Comparison of conjugate gradient method on solving unconstrained optimization problems
Conjugate gradient (CG) method approaches have been instrumental in solving unconstrained optimization problems. In 2020, Malik et al. have proposed a new hybrid coefficient (H-MS2), a combination of the RMIL coefficient and the new coefficient. In this paper, we propose the new method, which take...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/1823/1/FH03-FIK-20-42148.pdf http://eprints.unisza.edu.my/1823/2/FH03-FIK-20-42149.pdf http://eprints.unisza.edu.my/1823/ |
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| Summary: | Conjugate gradient (CG) method approaches have been instrumental in solving unconstrained optimization problems.
In 2020, Malik et al. have proposed a new hybrid coefficient (H-MS2), a combination of the RMIL coefficient and
the new coefficient. In this paper, we propose the new method, which takes the new coefficients from H-MS2. Also,
we will compare the new method and some of the classic methods that already based on the number of iterations and
central processing unit (CPU) time. The new method fulfills the sufficient descent condition and global convergence
properties, and it’s tested on a set functions under exact line search. The numerical results show that the new CG
method has the best efficiency between all the methods tested. |
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