Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier


Jacob Abernethy, Elad Hazan ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2520-2528, 2016.


This paper explores a surprising equivalence between two seemingly-distinct convex optimization methods. We show that simulated annealing, a well-studied random walk algorithms, is *directly equivalent*, in a certain sense, to the central path interior point algorithm for the the entropic universal barrier function. This connection exhibits several benefits. First, we are able improve the state of the art time complexity for convex optimization under the membership oracle model by devising a new temperature schedule for simulated annealing motivated by central path following interior point methods. Second, we get an efficient randomized interior point method with an efficiently computable universal barrier for any convex set described by a membership oracle. Previously, efficiently computable barriers were known only for particular convex sets.

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