Optimization and Engineering· 2018Q2· Review
A review and comparison of solvers for convex MINLP
- 299citations
- Q2SCImago
- 2018year
Short summary
This review compares 17 deterministic solvers for convex Mixed-Integer Non-Linear Programming (MINLP) problems using 335 instances from MINLPLib, finding that solver performance varies significantly based on problem characteristics like nonlinearity and discrete variable proportion.
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Key points
- 17 deterministic solvers for convex MINLP problems were reviewed and compared.
- The test set comprised 335 convex MINLP instances from the MINLPLib problem library.
- Solver performance was analyzed based on problem properties such as continuous relaxation gap, degree of nonlinearity, and relative number of discrete variables.
- The study provides insights into the suitability of specific solvers and methods for different MINLP problem types.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Optimization and Engineering, 2018 · DOI ↗
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Field: Numerical Analysis
Numerical AnalysisMathematics