Report icon

Report

Adaptive cubic overestimation methods for unconstrained optimization

Abstract:

An Adaptive Cubic Overestimation (ACO) algorithm for unconstrained optimization, generalizing a method due to Nesterov and Polyak (Math. Programming 108, 2006, pp 177-205), is proposed. At each iteration of Nesterov and Polyak's approach, the global minimizer of a local cubic overestimator of the objective function is determined, and this ensures a significant improvement in the objective so long as the Hessian of the objective is Lipschitz continuous and its Lipschitz constant is available. ...

Expand abstract

Actions


Access Document


Files:

Authors


Publisher:
Oxford University Computing Laboratory
Publication date:
2007-10-01
UUID:
uuid:417e0009-9d54-4b34-b412-15b85f962618
Local pid:
cs:10
Deposit date:
2015-03-31

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP