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Journal of Applied Mathematics and Computing
Erschienen in: Journal of Applied Mathematics and Computing 3/2022

12.07.2021 | Original Research

An inertial Bregman generalized alternating direction method of multipliers for nonconvex optimization

verfasst von: Jiawei Xu, Miantao Chao

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 3/2022

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Abstract

In this paper, a class of nonconvex optimization with linearly constrained is considered. An inertial Bregman generalized alternating direction method of multiplies is investigated for solving the nonconvex optimization. The iterative schemes are formulated in the spirit of the proximal alternating direction method of multipliers and its inertial variant. The proximal term is introduced via Bregman distance, a fact that allows us to derive new proximal splitting algorithms for large-scale separable optimization problems. Under some assumptions, we prove that the iterative sequence generated by the algorithm converges to a critical point of the considered problem. Finally, we report some preliminary numerical results on solving signal recovery and SCAD penalty problems to verify the efficiency of the proposed method.
Vorheriger Artikel On the resistance diameter of the Cartesian and lexicographic product of paths
Nächster Artikel Correction to: On -skew cyclic codes

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Metadaten
Titel
An inertial Bregman generalized alternating direction method of multipliers for nonconvex optimization
verfasst von
Jiawei Xu
Miantao Chao
Publikationsdatum
12.07.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Applied Mathematics and Computing / Ausgabe 3/2022
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI
https://doi.org/10.1007/s12190-021-01590-1

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