Yichen Chen

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Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the feasible set is the intersection of a large number of convex sets. We propose a class of algorithms that perform both stochastic gradient descent and random feasibility updates(More)
—The increasing popularity of smartphones, equipped with GPS, provides new opportunities for location-based service (LBS). Among all kinds of LBSs, targeted advertising based on users' locations takes great advantage of the rich location data to improve the accuracy of advertising and thus potentially increase the sellers' profits. However, location-based(More)
We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods use small storage(More)
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