Inexact Newton methods for the nonlinear complementarity problem
Mathematical Programming: Series A and B
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Nonmonotone derivative-free methods for nonlinear equations
Computational Optimization and Applications
A globally convergent derivative-free method for solving large-scale nonlinear monotone equations
Journal of Computational and Applied Mathematics
Structured Compressed Sensing: From Theory to Applications
IEEE Transactions on Signal Processing
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Frequently, the most important information in a signal is much sparser than the signal itself. In this paper, we study a projected conjugate gradient method for finding sparse solutions to an undetermined linear system arising from compressive sensing. The construction of this method consists of two main phases: (1) reformulate a l1 regularized least squares problem into an equivalent nonlinear system of monotone equations; (2) apply a conjugate gradient method with projection strategy to the resulting system. The derived method only needs matrix-vector products at each step and could be easily implemented. Global convergence result is established under some suitable conditions. Numerical results demonstrate that the proposed method can improve the computation time while obtaining similar reconstructed quality.