MIPLIBing: Seamless Benchmarking of Mathematical Optimization Problems and Metadata Extensions

Thiago Serra () and Ryan J. O’Neil ()
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Thiago Serra: Bucknell University
Ryan J. O’Neil: nextmv

SN Operations Research Forum, 2020, vol. 1, issue 3, 1-6

Abstract: Abstract Public libraries of problems such as Mixed Integer Programming Library (MIPLIB) are fundamental to creating a common benchmark for measuring algorithmic advances across mathematical optimization solvers. They also often provide metadata on problem structure, hardness with respect to state-of-the-art solvers, and solutions with the best objective function value on record. In this short paper, we discuss some ways in which such metadata can be leveraged to create a seamless testing experience. In particular, we present MIPLIBing: a Python library that automatically downloads queried subsets from the current versions of MIPLIB, MINLPLib, and QPLIB, provides a centralized local cache across projects, and tracks the best solution values and bounds on record for each problem. While inspired by similar use cases from other areas, we reflect on the specific needs of mathematical optimization and discuss opportunities to extend benchmark sets to facilitate experimentation with different model structures.

Keywords: Benchmarking; Mathematical optimization; Problem libraries (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s43069-020-00024-1

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