Configuring Mixed-Integer Programming Solvers for Large-Scale Instances

Algorithm configuration techniques automatically search for parameters of solvers and algorithms that provide minimal runtime or maximal solution quality on specified instance sets. Mixed-integer programming (MIP) solvers pose a particular challenge for algorithm configurators due to the difficulty...

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Bibliographic Details
Published in:Operations Research Forum Vol. 5; no. 2; p. 48
Main Authors: Kemminer, Robin, Lange, Jannick, Kempkes, Jens Peter, Tierney, Kevin, Weiß, Dimitri
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.06.2024
Springer Nature B.V
Subjects:
Algorithm configuration
Algorithms
Applications of Mathematics
Automation
Business and Management
Business metrics
Configurations
Critical components
Datasets
Genetic algorithms
Integer programming
Large-scale problem instances
Linear programming
Math Applications in Computer Science
Mathematical and Computational Engineering
Mixed integer
Mixed-integer programming
Operations Research & Machine Learning and Artificial Intelligence
Operations Research/Decision Theory
Optimization
Solvers
Large-scale problem instances
Mixed-integer programming
Algorithm configuration
ISSN:2662-2556, 2662-2556
Online Access:Get full text
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Summary:Algorithm configuration techniques automatically search for parameters of solvers and algorithms that provide minimal runtime or maximal solution quality on specified instance sets. Mixed-integer programming (MIP) solvers pose a particular challenge for algorithm configurators due to the difficulty of finding optimal, or even feasible, solutions on the large-scale problems commonly found in practice. We introduce the OPTANO Algorithm Tuner (OAT) to find configurations for MIP solvers and other optimization algorithms. We present and evaluate several critical components of OAT for solving MIPs in particular and show that OAT can find configurations that significantly improve the solution time of MIPs on two different datasets.
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ISSN:2662-2556
2662-2556
DOI:10.1007/s43069-024-00327-7