一类变尺度算法的收敛性质

本文讨论一类变尺度算法的收敛性质,在一定条件下,证明了 Huang 算法类、吴方和桂湘云算法类及 Flachs 算法类的收敛性与超线性收敛性.特别,还证明了一类带有非精确线性搜索的算法之收敛性与超线性收敛性.     

凸约束优化的非单调信赖域算法的收敛性

本文对凸约束优化问题提出一类新的非单调信赖域算法，在二次模型Hesse矩阵{Bk}一致有界条件下，证明了算法具有强收敛性；在{Bk}线性增长的条件下，证明了算法具有弱收敛性；这推广了现有约束或凸约束优化问题的各种信赖域算法，改进了收敛性结果。     

关于补几何规划算法收敛性的注记

文[1]提出了标准形式的补几何规划(SOGF)的一个算法,并在某种假定下证明了算法的收敛性.文[3]在一个大大减弱了的条件下证明了算法的收敛性,其中的一个主要定理是证明了标准形式的补几何规划的K-T点和一     

关于单降点到集映象族一般算法收敛性条件的改进

[1]给出了两个单降点到集映象簇的一般算法。在比[1]较弱的条件下,我们证明了算法的收敛性。对一般的点到集映象簇,我们定义了两个算法。在较弱的条件下,也证明了算法的收敛性。     

求解非线性互补问题的逐次逼近拟牛顿法的收敛性分析

提出了求解非线性互补问题的一个逐次逼近拟牛顿算法。在适当的假设下,证明了该算法的全局收敛性和局部超线性收敛性。     

半无限规划的一个序列线性方程组方法

本基于离散技术，给出了任意初始点下的半无限规划的一个序列线性方程组算法和算法的全局收敛性的证明。并在一定的假设下，证明了算法的一步超线性收敛性。     

求解伪单调变分不等式的两种投影算法

本文提出了两种求解伪单调变分不等式的定步长的投影算法．这与Solodov ＆ Tseng(1996)和He(1997)的变步长策略不同．我们证明了算法的全局收敛性，并且还在一定条件下证明了算法的Q-线性收敛性．     

一类带Wolfe条件的修改的Broyden算法

本文提出一类带Wolfe条件的修改的Broyden算法,证明了在一定条件下,算法具有整体收敛性、超线性收敛率和二阶收敛性,及Broyden算法的一些收敛性质。1.算法     

不等式约束优化的一个超线性收敛FSSLE算法

本文针对不等式约束优化问题,提出了一个可行序列线性方程组(FSSLE)算法.该算法每次迭代只需求解四个具有相同系数矩阵的线性方程组,因而计算量较小.在没有假设算法产生的聚点是孤立点和近似乘子列有界的条件下,证明了算法具有全局收敛性.在一般条件下,证明了算法具有超线性收敛性.     

修正Hestenes-Stiefel共轭梯度算法

本文探讨了Hestenes－Stiefel（HS）共轭梯度算法的收敛性条件．在无充分下降性条件下，证明了一种修正的HS共轭梯度算法的整体收敛性．     

Constrained interpolation and smoothing

Numerical and theoretical questions related to constrained interpolation and smoothing are treated. The prototype problem is that of finding the smoothest convex interpolant to given univariate data. Recent results have shown that this convex programming problem with infinite constraints can be recast as a finite parametric nonlinear system whose solution is closely related to the second derivative of the desired interpolating function. This paper focuses on the analysis of numerical techniques for solving the nonlinear system and on the theoretical issues that arise when certain extensions of the problem are considered. In particular, we show that two standard iteration techniques, the Jacobi and Gauss-Seidel methods, are globally convergent when applied to this problem. In addition we use the problem structure to develop an efficient implementation of Newton's method and observe consistent quadratic convergence. We also develop a theory for the existence, uniqueness, and representation of solutions to the convex interpolation problem with nonzero lower bounds on the second derivative (strict convexity). Finally, a smoothing spline analogue to the convex interpolation problem is studied with reference to the computation of convex approximations to noisy data.     

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     

Non-polynomial quintic spline solution for the system of third order boundary-value problems

In this paper we develop a non-polynomial quintic spline function to approximate the solution of third order linear and non-linear boundary value problems associated with odd-order obstacle problems. Such problems arise in physical oceanography and can be studied in the framework of variational inequality theory. The class of methods are second and fourth order convergent. End equations of the splines are derived and truncation error is obtained. Two numerical examples are given to illustrate the applicability and efficiency of proposed method. It is shown that the new method gives approximations, which are better than those produced by other methods.     

Construction of solutions for mixed hyperbolic elliptic Riemann initial value system of conservation laws

In this paper, we apply Adomian decomposition method (ADM) to develop a fast and accurate algorithm for systems of conservation laws of mixed hyperbolic elliptic type. The solutions of our model equations are calculated in the form of convergent power series with easily computable components. The results obtained are compared with our Modification of Adomian decomposition method (MADM) Az-Zo’bi and Al-Khaled (2010) [1]. The study outlines the significant features of the two methods. With application to van der Waals system, we obtain the stability of the approximate solutions graphically when the system changes type with more efficiency of the MADM.     

A convergent decomposition algorithm for support vector machines

In this work we consider nonlinear minimization problems with a single linear equality constraint and box constraints. In particular we are interested in solving problems where the number of variables is so huge that traditional optimization methods cannot be directly applied. Many interesting real world problems lead to the solution of large scale constrained problems with this structure. For example, the special subclass of problems with convex quadratic objective function plays a fundamental role in the training of Support Vector Machine, which is a technique for machine learning problems. For this particular subclass of convex quadratic problem, some convergent decomposition methods, based on the solution of a sequence of smaller subproblems, have been proposed. In this paper we define a new globally convergent decomposition algorithm that differs from the previous methods in the rule for the choice of the subproblem variables and in the presence of a proximal point modification in the objective function of the subproblems. In particular, the new rule for sequentially selecting the subproblems appears to be suited to tackle large scale problems, while the introduction of the proximal point term allows us to ensure the global convergence of the algorithm for the general case of nonconvex objective function. Furthermore, we report some preliminary numerical results on support vector classification problems with up to 100 thousands variables.     

A globally convergent derivative-free method for solving large-scale nonlinear monotone equations

In this paper, we propose two derivative-free iterative methods for solving nonlinear monotone equations, which combines two modified HS methods with the projection method in Solodov and Svaiter (1998) [5]. The proposed methods can be applied to solve nonsmooth equations. They are suitable to large-scale equations due to their lower storage requirement. Under mild conditions, we show that the proposed methods are globally convergent. The reported numerical results show that the methods are efficient.     

A Class of Nonmonotone Conjugate Gradient Methods for Unconstrained Optimization

In this paper, we introduce a class of nonmonotone conjugate gradient methods, which include the well-known Polak–Ribière method and Hestenes–Stiefel method as special cases. This class of nonmonotone conjugate gradient methods is proved to be globally convergent when it is applied to solve unconstrained optimization problems with convex objective functions. Numerical experiments show that the nonmonotone Polak–Ribière method and Hestenes–Stiefel method in this nonmonotone conjugate gradient class are competitive vis-à-vis their monotone counterparts.     

Two algorithms for global optimization

In this paper, we develop and compare two methods for solving the problem of determining the global maximum of a function over a feasible set. The two methods begin with a random sample of points over the feasible set. Both methods then seek to combine these points into “regions of attraction” which represent subsets of the points which will yield the same local maximums when an optimization procedure is applied to points in the subset. The first method for constructing regions of attraction is based on approximating the function by a mixture of normal distributions over the feasible region and the second involves attempts to apply cluster analysis to form regions of attraction. The two methods are then compared on a set of well-known test problems.     

Numerical approach to differential equations of fractional order

In this paper, the variational iteration method and the Adomian decomposition method are implemented to give approximate solutions for linear and nonlinear systems of differential equations of fractional order. The two methods in applied mathematics can be used as alternative methods for obtaining analytic and approximate solutions for different types of differential equations. In these schemes, the solution takes the form of a convergent series with easily computable components. This paper presents a numerical comparison between the two methods for solving systems of fractional differential equations. Numerical results show that the two approaches are easy to implement and accurate when applied to differential equations of fractional order.     

Wavelet regularization for an inverse heat conduction problem

In this paper we consider an inverse heat conduction problem which appears in some applied subjects. This problem is ill-posed in the sense that the solution (if it exists) does not depend continuously on the data. The Meyer wavelets are applied to formulate a regularized solution which is convergent to exact one on an acceptable interval when data error tends to zero.