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• W3202023649 abstract "We propose a general formulation of nonconvex and nonsmooth sparse optimization problems with convex set constraint, which can take into account most existing types of nonconvex sparsity-inducing terms, bringing strong applicability to a wide range of applications. We design a general algorithmic framework of iteratively reweighted algorithms for solving the proposed nonconvex and nonsmooth sparse optimization problems, which solves a sequence of weighted convex regularization problems with adaptively updated weights. First-order optimality condition is derived and global convergence results are provided under loose assumptions, making our theoretical results a practical tool for analyzing a family of various reweighted algorithms. The effectiveness and efficiency of our proposed formulation and the algorithms are demonstrated in numerical experiments on various sparse optimization problems." @default.
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• W3202023649 date "2021-10-05" @default.
• W3202023649 modified "2023-10-18" @default.
• W3202023649 title "Nonconvex and Nonsmooth Sparse Optimization via Adaptively Iterative Reweighted Methods" @default.
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• W3202023649 doi "https://doi.org/10.1007/s10898-021-01093-0" @default.
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