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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Veröffentlicht in:Operations Research Forum Jg. 5; H. 2; S. 48
Hauptverfasser: Kemminer, Robin, Lange, Jannick, Kempkes, Jens Peter, Tierney, Kevin, Weiß, Dimitri
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.06.2024
Springer Nature B.V
Schlagworte:
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
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Abstract 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.
AbstractList 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.
ArticleNumber 48
Author Tierney, Kevin
Weiß, Dimitri
Lange, Jannick
Kempkes, Jens Peter
Kemminer, Robin
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  surname: Weiß
  fullname: Weiß, Dimitri
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Algorithm configuration
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StartPage 48
SubjectTerms 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
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