Efficient ODE-based methods for unconstrained optimization

This paper presents some efficient methods for unconstrained optimization based upon approximating the gradient flow of the objective function. Most ODE-based methods would generate Levenberg-Marquardt-like steps that require the solution of linear systems. On the other hand our proposed methods use...

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Main Authors: Yap, Chui Ying, Leong, Wah June
Format: Article
Language:English
Published: Faculty of Science, Universiti Putra Malaysia 2018
Online Access:http://psasir.upm.edu.my/id/eprint/72536/
http://psasir.upm.edu.my/id/eprint/72536/1/Efficient%20ODE.pdf
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author Yap, Chui Ying
Leong, Wah June
author_facet Yap, Chui Ying
Leong, Wah June
author_sort Yap, Chui Ying
building UPM Institutional Repository
collection Online Access
description This paper presents some efficient methods for unconstrained optimization based upon approximating the gradient flow of the objective function. Most ODE-based methods would generate Levenberg-Marquardt-like steps that require the solution of linear systems. On the other hand our proposed methods used some quasi-Newton matrices to approximate the solution of these linear systems, thus avoiding the solution of linear systems repeatedly. Two implementations of the modified ODE-based methods - line search and trust region implementation are proposed. Under some suitable assumptions, the convergence of the proposed methods is then established. Numerical results indicate that the modified methods are more effective and comparable than the standard line search and trust region method using the well-known BFGS formula.
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spelling upm-725362020-10-22T05:00:12Z http://psasir.upm.edu.my/id/eprint/72536/ Efficient ODE-based methods for unconstrained optimization Yap, Chui Ying Leong, Wah June This paper presents some efficient methods for unconstrained optimization based upon approximating the gradient flow of the objective function. Most ODE-based methods would generate Levenberg-Marquardt-like steps that require the solution of linear systems. On the other hand our proposed methods used some quasi-Newton matrices to approximate the solution of these linear systems, thus avoiding the solution of linear systems repeatedly. Two implementations of the modified ODE-based methods - line search and trust region implementation are proposed. Under some suitable assumptions, the convergence of the proposed methods is then established. Numerical results indicate that the modified methods are more effective and comparable than the standard line search and trust region method using the well-known BFGS formula. Faculty of Science, Universiti Putra Malaysia 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72536/1/Efficient%20ODE.pdf Yap, Chui Ying and Leong, Wah June (2018) Efficient ODE-based methods for unconstrained optimization. Discovering Mathematics, 40 (1). 31 - 45. ISSN 2231-7023 https://einspem.upm.edu.my/ojs/index.php/dismath/article/view/28
spellingShingle Yap, Chui Ying
Leong, Wah June
Efficient ODE-based methods for unconstrained optimization
title Efficient ODE-based methods for unconstrained optimization
title_full Efficient ODE-based methods for unconstrained optimization
title_fullStr Efficient ODE-based methods for unconstrained optimization
title_full_unstemmed Efficient ODE-based methods for unconstrained optimization
title_short Efficient ODE-based methods for unconstrained optimization
title_sort efficient ode-based methods for unconstrained optimization
url http://psasir.upm.edu.my/id/eprint/72536/
http://psasir.upm.edu.my/id/eprint/72536/
http://psasir.upm.edu.my/id/eprint/72536/1/Efficient%20ODE.pdf