Approximation Algorithms for Cost-robust Discrete Minimization Problems Based on their LP-Relaxations

Khaled Elbassioni

doi:10.1007/s00453-022-00987-z

Approximation Algorithms for Cost-robust Discrete Minimization Problems Based on their LP-Relaxations

Khaled Elbassioni

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	British English
Pages (from-to)	3622-3654
Number of pages	33
Journal	Algorithmica (New York)
Volume	84
Issue number	12
DOIs	https://doi.org/10.1007/s00453-022-00987-z
State	Published - Dec 2022

Keywords

Approximation algorithms
Discrete optimization
Linear programming
Randomized rounding
Robust optimization

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10.1007/s00453-022-00987-z

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abstract = "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.",

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KW - Linear programming

KW - Randomized rounding

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Approximation Algorithms for Cost-robust Discrete Minimization Problems Based on their LP-Relaxations

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