无约束优化问题的混合共轭梯度法

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI:10.1109/CIS.2017.00121

B. Qiao, Liping Yang, Jie Liu, Yanru Yao

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引用次数: 0

摘要

本文在HS法和DY法的基础上，提出了一种求解无约束优化问题的混合共轭梯度法。这种新方法利用了两种方法的优点。证明了混合共轭梯度法在不需要下降条件的Wolfe线搜索下的全局收敛性。对一些经典问题的数值实验结果表明，该方法是有效的。

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A Mixed Conjugate Gradient Method for Unconstrained Optimization Problem

In this paper, we propose a mixed conjugate gradient method for unconstrained optimization problem based on the HS method and DY method. The new method has taken advantages of two methods. The global convergence of the mixed conjugate gradient method is proved under the Wolfe line search which is no need for the descent condition. The numerical experimental results on some classical problems show that the new method is efficient.

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2017 13th International Conference on Computational Intelligence and Security (CIS)

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