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A Review and Comparison of Solvers for Convex MINLP

Jan Kronqvist (jakronqv***at***abo.fi)
David E. Bernal (bernalde***at***cmu.edu)
Andreas Lundell (andreas.lundell***at***abo.fi)
Ignacio E. Grossmann (grossmann***at***cmu.edu)

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 366 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 integer relaxation gap, degree of nonlinearity, and the relative number of discrete variables. The results presented here also provide guidelines on how well suited a specific solver or method is for particular types of MINLP problems.

Keywords: Convex MINLP; MINLP solver; Solver comparison; Numerical benchmark

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Optimization Software and Modeling Systems (Optimization Software Benchmark )

Citation: Final paper (open access) at: https://link.springer.com/article/10.1007/s11081-018-9411-8


Entry Submitted: 06/06/2018
Entry Accepted: 06/06/2018
Entry Last Modified: 12/04/2018

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