A review and comparison of solvers for convex MINLP

Jan Kronqvist, David E. Bernal, Andreas Lundell, Ignacio E. Grossmann

    Research output: Contribution to journalArticleScientificpeer-review

    60 Citations (Scopus)

    Abstract

    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.
    Original languageUndefined/Unknown
    Pages (from-to)397–455
    Number of pages59
    JournalOptimization and Engineering
    Volume20
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA1 Journal article-refereed

    Keywords

    • MINLP solver
    • Solver comparison
    • Numerical benchmark
    • Convex MINLP

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