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Theory of Computing Report

Thursday, September 03

Every Day You See One More Card

from Ben Recht

The mathematics of transmuting frequencies of the past into odds of the future.

Hi there, argmin readers! As the fall semester picks up, posting volume will, too. So I’m going to commit to writing short descriptive headers to help you sort through the different threads. Today’s post is a live blog of Lecture 3 of my graduate seminar “Forecasting: A Critical Retrospective.” A table of contents is here.

Since the Great Depression, US law has required financial management companies that offer products like mutual funds to add a disclaimer to all of their advertising:

“Past performance is not indicative of future results”

The thing is, not a single person believes this. The fund managers don’t believe it, and neither do the customers. Why would you buy a mutual fund if you didn’t think its past performance told you something about how much you’ll have upon retirement? We tend to believe that some investments are riskier than others and some managers are more reputable than others. These beliefs are based upon past observations, and we use them to inform our investment decisions.

So what do we need to do to transform past observations into forecasts? We believe that the past can’t perfectly predict the future. We also believe that a forecaster is only as good as their track record. In today’s class, we’ll link these two together, showing how the evaluation metric for forecast track records leads us to particular forecasting algorithms.

Let’s start with the two main examples from Edmond Halley. We believe the past strongly predicts the future when talking about the motions of celestial bodies. We believe it far less when pricing individual insurance policies.

For his comet, Halley paired three observations together using insights about orbital shapes from Isaac Newton. Given the roughly 76-year gaps between these observations, he predicted we’d see the same object again 76 years later. In 1835, by the time we had seen Halley’s comet twice more, astronomers were uniformly convinced Halley was right and were certain we’d see the comet again in 1910 and 1986 (they were proven correct).

For his life table, Halley grouped people by age and used these cohorts to make demographic forecasts. The proportion of the population aged 25 was 1.668%, and the proportion aged 26 was 1.647%. Therefore, he concluded the odds a 25-year-old would live to see 26 were approximately 80 to 1 in favor.

The move in the demography example to consider odds and chance is interesting, and was something in the air at the time. Proto-demographer John Graunt had made similar calculations of hazard and risk in his tabulations thirty years earlier. Probability applied to casino games had only begun to be formalized forty years earlier. The transmutation of frequencies into risk was intuitive once you started assembling databases. Now we just take it for granted, having created a formal structure that hides the intuition.

In today’s lecture, we’ll work out some of the formalism of this map from rates to risks, deriving the mathematical formulas that encode our assumptions. If you assert that a forecaster will be evaluated on their track record, and if you believe that events are effectively the same, then you bind yourself to making future predictions a deterministic function of the observed rates. The assumptions here are usually implicit. We’re assuming a strong level of interchangeability between past and future events with a particular signature. And we tend to use metrics that beg the question: the common scoring rules always return probabilistic forecasts.

The evaluation ties your hands to making a particular form of forecast. Given a set of knowledge and a statistical score, you are forced to make a constant prediction for all future events. If you allow your predictions to be real-valued, they are suboptimal if they don’t obey the rules of probability. The score itself leads us into a probabilistic mindset. I’ve been calling this metrical determinism, and I find myself inserting some variant of this lecture in every class I teach.

Both the comet example and the life table example can be thought of as scoring track records on average. When you have highly predictable events, a perfect score is possible, but it takes a few hits to convince a skeptic that you really have nailed it down. When events are less predictable, you just want to make sure you’re not losing money on your annuity sales, and maximizing future profits again leads you into a particular form of forecasting.

What’s important here is we don’t have to assume some sort of generative model of randomness to buy into probabilistic prediction. Halley did not have to assume that god was playing dice with who lived and died. Instead, probabilities and odds were simply convenient tools for the actuary to price their products. Probability was the logical consequence of assuming past performance was indicative of future results.

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By Ben Recht

Amazing: There is no Percolation at the Critical Probability in all Dimensions. (Solved by AI via a conjecture of Gady Kozma and Shahaf Nitzan.)

from Gil Kalai

The θ(p꜀) = 0 conjecture is solved in all dimensions. In 2024 Gady Kozma and Shahaf Nitzan showed how to derive the dying percolation conjecture from a proposed conjecture about percolation on general graphs. (I briefly discussed it in this … Continue reading →
The θ(p꜀) = 0 conjecture is solved in all dimensions.

In 2024 Gady Kozma and Shahaf Nitzan showed how to derive the dying percolation conjecture from a proposed conjecture about percolation on general graphs. (I briefly discussed it in this post. Their conjecture was so general that many of us expected a counterexample to be discovered before long.) The dying percolation conjecture asserts that for percolation in \mathbb Z^d at the critical probability, with probability one, there is no infinite cluster. This was known for planar percolation and for percolation in high dimensions. It was a famous open problem in the intermediate dimensions starting with dimension 3. A Claude document, accompanied with a Lean verification, claims a positive solution to the Kozma-Nitzan conjecture. (h/t to Itai Benjamini who told me about it yesterday and also about Hugo’s post.) If verified, this is a remarkable breakthrough. See here and here for the AI’s documents.

There are very interesting related question about three-dimensional percolation.  Is critical percolation in dimension three noise sensitive? Is the total influence of critical finite percolation (for large finite n by n by n box) larger than \log^{1+\epsilon} n? Larger than some n^{\alpha}?  (Here \epsilon, \alpha are positive.)

I had a long research project around these questions with Gady, in which we managed to prove some interesting lemmas. We hoped to bring influences and Fourier tools to the picture.

Hugo’s Duminil-Copin’s post on “Proofs and Prompts”

There is a very interesting new blog called Proofs and Prompts and Hugo Duminil-Copin wrote a thoughtful post Care for a little more AI? about AI and mathematics, using the θ(p꜀) = 0 problem as a primary example. This was three days before Claude claimed the proof and several commentators remarked about the new AI proof.

I liked Alonso Castillo-Ramirez’ comment: “we can still have great joy and be marvelled by the beauty of mathematics on its own, independently if it was created by a human or an AI.”

And here is a moving Facebook post by another famous researcher in percolation theory – Jeff Steif.

By Gil Kalai

Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries

from arXiv: Computational Complexity

Authors: Surendra Ghentiyala, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

We study the question of answering linear queries with differential privacy using few (expected) random bits. We provide a randomness-efficient analog of the $\| \cdot \|_K$-norm mechanism of Hardt and Talwar [HT10]. For the $\ell_\infty$-error, our algorithm can answer $d$ linear queries with $O(d / \varepsilon)$ error using $O(\log d)$ random bits, improving upon algorithms of Canonne et al. and Ghentiyala [CSV25, Ghe26]; this is optimal when $\varepsilon \le 1/d$. We also provide a computationally efficient version of our algorithm, albeit with an $O(\log d)$ multiplicative increase in the error.

Authors: Surendra Ghentiyala, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

We study the question of answering linear queries with differential privacy using few (expected) random bits. We provide a randomness-efficient analog of the $\| \cdot \|_K$-norm mechanism of Hardt and Talwar [HT10]. For the $\ell_\infty$-error, our algorithm can answer $d$ linear queries with $O(d / \varepsilon)$ error using $O(\log d)$ random bits, improving upon algorithms of Canonne et al. and Ghentiyala [CSV25, Ghe26]; this is optimal when $\varepsilon \le 1/d$. We also provide a computationally efficient version of our algorithm, albeit with an $O(\log d)$ multiplicative increase in the error.

Online Non-Monotone DR-Submodular Maximization Matching the Offline $0.401$ Factor

from arXiv: Computational Complexity

Authors: Vaneet Aggarwal, Yiyang Lu

We study online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets of the $d$-dimensional unit cube. The best known constructive offline approximation factor is $0.401$ under the corresponding meta-solvability assumptions, whereas comparable adversarial online guarantees had remained at $1/e$. We show that this factor is also achievable online. In the post-decision full-information value-oracle model, our algorithm attains factor $0.401$ with sublinear approximate regret when oracle feedback is conditionally unbiased and bounded. The online algorithm does not run the offline construction on a changing objective. Instead, it replaces the offline objective-dependent box step by a weighted online learner that controls the required residual terms cumulatively. An exact asymmetric balance theorem preserves the offline coefficients despite adversarial variation. The direct implementation has $O(T^{3/4})$ regret and uses $O(dT^{1/4})$ oracle calls per round. More generally, for every $δ\in[0,1/4]$, batching gives $O(T^δ)$ calls per round and $O(T^{4/5-δ/5})$ regret, including a one-call $O(T^{4/5})$ endpoint. Under a positive-anchor condition, randomized blocking retains factor $0.401$ with $O(T^{5/6})$ one-point bandit regret.

Authors: Vaneet Aggarwal, Yiyang Lu

We study online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets of the $d$-dimensional unit cube. The best known constructive offline approximation factor is $0.401$ under the corresponding meta-solvability assumptions, whereas comparable adversarial online guarantees had remained at $1/e$. We show that this factor is also achievable online. In the post-decision full-information value-oracle model, our algorithm attains factor $0.401$ with sublinear approximate regret when oracle feedback is conditionally unbiased and bounded. The online algorithm does not run the offline construction on a changing objective. Instead, it replaces the offline objective-dependent box step by a weighted online learner that controls the required residual terms cumulatively. An exact asymmetric balance theorem preserves the offline coefficients despite adversarial variation. The direct implementation has $O(T^{3/4})$ regret and uses $O(dT^{1/4})$ oracle calls per round. More generally, for every $δ\in[0,1/4]$, batching gives $O(T^δ)$ calls per round and $O(T^{4/5-δ/5})$ regret, including a one-call $O(T^{4/5})$ endpoint. Under a positive-anchor condition, randomized blocking retains factor $0.401$ with $O(T^{5/6})$ one-point bandit regret.

On Top-Down and Local Lower Bounds for $\mathrm{AC^0}$ Circuits

from arXiv: Computational Complexity

Authors: Gülce Kardeş, Benjamin Rossman

Classical lower bounds for $\mathrm{AC^0}$ circuits proceed bottom-up by simplifying or approximating gates beginning at the input layer. We introduce a complementary top-down model called the Chopping Game, played by adversaries Spoiler and Duplicator on the sets of $0$- and $1$-inputs of a Boolean function. In each round, Spoiler keeps at least a $1/m$-fraction of one side, and Duplicator arbitrarily restricts the other; Spoiler seeks to minimize (and Duplicator to maximize) the number of rounds until some coordinate separates the two remaining sets. Every depth-$d$, fan-in-$m$ circuit induces a $d$-round winning strategy for Spoiler, while Duplicator strategies that survive $d$ rounds formalize top-down lower-bound arguments. Through the Chopping Game and using the polynomial-approximation method, we first obtain the classical lower bound for depth-$d$ $\mathrm{AC^0}$ circuits in a top-down fashion. We then consider a $k$-local variant of the Chopping Game, which relaxes Spoiler's win condition by requiring a separating coordinate within each Hamming ball of radius $k$, rather than a single coordinate globally. We put forward a conjecture that the $d$-round $k$-local Chopping Game for $\mathrm{PARITY}$ requires $m = n^{ω(1)}$ in the regime $d \ll k \ll n$. We prove such a lower bound $m \ge n^{Ω(k^{1/d}/d)}$ when Spoiler is restricted to so-called affine strategies, a class of strategies that achieves the best known upper bounds. Finally, we formulate a version of the $k$-local Chopping Game on $n$-regular graphs of girth $>2k$, and we conjecture a graph-theoretic analogue of ``$\mathrm{PARITY} \notin \mathrm{AC^0}$''.

Authors: Gülce Kardeş, Benjamin Rossman

Classical lower bounds for $\mathrm{AC^0}$ circuits proceed bottom-up by simplifying or approximating gates beginning at the input layer. We introduce a complementary top-down model called the Chopping Game, played by adversaries Spoiler and Duplicator on the sets of $0$- and $1$-inputs of a Boolean function. In each round, Spoiler keeps at least a $1/m$-fraction of one side, and Duplicator arbitrarily restricts the other; Spoiler seeks to minimize (and Duplicator to maximize) the number of rounds until some coordinate separates the two remaining sets. Every depth-$d$, fan-in-$m$ circuit induces a $d$-round winning strategy for Spoiler, while Duplicator strategies that survive $d$ rounds formalize top-down lower-bound arguments. Through the Chopping Game and using the polynomial-approximation method, we first obtain the classical lower bound for depth-$d$ $\mathrm{AC^0}$ circuits in a top-down fashion. We then consider a $k$-local variant of the Chopping Game, which relaxes Spoiler's win condition by requiring a separating coordinate within each Hamming ball of radius $k$, rather than a single coordinate globally. We put forward a conjecture that the $d$-round $k$-local Chopping Game for $\mathrm{PARITY}$ requires $m = n^{ω(1)}$ in the regime $d \ll k \ll n$. We prove such a lower bound $m \ge n^{Ω(k^{1/d}/d)}$ when Spoiler is restricted to so-called affine strategies, a class of strategies that achieves the best known upper bounds. Finally, we formulate a version of the $k$-local Chopping Game on $n$-regular graphs of girth $>2k$, and we conjecture a graph-theoretic analogue of ``$\mathrm{PARITY} \notin \mathrm{AC^0}$''.

Helly Type Theorems for Splitting Point-Sets

from arXiv: Computational Geometry

Authors: Lidor Portal, Natan Rubin

Let $0 < α\leq 1/2$. We say that a finite point set $P$ in $\mathbb{R}^d$ is $α$-split by a hyperplane $h$ if each of the closed half-spaces determined by $h$, contains at least $α|P|$ of the points of $P$. We further say $P$ is $α$-split by a $k$-dimensional flat $τ$ if $P$ is $α$-split by any hyperplane through $τ$. In the standard notation (which coincides with Tukey depth for $k= 0$), the $k$-flat $τ$ has depth $α$ with respect to $P$. We establish interesting Helly-type theorems for splitting families of finite point sets in $\mathbb{R}^d$. Unlike the classical sufficient Helly-type criteria for transversals to families of compact convex sets, which exist only for point and hyperplanes, our results extend to splitting families of point sets by collections of $k$-flats of arbitrary dimensionality $ 0 \leq k \leq d-1$.

Authors: Lidor Portal, Natan Rubin

Let $0 < α\leq 1/2$. We say that a finite point set $P$ in $\mathbb{R}^d$ is $α$-split by a hyperplane $h$ if each of the closed half-spaces determined by $h$, contains at least $α|P|$ of the points of $P$. We further say $P$ is $α$-split by a $k$-dimensional flat $τ$ if $P$ is $α$-split by any hyperplane through $τ$. In the standard notation (which coincides with Tukey depth for $k= 0$), the $k$-flat $τ$ has depth $α$ with respect to $P$. We establish interesting Helly-type theorems for splitting families of finite point sets in $\mathbb{R}^d$. Unlike the classical sufficient Helly-type criteria for transversals to families of compact convex sets, which exist only for point and hyperplanes, our results extend to splitting families of point sets by collections of $k$-flats of arbitrary dimensionality $ 0 \leq k \leq d-1$.

Almost Linear 3-Spanners of Temporal Cliques

from arXiv: Data Structures and Algorithms

Authors: Julia Baligacs, Davide Bilò, Václav Blažej, Maël Dumas, Anna Zych-Pawlewicz

Temporal graphs model dynamic networks by assigning positive integer time labels to the edges, while information propagates along temporal paths, whose edge labels are traversed in nondecreasing order. A temporal $α$-spanner of a temporal graph with $n$ vertices is a temporal subgraph that approximates the minimum-hop temporal distance between every pair of vertices within a factor of $α$. While general temporal graphs may not admit sparse temporal $α$-spanners for any value of $α$, temporal cliques are known to admit temporal $(2k-1)$-spanners of size $\widetilde{\mathcal{O}}(kn^{1+1/k})$ for every positive integer $k$. We present a simple recursive algorithm that computes, for every temporal clique on $n$ vertices, a temporal $3$-spanner of size $n^{1+2/\sqrt{\ln n}}=n^{1+o(1)}$, thereby improving the previous best upper bound of $\widetilde{\mathcal{O}}(n^{3/2})$. We also show that a modified version of our algorithm computes temporal $3$-spanners of size $\mathcal{O}(nL)$ when the lifetime is bounded by $L$, i.e., all time labels are in $\{1,\ldots,L\}$, thus improving the previous bound of $\mathcal{O}(2^Ln\log n)$. Both results are particularly striking in light of the known lower bound of $Ω(n^2)$ on the size of temporal $2$-spanners, which already holds for temporal cliques of lifetime $L\geq 3$. Both algorithms rely on a new simple recursive decomposition that certifies temporal connectivity for a large collection of source-target pairs using only $\mathcal{O}(n)$ carefully selected edges and recursively processes only the remaining pairs. Besides yielding substantially improved upper bounds, this approach is significantly simpler than previous constructions.

Authors: Julia Baligacs, Davide Bilò, Václav Blažej, Maël Dumas, Anna Zych-Pawlewicz

Temporal graphs model dynamic networks by assigning positive integer time labels to the edges, while information propagates along temporal paths, whose edge labels are traversed in nondecreasing order. A temporal $α$-spanner of a temporal graph with $n$ vertices is a temporal subgraph that approximates the minimum-hop temporal distance between every pair of vertices within a factor of $α$. While general temporal graphs may not admit sparse temporal $α$-spanners for any value of $α$, temporal cliques are known to admit temporal $(2k-1)$-spanners of size $\widetilde{\mathcal{O}}(kn^{1+1/k})$ for every positive integer $k$. We present a simple recursive algorithm that computes, for every temporal clique on $n$ vertices, a temporal $3$-spanner of size $n^{1+2/\sqrt{\ln n}}=n^{1+o(1)}$, thereby improving the previous best upper bound of $\widetilde{\mathcal{O}}(n^{3/2})$. We also show that a modified version of our algorithm computes temporal $3$-spanners of size $\mathcal{O}(nL)$ when the lifetime is bounded by $L$, i.e., all time labels are in $\{1,\ldots,L\}$, thus improving the previous bound of $\mathcal{O}(2^Ln\log n)$. Both results are particularly striking in light of the known lower bound of $Ω(n^2)$ on the size of temporal $2$-spanners, which already holds for temporal cliques of lifetime $L\geq 3$. Both algorithms rely on a new simple recursive decomposition that certifies temporal connectivity for a large collection of source-target pairs using only $\mathcal{O}(n)$ carefully selected edges and recursively processes only the remaining pairs. Besides yielding substantially improved upper bounds, this approach is significantly simpler than previous constructions.

Finding a Shortest Vector and More in $2^{n/2+o(n)}$ Time using $q$-ary Coset Difference Tree

from arXiv: Data Structures and Algorithms

Authors: Minki Hhan

This paper presents a new randomized algorithm for solving the exact shortest vector problem. For the $n$-dimensional lattice $\mathcal L$, our algorithm runs in time and space $2^{n/2+o(n)}$. Our algorithm can be viewed as a $q$-ary analogue of the midpoint Hessian for an odd prime $q$; more precisely, we use the fact that, for a shortest vector $v$, the gradient (rather than Hessian) of the periodic Gaussian function at $v/q$ is nearly proportional to $v$ (up to sign), even after aggregation over a relatively large random affine coset. We compute the relevant coset gradient along a chain of intermediate lattices using a combinatorial procedure inspired by Wagner's generalized birthday algorithm, yielding the $2^{n/2+o(n)}$ time and space complexity. A variant of the algorithm solves the exact closest vector problem on every input $(y,\mathcal L)$ with a distance guarantee $\operatorname{dist}(y,\mathcal L)\le 1.039λ_1(\mathcal L)$ within the same time and space complexity. This guarantee holds for a random target and a random lattice drawn according to the Haar-Siegel measure. Thus, this algorithm solves a closest vector problem on such random instances in time and space $2^{n/2+o(n)}$.

Authors: Minki Hhan

This paper presents a new randomized algorithm for solving the exact shortest vector problem. For the $n$-dimensional lattice $\mathcal L$, our algorithm runs in time and space $2^{n/2+o(n)}$. Our algorithm can be viewed as a $q$-ary analogue of the midpoint Hessian for an odd prime $q$; more precisely, we use the fact that, for a shortest vector $v$, the gradient (rather than Hessian) of the periodic Gaussian function at $v/q$ is nearly proportional to $v$ (up to sign), even after aggregation over a relatively large random affine coset. We compute the relevant coset gradient along a chain of intermediate lattices using a combinatorial procedure inspired by Wagner's generalized birthday algorithm, yielding the $2^{n/2+o(n)}$ time and space complexity. A variant of the algorithm solves the exact closest vector problem on every input $(y,\mathcal L)$ with a distance guarantee $\operatorname{dist}(y,\mathcal L)\le 1.039λ_1(\mathcal L)$ within the same time and space complexity. This guarantee holds for a random target and a random lattice drawn according to the Haar-Siegel measure. Thus, this algorithm solves a closest vector problem on such random instances in time and space $2^{n/2+o(n)}$.

The Price of Almost Navigability

from arXiv: Data Structures and Algorithms

Authors: Tomer Waizer, Yoav Danieli

Navigability is a fundamental property of graph-based search structures and plays an important role in the analysis of nearest-neighbor algorithms. Informally, a graph is navigable if, from any current point and toward any desired target, there is always an outgoing edge that moves strictly closer to that target. While this property provides a strong guarantee for greedy search, it can be inherently expensive: in the worst case, navigable graphs require $Ω(n^{3/2})$ edges, where $n$ is the size of the dataset. Recently, Avi and Musco introduced $(1-ε)$-almost navigability, a natural relaxation in which, from every current point, such a progress-making edge is required for almost all targets, while an $ε$ fraction of targets may fail this condition \cite{avimusco2026almost}. They showed that every dataset admits such a graph with $O(n/ε)$ edges. In this work, we prove a matching lower bound, establishing the optimality of their construction and giving a complete characterization of the sparsity achievable by almost-navigable graphs. For most values of $ε$, our hard instances lie in Euclidean spaces of polylogarithmic dimension, across the full worst-case range of $ε$, dimension $d=O(\sqrt{n}\log^{3/2} n)$ suffices. The same construction also sharpens our understanding of ordinary navigability: in the latter dimension, we exhibit datasets for which every navigable graph has $Ω(n^{3/2})$ edges. Our proof reveals a surprising connection between almost navigability and the classical Zarankiewicz problem of constructing dense graphs with limited pairwise neighborhood overlap. This connection lets us translate extremal graph constructions into hard geometric instances for navigation, linking two seemingly different notions of graph sparsity.

Authors: Tomer Waizer, Yoav Danieli

Navigability is a fundamental property of graph-based search structures and plays an important role in the analysis of nearest-neighbor algorithms. Informally, a graph is navigable if, from any current point and toward any desired target, there is always an outgoing edge that moves strictly closer to that target. While this property provides a strong guarantee for greedy search, it can be inherently expensive: in the worst case, navigable graphs require $Ω(n^{3/2})$ edges, where $n$ is the size of the dataset. Recently, Avi and Musco introduced $(1-ε)$-almost navigability, a natural relaxation in which, from every current point, such a progress-making edge is required for almost all targets, while an $ε$ fraction of targets may fail this condition \cite{avimusco2026almost}. They showed that every dataset admits such a graph with $O(n/ε)$ edges. In this work, we prove a matching lower bound, establishing the optimality of their construction and giving a complete characterization of the sparsity achievable by almost-navigable graphs. For most values of $ε$, our hard instances lie in Euclidean spaces of polylogarithmic dimension, across the full worst-case range of $ε$, dimension $d=O(\sqrt{n}\log^{3/2} n)$ suffices. The same construction also sharpens our understanding of ordinary navigability: in the latter dimension, we exhibit datasets for which every navigable graph has $Ω(n^{3/2})$ edges. Our proof reveals a surprising connection between almost navigability and the classical Zarankiewicz problem of constructing dense graphs with limited pairwise neighborhood overlap. This connection lets us translate extremal graph constructions into hard geometric instances for navigation, linking two seemingly different notions of graph sparsity.

Connectivity Oracles Under Vertex Failures via a Simple and Fast Low-Degree Steiner Forest Decomposition

from arXiv: Data Structures and Algorithms

Authors: Sayan Bhattacharya, Ermiya Farokhnejad, Thatchaphol Saranurak, Haoze Wang

We study the low-degree Steiner forest decomposition. Given a graph $G=(V,E)$ and a terminal set $U\subseteq V$, the standard decomposition returns a set $X\subseteq V$ of size at most $|U|/2$ and a forest $T\subseteq G-X$ of maximum degree $Δ$ such that, for every connected component $C$ of $G-X$, some connected component of $T$ contains all terminals in $U\cap V(C)$. This is the central decomposition behind several connectivity oracles under vertex failures [DP20, LS22, LW24]. The state-of-the-art algorithms either take $O(mn\log n)$ time with degree bound $4$ [DP20], or take $m^{1+o(1)}$ time with the weaker degree bound $O(\log^{2}n)$ [LW24]. We show that if $T$ is allowed to contain vertices of $X$, then a degree-$4$ decomposition can be computed by a very simple algorithm in $O(mα(n))$ time. Further, we show that this relaxed decomposition is equally useful for constructing connectivity oracles under vertex failures. As a consequence, we obtain a deterministic connectivity oracle under $d$ vertex failures with $\tilde{O}(m)$ space, $\tilde{O}(md_\star)$ preprocessing time ($d_\star$ is an upper bound on the number of failed vertices), $\tilde{O}(d^{2})$ update time, and $O(d)$ query time. Up to polylogarithmic factors, this oracle strictly improves all known oracles; in particular, it removes the $n^{o(1)}$ factors from the preprocessing and update times of [LS22, LW24].

Authors: Sayan Bhattacharya, Ermiya Farokhnejad, Thatchaphol Saranurak, Haoze Wang

We study the low-degree Steiner forest decomposition. Given a graph $G=(V,E)$ and a terminal set $U\subseteq V$, the standard decomposition returns a set $X\subseteq V$ of size at most $|U|/2$ and a forest $T\subseteq G-X$ of maximum degree $Δ$ such that, for every connected component $C$ of $G-X$, some connected component of $T$ contains all terminals in $U\cap V(C)$. This is the central decomposition behind several connectivity oracles under vertex failures [DP20, LS22, LW24]. The state-of-the-art algorithms either take $O(mn\log n)$ time with degree bound $4$ [DP20], or take $m^{1+o(1)}$ time with the weaker degree bound $O(\log^{2}n)$ [LW24]. We show that if $T$ is allowed to contain vertices of $X$, then a degree-$4$ decomposition can be computed by a very simple algorithm in $O(mα(n))$ time. Further, we show that this relaxed decomposition is equally useful for constructing connectivity oracles under vertex failures. As a consequence, we obtain a deterministic connectivity oracle under $d$ vertex failures with $\tilde{O}(m)$ space, $\tilde{O}(md_\star)$ preprocessing time ($d_\star$ is an upper bound on the number of failed vertices), $\tilde{O}(d^{2})$ update time, and $O(d)$ query time. Up to polylogarithmic factors, this oracle strictly improves all known oracles; in particular, it removes the $n^{o(1)}$ factors from the preprocessing and update times of [LS22, LW24].

The Exact Online Threshold for the Asymmetric Binary Perceptron

from arXiv: Data Structures and Algorithms

Authors: Sunghyeon Jo, Taekyun Lee

Let $G\in\mathbb{R}^{M\times N}$ have independent standard Gaussian entries. For a fixed margin $κ\in\mathbb{R}$, the asymmetric binary perceptron asks for $σ\in\{\pm1\}^N$ such that $Gσ/\sqrt{N}\geκ\mathbf{1}_M$. We study the online version of this problem, in which the columns of $G$ arrive sequentially and each sign must be chosen irrevocably before future columns are revealed. We determine the exact threshold $α_{\mathrm{on}}(κ)$ for every fixed $κ$: for $M/N\toα$ with $α<α_{\mathrm{on}}(κ)$, there is a deterministic online algorithm, using $O(MN)$ arithmetic operations and polynomial bit complexity, that succeeds with high probability, while for $α>α_{\mathrm{on}}(κ)$, no online algorithm succeeds with high probability. The threshold is characterized by a one-dimensional stochastic control problem for Brownian motion. The main difficulty is to upgrade a single-coordinate Brownian limit to simultaneous feasibility of all $M=Θ(N)$ constraints, which we do with half-line monotonicity and a short final correction block. At zero margin, we give a computer-assisted proof that $0.32747<α_{\mathrm{on}}(0)<0.36664$. In particular, every density below $0.32747$ is achievable online by such an algorithm, more than tripling the best density previously proved attainable by any polynomial-time algorithm, online or offline (the previous bound was $α\le0.1$, due to Li, Schramm, and Zhou). As $κ\to+\infty$, the online threshold agrees to first order with the offline storage capacity. As $κ\to-\infty$, it has the same asymptotic scale as the best known offline polynomial-time guarantee, while the storage capacity is larger by a factor of order $κ^2$.

Authors: Sunghyeon Jo, Taekyun Lee

Let $G\in\mathbb{R}^{M\times N}$ have independent standard Gaussian entries. For a fixed margin $κ\in\mathbb{R}$, the asymmetric binary perceptron asks for $σ\in\{\pm1\}^N$ such that $Gσ/\sqrt{N}\geκ\mathbf{1}_M$. We study the online version of this problem, in which the columns of $G$ arrive sequentially and each sign must be chosen irrevocably before future columns are revealed. We determine the exact threshold $α_{\mathrm{on}}(κ)$ for every fixed $κ$: for $M/N\toα$ with $α<α_{\mathrm{on}}(κ)$, there is a deterministic online algorithm, using $O(MN)$ arithmetic operations and polynomial bit complexity, that succeeds with high probability, while for $α>α_{\mathrm{on}}(κ)$, no online algorithm succeeds with high probability. The threshold is characterized by a one-dimensional stochastic control problem for Brownian motion. The main difficulty is to upgrade a single-coordinate Brownian limit to simultaneous feasibility of all $M=Θ(N)$ constraints, which we do with half-line monotonicity and a short final correction block. At zero margin, we give a computer-assisted proof that $0.32747<α_{\mathrm{on}}(0)<0.36664$. In particular, every density below $0.32747$ is achievable online by such an algorithm, more than tripling the best density previously proved attainable by any polynomial-time algorithm, online or offline (the previous bound was $α\le0.1$, due to Li, Schramm, and Zhou). As $κ\to+\infty$, the online threshold agrees to first order with the offline storage capacity. As $κ\to-\infty$, it has the same asymptotic scale as the best known offline polynomial-time guarantee, while the storage capacity is larger by a factor of order $κ^2$.

Optimal girth-dependent bounds for the Bethe approximation of the permanent

from arXiv: Data Structures and Algorithms

Authors: Dingding Dong, Vishesh Jain

For an $n\times n$ nonnegative matrix $A$, the Bethe permanent, which is computable in deterministic polynomial time, satisfies the tight universal comparison \[\operatorname{Bethe}(A) \leq \operatorname{per}(A) \leq 2^{n/2}\operatorname{Bethe}(A).\] The lower bound, due to Gurvits, is attained on forests. The upper bound, due to Anari and Rezaei, is attained by the adjacency matrix of a disjoint union of $4$-cycles. Confirming a conjecture of Anari, we provide an optimal girth-dependent refinement of the above comparison. More precisely, we show that if the bipartite support graph of $A$ has girth at least an even integer $g \geq 4$, then \[\operatorname{Bethe}(A) \leq \operatorname{per}(A) \leq 2^{2n/g}\operatorname{Bethe}(A).\] The upper bound is attained by the adjacency matrix of a disjoint union of $g$-cycles.

Authors: Dingding Dong, Vishesh Jain

For an $n\times n$ nonnegative matrix $A$, the Bethe permanent, which is computable in deterministic polynomial time, satisfies the tight universal comparison \[\operatorname{Bethe}(A) \leq \operatorname{per}(A) \leq 2^{n/2}\operatorname{Bethe}(A).\] The lower bound, due to Gurvits, is attained on forests. The upper bound, due to Anari and Rezaei, is attained by the adjacency matrix of a disjoint union of $4$-cycles. Confirming a conjecture of Anari, we provide an optimal girth-dependent refinement of the above comparison. More precisely, we show that if the bipartite support graph of $A$ has girth at least an even integer $g \geq 4$, then \[\operatorname{Bethe}(A) \leq \operatorname{per}(A) \leq 2^{2n/g}\operatorname{Bethe}(A).\] The upper bound is attained by the adjacency matrix of a disjoint union of $g$-cycles.

Forbidden Subgraphs of Graphs with Low Bandwidth

from arXiv: Data Structures and Algorithms

Authors: Maria Chudnovsky, Daniel Lokshtanov, Eran Nevo

A layout of a graph G is an injective function $f : V(G) \rightarrow Z$, and the bandwidth of a layout f is $bw(G,f) = max_{uv \in E(G)} |f(u) - f(v)|$. The bandwidth bw(G) of G is the minimum bandwidth of a layout of G. Computing the bandwidth of a graph is a notoriously hard problem: assuming P != NP, there is no polynomial time algorithm, even on very restricted classes of trees [Monien, SIAM Journal on Algebraic Discrete Methods, 1986], and no constant factor approximation, even on trees [Dubey et al., JCSS 2011]. Assuming the Exponential Time Hypothesis, there is no algorithm with running time $f(k)n^{o(k)}$ to determine whether an input graph has bandwidth at most k, even on very restricted classes of trees [Dregi and Lokshtanov, ICALP 2014]. In this paper we show that {\sc Bandwidth} on general graphs is FPT-approximable. In particular we give an algorithm that takes as input a graph G and an integer k, runs in time $2^{O(9^k)}n^{O(1)}$, and outputs a subtree T of G such that $bw(T) \geq k$ or a layout of G of bandwidth at most $(10^{85} k^{28})^{4^k}$. This resolves in the affirmative an open problem of Chung and Seymour [Discrete Mathematics, 1989], who asked whether the bandwidth of every graph G is upper bounded in terms of the maximum bandwidth of a subtree of G. Our theorem leads to a forbidden subgraph characterization for graphs of bounded bandwidth, and can be seen as an analog for bandwidth of the classic grid minor theorem for treewidth, the forbidden subtree theorem for pathwidth, and the forbidden subpath theorem for treedepth.

Authors: Maria Chudnovsky, Daniel Lokshtanov, Eran Nevo

A layout of a graph G is an injective function $f : V(G) \rightarrow Z$, and the bandwidth of a layout f is $bw(G,f) = max_{uv \in E(G)} |f(u) - f(v)|$. The bandwidth bw(G) of G is the minimum bandwidth of a layout of G. Computing the bandwidth of a graph is a notoriously hard problem: assuming P != NP, there is no polynomial time algorithm, even on very restricted classes of trees [Monien, SIAM Journal on Algebraic Discrete Methods, 1986], and no constant factor approximation, even on trees [Dubey et al., JCSS 2011]. Assuming the Exponential Time Hypothesis, there is no algorithm with running time $f(k)n^{o(k)}$ to determine whether an input graph has bandwidth at most k, even on very restricted classes of trees [Dregi and Lokshtanov, ICALP 2014]. In this paper we show that {\sc Bandwidth} on general graphs is FPT-approximable. In particular we give an algorithm that takes as input a graph G and an integer k, runs in time $2^{O(9^k)}n^{O(1)}$, and outputs a subtree T of G such that $bw(T) \geq k$ or a layout of G of bandwidth at most $(10^{85} k^{28})^{4^k}$. This resolves in the affirmative an open problem of Chung and Seymour [Discrete Mathematics, 1989], who asked whether the bandwidth of every graph G is upper bounded in terms of the maximum bandwidth of a subtree of G. Our theorem leads to a forbidden subgraph characterization for graphs of bounded bandwidth, and can be seen as an analog for bandwidth of the classic grid minor theorem for treewidth, the forbidden subtree theorem for pathwidth, and the forbidden subpath theorem for treedepth.

Wednesday, September 02

What is a Computer?

from Computational Complexity

Ben Brubaker has a new Quanta essay Does Computer Science Need Computers? 

Despite the title (and authors generally don't choose their titles), Brubaker's essay really addresses the question as to whether computer science is about computers. He starts with Dijkstra's apocryphal quote "Computer science is no more about computers than astronomy is about telescopes."

This is the wrong analogy: computers are not the telescopes, they are the stars. You just have to use a broad definition of computer.

The word "computer" goes back to at least 1613. The etymology

  1. Latin com- meant “together.”
  2. Putāre meant “to reckon” or “calculate”—and originally “to prune” or “clear up.”
  3. English added -er, meaning “someone or something that performs an action.”
The word originally meant one who computes, usually referring to a human performing a computational task. Its meaning as a machine didn't come into wide use until the mid-20th century. 
I start off every undergraduate theory class I teach with the question "What is a Computer", even in my Foundations of Complexity posts. After some discussion we end up with a diagram like this.
♦ A Computer
The computer doesn't need to be electrical, mechanical or biological. You can think of the postal service delivering a letter based on an address, an auction arriving at a price, or even a well that draws water as we pull a rope. 
The Church-Turing thesis says the process can always be represented by a Turing machine, and then we are off to the races.
When theoretical computer science stops talking about computing, it just becomes mathematics and no longer computer science. If we want to keep it computer science, we need a computer at the center, some kind of process.
How about the title "Does Computer Science Need Computers?" No, not for electronic computers, though they've become more helpful, especially in this AI era. But doing research in computing is a process in itself. Alan Turing drew inspiration for his machine from thinking about how a mathematician works. So yes, you need a computer for computer science, and a computer for astronomy and every other discipline, even if that computer is just yourself.

By Lance Fortnow

Ben Brubaker has a new Quanta essay Does Computer Science Need Computers

Despite the title (and authors generally don't choose their titles), Brubaker's essay really addresses the question as to whether computer science is about computers. He starts with Dijkstra's apocryphal quote "Computer science is no more about computers than astronomy is about telescopes."

This is the wrong analogy: computers are not the telescopes, they are the stars. You just have to use a broad definition of computer.

The word "computer" goes back to at least 1613. The etymology

  1. Latin com- meant “together.”
  2. Putāre meant “to reckon” or “calculate”—and originally “to prune” or “clear up.”
  3. English added -er, meaning “someone or something that performs an action.”
The word originally meant one who computes, usually referring to a human performing a computational task. Its meaning as a machine didn't come into wide use until the mid-20th century. 

I start off every undergraduate theory class I teach with the question "What is a Computer", even in my Foundations of Complexity posts. After some discussion we end up with a diagram like this.

A Computer

The computer doesn't need to be electrical, mechanical or biological. You can think of the postal service delivering a letter based on an address, an auction arriving at a price, or even a well that draws water as we pull a rope. 

The Church-Turing thesis says the process can always be represented by a Turing machine, and then we are off to the races.

When theoretical computer science stops talking about computing, it just becomes mathematics and no longer computer science. If we want to keep it computer science, we need a computer at the center, some kind of process.

How about the title "Does Computer Science Need Computers?" No, not for electronic computers, though they've become more helpful, especially in this AI era. But doing research in computing is a process in itself. Alan Turing drew inspiration for his machine from thinking about how a mathematician works. So yes, you need a computer for computer science, and a computer for astronomy and every other discipline, even if that computer is just yourself.

By Lance Fortnow

Annotated Slides – Micha A. Perles 90th Birthday Meeting

from Gil Kalai

Akiva Kadari, Pablo Soberon and the cascade conjecture The cascade conjecture is discussed in this post. I proposed the conjecture back in 1974, inspired by work by Meir Katchalski (though the name “Cascade Conjecture” only came into use over the … Continue reading →
Akiva Kadari, Pablo Soberon and the cascade conjecture

The cascade conjecture is discussed in this post. I proposed the conjecture back in 1974, inspired by work by Meir Katchalski (though the name “Cascade Conjecture” only came into use over the last decade or two).

Akiva Kadari, a master’s student of Micha Perles, proved the planar case in his M.Sc. thesis. Although the proof was ready in the early-to-mid 1980s, writing the thesis was delayed until 1990, when Micha was on sabbatical and I stepped in as a co-supervisor. (Akiva himself was present in the lecture.)

This is the conjecture

 


Recently. Pablo Soberón proved a remarkable weaker version of the conjecture using topological methods—we will devote a dedicated post to it soon. Very recently, Pablo also disproved the last open case of Grünbaum’s famous mass partition conjecture.

Yaacov Kupitz and Geometric graph theory

 

Yaacov Kupitz once decided to spend a year abroad and got in touch with the famous mathematician John Conway. Just a few weeks before taking off, Yaacov discovered he was going to a different John Conway—also famous, but in an entirely different field! Pivoting quickly, Yaacov instead spent a year in Aarhus, Denmark, where he wrote an influential monograph on geometric graphs (which later became his M.Sc. thesis). Vertices in geometric graphs are points in the plane, and edges are line segments (or sometimes pseudo-line segments) between them. It is a fascinating area, and we have written about it here before. János Pach took Micha Perles’s course on geometric graphs at Rutgers in 1989 and subsequently added the topic to his own research interests. In a previous post, we presented two of Micha’s proofs in geometric graph theory, both related to arthropods: his “proof by Lice” and his “proof for Caterpillars.”

A nice story: Micha was once invited to spend a sabbatical at Rutgers at the newly founded DIMACS. One evening, he received a phone call from Daniel Gorenstein, the founding director of DIMACS, who explained that they needed to lower their offer from $70,000 to $65,000. Micha was quite surprised and responded that when he had originally accepted the offer (also over the phone), he was sure it was $17,000!

Two Helly type problems from the 70s

I thought the proof would come from a certain extension of the Nerve Theorem, but it arrived from a different direction instead. Very recently, the conjecture was proved for  r=2 by Giuliamaria Menara in the paper A Helly-Type Theorem for two-component convex sets.

I presented this conjecture in a birthday party of another Micha—Micha Sharir. Shortly afterward, it was settled and since then further extended in various directions.

Meir Katchalski’s theorems about  the dimensions of intersections of convex sets.

 

Meir Katchalski’s beautiful theorems about the dimensions of intersections of convex sets were proved as part of his master’s and doctoral work. He obtained results regarding fractional Helly theorems, common transversals, and various other directions. (In my own doctoral work, I settled a conjecture by Katchalski and Perles.) Meir is the son of Israel’s fourth president, the renowned biologist Ephraim Katzir. I also spoke a bit about Branko Grünbaum, Micha’s doctoral supervisor. In one of the pictures , you can see representatives of five academic generations starting with Branko.

Ido Shemer and neighborly polytopes

Ido Shemer was a Ph.D. student around the same time as Noga Alon, Yaacov Kupitz, and me. His thesis was on neighborly polytopes, and he invented the “sewing” method.

The Kupitz-Perles conjecture

We devoted two posts to this beautiful conjecture (here and here), and Rom Pinchasi talked about it in greater detail in his lecture. When Rom proved his remarkable \log \log n result, I used to ask him in a friendly way (or so I thought!), as a gesture of appreciation for his abilities: “What about \log \log \log n?” From Rom’s lecture, I learned that he felt uncomfortable about this and regarded it as a form of pressure. To quote Rom: “Gil log-log-logged me every time he saw me in the corridors of the Einstein Institute.”

Four slides from Rom’s lecture.

Ziva Deutsch non convexity and graph homomorphisms

Nonconvexity is a very interesting topic closely related to graph homomorphisms—a subject that was greatly advanced by Perles and his students, as well as by Jarik Nešetřil and his colleagues. Two decades ago, Jarik gave a lecture series in Jerusalem on graph homomorphisms. The shapes in the slide are taken from a 1970 paper by Kay and Guay (see the picture below).

 

Interesting examples of nonconvexity. You can see two interesting extensions of the Magen David symbol, as well as two “dancing rulers.”

Here are Mazi, my mother, and me with Micha and members of his family. The younger daughter in the picture came to the session along with Micha’s oldest daughter. Two of Micha’s grandchildren, Shlomi and Itai, also attended—turns out they participated in our Math + AI project!

Moshe Rosenfeld, Yosi Zaks, and Amos Altshuler

 

I also wanted to mention three contemporaries of Micha: Moshe, Yosi, and Amos. Yosi Zaks and Moshe Rosenfeld were both students of Grünbaum—I mentioned their work and their problems here, here, and here. Amos Altshuler is the same age as Micha, but was unofficially Micha’s doctoral student (officially, he was Furstenberg’s student). Amos’s Ph.D. thesis discussed high-dimensional analogs of Hamiltonian cycles, which he tried to find in the boundary complexes of stacked polytopes.

An anecdote: Ehud, Ziva, and Micha

Michael Kallay and Zeev Smilansky

Michael Kallay (an older academic brother) and Zeev Smilansky (a younger one) studied the decomposition of polytopes. (In Hebrew, Michael’s surname and mine have the exact same spelling.) I was enthusiastic about Zeev’s extensions of cyclic polytopes—though Zeev himself did not share my enthusiasm! Zeev eventually moved into biotech, winemaking, and writing prose and poetry, all while spending decades trying to find an elementary geometric proof and strengthenings for the unimodality of the $h$-vector of simple polytopes. (See this post.)

Kallay’s father was a mathematician who wrote an early Hebrew book on calculus. Zeev Smilansky’s father was the famous writer Izhar Smilansky (S. Izhar).

Micha’s early work on Gale’s transform

Micha Perles used the Gale transform to translate the geometric and combinatorial properties of a d-dimensional polytope into a lower-dimensional vector configuration in \mathbb{R}^{k-1}. This made it possible to study, classify, enumerate and construct complex, higher-dimensional polytopes through lower-dimensional representation. One of the most famous applications was Perles constructions an 8-dimensional polytope with 12 vertices that cannot be realized with rational Cartesian coordinates. Gale transform record the affine dependencies among vertices, and many years ago I conjectured that the space of affine stresses could lead to a similar useful “transform”.  

Enumeration of skeletons of polytopes

This is another beautiful theorem by Micha Perles and a beautiful subsequent theorem by Arnau Padrol.

Jamil Kasem’s thesis

Kasem’s thesis dealt with neighborly families of standard boxes which are described by packing of a complete graph with complete bipartite graphs.

Another anecdote

I cannot translate it to English. Since that telephone call Ehud refers to ChatGPT as “my daughter Chatgi.”

 

By Gil Kalai

Depth-1 expanders on the unitary group and applications

from arXiv: Computational Complexity

Authors: Anurag Anshu, Shankar Balasubramanian, Jonas Haferkamp, Aram W. Harrow, Xinyu Tan

We construct a constant-degree and constant-gap quantum expander on $n$ qubits where each unitary can be implemented by a depth-$1$ and 1D circuit of Pauli or CNOT gates. We provide two applications of this expander. First, we use it to construct a family of frustration-free 1D Hamiltonians whose ground states obey the entanglement-gap relation $S = Θ(Δ^{-1/2})$; this is believed to be optimal, but achieving it had been open. Second, we use it to provide a streaming protocol that tests for closeness to a class of 1D volume-law entangled states. Moreover, we extend our quantum expander to a constant-degree and constant-gap expander on the unitary group where each unitary is a single $T$ gate, a single $T^{\dagger}$ gate, or a depth-$1$ Clifford circuit. This implies that a random sequence of unitaries from the expander yields a gapped walk on a dense subgroup of the unitary group. This improves upon previous work by Bourgain and Gamburd which did not control the dependence of the gap on the dimension.

Authors: Anurag Anshu, Shankar Balasubramanian, Jonas Haferkamp, Aram W. Harrow, Xinyu Tan

We construct a constant-degree and constant-gap quantum expander on $n$ qubits where each unitary can be implemented by a depth-$1$ and 1D circuit of Pauli or CNOT gates. We provide two applications of this expander. First, we use it to construct a family of frustration-free 1D Hamiltonians whose ground states obey the entanglement-gap relation $S = Θ(Δ^{-1/2})$; this is believed to be optimal, but achieving it had been open. Second, we use it to provide a streaming protocol that tests for closeness to a class of 1D volume-law entangled states. Moreover, we extend our quantum expander to a constant-degree and constant-gap expander on the unitary group where each unitary is a single $T$ gate, a single $T^{\dagger}$ gate, or a depth-$1$ Clifford circuit. This implies that a random sequence of unitaries from the expander yields a gapped walk on a dense subgroup of the unitary group. This improves upon previous work by Bourgain and Gamburd which did not control the dependence of the gap on the dimension.

SVP Is NP-Hard for Some Rank-2 Cyclotomic Modules

from arXiv: Computational Complexity

Authors: Jiaqi Liu, Yansong Feng, Yanbin Pan

Let $q$ range over primes congruent to $3$ modulo $4$. Let $ζ_q$ be a primitive $q$th root of unity, and put $K=\mathbb{Q}(ζ_q)$, with ring of integers $\mathcal{O}_K=\mathbb{Z}[ζ_q]$. We prove that the decision version of the Shortest Vector Problem ($\mathrm{SVP}$) in the $\ell_2$-norm is $\mathrm{NP}$-complete on full-rank free submodules of $\mathcal{O}_K^2$ by a deterministic polynomial-time many-one reduction from Exact Cover by 3-Sets (X3C). The module rank is fixed at two. As a $\mathbb{Z}$-lattice, the module has rank $2(q-1)$, which grows with $q$. The main obstacle is closure under the action of $\mathcal{O}_K$. A module containing a nonzero vector also contains every scalar multiple of that vector by a nonzero element of $\mathcal{O}_K$, and some of these multiples may be shorter. Three ideas overcome this obstacle. First, we map the Bennett--Peikert Reed--Solomon lattice to a principal cyclotomic ideal and use Wan's point-count estimates to prove that a coset of this ideal contains many binary coefficient representatives. Second, a checker based on a quadratic Gauss sum turns the X3C equations into a canonical squared norm. Third, the checker and a second module coordinate combine with a separation bound for ideal cosets to rule out every unintended vector created by the $\mathcal{O}_K$-action. Each constructed instance consists of a prime $q\equiv3\pmod4$, two integral generators whose $2\times2$ generator matrix has nonzero determinant, and an integer squared threshold. The construction also gives $\mathrm{NP}$-hardness of search-$\mathrm{SVP}$ under polynomial-time Turing reductions.

Authors: Jiaqi Liu, Yansong Feng, Yanbin Pan

Let $q$ range over primes congruent to $3$ modulo $4$. Let $ζ_q$ be a primitive $q$th root of unity, and put $K=\mathbb{Q}(ζ_q)$, with ring of integers $\mathcal{O}_K=\mathbb{Z}[ζ_q]$. We prove that the decision version of the Shortest Vector Problem ($\mathrm{SVP}$) in the $\ell_2$-norm is $\mathrm{NP}$-complete on full-rank free submodules of $\mathcal{O}_K^2$ by a deterministic polynomial-time many-one reduction from Exact Cover by 3-Sets (X3C). The module rank is fixed at two. As a $\mathbb{Z}$-lattice, the module has rank $2(q-1)$, which grows with $q$. The main obstacle is closure under the action of $\mathcal{O}_K$. A module containing a nonzero vector also contains every scalar multiple of that vector by a nonzero element of $\mathcal{O}_K$, and some of these multiples may be shorter. Three ideas overcome this obstacle. First, we map the Bennett--Peikert Reed--Solomon lattice to a principal cyclotomic ideal and use Wan's point-count estimates to prove that a coset of this ideal contains many binary coefficient representatives. Second, a checker based on a quadratic Gauss sum turns the X3C equations into a canonical squared norm. Third, the checker and a second module coordinate combine with a separation bound for ideal cosets to rule out every unintended vector created by the $\mathcal{O}_K$-action. Each constructed instance consists of a prime $q\equiv3\pmod4$, two integral generators whose $2\times2$ generator matrix has nonzero determinant, and an integer squared threshold. The construction also gives $\mathrm{NP}$-hardness of search-$\mathrm{SVP}$ under polynomial-time Turing reductions.

Subgroup Accessibility in Group Order Logic

from arXiv: Computational Complexity

Authors: Anatole Dahan

We investigate the expressive power of fixed-point logics (FP) and their extensions in defining generating sets for accessible subgroups of definable permutation groups. This operation, computable in polynomial time via the Schreier-Sims algorithm, plays a central role in the group-theoretic approach to Graph Isomorphism and Graph Canonisation. In particular, it underpins polynomial-time canonisation for bounded colour-class graphs--a class for which no natural logic capturing P is currently known. We first show that this operation cannot, in general, be expressed in any logic for P. This limitation arises from the fact that accessible subgroups need not admit symmetric generating sets of polynomial size. However, we prove that when the base group admits a definable ordered generating set, the accessible subgroup operation becomes definable in fixed-point logic with the group order operator (FP + ord). This is achieved by partially simulating the Schreier-Sims algorithm within FP + ord. As a corollary, we show that fixed-point logic with counting (FPC) can also define the operation when the base group is abelian. In particular, FPC can define the automorphism group of any graph with abelian colours--despite being unable to canonise such graphs.

Authors: Anatole Dahan

We investigate the expressive power of fixed-point logics (FP) and their extensions in defining generating sets for accessible subgroups of definable permutation groups. This operation, computable in polynomial time via the Schreier-Sims algorithm, plays a central role in the group-theoretic approach to Graph Isomorphism and Graph Canonisation. In particular, it underpins polynomial-time canonisation for bounded colour-class graphs--a class for which no natural logic capturing P is currently known. We first show that this operation cannot, in general, be expressed in any logic for P. This limitation arises from the fact that accessible subgroups need not admit symmetric generating sets of polynomial size. However, we prove that when the base group admits a definable ordered generating set, the accessible subgroup operation becomes definable in fixed-point logic with the group order operator (FP + ord). This is achieved by partially simulating the Schreier-Sims algorithm within FP + ord. As a corollary, we show that fixed-point logic with counting (FPC) can also define the operation when the base group is abelian. In particular, FPC can define the automorphism group of any graph with abelian colours--despite being unable to canonise such graphs.

Bounded Relative Boundary Implies Narrow DNF Approximation

from arXiv: Computational Complexity

Authors: Chenghua Liu, Boning Meng

Friedgut conjectured that an increasing family in the $p$-biased discrete cube with bounded relative boundary can be approximated arbitrarily well by one whose minimal elements have bounded size, with a bound independent of the dimension and the bias (J. Amer. Math. Soc. 12 (1999)). We prove this conjecture by showing that, for $0

Authors: Chenghua Liu, Boning Meng

Friedgut conjectured that an increasing family in the $p$-biased discrete cube with bounded relative boundary can be approximated arbitrarily well by one whose minimal elements have bounded size, with a bound independent of the dimension and the bias (J. Amer. Math. Soc. 12 (1999)). We prove this conjecture by showing that, for $0

A Dichotomy for Complex Boolean Holant with Binary Disequality

from arXiv: Computational Complexity

Authors: Chenghua Liu, Boning Meng

We prove a complexity dichotomy for Boolean Holant problems defined by arbitrary finite sets of algebraic complex-valued signatures when binary disequality is available. The tractable cases are characterized by an explicit, decidable criterion.

Authors: Chenghua Liu, Boning Meng

We prove a complexity dichotomy for Boolean Holant problems defined by arbitrary finite sets of algebraic complex-valued signatures when binary disequality is available. The tractable cases are characterized by an explicit, decidable criterion.

Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation

from arXiv: Computational Geometry

Authors: Sebastien Tchitchek, Julien Tierny

This paper introduces the Sierpiński-Knopp (SK) Wasserstein distance, a fast metric between persistence diagrams. The SK-Wasserstein distance, denoted $d_{\mathrm{SK}}$, maps diagram points and their diagonal projections to the unit interval via the Sierpiński-Knopp space-filling curve on the upper diagonal triangle. The encoded point sets are then efficiently matched via one-dimensional optimal assignment, in \(O(N\log N)\) steps, yielding an explicit diagonal-aware point assignment between the two input persistence diagrams. We show that the SK-Wasserstein distance controls the classical \(2\)-Wasserstein distance between diagrams, admits an explicit isometric embedding into a Hilbert space, and induces a positive-definite Gaussian kernel, making the resulting geometry directly compatible with Euclidean and kernel-based learning methods. A tighter surrogate dissimilarity, noted \(W_Γ\), is also introduced based on the point assignments along the curve. Experiments on 12 scientific collections comprising 227 diagrams show median per-collection speedup of \(d_{\mathrm{SK}}\) over state-of-the-art approximations of \(W_2\) is \(626\times\), while the aggregate speedup over the full benchmark is \(2100\times\). Average-linkage partitions obtained from \(d_{\mathrm{SK}}\) and \(W_Γ\) each exactly match the corresponding \(W_2\) partition on 8 of the 12 collections. Hilbert \(k\)-means and Gaussian spectral clustering, both based on \(d_{\mathrm{SK}}\), achieve mean adjusted Rand indices (ARI) of \(0.756\) and \(0.800\), respectively, with respect to the benchmark reference partitions, compared to \(0.750\) obtained by average linkage on \(W_2\). The Gaussian \(d_{\mathrm{SK}}\) kernel supports other kernel-based analysis tasks, as illustrated by its use for contiguous segmentation of ordered diagram collections in our experiments.

Authors: Sebastien Tchitchek, Julien Tierny

This paper introduces the Sierpiński-Knopp (SK) Wasserstein distance, a fast metric between persistence diagrams. The SK-Wasserstein distance, denoted $d_{\mathrm{SK}}$, maps diagram points and their diagonal projections to the unit interval via the Sierpiński-Knopp space-filling curve on the upper diagonal triangle. The encoded point sets are then efficiently matched via one-dimensional optimal assignment, in \(O(N\log N)\) steps, yielding an explicit diagonal-aware point assignment between the two input persistence diagrams. We show that the SK-Wasserstein distance controls the classical \(2\)-Wasserstein distance between diagrams, admits an explicit isometric embedding into a Hilbert space, and induces a positive-definite Gaussian kernel, making the resulting geometry directly compatible with Euclidean and kernel-based learning methods. A tighter surrogate dissimilarity, noted \(W_Γ\), is also introduced based on the point assignments along the curve. Experiments on 12 scientific collections comprising 227 diagrams show median per-collection speedup of \(d_{\mathrm{SK}}\) over state-of-the-art approximations of \(W_2\) is \(626\times\), while the aggregate speedup over the full benchmark is \(2100\times\). Average-linkage partitions obtained from \(d_{\mathrm{SK}}\) and \(W_Γ\) each exactly match the corresponding \(W_2\) partition on 8 of the 12 collections. Hilbert \(k\)-means and Gaussian spectral clustering, both based on \(d_{\mathrm{SK}}\), achieve mean adjusted Rand indices (ARI) of \(0.756\) and \(0.800\), respectively, with respect to the benchmark reference partitions, compared to \(0.750\) obtained by average linkage on \(W_2\). The Gaussian \(d_{\mathrm{SK}}\) kernel supports other kernel-based analysis tasks, as illustrated by its use for contiguous segmentation of ordered diagram collections in our experiments.

Efficient K-Visibility Query in Polygons

from arXiv: Computational Geometry

Authors: Yeganeh Bahoo, Roni Sherman

This paper investigates $k$-visibility, where a line of sight can penetrate up to $k$ obstacles. While computing the $k$-visibility polygon from a single query point is well-studied, existing spatial preprocessing approaches rely on full $O(n^2)$ line arrangements through all vertex pairs without characterizing the minimal set of topological boundaries. We present a refined cell decomposition framework that isolates the exact geometric events governing $k$-visibility: primary vertex horizon lines and secondary mutually critical hinge lines. We prove that this minimal set of partition lines yields a spatial decomposition of $Θ(n^4)$ cells within which the combinatorial structure of the $k$-visibility polygon remains strictly invariant. By leveraging a combinatorial $δ$-compression scheme across cell boundaries, we achieve an overall storage complexity of $\mathcal{O}(n^4)$ while supporting optimal $\mathcal{O}(\log n + m)$ query time to reconstruct explicit $k$-visibility polygons of size $m$. Our framework naturally extends to polygons containing holes.

Authors: Yeganeh Bahoo, Roni Sherman

This paper investigates $k$-visibility, where a line of sight can penetrate up to $k$ obstacles. While computing the $k$-visibility polygon from a single query point is well-studied, existing spatial preprocessing approaches rely on full $O(n^2)$ line arrangements through all vertex pairs without characterizing the minimal set of topological boundaries. We present a refined cell decomposition framework that isolates the exact geometric events governing $k$-visibility: primary vertex horizon lines and secondary mutually critical hinge lines. We prove that this minimal set of partition lines yields a spatial decomposition of $Θ(n^4)$ cells within which the combinatorial structure of the $k$-visibility polygon remains strictly invariant. By leveraging a combinatorial $δ$-compression scheme across cell boundaries, we achieve an overall storage complexity of $\mathcal{O}(n^4)$ while supporting optimal $\mathcal{O}(\log n + m)$ query time to reconstruct explicit $k$-visibility polygons of size $m$. Our framework naturally extends to polygons containing holes.

Exact curve counting of given word length on the once-punctured torus

from arXiv: Computational Geometry

Authors: Filippo Baroni, David Fisac, Mingkun Liu

On the once-punctured torus, we give an exact formula for the number of curves in any given mapping class group orbit of given word length. This settles a conjecture of Chas in [Cha16].

Authors: Filippo Baroni, David Fisac, Mingkun Liu

On the once-punctured torus, we give an exact formula for the number of curves in any given mapping class group orbit of given word length. This settles a conjecture of Chas in [Cha16].

The Discrete Harmonic Center of a Quadrilateral

from arXiv: Computational Geometry

Authors: Marc Alexa

Triangulate a simple quadrilateral by connecting all vertices to an additional point. If the vertices carry values, the piecewise linear function can be assigned a Dirichlet energy. We show that the minimal Dirichlet energy as a function of the location of the inserted point is convex, and the location of the minimum is independent of the values at the corners - a quadrilateral has a discrete harmonic center, characterized by an equilibrium of currents across the inserted edges. It turns out that the fixed points of the Möbius involution swapping opposite corners of the quadrilateral are critical points of this energy, so the discrete harmonic center is Möbius-covariant. For tangential and cyclic quadrilaterals the center admits simple closed forms related to the circle centers. The center and its data-independence generalize to polytopes with d + 2 vertices in dimension d, but the conformal characterizations are special to four points in the plane.

Authors: Marc Alexa

Triangulate a simple quadrilateral by connecting all vertices to an additional point. If the vertices carry values, the piecewise linear function can be assigned a Dirichlet energy. We show that the minimal Dirichlet energy as a function of the location of the inserted point is convex, and the location of the minimum is independent of the values at the corners - a quadrilateral has a discrete harmonic center, characterized by an equilibrium of currents across the inserted edges. It turns out that the fixed points of the Möbius involution swapping opposite corners of the quadrilateral are critical points of this energy, so the discrete harmonic center is Möbius-covariant. For tangential and cyclic quadrilaterals the center admits simple closed forms related to the circle centers. The center and its data-independence generalize to polytopes with d + 2 vertices in dimension d, but the conformal characterizations are special to four points in the plane.

A Dimension-Reducing Fréchet Simplification Oracle

from arXiv: Computational Geometry

Authors: Boris Aronov, Tsuri Farhana, Matthew J. Katz, Indu Ramesh

Let $P$ be a polygonal curve with $n$ vertices in the plane. We construct a data structure of size $O(n \log n)$ suited for simplification queries of the following kind. Given a query line $\ell$ and an integer $k\ge1$, find a curve $Q$ on $\ell$ with at most $k$ vertices that minimizes the discrete Fréchet distance to $P$, among all such curves. Using our data structure, a query can be handled in $O(k^2 \log^3 n + k\log^4 n)$ time. More generally, a geometric tree $T$ on $n$ vertices in the plane can be preprocessed into a near-linear-size structure so that, given a pair $u$, $v$ of its vertices, a line $\ell$, and an integer $k\ge1$, one can find a curve $Q$ on $\ell$ with at most $k$ vertices that minimizes the discrete Fréchet distance to the path from $u$ to $v$ in $T$, in time $O(k^2 \mathop{polylog} n)$. For the general dimension-reduction problem, where $P$ is a curve in $\mathbb{R}^d$ ($d \ge 3$), $0 < \varepsilon_0 < 1$ is a real parameter, and a query specifies a $g$-flat $h$ ($1 \le g \le d-1$) and an integer $k \ge 1$, we construct a data structure of size $O(n\log n + f(\varepsilon_0) n)$, where $f(\varepsilon_0)=(1+1/\varepsilon_0)^{(d-1)/2}$, that allows us to find a curve $Q$ on $h$ with at most $k$ vertices, whose discrete Fréchet distance to $P$ is at most $1+\varepsilon_0$ times the distance of $Q^*$ to $P$, where $Q^*$ is such a curve that minimizes the distance to $P$. The query handling time is $O(f(\varepsilon_0) k^2 \log^2 n)$.

Authors: Boris Aronov, Tsuri Farhana, Matthew J. Katz, Indu Ramesh

Let $P$ be a polygonal curve with $n$ vertices in the plane. We construct a data structure of size $O(n \log n)$ suited for simplification queries of the following kind. Given a query line $\ell$ and an integer $k\ge1$, find a curve $Q$ on $\ell$ with at most $k$ vertices that minimizes the discrete Fréchet distance to $P$, among all such curves. Using our data structure, a query can be handled in $O(k^2 \log^3 n + k\log^4 n)$ time. More generally, a geometric tree $T$ on $n$ vertices in the plane can be preprocessed into a near-linear-size structure so that, given a pair $u$, $v$ of its vertices, a line $\ell$, and an integer $k\ge1$, one can find a curve $Q$ on $\ell$ with at most $k$ vertices that minimizes the discrete Fréchet distance to the path from $u$ to $v$ in $T$, in time $O(k^2 \mathop{polylog} n)$. For the general dimension-reduction problem, where $P$ is a curve in $\mathbb{R}^d$ ($d \ge 3$), $0 < \varepsilon_0 < 1$ is a real parameter, and a query specifies a $g$-flat $h$ ($1 \le g \le d-1$) and an integer $k \ge 1$, we construct a data structure of size $O(n\log n + f(\varepsilon_0) n)$, where $f(\varepsilon_0)=(1+1/\varepsilon_0)^{(d-1)/2}$, that allows us to find a curve $Q$ on $h$ with at most $k$ vertices, whose discrete Fréchet distance to $P$ is at most $1+\varepsilon_0$ times the distance of $Q^*$ to $P$, where $Q^*$ is such a curve that minimizes the distance to $P$. The query handling time is $O(f(\varepsilon_0) k^2 \log^2 n)$.

Albertson's Conjecture Holds for r at Most 26

from arXiv: Computational Geometry

Authors: Ankan Sadhu

Albertson conjectured that every graph with chromatic number r has crossing number at least cr(K_r). The conjecture was verified for r <= 12 by Albertson, Cranston and Fox, for r <= 16 by Bar'at and T'oth, for r <= 18 by Ackerman, and recently for r <= 24 by Cranston, who reduced the remaining cases r in {25, 26} to three orders. We settle those three orders, so that Albertson's Conjecture holds for all r <= 26. Only published results are used, and an appendix reproves the range 19 <= r <= 24 so that the case r <= 26 does not rest on unpublished work. We also show that if chi(G) = 27 and cr(G) < cr(K_27), then G has a 27-critical subgraph of order 53 or 54 whose complement is connected.

Authors: Ankan Sadhu

Albertson conjectured that every graph with chromatic number r has crossing number at least cr(K_r). The conjecture was verified for r <= 12 by Albertson, Cranston and Fox, for r <= 16 by Bar'at and T'oth, for r <= 18 by Ackerman, and recently for r <= 24 by Cranston, who reduced the remaining cases r in {25, 26} to three orders. We settle those three orders, so that Albertson's Conjecture holds for all r <= 26. Only published results are used, and an appendix reproves the range 19 <= r <= 24 so that the case r <= 26 does not rest on unpublished work. We also show that if chi(G) = 27 and cr(G) < cr(K_27), then G has a 27-critical subgraph of order 53 or 54 whose complement is connected.

Disproving the Greedy Superstring Conjecture

from arXiv: Data Structures and Algorithms

Authors: Hiroki Shibata

The shortest common superstring problem is to find the shortest string that contains every string in a given set as a substring. It is conjectured that the greedy algorithm that repeatedly selects a pair of strings with maximum overlap and merges them is a $2$-approximation algorithm, and this conjecture had remained open for nearly four decades. In this paper, we disprove this conjecture and show that the approximation ratio of this algorithm is at least $9/4$.

Authors: Hiroki Shibata

The shortest common superstring problem is to find the shortest string that contains every string in a given set as a substring. It is conjectured that the greedy algorithm that repeatedly selects a pair of strings with maximum overlap and merges them is a $2$-approximation algorithm, and this conjecture had remained open for nearly four decades. In this paper, we disprove this conjecture and show that the approximation ratio of this algorithm is at least $9/4$.

Sensitivity Oracles for Matroid Packing, Matroid Covering, and Matching Problems with Applications

from arXiv: Data Structures and Algorithms

Authors: Keerti Choudhary, Amit Kumar, Lakshay Saggi

Sensitivity oracles preprocess a graph so that queries can be answered after any $f$ edge insertions and deletions, without recomputing from scratch. For structural optimization problems the known landscape is limited: for flows and cuts, all known compact oracles handle only $f\le2$ failures; existing oracles for $s$- and global min-cut apply only to undirected graphs; and for matchings, arborescence and spanning-tree packings, and arboricity, no efficient oracle is known for $f>1$. We present a unified algebraic framework based on sensitivity oracles for matroid packing, covering, and parity of sparse linear matroids, yielding the first oracles supporting an arbitrary number $f$ of updates across all of these problems (all constructions randomized Monte-Carlo). Concretely, we obtain efficient oracles for exact $(s,t)$-max-flow/min-cut, resolving an open problem of Baswana, Bhanja, and Pandey (ICALP'22) with near-optimal space; for all-pairs $k$-bounded flow, generalizing the near-optimal reachability oracle of Brand and Saranurak (FOCS'19, the case $k=1$); the first oracles for any $f$ for directed $s$- and global min-cut; oracles for $k$-disjoint arborescences, $k$-disjoint spanning trees, colorful spanning trees, and arboricity; and oracles for the existence of an $α$-factor, with perfect matching as the case $α=1$. We further introduce the \emph{subset sensitivity model}, in which updates are confined to a susceptible edge set of size $σ$ fixed during preprocessing. Here we decouple updates from the matroid representation and eliminate the dependence on $k$ and the matroid density altogether: all of the above are supported with $\widetilde O(f^ω)$ query time and $O(fσ^2)$ space. We also prove a matching $Ω(\min\{σ^2,n^2\})$-bit lower bound when $f\ge2$, establishing optimality.

Authors: Keerti Choudhary, Amit Kumar, Lakshay Saggi

Sensitivity oracles preprocess a graph so that queries can be answered after any $f$ edge insertions and deletions, without recomputing from scratch. For structural optimization problems the known landscape is limited: for flows and cuts, all known compact oracles handle only $f\le2$ failures; existing oracles for $s$- and global min-cut apply only to undirected graphs; and for matchings, arborescence and spanning-tree packings, and arboricity, no efficient oracle is known for $f>1$. We present a unified algebraic framework based on sensitivity oracles for matroid packing, covering, and parity of sparse linear matroids, yielding the first oracles supporting an arbitrary number $f$ of updates across all of these problems (all constructions randomized Monte-Carlo). Concretely, we obtain efficient oracles for exact $(s,t)$-max-flow/min-cut, resolving an open problem of Baswana, Bhanja, and Pandey (ICALP'22) with near-optimal space; for all-pairs $k$-bounded flow, generalizing the near-optimal reachability oracle of Brand and Saranurak (FOCS'19, the case $k=1$); the first oracles for any $f$ for directed $s$- and global min-cut; oracles for $k$-disjoint arborescences, $k$-disjoint spanning trees, colorful spanning trees, and arboricity; and oracles for the existence of an $α$-factor, with perfect matching as the case $α=1$. We further introduce the \emph{subset sensitivity model}, in which updates are confined to a susceptible edge set of size $σ$ fixed during preprocessing. Here we decouple updates from the matroid representation and eliminate the dependence on $k$ and the matroid density altogether: all of the above are supported with $\widetilde O(f^ω)$ query time and $O(fσ^2)$ space. We also prove a matching $Ω(\min\{σ^2,n^2\})$-bit lower bound when $f\ge2$, establishing optimality.

Kernelization of 2-Club Cluster Edge Deletion on Interval Graphs

from arXiv: Data Structures and Algorithms

Authors: Ajinkya Gaikwad

The \emph{$s$-Club Cluster Edge Deletion} problem asks whether, given a graph $G$ and an integer $k$, one can delete at most $k$ edges so that every remaining connected component has diameter at most~$s$. This generalizes the classical \emph{Cluster Edge Deletion} problem by permitting components of bounded diameter instead of requiring cliques. On general graphs, $2$-Club Cluster Edge Deletion is known to be fixed-parameter tractable when parameterized by $k$, but it remains open whether it admits a polynomial kernel, as posed in~\cite{ABUKHZAM2023113864}. Motivated by this question, we study the problem on interval graphs and obtain a polynomial vertex kernel of size $\mathcal{O}(k^{5})$. As a complementary result, we also show that the \emph{$s$-Club Cluster Edge Deletion} problem is polynomial time solvable on unit interval graphs. We also show that $2$-Club Cluster Edge Deletion is NP-hard even on split graphs.

Authors: Ajinkya Gaikwad

The \emph{$s$-Club Cluster Edge Deletion} problem asks whether, given a graph $G$ and an integer $k$, one can delete at most $k$ edges so that every remaining connected component has diameter at most~$s$. This generalizes the classical \emph{Cluster Edge Deletion} problem by permitting components of bounded diameter instead of requiring cliques. On general graphs, $2$-Club Cluster Edge Deletion is known to be fixed-parameter tractable when parameterized by $k$, but it remains open whether it admits a polynomial kernel, as posed in~\cite{ABUKHZAM2023113864}. Motivated by this question, we study the problem on interval graphs and obtain a polynomial vertex kernel of size $\mathcal{O}(k^{5})$. As a complementary result, we also show that the \emph{$s$-Club Cluster Edge Deletion} problem is polynomial time solvable on unit interval graphs. We also show that $2$-Club Cluster Edge Deletion is NP-hard even on split graphs.

Random-Priority Frontier Routing: Tight $Θ(n^c)$ Bounds Against $c$-Node Cartels

from arXiv: Data Structures and Algorithms

Authors: Krišjānis Petručena

We study path diversification in trusted-node networks, where sensitive material is relayed through intermediate nodes, some of which may be compromised. Our randomized routing rule assigns each vertex an independent random priority and repeatedly expands the highest-priority vertex on the global frontier of the explored region. Let $G$ have $n$ vertices, let $s,t$ be honest endpoints, and let $C$ be a set of $c$ compromised intermediate vertices, called a cartel, whose deletion leaves $s$ and $t$ connected. For every fixed $c$ and every fixed target probability $q\in(0,1)$, we prove that $Θ(n^c)$ independent executions are sufficient in the worst case for some route to avoid $C$ with probability at least $q$.

Authors: Krišjānis Petručena

We study path diversification in trusted-node networks, where sensitive material is relayed through intermediate nodes, some of which may be compromised. Our randomized routing rule assigns each vertex an independent random priority and repeatedly expands the highest-priority vertex on the global frontier of the explored region. Let $G$ have $n$ vertices, let $s,t$ be honest endpoints, and let $C$ be a set of $c$ compromised intermediate vertices, called a cartel, whose deletion leaves $s$ and $t$ connected. For every fixed $c$ and every fixed target probability $q\in(0,1)$, we prove that $Θ(n^c)$ independent executions are sufficient in the worst case for some route to avoid $C$ with probability at least $q$.

Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption

from arXiv: Data Structures and Algorithms

Authors: Patrick Wong

An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly multiple posted prices. The operational decision is not merely which model answers a prompt. A price changes purchase probability, a token cap changes both user value and the tail of resource consumption, and accepted requests compete for shared compute and premium-model capacity. Demand and output length are initially uncertain, while an offline model may provide useful but imperfect predictions. We formulate sequential pricing and admission with stochastic resource consumption. Each arriving request belongs to an observable segment. The platform chooses a product--price pair or makes no offer; purchase, revenue, and resource use are then random. An offline predictor supplies a uniform, validated error radius for every segment--product cell. We propose Prediction-Clipped UCB (PCUCB), which intersects the offline prediction interval with an online confidence interval, evaluates products using resource shadow prices, and reserves a sample-path envelope before commitment. The prior gives a fast start when accurate, while online learning protects the platform when predictions are coarse. The analysis is modular. On a simultaneous confidence event, regret against a buffered fluid benchmark is bounded by a pacing term plus the cumulative diameter of the intersected intervals. For $J$ segment-product cells and prediction radius $\varepsilon$, this yields \[ \widetilde O\left( \sqrt{T}+(1+\barΛ) \min\{T\varepsilon,\sqrt{JT}\} \right), \] where $\barΛ$ bounds operational shadow prices. Thus the algorithm smoothly interpolates between an almost full-information regime and learning from scratch. Hard feasibility holds on every sample path through reservation envelopes.

Authors: Patrick Wong

An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly multiple posted prices. The operational decision is not merely which model answers a prompt. A price changes purchase probability, a token cap changes both user value and the tail of resource consumption, and accepted requests compete for shared compute and premium-model capacity. Demand and output length are initially uncertain, while an offline model may provide useful but imperfect predictions. We formulate sequential pricing and admission with stochastic resource consumption. Each arriving request belongs to an observable segment. The platform chooses a product--price pair or makes no offer; purchase, revenue, and resource use are then random. An offline predictor supplies a uniform, validated error radius for every segment--product cell. We propose Prediction-Clipped UCB (PCUCB), which intersects the offline prediction interval with an online confidence interval, evaluates products using resource shadow prices, and reserves a sample-path envelope before commitment. The prior gives a fast start when accurate, while online learning protects the platform when predictions are coarse. The analysis is modular. On a simultaneous confidence event, regret against a buffered fluid benchmark is bounded by a pacing term plus the cumulative diameter of the intersected intervals. For $J$ segment-product cells and prediction radius $\varepsilon$, this yields \[ \widetilde O\left( \sqrt{T}+(1+\barΛ) \min\{T\varepsilon,\sqrt{JT}\} \right), \] where $\barΛ$ bounds operational shadow prices. Thus the algorithm smoothly interpolates between an almost full-information regime and learning from scratch. Hard feasibility holds on every sample path through reservation envelopes.

Two-State Max-Plus Comparison Is Decidable

from arXiv: Data Structures and Algorithms

Authors: Keigo Oka

Daviaud, Guillon, and Merlet proved that comparison of max-plus automata is undecidable under a fixed state bound of 553 and explicitly left the range from 2 to 552 states open. We resolve the two-state endpoint. More strongly, given an arbitrary finite max-plus automaton $A$ and a max-plus automaton $B$ with at most two states, it is decidable whether $[\![A]\!](w)\leq [\![B]\!](w)$ for every word $w$. The structural reason is a one-dimensional projective normal form for two-state dynamics. Outside an effective bounded region, a transition has one of three tail behaviors: it propagates the unbounded projective gap with gap-independent height increment, forgets the gap with gap-independent height increment, or reads the gap magnitude into the height increment and then forgets it. In particular, any transition whose output depends on the unbounded gap necessarily destroys that gap. This yields an exact one-counter realization of $B$. Effective semilinearity of context-free Parikh images then reduces comparison to Presburger arithmetic. As a consequence, two-state max-plus comparison, equivalence, and positivity are decidable.

Authors: Keigo Oka

Daviaud, Guillon, and Merlet proved that comparison of max-plus automata is undecidable under a fixed state bound of 553 and explicitly left the range from 2 to 552 states open. We resolve the two-state endpoint. More strongly, given an arbitrary finite max-plus automaton $A$ and a max-plus automaton $B$ with at most two states, it is decidable whether $[\![A]\!](w)\leq [\![B]\!](w)$ for every word $w$. The structural reason is a one-dimensional projective normal form for two-state dynamics. Outside an effective bounded region, a transition has one of three tail behaviors: it propagates the unbounded projective gap with gap-independent height increment, forgets the gap with gap-independent height increment, or reads the gap magnitude into the height increment and then forgets it. In particular, any transition whose output depends on the unbounded gap necessarily destroys that gap. This yields an exact one-counter realization of $B$. Effective semilinearity of context-free Parikh images then reduces comparison to Presburger arithmetic. As a consequence, two-state max-plus comparison, equivalence, and positivity are decidable.

Tuesday, September 01

TR26-163 | Quantitative Results on Super-Ramanujan Graphs | Gil Cohen, Gal Maor

from ECCC Papers

This work studies super-Ramanujan graphs: \(d\)-regular graphs whose spectral expansion lies strictly below the Ramanujan threshold \(2\sqrt{d-1}\). Although this threshold is asymptotically optimal for fixed \(d\) as \(n\) tends to infinity, it leaves open the finer question of how small the spectral expansion can be as a joint function of \(n\) and \(d\). Beyond their fundamental mathematical interest and potential applications to pseudorandomness, super-Ramanujan graphs may also have practical significance, since asymptotic analyses can obscure important finite-size effects. Our first result shows that, for every \(d\geq 3\) and every even \(n\), there exists a \(d\)-regular graph \(G\) on \(n\) vertices satisfying \[ \lambda_2(G) \leq 2\sqrt{d-1} - \frac{\sqrt{d}}{4n^{2/3}}. \] For each fixed \(d\), an asymptotic version of this bound also follows from the recent breakthrough work of Huang, McKenzie, and Yau. Related edge-universality results of Huang and Yau (The Annals of Probability, 2026) imply a bound of the same order in the regime \( n^\varepsilon\leq d\leq n^{1/3-\varepsilon}, \) for any fixed sufficiently small \(\varepsilon>0\), while He (Commun. Math. Phys., 2024) obtains a substantially larger advantage in the denser regime \(d\gg n^{2/3}\). These works provide a much richer probabilistic description of the spectral edge, whereas our proof is considerably simpler and gives a nonasymptotic bound that is uniform in both \(n\) and \(d\). Turning to explicit constructions, it is folklore that the Lubotzky-Phillips-Sarnak graphs are super-Ramanujan, but their guaranteed advantage over the Ramanujan threshold is only exponentially small in \(n\). Our second result gives a polynomial-time construction of bipartite super-Ramanujan expanders with advantage at least \( \frac{\sqrt{d}}{2n} \) over the Ramanujan threshold.
This work studies super-Ramanujan graphs: \(d\)-regular graphs whose spectral expansion lies strictly below the Ramanujan threshold \(2\sqrt{d-1}\). Although this threshold is asymptotically optimal for fixed \(d\) as \(n\) tends to infinity, it leaves open the finer question of how small the spectral expansion can be as a joint function of \(n\) and \(d\). Beyond their fundamental mathematical interest and potential applications to pseudorandomness, super-Ramanujan graphs may also have practical significance, since asymptotic analyses can obscure important finite-size effects. Our first result shows that, for every \(d\geq 3\) and every even \(n\), there exists a \(d\)-regular graph \(G\) on \(n\) vertices satisfying \[ \lambda_2(G) \leq 2\sqrt{d-1} - \frac{\sqrt{d}}{4n^{2/3}}. \] For each fixed \(d\), an asymptotic version of this bound also follows from the recent breakthrough work of Huang, McKenzie, and Yau. Related edge-universality results of Huang and Yau (The Annals of Probability, 2026) imply a bound of the same order in the regime \( n^\varepsilon\leq d\leq n^{1/3-\varepsilon}, \) for any fixed sufficiently small \(\varepsilon>0\), while He (Commun. Math. Phys., 2024) obtains a substantially larger advantage in the denser regime \(d\gg n^{2/3}\). These works provide a much richer probabilistic description of the spectral edge, whereas our proof is considerably simpler and gives a nonasymptotic bound that is uniform in both \(n\) and \(d\). Turning to explicit constructions, it is folklore that the Lubotzky-Phillips-Sarnak graphs are super-Ramanujan, but their guaranteed advantage over the Ramanujan threshold is only exponentially small in \(n\). Our second result gives a polynomial-time construction of bipartite super-Ramanujan expanders with advantage at least \( \frac{\sqrt{d}}{2n} \) over the Ramanujan threshold.

LLMs and self-referentiality

from Scott Aaronson

I woke up yesterday with the following thoughts, which are probably either obvious or dumb. A central thesis that many readers, including me, took from Douglas Hofstadter’s Gödel Escher Bach when young was that the secret of intelligence (and therefore, of AI) was going to have a lot to do with self-referentiality and “strange loops.” […]

I woke up yesterday with the following thoughts, which are probably either obvious or dumb.

A central thesis that many readers, including me, took from Douglas Hofstadter’s Gödel Escher Bach when young was that the secret of intelligence (and therefore, of AI) was going to have a lot to do with self-referentiality and “strange loops.”

Even Roger Penrose’s The Emperor’s New Mind, which in some ways was the anti-GEB, ironically agreed with GEB about the fundamental importance of self-reference to the success or failure of the whole AI project. It claimed (incorrectly, in my view and in most experts’) that AI could never work because there was something about Gödel’s Theorem and self-reference that no computer program could ever capture, but that could be captured by exotic physics accessible to the human brain.

Now, in 2026, we’ve succeeded at building AIs that outperform most humans at most intellectual tasks that are well-defined enough to judge. And at no point in the tech stack of those AIs — neither in the transformer neural nets, nor in the GPU clusters they run on, nor in the training process, nor anywhere else — did anyone need to build in anything about self-reference. (Excepting, eg, the system instructions that tell the model about its role and identity, which aren’t needed for intelligent behavior. Also, I’m not going to count the autoregressive nature of LLMs as “self-referential”; that’s just dynamical feedback.)

Of course, GPT 5.6 Pro and Fable can talk about themselves, about Gödel’s Theorem, about self-reference, about what we’re talking about right now, all of it, better than most humans. But at no point did anyone need to build self-referential abilities in. They popped out as a byproduct of the same pretraining that let the models talk about Pokémon and long-chain polymers and cognitive behavioral therapy and plate tectonics and everything else.

No wonder Hofstadter says he’s been stunned by the success of LLMs, and has seemed depressed about current AI capabilities in essays like this one. He’s way too smart to deny what’s happened or invent reasons why it doesn’t really count (the approach many have taken). But he realizes that we now have true conversational intelligence from a path that the GEB worldview would’ve regarded as far too cheap and simple, and that certainly has no “strange loops” built in anywhere.

Of course, a Hofstadterian could argue that a strange loop emerges in LLMs — indeed, nothing in GEB ever said that strange loops would need to be explicitly engineered at the outset. But would anyone who hadn’t been brought up on GEB arrive at this as a useful way of thinking about LLMs?

What can we say about this with hindsight? While the ideas of diagonalization and self-reference of course played a central role in the birth of modern mathematical logic and computer science, the most famous uses were negative: there is not a bijectjon between the natural numbers and the reals. There is not a complete sound proof system for arithmetic. There is not an algorithm to solve the halting problem.

If your goal was only to build the axioms of ZFC and the rules of first-order inference, or build an electronic computer, you wouldn’t explicitly need self-reference for that. You would just … start building, taking care that your instruction set didn’t fall short of universality.

Yes, ZFC can formalize and prove theorems about itself. Yes, electronic computers can run programs that take their own code as input. But no one ever needed to build those abilities in, any more than self-reference needed to be built in to the alphabet or the rules of grammar. It popped out as a free byproduct of universality.

In the same way, LLMs’ ability to talk about themselves popped out as a byproduct of their ability to talk about anything in the discourse universe they were trained on. The big, old ideas about intelligence that ended up basically vindicated were the ideas about how intelligence is about prediction, and prediction is about compression, and compression is about finding better and better upper bounds on Kolmogorov complexity. Not the self-reference stuff. (Although, if you wanted to know why Kolmogorov complexity can’t be computed perfectly, that negative statement would again require a self-referential argument.)

What’s left? Consciousness and subjective experience of course remain extremely mysterious. For all we know, Hofstadter could be right that those have something to do with self-reference. (For all we know, even Penrose could be right that they have something to do with exotic physics accessible to biological brains but not digital computers!)

But the idea that you’d need explicit self-referentiality before you could get convincing and world-changing conversational intelligence? Let it be buried in a Westminster Abbey or Arlington National Cemetery for the most important wrong ideas in human history — geocentrism, Aristotle’s teleological physics, aether, phlogiston, Freud’s psychology, Marx’s prediction of a workers’ uprising followed by a classless utopia, etc. But buried it needs to be.

By Scott

Halley The Superforecaster

from Ben Recht

Edmond Halley and the multiple purposes of forecasting.

Hi there, argmin readers! As the fall semester picks up, posting volume will, too. So I’m going to commit to writing short descriptive headers to help you sort through the different threads. Today’s post is a live blog of Lecture 2 of my graduate seminar “Forecasting: A Critical Retrospective.” A table of contents is here.

I’m always looking for how we did science and engineering before the social structures and norms were built up to normalize practice. So for a class on forecasting, let me ask how experts made forecasts before they had proper scoring rules, Bayesian statistics, and Stata.

Though there are plenty of places to start the search, why don’t we today look to the dawn of rationalism in the Enlightenment? Patterns, Predictions, and Actions opens and closes with stories about Edmond Halley. Halley was a master of prediction. He had a keen sense of statistical approximation, knowing how to round and manipulate data to explain the past and predict the future. Halley’s publication record showed a man obsessed with a wide range of forecasting applications.

Halley is best known for his comet, a celestial body that returns to our skies once every 76 years. [footnote: It’s due to return in 2061, but many people’s AGI timelines suggest we won’t be around to see it.] Halley, a strong proponent of Isaac Newton who helped fund the publication of the Principia, wanted to find definitive evidence to prove Newton right.

His proof would come from observations of a comet he had charted in his backyard in 1682. Using Newton’s rules, Halley computed the orbital parameters of the body. He found these parameters neatly matched those of comets observed by Johannes Kepler in 1607. Moreover, they matched those of one seen by German astronomer Petrus Apianus in 1531. Since the gaps between these observations were around 76 years, Halley forecast another observation in 1758. He’d die before its return, but he was right.

This successful celestial prediction is heralded as a crowning achievement of Enlightenment Science. In 1850, Yale astronomer Denison Olmsted, who observed the comet’s return in 1835, wrote, “The Return of Halley’s Comet in exact conformity with the predictions of astronomers established the truth of all those principles by which those predictions were made.” Explaining data we’ve already seen is fine, but there is nothing more convincing to scientists than when theory predicts the future.

The funny thing about this, and a theme we’ll frequently return to this semester, is that this conclusion is completely illogical. It’s a lovely example of affirming the consequent, a fallacy you’ll learn in an introductory logic course.

P implies Q
Q is true
Therefore, P is true.

This syllogism is clearly invalid. (“All the Rationalists live in Berkeley. Ben lives in Berkeley. Therefore, Ben is a Rationalist.” How dare you!) But this is how a lot of science works! Theory predicts a particular outcome. That outcome is observed. This makes scientists feel more convinced their theory is right.

We could go into a long rigmarole about logical positivism at this point, but I don’t want to argue with Bayesian epistemologists today. I just want to point out that even at the inception of the Enlightenment, science was based on the illogic of accurately divining the future. This is one of the things we love about forecasts. When we accurately predict the future, we feel like our internal narrative is true.

Halley’s intuition about forecasting extended far beyond the heavens. He was also a key contributor to modern demography and actuarial science. Protestant pastor Caspar Neumann had collected records of lives, births, and deaths in his hometown of Wroclaw in Poland. Neumann was apparently interested in using this data to disprove the existence of climacterics, where deaths were associated with specific ages like 63. Halley, who came across this data after Leibniz presented it to the Royal Society, had other predictive interests. He churned through Neumann’s data and produced his foundational actuarial life table.

The numbers here represent the counts of people of any given age at a particular snapshot. There were approximately 1000 infants between 0 and 1 (a number, perhaps a bit too convenient), and a total of approximately 34000 individuals in Wroclaw.

In presenting his table to the Royal Society, Halley saw numerous uses for it. He first explained how his table could be used to calculate the number of men draftable into the army. He computed this by counting the number of people aged 18 to 56 and dividing by two.

More relevant to our class, he also pioneered probabilistic forecasting. His second claimed use of the table was calculating the odds that someone might die in a particular time interval. To do this, he counted frequencies and assumed rates of the past were indicative of chance in the future. There were 567 people aged 25, and 560 aged 26. Therefore, the odds a 25-year-old lives to see 26 were 560 to 7 or, simplifying fractions, 80 to 1. Similarly, if you wanted to know the odds that person might live ten years, Halley advocated taking the number alive by age 35, 490, and computing odds: 490 to 77, or approximately 6 to 1.

Halley also used his table to estimate how long people would live by finding the point in a table at which the odds of living dropped below 2 to 1. He called this “The age to which it is an even wager.” Even back in the day before probability, people equated forecasts with fair betting odds.

Not surprisingly, if you could equate mortality forecasts with betting, you could price insurance. This was Halley’s fourth proposed use of his table. Similarly, for a fifth application, Halley worked out more sophisticated calculations and determined a clever scheme to value annuities, a popular means for the crown to raise money. Halley’s calculations set different prices for different ages, based on bets on how long an annuitant might live.

The life table is only one example of Halley’s keen sense that you could make forecasts without physics. Indeed, he seemed to appreciate that the key to forecasting was simply linking past observations with future extrapolations. These extrapolations could be used to confirm physics and create a shared model of reality. They could also guide the pricing of financial instruments wagering on matters of life and death. For Halley, as for us in this class, predicting the future served multiple purposes.

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By Ben Recht

On the Complexity of the Compatibility Problem for Succinctly Encoded Conditional Distributions

from arXiv: Computational Complexity

Authors: Guy Emerson

The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used to make predictions in the form of conditional probabilities. However, a pair of conditional distributions p(x|y) and p(y|x) may not be compatible with any joint distribution p(x,y). Given two such conditionals, determining if there exists a compatible joint is known as the compatibility problem. For discrete random variables, when the conditionals are encoded as probability tables, the compatibility problem has a known solution, which is computationally tractable. In this paper, we formalise and study a succinct version of the problem, encoding conditional distributions as arithmetic circuits. This is applicable to practical applications of probabilistic modelling in high-dimensional settings, including neural network models. We show that, for succinct circuit representations of conditionals, the compatibility problem is intractable. In the case that all probabilities are non-zero, the problem is co-NP-complete. In the case that probabilities can be zero, we give examples to demonstrate that several notions of compatibility can be distinguished, and we prove that multiple versions of the problem are PSPACE-complete. Furthermore, we show that, assuming the polynomial hierarchy does not collapse, there exist compatible succinct conditionals whose joint cannot be expressed succinctly. Implications of these results for probabilistic modelling and machine learning are discussed.

Authors: Guy Emerson

The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used to make predictions in the form of conditional probabilities. However, a pair of conditional distributions p(x|y) and p(y|x) may not be compatible with any joint distribution p(x,y). Given two such conditionals, determining if there exists a compatible joint is known as the compatibility problem. For discrete random variables, when the conditionals are encoded as probability tables, the compatibility problem has a known solution, which is computationally tractable. In this paper, we formalise and study a succinct version of the problem, encoding conditional distributions as arithmetic circuits. This is applicable to practical applications of probabilistic modelling in high-dimensional settings, including neural network models. We show that, for succinct circuit representations of conditionals, the compatibility problem is intractable. In the case that all probabilities are non-zero, the problem is co-NP-complete. In the case that probabilities can be zero, we give examples to demonstrate that several notions of compatibility can be distinguished, and we prove that multiple versions of the problem are PSPACE-complete. Furthermore, we show that, assuming the polynomial hierarchy does not collapse, there exist compatible succinct conditionals whose joint cannot be expressed succinctly. Implications of these results for probabilistic modelling and machine learning are discussed.

The (Parameterized) Complexity of Ordering a Graph While Avoiding a Forbidden Pattern

from arXiv: Computational Complexity

Authors: Thomas Depian, Simon D. Fink, Alexander Firbas, Robert Ganian, Martin Nöllenburg, Marie Diana Sieper

In this paper, we study the Pattern Avoidance problem of determining whether a given graph $G$ admits a linear vertex order which avoids a given pattern $P$, i.e., a vertex sequence with some forced and forbidden edges, on every suborder. Such patterns form a natural ordered counterpart to induced subgraphs in the order-invariant setting, and it is known that Pattern Avoidance captures a broad variety of graph problems including Bandwidth, Vertex Coloring, Queue Number, and extends to vertex-deletion problems such as Odd Cycle Transversal. We show that Pattern Avoidance is $Σ_2^{\textsf{P}}$-complete and furthermore remains intractable (in both the classical and parameterized sense) even under a variety of severe restrictions to both the pattern $P$ and the graph $G$. As our main contributions, we complement these lower bounds with the following tractability results, which provide a unifying framework for recognizing pattern-definable graph classes: - a fixed-parameter algorithm w.r.t. the vertex integrity of $G$ plus $|V(P)|$, - a fixed-parameter algorithm w.r.t. the neighborhood diversity of $G$ plus $|E(P)|$, and - a polynomial algorithm for Pattern Avoidance on forests for almost all constant-sized patterns.

Authors: Thomas Depian, Simon D. Fink, Alexander Firbas, Robert Ganian, Martin Nöllenburg, Marie Diana Sieper

In this paper, we study the Pattern Avoidance problem of determining whether a given graph $G$ admits a linear vertex order which avoids a given pattern $P$, i.e., a vertex sequence with some forced and forbidden edges, on every suborder. Such patterns form a natural ordered counterpart to induced subgraphs in the order-invariant setting, and it is known that Pattern Avoidance captures a broad variety of graph problems including Bandwidth, Vertex Coloring, Queue Number, and extends to vertex-deletion problems such as Odd Cycle Transversal. We show that Pattern Avoidance is $Σ_2^{\textsf{P}}$-complete and furthermore remains intractable (in both the classical and parameterized sense) even under a variety of severe restrictions to both the pattern $P$ and the graph $G$. As our main contributions, we complement these lower bounds with the following tractability results, which provide a unifying framework for recognizing pattern-definable graph classes: - a fixed-parameter algorithm w.r.t. the vertex integrity of $G$ plus $|V(P)|$, - a fixed-parameter algorithm w.r.t. the neighborhood diversity of $G$ plus $|E(P)|$, and - a polynomial algorithm for Pattern Avoidance on forests for almost all constant-sized patterns.

Upper and lower bounds on the OBDD-width of a special integer multiplication

from arXiv: Computational Complexity

Authors: Tong Qin

We consider the Boolean function ${\rm SMul}_{n-1}^n(\boldsymbol{x},\boldsymbol{y})$, which computes the middle bit of the multiplication of two natural numbers represented as $n$-bit binary strings $\boldsymbol{x}$ and $\boldsymbol{y}$, drawn from a restricted domain. We investigate the width of OBDDs computing ${\rm SMul}_{n-1}^n$. We introduce a combinatorially defined function $s_*(n)$ and show that the width of such OBDDs is $Θ(2^{s_*(n)})$.

Authors: Tong Qin

We consider the Boolean function ${\rm SMul}_{n-1}^n(\boldsymbol{x},\boldsymbol{y})$, which computes the middle bit of the multiplication of two natural numbers represented as $n$-bit binary strings $\boldsymbol{x}$ and $\boldsymbol{y}$, drawn from a restricted domain. We investigate the width of OBDDs computing ${\rm SMul}_{n-1}^n$. We introduce a combinatorially defined function $s_*(n)$ and show that the width of such OBDDs is $Θ(2^{s_*(n)})$.

An Optimal Separation Between Certificate Complexity and Approximate Degree

from arXiv: Computational Complexity

Authors: Kaspars Balodis

We prove that certificate complexity can be quartically larger than approximate degree. More precisely, we construct a family of total Boolean functions $G$ with $$ C(G) = \tildeΩ(\tilde{deg}(G)^4), $$ where $C$ denotes certificate complexity and $\tilde{deg}$ denotes $1/3$-approximate degree. This is optimal up to polylogarithmic factors, since every total Boolean function $f$ satisfies $C(f)\le O(\tilde{deg}(f)^4)$ by the classical block-sensitivity bounds of Nisan and Nisan--Szegedy. Thus the result closes the gap between these two measures and improves the previously best known separation $C(f)=\tildeΩ(\tilde{deg}(f)^3)$ by Balodis, Ben-David, Göös, Jain, and Kothari. The construction starts from the partial function they used to quadratically separate $0$-certificate complexity from unambiguous $1$-certificate complexity. It already has the required certificate hardness, but its $0$-certificates are unstructured, which blocks the derivation of a low-degree verifier. We keep its $1$-condition and restrict the $0$-inputs to those certified by a structured family whose validity admits a low-degree approximant, while preserving the quadratic hardness. The partial function with its low-degree verifier is then fed through the cheat-sheet framework to yield the total function $G$ with the claimed separation. The main technical ingredient is an approximate polynomial that verifies the certificate in degree $\tilde{O}(\sqrt n)$. The verifier forms a low-degree count $W$ of the candidate $1$-certificates that remain compatible with the asserted $0$-certificate, and tests whether this count is zero. Crucially, the construction ensures that $W$ never exceeds $\tilde{O}(n)$, instead of the $Θ(n^2)$ candidate pairs it counts bringing the verification down to degree $\tilde{O}(\sqrt n)$.

Authors: Kaspars Balodis

We prove that certificate complexity can be quartically larger than approximate degree. More precisely, we construct a family of total Boolean functions $G$ with $$ C(G) = \tildeΩ(\tilde{deg}(G)^4), $$ where $C$ denotes certificate complexity and $\tilde{deg}$ denotes $1/3$-approximate degree. This is optimal up to polylogarithmic factors, since every total Boolean function $f$ satisfies $C(f)\le O(\tilde{deg}(f)^4)$ by the classical block-sensitivity bounds of Nisan and Nisan--Szegedy. Thus the result closes the gap between these two measures and improves the previously best known separation $C(f)=\tildeΩ(\tilde{deg}(f)^3)$ by Balodis, Ben-David, Göös, Jain, and Kothari. The construction starts from the partial function they used to quadratically separate $0$-certificate complexity from unambiguous $1$-certificate complexity. It already has the required certificate hardness, but its $0$-certificates are unstructured, which blocks the derivation of a low-degree verifier. We keep its $1$-condition and restrict the $0$-inputs to those certified by a structured family whose validity admits a low-degree approximant, while preserving the quadratic hardness. The partial function with its low-degree verifier is then fed through the cheat-sheet framework to yield the total function $G$ with the claimed separation. The main technical ingredient is an approximate polynomial that verifies the certificate in degree $\tilde{O}(\sqrt n)$. The verifier forms a low-degree count $W$ of the candidate $1$-certificates that remain compatible with the asserted $0$-certificate, and tests whether this count is zero. Crucially, the construction ensures that $W$ never exceeds $\tilde{O}(n)$, instead of the $Θ(n^2)$ candidate pairs it counts bringing the verification down to degree $\tilde{O}(\sqrt n)$.

Exact quantum splitting and the structure of finite algebras

from arXiv: Computational Complexity

Authors: Muhammad Imran

Berlekamp's algorithm factors a squarefree polynomial $f\in\mathbb{F}_q[x]$ by deterministic linear algebra, reducing the problem to splitting an explicit commutative algebra $B\cong\mathbb{F}_q^r$ into its $r$ simple factors. For large odd $q$, the standard efficient splitting step is randomized, while known derandomizations are conditional on the Extended Riemann Hypothesis. We give an unconditional exact quantum implementation in a circuit model permitting single-qubit rotations through efficiently computable angles. The construction uses an unconditional counting argument. For a block containing $s\ge2$ irreducible factors, a quadratic-character test in odd characteristic and an absolute-trace test in characteristic $2$ yield a nonconstant test element with probability $p_{q,s}\ge\tfrac12$, known exactly in advance and depending only on $q$ and $s$, not on the unknown factorization. Exact amplitude amplification therefore converts each randomized test into a procedure succeeding with certainty after one amplification iteration. The resulting algorithm uses exactly $r-1$ quantum splitting rounds and $O(n^3\log q)$ quantum $\mathbb{F}_q$-operations and $O(n^3)$ classical operations, requiring no primitive root, quadratic non-residue, or distinct-degree preprocessing. The method also splits arbitrary finite-dimensional separable commutative $\\mathbb{F}_q$-algebras given by structure constants. Combined with R'onyai's classical structure theory, which computes the radical deterministically and reduces the remaining tasks deterministically to polynomial factorization, it yields the radical and the Wedderburn decomposition of $A/\mathrm{Rad}(A)$ into minimal two-sided ideals, with certainty, for any $n$-dimensional associative $\mathbb{F}_q$-algebra given by structure constants, using $O(n^4\log q)$ quantum $\mathbb{F}_q$-operations.

Authors: Muhammad Imran

Berlekamp's algorithm factors a squarefree polynomial $f\in\mathbb{F}_q[x]$ by deterministic linear algebra, reducing the problem to splitting an explicit commutative algebra $B\cong\mathbb{F}_q^r$ into its $r$ simple factors. For large odd $q$, the standard efficient splitting step is randomized, while known derandomizations are conditional on the Extended Riemann Hypothesis. We give an unconditional exact quantum implementation in a circuit model permitting single-qubit rotations through efficiently computable angles. The construction uses an unconditional counting argument. For a block containing $s\ge2$ irreducible factors, a quadratic-character test in odd characteristic and an absolute-trace test in characteristic $2$ yield a nonconstant test element with probability $p_{q,s}\ge\tfrac12$, known exactly in advance and depending only on $q$ and $s$, not on the unknown factorization. Exact amplitude amplification therefore converts each randomized test into a procedure succeeding with certainty after one amplification iteration. The resulting algorithm uses exactly $r-1$ quantum splitting rounds and $O(n^3\log q)$ quantum $\mathbb{F}_q$-operations and $O(n^3)$ classical operations, requiring no primitive root, quadratic non-residue, or distinct-degree preprocessing. The method also splits arbitrary finite-dimensional separable commutative $\\mathbb{F}_q$-algebras given by structure constants. Combined with R'onyai's classical structure theory, which computes the radical deterministically and reduces the remaining tasks deterministically to polynomial factorization, it yields the radical and the Wedderburn decomposition of $A/\mathrm{Rad}(A)$ into minimal two-sided ideals, with certainty, for any $n$-dimensional associative $\mathbb{F}_q$-algebra given by structure constants, using $O(n^4\log q)$ quantum $\mathbb{F}_q$-operations.

Unrestricted Boolean Multiplicative Complexity of Four-Term Binary Polynomial Multiplication: Rational Places, Hasse Jets, and the Failure of Nonlinear Feedback

from arXiv: Computational Complexity

Authors: Gregory Morse

Classical lower bounds show that multiplying two degree-three polynomials over $\mathbb F_2$ requires nine scalar products in bilinear or quadratic models. They do not settle unrestricted Boolean multiplicative complexity: an XOR--AND circuit may reuse nonlinear intermediate wires, and Boolean equality is taken modulo $x_i^2=x_i$, so a multiplication can lower algebraic degree. Let $\operatorname{Mul}_4:\mathbb F_2^8\to\mathbb F_2^7$ output the seven coefficients of the product of two four-term binary polynomials. We prove that its unrestricted XOR--AND multiplicative complexity is exactly nine. This resolves, for a natural vector-valued quadratic function, the Boyar--Find question of whether a quadratic-circuit lower bound can persist against unrestricted nonlinear reuse. The proof is structural rather than exhaustive. A useful purely quadratic prefix is forced onto the three rational places of $\mathbb P^1(\mathbb F_2)$. In a hypothetical eight-AND circuit, the unique non-useful gate must carry a cubic high part. Any useful continuation then forces a rational tangent and exposes a first Hasse jet, while exterior jet separation together with Boolean idempotence prevents the same defect from exposing the second Hasse jet. The required useful suffix therefore cannot exist. A complete Lean 4 formalization verifies the Boolean-ANF semantics, the unrestricted circuit model, and the exact theorem; it uses no project-specific axiom or native decision procedure. The same zero-defect flag argument gives multiplicative complexity six for three-term multiplication, and the method isolates the multi-defect obstruction for five terms.

Authors: Gregory Morse

Classical lower bounds show that multiplying two degree-three polynomials over $\mathbb F_2$ requires nine scalar products in bilinear or quadratic models. They do not settle unrestricted Boolean multiplicative complexity: an XOR--AND circuit may reuse nonlinear intermediate wires, and Boolean equality is taken modulo $x_i^2=x_i$, so a multiplication can lower algebraic degree. Let $\operatorname{Mul}_4:\mathbb F_2^8\to\mathbb F_2^7$ output the seven coefficients of the product of two four-term binary polynomials. We prove that its unrestricted XOR--AND multiplicative complexity is exactly nine. This resolves, for a natural vector-valued quadratic function, the Boyar--Find question of whether a quadratic-circuit lower bound can persist against unrestricted nonlinear reuse. The proof is structural rather than exhaustive. A useful purely quadratic prefix is forced onto the three rational places of $\mathbb P^1(\mathbb F_2)$. In a hypothetical eight-AND circuit, the unique non-useful gate must carry a cubic high part. Any useful continuation then forces a rational tangent and exposes a first Hasse jet, while exterior jet separation together with Boolean idempotence prevents the same defect from exposing the second Hasse jet. The required useful suffix therefore cannot exist. A complete Lean 4 formalization verifies the Boolean-ANF semantics, the unrestricted circuit model, and the exact theorem; it uses no project-specific axiom or native decision procedure. The same zero-defect flag argument gives multiplicative complexity six for three-term multiplication, and the method isolates the multi-defect obstruction for five terms.

A note on the $Σ_2^P$-completeness of the Frobenius number

from arXiv: Computational Complexity

Authors: Thomas Rothvoss

Given a finite set $A$ of natural numbers whose greatest common divisor is one, the Frobenius number $g(A)$ is the largest integer that is not a non-negative integer combination of the numbers in $A$. In a 2016 preprint, Matsubara states that given $A$ and $k$, deciding if $g(A) \geq k$ is $Σ_2^P$-complete. A decade has passed since without peer-reviewed publication of this result. At the same time, the community has found it difficult to verify this result. In this note, we give a write-up of the completeness proof based on Matsubara (2016).

Authors: Thomas Rothvoss

Given a finite set $A$ of natural numbers whose greatest common divisor is one, the Frobenius number $g(A)$ is the largest integer that is not a non-negative integer combination of the numbers in $A$. In a 2016 preprint, Matsubara states that given $A$ and $k$, deciding if $g(A) \geq k$ is $Σ_2^P$-complete. A decade has passed since without peer-reviewed publication of this result. At the same time, the community has found it difficult to verify this result. In this note, we give a write-up of the completeness proof based on Matsubara (2016).

On the Complexity of Bayesian Signal Processing

from arXiv: Computational Complexity

Authors: Yi Liu

We develop a computational framework for Bayesian decision-making. We show that as long as no action is optimal in every state, Bayes-optimal choice is intractable. This hardness need not arise from large action, state, or signal spaces, nor from a complicated represented utility function: extracting enough information from a hard-to-interpret signal to act optimally can itself be computationally hard. We also characterize tractability across approximation notions and identify their sources of difficulty. Under the probably approximately correct criterion, sample-based Bayesian learning is tractable if and only if the signal support is bounded. Our results provide justifications for bounded rationality, costly Bayesian inference, and sample-based Bayesian learning.

Authors: Yi Liu

We develop a computational framework for Bayesian decision-making. We show that as long as no action is optimal in every state, Bayes-optimal choice is intractable. This hardness need not arise from large action, state, or signal spaces, nor from a complicated represented utility function: extracting enough information from a hard-to-interpret signal to act optimally can itself be computationally hard. We also characterize tractability across approximation notions and identify their sources of difficulty. Under the probably approximately correct criterion, sample-based Bayesian learning is tractable if and only if the signal support is bounded. Our results provide justifications for bounded rationality, costly Bayesian inference, and sample-based Bayesian learning.

The Complexity of Coverability-Like Problems in Elementary Object Systems: Data-Nets to the Rescue

from arXiv: Computational Complexity

Authors: Francesco Di Cosmo, Soumodev Mal, Tephilla Prince

Elementary Object Systems (EOSs) are a model in the nets-within-nets (NWNs) paradigm, where tokens in turn can host standard Petri nets. We study the complexity of coverability-like problems, including termination and boundedness, over EOSs. Since coverability and boundedness are undecidable in general on EOSs, we focus on the relevant fragment of conservative EOSs (cEOSs). Our technique interprets cEOSs into the framework of data nets, whose tokens carry data from an infinite domain, thus bridging the nesting and the data-aware paradigms. Specifically, we show that cEOS coverability-like problems are equivalent to the coverability-like problems over an interesting fragment, called channel-$ν$PNs (c-$ν$PNs), of data nets that extends $ν$PN (featuring globally fresh name creation) with restricted forms of transfers with renaming. c-$ν$PNs remain less expressive than Unordered Data Nets, which feature lossy name creation as well as powerful forms of whole-place operations and broadcasts. These reductions allow us to analyze cEOS coverability taking advantage of known results on data nets. We conclude that the complexity of cEOS coverability is double-Ackermanian, $\mathcal{F}_{ω2}$-complete, while termination and boundedness are non-primitive recursive.

Authors: Francesco Di Cosmo, Soumodev Mal, Tephilla Prince

Elementary Object Systems (EOSs) are a model in the nets-within-nets (NWNs) paradigm, where tokens in turn can host standard Petri nets. We study the complexity of coverability-like problems, including termination and boundedness, over EOSs. Since coverability and boundedness are undecidable in general on EOSs, we focus on the relevant fragment of conservative EOSs (cEOSs). Our technique interprets cEOSs into the framework of data nets, whose tokens carry data from an infinite domain, thus bridging the nesting and the data-aware paradigms. Specifically, we show that cEOS coverability-like problems are equivalent to the coverability-like problems over an interesting fragment, called channel-$ν$PNs (c-$ν$PNs), of data nets that extends $ν$PN (featuring globally fresh name creation) with restricted forms of transfers with renaming. c-$ν$PNs remain less expressive than Unordered Data Nets, which feature lossy name creation as well as powerful forms of whole-place operations and broadcasts. These reductions allow us to analyze cEOS coverability taking advantage of known results on data nets. We conclude that the complexity of cEOS coverability is double-Ackermanian, $\mathcal{F}_{ω2}$-complete, while termination and boundedness are non-primitive recursive.

Separating Parsing Expression Grammars using Cell-Probe Lower Bounds

from arXiv: Computational Complexity

Authors: Jungyeom Kim, Jihyeok Park

We resolve three open problems concerning parsing expression grammars (PEGs). We construct a single language $C$ satisfying $C\in\mathsf{LIN}\cap\mathsf{PEG}$ and $C^R\in\mathsf{LIN}\setminus\mathsf{PEG}$. This proves that some linear context-free language is not a PEG language and that PEG languages are not closed under reversal, confirming a conjecture of Loff, Moreira, and Reis. Factoring the same witness resolves the concatenation-closure problem of Rubtsov and Chudinov negatively, in the strong form $\mathsf{PEG}\cdot\mathsf{REG}\not\subseteq\mathsf{PEG}$ despite $\mathsf{REG}\cdot\mathsf{PEG}\subseteq\mathsf{PEG}$. It also refutes closure under Kleene star, homomorphisms, and substitutions. Our main technique converts scaffolding automata (SCAs), which characterize reversals of PEG languages, into dynamic data structures in the cell-probe model. For any suitably local serialization of a problem with preprocessing, updates, and a final Boolean query, an SCA recognizer yields an exact deterministic cell-probe data structure whose operation costs are proportional to the corresponding encoding lengths. Cell-probe lower bounds can therefore prove SCA non-membership and, by reversal, PEG non-membership. We apply this transfer to Multiphase Inner Product using one-symbol update blocks and a query suffix of length $O(\log n)$, while keeping both the language and its reversal linear context-free. Ko's cell-probe lower bound then yields the witness above. The arguments are additionally formalized in Lean 4.

Authors: Jungyeom Kim, Jihyeok Park

We resolve three open problems concerning parsing expression grammars (PEGs). We construct a single language $C$ satisfying $C\in\mathsf{LIN}\cap\mathsf{PEG}$ and $C^R\in\mathsf{LIN}\setminus\mathsf{PEG}$. This proves that some linear context-free language is not a PEG language and that PEG languages are not closed under reversal, confirming a conjecture of Loff, Moreira, and Reis. Factoring the same witness resolves the concatenation-closure problem of Rubtsov and Chudinov negatively, in the strong form $\mathsf{PEG}\cdot\mathsf{REG}\not\subseteq\mathsf{PEG}$ despite $\mathsf{REG}\cdot\mathsf{PEG}\subseteq\mathsf{PEG}$. It also refutes closure under Kleene star, homomorphisms, and substitutions. Our main technique converts scaffolding automata (SCAs), which characterize reversals of PEG languages, into dynamic data structures in the cell-probe model. For any suitably local serialization of a problem with preprocessing, updates, and a final Boolean query, an SCA recognizer yields an exact deterministic cell-probe data structure whose operation costs are proportional to the corresponding encoding lengths. Cell-probe lower bounds can therefore prove SCA non-membership and, by reversal, PEG non-membership. We apply this transfer to Multiphase Inner Product using one-symbol update blocks and a query suffix of length $O(\log n)$, while keeping both the language and its reversal linear context-free. Ko's cell-probe lower bound then yields the witness above. The arguments are additionally formalized in Lean 4.

It's Hard to PArcK

from arXiv: Computational Complexity

Authors: Kyle Burke, Jeffrey Leman, Craig Tennenhouse

We show that Partizan Arc Kayles (PArcK), a generalization of Domineering to graphs, is PSPACE-complete via a reduction from Positive CNF and with recently-discovered techniques for creating PArcK positions with high temperature. The reduction uses only red and blue edges.

Authors: Kyle Burke, Jeffrey Leman, Craig Tennenhouse

We show that Partizan Arc Kayles (PArcK), a generalization of Domineering to graphs, is PSPACE-complete via a reduction from Positive CNF and with recently-discovered techniques for creating PArcK positions with high temperature. The reduction uses only red and blue edges.

Algorithmic threshold for high-dimensional projection pursuit I: general theory

from arXiv: Computational Complexity

Authors: Brice Huang, Mark Sellke, Nike Sun

We study a null model of high-dimensional projection pursuit: we are given $M$ points sampled i.i.d. from a standard gaussian in $N$ dimensions, where $M,N\to\infty$ with $M/N\toα\in(0,\infty)$. Our goal is to characterize the possible empirical distributions of these points' projections along a data-dependent direction $x$, which ranges over either the sphere $S_N=\sqrt{N}\mathbb{S}^{N-1}$ or cube $Σ_N=\{-1,+1\}^N$. We consider this problem in an algorithmic setting, where $x$ must be the output of an algorithm with dimension-free Lipschitz dependence on the input; this class of algorithms includes general gradient-based methods such as Langevin dynamics and approximate message passing (AMP). Our main result exactly characterizes the set of empirical distributions attainable by this class in terms of a one-dimensional stochastic control problem. As a consequence of our main result, we obtain exact algorithmic thresholds for optimizing the Hamiltonian of a spherical or Ising perceptron model with general bounded continuous activation. For the spherical problem, independent work of Montanari and Zhou (2024) characterized the empirical distributions attainable by a related two-stage AMP algorithm, also in terms of stochastic control. Our proof of hardness builds on the branching overlap gap property introduced in earlier work by the first two authors. Our main innovation is to develop stochastic control theory within the branching OGP framework, significantly expanding the settings in which it locates an exact algorithmic threshold. Notably, our methods apply even though the non-algorithmic problem of characterizing all feasible projections remains a major outstanding challenge. For the matching algorithmic result, we construct a new incremental AMP algorithm that acts on a Brownian-bridge revelation of the gaussian disorder and simulates the same family of controlled SDEs.

Authors: Brice Huang, Mark Sellke, Nike Sun

We study a null model of high-dimensional projection pursuit: we are given $M$ points sampled i.i.d. from a standard gaussian in $N$ dimensions, where $M,N\to\infty$ with $M/N\toα\in(0,\infty)$. Our goal is to characterize the possible empirical distributions of these points' projections along a data-dependent direction $x$, which ranges over either the sphere $S_N=\sqrt{N}\mathbb{S}^{N-1}$ or cube $Σ_N=\{-1,+1\}^N$. We consider this problem in an algorithmic setting, where $x$ must be the output of an algorithm with dimension-free Lipschitz dependence on the input; this class of algorithms includes general gradient-based methods such as Langevin dynamics and approximate message passing (AMP). Our main result exactly characterizes the set of empirical distributions attainable by this class in terms of a one-dimensional stochastic control problem. As a consequence of our main result, we obtain exact algorithmic thresholds for optimizing the Hamiltonian of a spherical or Ising perceptron model with general bounded continuous activation. For the spherical problem, independent work of Montanari and Zhou (2024) characterized the empirical distributions attainable by a related two-stage AMP algorithm, also in terms of stochastic control. Our proof of hardness builds on the branching overlap gap property introduced in earlier work by the first two authors. Our main innovation is to develop stochastic control theory within the branching OGP framework, significantly expanding the settings in which it locates an exact algorithmic threshold. Notably, our methods apply even though the non-algorithmic problem of characterizing all feasible projections remains a major outstanding challenge. For the matching algorithmic result, we construct a new incremental AMP algorithm that acts on a Brownian-bridge revelation of the gaussian disorder and simulates the same family of controlled SDEs.

Hardness of Approximation of Rank Aggregation on Ulam Metric

from arXiv: Computational Complexity

Authors: Sk Ruhul Azgor, Diptarka Chakraborty, Le Van Cuong, Debarati Das, Tien Long Nguyen

We study the approximability of rank aggregation under the Ulam metric. In the \emph{Ulam median} problem, the goal is to find a permutation minimizing the sum of its Ulam distances to the input permutations, while in the \emph{Ulam center} problem the objective is to minimize the maximum such distance. Both problems are known to be NP-hard, but no explicit approximation hardness was previously known. We prove that, for every $\varepsilon>0$, it is NP-hard to approximate either Ulam median or Ulam center within a factor of $51/50-\varepsilon$, even when the input consists of only four permutations. We further show that unless P = NP, neither problem admits a polynomial-time additive approximation scheme. The hardness result for Ulam median is established via a reduction from MAX-E3-LIN-2. The corresponding hardness for Ulam center is then obtained through a reduction from Ulam median.

Authors: Sk Ruhul Azgor, Diptarka Chakraborty, Le Van Cuong, Debarati Das, Tien Long Nguyen

We study the approximability of rank aggregation under the Ulam metric. In the \emph{Ulam median} problem, the goal is to find a permutation minimizing the sum of its Ulam distances to the input permutations, while in the \emph{Ulam center} problem the objective is to minimize the maximum such distance. Both problems are known to be NP-hard, but no explicit approximation hardness was previously known. We prove that, for every $\varepsilon>0$, it is NP-hard to approximate either Ulam median or Ulam center within a factor of $51/50-\varepsilon$, even when the input consists of only four permutations. We further show that unless P = NP, neither problem admits a polynomial-time additive approximation scheme. The hardness result for Ulam median is established via a reduction from MAX-E3-LIN-2. The corresponding hardness for Ulam center is then obtained through a reduction from Ulam median.

Unconditional $V^0_1$-independence of a certified hitting-set principle

from arXiv: Computational Complexity

Authors: Martin Kolář

We show that a certified formalization of the hitting-set-existence axiom of Atserias and Tzameret, instantiated on the parity-based Nisan-Wigderson compression class of Khaniki, is independent of the two-sorted theory $V^0_1$ of $\mathrm{AC}^0$-reasoning, unconditionally: $V^0_1$ proves neither it nor its negation. The same holds for the corresponding certified dual weak pigeonhole principle, whose refutation is witnessed by a single seed that certified-computes every string of the model simultaneously. The mechanism is a bounded-arithmetic transfer of Atserias-Tzameret's reduction from hitting sets to the dual weak pigeonhole principle: the amplification half of that reduction, the sole source of its NP-oracle, is unnecessary at the native stretch of the Nisan-Wigderson map, and the compression half becomes a $V^0_1$-provable implication once circuit evaluation is replaced by its certified $Σ^B_0$ unfolding. This is, to our knowledge, the first independence result for a derandomization-flavoured existence principle at the $\mathrm{AC}^0$-reasoning level, and it makes explicit the bridge between the Khaniki Nisan-Wigderson line and the Atserias-Tzameret reverse mathematics of hitting sets.

Authors: Martin Kolář

We show that a certified formalization of the hitting-set-existence axiom of Atserias and Tzameret, instantiated on the parity-based Nisan-Wigderson compression class of Khaniki, is independent of the two-sorted theory $V^0_1$ of $\mathrm{AC}^0$-reasoning, unconditionally: $V^0_1$ proves neither it nor its negation. The same holds for the corresponding certified dual weak pigeonhole principle, whose refutation is witnessed by a single seed that certified-computes every string of the model simultaneously. The mechanism is a bounded-arithmetic transfer of Atserias-Tzameret's reduction from hitting sets to the dual weak pigeonhole principle: the amplification half of that reduction, the sole source of its NP-oracle, is unnecessary at the native stretch of the Nisan-Wigderson map, and the compression half becomes a $V^0_1$-provable implication once circuit evaluation is replaced by its certified $Σ^B_0$ unfolding. This is, to our knowledge, the first independence result for a derandomization-flavoured existence principle at the $\mathrm{AC}^0$-reasoning level, and it makes explicit the bridge between the Khaniki Nisan-Wigderson line and the Atserias-Tzameret reverse mathematics of hitting sets.

Parameterized Complexity of Edge-Constrained Graph Partitioning

from arXiv: Computational Complexity

Authors: Ajinkya Gaikwad, Jan Pokorný, Tomáš Valla

We study the Edge-Constrained Graph Partitioning Problem (ECGP), which asks whether the vertices of a graph can be partitioned into r parts, each inducing at least gamma edges. We also consider a balanced variant (BECGP), requiring equal-sized parts, and signed variants, where the utility of a part is the difference between its numbers of positive and negative edges. We show that ECGP and BECGP remain NP-hard for fixed gamma, while BECGP is also NP-hard for fixed r. For the natural parameterization r+gamma, both problems admit polynomial kernels. We obtain FPT algorithms for ECGP and BECGP parameterized by maximum leaf number, vertex deletion distance to a clique, cluster vertex deletion number plus gamma, and vertex integrity. Furthermore, ECGP is FPT parameterized by vertex deletion distance to stars plus gamma and vertex deletion distance to paths plus gamma. On the negative side, ECGP and BECGP are W[1]-hard when parameterized by r together with several structural parameters. In particular, hardness holds for feedback edge set, vertex deletion distance to stars or paths, and modular width even when the corresponding parameter is zero. The problems are also W[1]-hard parameterized by cluster vertex deletion number plus r, and by clique-width even when gamma=3. For signed graphs, both variants are NP-hard even when r+gamma=3 and the input is a disjoint union of two cliques. Finally, the balanced signed variant is W[1]-hard parameterized by treedepth plus r, even when gamma=0.

Authors: Ajinkya Gaikwad, Jan Pokorný, Tomáš Valla

We study the Edge-Constrained Graph Partitioning Problem (ECGP), which asks whether the vertices of a graph can be partitioned into r parts, each inducing at least gamma edges. We also consider a balanced variant (BECGP), requiring equal-sized parts, and signed variants, where the utility of a part is the difference between its numbers of positive and negative edges. We show that ECGP and BECGP remain NP-hard for fixed gamma, while BECGP is also NP-hard for fixed r. For the natural parameterization r+gamma, both problems admit polynomial kernels. We obtain FPT algorithms for ECGP and BECGP parameterized by maximum leaf number, vertex deletion distance to a clique, cluster vertex deletion number plus gamma, and vertex integrity. Furthermore, ECGP is FPT parameterized by vertex deletion distance to stars plus gamma and vertex deletion distance to paths plus gamma. On the negative side, ECGP and BECGP are W[1]-hard when parameterized by r together with several structural parameters. In particular, hardness holds for feedback edge set, vertex deletion distance to stars or paths, and modular width even when the corresponding parameter is zero. The problems are also W[1]-hard parameterized by cluster vertex deletion number plus r, and by clique-width even when gamma=3. For signed graphs, both variants are NP-hard even when r+gamma=3 and the input is a disjoint union of two cliques. Finally, the balanced signed variant is W[1]-hard parameterized by treedepth plus r, even when gamma=0.