Partially distributed outer approximation

Alexander Murray (), Timm Faulwasser (), Veit Hagenmeyer (), Mario E. Villanueva () and Boris Houska ()
Additional contact information
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

Journal of Global Optimization, 2021, vol. 80, issue 3, No 2, 523-550

Abstract: 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.

Keywords: Mixed integer programming; Distributed optimization; Outer approximation; Global optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10898-021-01015-0

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