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- W3175936473 abstract "In this paper, we study a parameterized Douglas–Rachford splitting method in Wang-Wang (2019)[5] for a class of nonconvex optimization problem. A new merit function is constructed to establish the convergence of the whole sequence generated by the parameterized Douglas–Rachford splitting method. As a by-product, this also provides convergence results of a special case of the adaptive Douglas–Rachford algorithm proposed by Dao and Phan (2019)[22] in nonconvex settings. We then apply the parameterized Douglas–Rachford splitting method to three important classes of nonconvex optimization problems arising in data science: sparsity constrained least squares problem, feasibility problem and low rank matrix completion. Numerical results validate the effectiveness of the parameterized Douglas–Rachford splitting method compared with some other classical methods." @default.
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- W3175936473 date "2021-12-01" @default.
- W3175936473 modified "2023-10-16" @default.
- W3175936473 title "A parameterized Douglas–Rachford splitting algorithm for nonconvex optimization" @default.
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- W3175936473 doi "https://doi.org/10.1016/j.amc.2021.126425" @default.
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