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DOI 10.5286/raltr.2010029
Persistent URL http://purl.org/net/epubs/work/53954
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Record Id 53954
Title 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 Regularizations 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 (2010) is also discussed, giving some theoretical insight on the relative merits of various methods in this popular class of algorithms.
Organisation CSE , CSE-NAG , STFC
Funding Information
Related Research Object(s): 62623
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Language English (EN)
Type Details URI(s) Local file(s) Year
Report RAL Technical Reports RAL-TR-2010-029. 2010. RAL-TR-2010-029.pdf 2010