Partially Distributed Outer Approximation
Abstract
This paper presents a novel partially distributed outer approximation algorithm, named PaDOA, for solving a class of structured mixed integer convex programming (MICP) 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.
Cite
@article{arxiv.1911.08296,
title = {Partially Distributed Outer Approximation},
author = {Alexander Murray and Timm Faulwasser and Veit Hagenmeyer and Mario E. Villanueva and Boris Houska},
journal= {arXiv preprint arXiv:1911.08296},
year = {2019}
}