Stochastic Proximal Gradient Descent with Acceleration Techniques

NeurIPS 2014 Atsushi Nitanda

Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting... (read more)

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