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Journal of Applied Mathematics and Computing

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22.10.2022 | Original Research

An inertial alternating minimization with Bregman distance for a class of nonconvex and nonsmooth problems

verfasst von: Miantao Chao, Feifan Nong, Meiyu Zhao

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 2/2023

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Abstract

In this paper, we propose an inertial alternating minimization with Bregman distance (BIAM) for a class of nonconvex nonsmooth optimization problems. Under more general conditions, we analyzed the global convergence of the proposed algorithm. In particular, the analysis of the decline of merit function is simpler than the existing algorithm, and we optimize the parameters selection in the literature. Furthermore, suppose that the merit function satisfies the Kurdyka-Łojasiewicz property and the parameters are selected appropriately, we also prove the convergence of BIAM algorithm. Finally, some preliminary numerical results are reported to illustrate the effectiveness of the algorithm.

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Titel: An inertial alternating minimization with Bregman distance for a class of nonconvex and nonsmooth problems
verfasst von: Miantao Chao
Feifan Nong
Meiyu Zhao
Publikationsdatum: 22.10.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 2/2023
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-022-01799-8

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