- 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,...
Expand abstract - Publisher copy:
- 10.1137/100812276
-
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- 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
- Language:
- English
- Keywords:
- Copyright date:
- 2012
Journal article
On the Oracle Complexity of First-Order and Derivative-Free Algorithms for Smooth Nonconvex Minimization.
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