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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Fully Polynomial-Time Distributed Computation in Low-Tree...
Taisuke Izumi, Naoki Kitamura, Takamasa Naruse, Gregory Schwartz · 2022-05-30 · via cs.DS updates on arXiv.org

We consider global problems, i.e. problems that take at least diameter time, even when the bandwidth is not restricted. We show that all problems considered admit efficient solutions in low-treewidth graphs. By ``efficient'' we mean that the running time has polynomial dependence on the treewidth, a linear dependence on the diameter (which is unavoidable), and only a polylogarithmic dependence on $n$, the number of nodes in the graph. We present the algorithms solving the following problems in the CONGEST model which all attain $\tilde{O(τ^{O(1)}D)}$-round complexity (where $τ$ and $D$ denote the treewidth and diameter of the graph, respectively): (1) Exact single-source shortest paths (actually, the more general problem of computing a distance labeling scheme) for weighted and directed graphs, (2) exact bipartite unweighted maximum matching, and (3) the weighted girth for both directed and undirected graphs. We derive all of our results using a single unified framework, which consists of two novel technical ingredients, The first is a fully polynomial-time distributed tree decomposition algorithm, which outputs a decomposition of width $O(τ^2\log n)$ in $\tilde{O}(τ^{O(1)}D)$ rounds (where $n$ is the number of nodes in the graph). The second ingredient, and the technical highlight of this paper, is the novel concept of a \emph{stateful walk constraint}, which naturally defines a set of feasible walks in the input graph based on their local properties (e.g., augmenting paths). Given a stateful walk constraint, the constrained version of the shortest paths problem (or distance labeling) requires the algorithm to output the shortest \emph{constrained} walk (or its distance) for a given source and sink vertices. We show that this problem can be efficiently solved in the CONGEST model by reducing it to an \emph{unconstrained} version of the problem.