Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization

JOURNAL OF MACHINE LEARNING RESEARCH, (2010): 2543-2596

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Abstract

We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning task, and the other is a simple regularization term such as l1-norm for promoting sparsity. We develop extensions of Nesterov's dual averaging method, that can ...More

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