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Adaptive cubic overestimation methods for unconstrained optimization

Abstract:

An Adaptive Cubic Overestimation (ACO) algorithm for unconstrained optimization, generalizing a method due to Nesterov & Polyak (Math. Programming 108, 2006, pp 177-205), is proposed. At each iteration of Nesterov & 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 availab...

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Publication date:
2007-10-01
UUID:
uuid:68c5c310-dec1-43aa-a3f2-feaca56d8222
Local pid:
oai:eprints.maths.ox.ac.uk:1082
Deposit date:
2011-05-20

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