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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
Efficient constructions of convex combinations for 2-edge...
Arash Haddadan, Alantha Newman · 2018-11-25 · via cs.DS updates on arXiv.org

Finding the exact integrality gap $α$ for the LP relaxation of the 2-edge-connected spanning multigraph problem (2EC) is closely related to the same problem for the Held-Karp relaxation of the metric traveling salesman problem (TSP). While the former problem seems easier than the latter, since it is less constrained, currently the upper bounds on the respective integrality gaps for the two problems are the same. An approach to proving integrality gaps for both of these problems is to consider fundamental classes of extreme points. For 2EC, better bounds on the integrality gap are known for certain important special cases of these fundamental points. For example, for half-integer square points, the integrality gap is between $\frac{6}{5}$ and $\frac{4}{3}$. Our main result is to improve the approximation factor to $\frac{9}{7}$ for 2EC for these points. Our approach is based on constructing convex combinations and our key tool is the top-down coloring framework for tree augmentation, whose flexibility we employ to exploit beneficial properties in both the initial spanning tree and in the input graph. We also show how these tools can be tailored to the closely related problem of uniform covers for which the proofs of the best-known bounds do not yield polynomial-time algorithms. Another key ingredient is to use a rainbow spanning tree decomposition, which allows us to obtain a convex combination of spanning trees with particular properties