A nonlinear conjugate gradient method with a strong global convergence property
A nonlinear conjugate gradient method with a strong global convergence property
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DOI：
10.1137/s1052623497318992
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发表时间：
1999-11-29
影响因子：
3.1
通讯作者：
Yuan, Y
中科院分区：
文献类型：
--
作者：
Dai, YH;Yuan, Y
共轭梯度法广泛用于无约束优化，特别是大规模问题。在共轭梯度法的分析和实现中通常使用强沃尔夫条件。本文提出了一种共轭梯度法的新版本，若线搜索满足标准沃尔夫条件，该方法全局收敛。对目标函数的条件也很弱，类似于佐藤迪克条件所要求的那些条件。
Conjugate gradient methods are widely used for unconstrained optimization, especially large scale problems. The strong Wolfe conditions are usually used in the analyses and implementations of conjugate gradient methods. This paper presents a new version of the conjugate gradient method, which converges globally, provided the line search satisfies the standard Wolfe conditions. The conditions on the objective function are also weak, being similar to those required by the Zoutendijk condition.