一个求解一类投影问题的总体线性收敛的迭代法

所求的解就是c在p上的投影。 对于问题(1.1),He基于求解线性互补问题的投影收缩(PC)法,把投影问题转化为等价的广义线性互补问题,提出了一个求解这类问题的迭代方法。 原始的PC方法只能证明迭代是全局收敛的,而无法估计其收敛速度。为此,[4]和[5]对原始的PC方法作了改进,提出了固定步长的PC法并证明了其收敛速度是线性的。但在实际应用中,固定步长的PC法比原始的PC法慢的多,而且在求步长时,还要估计约束矩阵范数的大小。 本文基于[5]的思想,对于(1.1)提出了一个新的PC方法,该方法是全局线性收敛的。 本文中用到的符号说明如下:x_i表示x的第i个分量。如果u∈(?)且Ω(?)(?)为凸闭集,则P_Ω[u]定义为u到Ω上的投影。特别地,u_+定义为u到非负卦限(?)上的投影,对于一个正定矩阵G,范数||u++G表示(u~TGu)(?)。     

一个求解二阶锥互补问题的非单调光滑算法

光滑算法是求解二阶锥互补问题非常有效的方法,而这类算法通常采用单调线性搜索.给出了一个求解二阶锥互补问题的非单调光滑算法,在不需要满足严格互补条件下证明了算法是全局和局部二阶收敛的.数值试验表明算法是有效的.     

一个求解二阶锥互补问题的非单调光滑算法北大核心

光滑算法是求解二阶锥互补问题非常有效的方法,而这类算法通常采用单调线性搜索.给出了一个求解二阶锥互补问题的非单调光滑算法,在不需要满足严格互补条件下证明了算法是全局和局部二阶收敛的.数值试验表明算法是有效的.     

二阶锥线性互补问题的广义模系矩阵分裂迭代算法

通过将二阶锥线性互补问题转化为等价的不动点方程,介绍了一种广义模系矩阵分裂迭代算法,并研究了该算法的收敛性.进一步,数值结果表明广义模系矩阵分裂迭代算法能够有效地求解二阶锥线性互补问题.     

线性不等式约束的广义非线性互补问题的仿射内点信赖域方法

提供了一种新的非单调内点回代线搜索技术的仿射内点信赖域方法解线性不等式约束的广义非线性互补问题(GCP).基于广义互补问题构成的半光滑方程组的广义Jacobian矩阵,算法使用l2范数作为半光滑方程组的势函数,形成的信赖域子问题为一个带椭球约束的线性化的二次模型.利用广义牛顿方程计算试探迭代步,通过内点映射回代技术确保迭代点是严格内点,保证了算法的整体收敛性.在合理的条件下,证明了信赖域算法在接近最优点时可转化为广义拟牛顿步,进而具有局部超线性收敛速率.非单调技术将克服高度非线性情况加速收敛进展.最后,数值结果表明了算法的有效性.     

线性不等式约束的广义非线性互补问题的仿射内点信赖域方法

提供了一种新的非单调内点回代线搜索技术的仿射内点信赖域方法解线性不等式约束的广义非线性互补问题(GCP).基于广义互补问题构成的半光滑方程组的广义Jacobian矩阵,算法使用l_2范数作为半光滑方程组的势函数,形成的信赖域子问题为一个带椭球约束的线性化的二次模型.利用广义牛顿方程计算试探迭代步,通过内点映射回代技术确保迭代点是严格内点,保证了算法的整体收敛性.在合理的条件下,证明了信赖域算法在接近最优点时可转化为广义拟牛顿步,进而具有局部超线性收敛速率.非单调技术将克服高度非线性情况加速收敛进展.最后,数值结果表明了算法的有效性.     

无约束非光滑优化问题信赖域算法的收敛条件

1 引言 考虑下列无约束非光滑优化问题 minf(x),(1) x∈R~n,其中f为R~n上的局部Lipschitz函数,本文将‖·‖_2简记为‖·‖.记下列信赖域子问题为S∪B(x,△). min m(x,s)=φ(x,s)+1/2s~TBs, 其中φ:R~(2m)→R为f的迭代函数。 对于无约束非光滑优化问题(1),[11],[13],[3]、[4]和[5]分别在特殊的条件下给出了信赖域算法用以求解(1)的收敛性结果。最近,[10]、[2]和[6]在不同的假设条件下分别给出了信赖域算法求解无约束非光滑优化问题的一般模型,并在子问题的目标函数满足局部一致有界性条件时证明了算法模型的整体收敛性。在目标函数满足某种正则性条件时,[11]和[9]给出了当信赖域子问题的目标函数中二次项不满足一致有界性条件时的收敛性结果.本文则在目标函数仅为局部Lipschitz函数时得到了和[8]、[11]、[9]相同的收敛性结果。     

改进的分块模方法求解对角占优线性互补问题

为了高效求解中小型线性互补问题,本文提出了改进的分块模方法,并证明了关于严格对角占优(对角元素均为正数)线性互补问题的收敛性.对于广义对角占优线性互补问题,先将其转化为严格对角占优线性互补问题,再采用改进的分块模方法求解.数值结果表明,改进的分块模方法在求解广义对角占优线性互补问题时在内迭代次数和计算时间上均明显优于分...     

偏序下的区间松弛法

§1. 引言 本文给出了求解非线性方程组 f(x)=0,f:D?R~n→R~m (1.1)在偏序下的区间松弛法,它是在[1]的基础上将区间迭代与Newton-SOR 迭代结合得到的一种便于计算且收敛较快的序区间N-SOR松弛法,也是单调N-SOR迭代法的推广.§2给出了偏序下的区间Krawczyk算子,它是区间 Newton算子的推广,同样具     

偏序下的区间松弛法

§1. 引言 本文给出了求解非线性方程组 f(x)=0,f:D?R~n→R~m (1.1)在偏序下的区间松弛法,它是在[1]的基础上将区间迭代与Newton-SOR 迭代结合得到的一种便于计算且收敛较快的序区间N-SOR松弛法,也是单调N-SOR迭代法的推广.§2给出了偏序下的区间Krawczyk算子,它是区间 Newton算子的推广,同样具     

求解非单调变分不等式的一种二次投影算法北大核心CSCD

投影算法是求解变分不等式问题的主要方法之一.目前,有关投影算法的研究通常需要假设映射是单调且Lipschitz连续的,然而在实际问题中,往往不满足这些假设条件.该文利用线搜索方法,提出了一种新的求解非单调变分不等式问题的二次投影算法.在一致连续假设下,证明了算法产生的迭代序列强收敛到变分不等式问题的解.数值实验结果表明了该文所提算法的有效性和优越性.     

SINGLE PROJECTION ALGORITHM FOR VARIATIONAL INEQUALITIES IN BANACH SPACES WITH APPLICATION TO CONTACT PROBLEM

We study the single projection algorithm of Tseng for solving a variational inequality problem in a 2-uniformly convex Banach space. The underline cost function of the variational inequality is assumed to be monotone and Lipschitz continuous. A weak convergence result is obtained under reasonable assumptions on the variable step-sizes. We also give the strong convergence result for when the underline cost function is strongly monotone and Lipchitz continuous. For this strong convergence case, the proposed method does not require prior knowledge of the modulus of strong monotonicity and the Lipschitz constant of the cost function as input parameters, rather, the variable step-sizes are diminishing and non-summable. The asymptotic estimate of the convergence rate for the strong convergence case is also given. For completeness, we give another strong convergence result using the idea of Halpern's iteration when the cost function is monotone and Lipschitz continuous and the variable step-sizes are bounded by the inverse of the Lipschitz constant of the cost function.Finally, we give an example of a contact problem where our proposed method can be applied.     

一种求解半定规划的邻近外梯度算法

本文提出了一种求解半定规划的邻近外梯度算法.通过转化半定规划的最优性条件为变分不等式,在变分不等式满足单调性和Lipschitz连续的前提下,构造包含原投影区域的半空间,产生邻近点序列来逼近变分不等式的解,简化了投影的求解过程.将该算法应用到教育测评问题中,数值实验结果表明,该方法是解大规模半定规划问题的一种可行方法.     

交替方向法求解带线性约束的变分不等式

１引言变分不等式是一个有广泛应用的数学问题,它的一般形式是：确定一个向量,使其满足这里ｆ是一个从到自身的一个映射,Ｓ是Ｒ中的一个闭凸集．在许多实际问题中集合Ｓ往往具有如下结构其中ＡｂＫ是中的一个简单闭凸集．例如一个正卦限,一个框形约束结构,或者一个球简言之,Ｓ是Ｒ中的一个超平面与一个简单闭凸集的交．求解问题（１）－（２）,往往是通过对线性约束Ａ引人Ｌａｇｒａｎｇｅ乘子,将原问题化为如下的变分不等式：确定使得我们记问题（３）－（４）为ＶＩ（Ｆ）．熟知［３］,ＶＩ（,Ｆ）等价于投影方程其中凡（·）表…     

关于两个非线性算子的一个分裂方法(英文)

<正>This paper generalizes a class of projection type methods for monotone variational inequalities to general monotone inclusion.It is shown that when the normal cone operator in projection is replaced by any maximal monotone operator,the resulting method inherits all attractive convergence properties of projection type methods,and allows an adjusting step size rule.Weaker convergence assumption entails an extra projection at each iteration.Moreover,this paper also addresses applications of the resulting method to convex programs and monotone variational inequalities.     

Strong convergence of an iterative method for pseudo-contractive and monotone mappings

In this paper, we introduce an iterative process which converges strongly to a common element of fixed points of pseudo-contractive mapping and solutions of variational inequality problem for monotone mapping. As a consequence, we provide an iteration scheme which converges strongly to a common element of set of fixed points of finite family continuous pseudo-contractive mappings and solutions set of finite family of variational inequality problems for continuous monotone mappings. Our theorems extend and unify most of the results that have been proved for this class of nonlinear mappings.     

A Modified Alternating Direction Method for Variational Inequality Problems

The alternating direction method is an attractive method for solving large-scale variational inequality problems whenever the subproblems can be solved efficiently. However, the subproblems are still variational inequality problems, which are as structurally difficult to solve as the original one. To overcome this disadvantage, in this paper we propose a new alternating direction method for solving a class of nonlinear monotone variational inequality problems. In each iteration the method just makes an orthogonal projection to a simple set and some function evaluations. We report some preliminary computational results to illustrate the efficiency of the method. Accepted 4 May 2001. Online publication 19 October, 2001.     

Some Methods Based on the D-Gap Function for Solving Monotone Variational Inequalities

The D-gap function has been useful in developing unconstrained descent methods for solving strongly monotone variational inequality problems. We show that the D-gap function has certain properties that are useful also for monotone variational inequality problems with bounded feasible set. Accordingly, we develop two unconstrained methods based on them that are similar in spirit to a feasible method of Zhu and Marcotte based on the regularized-gap function. We further discuss a third method based on applying the D-gap function to a regularized problem. Preliminary numerical experience is also reported.     

一般单调变分不等式的一个改进的预估-校正算法

最近何炳生等提出了解大规模单调变分不等式的一种预估-校正算法,然而,这个方法在计算每一个试验点时需要一次投影运算,因而计算量较大．为了克服这个缺点,我们提出了一个解一般大规模g-单调变分不等式的新的预估-校正算法,该方法使用了一个非常有效的预估步长准则,每个步长的选取只需要计算一次投影,这将大大减少计算量．数值试验说明我们的算法比最新文献中出现的投影类方法有效．     

An efficient iterative method for finding common fixed point and variational inequalities in Hilbert spaces

ABSTRACT

In this paper, we investigate the problem of finding a common solution to a fixed point problem involving demi-contractive operator and a variational inequality with monotone and Lipschitz continuous mapping in real Hilbert spaces. Inspired by the projection and contraction method and the hybrid descent approximation method, a new and efficient iterative method for solving the problem is introduced. Strong convergence theorem of the proposed method is established under standard and mild conditions. Our scheme generalizes and extends some of the existing results in the literature, and moreover, its computational effort is less per each iteration compared with related works.