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In this paper, we propose an adaptive sieving (AS) strategy for solvinggeneral sparse machine learning models by effectively exploring the intrinsicsparsity of the solutions, wherein only a sequence of reduced problems withmuch smaller sizes need to be solved. We further apply the proposed AS strategyto generate solution paths for large-scale sparse optimization problemsefficiently. We establish the theoretical guarantees for the proposed ASstrategy including its finite termination property. Extensive numericalexperiments are presented in this paper to demonstrate the effectiveness andflexibility of the AS strategy to solve large-scale machine learning models.

Adaptive sieving: A dimension reduction technique for sparse optimization problems