Badereddine SELLAMI and Yacine CHAIB (2016) New conjugate gradient method for unconstrained optimization. RAIRO Operations Research , (), 1-16, EDP Sciences
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Abstract
In this paper, a new conjugate gradient method is pro-
posed for large-scale unconstrained optimization. This method includes
the already existing three practical nonlinear conjugate gradient meth-
ods, which produces a descent search direction at every iteration and
converges globally provided that the line search satis?es the Wolfe con-
ditions. The numerical experiments are done to test the e?ciency of
the new method, which con?rms the promising potentials of the new
method.
posed for large-scale unconstrained optimization. This method includes
the already existing three practical nonlinear conjugate gradient meth-
ods, which produces a descent search direction at every iteration and
converges globally provided that the line search satis?es the Wolfe con-
ditions. The numerical experiments are done to test the e?ciency of
the new method, which con?rms the promising potentials of the new
method.
Information
| Item Type | Journal |
|---|---|
| Divisions |
» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology |
| ePrint ID | 515 |
| Date Deposited | 2016-04-01 |
| Further Information | Google Scholar |
| URI | https://univ-soukahras.dz/en/publication/article/515 |
BibTex
@article{uniusa515,
title={New conjugate gradient method for unconstrained optimization},
author={Badereddine SELLAMI and Yacine CHAIB},
journal={RAIRO Operations Research}
year={2016},
volume={},
number={},
pages={1-16},
publisher={EDP Sciences}
}
title={New conjugate gradient method for unconstrained optimization},
author={Badereddine SELLAMI and Yacine CHAIB},
journal={RAIRO Operations Research}
year={2016},
volume={},
number={},
pages={1-16},
publisher={EDP Sciences}
}