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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
Flow-Cut Gaps for Integer and Fractional Multiflows
Chandra Chekuri, F. Bruce Shepherd, Christophe Weibel · 2010-08-12 · via cs.DS updates on arXiv.org

Consider a routing problem consisting of a demand graph H and a supply graph G. If the pair obeys the cut condition, then the flow-cut gap for this instance is the minimum value C such that there is a feasible multiflow for H if each edge of G is given capacity C. The flow-cut gap can be greater than 1 even when G is the (series-parallel) graph K_{2,3}. In this paper we are primarily interested in the "integer" flow-cut gap. What is the minimum value C such that there is a feasible integer valued multiflow for H if each edge of G is given capacity C? We conjecture that the integer flow-cut gap is quantitatively related to the fractional flow-cut gap. This strengthens the well-known conjecture that the flow-cut gap in planar and minor-free graphs is O(1) to suggest that the integer flow-cut gap is O(1). We give several results on non-trivial special classes of graphs supporting this conjecture and further explore the "primal" method for understanding flow-cut gaps. Our results include: - Let G be obtained by series-parallel operations starting from an edge st, and consider orienting all edges in G in the direction from s to t. A demand is compliant if its endpoints are joined by a directed path in the resulting oriented graph. If the cut condition holds for a compliant instance and G+H is Eulerian, then an integral routing of H exists. - The integer flow-cut gap in series-parallel graphs is 5. We also give an explicit class of instances that shows via elementary calculations that the flow-cut gap in series-parallel graphs is at least 2-o(1); this simplifies the proof by Lee and Raghavendra. - The integer flow-cut gap in k-Outerplanar graphs is c^{O(k)} for some fixed constant c. - A simple proof that the flow-cut gap is O(\log k^*) where k^* is the size of a node-cover in H; this was previously shown by Günlük via a more intricate proof.