Two extragradient methods for generalized mixed equilibrium problems, nonexpansive mappings and monotone mappings

In this paper, we introduce iterative schemes based on the extragradient method for finding a common element of the set of solutions of a generalized mixed equilibrium problem and the set of fixed points of an infinite (a finite) family of nonexpansive mappings and the set of solutions of a variational inequality problem for a monotone, Lipschitz continuous mapping. We obtain some weak convergence theorems for the sequences generated by these processes in Hilbert spaces. The results in this paper generalize, extend and unify some well-known weak convergence theorems in the literature.     

Convergence of an Ishikawa type iterative scheme for asymptotically quasi-nonexpansive mappings

In convex metric spaces, the Ishikawa iteration process and the Ishikawa iteration process with errors is defined for asymptotically quasi-nonexpansive mappings. It is proved some sufficient and necessary conditions for the iterative scheme converges to the fixed point of the asymptotically quasi-nonexpansive mappings. These results generalize and unify many important known results in recent literature.     

Halpern-type iterations for strongly relatively nonexpansive mappings in Banach spaces

In this paper, we note that the main convergence theorem in Zhang et al. (2011) [21] is incorrect and we prove a correction. We also modify Halpern’s iteration for finding a fixed point of a strongly relatively nonexpansive mapping in a Banach space. Consequently, two strong convergence theorems for a relatively nonexpansive mapping and for a mapping of firmly nonexpansive type are deduced. Using the concept of duality theorems, we obtain analogue results for strongly generalized nonexpansive mappings and for mappings of firmly generalized nonexpansive type. In addition, we study two strong convergence theorems concerning two types of resolvents of a maximal monotone operator in a Banach space.     

Mann and Ishikawa iterations with errors for asymptotically nonexpansive mappings

In a recent paper, Rhoades [1] presented some generalizations of Schu [2] on the convergence of the Mann and Ishikawa iterations of asymptotically nonexpansive mappings in uniformly convex Banach spaces. We continue the study on the Ishikawa (and Mann) iteration process with errors and prove that if X is a uniformly convex Banach space, ø ≠ E ⊂ X closed bounded and convex, and T : E → E is an asymptotically nonexpansive mapping, then the Ishikawa (and Mann) iteration process with errors converges strongly to some fixed point of T.     

Convergence theorems by a new iteration process for a finite family of nonself asymptotically nonexpansive mappings with errors in Banach spaces

In this paper, we study a new iteration process for a finite family of nonself asymptotically nonexpansive mappings with errors in Banach spaces. We prove some weak and strong convergence theorems for this new iteration process. The results of this paper improve and extend the corresponding results of Chidume et al. (2003) [10], Osilike and Aniagbosor (2000) [3], Schu (1991) [4], Takahashi and Kim (1998) [9], Tian et al. (2007) [18], Wang (2006) [11], Yang (2007) [17] and others.     

Strong convergence theorems for equilibrium problems and relatively nonexpansive mappings in Banach spaces

The purpose of the paper is to introduce modified Halpern and Ishikawa iteration for finding a common element of the set of fixed points of a relatively nonexpansive mapping and the set of solutions of an equilibrium problem in Banach spaces. We also consider two strong convergence theorems for relatively nonexpansive mappings with some proper restriction.     

Convergence of an adaptive linear estimation algorithm

In this work we prove the almost sure convergence of an adaptive linear estimator governed by a stochastic gradient algorithm with decreasing step size in the presence of correlated observations. Two complementary contributions are added to the famous 1977 Ljung theorem. First we drop the condition of nondivergence of the algorithm assumed by Ljung. While that condition can be ensured by adding a barrier, the convergence of the suitably bounded algorithm itself is not established even on the basis of Ljung theorem. Here, the barrier problem is overcome by proving that it is not necessary for the convergence. Our second contribution is to generalize the model describing the correlated observations. No state space model is used and no linear relationship between the observations and the signal to be estimated needs to be assumed. Instead we use a decreasing covariance model that agrees with a very wide class of practical applications.     

Convergence of an algorithm in optimal design

The convergence of an algorithm in optimal design for problems in which the material properties are described by a second-order tensor is proved in this paper. The heat conductance context has been chosen for the presentation. Numerical results by using this kind of algorithm have already been obtained by Allaireet al. (1996) in elasticity.     

Convergence characterisation of an iterative algorithm for periodic Lyapunov matrix equations

This paper is concerned with convergence characterisation of an iterative algorithm for a class of reverse discrete periodic Lyapunov matrix equation associated with discrete-time linear periodic systems. Firstly, a simple necessary condition is given for this algorithm to be convergent. Then, a necessary and sufficient condition is presented for the convergence of the algorithm in terms of the roots of polynomial equations. In addition, with the aid of the necessary condition explicit expressions of the optimal parameter such that the algorithm has the fastest convergence rate are provided for two special cases. The advantage of the proposed approaches is illustrated by numerical examples.     

Convergence theorems for asymptotically pseudocontractive mappings in the intermediate sense

In this paper, we prove a strong convergence of Ishikawa scheme to a uniformly L-Lipschitzian and asymptotically pseudocontractive mappings in the intermediate sense. No compactness assumption is imposed either on T or C, and computation of intersection of closed convex sets C_n and Q_n for each n≥1 is not required. We also obtain convergence results in this direction for asymptotically strict pseudocontractive mappings in the intermediate sense. Our theorems improve and unify most of the results that have been proved for this important class of nonlinear mappings.     

Strong convergence of the modified Ishikawa iterative method for infinitely many nonexpansive mappings in Banach spaces

In this paper, we introduce a new modified Ishikawa iterative process for computing fixed points of an infinite family nonexpansive mapping in the framework of Banach spaces. Then, we establish the strong convergence theorem of the proposed iterative scheme under some mild conditions which solves a variational inequality. The results obtained in this paper extend and improve on the recent results of Qin et al. [Strong convergence theorems for an infinite family of nonexpansive mappings in Banach spaces, Journal of Computational and Applied Mathematics 230 (1) (2009) 121–127], Cho et al. [Approximation of common fixed points of an infinite family of nonexpansive mappings in Banach spaces, Computers and Mathematics with Applications 56 (2008) 2058–2064] and many others.     

Convergence of multivalued mappings via approximable measurable selectors

We investigate the convergence of measurable selectors for the limit of measurable multivalued maps. The relationship between the convergence of measurable selectors and lower and upper limits of measurable multivalued mappings with closed images is also derived.     

隐多项式曲线的快速逐点生成算法

隐多项式曲线一直没有理想的生成算法,给出了一种针对二维n次隐多项式曲线的快速逐点生成算法,该算法思路简洁,在逐点生成过程中,只用到整数加减法,故速度快,效率高,具有广泛的应用价值。最后,运用算法给出了曲线生成实例和对算法效率的比较,比较结果表明本文提出的算法有效的提高了生成曲线的效率。