Convergence analysis of a new coefficient conjugate gradient method under exact line search

Maulana Malik; Mustafa Mamat; Siti Sabariah Abas; Sukono

Convergence analysis of a new coefficient conjugate gradient method under exact line search

Maulana Malik, Mustafa Mamat, Siti Sabariah Abas, Sukono

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

Conjugate gradient (CG) methods were instrumental in solving unconstrained, wide-ranging optimization. In this paper, we propose a new CG coefficient family, which holds conditions of sufficient descent and global convergence properties. Under exact line search this new CG is evaluated on a set of functions. Based on number of iterations (NOI) and central processing unit (CPU) time, it then compared its output with that of some of the well-known previous CG methods. The results show that of all the methods tested, the latest CG method has the best performance.

Original language	English
Pages (from-to)	187-198
Number of pages	12
Journal	International Journal of Advanced Science and Technology
Volume	29
Issue number	5
Publication status	Published - 10 Apr 2020

Keywords

Conjugate Gradient Coefficient
Conjugate Gradient Method
Exact Line Search
Global Convergence
Sufficient Descent

Cite this

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abstract = "Conjugate gradient (CG) methods were instrumental in solving unconstrained, wide-ranging optimization. In this paper, we propose a new CG coefficient family, which holds conditions of sufficient descent and global convergence properties. Under exact line search this new CG is evaluated on a set of functions. Based on number of iterations (NOI) and central processing unit (CPU) time, it then compared its output with that of some of the well-known previous CG methods. The results show that of all the methods tested, the latest CG method has the best performance.",

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AU - Abas, Siti Sabariah

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N2 - Conjugate gradient (CG) methods were instrumental in solving unconstrained, wide-ranging optimization. In this paper, we propose a new CG coefficient family, which holds conditions of sufficient descent and global convergence properties. Under exact line search this new CG is evaluated on a set of functions. Based on number of iterations (NOI) and central processing unit (CPU) time, it then compared its output with that of some of the well-known previous CG methods. The results show that of all the methods tested, the latest CG method has the best performance.

AB - Conjugate gradient (CG) methods were instrumental in solving unconstrained, wide-ranging optimization. In this paper, we propose a new CG coefficient family, which holds conditions of sufficient descent and global convergence properties. Under exact line search this new CG is evaluated on a set of functions. Based on number of iterations (NOI) and central processing unit (CPU) time, it then compared its output with that of some of the well-known previous CG methods. The results show that of all the methods tested, the latest CG method has the best performance.

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KW - Conjugate Gradient Method

KW - Exact Line Search

KW - Global Convergence

KW - Sufficient Descent

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Convergence analysis of a new coefficient conjugate gradient method under exact line search

Abstract

Keywords

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