Adaptive Projected Gradient Thresholding Methods for Constrained l 0 Problems

In this paper, we propose and analyze Adaptive Projected Gradient Thresholding (APGT) methods for finding sparse solutions of the underdetermined linear system with equality and box constrains. The general convergence will be demonstrated, and in addition the bound of the number of iterations is established in some special cases. Under suitable assumptions, it is proved that any accumulation point of the sequence generated by the APGT methods is a local minimizer of the underdetermined linear system. Moreover, the APGT methods, under certain conditions, can find all s-sparse solutions for accurate measurement cases and guarantee the stability and robustness for awed measurement cases. Numerical examples are presented to show the accordance with theoretical results in compressed sensing and verify high out-of-sample performance in index tracking