摘要
给出了一种新的求解非线性无约束优化问题的共轭梯度法,我们证明了该方法对相应的算法具有全局收敛性,同时我们还证明了该方法在强Wolfe线搜索下具有充分下降性.并且本算法给出了比较好的数值结果.
In this paper,we present a new conjugate gradient method for unconstrained optimization based on Fletcher-Reeves algorithms and proved that with the strong Wolfe line search the new method can support the global convergence result. At the same time, the new conjugate gradient algorithm satisfies the sufficient descent property. We also obtainded better numerical results by this algorithm.
出处
《首都师范大学学报(自然科学版)》
2006年第3期1-4,共4页
Journal of Capital Normal University:Natural Science Edition
基金
国家自然科学基(60472071)
北京市教委科研基金(KM200510028019)资助
关键词
无约束最优化
共轭梯度法
强WOLFE线性搜索
全局收敛性
unconstrained optimization, conjugate gradient method, strong Wolfe line search, global convergence.