Abstract
D’Ambrosio, Lee, and Wächter (2009, 2012) introduced an algorithmic approach for handling separable non-convexities in the context of global optimization. That algorithmic framework calculates lower bounds (on the optimal min objective value) by solving a sequence of convex MINLPs. We propose a method for addressing the same setting, but employing disjunctive cuts (generated via LP), and solving instead a sequence of convex NLPs. We present computational results which demonstrate the viability of our approach.
C. D’Ambrosio and D. Thomopulos were supported by a public grant as part of the Investissement d’avenir project, reference ANR-11-LABX-0056-LMH, LabEx LMH. This research benefited from the support of the FMJH Program PGMO and from the support of EDF. J. Lee was supported in part by ONR grant N00014-17-1-2296 and LIX, École Polytechnique. D. Skipper was supported in part by ONR grant N00014-18-W-X00709.
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D’Ambrosio, C., Lee, J., Skipper, D., Thomopulos, D. (2020). Handling Separable Non-convexities Using Disjunctive Cuts. In: Baïou, M., Gendron, B., Günlük, O., Mahjoub, A.R. (eds) Combinatorial Optimization. ISCO 2020. Lecture Notes in Computer Science(), vol 12176. Springer, Cham. https://doi.org/10.1007/978-3-030-53262-8_9
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