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Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT

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Listed:
  • Andreas Lundell

    (Åbo Akademi University)

  • Jan Kronqvist

    (Imperial College London)

Abstract

Different versions of polyhedral outer approximation are used by many algorithms for mixed-integer nonlinear programming (MINLP). While it has been demonstrated that such methods work well for convex MINLP, extending them to solve nonconvex problems has traditionally been challenging. The Supporting Hyperplane Optimization Toolkit (SHOT) is a solver based on polyhedral approximations of the nonlinear feasible set of MINLP problems. SHOT is an open source COIN-OR project, and is currently one of the most efficient global solvers for convex MINLP. In this paper, we discuss some extensions to SHOT that significantly extend its applicability to nonconvex problems. The functionality include utilizing convexity detection for selecting the nonlinearities to linearize, lifting reformulations for special classes of functions, feasibility relaxations for infeasible subproblems and adding objective cuts to force the search for better feasible solutions. This functionality is not unique to SHOT, but can be implemented in other similar methods as well. In addition to discussing the new nonconvex functionality of SHOT, an extensive benchmark of deterministic solvers for nonconvex MINLP is performed that provides a snapshot of the current state of nonconvex MINLP.

Suggested Citation

  • Andreas Lundell & Jan Kronqvist, 2022. "Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT," Journal of Global Optimization, Springer, vol. 82(4), pages 863-896, April.
    • Handle: RePEc:spr:jglopt:v:82:y:2022:i:4:d:10.1007_s10898-021-01006-1
    DOI: 10.1007/s10898-021-01006-1
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    References listed on IDEAS

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    1. Ivo Nowak & Norman Breitfeld & Eligius M. T. Hendrix & Grégoire Njacheun-Njanzoua, 2018. "Decomposition-based Inner- and Outer-Refinement Algorithms for Global Optimization," Journal of Global Optimization, Springer, vol. 72(2), pages 305-321, October.
    2. Omprakash K. Gupta & A. Ravindran, 1985. "Branch and Bound Experiments in Convex Nonlinear Integer Programming," Management Science, INFORMS, vol. 31(12), pages 1533-1546, December.
    3. Fischetti, Matteo & Monaci, Michele, 2020. "A branch-and-cut algorithm for Mixed-Integer Bilinear Programming," European Journal of Operational Research, Elsevier, vol. 282(2), pages 506-514.
    4. Andreas Lundell & Anders Skjäl & Tapio Westerlund, 2013. "A reformulation framework for global optimization," Journal of Global Optimization, Springer, vol. 57(1), pages 115-141, September.
    5. Pavlo Muts & Ivo Nowak & Eligius M. T. Hendrix, 2020. "The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming," Journal of Global Optimization, Springer, vol. 77(1), pages 75-96, May.
    6. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    7. Harsha Nagarajan & Mowen Lu & Site Wang & Russell Bent & Kaarthik Sundar, 2019. "An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs," Journal of Global Optimization, Springer, vol. 74(4), pages 639-675, August.
    8. Jan Kronqvist & Andreas Lundell & Tapio Westerlund, 2018. "Reformulations for utilizing separability when solving convex MINLP problems," Journal of Global Optimization, Springer, vol. 71(3), pages 571-592, July.
    9. Claudia D’Ambrosio & Andrea Lodi, 2013. "Mixed integer nonlinear programming tools: an updated practical overview," Annals of Operations Research, Springer, vol. 204(1), pages 301-320, April.
    10. Kai Zhou & Mustafa R. Kılınç & Xi Chen & Nikolaos V. Sahinidis, 2018. "An efficient strategy for the activation of MIP relaxations in a multicore global MINLP solver," Journal of Global Optimization, Springer, vol. 70(3), pages 497-516, March.
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