非凸两分块优化问题的一类惯性对称正则化交替方向乘子法

doi:10.15960/j.cnki.issn.1007-6093.2023.03.003

Abstract

Abstract:

The alternating direction method of multipliers(ADMM) is an valid method for solving separable convex optimization problems, nevertheless, when the objective function has a nonconvex function, ADMM may not converge. This paper proposes an inertial symmetric regularization alternating direction method of multipliers for nonconvex two-block optimization problem with linear equality constraints. Under the appropriate hypothesis conditions, the global convergence of the algorithm is established. Secondly, When the benefit function satisfies the Kurdyka-Łojasiewicz(KL) property, the strong convergence of the algorithm is established. Finally, numerical experiments are performed on the algorithm, and the results show that the algorithm is an effective method.

Key words: the alternating direction method of multipliers, nonconvex optimization problem, Kurdyka-Łojasiewicz property, convergence

CLC Number:

O221.6

Jianwen PENG, Hongwang LEI. A class of inertial symmetric regularization alternating direction method of multipliers for nonconvex two-block optimization[J]. Operations Research Transactions, 2023, 27(3): 37-52.

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References 31

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		ISRADMM		PSRADMM
$ m $	$ n $	iter(k)	Time/s	iter(k)	Time/s
500	600	100	1.180 8	114	1.534 5
600	700	97	1.679 5	114	2.216 4
700	800	96	2.468 8	114	3.301 1
800	900	94	3.278 6	115	4.410 8
900	1 000	93	4.284 4	115	5.716 4

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A class of inertial symmetric regularization alternating direction method of multipliers for nonconvex two-block optimization

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