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Algorithms to Approximate Column-Sparse Packing Problems
Column-sparse packing problems arise in several contexts in both deterministic and stochastic discrete optimization. We present two unifying ideas, (non-uniform) attenuation and multiple-chance algorithms, to obtain improved approximation algorithms for some well-known families of such problems. As three main examples, we attain the integrality gap, up to lower-order terms, for known LP relaxations for k-column sparse packing integer programs (Bansal et al., Theory of Computing, 2012) and stochastic k-set packing (Bansal et al., Algorithmica, 2012), and go “half the remaining distance” to optimal for a major integrality-gap conjecture of Furedi, Kahn and Seymour on hypergraph matching (Combinatorica, 1993).
Authors:
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Award ID(s):
Publication Date:
NSF-PAR ID:
10065499
Journal Name:
ACM-SIAM Symposium on Discrete Algorithms (SODA)
Page Range or eLocation-ID:
311-330
Sponsoring Org:
National Science Foundation
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