Code and Data Repository for Recursive McCormick Linearization of Multilinear Programs

We compare algorithms for Minimum-size and Best-Bound McCormick linearizations with alternative techniques addressing similar problems. Moreover, we compare the performance of quadratized models with the state-of-the-art technique to solve multilinear optimization problems directly.

Authors

David Bergman (ORCID: https://orcid.org/0000-0002-5566-5224)
Carlos Nohra (ORCID: https://orcid.org/0000-0003-4838-5336)
Arvind U. Raghunathan (ORCID: https://orcid.org/0000-0003-3173-3875)
Carlos Cardonha (ORCID: https://orcid.org/0000-0002-1439-5205)

Publication Details

Journal: INFORMS journal on computing
Published: 2025-01-10
DOI: https://doi.org/10.1287/ijoc.2023.0390.cd
Primary Topic: Advanced Optimization Algorithms Research
Type: article
Field-Weighted Citation Impact: 0.00

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article

Code and Data Repository for Recursive McCormick Linearization of Multilinear Programs

David Bergman, Carlos Nohra, Arvind U. Raghunathan, Carlos Cardonha

Jan 10, 2025

INFORMS journal on computing

Advanced Optimization Algorithms Research

article

Code and Data Repository for Recursive McCormick Linearization of Multilinear Programs

David Bergman, Carlos Nohra, Arvind U. Raghunathan, Carlos Cardonha

January 10, 2025article en

Abstract

We compare algorithms for Minimum-size and Best-Bound McCormick linearizations with alternative techniques addressing similar problems. Moreover, we compare the performance of quadratized models with the state-of-the-art technique to solve multilinear optimization problems directly.

INFORMS journal on computing

Openalex Percentile: Top 98%

Advanced Optimization Algorithms Research

DOI Source OpenAlex

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Code and Data Repository for Recursive McCormick Linearization of Multilinear Programs — David Bergman, Carlos Nohra, et al. · INFORMS journal on computing (2025) | TGRS Research Map | TGRS