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On the Oracle Complexity of First-Order and Derivative-Free Algorithms for Smooth Nonconvex Minimization.

Abstract:

The (optimal) function/gradient evaluations worst-case complexity analysis available for the adaptive regularization algorithms with cubics (ARC) for nonconvex smooth unconstrained optimization is extended to finite-difference versions of this algorithm, yielding complexity bounds for first-order and derivative-free methods applied on the same problem class. A comparison with the results obtained for derivative-free methods by Vicente [Worst Case Complexity of Direct Search, Technical report,...

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Publisher copy:
10.1137/100812276

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Institution:
University of Oxford
Department:
Oxford, MPLS, Mathematical Inst
Role:
Author
Journal:
SIAM Journal on Optimization
Volume:
22
Issue:
1
Pages:
66-86
Publication date:
2012-01-01
DOI:
EISSN:
1095-7189
ISSN:
1052-6234
URN:
uuid:6a4fb5df-cb34-4fbd-b318-725ed9ca6cb2
Source identifiers:
400606
Local pid:
pubs:400606

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