Journal article
A concise second-order complexity analysis for unconstrained optimization using high-order regularized models
- Abstract:
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An adaptive regularization algorithm is proposed that uses Taylor models of the objective of order p, p≥2, of the unconstrained objective function, and that is guaranteed to find a first- and second-order critical point in at most O(max{ϵ−p+1p1,ϵ−p+1p−12}) function and derivatives evaluations, where ϵ1 and ϵ2 are prescribed first- and second-order optimality tolerances. This is a simple algorithm and associated analysis compared to the much more general approach in Cartis et al. [Sharp worst-...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Accepted manuscript, pdf, 176.0KB)
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- Publisher copy:
- 10.1080/10556788.2019.1678033
Authors
Bibliographic Details
- Publisher:
- Taylor and Francis Publisher's website
- Journal:
- Optimization Methods and Software Journal website
- Volume:
- 35
- Issue:
- 2
- Pages:
- 243-256
- Publication date:
- 2019-10-24
- Acceptance date:
- 2019-10-01
- DOI:
- EISSN:
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1029-4937
- ISSN:
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1055-6788
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:1067133
- UUID:
-
uuid:09b543f4-83e2-4e03-9846-84cab74dad6c
- Local pid:
- pubs:1067133
- Source identifiers:
-
1067133
- Deposit date:
- 2019-11-23
Terms of use
- Copyright holder:
- Taylor and Francis
- Copyright date:
- 2019
- Rights statement:
- © 2019 Informa UK Limited, trading as Taylor and Francis Group
- Notes:
- This is the accepted manuscript version of the article. The final version is available from Taylor and Francis at: https://doi.org/10.1080/10556788.2019.1678033
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