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Partially distributed outer approximation

Author

Listed:
  • Alexander Murray

    (Karlsruhe Institute of Technology)

  • Timm Faulwasser

    (Karlsruhe Institute of Technology)

  • Veit Hagenmeyer

    (Karlsruhe Institute of Technology)

  • Mario E. Villanueva

    (ShanghaiTech University)

  • Boris Houska

    (ShanghaiTech University)

Abstract

This paper presents a novel partially distributed outer approximation algorithm, named PaDOA, for solving a class of structured mixed integer convex programming problems to global optimality. The proposed scheme uses an iterative outer approximation method for coupled mixed integer optimization problems with separable convex objective functions, affine coupling constraints, and compact domain. PaDOA proceeds by alternating between solving large-scale structured mixed-integer linear programming problems and partially decoupled mixed-integer nonlinear programming subproblems that comprise much fewer integer variables. We establish conditions under which PaDOA converges to global minimizers after a finite number of iterations and verify these properties with an application to thermostatically controlled loads and to mixed-integer regression.

Suggested Citation

  • Alexander Murray & Timm Faulwasser & Veit Hagenmeyer & Mario E. Villanueva & Boris Houska, 2021. "Partially distributed outer approximation," Journal of Global Optimization, Springer, vol. 80(3), pages 523-550, July.
    • Handle: RePEc:spr:jglopt:v:80:y:2021:i:3:d:10.1007_s10898-021-01015-0
    DOI: 10.1007/s10898-021-01015-0
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    Cited by:

    1. Alireza Olama & Eduardo Camponogara & Paulo R. C. Mendes, 2023. "Distributed primal outer approximation algorithm for sparse convex programming with separable structures," Journal of Global Optimization, Springer, vol. 86(3), pages 637-670, July.

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