Two-step inertial Bregman alternating minimization algorithm for nonconvex and nonsmooth problems

Jing Zhao (), Qiao-Li Dong (), Michael Th. Rassias () and Fenghui Wang ()
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Jing Zhao: Civil Aviation University of China
Qiao-Li Dong: Civil Aviation University of China
Michael Th. Rassias: Hellenic Military Academy
Fenghui Wang: Luoyang Normal University

Journal of Global Optimization, 2022, vol. 84, issue 4, No 5, 966 pages

Abstract: Abstract In this paper, we propose an algorithm combining Bregman alternating minimization algorithm with two-step inertial force for solving a minimization problem composed of two nonsmooth functions with a smooth one in the absence of convexity. For solving nonconvex and nonsmooth problems, we give an abstract convergence theorem for general descent methods satisfying a sufficient decrease assumption, and allowing a relative error tolerance. Our result holds under the assumption that the objective function satisfies the Kurdyka–Łojasiewicz inequality. The proposed algorithm is shown to satisfy the requirements of our abstract convergence theorem. The convergence is obtained provided an appropriate regularization of the objective function satisfies the Kurdyka–Łojasiewicz inequality. Finally, numerical results are reported to show the effectiveness of the proposed algorithm.

Keywords: Nonconvex optimization; Inertial algorithm; Bregman distance; Kurdyka–Łojasiewicz inequality; Alternating minimization; 47J06; 49J52; 65K10; 90C26; 90C30 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10898-022-01176-6

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