Journal article
Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
- Abstract:
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We present global convergence rates for a line-search method which is based on random first-order models and directions whose quality is ensured only with certain probability. We show that in terms of the order of the accuracy, the evaluation complexity of such a method is the same as its counterparts that use deterministic accurate models; the use of probabilistic models only increases the complexity by a constant, which depends on the probability of the models being good. We particularize a...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, pdf, 402.6KB)
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- Publisher copy:
- 10.1007/s10107-017-1137-4
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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Cartis, C
Grant:
EP/I01893X/1
Bibliographic Details
- Publisher:
- Springer Berlin Heidelberg Publisher's website
- Journal:
- Mathematical Programming Journal website
- Volume:
- 169
- Issue:
- 2
- Pages:
- 337–375
- Publication date:
- 2017-04-01
- Acceptance date:
- 2017-03-22
- DOI:
- EISSN:
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1436-4646
- ISSN:
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0025-5610
- Source identifiers:
-
689453
Item Description
- Keywords:
- Pubs id:
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pubs:689453
- UUID:
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uuid:3ad9d914-488f-465c-be2f-cab804e30ceb
- Local pid:
- pubs:689453
- Deposit date:
- 2017-04-18
Terms of use
- Copyright holder:
- Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society
- Copyright date:
- 2017
- Notes:
- Copyright © 2017 Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/s10107-017-1137-4
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