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A New Nonmonotone Adaptive Retrospective Trust Region Method for Unconstrained Optimization Problems
In this paper, we propose a new nonmonotone adaptive retrospective Trust Region (TR) method for solving unconstrained optimization problems. Inspired by the retrospective ratio proposed in Bastin et al. (Math Program Ser A 123:395–418, 2010 ), a new nonmonotone TR ratio is introduced based on a conv...
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Published in: | Journal of optimization theory and applications 2015-11, Vol.167 (2), p.676-692 |
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creator | Tarzanagh, D. Ataee Peyghami, M. Reza Bastin, F. |
description | In this paper, we propose a new nonmonotone adaptive retrospective Trust Region (TR) method for solving unconstrained optimization problems. Inspired by the retrospective ratio proposed in Bastin et al. (Math Program Ser A 123:395–418,
2010
), a new nonmonotone TR ratio is introduced based on a convex combination of the nonmonotone classical and retrospective ratios. Due to the value of the new ratio, the TR radius is updated adaptively by a variant of the rule as proposed in Shi and Guo (J Comput Appl Math 213:509–520,
2008
). Global convergence property of the new algorithm, as well as its superlinear convergence rate, is established under some standard assumptions. Numerical results on some test problems show the efficiency and effectiveness of the new method in practice, too. |
doi_str_mv | 10.1007/s10957-015-0790-0 |
format | article |
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2010
), a new nonmonotone TR ratio is introduced based on a convex combination of the nonmonotone classical and retrospective ratios. Due to the value of the new ratio, the TR radius is updated adaptively by a variant of the rule as proposed in Shi and Guo (J Comput Appl Math 213:509–520,
2008
). Global convergence property of the new algorithm, as well as its superlinear convergence rate, is established under some standard assumptions. Numerical results on some test problems show the efficiency and effectiveness of the new method in practice, too.</description><identifier>ISSN: 0022-3239</identifier><identifier>EISSN: 1573-2878</identifier><identifier>DOI: 10.1007/s10957-015-0790-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Applications of Mathematics ; Calculus of Variations and Optimal Control; Optimization ; Computational efficiency ; Computing time ; Convergence ; Engineering ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Methods ; Operations Research/Decision Theory ; Optimization ; Ratios ; Studies ; Theory of Computation</subject><ispartof>Journal of optimization theory and applications, 2015-11, Vol.167 (2), p.676-692</ispartof><rights>Springer Science+Business Media New York 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c419t-85f29d9b43a87527b46abd05b7f7de0b1299032f4b0992d98861d25781411d073</citedby><cites>FETCH-LOGICAL-c419t-85f29d9b43a87527b46abd05b7f7de0b1299032f4b0992d98861d25781411d073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1722620335/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1722620335?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11687,27923,27924,36059,36060,44362,74766</link.rule.ids></links><search><creatorcontrib>Tarzanagh, D. Ataee</creatorcontrib><creatorcontrib>Peyghami, M. Reza</creatorcontrib><creatorcontrib>Bastin, F.</creatorcontrib><title>A New Nonmonotone Adaptive Retrospective Trust Region Method for Unconstrained Optimization Problems</title><title>Journal of optimization theory and applications</title><addtitle>J Optim Theory Appl</addtitle><description>In this paper, we propose a new nonmonotone adaptive retrospective Trust Region (TR) method for solving unconstrained optimization problems. Inspired by the retrospective ratio proposed in Bastin et al. (Math Program Ser A 123:395–418,
2010
), a new nonmonotone TR ratio is introduced based on a convex combination of the nonmonotone classical and retrospective ratios. Due to the value of the new ratio, the TR radius is updated adaptively by a variant of the rule as proposed in Shi and Guo (J Comput Appl Math 213:509–520,
2008
). Global convergence property of the new algorithm, as well as its superlinear convergence rate, is established under some standard assumptions. 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2010
), a new nonmonotone TR ratio is introduced based on a convex combination of the nonmonotone classical and retrospective ratios. Due to the value of the new ratio, the TR radius is updated adaptively by a variant of the rule as proposed in Shi and Guo (J Comput Appl Math 213:509–520,
2008
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subjects | Algorithms Applications of Mathematics Calculus of Variations and Optimal Control Optimization Computational efficiency Computing time Convergence Engineering Mathematical models Mathematics Mathematics and Statistics Methods Operations Research/Decision Theory Optimization Ratios Studies Theory of Computation |
title | A New Nonmonotone Adaptive Retrospective Trust Region Method for Unconstrained Optimization Problems |
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