In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition -basedsolution method. In this approach, an optimization problem is represented a...
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In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition -basedsolution 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 can be built to assist a solver in selecting the best solution strategy for a given problem with respect to some metric of choice. 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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