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arXiv2026

Sequential Offering in On-Demand Platforms: On the Optimality of Greedy Ranking

On-demand platforms face the fundamental challenge of fulfilling time-sensitive jobs with independent workers who may decline offers. To minimize delays and unfulfilled jobs, platforms frequently raise the offered wage sequentially following each rejection. However, the interaction between these dynamic price adjustments and the specific sequence in which workers are approached has been overlooked. In particular, if the best-suited workers (e.g., closest to the job) are also ranked earliest in the sequence, then those workers would see the lowest offered wages and may decline, leading to poor system outcomes where less-suited workers end up seeing the raised wages and accepting the job. We study the sequential offering problem to maximize expected welfare or platform profit by jointly optimizing the ranking of workers and the pricing trajectory. Surprisingly, our main result establishes that if the reservation wage distribution exhibits a non-increasing and convex density function (e.g., Uniform, Exponential), welfare is maximized by greedy ranking and wages optimized via backward induction. For arbitrary distributions, we prove that greedy ranking achieves a tight $n/(2n - 1)$ fraction of the prophet benchmark. Numerical results for settings beyond the distributional assumptions find welfare losses well below those allowed by the universal guarantee, even in families where greedy is provably suboptimal. This suggests that rather than sending initial "low ball'' offers to worse matches, platforms should stick with greedy ranking and optimize the wage offerings by appropriately taking the continuation value of the downstream offers into consideration.

cs.GT↗
arXiv2026

Random Schreier graphs as expanders

Expander graphs, due to their mixing properties, are useful in many algorithms and combinatorial constructions. One can produce an expander graph with high probability by taking a random graph (e.g., the union of $d$ random bijections for a bipartite graph of degree $d$). This construction is much simpler than all known explicit constructions of expanders and gives graphs with good mixing properties (small second largest eigenvalue) with high probability. However, from the practical viewpoint, it uses too many random bits. The natural idea is to restrict the class of the bijections that we use. For example, if both sides are vector spaces $\mathbb{F}_q^k$ over a finite field $\mathbb{F}_q$, we may consider only \emph{linear} bijections, making the number of random bits polynomial in $k$ (and not $q^k$). In this paper we provide some experimental data that shows that this approach conserves the mixing properties (the second eigenvalue) for several types of graphs (undirected regular and biregular bipartite graphs). We also prove some upper bounds for the second eigenvalue (though they are quite weak compared with the experimental results) and a lower bound for a special class of graphs. Finally, we discuss the possibility to decrease the number of random bits further by using Toeplitz matrices; our experiments show that this change makes the mixing properties only marginally worse while the number of random bits decreases significantly.

math.CO↗
arXiv2026

Hardness of Regular Expression Matching with Extensions

Regular expression matching asks whether a given regular expression of length $m$ matches a given string of length $n$, and its time complexity is fairly well understood. Meanwhile, regular expressions have been extended to support various extensions for both theoretical and practical reasons, which can substantially change the time complexity of the matching problem. In this work, we consider four well-known extensions to regular expressions called intersection, squaring, complement and backreference, and we prove a number of novel conditional time complexity lower bounds for the regular expression matching problems with these extensions under the Orthogonal Vectors Conjecture, the $k$-Orthogonal Vectors Conjecture or the $k$-Clique hypotheses. Our lower bounds significantly narrow the gaps to the known upper bounds, with some even being (conditionally) optimal in that the dependence on $n$ cannot be polynomially reduced while keeping the dependence on $m$ polynomial (or even elementary, for complement). Additionally, to obtain our results for intersection, squaring and complement in a unified manner, we introduce novel problems called Dyck Selection, Orthogonal Dyck Selection and Generalized Dyck Selection. They are arguably natural formal language and stringology problems that are based on Greibach's hardest context-free language and capture the essence of why the regular expression matching problems with these extensions are hard.

cs.CC↗
arXiv2026

Bounded Independence for $k$-Min-Wise Hashing: Tight Bounds and Limitations of Structured Hashing

Min-wise hashing and its $k$-min-wise extension are fundamental tools in sampling, sketching, similarity estimation, etc. A standard approach to constructing such families is bounded independence. For ordinary min-wise hashing, the required degree of independence is fully understood: $Θ(\log 1/δ)$-wise independence is both sufficient and necessary. For $k$-min-wise hashing, however, the best previous result only showed that $O(k\log\log1/δ+\log1/δ)$-wise independence suffices, with no matching lower bound. We give a tight characterization of the amount of bounded independence required for $k$-min-wise hashing, proving that $Θ(k+\log 1/δ)$-wise independence is both sufficient and necessary. This improves the previous upper bound and provides a matching lower bound. Consequently, the standard construction of bounded-independent hash families has seed length $O\bigl((k+\log 1/δ)\cdot\log(N/δ)\bigr)$. In particular, for polynomially small $δ$ and any $Ω(\log N) \le k \le N^{1 - c}$, it achieves the optimal seed length $O(k\log N)$. We further investigate two standard low-independence hash families. For random affine functions over $\mathbb{F}_2$, which form a pairwise independent family, we show that the multiplicative error is $Ω(\log N)$ even for ordinary min-wise hashing. For simple tabulation hashing, which is $3$-wise independent and performs well for ordinary min-wise hashing, we show that it incurs a multiplicative error $Ω(N)$ for $k$-min-wise hashing whenever $k\ge 4$.

cs.DS↗
arXiv2026

On Randomized Online Span Minimization

We study the online Busy Time scheduling model on a single machine of unbounded capacity, with non-preemptive jobs. In our setting, flexible jobs arrive online with a processing time and deadline, both of which become known to the algorithm at the job's arrival time. The goal is to schedule jobs on the machine to finish all jobs by their deadlines, so that the total time when the machine is turned on (busy time, also called span in this setting) is minimized. We present a randomized online algorithm with an exact competitive ratio of exactly (1 + e) < 3.72, where e is Euler's number, against an oblivious adversary, and show that no randomized algorithm can have a competitive ratio better than $e$ against an oblivious adversary. This lower bound holds even for algorithms that are allowed to restart jobs and are given lookahead. Previous work either dealt with special cases, or with deterministic algorithms, for which the known upper bound on the competitive ratio is 5 and the known lower bound is 4. Our findings offer fresh insights into randomization in online energy-aware scheduling. In the setting where jobs have uniform processing times, and a job that is started by the algorithm must be finished, we show that no deterministic algorithm can do better than 2, even when the jobs are agreeable. This deterministic lower bound also holds for uniform processing times and restarts in the scenario where a job's deadline is only revealed at its starting deadline. For Capacitated Busy Time with p_max-lookahead, we obtain a deterministic online algorithm with competitive ratio at most 8 and a randomized online algorithm with competitive ratio at most (4+ e) < 6.72, against an oblivious adversary.

cs.DS↗
arXiv2026

Computation and Applications of Euclidean and Normed Representations of Massive Data

This thesis investigates Euclidean-space and $\ell_p$-norm representations of different forms of data, with a focus on efficient algorithms for computing these representations in settings where the amount of data is very large. The applications of such representations are also discussed: they may be used to summarize the data in a more compact form for downstream tasks while preserving its most salient properties; and further, they may also be used to extract insights about the data that may not be apparent in its original form. This thesis presents novel algorithms for computing Euclidean representations in the case where the data comes with linear structure, as well as in the case where the data comes only with metric, or distance structure. In addition, we give algorithms for computing $\ell_p$-norm representations in linear structured cases. The analyses of these algorithms use tools from geometry, probability, and optimization, and these tools are used to illuminate other algorithms for similar problems.

cs.DS↗
arXiv2026

PACO: A Fully Cache-Oblivious Parallel FFT with One Global Redistribution

Fast Fourier transforms on parallel machines incur two forms of data movement: processor-local transfers through the memory hierarchy and global redistribution between processors. Four-step FFT organizations switch the active transform dimension with one global transpose-like exchange, but this alone does not yield cache-efficient local computation. Cache-oblivious FFTs achieve asymptotically optimal local memory traffic through recursive layout transformations, whose direct materialization can require extra data-rearrangement passes and global exchanges. We present PACO, a fully cache-oblivious parallel FFT framework that reconciles these objectives. PACO executes LocalFFT -> OneGlobalPermutation -> LocalFFT. Its local stages recursively partition both transform and batch dimensions without knowledge of the cache parameters. Rather than materializing the transpose-like layouts induced by this recursion, PACO defers them, showing that they compose into a base-b digit-reversal permutation that is fused with the redistribution already required to change the local transform dimension. The resulting middle stage is a perfectly balanced parallel cache-oblivious digit-reversal permutation in which each source-destination processor pair exchanges exactly $N/p^2$ elements. Under an exact base-b slab decomposition in a hybrid ideal-cache/BSP model, PACO computes an N-point DFT exactly with maximum per-processor work $Θ((N/p) \log N)$ and maximum per-processor cache complexity $Θ((N/(pB))(1 + \log_M N))$, using exactly one global redistribution round. PACO returns canonical logical DFT coefficients under a factor-swapped target slab ownership. The single redistribution is necessary under the stated no-replication ownership model, while its communication volume is optimal for the prescribed fused permutation and source-target slab distributions.

cs.DS↗
arXiv2026

Minimization Prophet Inequality with Bounded Costs

We study the cost-minimization prophet inequality problem, in which a decision-maker sequentially observes $n$ independent and identically distributed (IID) random variables. After each observation, the decision-maker must either accept the current realization and stop, or reject it and continue with the next variable. The goal is to minimize the selected value. Unlike the classical maximization setting, if none of the first $n-1$ values is accepted, the final realization must be selected. Esfandiari et al. (SIDMA 2017) showed that this problem does not admit constant-competitive online algorithms in general, which motivates subsequent work on restricted distribution classes, such as entire distributions (Livanos and Mehta, SODA 2024) and distributions with bounded extreme values (Livanos and Mehta, EC 2025). In this work, we focus on distributions with bounded support. For distributions supported on $[1,b]$, we characterize the competitive ratio of online algorithms as a function of both $b$ and $n$. We consider both distribution-aware and distribution-oblivious settings. In the distribution-aware case, we provide a nearly tight characterization of the optimal online algorithm, showing that its competitive ratio is at most $b^{(1-1/n)^n}$ for all $n\geq 2$. More significantly, we design a distribution-oblivious algorithm that achieves the same guarantee, with a sequence of predetermined thresholds that depend only on $b$ and $n$. Furthermore, we extend our analysis to the non-IID setting, where we show that a simple single-threshold algorithm attains an asymptotically optimal competitive ratio of $Θ(\sqrt{b})$.

cs.DS↗
arXiv2026

A Near-Linear Element Kernel for \(d\)-Hitting Set

In \(d\)-\textsc{Hitting Set}, the input consists of a finite universe \(U\), a family \(\mathcal S\) of subsets of \(U\) with size at most \(d\), and an integer \(k\). The task is to decide whether at most \(k\) elements of \(U\) can intersect every set in \(\mathcal S\). For every fixed \(d\geq3\), we give a one-sided randomized kernel with \(O(k\log^3k)\) elements and a deterministic kernel with \(O(k^2\log k)\) elements for \(d\)-\textsc{Hitting Set}. In the one-sided randomized kernel, every NO-instance is always mapped to a NO-instance, and a YES-instance is mapped to a YES-instance with constant probability. The previously known kernels for \(d\)-\textsc{Hitting Set} contain \(O(k^{d-1})\) elements and \(O(k^d)\) sets. It has been asked in the literature whether \(d\)-\textsc{Hitting Set} allows kernels with \(O(k^{d-1-\varepsilon})\) elements for some constant \(\varepsilon>0\). In this paper, we answer this question affirmatively by giving near-linear element-kernels through a re-encoding of the instance. On the other hand, our kernel may still contain \(k^{O(d)}\) sets and the parameter $k$ may grow polynomially.

cs.DS↗
arXiv2026

A 52-Addition, Rank-23 Scheme for Exact 3 x 3 Matrix Multiplication

We present a rank-23 algorithm for general 3 x 3 matrix multiplication using 52 additions or subtractions, together with a Karstadt-Schwartz-style basis change [1, 2]. This gives, to the best of our knowledge, a new best-known candidate for the addition-minimization problem for rank-23 general 3 x 3 matrix multiplication.

cs.DS↗
arXiv2026

Tight Bounds on the Cost of Adaptivity for the Meyerson Sketch

In online facility location, points arrive one at a time, and the algorithm must either open a facility at the arriving point or route the point to an existing facility. The Meyerson sketch opens a facility at each arriving point with probability proportional to the point's distance to the closest open facility, and requires no state beyond the set of open centers. Due to its simplicity, space efficiency, and strong guarantees against the offline optimum, the Meyerson sketch has become a workhorse of streaming and online clustering. In many such applications, however, the set of open facilities are visible to the process that generates the stream, which can adaptively select future points based on the algorithm's past random choices, voiding its classical guarantees. In this work, we quantify the effect of such adaptivity. We compare an adaptively generated run of the sketch against an \emph{oblivious replay}, an independent execution, with fresh coins, on the very same generated sequence, and study the \emph{adaptivity ratio} of expected adaptive cost to expected replay cost. We determine the worst-case ratio in both directions: adaptivity can neither inflate nor deflate the expected cost, or the number of open facilities, by more than an $O(\logΔ/\log\logΔ)$ factor, where $Δ$ is the aspect ratio of the input points (the ratio of the largest to the smallest pairwise distance). This is asymptotically tight as there are deterministic generators on the real line that inflate or deflate the cost by an $Ω(\logΔ/\log\logΔ)$ factor. We show that these robustness guarantees carry over to Meyerson-based sketches for approximate $k$ clustering with sketch size $O(k\,\mathrm{polylog}(n))$.

cs.DS↗
arXiv2026

Improved Multilayered PCPs and Hypergraph Vertex Cover

We present two elementary constructions of multilayered PCPs that improve upon prior constructions in two ways. Specifically, we give one construction of quasi-linear size, and another one with $2$-to-$2$ constraints. Using these constructions we obtain the following results for the hypergraph vertex cover problem: $\bullet$ For $k=3$, for all $\varepsilon>0$, approximating the minimum vertex cover of a given $3$-uniform hypergraph within factor $1+\sqrt{2}-\varepsilon$ is NP-hard. Previously, the best known result due to [Dinur, Guruswami, Khot, Regev, SICOMP 2005] achieved a factor of $2-\varepsilon$. $\bullet$ For $k\geq 4$, for all $\varepsilon>0$, approximating the minimum vertex cover of a given $k$-uniform hypergraph within factor $k-\varepsilon$ is NP-hard, which is tight. Previous works established this result assuming the Unique-Games Conjecture [Khot, Regev, JCSS 2008], and a weaker factor of $k-1-\varepsilon$ for standard NP-hardness [Dinur, Guruswami, Khot, Regev, SICOMP 2005]. $\bullet$ Assuming the Exponential Time Hypothesis, for all $k\geq 3$ and $\varepsilon>0$ there is $C>0$ such that no $2^{n/\log^C n}$-time algorithm approximates the minimum vertex cover in a $k$-uniform, $n$-vertex hypergraph within factor $k-1-\varepsilon$. The proofs were obtained using ChatGPT 5.6 Pro and subsequently rewritten by the communicators.

cs.CC↗
arXiv2026

Don't Be Afraid to Die: Black Hole Search in Dynamic Graphs with Fewer Agents

We consider a team of synchronous mobile agents operating in a port-labeled network. There is one node in the network, called a black hole, that permanently destroys any agent that visits the node. The team of agents must safely locate the black hole, i.e., at least one agent must survive, terminate its algorithm at a node adjacent to the black hole, and output the port number that leads to the black hole from its current position. In the setting where the network is a 1-bounded 1-interval connected dynamic graph, Kaur et al. (SSS 2025) showed that a team consisting of $2δ_{BH}+17$ agents is sufficient to solve the task from a scattered configuration, where $δ_{BH}$ denotes the degree of the black hole node. We show that $2δ_{BH}+3$ agents are sufficient, nearly matching the $2δ_{BH}+1$ lower bound provided in Kaur et al. (ICDCN 2025).

cs.DC↗
arXiv2026

Quantum Algorithm for Elliptic Curve Discrete Logarithms with Space-Efficient Point Addition

The Elliptic Curve Discrete Logarithm Problem (ECDLP) is a fundamental problem in cryptography, and reducing the resource requirements of quantum algorithms for solving ECDLP is an important goal. In this work, we present a space-efficient quantum algorithm for solving the ECDLP over prime fields, achieving an implementation with only $3n+6\lfloor \log_2 n \rfloor+O(1)$ logical qubits and $1056n^3/\log_2 n+O(n^2)$ Toffoli gates, where $n$ is the bit-length of the prime. For a 256-bit prime-field curve, our construction requires only 835 logical qubits, reducing the previous best estimates of 1098 and 1175 logical qubits by Chevignard et al. [EUROCRYPT 2026] and Babbush et al. [ArXiv Preprint 2026], respectively. The key to our improvement is a new space-efficient reversible modular inversion circuit, which addresses the dominant space bottleneck in affine-coordinate point addition. Starting from the extended Euclidean algorithm (EEA), we refine the register-sharing technique of Proos and Zalka by introducing length registers and location-controlled arithmetic to compactly store and update intermediate variables. We further optimize the reversible update procedures and construct the corresponding controlled arithmetic circuits, resulting in a modular inversion circuit implemented by only $2n+6\lfloor \log_2 n \rfloor+O(1)$ logical qubits and $229n^2+O(n\log_2 n)$ Toffoli gates. This modular inversion circuit together with mid-circuit measurements and classical feed-forward operations provides a space-efficient controlled affine point-addition circuit and a complete implementation of Shor's algorithm for ECDLP.

quant-ph↗
arXiv2026

Simple and Almost Non-Adaptive \(\frac{1}{2}\)-Approximation for Matroid Prophet Inequalities

Prophet inequalities are a fundamental model for online decision-making under uncertainty. For matroid constraints, Kleinberg and Weinberg gave a tight $\frac{1}{2}$-approximation using adaptive thresholds, while Feldman, Svensson, and Zenklusen obtained a $\frac{1}{4}$-approximation via an online contention resolution scheme (OCRS). We give the first almost non-adaptive algorithm for general matroid prophet inequalities achieving the optimal $\frac{1}{2}$ guarantee, in fact with respect to the stronger ex-ante relaxation. Starting from an optimal ex-ante solution $x$, we reduce to a Bernoulli instance, replace the original matroid by a stricter direct sum of minors, and assign fixed thresholds to the resulting components. Translating the rule back to the original distributions, an element $e$ can be accepted only when its realized value lies in its top $x_e$-quantile and adding it preserves the corresponding stricter matroid constraint. We also give a second almost non-adaptive $\frac{1}{2}$-approximation based on a different threshold rule. This formulation extends naturally to intersections of matroids and yields an almost non-adaptive $(q+1)$-approximation for prophet inequalities under the intersection of $q$ arbitrary matroids, again with respect to the ex-ante relaxation. This matches the previously known $(q+1)$ guarantee for intersections of $q$ partition matroids, due to Alon, Pollner, and Weinberg, while extending it to arbitrary matroids. For the intersection result, each matroid is replaced by a stricter direct sum of minors, and a common surplus vector determines fixed element thresholds across all $q$ constraints. We prove the existence of such a vector using Brouwer's fixed-point theorem and give a polynomial-time procedure to compute it.

cs.DS↗
arXiv2026

Hit-and-Run Mixes as Fast as the Ball Walk

Let $K\subset\mathbb{R}^n$ be an isotropic convex body. We prove that the hit-and-run walk, started from any $M$-warm distribution, reaches total-variation distance $\varepsilon$ from the uniform distribution on $K$ in $O\!\left(n^2ψ_n^{-2}\log^3(M/\varepsilon)\right)$ steps, where $ψ_n^{-1}$ is the Kannan-Lovász-Simonovits (KLS) constant. Up to logarithmic factors, this matches the best-known warm-start mixing time for the ball walk. Chen and Eldan [Discrete Comput. Geom. 2026] obtained the same $n^2ψ_n^{-2}$ dependence for hit-and-run, but with polynomial dependence on $M/\varepsilon$. Our result improves that polynomial dependence to a polylogarithmic one, fully resolving their open question about warm-start mixing of hit-and-run in isotropic convex bodies.

cs.DS↗
arXiv2026

Polynomial Lower Bounds for Distributed Graph Sketching with Tiny Error: Connectivity and Spanning Tree Construction

We present the first polynomial lower bounds for several fundamental problems in the distributed graph sketching model in the tiny-error regime, which includes deterministic algorithms as a special case. In the graph sketching model, every node sends a single message to the referee who does not have any prior knowledge of the graph and must output the answer. While the work of Nelson and Yu (SODA 2019) and Yu (SODA 2021) showed that $Θ( \log^3n )$ is optimal for constructing a spanning forest or deciding whether the graph is connected with error at most $\frac{1}{\text{poly}(n)}$ , their approach does not yield any stronger bounds for significantly smaller error probabilities. Our main result is to show that solving either connectivity or spanning tree construction with error at most $δ$ requires messages of length $Ω( \min\{n, \log_2 \frac{1}δ\}^{1/3} )$, which implies that algorithms with exponentially small error must send messages of $Ω( n^{1/3} )$ bits in the worst case. Our results significantly narrows the current gap between the Jelani-Yu threshold of $Θ( \log^3n )$ and the trivial upper bound of sending $O(n)$ bits per node for deterministic graph sketching. We also extend our results to $k$-edge connectivity. For any $k=O(n^{1/7})$, we recover the same bound of $Ω( k )$ on the message length for algorithms with exponentially small error that was shown by Robinson and Tan (PODS 2026) only for deterministic algorithms. Finally, for $k=n^{o(1)}$, our result implies a stronger lower bound of $Ω_ε( n^ε )$ bits, for any constant $ε<\tfrac{1}{3}$.

cs.DS↗
arXiv2026

Quantum Query Algorithms for the Constructive Diagonal Ramsey Theorem

The constructive diagonal Ramsey problem asks, given adjacency-oracle access to an $N$-vertex graph, for a clique or independent set of the order guaranteed by Ramsey's theorem. We give a bounded-error quantum algorithm that, for every $K\ge2$ and $N\ge4^{K-1}$, finds and verifies a homogeneous $K$-set using $O\!\left(2^K K\log\frac Kη\right)$ edge queries with failure probability at most $η$. At the Ramsey scale $N=2^n$, this yields a homogeneous set of order $\lfloor n/2\rfloor+1$ using $O(\sqrt N\log N\log(\log N/η))$ queries, improving on the $O(N)$ queries of the explicit classical recursion and giving, to our knowledge, the first sublinear worst-case algorithm for the Ramsey relation. We also derive an $Ω(N^{1/12})$ quantum lower bound by a reduction from collision finding. The algorithm runs the constructive recursion over implicit candidate sets. Each set is represented by a short conjunction of adjacency constraints and sampled using capped unknown-solution quantum search, and a scale-aware concentration schedule balances estimation accuracy against the increasing cost of sampling deeper sets. We complement the upper bound with an $Ω(N^{1-1/\sqrt2})$ randomized lower bound, transported from the random-Painter analysis of online Ramsey numbers, which holds on the uniform distribution $G(N,1/2)$. On that distribution a greedy quantum search uses only $\widetilde O(N^{1/4})$ queries, giving a provable polynomial quantum speedup for Ramsey search on random graphs. We also give an estimation-free size-biased recursion and extend it to every fixed number of edge colours.

quant-ph↗