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optimization_layer [2018/07/30 01:56]
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optimization_layer [2018/11/17 22:26] (current)
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 http://​proceedings.mlr.press/​v80/​rosenfeld18a.html Learning to Optimize Combinatorial Functions http://​proceedings.mlr.press/​v80/​rosenfeld18a.html Learning to Optimize Combinatorial Functions
  
 +https://​arxiv.org/​abs/​1804.06355v1 An Exponential Speedup in Parallel Running Time for Submodular Maximization without Loss in Approximation
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 +https://​arxiv.org/​abs/​1810.10659 Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
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 +https://​arxiv.org/​abs/​1811.06128 Machine Learning for Combinatorial Optimization:​ a Methodological Tour d'​Horizon
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 +This paper surveys the recent attempts, both from the machine learning and operations research communities,​ at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art methodologies involve algorithmic decisions that either require too much computing time or are not mathematically well defined. Thus, machine learning looks like a promising candidate to effectively deal with those decisions. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.