Discriminating instance generation from abstract specifications: a case study with CP and MIP

Özgür Akgün, Nguyen Dang*, Ian Miguel, András Z. Salamon, Patrick Spracklen, Christopher Stone

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

We extend automatic instance generation methods to allow cross-paradigm comparisons. We demonstrate that it is possible to completely automate the search for benchmark instances that help to discriminate between solvers. Our system starts from a high level human-provided problem specification, which is translated into a specification for valid instances. We use the automated algorithm configuration tool irace to search for instances, which are translated into inputs for both MIP and CP solvers by means of the Conjure, Savile Row, and MiniZinc tools. These instances are then solved by CPLEX and Chuffed, respectively. We constrain our search for instances by requiring them to exhibit a significant advantage for MIP over CP, or vice versa. Experimental results on four optimisation problem classes demonstrate the effectiveness of our method in identifying instances that highlight differences in performance of the two solvers.

Original languageEnglish
Title of host publicationIntegration of Constraint Programming, Artificial Intelligence, and Operations Research
Subtitle of host publication17th International Conference, CPAIOR 2020, Vienna, Austria, September 21–24, 2020, Proceedings
EditorsEmmanuel Hebrard, Nysret Musliu
Place of PublicationCham
PublisherSpringer
Pages41-51
Number of pages11
ISBN (Electronic)9783030589424
ISBN (Print)9783030589417
DOIs
Publication statusPublished - 2020
Event17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2020 - Vienna, Online, Austria
Duration: 21 Sept 202024 Sept 2020
https://cpaior2020.dbai.tuwien.ac.at/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12296 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2020
Country/TerritoryAustria
CityVienna, Online
Period21/09/2024/09/20
Internet address

Keywords

  • Constraint Programming
  • Instance generation
  • MIP

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