Relaxation strength for multilinear optimization: McCormick strikes back

Fuente: arXiv
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Main Authors: Schutte, Emily, Walter, Matthias
Format: Preprint
Published: 2023
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author Schutte, Emily
Walter, Matthias
author_facet Schutte, Emily
Walter, Matthias
contents We consider linear relaxations for multilinear optimization problems. In a recent paper, Khajavirad proved that the extended flower relaxation is at least as strong as the relaxation of any recursive McCormick linearization (Operations Research Letters 51 (2023) 146-152). In this paper we extend the result to more general linearizations, and present a simpler proof. Moreover, we complement Khajavirad's result by showing that the intersection of the relaxations of such linearizations and the extended flower relaxation are equally strong.
format Preprint
id arxiv_https___arxiv_org_abs_2311_08570
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Relaxation strength for multilinear optimization: McCormick strikes back
Schutte, Emily
Walter, Matthias
Optimization and Control
Discrete Mathematics
Combinatorics
90C57
F.2.2
We consider linear relaxations for multilinear optimization problems. In a recent paper, Khajavirad proved that the extended flower relaxation is at least as strong as the relaxation of any recursive McCormick linearization (Operations Research Letters 51 (2023) 146-152). In this paper we extend the result to more general linearizations, and present a simpler proof. Moreover, we complement Khajavirad's result by showing that the intersection of the relaxations of such linearizations and the extended flower relaxation are equally strong.
title Relaxation strength for multilinear optimization: McCormick strikes back
topic Optimization and Control
Discrete Mathematics
Combinatorics
90C57
F.2.2
url https://arxiv.org/abs/2311.08570