Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286966 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Global Optimization [ISSN:] 1573-2916 [Volume:] 80 [Issue:] 3 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 523-550
Publisher: 
Springer US, New York, NY
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.
Subjects: 
Mixed integer programming
Distributed optimization
Outer approximation
Global optimization
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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