Robust optimization in countably infinite linear programs

Archis Ghate

doi:10.1007/s11590-015-0958-4

Robust optimization in countably infinite linear programs

Archis Ghate

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

4 Scopus citations

Abstract

A robust optimization framework for countably infinite linear programs (CILPs) is developed. It is shown that a particular robust counterpart of a nominal CILP can be reformulated as another CILP. A bound on the probability of constraint violation is derived. A convergent algorithm for solving this robust CILP is proposed.

Original language	English (US)
Pages (from-to)	847-863
Number of pages	17
Journal	Optimization Letters
Volume	10
Issue number	4
DOIs	https://doi.org/10.1007/s11590-015-0958-4
State	Published - Apr 1 2016
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.

Keywords

Infinite-dimensional optimization
Planning horizon algorithm
Uncertainty sets

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10.1007/s11590-015-0958-4

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abstract = "A robust optimization framework for countably infinite linear programs (CILPs) is developed. It is shown that a particular robust counterpart of a nominal CILP can be reformulated as another CILP. A bound on the probability of constraint violation is derived. A convergent algorithm for solving this robust CILP is proposed.",

keywords = "Infinite-dimensional optimization, Planning horizon algorithm, Uncertainty sets",

author = "Archis Ghate",

note = "Publisher Copyright: {\textcopyright} 2015, Springer-Verlag Berlin Heidelberg.",

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AB - A robust optimization framework for countably infinite linear programs (CILPs) is developed. It is shown that a particular robust counterpart of a nominal CILP can be reformulated as another CILP. A bound on the probability of constraint violation is derived. A convergent algorithm for solving this robust CILP is proposed.

KW - Infinite-dimensional optimization

KW - Planning horizon algorithm

KW - Uncertainty sets

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Robust optimization in countably infinite linear programs

Abstract

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