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您的检索式：作者名="KOU CaiXia"
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| 1 | A Barzilai-Borwein conjugate gradient method显示文摘The linear conjugate gradient method is an optimal method for convex quadratic minimization due to the Krylov subspace minimization property. The proposition of limited-memory BFGS method and Barzilai-Borwein gradient method, however, heavily restricted the use of conjugate gradient method for largescale nonlinear optimization. This is, to the great extent, due to the requirement of a relatively exact line search at each iteration and the loss of conjugacy property of the search directions in various occasions. On the contrary, the limited-memory BFGS method and the Barzilai-Bowein gradient method share the so-called asymptotical one stepsize per line-search property, namely, the trial stepsize in the method will asymptotically be accepted by the line search when the iteration is close to the solution. This paper will focus on the analysis of the subspace minimization conjugate gradient method by Yuan and Stoer(1995). Specifically, if choosing the parameter in the method by combining the Barzilai-Borwein idea, we will be able to provide some efficient Barzilai-Borwein conjugate gradient(BBCG) methods. The initial numerical experiments show that one of the variants, BBCG3, is specially efficient among many others without line searches. This variant of the BBCG method might enjoy the asymptotical one stepsize per line-search property and become a strong candidate for large-scale nonlinear optimization. | DAI YuHong KOU CaiXia | 2016 | Science China Mathematics2016，59，8： | 4 |
| 2 | An improved nonlinear conjugate gradient method with an optimal property显示文摘Conjugate gradient methods have played a special role in solving large scale nonlinear problems.Recently,the author and Dai proposed an efcient nonlinear conjugate gradient method called CGOPT,through seeking the conjugate gradient direction closest to the direction of the scaled memoryless BFGS method.In this paper,we make use of two types of modified secant equations to improve CGOPT method.Under some assumptions,the improved methods are showed to be globally convergent.Numerical results are also reported. | KOU CaiXia | 2014 | Science China Mathematics2014，57，3： | 2 |
| 3 | AN AUGMENTED LAGRANGIAN TRUST REGION METHOD WITH A BI-OBJECT STRATEGY显示文摘有双性人目标策略的一个扩充 Lagrangian 信任区域方法被建议因为解决非线性的平等抑制了优化,它在惩罚类型方法和没有惩罚的之间掉落。在每次重复,试用步被在一个信任区域以内最小化一个二次的近似模型到扩充 Lagrangian 功能计算。模型是为非强迫的优化的标准信任区域 subproblem 并且能被许多存在方法高效地因此解决。选择惩罚参数,辅助信任区域 subproblem 与限制违背有关被介绍。结果,惩罚参数不必 monotonically 正在增加并且不趋于到无穷。双性人目标策略,与客观功能和限制违背的措施有关,被利用决定试用步是否将被接受。方法的全球集中在温和假设下面被建立。数字实验被做,它在各种各样的困难的状况上说明算法的效率。 | Caixia Kou Zhongwen Chen Yuhong Dai Haifei Han | 2018 | Journal of Computational Mathematics2018，36，3： | 1 |
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