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Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

2020-03-05 | |  66 |   35 |   0

Abstract

We propose a stochastic variance reduced optimization algorithm for solving a class of largescale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.

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