A sufficient descent LS conjugate gradient method for unconstrained optimization problems
A sufficient descent LS conjugate gradient method for unconstrained optimization problems
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DOI：
10.1016/j.amc.2011.06.034
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Min Li;Heying Feng
中科院分区：
文献类型：
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作者：
Min Li;Heying Feng
本文对Liu-Storey（LS）共轭梯度法进行了改进，提出了一种下降LS法。该方法可以为目标函数生成足够的下降方向。此属性与所使用的行搜索无关。我们证明了在强Wolfe线搜索下，改进的LS方法是全局收敛的。数值结果表明，所提出的下降LS方法是有效的CUTEr库中的无约束问题。
In this paper, we make a modification to the Liu–Storey (LS) conjugate gradient method and propose a descent LS method. The method can generate sufficient descent directions for the objective function. This property is independent of the line search used. We prove that the modified LS method is globally convergent with the strong Wolfe line search. The numerical results show that the proposed descent LS method is efficient for the unconstrained problems in the CUTEr library.