Partially distributed outer approximation

Publikationstyp
Journal Article
Publikationsdatum
2021-04-17
Sprache
English
Author
Murray, Alexander
Faulwasser, Timm 
Hagenmeyer, Veit
Villanueva, Mario E.
Houska, Boris
DOI
10.15480/882.9196
TORE-URI
https://hdl.handle.net/11420/45676
Enthalten in
Journal of global optimization 
Volume
80
Issue
3
Start Page
523
End Page
550
Citation
Journal of Global Optimization 80 (3): 523-550 (2021-04-17)
Publisher DOI
10.1007/s10898-021-01015-0
Scopus ID
2-s2.0-85104878649
Publisher
Springer
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.
Schlagworte
Distributed optimization
Global optimization
Mixed integer programming
Outer approximation
DDC Class
004: Computer Sciences
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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