Ailon showed that you couldn’t compute an FFT faster than n log n without severe numerical instabilities. This was also too restrictive for some people, I guess.

This new OpenAI result is thus less a mathematical breakthrough and more an oddity of computer scientists being bad at formalizing their design specifications. The “theoretical” definition of a computer is very far removed from the computers we actually build. The “faster” Fourier transform is merely an example where theory gets so unmoored from the original question that an “open problem” becomes nothing more than an academic status puzzle. The solution is like what happens when you set computational bot streams at impossible problems and find them engaging in illegal hacking.

Many of the other “big” results in this OpenAI slopository could be critiqued in the same way. For example, one of those results was a galactic algorithm to multiply two matrices in O(n^2.25) operations. The construction used in the paper does not let you even estimate the constant that hides inside big O.3 It may as well be infinite, computable only with the divine music of the spheres.

Still, if written substantially better, these papers would have made it into the most prestigious publication venues for the theory of computer science (i.e., refereed conference proceedings from STOC or FOCS).4 The matrix multiplication paper would have likely won awards. But neither of these has anything to do with computers!

This is because the theory of computer science has a culture that actively discourages running algorithms on a computer. It’s acceptable to write papers in theory conferences that assert an algorithm exists without ever writing it down. The theory of computer science has become a form of mathematics that doesn’t concern itself with computers, and it offers little reward for impact outside those mathematical pursuits. I explained this to a friend who remarked to me that “theory” has the transdisciplinary meaning of “hiding in obscurity of one’s own making.”

AI is really good at automating our worst tendencies. It amplifies the unspoken cultural practices no one likes, but no individual can reasonably deviate from. It exploits the mechanized traditions recalcitrant to collective action. In that vein, AI can now write useless theory papers. But perhaps in the absurdity of the distasteful OpenAI trash dump of proofs, we can all look up and realize together that we should consider doing something else.

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1

Kids these days call it cosine similarity.

2

The wikipedia page says it should be credited to Gauss in 1805! Proper credit assignment is a perennial problem in academia.

3

That one shows that for every epsilon greater than 0, there is a constant C so that T(n) <= n^(2.25 +epsilon + C/log(n)). No matter which queries I try, I can’t get an OpenAI model to give a quantitative estimate of C. Whatever!

4

Another bad tradition is computer science choosing its most prestigious venues as hastily reviewed conference proceedings.

By Ben Recht

TR26-250 | Input-oblivious complexity classes in the second level of the Polynomial Hierarchy | Edward Hirsch, Ilya Volkovich, Ryan Williams

from ECCC Papers
We continue to investigate input-oblivious computations and build a map of known and new complexity classes. In particular, * We prove a new upper bound for $OL_2^P$ defined by Hirsch and Volkovich (STACS, 2026), which is a natural input-oblivious version of the $L_2^P$ class of Korten and Pitassi (FOCS, 2024): it is captured by symmetric (shared-witness) input-oblivious unambiguous computations with one query to a nondeterministic input-oblivious computation. We introduce the corresponding class $SOUP$ and prove that it is closed under polynomial-time Turing reductions. * We give an equivalent characterization of $OL_2^P$: a new symmetric input-oblivious class $OMIN$. We prove that $OMIN$ contains hard functions against fixed-polynomial size circuits. * We prove a number of query aggregation results showing a number of containments and equivalences between input-oblivious complexity classes. In particular, we prove that $BPP^{OL_2^P}=OL_2^P$ and $O_2^P=ONP^{prONP}$. We also characterize a number of input-oblivious classes using the dot-operator. * We extract a number of derandomization corollaries from our results: an upper bound for $BPP^{ONP}$, as well as the new characterization $OMA=BP\cdot prONP$, and new upper bounds for $MA$, $OMA$, $P^{prMA}$, $P^{OprMA}$. These improve and extend earlier bounds of Chakaravarthy and Roy (STACS, 2006) and Hirsch and Volkovich (STACS, 2026).
We continue to investigate input-oblivious computations and build a map of known and new complexity classes. In particular, * We prove a new upper bound for $OL_2^P$ defined by Hirsch and Volkovich (STACS, 2026), which is a natural input-oblivious version of the $L_2^P$ class of Korten and Pitassi (FOCS, 2024): it is captured by symmetric (shared-witness) input-oblivious unambiguous computations with one query to a nondeterministic input-oblivious computation. We introduce the corresponding class $SOUP$ and prove that it is closed under polynomial-time Turing reductions. * We give an equivalent characterization of $OL_2^P$: a new symmetric input-oblivious class $OMIN$. We prove that $OMIN$ contains hard functions against fixed-polynomial size circuits. * We prove a number of query aggregation results showing a number of containments and equivalences between input-oblivious complexity classes. In particular, we prove that $BPP^{OL_2^P}=OL_2^P$ and $O_2^P=ONP^{prONP}$. We also characterize a number of input-oblivious classes using the dot-operator. * We extract a number of derandomization corollaries from our results: an upper bound for $BPP^{ONP}$, as well as the new characterization $OMA=BP\cdot prONP$, and new upper bounds for $MA$, $OMA$, $P^{prMA}$, $P^{OprMA}$. These improve and extend earlier bounds of Chakaravarthy and Roy (STACS, 2006) and Hirsch and Volkovich (STACS, 2026).

TR26-249 | Learning, Compression, and Meta-Complexity | Halley Goldberg, Mikito Nanashima, Igor Oliveira

from ECCC Papers
The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine learning and learning theory. Here we focus on settings in which that intuition can be made mathematically precise, surveying formal connections among learning algorithms, data compression, and computational complexity theory, with an emphasis on recent progress and current frontiers. We describe how techniques from complexity theory can guide the design of both supervised and unsupervised learning algorithms with provable guarantees, and conversely, how learning algorithms can yield new insights and results in complexity theory. A central theme is the role of data compression as a bridge between learning and complexity, especially through an area called meta-complexity, which studies the computational difficulty of compression problems and time-bounded variants of Kolmogorov complexity.
The idea that learning is closely tied to compression has long served as both a powerful principle and, at times, an informal analogy across machine learning and learning theory. Here we focus on settings in which that intuition can be made mathematically precise, surveying formal connections among learning algorithms, data compression, and computational complexity theory, with an emphasis on recent progress and current frontiers. We describe how techniques from complexity theory can guide the design of both supervised and unsupervised learning algorithms with provable guarantees, and conversely, how learning algorithms can yield new insights and results in complexity theory. A central theme is the role of data compression as a bridge between learning and complexity, especially through an area called meta-complexity, which studies the computational difficulty of compression problems and time-bounded variants of Kolmogorov complexity.

The Complexity of Finding Additional Solutions

from arXiv: Computational Complexity

Authors: Saman Farhat

We study the difficulty of finding a new solution when a list of distinct solutions is already supplied. For any fixed finite collection of list lengths, we can prescribe the additional-solution degrees while preserving a given $1$-paddable domain and first-solution degree. We also prove an exact compatibility condition for prescribing an arbitrary positive-list degree at the same time: it must dominate every prescribed fixed-level degree, and this condition suffices. All degrees use polynomial-time reductions with at most one oracle query and correct behavior on empty fibers; the realization needs no assumption about P versus NP. The combined construction gives each underlying witness exponentially many short counter copies. A list that exhausts these copies is long enough to pay for exhaustive search through the underlying witness bound. Separately, we construct a total problem whose first-solution task and every fixed-length additional-solution task are polynomial-time solvable, while its arbitrary-list task is FNP-complete. Assuming P differs from NP, the domain, first degree, and entire fixed-level degree sequence therefore do not determine the arbitrary-list degree. A constant fiber cap restores determination by finitely many fixed levels. The constructions change witness sets and do not prescribe an infinite sequence of degrees or enumerate all remaining solutions.

Authors: Saman Farhat

We study the difficulty of finding a new solution when a list of distinct solutions is already supplied. For any fixed finite collection of list lengths, we can prescribe the additional-solution degrees while preserving a given $1$-paddable domain and first-solution degree. We also prove an exact compatibility condition for prescribing an arbitrary positive-list degree at the same time: it must dominate every prescribed fixed-level degree, and this condition suffices. All degrees use polynomial-time reductions with at most one oracle query and correct behavior on empty fibers; the realization needs no assumption about P versus NP. The combined construction gives each underlying witness exponentially many short counter copies. A list that exhausts these copies is long enough to pay for exhaustive search through the underlying witness bound. Separately, we construct a total problem whose first-solution task and every fixed-length additional-solution task are polynomial-time solvable, while its arbitrary-list task is FNP-complete. Assuming P differs from NP, the domain, first degree, and entire fixed-level degree sequence therefore do not determine the arbitrary-list degree. A constant fiber cap restores determination by finitely many fixed levels. The constructions change witness sets and do not prescribe an infinite sequence of degrees or enumerate all remaining solutions.

Quantum Query Complexity of Maximum Degree and its Generalizations

from arXiv: Computational Complexity

Authors: Amin Shiraz Gilani, Nikhil S. Mande, Subhasree Patro, Nithish Raja

We study the quantum query complexity of several basic graph problems. 1) We prove a tight $Θ(n^{3/2})$ bound for finding a maximum-out-degree vertex in an $n$-vertex tournament, resolving a conjecture of Mande, Paraashar, and Saurabh [FSTTCS'23]. 2) Using the same proof idea, we give a direct proof that finding a maximum-degree vertex in an $n$-vertex undirected graph requires $Θ(n^{3/2})$ quantum queries, a bound implicit in the work of Apers and Lee [CCC'21]. Both lower bounds above hold even for computing only the maximum degree or maximum out-degree. 3) We also study Maximum $(k,n-k)$-Cut, which asks for the largest cut with one side of size $k$. For every constant $k\geq 1$, we give an $O(n^{2-1/(k+1)})$-query quantum algorithm. 4) We establish a reduction to Maximum $(k,n-k)$-Cut involving $k$-Graph Collision, which asks whether the marked vertices of a known graph contain a $k$-clique. For $k\geq 3$, this yields unconditional lower bounds with exponents strictly larger than $3/2$, showing that this problem is strictly harder than computing maximum degree. Additionally, under a conjecture we propose for $k$-Graph Collision, the reduction yields a lower bound matching our upper bound above.

Authors: Amin Shiraz Gilani, Nikhil S. Mande, Subhasree Patro, Nithish Raja

We study the quantum query complexity of several basic graph problems. 1) We prove a tight $Θ(n^{3/2})$ bound for finding a maximum-out-degree vertex in an $n$-vertex tournament, resolving a conjecture of Mande, Paraashar, and Saurabh [FSTTCS'23]. 2) Using the same proof idea, we give a direct proof that finding a maximum-degree vertex in an $n$-vertex undirected graph requires $Θ(n^{3/2})$ quantum queries, a bound implicit in the work of Apers and Lee [CCC'21]. Both lower bounds above hold even for computing only the maximum degree or maximum out-degree. 3) We also study Maximum $(k,n-k)$-Cut, which asks for the largest cut with one side of size $k$. For every constant $k\geq 1$, we give an $O(n^{2-1/(k+1)})$-query quantum algorithm. 4) We establish a reduction to Maximum $(k,n-k)$-Cut involving $k$-Graph Collision, which asks whether the marked vertices of a known graph contain a $k$-clique. For $k\geq 3$, this yields unconditional lower bounds with exponents strictly larger than $3/2$, showing that this problem is strictly harder than computing maximum degree. Additionally, under a conjecture we propose for $k$-Graph Collision, the reduction yields a lower bound matching our upper bound above.

Complexity of Autobidding Equilibria in GSP Sponsored Shopping

from arXiv: Computational Complexity

Authors: Kristoffer Arnsfelt Hansen, Yuhao Li, Xinhao Nie

We revisit autobidding equilibria in GSP sponsored shopping auctions, recently proposed and studied in Dütting et al. On the existence side, we show that autobidding equilibria under both budget constraints (ABE-B) and return-on-investment constraints (ABE-R) always exist and, moreover, always admit equilibria with polynomial-size support over the allocation space. On the computational side, we show that computing an exact equilibrium is in FIXP, and that computing an approximate equilibrium is in PPAD. We also prove that computing an approximate equilibrium is PPAD-hard for both ABE-B and ABE-R. Together, these results provide a fairly comprehensive picture of the computational complexity of autobidding equilibria in GSP sponsored shopping auctions.

Authors: Kristoffer Arnsfelt Hansen, Yuhao Li, Xinhao Nie

We revisit autobidding equilibria in GSP sponsored shopping auctions, recently proposed and studied in Dütting et al. On the existence side, we show that autobidding equilibria under both budget constraints (ABE-B) and return-on-investment constraints (ABE-R) always exist and, moreover, always admit equilibria with polynomial-size support over the allocation space. On the computational side, we show that computing an exact equilibrium is in FIXP, and that computing an approximate equilibrium is in PPAD. We also prove that computing an approximate equilibrium is PPAD-hard for both ABE-B and ABE-R. Together, these results provide a fairly comprehensive picture of the computational complexity of autobidding equilibria in GSP sponsored shopping auctions.

How far is Kolmogorov-Loveland randomness from Martin-Löf randomness?

from arXiv: Computational Complexity

Authors: Tomislav Petrović

We'll call a map from basic to clopen sets of binary sequences an \textit{encoding} if it preserves uniform Lebesgue measure and the disjointedness relation. For some notion of randomness $R$ we can ask if there is a computable encoding $E$ such that for every non-Martin-Löf-random (non-MLR) sequence $α$, the sequences in $E(α)$ are non-$R$. Thus we can use $E$ and randomness test for $R$ to construct a randomness test for MLR. We show that there is no such $E$ for partial computable randomness (PCR), but there is one for Kolmogorov-Loveland randomness. Indeed, there is a total computable $E$ and a pair of total computable non-monotonic betting strategies such that on every sequence in $E(α)$ at least one of the strategies wins whenever $α$ is non-MLR.

Authors: Tomislav Petrović

We'll call a map from basic to clopen sets of binary sequences an \textit{encoding} if it preserves uniform Lebesgue measure and the disjointedness relation. For some notion of randomness $R$ we can ask if there is a computable encoding $E$ such that for every non-Martin-Löf-random (non-MLR) sequence $α$, the sequences in $E(α)$ are non-$R$. Thus we can use $E$ and randomness test for $R$ to construct a randomness test for MLR. We show that there is no such $E$ for partial computable randomness (PCR), but there is one for Kolmogorov-Loveland randomness. Indeed, there is a total computable $E$ and a pair of total computable non-monotonic betting strategies such that on every sequence in $E(α)$ at least one of the strategies wins whenever $α$ is non-MLR.

A Bicriteria Approximation Scheme for the Minimum Well-Separated Pair Decomposition

from arXiv: Computational Geometry

Authors: Guilherme D. da Fonseca

A well-separated pair decomposition (WSPD) of a set $P$ of $n$ points covers all $\binom{n}{2}$ pairs of points by a family of dumbbells $\{X,Y\}$, each covering the pairs of $X\times Y$, such that the diameters of $X$ and $Y$ are at most an $\varepsilon$-fraction of their distance. In $\mathbb{R}^d$, WSPDs with $O(n/\varepsilon^d)$ dumbbells always exist, but many inputs admit much smaller ones. However, computing a WSPD of minimum size is NP-hard in the plane. For $d\ge2$, the only known approximation that preserves the separation has a factor of $2^{O(d)}$, and Buchin et al. (ESA 2026) asked whether a $(1+δ)$-approximation exists. We give a bicriteria approximation scheme: for fixed $d$ and $δ\in(0,1]$, we compute in $f(d,δ)\cdot n^{6}$ time a $(1+δ)\varepsilon$-WSPD whose size is at most $(1+δ)$ times the minimum size of an $\varepsilon$-WSPD. The same holds in metric spaces of bounded doubling dimension. The algorithm combines shifting over scales with the local-growth technique of Nieberg, Hurink, and Kern, applied to a graph whose vertices are the pairs of points. In contrast, we show that in general metric spaces, relaxing the separation by a factor below $2$ keeps the problem hard: for every fixed rational $\varepsilon\le1/4$, it is NP-hard to compute such a relaxed WSPD whose size is within a factor $n^c$ of the minimum, for every $c<2/3$.

Authors: Guilherme D. da Fonseca

A well-separated pair decomposition (WSPD) of a set $P$ of $n$ points covers all $\binom{n}{2}$ pairs of points by a family of dumbbells $\{X,Y\}$, each covering the pairs of $X\times Y$, such that the diameters of $X$ and $Y$ are at most an $\varepsilon$-fraction of their distance. In $\mathbb{R}^d$, WSPDs with $O(n/\varepsilon^d)$ dumbbells always exist, but many inputs admit much smaller ones. However, computing a WSPD of minimum size is NP-hard in the plane. For $d\ge2$, the only known approximation that preserves the separation has a factor of $2^{O(d)}$, and Buchin et al. (ESA 2026) asked whether a $(1+δ)$-approximation exists. We give a bicriteria approximation scheme: for fixed $d$ and $δ\in(0,1]$, we compute in $f(d,δ)\cdot n^{6}$ time a $(1+δ)\varepsilon$-WSPD whose size is at most $(1+δ)$ times the minimum size of an $\varepsilon$-WSPD. The same holds in metric spaces of bounded doubling dimension. The algorithm combines shifting over scales with the local-growth technique of Nieberg, Hurink, and Kern, applied to a graph whose vertices are the pairs of points. In contrast, we show that in general metric spaces, relaxing the separation by a factor below $2$ keeps the problem hard: for every fixed rational $\varepsilon\le1/4$, it is NP-hard to compute such a relaxed WSPD whose size is within a factor $n^c$ of the minimum, for every $c<2/3$.