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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
Augmentation based Approximation Algorithms for Flexible ...
Chandra Chekuri, Rhea Jain · 2022-09-26 · via cs.DS updates on arXiv.org

Adjiashvili introduced network design in a non-uniform fault model: the edge set of a given graph is partitioned into safe and unsafe edges. A vertex pair $(s,t)$ is $(p,q)$-flex-connected if $s$ and $t$ have $p$ edge-connectivity even after the removal of any $q$ unsafe edges. Given a graph $G$, the goal is to choose a min-cost subgraph $H$ of $G$ that has desired flex-connectivity for a given set of vertex pairs. This model generalizes the well-studied edge-connectivity based network design, however, even special cases are provably much harder to approximate. The approximability of network design in this model has been mainly studied for two settings of interest: (i) single pair setting under the names FTP and FTF (fault tolerant path and fault tolerant flow), (ii) spanning setting under the name FGC (flexible graph connectivity). There have been several positive results in these papers. However, despite similarity to the well-known network design problems, this new model has been challenging to design approximation algorithms for, especially when $p,q \ge 2$. We obtain two results that advance our understanding of algorithm design in this model. 1. We obtain a $5$-approximation for the $(2,2)$-flex-connectivity for a single pair $(s,t)$. Previously no non-trivial approximation was known for this setting. 2. We obtain $O(p)$ approximation for $(p,2)$ and $(p,3)$-FGC for any $p \ge 1$, and for $(p,4)$-FGC for any even $p$. We obtain an $O(q)$-approximation for $(2,q)$-FGC for any $q \ge 1$. Previously only a $O(q \log n)$-approximation was known for these settings. Our results are obtained via the augmentation framework where we identify a structured way to use the well-known $2$-approximation for covering uncrossable families of cuts. Our analysis also proves corresponding integrality gap bounds on an LP relaxation that we formulate.