Two optimal Dai–Liao conjugate gradient methods

Two adaptive choices for the parameter of Dai–Liao conjugate gradient (CG) method are suggested. One of which is obtained by minimizing the distance between search directions of Dai–Liao method and a three-term CG method proposed by Zhang et al. and the other one is obtained by minimizing Frobenius condition number of the search direction matrix. Global convergence analyses are made briefly. Numerical results are reported; they demonstrate effectiveness of the suggested adaptive choices.     

一种修正的CD共轭梯度法及其全局收敛性

求解无约束优化问题的共轭梯度法,其搜索方向的下降性往往依赖于所采用的线性搜索.将提出一种修正的CD算法,其搜索方向d_k始终满足1-1/u≤(-g_k~Td_k)/(‖g_k‖~2)≤1+1/u(u1),即算法在不依赖任何线性搜索的情况下能始终产生充分下降方向.同时,当采用精确线性搜索时,该修正的CD算法就是标准的CD共轭梯度法.在适当条件下,还证明了修正的CD算法在强Wolfe线性搜索下具有全局收敛性.最后,我们给出了相应的数值结果,说明了算法是一种有效的算法.     

DFP变尺度法与FR共轭梯度法的二次等价性

研究了求解无约束极值问题的DFP变尺度法和FR共轭梯度法的关系问题.证明了在应用于求解二次函数的极值问题时,若将初始尺度矩阵取为单位矩阵,二者实际上是等价的,即两种方法求出的极小化点列是相同的.     

关于正定可对称化线性代数方程组的预对称正则化算法

1引言考虑线性代数方程组A_x=b,A∈R~(n×n)非奇异,x,b∈R~n(1)的求解．当系数矩阵是大型稀疏的正定可对称化矩阵,文[1,2]讨论了一类预对称共轭梯度算法(LRSCG算法是其中之一),这类算法的实质是利用非对称的系数矩阵可对称化的性质,并结合共轭梯度法而构造的一种预处理的共轭梯度法[12,16,17]．但非对称的系数     

L-曲线估计确定正则参数的双网格迭代法

本文考虑对不适定问题离散化得到的大规模不适定线性方程组进行Tiknonov正则化,然后用双网格迭代法求解得到的Tikhonov正则化方程组,并用L-曲线估计法来确定正则参数.试验问题的数值结果表明双网格迭代法求解正则化后的对称正定线性方程组效果很好,且L-曲线估计法确定正则参数计算量很小.     

Global Convergence of Conjugate Gradient Methods without Line Search

Global convergence results are derived for well-known conjugate gradient methods in which the line search step is replaced by a step whose length is determined by a formula. The results include the following cases: (1) The Fletcher–Reeves method, the Hestenes–Stiefel method, and the Dai–Yuan method applied to a strongly convex LC ¹ objective function; (2) The Polak–Ribière method and the Conjugate Descent method applied to a general, not necessarily convex, LC ¹ objective function.     

On Properties of Rectangular Hyperbolas

This paper improves an old theorem about a rectangular hyperbola : its centre lies on the pedal circle of any point on with respect to any triangle inscribed in . We also prove that an analogous result holds for Cevian circles. These results are used to obtain new characterisations of the Feuerbach, Jarabek, and Kiepert hyperbolas of a triangle.     

随机度量理论及其应用在我国最近进展的综述

本旨在全面综述随机度量理论及其应用过去十年在我国发展过程中所获得的主要结果与思想方法,本由十节组成,第一节对我们工作的背景-概率度量空间与随机度量空间理论作一简单的介绍;第二节给出某些有关随机泛函分析及取值于抽象空间的可测函数的预备知识,第三节阐明随机泛函分析与原始随机度量理论（本称之为F-随机度量理论）的整体关系,主要结果是在随机元生成空间上给出自然且合理的随机度量与随机范数的构造,从而将随机元与随机算子理论的研究纳入随机度量理论框架,主要思想是将随机泛函分析视为随机度量空间体系上的分析学而统一地发展;从而形成了发展随机泛函分析的一个新的途径-空间随机化途径;除此之外,在本节我们也从随机过程理论的观点出发首次提出对应于随机度量理论原始版本的一种新的随机共轭空间理论（叫作F-随机共轭空间理论）,它的突出优点是能保持象随机过程的样本性质这样更精细的特性（本节由作的工作构成）,在第四节,基于作最近提出的随机度量理论的一个新的版本（本称之为E-随机度量理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E-随机共轭空间理论）的基本结果进行系统整理并给以全新的处理（本节内容整体上由作最近的一篇论构成,也尤其提到朱林户等人的重要工作）,在本节我们也以相当的篇幅论述F-随机共轭空间理论与E-随机共轭空间理论的内在关系与本质差异,在下面紧跟的两节,致力于E-随机共轭空间理论深层次的结果,尤其突出了E-随机赋范模与传统的赋范空间、E-随机共轭空间与经典共轭空间之间的内在联系;在第五节给出了几类E-随机赋范模的E-随机共轭空间的表示定理（主要由作的工作,作与游兆水及林熙合作的工作,还有巩馥州与刘清荣合作的工作组成）,在六节给出完备E-随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）,尤其是第五及第六节中,我们给出随机度量理论在随机泛函分析及经典Banach空间中若干实质性的应用;第七节简要给出E-随机赋半范模及E-随机对偶系理论初步;第八节简单阐明随机度量理论与泛函分析的关系;第九节简单阐明了随机度量理论与概率度量空间理论的关系,最后在第十节结合随机度量理论,Banach空间理论及随机泛函分析对发展随机泛函分析的空间随机化途径的合理性与优越性作了进一步的分析。     

A three-parameter family of nonlinear conjugate gradient methods

In this paper, we propose a three-parameter family of conjugate gradient methods for unconstrained optimization. The three-parameter family of methods not only includes the already existing six practical nonlinear conjugate gradient methods, but subsumes some other families of nonlinear conjugate gradient methods as its subfamilies. With Powell's restart criterion, the three-parameter family of methods with the strong Wolfe line search is shown to ensure the descent property of each search direction. Some general convergence results are also established for the three-parameter family of methods. This paper can also be regarded as a brief review on nonlinear conjugate gradient methods.

     

A new globalization technique for nonlinear conjugate gradient methods for nonconvex minimization

It is well-known that the HS method and the PRP method may not converge for nonconvex optimization even with exact line search. Some globalization techniques have been proposed, for instance, the PRP+ globalization technique and the Grippo-Lucidi globalization technique for the PRP method. In this paper, we propose a new efficient globalization technique for general nonlinear conjugate gradient methods for nonconvex minimization. This new technique utilizes the information of the previous search direction sufficiently. Under suitable conditions, we prove that the nonlinear conjugate gradient methods with this new technique are globally convergent for nonconvex minimization if the line search satisfies Wolfe conditions or Armijo condition. Extensive numerical experiments are reported to show the efficiency of the proposed technique.