Some Black-box Reductions for Objective-robust Discrete Optimization Problems Based on their LP-Relaxations

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.