Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction

Understand

We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints.

• Sufficient conditions are provided, under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions.
• We further extend the proposed algorithm to an asynchronous parallel variant with a near linear speedup.
• Numerical experiments demonstrate the efficiency of our algorithm in terms of both parameter estimation and computational performance.