Online Stochastic Convex Optimization: Wasserstein Distance Variation

• URL: http://arxiv.org/abs/2006.01397v2
• Date: Tue, 29 Sep 2020 22:14:45 GMT
• Title: Online Stochastic Convex Optimization: Wasserstein Distance Variation
• Authors: Iman Shames and Farhad Farokhi
• Abstract summary: We consider an online proximal-gradient method to track the minimizers of expectations of smooth convex functions. We revisit the concepts of estimation and tracking error inspired by systems and control literature. We provide bounds for them under strong convexity, Lipschitzness of the gradient, and bounds on the probability distribution drift.
• Score: 15.313864176694832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributionally-robust optimization is often studied for a fixed set of distributions rather than time-varying distributions that can drift significantly over time (which is, for instance, the case in finance and sociology due to underlying expansion of economy and evolution of demographics). This motivates understanding conditions on probability distributions, using the Wasserstein distance, that can be used to model time-varying environments. We can then use these conditions in conjunction with online stochastic optimization to adapt the decisions. We considers an online proximal-gradient method to track the minimizers of expectations of smooth convex functions parameterised by a random variable whose probability distributions continuously evolve over time at a rate similar to that of the rate at which the decision maker acts. We revisit the concepts of estimation and tracking error inspired by systems and control literature and provide bounds for them under strong convexity, Lipschitzness of the gradient, and bounds on the probability distribution drift. Further, noting that computing projections for a general feasible sets might not be amenable to online implementation (due to computational constraints), we propose an exact penalty method. Doing so allows us to relax the uniform boundedness of the gradient and establish dynamic regret bounds for tracking and estimation error. We further introduce a constraint-tightening approach and relate the amount of tightening to the probability of satisfying the constraints.

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