Serial and parallel two-column probing for mixed-integer programming

Abstract Probing in mixed-integer programming (MIP) is a technique of temporarily fixing variables to discover implications that are useful to branch-and-cut solvers. Such fixing is typically performed one variable at a time—this paper develops instead a two-column probing scheme that instead fixes a pair of variables per iteration. Although the scheme involves more work per iteration compared to the one-column approach, stronger implied bounds as well as more conflicts identified may compensate. Indeed, our prototype implementation was awarded first prize at the MIP Workshop 2024 Computational Competition on novel presolving approaches. This paper presents the aforementioned (serial) prototype and additionally develops an efficient parallelization, leveraging hardware acceleration to further improve overall solve times. Compared to serial two-column probing, our parallel version sacrifices some strength per-pair probed in exchange for greatly increasing the total number of such probings; computational experiments demonstrate its promise.

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Publication Details

Journal
Mathematical Programming Computation
Published
2026-03-31
DOI
https://doi.org/10.1007/s12532-026-00314-5
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Serial and parallel two-column probing for mixed-integer programming

Yongzheng Dai, Chen Chen
Mathematical Programming Computation
Advanced Optimization Algorithms Research
article

Serial and parallel two-column probing for mixed-integer programming

Yongzheng Dai, Chen Chen
article en

Abstract

Abstract Probing in mixed-integer programming (MIP) is a technique of temporarily fixing variables to discover implications that are useful to branch-and-cut solvers. Such fixing is typically performed one variable at a time—this paper develops instead a two-column probing scheme that instead fixes a pair of variables per iteration. Although the scheme involves more work per iteration compared to the one-column approach, stronger implied bounds as well as more conflicts identified may compensate. Indeed, our prototype implementation was awarded first prize at the MIP Workshop 2024 Computational Competition on novel presolving approaches. This paper presents the aforementioned (serial) prototype and additionally develops an efficient parallelization, leveraging hardware acceleration to further improve overall solve times. Compared to serial two-column probing, our parallel version sacrifices some strength per-pair probed in exchange for greatly increasing the total number of such probings; computational experiments demonstrate its promise.

Mathematical Programming Computation
The Ohio State University (US)
Office of Naval Research
Openalex Percentile: Top 13%
Advanced Optimization Algorithms Research
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