# 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)

PDF Abstract

No code implementations yet. Submit your code now