Theory & Algorithms · Computer Science & AI
Data Structures & Algorithms
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1,174 papers, from 4 September 2026 to 5 November 2026: 1,174 arXiv preprints.
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Newest papers, page 57
A Deadline-Driven Algorithm for Polyamorous Scheduling
In Polyamorous Scheduling Problem, we are given an edge-weighted graph and must find a periodic schedule of matchings in this graph which minimizes the maximal weighted waiting time between…Tight Time-Space Lower Bounds for Collision Finding and Element Distinctness under Label Symmetry
How much memory is needed to retain the quantum speedup for collision finding?Lower Bounds for Private Graph Optimization Problems using Reconstruction Attacks
This paper studies fundamental graph optimization problems under differential privacy (DP) and shows new, reconstruction-based lower bounds.Navigating Small-World Networks with Distance Predictions
The small-world phenomenon was given an algorithmic foundation by Kleinberg, who showed that in an augmented $k$-dimensional lattice a decentralized greedy algorithm delivers a message in $O(\log^2…Sparse Polynomial GCD Algorithms Asymptotically Linear in All Fundamental Parameters
Let $A, B \in \mathbb{Z}[x_1, \dots, x_n]$ be multivariate polynomials with integer coefficients and let $G = \gcd(A, B)$.Beyond Cut Balance: Spectral Sparsification of the Nonlinear Directed Laplacian
Digraphs with constant cut balance admit nearly linear directed cut sparsifiers.Generalized Graph Search Trees
Graph search algorithms and their corresponding graph search trees are commonly used in algorithmic graph theory.On the Power of Adaptivity in Testing Quantum States in Fidelity
We study the problems of quantum state certification, equivalence testing and independence testing.Distributed Quantum Property Testing with Quantum Carrier Pigeons
We introduce a framework for distributed quantum inference under communication constraints.High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
Sparsity is a powerful structural resource in optimization and statistics.A $(\log n)^{1/4}$ Bound for the Komlós Problem
Let $A\in\mathbb{R}^{m\times n}$ have columns of Euclidean norm at most one.Deterministic Edge-Fault-Tolerant Connectivity Labeling Schemes with Nearly Optimal Label Size
For an undirected graph $G = (V,E)$ and a fault bound $f$, an edge-fault-tolerant connectivity labeling scheme assigns short labels to vertices and edges, so that for any vertex pair $(s,t)$ and…Near-Optimal Quantum Lower Bounds for Convex Optimization via Fourier Rank
We establish a near-linear quantum query lower bound for high-accuracy convex optimization over an explicit family of $n$-dimensional ellipsoids.Parity and Pattern Detection in Permutation Streams
Consider a permutation of $[n]$ whose values arrive one at a time.Approximate Nearest Neighbor in Ultra-High Dimensional $\ell_\infty$
We study the approximate nearest neighbor problem under $\ell_\infty$ in the ultra-high dimensional setting where the dimension $d$ is significantly larger than the number of points $n$.Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps
We study the oracle complexity of computing a point with small fixed-point residual $\|T(x)-x\| \leq ε$, for a general norm $\|\cdot\|$ and a self-map $T$ of a compact convex set.Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler
We study the problem of sampling from $μ(\mathrm{d}x)\propto e^{-V(x)}\,\mathrm{d}x$ on $\mathbb{R}^d$, where $V$ is $α$-strongly convex and $β$-smooth, and write $κ:=β/α$.Smoothed Picard Hamiltonian Monte Carlo
We develop a new low-accuracy sampler, called \emph{smoothed Picard Hamiltonian Monte Carlo}, which combines Gaussian smoothing, Picard iteration, and higher-order discretization.Frequency Moments Beyond Equality: Streaming Cosine Density Moments
For a stream of nonzero vectors $x_1,\ldots,x_n\in\mathbb{R}^d$, let $u_i=x_i/\|x_i\|_2$.Almost Sharp Equivalence between Approximate Message Passing and Low-Degree Polynomials
We prove a sharp lower bound for growing-degree polynomial estimation in the Gaussian planted submatrix model.