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arXiv · 1410.7596

Fast Algorithms for Online Stochastic Convex Programming

Shipra Agrawal·Nikhil R. Devanur
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Abstract

We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints. Many well-studied problems like online stochastic packing and covering, online stochastic matching with concave returns, etc. form a special case of online stochastic CP. We present fast algorithms for these problems, which achieve near-optimal regret guarantees for both the i.i.d. and the random permutation models of stochastic inputs. When applied to the special case online packing, our ideas yield a simpler and faster primal-dual algorithm for this well studied problem, which achieves the optimal competitive ratio. Our techniques make explicit the connection of primal-dual paradigm and online learning to online stochastic CP.

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Shipra Agrawal, Nikhil R. Devanur. 2014-10-28. Fast Algorithms for Online Stochastic Convex Programming. https://arxiv.org/abs/1410.7596

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