A Randomized Algorithm for Nonconvex Minimization With Inexact Evaluations and Complexity Guarantees

We consider minimization of a smooth nonconvex function with inexact oracle access to gradient and Hessian (without assuming access to the function value) to achieve approximate second-order optimality. A novel feature of our method is that if an approximate direction of negative curvature is chosen...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of optimization theory and applications Vol. 207; no. 3; p. 66
Main Authors:	Li, Shuyao, Wright, Stephen J.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2025 Springer Nature B.V
Subjects:	Algorithms Applications of Mathematics Calculus of Variations and Optimal Control; Optimization Complexity Engineering Folklore Martingales Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Probability Regularization methods Theory of Computation 68W20 Nonconvex optimization Second-order methods 90C17 90C26 Second-order stationarity Inexact Hessian Inexact gradient
ISSN:	0022-3239, 1573-2878
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!