Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure

The alternating direction method of multipliers (ADMM) is an effective method for solving two-block separable convex problems and its convergence is well understood. When either the involved number of blocks is more than two, or there is a nonconvex function, or there is a nonseparable structure, AD...

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Main Authors: Miantao Chao, Zhao Deng, Jinbao Jian
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/6237942
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author Miantao Chao
Zhao Deng
Jinbao Jian
author_facet Miantao Chao
Zhao Deng
Jinbao Jian
author_sort Miantao Chao
collection DOAJ
description The alternating direction method of multipliers (ADMM) is an effective method for solving two-block separable convex problems and its convergence is well understood. When either the involved number of blocks is more than two, or there is a nonconvex function, or there is a nonseparable structure, ADMM or its directly extend version may not converge. In this paper, we proposed an ADMM-based algorithm for nonconvex multiblock optimization problems with a nonseparable structure. We show that any cluster point of the iterative sequence generated by the proposed algorithm is a critical point, under mild condition. Furthermore, we establish the strong convergence of the whole sequence, under the condition that the potential function satisfies the Kurdyka–Łojasiewicz property. This provides the theoretical basis for the application of the proposed ADMM in the practice. Finally, we give some preliminary numerical results to show the effectiveness of the proposed algorithm.
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language English
publishDate 2020-01-01
publisher Wiley
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series Complexity
spelling doaj-art-2c2609ed91854bd58b47f34f96bb8f9a2025-02-03T06:46:23ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/62379426237942Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable StructureMiantao Chao0Zhao Deng1Jinbao Jian2Department of Mathematics, Guangxi University, Nanning 530004, ChinaDepartment of Mathematics, Guangxi University, Nanning 530004, ChinaGuangxi University for Nationalities, Nanning, ChinaThe alternating direction method of multipliers (ADMM) is an effective method for solving two-block separable convex problems and its convergence is well understood. When either the involved number of blocks is more than two, or there is a nonconvex function, or there is a nonseparable structure, ADMM or its directly extend version may not converge. In this paper, we proposed an ADMM-based algorithm for nonconvex multiblock optimization problems with a nonseparable structure. We show that any cluster point of the iterative sequence generated by the proposed algorithm is a critical point, under mild condition. Furthermore, we establish the strong convergence of the whole sequence, under the condition that the potential function satisfies the Kurdyka–Łojasiewicz property. This provides the theoretical basis for the application of the proposed ADMM in the practice. Finally, we give some preliminary numerical results to show the effectiveness of the proposed algorithm.http://dx.doi.org/10.1155/2020/6237942
spellingShingle Miantao Chao
Zhao Deng
Jinbao Jian
Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
Complexity
title Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
title_full Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
title_fullStr Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
title_full_unstemmed Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
title_short Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
title_sort convergence of linear bregman admm for nonconvex and nonsmooth problems with nonseparable structure
url http://dx.doi.org/10.1155/2020/6237942
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