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Some Black-box Reductions for Objective-robust Discrete Optimization Problems Based on their LP-Relaxations
July 15, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Khaled Elbassioni
arXiv ID
1907.06786
Category
cs.DS: Data Structures & Algorithms
Cross-listed
math.OC
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We consider robust discrete minimization problems where uncertainty is defined by a convex set in the objective. We show how an integrality gap verifier for the linear programming relaxation of the non-robust version of the problem can be used to derive approximation algorithms for the robust version.
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