Some Black-box Reductions for Objective-robust Discrete Optimization Problems Based on their LP-Relaxations
Data Structures and Algorithms
2019-07-17 v1 Optimization and Control
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.
Cite
@article{arxiv.1907.06786,
title = {Some Black-box Reductions for Objective-robust Discrete Optimization Problems Based on their LP-Relaxations},
author = {Khaled Elbassioni},
journal= {arXiv preprint arXiv:1907.06786},
year = {2019}
}