Implementation of trust region methods in optimization. (c1998)

Includes bibliographical references (l. 37).

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Hajj, Mohammed Omar (author)
التنسيق: masterThesis
منشور في: 1998
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/10725/143
https://doi.org/10.26756/th.1998.1
الوسوم: إضافة وسم
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author Hajj, Mohammed Omar
author_facet Hajj, Mohammed Omar
author_role author
dc.creator.none.fl_str_mv Hajj, Mohammed Omar
dc.date.none.fl_str_mv 1998
1998-05
2010-11-26T13:56:54Z
2010-11-26T13:56:54Z
2010-11-26
dc.identifier.none.fl_str_mv http://hdl.handle.net/10725/143
https://doi.org/10.26756/th.1998.1
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv Lebanese American University
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Mathematical optimization
dc.title.none.fl_str_mv Implementation of trust region methods in optimization. (c1998)
dc.type.none.fl_str_mv Thesis
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info:eu-repo/semantics/masterThesis
description Includes bibliographical references (l. 37).
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publishDate 1998
publisher.none.fl_str_mv Lebanese American University
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spelling Implementation of trust region methods in optimization. (c1998)Hajj, Mohammed OmarMathematical optimizationIncludes bibliographical references (l. 37).This project presents a new approach to Quasi-Newton methods for unconstrained optimization. Quasi-Newton Methods update at each iteration the existing Hessian approximation (or its inverse) cheaply by integrating data derived from the previously completed one, which is soon ignored. These methods are based on the so-called Secant equation. In our project we focus on solving a critical subproblem of the Quasi-Newton algorithm that requires determining a proper, suitable step size that takes from the current approximation to the minimum to a new 'better' one. The subproblem can either be posed as doing a Line Search along some generated search direction in order to determine a minimum along the search vector. Another technique, on which we focus primarily in this work, is to use a Trust Region method that directly computes the step vector without doing a focused Line Search. The subproblem is critical to the numerical success of Q-N methods. We emphasize features of successful implementation to pinpoint assess merits of Trust Region methods. Our Numerical Results reveal that Trust Region algorithms seem to markedly improve as the dimension of the problem increases, while for small dimensional problems performance of both methods is comparable.1 bound copy: vii, 44 leaves; ill.; 30 cm. available at RNL.Lebanese American University2010-11-26T13:56:54Z2010-11-26T13:56:54Z19982010-11-261998-05Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/143https://doi.org/10.26756/th.1998.1eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/1432021-03-19T09:58:56Z
spellingShingle Implementation of trust region methods in optimization. (c1998)
Hajj, Mohammed Omar
Mathematical optimization
status_str publishedVersion
title Implementation of trust region methods in optimization. (c1998)
title_full Implementation of trust region methods in optimization. (c1998)
title_fullStr Implementation of trust region methods in optimization. (c1998)
title_full_unstemmed Implementation of trust region methods in optimization. (c1998)
title_short Implementation of trust region methods in optimization. (c1998)
title_sort Implementation of trust region methods in optimization. (c1998)
topic Mathematical optimization
url http://hdl.handle.net/10725/143
https://doi.org/10.26756/th.1998.1