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Global convergence rate analysis of unconstrained optimization methods based on probabilistic models

Abstract:

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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Publisher copy:
10.1007/s10107-017-1137-4

Authors


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Institution:
University of Oxford
Oxford college:
Balliol College
Role:
Author
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Funding agency for:
Cartis, C
Grant:
EP/I01893X/1
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:
1436-4646
ISSN:
0025-5610
Source identifiers:
689453
Keywords:
Pubs id:
pubs:689453
UUID:
uuid:3ad9d914-488f-465c-be2f-cab804e30ceb
Local pid:
pubs:689453
Deposit date:
2017-04-18

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