Theory of Computing Report

We construct an $\varepsilon$-error PRG for permutation read-once branching programs of length $n$ and width $w$ with seed length $$ O\left((\log w+\log(1/\varepsilon))\cdot \log n\right). $$ This gives an exponential improvement in the dependence on $w$ compared with the constructions of De (CCC 2011) and Steinke (ECCC 2012). Compared with the work of Braverman, Rao, Raz, and Yehudayoff (FOCS 2010; SICOMP 2014), which applies more generally to regular branching programs and already achieves the optimal dependence on $w$, our result improves the dependence on the length $n$, attaining the optimal logarithmic dependence. The generator itself is the classical INW PRG of Impagliazzo, Nisan, and Wigderson (STOC 1994). We show that, for permutation branching programs, the INW generator can be instantiated with expanders whose degrees are polynomial in $w$ and $1/\varepsilon$ and, crucially, independent of $n$. To prove this, we analyze error propagation using program-dependent seminorms tailored to the branching program at hand. These seminorms build on the weight function introduced by Braverman et al. The key point is that, when measured in these adapted seminorms, the error does not accumulate throughout the recursion. Since our analysis relies only on the spectral expansion of the underlying expanders, our seed length tightly matches the recent lower bound for spectral analyses of the INW generator due to Hoza, Pyne, and Vadhan (Algorithmica 2024).

Authors: Yunbei Xu

A randomized algorithm may terminate almost surely even though exceptional random tapes make it run forever. This paper studies the survival tail, the Kolmogorov complexity of one such tape, and the Hausdorff dimension of all of them. For each $s>0$ at which the powered repair matrices commute, the main theorem bounds $\sum_wP[w]^s$ over surviving prefixes $w$, uniformly over deterministic nonanticipating selectors. The case $s=1$ controls termination; the full family gives weak-source and dimension bounds. The source powers contain information absent even from the ordinary repair kernel and the complete stopping-time law. Under one common finite tape source, two overlapping disagreement-repair rules on a four-vertex path have the same ordinary kernels and the same stopping-time law for every selector, yet their nontermination dimensions can be arbitrarily close to zero and one. At one common source-power level, the same dominated tape source makes one rule run forever but gives the other an exponential stopping tail. The separation is caused by action labels that produce the same state transition and are therefore invisible at power one. For bounded-dependence $k$-SAT, conditional block min-entropy above the trace-growth threshold gives exponential termination, and the effective dimension of an individual infinite run is bounded by the trace growth induced by the clauses repaired infinitely often. Tree formulas asymptotically attain the maximum-degree dimension and global source bounds, while clique formulas attain the graph-specific one-step threshold in the stated regime. An exact backward likelihood identity complements these setwise results with tail and coding bounds for each run.

Authors: Arman Adibi, Piotr Krysta

Estimating entropy from samples is fundamental in information theory and property testing. Shannon entropy measures average uncertainty and can be estimated to constant additive accuracy over a $k$-symbol alphabet using $Θ(k/\log k)$ samples. Min-entropy depends only on the most likely symbol. Both are special cases of order-$α$ R'{e}nyi entropy, $H_α$. We characterize the sample complexity of estimating min-entropy and R'{e}nyi entropy for $k$ and integer $α>1$; our lower bounds also hold for noninteger $α\ge1.001$. We prove that min-entropy estimation to constant additive accuracy has sample complexity $Θ(k\log k)$. The upper bound uses the largest empirical frequency and concentration via dyadic grouping. The matching lower bound hides a slightly heavier symbol at a uniformly random location. Thus, min-entropy requires $Θ(\log^2 k)$ more samples than Shannon entropy and corrects a previously stated $Θ(k/\log k)$ characterization. For every integer $2\leα\le c_0\log k$, we prove the matching fixed-accuracy bound $Θ_{c_0}(αk^{1-1/α})$. Previous results gave $Ω_α(k^{1-1/α})$ for fixed integer $α>1$ and $O_{c_0}(α^2k^{1-1/α})$ for all integer $α>1$. Our upper bound analyzes an unbiased falling-factorial estimator based on $α$-way collisions, while a hidden-heavy-coordinate construction gives the matching lower bound and shows that the factor $α$ is unavoidable. For every real $1.001\leα\le c_0\log k$, we prove the uniform lower bound $Ω_{c_0}(αk^{1-1/α})$. Finally, since $0\le H_α(p)-H_\infty(p)\le\log k/(α-1)$, min-entropy uniformly approximates $H_α$ when $α$ is a sufficiently large multiple of $\log k$. Combining this reduction with our min-entropy bounds gives $Θ_\varepsilon(k\log k)$ sample complexity in the high-order regime.

Authors: Dmitry Babichev, Denis Pinchuk

Let $F(n)$ be the number of rectangles, not necessarily axis-parallel, whose vertices belong to the $n\times n$ square grid of lattice points. We compute the complete table $F(1),\ldots,F(N)$ exactly in $O(M(N)\log N)$ coefficient-ring operations and $O(N\log N)$ ring elements of working memory, where $M(N)$ is a regular bound for multiplying degree-$N$ polynomials. The ring-level statement assumes that $6$ is invertible; over $\mathbb Z$ the only division is instead performed exactly in the elementary boundary term. With quasi-linear polynomial multiplication the arithmetic bound is $O(N\log^2 N)$. The algorithm applies a square-root cover before coefficient extraction and evaluates the resulting rational wedge and triangular sums by a local-denominator divide-and-conquer recursion. Primitive directions are recovered coefficientwise by Möbius inversion, followed by five prefix sums. A modular number-theoretic-transform (NTT) implementation with certified Chinese-remainder (CRT) recovery is evaluated experimentally against the $O(N^{3/2})$ all-values algorithm.

Authors: Dmitry Babichev, Tatiana Shpakova

Let $F(n)$ be the number of rectangles, not necessarily axis-parallel, whose vertices belong to the $n\times n$ square grid of lattice points. We give an exact algorithm that computes one prescribed value $F(n)$ in $O(n\log n)$ arithmetic operations and $O(n^{3/4})$ arithmetic words of working memory. The algorithm decomposes the count into Möbius divisor layers, partitions weighted floor-moment queries by a truncated Euclidean coefficient-cone recursion, and reuses uniform marker grids along common coefficient paths. Each marker requires only its uniform cell and constant-size corrections at nearby boundaries, which select an exact precompiled cell operator. All integer operands have $O(\log n)$ bits. An exact 128-bit C++ implementation for the reported input range is compared experimentally with the previous $O(n\log^2 n)$ algorithm.

Authors: Zhou He, T. C. E. Cheng, Jichang Dong

We study continuous planar facility location in which a demand point's captured value decays with distance, with the per-point decay scale varying across points. This heterogeneity is ubiquitous yet underexploited, and one nearest-facility objective unifies decay, clustering, and median goals, containing k-means, the Weber/p-median problem, and maximum covering as special cases. We make four contributions. (i) A tractability classification: the discrete objective is always monotone submodular, so the (1-1/e) greedy guarantee holds regardless of decay shape or heterogeneity, and the continuous cooperative objective is concave if and only if the decay is concave in distance; the clip max(0,d) in common coverage specifications is what destroys concavity, and the classification is tight. (ii) An exact discrete method: the candidate-discretized maximum-cover MIP has an empirically tight LP relaxation (~0% gap) and is solved by branch-and-bound in seconds for n <= 500. (iii) A force-as-gradient / large-neighborhood-search heuristic, within 0.5% of the discrete optimum, that outperforms the (1-1/e) greedy, Cooper-style alternating location-allocation, particle swarm optimization, and weighted k-means (30/30 per-instance wins at K=30, p<10^-9) and is competitive with bespoke solvers on k-means, Weber/p-median, and shape-demand instances. (iv) A real-world study: on 592,667 urban-delivery orders, ignoring the calibrated decay variation loses up to 9.7% of captured demand and relocates facilities by up to 37% of the map; a retail dataset calibrates the decay as exponential with scale R ~ 1.4 km.

Authors: Gil Cohen, Dean Doron, Noam Goldgraber

We construct an $\varepsilon$-error PRG for permutation read-once branching programs of length $n$ and width $w$ with seed length \[ O\left((\log w+\log(1/\varepsilon))\cdot \log n\right). \] This gives an exponential improvement in the dependence on $w$ compared with the constructions of De (CCC 2011) and Steinke (ECCC 2012). Compared with the work of Braverman, Rao, Raz, and Yehudayoff (FOCS 2010; SICOMP 2014), which applies more generally to regular branching programs and already achieves the optimal dependence on $w$, our result improves the dependence on the length $n$, attaining the optimal logarithmic dependence. The generator itself is the classical INW PRG of Impagliazzo, Nisan, and Wigderson (STOC 1994). We show that, for permutation branching programs, the INW generator can be instantiated with expanders whose degrees are polynomial in $w$ and $1/\varepsilon$ and, crucially, independent of $n$. To prove this, we analyze error propagation using program-dependent seminorms tailored to the branching program at hand. These seminorms build on the weight function introduced by Braverman et al. The key point is that, when measured in these adapted seminorms, the error does not accumulate throughout the recursion. Since our analysis relies only on the spectral expansion of the underlying expanders, our seed length tightly matches the recent lower bound for spectral analyses of the INW generator due to Hoza, Pyne, and Vadhan (Algorithmica 2024).

Authors: Che Cheng, Daniel Mock, Peter Rossmanith

We extend the algorithmic framework of progressive exploration [Fabiański et al., STACS 2019], which yields simple, yet surprisingly general and efficient parameterized algorithms for Dominating Set, Independent Set, and some of their variants. While they identified stability and the Helly property as necessary for their approach, we show that -- with a simple change -- in the case of Dominating Set, one can get rid of the stability requirement. This yields a fixed-parameter tractable algorithm on exactly those graph classes which do not contain long co-matchings or double-ladders as semi-induced subgraphs. Lifting one of these two restrictions makes Dominating Set W[1]-hard on these classes. Our algorithm generalizes results on weakly $γ$-closed graphs, and results from Sparsity theory, e.g., nowhere dense and biclique-free classes. At the same time, we match the time complexity of the previously known algorithms on those classes. We demonstrate that this technique can easily be applied to the Distance-$r$ Dominating Set and the Set Cover problem.

Authors: Simon D. Fink, Matthias Pfretzschner, Ignaz Rutter, Marie Diana Sieper

We consider the combination of the two constrained planarity problems Level- and Clustered Planarity. Traditionally, level-planar drawings with convex clusters have been studied in this setting. Fink et al. (EuroCG 2024) recently introduced a different way of combining level- and clustered planarity by mimicking a classic characterization of clustered planarity in the level-planar setting: The problem (y-)monotone Clustered Level Planarity (mCLP) seeks a level-planar drawing in which it is possible to augment each cluster with edges that do not cross cluster boundaries so that it becomes connected while maintaining level-planarity. This is in line with previous research on clustered planarity that poses certain requirements on the augmentation edges that make each cluster connected, e.g., that they form a path. Fink et al. (EuroCG 2024) showed that mCLP is NP-complete even for biconnected single-source graphs and instances with a constant number of levels and clusters. We further classify the parameterized complexity of the mCLP problem by, on the one hand, showing hardness even for instances that consist of a forest with trees of bounded size, no isolated vertices, and a small constant number of either clusters or levels. This excludes fixed-parameter tractability for almost all graph-structural parameters, except for vertex cover, even in conjunction with the number of clusters. We complement this by showing fixed-parameter tractability when parameterizing by the vertex cover number and the number of clusters. A major obstacle is the fact that mCLP is non-hereditary, i.e., subinstances of yes-instances may be no-instances and vice versa, which makes it challenging to apply usual reduction techniques.

Authors: Harish Chandramouleeswaran, Prajakta Nimbhorkar

The existence of allocations of indivisible goods that are simultaneously fair (envy-free up to one item (EF1)) and efficient (Pareto optimal (PO)) when agents have monotone submodular valuations has been a longstanding open problem. We settle this question negatively by giving an example with two agents where no allocation is simultaneously EF1 and PO. We also show that determining the existence of such allocations is NP-hard for monotone submodular valuations. Our example uses (unweighted) coverage valuations, which is a strict subclass of monotone submodular valuations. Since EF1+PO allocations are known to always exist for additive valuations via the maximization of Nash Social Welfare (Caragiannis et al. (ACM TEAC 2019)), and for matroid-rank valuations (Benabbou et al. (ACM TEAC 2021)), nonexistence was known only for monotone subadditive valuations (Caragiannis et al. (ACM TEAC 2019)). Our work moves the nonexistence frontier to unweighted coverage valuations. We also show that the example we designed for goods also proves nonexistence of EF1+PO in general, for chores with unweighted coverage costs, by interpreting the valuations as disutilities.

Authors: Zhiyi Huang, Qinpei Lou, Tao Xiao

Mixture-of-Experts (MoE) models route each token to only a few expert networks, distributing the serving load across experts whose popularity shifts over time. A serving system must therefore dynamically decide how many GPUs to assign to each expert, trading off service latency against the cost of reconfiguring the assignment. We introduce a formal model of MoE Serving and initiate a principled study of online and offline algorithms for it. Our main result is a polynomial-time $O(\sqrt{\log k})$-competitive online algorithm, where $k$ is the number of GPUs beyond one per expert. We complement it with a matching $Ω(\sqrt{\log k})$ barrier for the online dual problem underlying our analysis. In the offline setting, we give a constant-factor approximation, show that MoE Serving is NP-hard, and rule out an FPTAS assuming ETH.

Authors: Romain Cosson, Jingwei Li, Alexander Lindermayr, Jens Schlöter

Nonclairvoyant scheduling is a fundamental online model in which processing times are initially unknown to the scheduler. Unfortunately, for important objectives such as total completion time and makespan, worst-case analysis yields pessimistic guarantees: every nonclairvoyant algorithm has a competitive ratio of at least $2$ for these objectives. Recent work introduced $\varepsilon$-clairvoyance, where a scheduler receives a signal once an $\varepsilon$-fraction of a job remains (FOCS'25, NeurIPS'25). This model avoids giving the algorithm a priori predictions as done in learning-augmented algorithms, a practice that is often hard to justify in practice. However, existing algorithms and analyses rely crucially on signal times being precise, an assumption hardly justifiable in applications such as task profiling. We introduce stochastic clairvoyance, a beyond-worst-case model in which each job emits a randomly timed signal during its execution, drawn from a distribution over its processing length. For this model, we design new online scheduling algorithms whose competitive ratios are strictly below $2$ for minimizing total completion time and makespan. On the technical side, we prove a new black-box theorem that converts bounds on expected pairwise job delays into competitive guarantees via a continuous amortized charging argument. Our results show that stochastic clairvoyance is not merely a curiosity: it yields robust improvements across different scheduling objectives and machine environments. More broadly, stochastic clairvoyance suggests a new direction in beyond-worst-case analysis for online algorithms, and builds a bridge between learning-augmented algorithms and stochastic information models.

Authors: Jiří Fink, Petr Hladík, Arturo Merino, Ondřej Mička, Torsten Mütze

Recently, Merino and Mütze (FOCS'23+SICOMP'24) presented an algorithm for computing a Hamilton path on the skeleton of any 0/1-polytope ${\rm conv}(X)$, where $X\subseteq\{0,1\}^n$. The algorithm uses as a black box an algorithm for solving the classical linear optimization problem $\min\{w\cdot x\mid x\in X\}$ for some weight vector $w\in\mathbb{R}^n$. The resulting delay per visited vertex on the Hamilton path is only by a $\log n$ factor larger than the time to solve one instance of the optimization algorithm. In this paper, we make the Hamilton path algorithm simpler and faster. Namely, we obtain an amortized delay that is only by a constant factor larger than the running time of the optimization algorithm, thus removing the $\log n$ factor. As concrete results, this yields improved algorithms for generating bases and independent sets in a matroid, spanning trees, forests, matchings and maximum matchings in a graph, vertex covers, minimum vertex covers, independent sets and maximum independent sets in a bipartite graph, and antichains, maximum antichains and ideals in a poset. All of these listings correspond to Hamilton paths on the corresponding polytopes. Furthermore, we obtain an $\mathcal{O}(t_{\rm LP})$ amortized delay algorithm for the vertex enumeration problem on 0/1-polytopes $\{x\in\mathbb{R}^n\mid Ax\leq b\}$, where $A\in \mathbb{R}^{m\times n}$ and $b\in\mathbb{R}^m$, and $t_{\rm LP}$ is the time needed to solve the linear program $\min\{w\cdot x\mid Ax\leq b\}$. This improves upon the $\mathcal{O}(t_{\rm LP} \log n)$ delay algorithm of Merino and Mütze, and the previous $\mathcal{O}(t_{\rm LP}\,n)$ delay algorithm of Bussieck and Lübbecke from 1998.

Authors: Andrew Krapivin, William Kuszmaul, Jolyne Wang

Linear probing hash tables classically use a \emph{greedy} insertion strategy, placing a key $u$ in the first available position out of $h(u), h(u) + 1, h(u) + 2, \ldots$. If the hash table is filled to $1 - 1/x$ full, this results in $Θ(x^{2})$ worst-case expected insertion time. In this note, we show that there is a simple \emph{non-greedy} insertion strategy that does better, and without requiring elements to be reordered within the table over time. Given $x$ in advance, the strategy is able to bring the worst-case expected insertion time down to $O(x \log x)$.

Authors: Vanessa Kosoy

In a previous paper, we began the study of sequence prediction algorithms adapted to stringological word complexity measures. One measure we considered was left-to-right (most-significant-digit-first) automaticity. Here, we show a statistically and computationally efficient algorithm adapted to the ``dual'' right-to-left (least-significant-digit-first) automaticity, which turns out to be substantially different for our purpose. We also demonstrate a prediction algorithm for a more expressive measure that we call ``arithmetic repetition complexity''. In particular, the latter can be used for predicting the so-called mix-automatic sequences.

Authors: John N. Bray, Henrik Bäärnhielm

We present a black box algorithm that constructs standard generators for the Suzuki groups $Sz(q)$, where $q = 2^{2m+1}$ for some $m > 0$. The algorithm is one-sided Monte Carlo, with no false positives. We also present a black box algorithm that performs constructive membership testing in $Sz(q)$, and writes an element as a straight line program in the standard generators. Finally, we give a presentation for $Sz(q)$ that is efficient to verify. The algorithms have been implemented in the computer algebra system Magma.

Authors: Charalampos Platanos, Thanos Tolias

In Online Sorting, we are given an array $A$ of $n$ initially empty cells. At each time step $t\in[n]$, an element $x_t\in[0,1]$ arrives and must be placed irrevocably into an empty cell, without knowledge of future arrivals. The objective is to minimize the sum of absolute differences between elements assigned to adjacent cells. The problem has been studied under both adversarial and stochastic input models. For adversarial sequences, Aamand, Abrahamsen, Beretta, and Kleist (SODA'23) gave a tight $O\sqrt n)$-competitive algorithm, fully resolving the worst-case setting. For stochastic sequences, in which the elements are drawn i.i.d.\ from $U[0,1]$, Hu (SODA'26) gave an $\log n\cdot 2^{O(\log^* n)}$-competitive algorithm in expectation and proved an $Ω(\log n)$ lower bound, while Kalavas, Platanos, and Tolias (STACS'26) gave an $O(\log^2 n)$-competitive algorithm with high probability. Very recently, Hermansen (ESA'26) closed the remaining gap by designing an $O(\log n)$-competitive algorithm in expectation. In this work, we study Random-Order Online Sorting, a model interpolating between the adversarial and stochastic settings, that was posed as a challenging open question by Hermansen (ESA'26). Here, the input is a multiset chosen adversarially, but its elements arrive in uniformly random order. We take a different point of view by solving the problem in rank space, and prove an $O(\log^2 n)$-competitive algorithm with high probability, matching the state-of-the-art high probability guarantee for the stochastic setting in this more general model. We also study a multidimensional generalization, which we call Random-Order Online TSP, and obtain an $O(\log^3 n)$-competitive algorithm with high probability.

Authors: Sharma V. Thankachan

The suffix array ($\SA$) and inverse suffix array ($\ISA$) are fundamental data structures in string algorithms. Given a text of length $n$ over an integer alphabet of size $σ$, explicitly storing these structures requires $Θ(n\log n)$ bits, motivating compressed representations that approach the information-theoretic minimum of $n\logσ$ bits. Two landmark solutions are the FM-index, which achieves succinct space and supports $\SA$ and $\ISA$ queries in roughly logarithmic time, and the Compressed Suffix Array (CSA), which uses $O(n\logσ)$ bits and answers both queries in $O(\log_σ^εn)$ time. Nearly all subsequent compressed indexes, including entropy-compressed and repetitiveness-aware variants, support $\SA$ and $\ISA$ using essentially the same techniques, leading to similar space--time trade-offs. This raises a fundamental question: under the same asymptotic space bound, do $\SA$ and $\ISA$ have the same inherent query complexity? We provide evidence that this symmetry is not intrinsic. Under an $O(n\logσ)$-bit space bound, we show that $\SA$ queries are unlikely to admit $\log^{o(1)} n$ query time, suggesting that improving the classical CSA bound would require a major breakthrough. In contrast, we present a new encoding supporting $\ISA$ queries in $O(τ+\log\log n/\log\logσ)$ time using $(1+1/τ+o(1))n\logσ$ bits, for any $τ\ge1$. Our main insight is that the $\LF$-mapping, traditionally implemented using the Burrows--Wheeler Transform, can be adapted to support $\ISA$ queries directly from the packed text, eliminating the need to store the BWT while achieving substantially faster query times.

Authors: Ron Zadicario, Tova Milo

Result diversification is crucial for generating informative, non-redundant data summaries and query outputs. Although its various formulations have been extensively studied across an array of data-driven disciplines, existing methods fail to address the privacy concerns that arise when the underlying data is sensitive. In this work, we initiate the study of result diversification under differential privacy, focusing on the max-sum diversification (MSD) problem, a widely adopted model with the objective of maximizing a linear combination of a submodular function, quantifying relevance, and the sum of pairwise distances between selected items, quantifying diversity. We propose differentially private algorithms for MSD under both cardinality and matroid constraints, achieving nearly optimal utility guarantees. At the same time, we design more efficient algorithms that maintain strong guarantees. Notably, the proposed algorithms are faster than existing non-private methods, making them appealing even in non-private settings. Experimental evaluations on real-world datasets demonstrate that the proposed approach achieves utility comparable to that of non-private baselines even under strong privacy guarantees, and significantly improves execution times for cardinality constraints.

Authors: Toluwanimi O. Odemuyiwa, Serban D. Porumbescu, Muhammad Osama, Joel S. Emer, John D. Owens

We propose a principled approach to reasoning about various graph algorithm implementations. We leverage the extended general Einsum notation (EDGE) which allows us to factor complexity along four axes: algebraic manipulation, mapping, format, and low-level implementations. Using breadth-first search (BFS) as a driving example and case study, we apply our methodology to explore over 90 variations across 26 categories of optimization choices for our GPU-based implementations. In addition to showing that our approach is general enough to represent previously discovered algorithmic techniques such as the pull variant of BFS, we discover novel variants that lead to geomean performance benefits ranging from 1.2x to 1.7x over the best Gunrock baseline variation for graphs with mid- to high- normalized degree variance.

Authors: Rong Fu, Yongtai Liu, Xiaowen Ma, Wangyu Wu, Long Zhang, Hongbo Zhang, Yangchen Zeng, Hoi Leong Lee, Hao Zhang

We study degeneracy guided list compression for greedy graph coloring when graph structure is available before colors are sampled. Our exposure calibrated ordering framework assigns each vertex an independent uniform list according to its backward neighborhood in a color independent order. Its certified instantiation, Profiled Structure Aware Asymmetric Palette Sparsification, or P-SAPST, reverses a minimum degree removal sequence and obtains every backward exposure from the removal profile. For each fixed profile, we characterize the exact local budget required by independent uniform lists under history robust greedy recovery. The profile yields linear list volume on high degree forests and on a core fringe family where reciprocal rank allocation requires Theta(n log^2 n) sampled colors. Exact conflict expectation, concentration, and a dense exposure barrier complete the theoretical description. The evaluation contains 40,320 runs over SAPBench and two SNAP networks. At the theorem scale, P-SAPST reduces mean list size by 47.6 percent relative to calibrated APST while attaining 99.8 percent observed greedy success. P-SAPST Lite replaces peeling with a degree order and provides a lower latency order choice within the same framework. On stress graphs with 250,000 vertices and up to 1,251,868 edges, Lite obtains a payload ratio of 0.865, while calibrated APST obtains 7.886. On email Enron, the corresponding ratios are 0.193 and 5.814. Compression is strongest on hub dominated and power law graphs and disappears near the dense exposure barrier. The method complements edge oblivious streaming APST by addressing an offline regime in which structural plans can be reused.

Authors: Keren Zhu

Large language models may make precise but dormant algorithmic problems practical to revisit, and may expose new paths toward fundamental ones. We demonstrate this possibility through Prim-Dijkstra routing, a classic VLSI problem whose terminal-only Manhattan complexity remained open despite decades of practical work. We prove weak NP-completeness, derive a continuous cost-radius tradeoff with a balanced (2,2) guarantee, and build HP-RCRST, a height-partition-based multi-mode solver. On 28 development instances, its stronger modes Pareto-dominate the published-method union on 23 and tie on five. The case shows how conflicting conjectures, counterexamples, formal checks, and implementation can reopen neglected questions. Code and reproducibility materials are available at github.com/CODA-Team/hp-rcrst.

Authors: Niu Yannian, Han Song, Wang Minmei

Multi-set membership queries are ubiquitous in networking and database systems. Current solutions force a difficult compromise: hash tables guarantee correctness but suffer from high memory footprints, while filter-based approaches optimize space at the cost of probabilistic errors. In this paper, we propose STEM2, a fast and space-efficient data structure that achieves 100% query accuracy and can support dynamic key updates for multi-set membership queries. STEM2 utilizes a balanced binary tree architecture where each non-leaf node incorporates a novel Exact Binary Set Separator (XBSS) to partition keys into two disjoint groups. A key innovation of our design is a minimized hashing scheme that requires only two hash computations per key lookup, significantly reducing computational overhead. Additionally, STEM2 separates the control plane and the data plane: the control plane handles construction and dynamic updates, while the data plane is dedicated to serving efficient membership queries. Extensive experiments show that STEM2 achieves over 120 million operations per second (Mops) in lookup throughput, outperforming the state-of-the-art Coloring Embedder by 20% and the Ludo hashing by up to 21.6X, while maintaining compact memory cost and exact correctness.

Authors: Rémy Belmonte, Juan Pablo Bravo, Noleen Köhler, Haiko Müller

We study variants of graph colouring with parity constraints. More specifically, we consider $q$-colourings $c\colon V(G)\rightarrow \{1,\dots,q\}$ of a graph $G$ where, for every vertex $v\in V(G)$, the number of neighbours $w$ of $v$ with $c(w)=c(v)$ is restricted to be odd, even, positive, zero or a combination thereof. For every colour $i\neq c(v)$ the number of neighbours $w$ of $v$ with $c(w)=i$ is restricted by a constraint of similar type. Many known colouring problems such as proper colouring, defective colouring, exact defective colouring, odd colouring, and strong odd colouring can be described within this framework of constraining graph colourings, and therefore considering variants constitutes a natural generalisation of known colouring problems. We provide a comprehensive study of the computational complexity of different combinations of constraints involving parity.

A new essay and some new stories about diet optimization.

The Department of Computer Science at RPTU at campus Kaiserslautern invites applications for a professorship in Algorithms and Complexity. The position is a tenured professorship at the salary level W2 equivalent to an associate professorship, and is to be filled as soon as possible. Website: www.cs.rptu.de/en/forschung/stellen/w2-ak/ Email: lin@cs.uni-kl.de

Authors: Álvaro Gutiérrez, Christian Ikenmeyer, Greta Panova

We study the class $\#\mathsf{L}$ of functions counting accepting paths of non-deterministic log-space Turing machines and construct methods to prove containment in $\#\mathsf{L}$. We prove that a large number of classical combinatorial and number theoretic functions belong to this class: classical functions from enumerative combinatorics (multinomial coefficients, Catalan numbers, linear extensions of trees, Stirling numbers, etc), algebraic combinatorics (number of standard Young tableaux, etc), discrete geometry, number theoretic functions, representation theoretic multiplicities in a large class of cases. We show that $\mathrm{GL}_2$-plethysm coefficients of bounded length outer partition can be counted by log$^2$-space polytime verifiers. We pose numerous questions and conjectures on $\#\mathsf{L}$ containment and its generalizations, that suggest venues for conditionally disproving $\#\mathsf{P}$-completeness. While studying which combinatorial functions are in $\#\mathsf{P}$ provides a formal way of (dis)proving the existence of combinatorial interpretations, the lower class $\#\mathsf{L}$ serves as an analogue for functions computable in polynomial time.

Authors: Anand Kumar Narayanan

Raz proposed a program to prove arithmetic circuit lower bounds through the explicit construction of elusive functions. These are polynomial maps from a low dimensional space to a high dimensional ambient space whose image is contained in no subvariety of low complexity. Here, complexity is prescribed in terms of the dimension and degree of parametric maps into the ambient space defining the subvariety. Elusive functions are abundant: finding explicit ones with parameters typical of generic polynomial maps implies Valiant's hypothesis that VP$\neq$VNP. But no such construction is known. Raz devised elusive functions with weaker parameters to derive explicit degree d polynomials in n variables requiring superlinear circuit size at depth $d=o(\log n)$. We present a new method to analyse and construct elusive functions, with coordinate maps restricted to monomials. To prove elusiveness, we identify a hitting set of points, each a tuple of roots of unity coupled based on the exponents of the monomial maps. Using Chebotarev's theorem on roots of unity, we show that for every low complexity subvariety, the function evaluated at some point in the hitting set eludes it. For this strategy to work, it suffices that the iterated sumset of a certain set of numbers (derived from the exponents) expands exponentially. We thus reduce open explicit construction problems in elusive functions to purely additive combinatorial ones, whose resolutions imply as yet unknown lower bounds. Informed by iterated sumset expansion, we devise new elusive functions. We construct explicit elusive curves of exponential degree, resolving an open problem posed by Garg, Makam, Oliveira, and Wigderson as a testament to the difficulty of elusiveness proofs. We improve Raz's superlinear bound quadratically (with circuit size to input size ratio as the metric) below $o(\log n/\log\log n)$ depths.

Authors: Gal Yehuda

We construct quantitative almost laws for $SO(3)$. More precisely, there exist a constant $c>0$ and non-trivial words $W_n\in F_2$ such that, for every $A,B\in SO(3)$, \[ \|W_n(A,B)-I\| \le \exp\!\left(-c |W_n|^δ\right), \] where $δ=\log_2(x_0)=0.879146\ldots$ and $x_0>1$ is the real root of $x^3=x^2+x+1$. This improves the exponent $\log_2\varphi$ obtained from Elkasapy's lower-central-series construction. As an application, we show how this result improves the word-length threshold in Kuperberg's Solovay--Kitaev algorithm for single-qubit gates.

Authors: Tamal K. Dey, Tao Hou, Dmitriy Morozov

Computation of zigzag persistence has progressed in recent years, with results showing that complexities of many problems closely align with those in the non-zigzag setting. The major efficiency gap now lies in the updating of zigzag representatives. In this paper, we propose efficient algorithms for updating zigzag representatives based on a recent algorithm for extracting zigzag representatives from a $R=DV$ decomposition of a constructed non-zigzag. The main difficulty for designing our update algorithms lies in the adjacency change occurring in two operations that elongate or shorten a filtration. Despite the adjacency change, we find that the update can still be done efficiently in quadratic time.

Authors: Mark de Berg, Ulrike Schmidt-Kraepelin, Andree-Ovidiu Stef

Computing a minimum-weight perfect matching for a point set $P$ in Euclidean space is a classic geometric optimization problem. We consider the problem in a dynamic setting, where pairs of points may be added to or removed from the set $P$. Our focus is on maintaining an approximately optimal solution without making too many changes to the solution. More precisely, we are interested in $k$-stable algorithms, which change at most $k$ edges in the matching after each update to the set $P$. In other words, we consider an online setting (with insertions and deletions) with bounded recourse. We study trade-offs between the stability of the algorithm and the approximation ratio of the maintained solution for point sets in $\mathbb{R}^1$. First, we present an $O(\sqrt{n})$-stable algorithm that maintains a $2$-approximation, which we show to be optimal among all algorithms with sublinear stability. Second, we prove that any $o(\log n)$-stable algorithm has unbounded approximation ratio. Our lower bounds hold even in the insertion-only case, while our algorithm works in the fully dynamic case. Moreover, our lower bounds also hold for the bipartite variant of the problem.

Authors: Žiga Sajovic, Dejan Knez

Mesh CSG output is consumed in floating point: however exact the computation, every emitted coordinate is materialised -- rounded to a representable position -- and the next stage can observe crossings and orderings the exact result never had. Only index-based topology survives materialisation. We keep it exact: within the build, the arrangement's radial structure is ordered by exact predicates on the original input planes -- exact without exact constructions -- and where a decision spans faces, the intended answer is recovered by topological aggregation: a majority vote over the disagreeing geometric observations within their topological unit. We compute the arrangement locally with integer-exact predicates, every stage a graph problem on graphs it never explicitly constructs. Pairwise intersections are classified into five canonical types (VV, VE, VF, EE, EF), each cut face is arranged in its own plane, and a two-level identity keeps the result consistent across faces with no global structure. The arrangement and its domain partition are built once and queried arbitrarily often: a boolean of any arity is a per-domain bit test, volumetric regions read straight off the partition, and open surfaces -- declared as oriented sheets -- cut volumes through the same algebra. The method is implemented in the header-only trueform library, in C++ with Python and TypeScript bindings. Compared to prior art, it produces valid, watertight output while running up to two orders of magnitude faster, and stays interactive in the browser.

Authors: Ronald Katende

For an explicitly represented finite empirical model, deciding whether the contextual fraction is strictly below one is NP-complete, while the standard exact linear program has one column for every global assignment. We identify a permutation-transport class in which this global problem collapses to a fixed-point calculation. Let a connected permutation gain graph act on a finite state set $O$, let $H \leq{ \rm Sym}(O)$ be its holonomy subgroup, let $F = {\rm Fix}(H)$, and let $p$ be an $H$-invariant root distribution. For the induced empirical model, \[ {\rm NCF}(e)=p(F),\qquad {\rm CF}(e)=1-p(F). \] Consequently, compatibility, $F$, and ${\rm CF}(e)$ are computable in $O(|O|(|V|+|E|))$ arithmetic and table operations. For every finite simple $2$-edge-connected graph, any deterministic exact algorithm in the explicit permutation-table query model requires at least $(|O|-1)|E|$ probes in the worst case, making the dependence on the input tables optimal up to constant factors. With a fixed spanning tree, chord insertions and deletions require $O(|O|)$ worst-case time, or time proportional to the moved-set representation, while compatibility and contextual-fraction queries take $O(1)$ time. Finally, for common-marginal realizable binary constraint languages, the support threshold ${\rm CF} < 1$ is polynomial-time equivalent to the associated finite-domain constraint-satisfaction problem and therefore inherits the Bulatov--Zhuk dichotomy. The results identify a query-optimal and dynamically maintainable tractability island inside the general contextual-fraction problem.

Authors: Naoki Kitamura, Seitaro Kawaguchi, Yuya Terashima, Taisuke Izumi

We study the Connected Components Reconfiguration problem (CCR), in which connected components on a graph are transformed according to a specified reconfiguration rule. CCR generalizes Independent Set Reconfiguration by treating tokens not as individual vertices but as connected components of prescribed sizes. Among the variants of CCR, we focus on the component-jumping model, denoted by \CCRCJ. Nakahata.\ introduced this problem and showed that the decision problem for \CCRCJ~can be solved in $O(n^2)$ time on path graphs for arbitrary component sizes, and in polynomial time on chordal graphs when all connected components have the same size. However, the complexity on chordal graphs under a multiset size constraint remained open. In this paper, we study this multiset version of \CCRCJ~from both complexity-theoretic and algorithmic viewpoints. First, we prove that \CCRCJ~is NP-hard even on caterpillar graphs, which is a very restricted subclass of trees and chordal graphs minimally above path graphs. This result immediately implies NP-hardness for chordal graphs under a multiset size constraint, thereby resolving Nakahata's open problem on chordal graphs under multiset size constraints. Second, we revisit \CCRCJ~on path graphs. We improve the previous $O(n^2)$-time algorithm for the decision problem by giving an $O(n\log n)$-time decision algorithm. Moreover, when the instance has sufficiently large empty space, we show that there exists a reconfiguration sequence of length $O(n\log n)$, and such a sequence can be output efficiently.

Authors: Mark Bun, Rathin Desai, Renato Ferreira Pinto

Motivated by the challenge of testing distributions over high-dimensional or continuous domains, we study distribution testing with respect to bounded classes of distinguishers. A representative task is to use samples from an unknown distribution $P$ over a very large domain to decide between two cases: $P = P_{\mathsf{ref}}$ for a fixed reference distribution $P_{\mathsf{ref}}$, or there exists a distinguisher $f$ in a bounded class $\mathcal{F}$ which witnesses the separation $|\mathbf{E}_P[f] - \mathbf{E}_{P_{\mathsf{ref}}}[f]| > ε$. This is the task of identity testing with respect to fooling distance, a name inspired by the conceptual connection with pseudorandomness. (Formally, our model instantiates integral probability metrics from Boolean classes of bounded expressivity.) We show that testing with respect to fooling distance is not only a natural computational problem that admits sample-efficient algorithms even in high-dimensional settings, but also one that reveals and underlies connections between three seemingly unrelated areas of study: testable learning, verification of learning algorithms, and testing of structured distributions (whose "$\mathcal{A}_k$-testing" model our framework extends). These connections yield new results for all of these models, including: 1. Testable proper learners using membership queries for halfspaces and decision trees. 2. A lower bound for testable PAC verification in terms of Rademacher complexity, and a distribution-free verification protocol for disjoint unions of $k$ multidimensional rectangles. 3. Identity testers (with respect to total variation distance) for decision tree distributions and distributions with low-degree polynomial densities, over Boolean and continuous hypercube domains.

Authors: Swastik Kopparty, Shubhangi Saraf

The recent breakthrough work of Chatterjee, Ghosh, Gurjar, Raj and Thierauf [CGGRT26] gives the first deterministic NC algorithm for the bipartite matching problem. They show how to detect as well as find perfect matchings in bipartite graphs in NC. In this note we present an arguably simpler-to-state variation of the NC detection criterion of [CGGRT26], with improved parameters.

Authors: Allan Borodin, Changdao He, Nadim Mottu

Job throughput maximization is the central maximization problem in scheduling. Interval scheduling is the special case of throughput maximization when jobs are intervals and therefore there is no slack available in which to schedule a job. It is interesting to know to what extent results for interval scheduling can be extended to the more general throughput problem in the real-time model. For the unweighted and proportionally weighted throughput problem (where the weight or value $w_i$ of a job $J_i$ is its processing time $p_i$), there are constant competitive real-time scheduling algorithms using preemption with restarting. More generally, the result for proportionally weighted interval scheduling can be extended to C-Benevolent weight functions. We also introduce a new real-time model in which jobs are announced before the actual release time of a job. We show that with sufficient advance notice, we can obtain a constant competitive ratio for proportionally weighted throughput {\it without any preemption}. However, this advance notice result does not extend to arbitrary C-Benevolent and D-Benevolent weight functions. Finally, we show that unlike interval scheduling, unweighted throughput using preemption with revoking admits no constant competitive ratio when the number of distinct processing times is unrestricted. More precisely, for instances with at most $k$ distinct processing times, we give a lower bound of $1/(k+1)$ and a deterministic $1/(2k)$-competitive algorithm.

Authors: Ole-Christian Galbo Engstrøm

Improved Kernel Partial Least Squares (IKPLS) algorithms 1 and 2 are among the fastest PLS calibration algorithms. This article focuses on two shared steps, the computation of the $\mathbf{X}$ rotations, $\mathbf{R}$, and the $\mathbf{Y}$ loadings, $\mathbf{Q}$, and accelerates both. For $\mathbf{R}$, term-by-term accumulation is replaced by a direct evaluation strategy that requires the same number of multiplications but parallelizes better on modern hardware. For $\mathbf{Q}$, I identify - to the best of my knowledge, for the first time - equivalences showing that each $\mathbf{Y}$ loading is obtainable, up to explicitly derived constants, from quantities already computed earlier in the same iteration, and I exploit them in IKPLS to reduce the cost of each loading from $Θ\left(KM\right)$ to $Θ\left(M\right)$ operations whenever $M = 1$ or $2 \leq M < K$, with $K$ predictor variables (number of columns in $\mathbf{X}$) and $M$ response variables (number of columns in $\mathbf{Y}$). Both improvements provably yield exactly the same $\mathbf{W}$, $\mathbf{P}$, $\mathbf{Q}$, $\mathbf{R}$, and $\mathbf{T}$ as the original algorithms. Benchmarks with NumPy (CPU) and JAX (GPU) show speedups of up to two orders of magnitude for the isolated steps and of approximately $2\times$ (CPU) and $6\times$ (GPU) for entire fits. Both improvements are implemented in the free, open-source Python package \texttt{ikpls}.

Authors: Sanjeev Khanna, Aaron Putterman, Madhu Sudan

We study the sparsifiability of \emph{real-valued codes}, a unifying abstraction that generalizes both combinatorial and continuous notions of sparsification, including spectral sparsification. In our setting, a code $C \subseteq \mathbb{R}_{\geq 0}^m$ is simply a collection of nonnegative real-valued vectors, and for a parameter $ε> 0$, a \emph{$(1 \pm ε)$-sparsifier} of $C$ is a subset $T \subseteq [m]$, together with weights $w \in \mathbb{R}_{\geq 0}^T$, such that, for every $c \in C$, $\sum_{i \in T} w_i c_i \in (1 \pm ε)\sum_{i=1}^m c_i$. When $C \subseteq \{0,1\}^m$, this specializes to code sparsification, and hence captures CSP sparsification, as studied by Khanna--Putterman--Sudan (SODA 2024, STOC 2025) and Brakensiek--Guruswami (STOC 2025). Similarly, for a graph $G=(V,E)$, if one defines $C=\{c^{(x)}:x\in\mathbb R^V\}\subseteq\mathbb R_{\geq 0}^E$ by $c^{(x)}_{(u,v)}=(x_u-x_v)^2$, then sparsifying $C$ is exactly spectral graph sparsification, as studied by Spielman--Teng (SICOMP 2011). Although the techniques driving combinatorial and continuous sparsification have traditionally been largely disjoint, our main result is a single structural theorem governing the sparsifiability of arbitrary real-valued codes $C\subseteq\mathbb{R}_{\geq 0}^m$. The central parameter is \emph{continuous-valued non-redundancy} ($\mathrm{CVNRD}$), a real-valued analogue of non-redundancy that captures the largest approximately block-diagonal obstruction contained in $C$. Our theorem gives sparsifiers of size nearly-linear in $\mathrm{CVNRD}$, and shows that $\mathrm{CVNRD}$ is also a lower-bound obstruction for the broad class of coordinate-wise unbiased randomized sparsification schemes.

Authors: Dominik Scheder, Johannes Tantow

The Stackelberg Vertex Cover problem is a bilevel optimization problem with two players on a graph $G = (F \cup P, E)$ where each vertex from $F$ has a weight and the first player selects a price for each vertex in $P$. Afterwards, the second player finds a minimum vertex cover $X$ and the first player receives the set price for each vertex from $X \cap P$. The goal is to maximize the revenue of the first player. This problem was recently shown to be NP-complete for bipartite graphs while being solvable in linear time on paths. We present three new algorithms for solving Stackelberg Vertex Cover on certain kinds of trees: (1) a pseudo-polynomial algorithm working on general trees when all weights are integer, i.e., it is FPT with the maximum weight as a parameter; (2) a strongly polynomial algorithm for trees having the property that the least common ancestor of any two vertices from $P$ is again in $P$ (this case includes paths); and (3) an FPT-algorithm for trees, where the parameter is the maximum number $P$-vertices $v_i$ that an $F$-vertex $u$ can reach while using no other $P$-vertices. These algorithms are based on a lemma that allows us to split instances at a vertex $u$ into multiple sub-instances, which follows from LP duality and integrality of the vertex cover LP on bipartite graphs. The lemma requires that the minimum vertex covers of the sub-instances agree on $u$ (either all include $u$ or all don't). For this we introduce the concept of commitments. Finally, we show that the Stackelberg Vertex Cover problem with commitments is weakly NP-complete.

Authors: Arya Mazumdar, Prateeti Mukherjee

Binary Iterative Hard Thresholding (BIHT) is a simple, yet effective, greedy method for recovering a sparse vector from one-bit sign measurements. In its original form, BIHT performs a ``gradient-descent'' step, followed by hard thresholding. A convergence analysis of this algorithm was left open in the introductory work of [Jac+11] and has remained unresolved for over a decade, with subsequent sharp analyses studying a normalized variant instead, that additionally projects every iterate onto the unit sphere. This paper resolves that gap and characterizes when per-iteration normalization is algorithmically necessary. In the noiseless setting, we prove a universal, sample-optimal convergence theorem for the original BIHT algorithm. Specifically, with $\widetilde O(s/ε)$ measurements, a deterministic finite-time iterate has directional error at most $ε$, simultaneously for every $s$-sparse unit vector. This matches the optimal sample dependence achieved by normalized BIHT in prior work. Thus, in the noiseless regime, per-iterate normalization is unnecessary for optimal recovery. Under sign corruptions, we prove a sharp separation. If at most a $τ$ fraction of signs are flipped adversarially, then BIHT, without per-iterate normalization, still reaches the robust error floor at an early iterate with a matching $\widetilde O(s/ε)$ sample complexity rate as its normalized variant. This recovery, however, is not stable. We prove a scalar lower bound showing that any nontrivial corruption pattern, even one that involves only one flipped sign together with one clean sign, forces the iterates to oscillate indefinitely. Consequently, no general last-iterate convergence theorem can hold for BIHT under sign corruptions, while its normalized surrogate provably escapes this instance.

Authors: Michael Ngo, Michael P. Kim

Following Goldwasser, Rothblum, Shafer, and Yehudayoff, who defined a framework for interactive proofs of learning [ITCS'21], we initiate the study of non-interactive proofs of learning. We define and study a new notion: Publicly-Verifiable Certificates of Statistical Validity (pvCSVs), which allow for public, distributionally-robust certification that the result of a learning algorithm is valid. In a pvCSV, a learner publishes a hypothesis $h$ and corresponding certificate $π$; then, any user, who holds a user-specific distribution, can read the pair $(h,π)$ and determine efficiently whether the hypothesis is valid according to the user-specific distribution. We construct pvCSVs in the context of Adaptive Statistical Query (SQ) Algorithms. To certify SQ algorithms that makes $k$ adaptive queries, we construct pvCSVs where the sample complexity scales with $O(\log k)$, whereas the sample complexity of the best learning algorithms scale with $\tilde{O}(\sqrt{k})$. More generally, we study proof systems for learning in the SQ model, demonstrating the model's strengths as well as its limitations.

Authors: Linh Anh Nguyen

Fuzzy minimax nets were recently introduced as a tool for computing the greatest fuzzy bisimulation and simulation between two finite fuzzy graph-based structures. In this work, we provide an efficient algorithm for computing the greatest correct marking of a finite fuzzy minimax net over the Gödel structure. Its time complexity is linear in the number of nodes and positive edges in the input net. Building on this result, we derive the first algorithm with time complexity $O((m+n)n)$ for computing the greatest fuzzy directed simulation between two finite fuzzy graphs over the Gödel structure, where $n$ and $m$ denote the total numbers of vertices and positive edges, respectively, in the input graphs.

Authors: Daniel Yang, Thaxter Shaw, TJ Tsai

This article investigates several parallelizable alternatives to DTW for estimating the alignment between two long sequences. Whereas most previous work has focused on reducing the total computation and/or memory costs of DTW, our focus is instead on reducing wall clock time by utilizing common hardware like GPUs that are optimized for parallel processing. We propose and study four different parallelizable alignment algorithms: the first three algorithms compute approximations of DTW by breaking the pairwise cost matrix into rectangular regions and processing the regions in parallel, and the fourth algorithm computes an exact DTW alignment by processing the cost matrix along diagonals rather than rows or columns. We characterize the performance of our proposed alignment algorithms on an audio-audio alignment task, and we develop GPU-based implementations for the two best-performing algorithms, which we call weakly-ordered Segmental DTW (WSDTW) and Parallelized Diagonal DTW (ParDTW). Our experiments indicate that ParDTW is the most practical and useful of the four algorithms: it computes an exact DTW alignment and reduces runtime by 1.5 to 2 orders of magnitude on long sequences compared to current alternatives. We present a comprehensive evaluation and study of the alignment accuracy, runtime, and practical limitations of the proposed alignment algorithms.

Authors: Ricardo Arancibia-Castillo, José A. Soto

We study a single-machine scheduling problem in which each job $j$ has a nonnegative processing time $p_j\ge 0$ and a due date $d_j\in\mathbb{R}$. For a non-idling schedule $S$, let $C_j(S)$ be the completion time and let $L_j(S)=C_j(S)-d_j$ be the (possibly negative) lateness. The objective is to minimize the sum of the $k$ largest lateness values, interpolating between maximum lateness ($k=1$) and total lateness ($k=n$). We prove that the decision version is weakly NP-complete when $k$ is part of the input. For fixed $k$, we give an $O(k^2 n^{k+2})$ algorithm. As a consequence, we resolve a conjecture of Woeginger on the top-$k$ tardiness problem and obtain an $O(n^{k+2})$ algorithm for every fixed $k$. Our main structural result shows that there exists an optimal schedule that admits a block-island decomposition. Outside a suitable top-$k$ set, jobs form due-date blocks ordered by due date. Within each due-date class, the top-$k$ jobs form a suffix in lexicographic shortest-processing-time (SPT) order. This structure also yields an FPT algorithm parameterized by $D+k$, where $D$ is the number of distinct due dates. Independently, a standard dual representation of the top-$k$ objective reduces the problem to a family of total-tardiness instances with uniformly shifted due dates. For integral data, this gives a pseudopolynomial algorithm and a fully polynomial additive approximation scheme with error at most $\varepsilon M$, where $M=\max\{1,\max_j p_j,\max_j |d_j|\}$. The same route also gives XP algorithms for fixed $P$ and fixed $D$, where $P$ is the number of distinct processing times.

Authors: Matías Ortiz-Angel, José A. Soto

We introduce and study the multiple-choice matroid secretary problem, denoted $(J,κ)$-MSP. For rank-one matroids and $κ=\infty$, it reduces to the classical secretary problem with $J$ choices. Elements arrive in uniformly random order. Algorithms may keep a candidate pool $\mathrm{AUX}$ feasible in the $J$-fold union matroid $\mathcal{M}^{(J)}$ satisfying $|\mathrm{AUX}|\le κ\cdot\mathrm{rank}(\mathcal{M})$. Finally, one extracts the maximum-weight independent subset of $\mathrm{AUX}$ in $\mathcal{M}$. This model separates online storage from the final feasible solution. We study two multiple-choice implementations: multi-track algorithms (maintaining $J$ independent sets of $\mathcal{M}$) and union-based algorithms (maintaining the pool directly in $\mathcal{M}^{(J)}$). Our main result is an exact optimal algorithm for transversal matroids in the uncapacitated $(J,\infty)$ setting. For fixed $J$, its probability-competitive ratio equals the optimal success probability of the classical $J$-choice secretary problem. Thus, rank-one instances are the worst case for the whole transversal class, and the optimal guarantee converges exponentially fast to $1$ as $J$ grows. We also analyze a simple single-threshold routing algorithm for capacitated transversal matroids with local capacities $b$ and global capacity $κ\cdot\mathrm{rank}(\mathcal{M})$. Its analysis provides explicit finite-parameter bounds and asymptotic formulas, showing how finite-rank loss caused by global capacity decays, and how $b$, $J$, and $κ$ interact. Finally, we instantiate the multi-track approach for $k$-column-sparse matroids (guarantee $1-O(e^{-J/(ke)})$) and the union-based approach for laminar matroids (guarantee $1-O(e^{-J/e})$).

Authors: Or Zamir

Open-addressed hash tables without reordering, such as linear probing and uniform probing, are among the simplest and most widely used data structures. Their performance is traditionally measured by probe count. We study a complementary parameter: locality, defined as the geometric distance from the first probed location to the farthest cell inspected or used. At load factor $1-\varepsilon$, uniform probing achieves the optimal $Θ(1/\varepsilon)$ probe count among greedy schemes, but has essentially no locality, whereas linear probing is highly local but performs $Θ(1/\varepsilon^2)$ probes. We show that this quadratic locality scale is fundamental: no open-addressing algorithm without reordering can achieve locality $o(1/\varepsilon^2)$ simultaneously at every load $1-\varepsilon$. We also prove an amortized expected-locality lower bound of $Ω(1/\varepsilon)$ over any sequence of $(1-\varepsilon)n$ insertions, even when the final load is known in advance. Our lower bound further implies that page size $B=Ω(1/\varepsilon^2)$ is necessary for $1+o(1)$ expected page span in immutable open addressing. We complement these lower bounds with two upper bounds. When the target load is known in advance, every insertion and every successful or unsuccessful search can be given expected probe count and locality $\widetilde O(1/\varepsilon)$, essentially deamortizing the amortized lower bound. We also give a load-oblivious greedy scheme with optimal expected probe count $Θ(1/\varepsilon)$ whose $i$-th probe is at distance $O(i^2)$ from the first probe. Its analysis gives a general variance bound for occupied-cell densities in symmetric probing schemes, implying an $O(\log n/\varepsilon^2)$ expected probe bound for every fixed-shift probing sequence and every load $1-\varepsilon$.

Authors: Dean Matthew Menezes, C. Gregory Plaxton

We study the classical single-machine deadline problem $1 \mid\mid \sum U_j$, in which each task has a deadline and an execution requirement and the goal is to select as many on-time tasks as possible. The standard Moore-Hodgson algorithm processes tasks by deadline and may later delete a previously accepted task. We study the insertion-only shortest-job-first rule of Lin and Wang: process the tasks in nondecreasing execution requirement, and accept a task exactly when doing so preserves feasibility. We give a direct $O(n\log n)$-time implementation using a balanced augmented BST keyed by deadline. Unlike the previous $O(n\log n)$ implementation of this SJF rule, our implementation needs neither a preëmptive schedule nor an amortized analysis of interval changes. Our analysis gives an explicit threshold form of the rule's lexicographic (\emph{lex-first}) optimality: for every threshold~$e$, its outputs maximize the number of selected tasks whose execution requirement is at most~$e$. The analysis also reveals additional combinatorial structure. After the shorter tasks have been greedily fixed, the feasible choices within a single execution-requirement tier form a nested matroid. These tier matroids assemble, as a direct sum, into an overall laminar matroid whose bases are exactly the greedy outputs. Finally, a flow network encoding the deadline-prefix constraints gives a polymatroid rank function for the underlying scheduling feasibility structure. This flow view also recovers the nested matroids that govern the equal-execution tiers.

Nutan Limaye and Thore Husfeldt guest post on the new deterministic parallel algorithm for bipartite perfect matching by Abhranil Chatterjee, Sumanta Ghosh, Rohit Gurjar, Roshan Raj and Thomas Thierauf.
The post will try to explain three main things about the result. What is the result? Why is it important? And finally, how did the authors prove it?

We will assume that the reader is an undergraduate student in CS (i.e., the reader knows basics of discrete mathematics, linear algebra, and algorithm design).
What? The main result can be stated in just one line! Bipartite Perfect Matching can be solved in NC. Let's now understand what each of these terms means.

Bipartite Perfect Matching. A bipartite graph has two disjoint sets of vertices, say \(L, R\), and any edge connects one vertex of \(L\) and one vertex of \(R\). A matching in a graph is a subset of edges such that no two edges have a vertex in common. A perfect matching is a matching in which each vertex of the graph appears exactly once. Let's take the following example. Here is a bipartite graph. 
♦It has two perfect matchings. One is \(\{(v_1, w_1), (v_2, w_3), (v_3, w_2)\}\) and the other is \(\{(v_1, w_2), (v_2, w_1), (v_3, w_3)\}\).

We will assume that the graph is given as a matrix, known as the bi-adjacency matrix. The bi-adjacency matrix of a bipartite graph has \(A_{ij} = 1\) whenever \((i,j)\in E(G)\). For our running example, here is how we will receive the input: \[ A_G = \begin{bmatrix} 1 & 1 & 0 \\ 1 & 0 & 1 \\ 0 & 1 & 1 \end{bmatrix}\,. \] Now, we are ready to define the Bipartite Perfect Matching (BPM for short) problem. Given a bipartite graph as a bi-adjacency matrix, check whether there is a perfect matching in the graph or not.

NC. The next term we need to explain is NC. It is a complexity class named after Nick Pippenger, which consists of a class of problems solvable by parallel algorithms that use polynomially many processors and have parallel running time that is considerably smaller than polynomial. More specifically, the parallel running time is polylogarithmic. You can simply think of this as a class of problems that have efficient parallel algorithms.

Let us begin with a simple example. Suppose we want to multiply \(n\) numbers, \(x_1,\ldots,x_n\). One approach is to first multiply \(x_1\) and \(x_2\), then multiply the result by \(x_3\), and continue in this way until we reach \(x_n\). This is a sequential algorithm.

A parallel algorithm proceeds differently. It first pairs up the numbers and multiplies all pairs simultaneously. This produces \(n/2\) numbers, giving a new instance of the same problem with only half as many inputs. We then repeat the process: pair up the remaining numbers, multiply each pair in parallel, and continue until only one number remains.♦ Consider another example. Given an integer matrix \(A\), suppose we wish to compute its determinant; we refer to this as the DET problem. Sequentially, the determinant can be computed efficiently using Gaussian elimination. More surprisingly—and this is far from obvious—the problem also admits an efficient parallel algorithm (DET \(\in\) NC). (Here are some references: Csanky's paper, Berkowitz' paper.) We will use this statement as a black box many times below.
So, overall the new result states that given a bi-adjacency matrix of a graph, checking whether it has a perfect matching or not can be solved efficiently by a parallel algorithm. Why it matters: TL;DR

• BPM is an extremely well-studied graph problem because it arises naturally in many practical scenarios. The problem has been a focus of intense research for more than 5 decades. (See an excellent introduction to matching and related problems here and here.)
• Derandomization is a central problem in theoretical computer science. (For a deep dive into derandomization see this or this survey.) It asks whether randomness truly adds computational power or merely provides a convenient shortcut. Bipartite Perfect Matching has long been known to admit a randomized NC algorithm. Finding a deterministic NC algorithm for the problem therefore fits naturally into the broader derandomization program. In fact, bipartite perfect matching is one of the most natural and simply stated problems in this setting. Its resolution provides a particularly striking example of randomness being removed from an efficient parallel algorithm.

How did the authors solve the problem? Polynomial-time algorithms for this problem---Kuhn's algorithm, Hopcroft--Karp, or matching-via-max-flow with Ford--Fulkerson---are a staple of undergraduate algorithms courses and have been known since the 1960s. They work by repeatedly finding an augmenting path: a path between two unmatched vertices that alternates between edges outside and inside the current matching; augmenting along it grows the matching by one edge (Berge's theorem)---an inherently sequential process. The matching that admits an augmenting path at step \(k\) depends on exactly which edges were chosen at steps \(1\) through \(k-1\); there's no obvious way to precompute or guess ahead of time which augmentations will happen. Thus, the classical algorithms for the problem are sequential.

The randomized NC algorithm for BPM. As a warm up, let us see the randomized NC algorithm for BPM. The algorithm is as follows.

• Start with the given bi-adjacency matrix \(A_G\) of the bipartite graph \(G\).
• Replace each \(1\) entry by a random number from the range \([1, 100n]\), where \(n\) is the number of vertices in \(G\). This creates a new matrix \(A_G'\).
• Compute DET of \(A_G'\).
• If it is non-zero, then accept else reject.

Using the fact that DET \(\in\) NC as a black box, it is straightforward to see that the algorithm above runs in randomized NC. But why is it correct? Establishing correctness requires a connection between determinants and perfect matchings. At a very high level, the terms of the determinant of \(A_G'\) are in one-to-one correspondence with the perfect matchings of \(G\). Thus computing the determinant leads to checking the existence of a matching. For more rigorous details about this connection, we refer the reader to Lovász' paper.

The next question is whether the randomness can be removed. Informally, the randomization is needed to prevent cancellations in the determinant. Recall that computing a determinant involves both additions and subtractions. Thus, even if the graph has a perfect matching, different terms in the determinant expansion may cancel, causing the determinant to vanish. This would make the algorithm incorrect.

To avoid this, each nonzero entry is replaced by a random value. With high probability, the resulting determinant is nonzero whenever a perfect matching exists. The challenge, then, is to achieve the same effect deterministically: how can we prevent these cancellations without relying on randomness?

The first major breakthrough came about a decade ago, in work by Fenner, Gurjar, and Thierauf -- notice the overlap with the authors of the new result. Their approach traded randomness for parallel resources: they showed that the algorithm could indeed be derandomized, but only by allowing many more processors. Specifically, their algorithm required quasi-polynomially many processors. Recall that a polynomial function grows as \(n^c\) for some constant \(c\), whereas a quasi-polynomial function may grow as \(n^{\log n}\). In the latter case, the exponent of \(n\) is itself a growing function of \(n\), rather than a fixed constant.
The new approach Let us reiterate the question we posed after reviewing the randomized NC algorithm.

How can we prevent these cancellations without relying on randomness?

The authors say, the answer lies in coding theory! At a very high level, the algorithm can be described as follows.

• Start with the given bi-adjacency matrix \(A_G\) of the bipartite graph \(G\).
• Replace each \(1\) entry in location \((i,j)\) with a fixed thin and tall matrix \(V_{ij}\) to produce a new matrix \(A_G'\).
• Compute DET of \(A_G'\).
• If it is non-zero, then accept else reject.

If you feed this algorithm to a computer, it will complain: Cannot compute DET because AG' is not a square matrix. But this is not a big deal. One could simply say, check whether it has full rank (either row-rank or column rank, whichever is smaller). This problem is also solvable in NC.

However, instead of replacing the original adjacency matrix, they first create an intermediate matrix \(\widehat{A_G'}\) from \(A_G\) by padding \(A_G\) with \(n(n-1)\) columns of unit vectors. For our example, \[ \widehat{A_G'} = \begin{bmatrix} 1 & 1 & 0 \quad & 1 & 1 \,& 0 & 0\, & 0& 0\\ 1 & 0 & 1 \quad & 0 & 0 \,& 1 & 1\, & 0& 0\\ 0 & 1 & 1 \quad & 0 & 0 \,& 0 & 0\, & 1& 1\\ \end{bmatrix} \] Now, in \(\widehat{A_G'}\), replace each non-zero entry \((i,j)\) with a thin and tall matrix \(V_{ij}\) to obtain \(\widehat{A_G}\). The matrix dimension and the padding length are chosen such that the overall matrix \(\widehat{A_G}\) becomes almost square. It now has slightly fewer rows than columns.

Note that the entire algorithm is deterministic. The main work is to show that this deterministic replacement works. Specifically, the authors show that
\(G\) has a perfect matching if and only if \(\widehat{A_G}\) has full row rank.
While we don't plan to present the proof here, the goal is to give you a high-level outline of the approach. In order to do that, let us first see what these matrices \(V_{ij}\) look like. This is exactly where the connection to coding theory comes up! Each matrix is indeed a Folded Vandermonde matrix, which comes up in folded Reed-Solomon codes and subspace designs.

Fix \(\gamma\in \mathbb{F}\) that has a large order, i.e. it needs to be raised to a very large power before it circles back to itself. (This is relevant when we are over finite fields.) For parameters \(r,D\in \mathbb{N}\), the folded Vandermonde matrix is the \(D\times r\) matrix whose \(ij\)th entry is \(\left(\alpha\gamma^{(j-1)}\right)^{i-1}\): \[ V(\alpha_{}, \gamma) = \begin{bmatrix} 1 & 1 & \cdots & 1 \\ \alpha_{} & \alpha_{}\gamma & \cdots & \alpha_{}\gamma^{r-1} \\ \alpha_{}^2 & (\alpha_{}\gamma)^2 & \cdots & (\alpha_{}\gamma^{r-1})^2 \\ \vdots & \vdots & \vdots & \vdots \\ \vdots & \vdots & \vdots & \vdots \\ \vdots & \vdots & \vdots & \vdots \\ \vdots & \vdots & \vdots & \vdots \\ \vdots & \vdots & \vdots & \vdots \\ \vdots & \vdots & \vdots & \vdots \\ \alpha_{}^{D-1} & (\alpha_{}\gamma)^{D-1} & \cdots & (\alpha_{}\gamma^{r-1})^{D-1} \\ \end{bmatrix} \] The parameters \(r,D\) are chosen such that the matrix is thin and tall. For our example \(G\) with \(n=3\), the matrix ends up as having \(D=n(n^3+1-n)=75\) rows and \(r=n^3+1=28\) columns. To obtain \(\widehat{A_G}\) from \(\widehat{A_G'}\), replace all the \(1\) entries from each column \(j\) of \(\widehat{A_G'}\) by \(V(\alpha_j, \gamma)\), where all \(\alpha\)s are distinct.
Swastik Kopparty and Shubhangi Saraf recently announced a variation of this approach. 

How does the proof proceed from here? One direction of the proof is quite easy. When there is no perfect matching, it is quite easy to see that the matrix cannot achieve full row rank. This is because, when there is no perfect matching, then by Hall's Theorem we know that there is a Hall blocker. That is, there is a set \(S \subseteq L\) such that if you look at the neighbours of \(S\), denoted as \(N(S) \subseteq R\), then \(|S| > |N(S)|\). This deficit in the size of the neighbour set for one of the subsets in \(L\) suffices to observe that the number of columns with nonzero entries is strictly less than the number of rows. Thus, one cannot have full row rank.

For the other direction, things are more intricate. The proof proceeds by proving the contrapositive. They show that if the row rank of the matrix is not full, then there will be a subset \(S \subseteq L\) such that \(|S| > |N(S)|\).

To prove this, they start with a vector \(v\) that certifies the linear dependence between the rows of \(\widehat{A_G}\). This vector is viewed as a tuple of \(n\) vectors of dimension \(D\) each. Now, they prove two things about it.

• First, they show that the non-zero elements of the tuple, say \(\mathcal{P}\), are linearly independent vectors.
• Second, they view these vectors as polynomials and consider the span of these linearly independent polynomials. They count the folded roots of the span of \(\mathcal{P}\) in two ways to derive the fact that \(|S| > |N(S)|\).

Both the parts above invoke a Lemma due to Guruswami and Kopparty which proves an upper bound on the number of folded roots of a linear space of polynomials.Conclusion Here are some final thoughts.

• The paper proves many more results. The proofs are self-contained and well-written. So, I highly encourage you to read it.
• Venkat Guruswami gave a keynote at ICALP 2026, where he explained how one can view the idea of subspace designs as a derandomization tool. He explained subspace designs by analogy with the more familiar notion of hash functions: subspace designs play for linear spaces a role similar to that played by hash functions for sets. He then sketched the BPM in NC proof from this perspective.
• One big open problem is resolved by humans. Going forward, will we have such only-human proofs? In fact, the authors first developed an algorithm for the problem, then used AI to help adapt the underlying idea to a broader setting. That is, they used AI to extend human ideas. Currently, this seems to be a trend in TCS. What can we expect going forward?

By Lance Fortnow

The integer complexity of a number \(n\) is the minimum number of ones needed to express \(n\) as a parenthesized combination of sums and products of ones. For instance, 10 has complexity 7 as it can be expressed using seven ones, but not fewer: