An accelerated preconditioned primal-dual gradient algorithm for structured nonconvex optimization problems

•An novel accelerated preconditioned primal-dual gradient algorithm for solving nonconvex optimization problems by the conjugate duality theory of nonconvex functions.•Our algorithm only needs to calculate the proximal mapping of the conjugate function which is always convex and lower semicontinuous...

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Bibliographic Details

Published in:	Communications in nonlinear science & numerical simulation Vol. 153; p. 109480
Main Authors:	Long, Xian-Jun, Nie, Jia-Lin, Gou, Zhun, Sun, Xiang-Kai, Li, Gao-Xi
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2026
Subjects:	Conjugate duality theory Global convergence Kurdyka-Łojasiewicz property Primal-dual gradient algorithm Structured nonconvex and nonsmooth problem Structured nonconvex and nonsmooth problem Global convergence Kurdyka-Łojasiewicz property 90C30 90C33 Primal-dual gradient algorithm 90C26 Conjugate duality theory
ISSN:	1007-5704
Online Access:	Get full text
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