A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems
The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. This paper proposes a conjugate gradient method which is similar to Dai-Liao conjugate gradient method (Dai and Liao, 2001)...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/730454 |
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| Summary: | The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear
optimization problems due to the simplicity of their very low memory requirements. This paper
proposes a conjugate gradient method which is similar to Dai-Liao conjugate gradient method (Dai and Liao, 2001)
but has stronger convergence properties. The given method possesses the sufficient descent condition,
and is globally convergent under strong Wolfe-Powell (SWP) line search for general function. Our
numerical results show that the proposed method is very efficient for the test problems. |
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| ISSN: | 1110-757X 1687-0042 |