Fast Reflected Forward-Backward algorithm: achieving fast convergence rates for convex optimization with linear cone constraints

In this paper, we derive a Fast Reflected Forward-Backward (Fast RFB) algorithm to solve the problem of finding a zero of the sum of a maximally monotone operator and a monotone and Lipschitz continuous operator in a real Hilbert space. Our approach extends the class of reflected forward-backward me...

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Veröffentlicht in:	Journal of scientific computing Jg. 105; H. 3; S. 73
Hauptverfasser:	Boţ, Radu Ioan, Nguyen, Dang-Khoa, Zong, Chunxiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2025 Springer Nature B.V
Schlagworte:	Algorithms Computational Mathematics and Numerical Analysis Constraints Convergence Convex analysis Convexity Distance learning Hilbert space Lipschitz condition Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Methods Numerical analysis Operators (mathematics) Optimization Theoretical saddle point problem 68Q25 Lyapunov analysis 65K05 fast primal-dual algorithm reflected forward-backward splitting algorithm 90C25 90C47 Nesterov momentum 49M29 convergence of the iterates monotone inclusion fast convergence rates
ISSN:	0885-7474, 1573-7691
Online-Zugang:	Volltext
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