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Volume 11, Issue 2
An Efficient Mixed Conjugate Gradient Method for Solving Unconstrained Optimisation Problems

P. Mtagulwa & P. Kaelo

DOI: 10.4208/eajam.140720.251220

East Asian J. Appl. Math., 11 (2021), pp. 421-434.

Published online: 2021-02

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  • Abstract

Conjugate gradient algorithms are most commonly used to solve large scale unconstrained optimisation problems. They are simple and do not require the computation and/or storage of the second derivative information about the objective function. We propose a new conjugate gradient method and establish its global convergence under suitable assumptions. Numerical examples demonstrate the efficiency and effectiveness of our method.

  • Keywords

Global convergence, conjugate gradient method, sufficient descent, strong Wolfe conditions.

  • AMS Subject Headings

90C06, 90C30, 65K05, 65K10

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{EAJAM-11-421, author = {Mtagulwa , P. and Kaelo , P.}, title = {An Efficient Mixed Conjugate Gradient Method for Solving Unconstrained Optimisation Problems}, journal = {East Asian Journal on Applied Mathematics}, year = {2021}, volume = {11}, number = {2}, pages = {421--434}, abstract = {

Conjugate gradient algorithms are most commonly used to solve large scale unconstrained optimisation problems. They are simple and do not require the computation and/or storage of the second derivative information about the objective function. We propose a new conjugate gradient method and establish its global convergence under suitable assumptions. Numerical examples demonstrate the efficiency and effectiveness of our method.

}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.140720.251220}, url = {http://global-sci.org/intro/article_detail/eajam/18642.html} }
TY - JOUR T1 - An Efficient Mixed Conjugate Gradient Method for Solving Unconstrained Optimisation Problems AU - Mtagulwa , P. AU - Kaelo , P. JO - East Asian Journal on Applied Mathematics VL - 2 SP - 421 EP - 434 PY - 2021 DA - 2021/02 SN - 11 DO - http://doi.org/10.4208/eajam.140720.251220 UR - https://global-sci.org/intro/article_detail/eajam/18642.html KW - Global convergence, conjugate gradient method, sufficient descent, strong Wolfe conditions. AB -

Conjugate gradient algorithms are most commonly used to solve large scale unconstrained optimisation problems. They are simple and do not require the computation and/or storage of the second derivative information about the objective function. We propose a new conjugate gradient method and establish its global convergence under suitable assumptions. Numerical examples demonstrate the efficiency and effectiveness of our method.

P. Mtagulwa & P. Kaelo. (2021). An Efficient Mixed Conjugate Gradient Method for Solving Unconstrained Optimisation Problems. East Asian Journal on Applied Mathematics. 11 (2). 421-434. doi:10.4208/eajam.140720.251220
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