First-order optimization algorithms via inertial systems with Hessian driven damping

In a Hilbert space setting, for convex optimization, we analyze the convergence rate of a class of first-order algorithms involving inertial features. They can be interpreted as discrete time versions of inertial dynamics involving both viscous and Hessian-driven dampings. The geometrical damping dr...

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Veröffentlicht in:	Mathematical programming Jg. 193; H. 1; S. 113 - 155
Hauptverfasser:	Attouch, Hedy, Chbani, Zaki, Fadili, Jalal, Riahi, Hassan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022 Springer Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Analysis Calculus of Variations and Optimal Control; Optimization Combinatorics Computational geometry Convergence Convex analysis Convexity Damping First order algorithms Full Length Paper Hilbert space Inventory control Machine Learning Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical optimization Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Optimization Optimization and Control Regularization Statistics Theoretical 65K05 Time rescaling 37N40 65B99 49M30 Nesterov accelerated gradient method Ravine method 90C25 Inertial optimization algorithms 65K10 90B50 46N10 Hessian driven damping inertial optimization algorithms
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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