不可微合成函数的极小化方法

本文提出了一种极小化不可微合成函数的下降算法。该算法通过内部迭代寻找下降方向，每次内部迭代求解一个二次规划。外部迭代点不精确线搜索求得，算法在有限步内得到近似平稳点，经过适当修正后，算法全局收敛到平衡点。     

Hilbert尺度下求解非线性不适定问题的多水平迭代法

本文吸取了多水平方法的思想，采用多水平方法提供了离散化参数和迭代初值的合理的选择方法，提出了Hilbert尺度下求解非线性不适定问题的多水平Landweber迭代算法，并给出了算法的收敛性分析，证明了算法在整体上提高了Hilbert尺度下的Landweber迭代法的迭代效率。     

离散信道容量的迭代算法

引入信息量偏差概念，给出平均交互信息量关于输入概率的增量公式，设计出离散信道容量线性乘法迭代和线性常系数迭代算法，它们都优于现有的指数迭代算法．并证明在所有单步迭代算法中它们几乎是最好的算法.     

无约束优化的超记忆梯度算法及其收敛特征

研究一种新的无约束优化超记忆梯度算法，算法在每步迭代中充分利用前面迭代点的信息产生下降方向，利用Wolfe线性搜索产生步长，在较弱的条件下证明了算法的全局收敛性。新算法在每步迭代中不需计算和存储矩阵，适于求解大规模优化问题。     

一类教育最优投资模型的快速瓶颈消除算法

本提出了一类教育最优投资模型的快速瓶颈消除算法，给出了算法的思想和具体迭代过程，对算法的最优性进行了证明。最后通过实例给出了算法直观的表上作业法。该算法迭代次数非常少，是一种实用的好算法。     

一种提取小波脊线的迭代算法

在实际问题中，经常会需要识别信号的调制类型．小波脊线就包含了信号的重要特征．本文给出了提取小波脊线的一种迭代算法，以达到识别信号的目的，并将该算法与其他算法比较，表明迭代算法是一种行之有效的方法．     

非线性不等式约束最优化一个超线性与二次收敛的强次可行方法

本文讨论非线性不等式约束最优化问题，借助于序列线性方程组技术和强次可行方法思想，建立了问题的一个初始点任意的快速收敛新算法．在每次迭代中，算法只需解一个结构简单的线性方程组．算法的初始迭代点不仅可以是任意的，而且不使用罚函数和罚参数，在迭代过程中，迭代点列的可行性单调不减．在相对弱的假设下，算法具有较好的收敛性和收敛速度，即具有整体与强收敛性，超线性与二次收敛性．文中最后给出一些数值试验结果．     

一类非单调线性互补问题的高阶Dikin型仿射尺度算法

对于一类非单调线性互补问题提出了一个新算法：高阶Dikin型仿射尺度算法，算法的每步迭代．基于线性规划Dikin原始-对偶算法思想来求解一个线性方程组得到迭代方向，再适当选取步长，得到了算法的多项式复杂性。     

一个基于近似下降方向的免梯度算法

本构造一个求解非线性无约束优化问题的免梯度算法，该算法基于传统的模矢法，每次不成功迭代后，充分利用已有迭代点的信息，构造近似下降方向，产生新的迭代点。在较弱条件下，算法是总体收敛的。通过数值实验与传统模矢法相比，计算量明显减少。     

二次规划的内椭球算法

对于标准型的凸二次规划问题本文给出了一个新算法，算法的一每步迭代，利用内椭球的思想来近似求解一个线性质规划子问题而得到迭代方向，再适当选取步长而使之成为多项式算法，其迭代步数为Ｏ（ｎＬ＾２），每一步迭代所需计算量为Ｏ（ｎ＾３）。其中ｎ为变量个数，Ｌ为问题的输入长度。     

无约束最优化问题中一类新黄族及其性质

本文提出了一类新的用于解决无约束最优化问题的拟牛顿方法,并证明了这样的性质,在 精确线性搜索条件下,每一步该族所有方法所产生的迭代方向和迭代点列仅依赖于参数ρ.该方 法可视为拟牛顿方法中黄族的推广.     

A class of globally convergent conjugate gradient methods

Conjugate gradient methods are very important ones for solving nonlinear optimization problems, especially for large scale problems. However, unlike quasi-Newton methods, conjugate gradient methods were usually analyzed individually. In this paper, we propose a class of conjugate gradient methods, which can be regarded as some kind of convex combination of the Fletcher-Reeves method and the method proposed by Dai et al. To analyze this class of methods, we introduce some unified tools that concern a general method with the scalar βk having the form of φk/φk-1. Consequently, the class of conjugate gradient methods can uniformly be analyzed.     

Multi-step hybrid methods for special second-order differential equations y″(t) = f(t,y(t))

In this paper, multi-step hybrid methods for solving special second-order differential equations y^″(t) = f(t,y(t)) are presented and studied. The new methods inherit the frameworks of RKN methods and linear multi-step methods and include two-step hybrid methods proposed by Coleman (IMA J. Numer. Anal. 23, 197–220, 8) as special cases. The order conditions of the methods were derived by using the SN-series defined on the set SNT of SN-trees. Based on the order conditions, we construct two explicit four-step hybrid methods, which are convergent of order six and seven, respectively. Numerical results show that our new methods are more efficient in comparison with the well-known high quality methods proposed in the scientific literature.     

Generalization of Backward Differentiation Formulas for Parallel Computers

We analyze an extension of backward differentiation formulas, used as boundary value methods, that generates a class of methods with nice stability and convergence properties. These methods are obtained starting from the boundary value GBDFs class, and are in the class of EBDF-type methods. We discuss different ways of using these linear multistep formulas in order to have efficient parallel implementations. Numerical experiments show their effectiveness.     

Smooth methods of multipliers for complementarity problems

This paper describes several methods for solving nonlinear complementarity problems. A general duality framework for pairs of monotone operators is developed and then applied to the monotone complementarity problem, obtaining primal, dual, and primal-dual formulations. We derive Bregman-function-based generalized proximal algorithms for each of these formulations, generating three classes of complementarity algorithms. The primal class is well-known. The dual class is new and constitutes a general collection of methods of multipliers, or augmented Lagrangian methods, for complementarity problems. In a special case, it corresponds to a class of variational inequality algorithms proposed by Gabay. By appropriate choice of Bregman function, the augmented Lagrangian subproblem in these methods can be made continuously differentiable. The primal-dual class of methods is entirely new and combines the best theoretical features of the primal and dual methods. Some preliminary computation shows that this class of algorithms is effective at solving many of the standard complementarity test problems. Received February 21, 1997 / Revised version received December 11, 1998? Published online May 12, 1999     

Iterative algorithms for large partitioned linear systems,with applications to image reconstruction

We present a unifying framework for a wide class of iterative methods in numerical linear algebra. In particular, the class of algorithms contains Kaczmarz's and Richardson's methods for the regularized weighted least squares problem with weighted norm. The convergence theory for this class of algorithms yields as corollaries the usual convergence conditions for Kaczmarz's and Richardson's methods. The algorithms in the class may be characterized as being group-iterative, and incorporate relaxation matrices, as opposed to a single relaxation parameter. We show that some well-known iterative methods of image reconstruction fall into the class of algorithms under consideration, and are thus covered by the convergence theory. We also describe a novel application to truly three-dimensional image reconstruction.     

Explicit symplectic multidimensional exponential fitting modified Runge-Kutta-Nyström methods

This paper is concerned with multidimensional exponential fitting modified Runge-Kutta-Nyström (MEFMRKN) methods for the system of oscillatory second-order differential equations q″(t)+Mq(t)=f(q(t)), where M is a d×d symmetric and positive semi-definite matrix and f(q) is the negative gradient of a potential scalar U(q). We formulate MEFMRKN methods and show clearly the relationship between MEFMRKN methods and multidimensional extended Runge-Kutta-Nyström (ERKN) methods proposed by Wu et al. (Comput. Phys. Comm. 181:1955–1962, 2010). Taking into account the fact that the oscillatory system is a separable Hamiltonian system with Hamiltonian \(H(p,q)=\frac{1}{2}p^{T}p+ \frac{1}{2}q^{T}Mq+U(q)\), we derive the symplecticity conditions for the MEFMRKN methods. Two explicit symplectic MEFMRKN methods are proposed. Numerical experiments accompanied demonstrate that our explicit symplectic MEFMRKN methods are more efficient than some well-known numerical methods appeared in the scientific literature.     

A CLASS OF GENERALIZED MULTISPLITTING RELAXATION METHODS FOR LINEAR COMPLEMENTARITY PROBLEMS

In this paper,a class of generalized parallel matrix multisplitting relaxation methods for solving linear complementarity problems on the high-speed multiprocessor systems is set up. This class of methods not only includes all the existing relaxation methods for the linear complementarity problems ,but also yields a lot of novel ones in the sense of multisplittlng. We establish the convergence theories of this class of generalized parallel multisplitting relaxation methods under the condition that the system matrix is an H-metrix with positive diagonal elements.     

Natural Runge-Kutta and projection methods

Summary Recently the author defined the class of natural Runge-Kutta methods and observed that it includes all the collocation methods. The present paper is devoted to a complete characterization of this class and it is shown that it coincides with the class of the projection methods in some polynomial spaces.This work was supported by the Italian Ministero della Pubblica Istruzione, funds 40%     

一类多值多导数方法的收敛性与稳定性

1 引言 对于多值多导数方法,由于其多值多导的结构特点有利于提高解的精度,以及其包容性大,它包含了当今常用的多种常微数值方法,诸如:线性多步法,单支方法,多步多导方法,多(单)步Runge—Kutta方法,多导Runge-Kutta方法以及混合方法等.因此收敛性与稳定性的研究具有重要的实践意义和广泛的理论指导意义,也正因如此,这方面的研究工作引起了众多数值工作者们的兴趣,近年来,多值多导法求解刚性问题的B—收敛及其非线性稳定性的研究工作巳获得较大进展,其相应成果可参见文献[1—3],在文献[4,5]中笔者则针对Banach空间中一类非刚性问题-K~((p))类问题,分别探讨了多步多导法及单支方法的收敛性