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Strengthening integrality gaps for capacitated network design and covering problems

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TLDR
The authors show that by adding additional inequalities, the ratio between the optimal integer solution and the optimal solution to the linear program relaxation can be improved significantly, and provide a polynomial-time approximation algorithm to achieve this bound.
Abstract
A capacitated covering IP is an integer program of the form min{l_brace}ex{vert_bar}Ux {ge} d, 0 {le} x {le} b, x {element_of} Z{sup +}{r_brace}, where all entries of c, U, and d are nonnegative. Given such a formulation, the ratio between the optimal integer solution and the optimal solution to the linear program relaxation can be as bad as {parallel}d{parallel}{sub {proportional_to}}, even when U consists of a single row. They show that by adding additional inequalities, this ratio can be improved significantly. In the general case, they show that the improved ratio is bounded by the maximum number of non-zero coefficients in a row of U, and provide a polynomial-time approximation algorithm to achieve this bound. This improves upon the results of Bertsimas and Vohra, strengthening their extension of Hall and Hochbaum.

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References
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Book ChapterDOI

Reducibility Among Combinatorial Problems

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Knapsack Problems: Algorithms and Computer Implementations

TL;DR: This paper focuses on the part of the knapsack problem where the problem of bin packing is concerned and investigates the role of computer codes in the solution of this problem.
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Geometric Algorithms and Combinatorial Optimization

TL;DR: In this article, the Fulkerson Prize was won by the Mathematical Programming Society and the American Mathematical Society for proving polynomial time solvability of problems in convexity theory, geometry, and combinatorial optimization.
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Approximation Algorithms for NP-Hard Problems

TL;DR: This book reviews the design techniques for approximation algorithms and the developments in this area since its inception about three decades ago and the "closeness" to optimum that is achievable in polynomial time.
Journal ArticleDOI

Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems

TL;DR: An algorithm is presented which finds for any 0 < e < 1 an approximate solution P satisfying (P* P)/P* < ~, where P* is the desired optimal sum.
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