Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results.

Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results.

An Adaptive Regularisation algorithm using Cubics (ARC) is proposed for unconstrained optimization, generalizing at the same time an unpublished method due to Griewank (Technical Report NA/12, 1981, DAMTP, University of Cambridge), an algorithm by Nesterov and Polyak (Math Program 108(1):177-205, 20...

Bibliográfalaš dieđut
Váldodahkkit:	Cartis, C, Gould, N, Toint, P
Materiálatiipa:	Journal article
Giella:	English
Almmustuhtton:	2011

Geahča maid

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Adaptive cubic overestimation methods for unconstrained optimization
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Evaluation complexity of adaptive cubic regularization methods for convex unconstrained optimization.
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Trust-region and other regularisations of linear least-squares problems
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On the Evaluation Complexity of Composite Function Minimization with Applications to Nonconvex Nonlinear Programming.
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On the Oracle Complexity of First-Order and Derivative-Free Algorithms for Smooth Nonconvex Minimization.
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Adaptive Cuckoo Search Algorithm for Unconstrained Optimization
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