2-一致光滑空间中严格伪压缩映像的弱收敛定理

讨论了严格伪压缩映像的不动点问题.在2-一致光滑一致凸的Banach空间中,通过Mann迭代方法得到严格伪压缩映像的不动点的弱收敛结果.这个结果推广了目前的已知结果.     

Strong Convergence Theorems for a Finite Family of Sequences of Nearly Nonexpansive Mappings in Hilbert Spaces

In this article, by using the hybrid projection method or the shrinking projection method, we introduce two strong convergence theorems for finding a common fixed point of a finite family of sequences of nearly nonexpansive mappings in Hilbert spaces.     

渐近非扩张非自映射的收敛定理(英文)

设E是实的一致凸Banach空间,K是E的一个非空闭凸集,P是E到K上的非扩张的保核收缩映射.设T1,T2,T3:K→E分别是具有数列{hn},{ln},{kn}[1,∞)的渐近非扩张非自映射,使得sum (hn-1) from n=1 to ∞<∞,sum ((ln-1)) from n=1 to ∞<∞及sum (n=1(kn-1) from n=1 to ∞<∞,且F=F(T1)∩F(T2)∩F(T3)={x∈K:T1x=T2x=T3x}≠Ф.定义迭代序列{xn}:x1∈K,xn+1=P((1-αn)xn+αnT1(PT1)n-1yn),yn=P((1-βn)xn+βnT2(PT2)n-1zn),zn=P((1-γn)xn+γnT3(PT3)n-1xn),其中{αn},{βn},{γn}[ε,1-ε],ε是大于零的实数.(i)如果T1,T2,T3中有一个是全连续的或者半紧的,则{xn}强收敛于某一点q∈F;(ii)如果E具有Frechet可微范数或者满足Opial’s条件或者E的对偶空间E~*具有Kadec-Klee性质,则{xn}弱收敛于某一点q∈F.     

极大单调算子和非扩展映射的迭代收敛定理及其应用

In this paper, two iterative schemes for approximating common element of the set of zero points of maximal monotone operators and the set of fixed points of a kind of generalized nonexpansive mappings in a real uniformly smooth and uniformly convex Banach space are proposed. Two strong convergence theorems are obtained and their applications on finding the minimizer of a kind of convex functional are discussed, which extend some previous work.     

Weak Convergence Theorems for Mixed Type Asymptotically Nonexpansive Mappings

The purpose of this paper is to prove some weak convergence theorems for mixed type asymptotically nonexpansive mappings with mean errors in uniformly convex Banach spaces. The results presented in this paper extend the corresponding results in the references.     

Strong Convergence Theorems for Mixed Typ e Asymptotically Nonexpansive Mappings

The purpose of this paper is to study a new two-step iterative scheme with mean errors of mixed type for two asymptotically nonexpansive self-mappings and two asymptotically nonexpansive nonself-mappin...     

Banach空间中一族拟$\phi$-严格渐近伪压缩映像的收缩投影方法

The purpose of this article is to propose a shrinking projection method and prove a strong convergence theorem for a family of quasi-φ-strict asymptotically pseudo-contractions. Its results hold in reflexive, strictly convex, smooth Banach spaces with the property （K）. The results of this paper improve and extend the results of Matsushita and Takahashi, Marino and Xu, Zhou and Gao and others.     

Banach空间中渐近非扩张映象具误差的强收敛定理

设E是一实的Banach空间,其范数是一致Gteaux可微的;D是E的一非空闭凸子集,设T:D→D是具有序列{k_n}[1,∞),lim_(n→∞) k_n=1的渐近非扩张映象.本文证明了,在一定条件下,由(1.3)和(1.5)式定义的具误差的迭代序列{x_n}强收敛于T的不动点.本文结果也推广和改进了最近一些人的最新结果.     

Convergence of over-relaxed contraction-proximal point algorithm in Hilbert spaces

The proximal point algorithm (PPA) is a classical method for finding zeros of maximal monotone operators. It is known that the algorithm only has weak convergence in a general Hilbert space. Recently, Wang, Wang and Xu proposed two modifications of the PPA and established strong convergence theorems on these two algorithms. However, these two convergence theorems exclude an important case, namely, the over-relaxed case. In this paper, we extend the above convergence theorems from under-relaxed case to the over-relaxed case, which in turn improve the performance of these two algorithms. Preliminary numerical experiments show that the algorithm with over-relaxed parameter performs better than that with under-relaxed parameter.     

Approximating Solutions of Maximal Monotone Operators in Hilbert Spaces

Let H be a real Hilbert space and let T: H→2^H be a maximal monotone operator. In this paper, we first introduce two algorithms of approximating solutions of maximal monotone operators. One of them is to generate a strongly convergent sequence with limit vT⁻¹0. The other is to discuss the weak convergence of the proximal point algorithm. Next, using these results, we consider the problem of finding a minimizer of a convex function. Our methods are motivated by Halpern's iteration and Mann's iteration.     

非自渐近非扩张映象的Reich—Takahashi型迭代法的强收敛定理

设E是一实的Banach空间，其范数是一致Gateaux可微的；D是E的非空闭凸子集而且是E的非扩张收缩核．设T：D→E是具有序列{kn}包含[1，∞），limn→∞kn=1的非自渐近非扩张映象，P：E→D是一非扩张保核收缩．本文证明了，在一定条件下，由修正的Reich—Takahashi迭代法（1．2）和（1.3）式定义的迭代序列{xn}强收敛于非自渐近非扩张映象T的不动点.     

非自渐近非扩张映象的Reich-Takahashi型迭代法的强收敛定理

设E是一实的Banach空间,其范数是一致Gteaux可微的;D是E的非空闭凸子集而且是E的非扩张收缩核.设T:D→E是具有序列{kn}[1,∞),limn→∞kn=1的非自渐近非扩张映象,P:E→D是一非扩张保核收缩.本文证明了,在一定条件下,由修正的Reich-Takahashi迭代法(1.2)和(1.3)式定义的迭代序列{xn}强收敛于非自渐近非扩张映象T的不动点.     

Banach空间中一致L-Lipschitz映象的强收敛定理

在任意实Banach空间中引入了一对一致L-Lipschitz映象的具误差的新迭代序列,并证明了这些迭代序列的强收敛性定理.结果推广、完善和改进了最近的一些结果.     

On Weak and Strong Convergence of the Projected Gradient Method for Convex Optimization in Real Hilbert Spaces

This work focuses on convergence analysis of the projected gradient method for solving constrained convex minimization problems in Hilbert spaces. We show that the sequence of points generated by the method employing the Armijo line search converges weakly to a solution of the considered convex optimization problem. Weak convergence is established by assuming convexity and Gateaux differentiability of the objective function, whose Gateaux derivative is supposed to be uniformly continuous on bounded sets. Furthermore, we propose some modifications in the classical projected gradient method in order to obtain strong convergence. The new variant has the following desirable properties: the sequence of generated points is entirely contained in a ball with diameter equal to the distance between the initial point and the solution set, and the whole sequence converges strongly to the solution of the problem that lies closest to the initial iterate. Convergence analysis of both methods is presented without Lipschitz continuity assumption.     

Hilbert空间中Lipschitz单调映像变分不等式解的另一个逼近定理

首先给出了Hilbert空间中Lipschitz单调映像变分不等式解的迭代格式,并证明了其收敛性.作为应用,证明了Hilbert空间中Lipschitz伪压缩映像的强收敛定理,扩展了已知的相关结果.     

Banach空间中渐近伪压缩映射的黏滞近似不动点序列

首先给出了渐近伪压缩映射的黏滞近似不动点序列的新定义,继而证明了如下逼近定理:令K为实Banach空间E的非空闭凸有界子集,T:K→K为一致L-Lipschitz、具数列{εn}的一致渐近正则、具数列{kn}的渐近伪压缩映射.假设迭代序列{xn}定义为:x1∈K,对n≥1,xn+1:=λnθnf(xn)+[1-λn(1+θn)]xn+λnTnxn,其中{λn},{θn}(0,1)且满足一定条件,则:当n→∞时,‖xn-Txn‖→0.     

Hilbert空间框架下严格渐近伪压缩映像的强收敛定理

The Mann iterations have no strong convergence even for nonexpansive mappings in Hilbert spaces. The aim of this paper is to propose a modification of the Mann iterations for strictly asymptotically pseudocontractive maps in Hilbert spaces to have strong convergence. Our results extend those of Kim, Xu, Nakajo, Takahashi and many others.     

Strong Convergence Theorems for Quasi-Nonexpansive Mappings and Uniformly L-Lipschitzian Asymptotically Pseudo-Contractive Mappings in Banach Spaces

A new 𝒮-generated Ishikawa iteration with errors is proposed for a pair of quasi-nonexpansive mapping and uniformly L-Lipschitzian asymptotically pseudo-contractive mapping in real Banach spaces. We show that the proposed iterative scheme converges strongly to a common solution of quasi-nonexpansive mapping and uniformly L-Lipschitzian asymptotically pseudo-contractive mapping in real Banach spaces. A comparison table is prepared using a numeric example which shows that the proposed iterative algorithm is faster than some known iterative algorithms.     

Weak and Strong Convergence Theorems of G-Monotone Nonexpansive Mapping in Banach Spaces with a Graph

In this article, we introduce a faster iteration for finding a fixed point of G-monotone nonexpansive mapping in a uniformly convex Banach space with a directed graph. We establish weak and strong convergence theorems of fixed point for G-monotone nonexpansive mapping with a more convenient G-convex interval instead of the previous fixed point dominated conditions in convergence analysis. Moreover, we provide two numerical examples to illustrate the convergence behavior and advantages of the proposed method.     

Outer approximation methods for solving variational inequalities in Hilbert space

In this paper, we study variational inequalities in a real Hilbert space, which are governed by a strongly monotone and Lipschitz continuous operator F over a closed and convex set C. We assume that the set C can be outerly approximated by the fixed point sets of a sequence of certain quasi-nonexpansive operators called cutters. We propose an iterative method, the main idea of which is to project at each step onto a particular half-space constructed using the input data. Our approach is based on a method presented by Fukushima in 1986, which has recently been extended by several authors. In the present paper, we establish strong convergence in Hilbert space. We emphasize that to the best of our knowledge, Fukushima’s method has so far been considered only in the Euclidean setting with different conditions on F. We provide several examples for the case where C is the common fixed point set of a finite number of cutters with numerical illustrations of our theoretical results.