A review and comparison of solvers for convex MINLP

A1 Originalartikel i en vetenskaplig tidskrift (referentgranskad)

Interna författare/redaktörer

Publikationens författare: Jan Kronqvist, David E. Bernal, Andreas Lundell, Ignacio E. Grossmann
Förläggare: Springer
Publiceringsår: 2019
Tidskrift: Optimization and Engineering
Tidskriftsakronym: Optim Eng
Volym: 20
Artikelns första sida, sidnummer: 397
Artikelns sista sida, sidnummer: 455
Antal sidor: 59
ISSN: 1389-4420
eISSN: 1573-2924


In this paper, we present a review of deterministic software for solving convex MINLP problems as well as a comprehensive comparison of a large selection of commonly available solvers. As a test set, we have used all MINLP instances classified as convex in the problem library MINLPLib, resulting in a test set of 335 convex MINLP instances. A summary of the most common methods for solving convex MINLP problems is given to better highlight the differences between the solvers. To show how the solvers perform on problems with different properties, we have divided the test set into subsets based on the continuous relaxation gap, the degree of nonlinearity, and the relative number of discrete variables. The results also provide guidelines on how well suited a specific solver or method is for particular types of MINLP problems.


Convex MINLP, MINLP solver, Numerical benchmark, Solver comparison

Senast uppdaterad 2020-07-04 vid 06:55