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Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
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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.
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Cited by 1 Pith paper
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New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
A generalized variance-reduced ZO hard-thresholding algorithm removes prior limits on random directions for gradient estimates, yielding improved convergence rates under standard assumptions.
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