Stochastic Proximal Gradient Descent for Nuclear Norm Regularization

5 Nov 2015Lijun ZhangTianbao YangRong JinZhi-Hua Zhou

In this paper, we utilize stochastic optimization to reduce the space complexity of convex composite optimization with a nuclear norm regularizer, where the variable is a matrix of size $m \times n$. By constructing a low-rank estimate of the gradient, we propose an iterative algorithm based on stochastic proximal gradient descent (SPGD), and take the last iterate of SPGD as the final solution... (read more)

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