Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal

Conjugate gradient method is an efficient technique to solve unconstrained optimization problem. This method was proposed by Magnus Hestenes and Eduard Stiefel in 1952. For our research, it focus on non-classical which is Fletcher-Reeves (FR) and PRP for hybrid conjugate gradient, modified, scaled a...

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Main Authors:	Daud, Nik Nurshahfini, Mohamad Fadzal, Farah Ellyna
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Mathematical statistics Probabilities Analysis Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/39689/1/39689.pdf
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id	my-uitm-ir.39689
record_format	uketd_dc
spelling	my-uitm-ir.396892020-12-27T05:04:01Z Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal 2019-07 Daud, Nik Nurshahfini Mohamad Fadzal, Farah Ellyna Mathematical statistics. Probabilities Analysis Algorithms Conjugate gradient method is an efficient technique to solve unconstrained optimization problem. This method was proposed by Magnus Hestenes and Eduard Stiefel in 1952. For our research, it focus on non-classical which is Fletcher-Reeves (FR) and PRP for hybrid conjugate gradient, modified, scaled and parametrized methods. Hybrid conjugate gradient is the combination of attractive features of known conjugate gradient such as PRP and FR. Modified is where the existing numerator and denominator is modified with new terms. Scaled is when a parameter is added at search direction. Parametrized is added a parameter to a classical conjugate gradient. To find the best method, we compare the methods in terms of its efficiency and robustness. Efficiency is measured by the number of iteration and CPU time. Whereas, robustness is the ability of method to solve the most problems or test function than other methods. As a conclusion, from this study, we could determine what are the factors could make PRP-FR Hybrid, Scaled, Modified and Parametrized methods more efficient and more robust. 2019-07 Thesis https://ir.uitm.edu.my/id/eprint/39689/ https://ir.uitm.edu.my/id/eprint/39689/1/39689.pdf text en public degree Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences Jusoh, Ibrahim
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jusoh, Ibrahim
topic	Mathematical statistics Probabilities Analysis Algorithms
spellingShingle	Mathematical statistics Probabilities Analysis Algorithms Daud, Nik Nurshahfini Mohamad Fadzal, Farah Ellyna Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal
description	Conjugate gradient method is an efficient technique to solve unconstrained optimization problem. This method was proposed by Magnus Hestenes and Eduard Stiefel in 1952. For our research, it focus on non-classical which is Fletcher-Reeves (FR) and PRP for hybrid conjugate gradient, modified, scaled and parametrized methods. Hybrid conjugate gradient is the combination of attractive features of known conjugate gradient such as PRP and FR. Modified is where the existing numerator and denominator is modified with new terms. Scaled is when a parameter is added at search direction. Parametrized is added a parameter to a classical conjugate gradient. To find the best method, we compare the methods in terms of its efficiency and robustness. Efficiency is measured by the number of iteration and CPU time. Whereas, robustness is the ability of method to solve the most problems or test function than other methods. As a conclusion, from this study, we could determine what are the factors could make PRP-FR Hybrid, Scaled, Modified and Parametrized methods more efficient and more robust.
format	Thesis
qualification_level	Bachelor degree
author	Daud, Nik Nurshahfini Mohamad Fadzal, Farah Ellyna
author_facet	Daud, Nik Nurshahfini Mohamad Fadzal, Farah Ellyna
author_sort	Daud, Nik Nurshahfini
title	Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal
title_short	Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal
title_full	Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal
title_fullStr	Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal
title_full_unstemmed	Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal
title_sort	comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / nik nurshahfini daud and farah ellyna mohamad fadzal
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/39689/1/39689.pdf
_version_	1783734529914044416

Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal

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