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A computational study of primal heuristics inside an MI(NL)P solver

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  • Timo Berthold

    (FICO)

Abstract

Primal heuristics are a fundamental component of state-of-the-art global solvers for mixed integer linear programming (MIP) and mixed integer nonlinear programming (MINLP). In this paper, we investigate the impact of primal heuristics on the overall solution process. We present a computational study, in which we compare the performance of the MIP and MINLP solver SCIP with and without primal heuristics on six test sets with altogether 983 instances from academic and industrial sources. We analyze how primal heuristics affect the solver regarding seven different measures of performance and show that the impact differs by orders of magnitude. We further argue that the harder a problem is to solve to global optimality, the more important the deployment of primal heuristics becomes.

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  • Timo Berthold, 2018. "A computational study of primal heuristics inside an MI(NL)P solver," Journal of Global Optimization, Springer, vol. 70(1), pages 189-206, January.
    • Handle: RePEc:spr:jglopt:v:70:y:2018:i:1:d:10.1007_s10898-017-0600-3
    DOI: 10.1007/s10898-017-0600-3
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    Cited by:

    1. Meenarli Sharma & Prashant Palkar & Ashutosh Mahajan, 2022. "Linearization and parallelization schemes for convex mixed-integer nonlinear optimization," Computational Optimization and Applications, Springer, vol. 81(2), pages 423-478, March.

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