3 years ago

Interior Point Methods with Adversarial Networks.

Aaron Babier, Timothy C. Y. Chan, Rafid Mahmood, Adam Diamant

We present a new methodology, called IPMAN, that combines interior point methods and generative adversarial networks to solve constrained optimization problems with feasible sets that are non-convex or not explicitly defined. Our methodology produces {\epsilon}-optimal solutions and demonstrates that, when there are multiple global optima, it learns a distribution over the optimal set. We apply our approach to synthetic examples to demonstrate its effectiveness and to a problem in radiation therapy treatment optimization with a non-convex feasible set.

Publisher URL: http://arxiv.org/abs/1805.09293

DOI: arXiv:1805.09293v1

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