Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose

• URL: http://arxiv.org/abs/2310.07068v1
• Date: Tue, 10 Oct 2023 23:31:06 GMT
• Title: Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose
• Authors: Ilias Mitrai, Prodromos Daoutidis
• Abstract summary: We propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition-based solution method. It is shown how the learned classifier can be incorporated into existing structural mixed integer optimization solvers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition-based solution method. In this approach, an optimization problem is represented as a graph that captures the structural and functional coupling among the variables and constraints of the problem via an appropriate set of features. Given this representation, a graph classifier is built to determine the best solution method for a given problem. The proposed approach is used to develop a classifier that determines whether a convex Mixed Integer Nonlinear Programming problem should be solved using branch and bound or the outer approximation algorithm. Finally, it is shown how the learned classifier can be incorporated into existing mixed integer optimization solvers.

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