Highlights · A s-difference type regularization is proposed, which is the difference of the normal penalty function and its corresponding s-truncated function.
May 11, 2019 · In this paper, a s-difference type regularization for sparse recovery problem is proposed, which is the difference of the normal penalty function R(x) and its ...
May 11, 2019 · Abstract—In this paper, a s-difference type regularization for sparse recovery problem is proposed, which is the difference.
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Mar 23, 2017 · If N = M, then A is the inverse N-point DFT matrix. 2. If N > M, then A is the first M rows of the inverse N-point DFT matrix.
where s is the given sparsity level. For the sparse optimization problem, a popular and practical technique is the regulariza- tion method, which is to ...
Several special purpose techniques have been proposed for solving ℓ1-regularized least squares: Iterative Shrinkage/Thresholding (IST) methods (e.g..
Many applications need structured, approximate solutions of optimization formulations, rather than exact solutions. More Useful, More Credible.
Feb 7, 2022 · Several numerical studies have shown that non-convex sparsity- induced regularization can outperform the convex `1-penalty. In this.
Sparse optimization involving the L 0-norm function as the regularization in objective function has a wide application in many fields.
Abstract—Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares,.