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Full Record Details
DOI
10.5286/raltr.2009006
Persistent URL
http://purl.org/net/epubs/work/56166
Record Status
Checked
Record Id
56166
Title
An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
Contributors
C Cartis (SFTC Rutherford Appleton Lab.)
,
NIM Gould (STFC Rutherford Appleton Lab.)
,
Ph L Toint
Abstract
The adaptive cubic overestimation algorithm described by Cartis, Gould and Toint (RAL-TR- 2007-007) is adapted to the problem of minimizing a nonlinear, possibly nonconvex, smooth objective function over a convex domain. Convergence to first-order critical points is shown under standard assumptions, but without any Lipschitz continuity requirement on the objective?s Hessian. A worst-case complexity analysis in terms of evaluations of the problem?s function and derivatives is also presented for the Lipschitz continuous case and for a variant of the resulting algorithm. This analysis extends the best known bound for general unconstrained problems to nonlinear problems with convex constraints.
Organisation
CSE
,
CSE-NAG
,
STFC
Keywords
Funding Information
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Language
English (EN)
Type
Details
URI(s)
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Year
Report
RAL Technical Reports
RAL-TR-2009-006. STFC, 2009.
cgtRALTR2009006.pdf
2009
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