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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Veröffentlicht in:	Communications in nonlinear science & numerical simulation Jg. 153; S. 109480
Hauptverfasser:	Long, Xian-Jun, Nie, Jia-Lin, Gou, Zhun, Sun, Xiang-Kai, Li, Gao-Xi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.02.2026
Schlagworte:	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-Zugang:	Volltext
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