We consider robust discrete minimization problems where uncertainty is defined by a convex set in the objective. Assuming the existence of an integrality gap verifier with a bounded approximation guarantee for the LP relaxation of the non-robust version of the problem, we derive approximation algorithms for the robust version under different types of uncertainty, including polyhedral and ellipsoidal uncertainty.
CITATION STYLE
Elbassioni, K. (2020). Approximation Algorithms for Cost-Robust Discrete Minimization Problems Based on Their LP-Relaxations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12118 LNCS, pp. 27–37). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61792-9_3
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