 A parameterized Douglas–Rachford splitting algorithm for nonconvex optimizationFengmiao Bian and Xiaoqun Zhang Applied Mathematics and Computation, 2021, vol. 410, issue C 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. Keywords: Parameterized Douglas–Rachford splitting method; Nonconvex optimization problems; Global convergence; Sparsity constrained least squares problem; Low rank matrix completion; Feasibility problem (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:410:y:2021:i:c:s0096300321005142 DOI: 10.1016/j.amc.2021.126425 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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