Web: http://arxiv.org/abs/2201.11945

Jan. 31, 2022, 2:11 a.m. | Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

cs.LG updates on arXiv.org arxiv.org

Finding multiple solutions of non-convex optimization problems is a
ubiquitous yet challenging task. Typical existing solutions either apply
single-solution optimization methods from multiple random initial guesses or
search in the vicinity of found solutions using ad hoc heuristics. We present
an end-to-end method to learn the proximal operator across a family of
non-convex problems, which can then be used to recover multiple solutions for
unseen problems at test time. Our method only requires access to the objectives
without needing the …

arxiv learning operators

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