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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
LP-rounding Algorithms for the Fault-Tolerant Facility Pl...
Li Yan, Marek Chrobak · 2012-05-07 · via cs.DS updates on arXiv.org

The Fault-Tolerant Facility Placement problem (FTFP) is a generalization of the classic Uncapacitated Facility Location Problem (UFL). In FTFP we are given a set of facility sites and a set of clients. Opening a facility at site $i$ costs $f_i$ and connecting client $j$ to a facility at site $i$ costs $d_{ij}$. We assume that the connection costs (distances) $d_{ij}$ satisfy the triangle inequality. Multiple facilities can be opened at any site. Each client $j$ has a demand $r_j$, which means that it needs to be connected to $r_j$ different facilities (some of which could be located on the same site). The goal is to minimize the sum of facility opening cost and connection cost. The main result of this paper is a 1.575-approximation algorithm for FTFP, based on LP-rounding. The algorithm first reduces the demands to values polynomial in the number of sites. Then it uses a technique that we call adaptive partitioning, which partitions the instance by splitting clients into unit demands and creating a number of (not yet opened) facilities at each site. It also partitions the optimal fractional solution to produce a fractional solution for this new instance. The partitioned instance satisfies a number of properties that allow us to exploit existing LP-rounding methods for UFL to round our partitioned solution to an integral solution, preserving the approximation ratio. In particular, our 1.575-approximation algorithm is based on the ideas from the 1.575-approximation algorithm for UFL by Byrka et al., with changes necessary to satisfy the fault-tolerance requirement.