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Towards an objective feasibility pump for convex MINLPs

Author

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  • Shaurya Sharma
  • Brage Knudsen
  • Bjarne Grimstad

Abstract

This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Shaurya Sharma & Brage Knudsen & Bjarne Grimstad, 2016. "Towards an objective feasibility pump for convex MINLPs," Computational Optimization and Applications, Springer, vol. 63(3), pages 737-753, April.
    • Handle: RePEc:spr:coopap:v:63:y:2016:i:3:p:737-753
    DOI: 10.1007/s10589-015-9792-y
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    References listed on IDEAS

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    1. Marianna De Santis & Stefano Lucidi & Francesco Rinaldi, 2013. "A new class of functions for measuring solution integrality in the Feasibility Pump approach: Complete Results," DIAG Technical Reports 2013-05, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
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    Cited by:

    1. Massimo De Mauri & Joris Gillis & Jan Swevers & Goele Pipeleers, 2020. "A proximal-point outer approximation algorithm," Computational Optimization and Applications, Springer, vol. 77(3), pages 755-777, December.

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