Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan

Optimization problems always arise in daily life and in fields such as economics, management science, and engineering, where design and performance optimization are common objectives. Industrial applications also focus on maximizing efficiency and minimizing costs. This research explores the applica...

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Main Author:	Anizan, Nurfatihah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/106016/1/106016.pdf
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id	my-uitm-ir.106016
record_format	uketd_dc
spelling	my-uitm-ir.1060162024-11-30T22:59:43Z Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan 2024 Anizan, Nurfatihah Algorithms Optimization problems always arise in daily life and in fields such as economics, management science, and engineering, where design and performance optimization are common objectives. Industrial applications also focus on maximizing efficiency and minimizing costs. This research explores the application of the Scaled Conjugate Gradient (SCG) method, utilizing a strong Wolfe line search, in the context of portfolio selection. The study systematically compares four SCG methods which are RMIL, AMRI, NMR, and LAMR, to identify the most effective approach. These methods are tested using 20 test functions with different variables also with four initial points have been for each variable. In order to find the best method, the iteration number (NOI) and CPU time are computed. Numerically, SLAMR outperforms others by solving all the test functions, followed by SAMRI (99.06%), SRMIL (91.25%), and SNMR (81.56%). LAMR is the most effective algorithm among those compared, balancing quick problem-solving and solving a larger number of problems. Based on the results, the Scaled LAMR method is the most robust method among others as it can solve 100% of the test functions. Then, the scaled LAMR method is applied in portfolio selection problems, to determine the more suitable investment between CIMB Bank and Maybank. The results showed that CIMB Bank has a lower risk than Maybank, making it the better investment. This study demonstrates the SLAMR method's effectiveness in solving portfolio selection problems. 2024 Thesis https://ir.uitm.edu.my/id/eprint/106016/ https://ir.uitm.edu.my/id/eprint/106016/1/106016.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Zull Pakkal, Norhaslinda
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Zull Pakkal, Norhaslinda
topic	Algorithms
spellingShingle	Algorithms Anizan, Nurfatihah Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
description	Optimization problems always arise in daily life and in fields such as economics, management science, and engineering, where design and performance optimization are common objectives. Industrial applications also focus on maximizing efficiency and minimizing costs. This research explores the application of the Scaled Conjugate Gradient (SCG) method, utilizing a strong Wolfe line search, in the context of portfolio selection. The study systematically compares four SCG methods which are RMIL, AMRI, NMR, and LAMR, to identify the most effective approach. These methods are tested using 20 test functions with different variables also with four initial points have been for each variable. In order to find the best method, the iteration number (NOI) and CPU time are computed. Numerically, SLAMR outperforms others by solving all the test functions, followed by SAMRI (99.06%), SRMIL (91.25%), and SNMR (81.56%). LAMR is the most effective algorithm among those compared, balancing quick problem-solving and solving a larger number of problems. Based on the results, the Scaled LAMR method is the most robust method among others as it can solve 100% of the test functions. Then, the scaled LAMR method is applied in portfolio selection problems, to determine the more suitable investment between CIMB Bank and Maybank. The results showed that CIMB Bank has a lower risk than Maybank, making it the better investment. This study demonstrates the SLAMR method's effectiveness in solving portfolio selection problems.
format	Thesis
qualification_level	Bachelor degree
author	Anizan, Nurfatihah
author_facet	Anizan, Nurfatihah
author_sort	Anizan, Nurfatihah
title	Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
title_short	Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
title_full	Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
title_fullStr	Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
title_full_unstemmed	Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan
title_sort	scaled conjugate gradient using strong wolfe line search for portfolio selection / nurfatihah anizan
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/106016/1/106016.pdf
_version_	1818588166898057216

Scaled Conjugate Gradient using strong Wolfe line search for portfolio selection / Nurfatihah Anizan

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