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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
Max-Flows on Sparse and Dense Networks
Rahul Mehta · 2013-09-10 · via cs.DS updates on arXiv.org

In this paper, we present an improved algorithm for the maximum flow problem on general networks with $n$ vertices and $m$ arcs. We show how to solve the problem in $O(mn)$ time, when $m = O(n^{2-ε})$, for some $0 <ε\leq 1$. This improves upon the results of both Orlin and King, et. al., who solved the problem in $O(mn + m^{31/16} \log^2 n)$ and $O(mn\log_{m/n\log n}n)$ time, respectively. Our main result is reducing the number of nonsaturating pushes to $O(mn)$ across all scaling phases. Our algorithm can be seen as complementary to King, et. al., in the sense that we solve the max-flow problem in $O(mn)$ time when $m = O(n^{2-ε})$ (all sparse and non-dense networks), whereas King, et. al. solve it in $O(mn)$ time when $m = Ω(n^{1+ε})$ (all dense and non-sparse networks). Our improvement is reached by a novel combination of Ahuja and Orlin's excess scaling method and Orlin's compact flow networks. To our knowledge, this is the first $O(mn)$ time max-flow algorithm that runs on this range of networks. Further, we extend the range of Orlin's $O(mn)$ time algorithm from $O(n^{16/15-ε})$ to $O(n^{2-ε})$, which is an improvement of approximately $O(n^{0.94})$. Our result also establishes that the problem can be solved for all $n$ and $m$ using exclusively the push-relabel method. We also give improved algorithms for parametric flows and efficiently constructing Gomory-Hu trees, and suggest a new approach to the minimum-cost flow problem.