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Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method
Constantine Caramanis · Dimitris Fotakis · Alkis Kalavasis · Vasilis Kontonis · Christos Tzamos

Thu Dec 14 08:30 AM -- 08:45 AM (PST) @
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems. In those methods a deep neural network is used as a solution generator which is then trained by gradient-based methods (e.g., policy gradient) to successively obtain better solution distributions.In this work we introduce a novel theoretical framework for analyzing the effectiveness of such methods. We ask whether there exist generative models that (i) are expressive enough to generate approximately optimal solutions; (ii) have a tractable, i.e, polynomial in the size of the input, number of parameters; (iii) their optimization landscape is benign in the sense that it does not contain sub-optimal stationary points. Our main contribution is a positive answer to this question. Our result holds for a broad class of combinatorial problems including Max- and Min-Cut, Max-$k$-CSP, Maximum-Weight-Bipartite-Matching, and the Traveling Salesman Problem. As a byproduct of our analysis we introduce a novel regularization process over vanilla gradient descent and provide theoretical and experimental evidence that it helps address vanishing-gradient issues and escape bad stationary points.

Author Information

Constantine Caramanis (UT Austin)
Dimitris Fotakis (National Technical University of Athens)
Alkis Kalavasis (Yale University)
Vasilis Kontonis (University of Texas at Austin)
Christos Tzamos (UW-Madison)

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