On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions

We consider the gradient (or steepest) descent method with exact line search applied to a strongly convex function with Lipschitz continuous gradient. We establish the exact worst-case rate of convergence of this scheme, and show that this worst-case behavior is exhibited by a certain convex quadrat...

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Veröffentlicht in:	Optimization letters Jg. 11; H. 7; S. 1185 - 1199
Hauptverfasser:	de Klerk, Etienne, Glineur, François, Taylor, Adrien B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017
Schlagworte:	Computational Intelligence Mathematics Mathematics and Statistics Numerical and Computational Physics Operations Research/Decision Theory Optimization Original Paper Simulation Semidefinite programming Performance estimation problem Steepest descent Gradient method
ISSN:	1862-4472, 1862-4480
Online-Zugang:	Volltext
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