Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi
The conjugate gradient (CG) method is one of the optimization methods that is often used in practical applications. The continuous and numerous studies conducted on the CG method have led to vast improvements in its convergence properties and efficiency. In this project, a few CG methods are chosen...
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my-uitm-ir.778182023-05-22T04:54:43Z Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi 2021-01 Mohd Azmi, Nurfarahi Naabihah Equations Mathematical statistics. Probabilities Analytical methods used in the solution of physical problems Algorithms The conjugate gradient (CG) method is one of the optimization methods that is often used in practical applications. The continuous and numerous studies conducted on the CG method have led to vast improvements in its convergence properties and efficiency. In this project, a few CG methods are chosen to be tested under Armijo Line Search based on their efficiency and robustness. These methods are tested with a set of test functions with different variable. There are three initial points used for each method. The number of iteration and CPU time are evaluated in order to find the best method. Based on the results, LAMR method is known to be the best method compared to AMRI, NRMI, AMRO and MRM as it has quite good performance as well as it can solve 100% of the test functions 2021-01 Thesis https://ir.uitm.edu.my/id/eprint/77818/ https://ir.uitm.edu.my/id/eprint/77818/1/77818.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Zull Pakkal, Norhaslinda |
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Universiti Teknologi MARA |
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UiTM Institutional Repository |
language |
English |
advisor |
Zull Pakkal, Norhaslinda |
topic |
Equations Equations Analytical methods used in the solution of physical problems Algorithms |
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Equations Equations Analytical methods used in the solution of physical problems Algorithms Mohd Azmi, Nurfarahi Naabihah Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi |
description |
The conjugate gradient (CG) method is one of the optimization methods that is often used in practical applications. The continuous and numerous studies conducted on the CG method have led to vast improvements in its convergence properties and efficiency. In this project, a few CG methods are chosen to be tested under Armijo Line Search based on their efficiency and robustness. These methods are tested with a set of test functions with different variable. There are three initial points used for each method. The number of iteration and CPU time are evaluated in order to find the best method. Based on the results, LAMR method is known to be the best method compared to AMRI, NRMI, AMRO and MRM as it has quite good performance as well as it can solve 100% of the test functions |
format |
Thesis |
qualification_level |
Bachelor degree |
author |
Mohd Azmi, Nurfarahi Naabihah |
author_facet |
Mohd Azmi, Nurfarahi Naabihah |
author_sort |
Mohd Azmi, Nurfarahi Naabihah |
title |
Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi |
title_short |
Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi |
title_full |
Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi |
title_fullStr |
Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi |
title_full_unstemmed |
Comparative study of Conjugate Gradient methods under Armijo Line search / Nurfarahi Naabihah Mohd Azmi |
title_sort |
comparative study of conjugate gradient methods under armijo line search / nurfarahi naabihah mohd azmi |
granting_institution |
Universiti Teknologi MARA, Terengganu |
granting_department |
Faculty of Computer and Mathematical Sciences |
publishDate |
2021 |
url |
https://ir.uitm.edu.my/id/eprint/77818/1/77818.pdf |
_version_ |
1783736172115132416 |