非光滑多目标规划的不动点算法

本文讨论不动点算法在非光滑多目标规划中的应用，得到了一些新的最优性条件以及不动点与非光滑多目标的解之间的关系，并且给出了解非光滑多目标规划的不动点算法的收敛性。     

非光滑多目标规划的不动点算法(英)

本文讨论不动点算法在非光滑多目标规划中的应用，得到了一些新的最优性条件以及不动点与非光滑多目标的解之间的关系，并且给出了解非光滑多目标规划的不动点算法的收敛性．     

非扩张映像不动点新的简单逼近算法

提出了一个简单的非扩张映像不动点的逼近算法,该算法通过非迭代的逼近序列来实现.从算法的复杂性来看,提出的算法比经典的Mann迭代算法、Ishikawa迭代算法和Halpern迭代算法更简单.提出的算法紧密联系着非扩张映像不动点的存在性,因此,还得到了非扩张映像的新不动点定理, 拓展和改进了经典的Goebel-Kirk,Kim-Xu等作者的结果.     

用单纯算法求解三解定理

在不动点的计算中,人们越来越注意单纯算法,特别是各种基于Brouwer不动点原理的定理,都可以有相应的单纯算法.本文讨论三解定理的单纯算法.§1介绍三解定理,它是由Amann最早提出的;§2介绍用单纯算法计算Brouwer不动点;§ 3考虑Amann三解定理的计算方案.     

Banach空间中可数非扩张映像族公共不动点迭代算法

提出一种新的迭代算法用于求解实一致光滑Banach空间上可数非扩张映像族的公共不动点.在一定条件下证明了迭代算法产生的序列强收敛到一个公共不动点,并且此不动点也是一个变分不等式的解.此结果改进和推广了已有的相关结果.     

单纯不动点算法

§1.引言 不动点问题有广泛的实际背景。自Scarf引进本原集概念,提出逼近连续映射的不动点的一种有限算法以来,不动点算法作为数学研究的一个新方向和作为非线性问题数值解的有效方法,得到迅速发展,取得一系列有意义的成果。     

不动点的单纯型算法

不动点理论是处理非线性问题的一个重要工具,非线性规划、数理经济学和其他应用领域中的许多问题都可归结为不动点问题,早在1912年,Brouwer就证明了著名的不动点定理,之后又有了Schauder、Kakutani等人的各种推广,但这些结果都是作为存在性定理(非构造方式)来处理问题的.直到十余年前才出现计算不动点的数值方法,从而形成了“不动点算法”这一迅速发展的数学新分支.第一代不动点算法是Scarf于1967年提出来的,他引进了所谓“本原集”的概念和“替代步骤的唯一性”,并利用Sperner引理和Brouwer不动点定理,得到了计算不动点近     

关于有限族Lipschitz映射多步Ishikawa迭代算法的收敛性及其应用

本文的目的是研究Lipschitz映射公共不动点问题.基于传统的Ishikawa迭代和Noor迭代方法,我们引入多步Ishikawa迭代算法,并且分别给出了该算法强收敛于有限族拟-Lipschitz映射和伪压缩映射公共不动点的充分必要条件.此外,我们证明了该算法强收敛到非扩张映射的公共不动点.作为应用,我们给出数值试验证实所得的结论.     

同伦算法几何理论

自Scarf[1967]引进本原集概念提出逼近单纯形连续自映射的不动点的一种有限算法以来,不动点算法作为非线性问题的有效算法得到发展。 Kuhn[1968],Kuhn[1969]将整数标号和单纯剖分引进算法,形成采用整数标号的单纯不动点算法。它在实质上是半个世纪以前两个著名的组合引理Sperner引理(Sperner[1928])和K-K-M引理(Knaster,Kuratowski&Mazurkiewicz[1929])的算     

高维不动点的复杂性

§1.引言 本文讨论计算高维压缩函数不动点的ε逼近的复杂性、构造高维不动点包络(MFPE)算法并证明此算法在绝对误差标准下为最优误差算法,同时给出误差估计的一个递推关系式,从而解决了[3]中未解决的问题.     

A restart algorithm for computing fixed points without an extra dimension

An algorithm to compute a fixed point of an upper semicontinuous point to set mapping using a simplicial subdivision is introduced. The new element of the algorithm is that for a given grid it does not start with a subsimplex but with one (arbitrary) point only; the algorithm will terminate always with a subsimplex. This subsimplex yields an approximation of a fixed point and provides the starting point for a finer grid. Some numerical results suggest that this algorithm converges more rapidly than the known algorithms. Moreover, it is very simple to implement the algorithm on the computer.     

Strong Convergence of a Split Common Fixed Point Problem

In this article, we present a new general algorithm for solving the split common fixed point problem in an infinite dimensional Hilbert space, which is to find a point which belongs to the common fixed point of a family of quasi-nonexpansive mappings such that its image under a linear transformation belongs to the common fixed point of another family of quasi-nonexpansive mappings in the image space. We establish the strong convergence for the algorithm to find a unique solution of the variational inequality, which is the optimality condition for the minimization problem. The algorithm and its convergence results improve and develop previous results in this field.     

一种求解无约束极值问题的无记忆拟牛顿算法

§1.引言 求无约束极值常用的方法,有CG算法、变尺度算法以及拟牛顿算法等等.变尺度算法虽然收敛速度快,但是存贮量大(为O(n~2))。CG算法所需存贮量(为O(n))虽小,但在收敛速度上一般不如变尺度法.因此,本文探索收敛速度快且所需存贮量小的算法,以     

General algorithms for split common fixed point problem of demicontractive mappings

In the first part of this paper, we present a new general algorithm for solving the split common fixed point problem for an infinite family of demicontractive mappings. We establish strong convergence of the algorithm in an infinite dimensional Hilbert space. As applications, we consider algorithms for split variational inequality problem and split common null point problem. In the second part of this paper, we present a new algorithm and strong convergence theorem for approximation of solutions of split equality fixed point problems for an infinite family of demicontractive mappings. Our results improve and generalize some recent results in the literature.     

A revised bound improvement sequence algorithm

In this paper we present a generalization and a computational improvement of the Bound Improvement Sequence Algorithm. The main computational burden of this algorithm consists in determining whether there exists a feasible point on the objective hyperplane, when the algorithm encounters a fixed point. By generalizing the algorithm, such that the objective function and constraints are treated alike, the number of fixed points that are required can be reduced. The computational results that we report allow us to conclude that the number of fixed points can generally be reduced for loosely constrained problems. For this class of problems the new algorithm appears to be more efficient than a standard MIP code such as FMPS.     

A new iterative method for the split common fixed point problem in Hilbert spaces

The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed-point set. In this paper, we propose a new algorithm for this problem that is completely different from the existing algorithms. Moreover, our algorithm does not need any prior information of the operator norm. Under standard assumptions, we establish a weak convergence theorem of the proposed algorithm and a strong convergence theorem of its variant.     

A new iterative algorithm for the variational inequality problem over the fixed point set of a firmly nonexpansive mapping

Many problems to appear in signal processing have been formulated as the variational inequality problem over the fixed point set of a nonexpansive mapping. In particular, convex optimization problems over the fixed point set are discussed, and operators which are considered to the problems satisfy the monotonicity. Hence, the uniqueness of the solution of the problem is not always guaranteed. In this article, we present the variational inequality problem for a monotone, hemicontinuous operator over the fixed point set of a firmly nonexpansive mapping. The main aim of the article is to solve the proposed problem by using an iterative algorithm. To this goal, we present a new iterative algorithm for the proposed problem and its convergence analysis. Numerical examples for the proposed algorithm for convex optimization problems over the fixed point set are provided in the final section.     

Split common fixed point and common null point problem

In this paper, we present a new algorithm for solving the split common null point and common fixed point problem, to find a point that belongs to the common element of common zero points of an infinite family of maximal monotone operators and common fixed points of an infinite family of demicontractive mappings such that its image under a linear transformation belongs to the common zero points of another infinite family of maximal monotone operators and its image under another linear transformation belongs to the common fixed point of another infinite family of demicontractive mappings in the image space. We establish strong convergence for the algorithm to find a unique solution of the variational inequality, which is the optimality condition for the minimization problem. As special cases, we shall use our results to study the split equilibrium problems and the split optimization problems.     

A modified fixed-point iterative algorithm for image restoration using fourth-order PDE model

In this paper, we propose a modified fixed point iterative algorithm to solve the fourth-order PDE model for image restoration problem. Compared with the standard fixed point algorithm, the proposed algorithm needn?t to compute inverse matrices so that it can speed up the convergence and reduce the roundoff error. Furthermore, we prove the convergence of the proposed algorithm and give some experimental results to illustrate its effectiveness by comparing with the standard fixed point algorithm, the time marching algorithm and the split Bregman algorithm.     

Further investigation into split common fixed point problem for demicontractive operators

Our contribution in this paper is to propose an iterative algorithm which does not require prior knowledge of operator norm and prove strong convergence theorem for approximating a solution of split common fixed point problem of demicontractive mappings in a real Hilbert space. So many authors have used algorithms involving the operator norm for solving split common fixed point problem, but as widely known the computation of these algorithms may be difficult and for this reason, authors have recently started constructing iterative algorithms with a way of selecting the step-sizes such that the implementation of the algorithm does not require the calculation or estimation of the operator norm. We introduce a new algorithm for solving the split common fixed point problem for demicontractive mappings with a way of selecting the step-sizes such that the implementation of the algorithm does not require the calculation or estimation of the operator norm and then prove strong convergence of the sequence in real Hilbert spaces. Finally, we give some applications of our result and numerical example at the end of the paper.