AOR迭代法的收敛性

1.引言 [1]定义了解线性方程组A_x=b的AOR迭代法,它以SOR迭代为特例,而且适当选取参数,有可能比SOR方法收敛快(见[2]).众所周知,使 AOR方法有意义的最基本条件是A的对角元素都不为零.然而,在实际计算中,有时需要求解的线性方程组其系数矩阵存在零对角元素.例如[3]中研究的线性方程组的系数矩阵具有如下形式:     

The split common fixed point problem for generalized demimetric mappings in two Banach spaces

ABSTRACT

In this paper, we consider the split common fixed point problem for new demimetric mappings in two Banach spaces. Using the hybrid method, we prove a strong convergence theorem for finding a solution of the split common fixed point problem in two Banach spaces. Furthermore, using the shrinking projection method, we obtain another strong convergence theorem for finding a solution of the problem in two Banach spaces. Using these results, we obtain well-known and new strong convergence theorems in Hilbert spaces and Banach spaces.     

Rate of Convergence in Trotter’s Approximation Theorem

We give a quantitative estimate of the convergence in Trotter’s approximation theorem on the convergence of iterates of linear operators to an assigned semigroup. An application is given concerning the classical Bernstein operator on the d-dimensional simplex.      

Convergence analysis of the parallel multisplitting TOR method

In this paper, we propose the parallel multisplitting TOR method, for solving a large nonsingular systems of linear equations Ax = b. These new methods are a generalization and an improvement of the relaxed parallel multisplitting method (Formmer and Mager, 1989) and parallel multisplitting AOR Algorithm (Wang Deren, 1991). The convergence theorem of this new algorithm is established under the condition that the coefficient matrix A of linear systems is an H-matrix. Some results also yield new convergence theorem for TOR method.     

Matrix Summability and Positive Linear Operators

In the present paper we prove a Korovkin type approximation theorem for a sequence of positive linear operators acting from a weighted space C_ρ1 into a weighted space B_ρ2 with the use of a matrix summability method which includes both convergence and almost convergence. We also study the rates of convergence of these operators.     

Convergence of GAOR method for doubly diagonally dominant matrices

In this paper, we obtain bounds for the spectral radius of the matrix l_ω,r which is the iterative matrix of the generalized accelerated overrelaxation (GAOR) iterative method. Moreover, we present one convergence theorem of the GAOR method. Finally, we present two numerical examples.     

Some extensions of the improved modified Gauss–Seidel iterative method for H‐matrices

In this paper, first we present a convergence theorem of the improved modified Gauss–Seidel iterative method, referred to as the IMGS method, for H‐matrices and compare the range of parameters α_i with that of the parameter ω of the SOR iterative method. Then with a more general splitting, the convergence analysis of this method for an H‐matrix and its comparison matrix is given. The spectral radii of them are also compared. Copyright © 2006 John Wiley & Sons, Ltd.     

Abstract convergence theorem for quasi-convex optimization problems with applications

Abstract

Quasi-convex optimization is fundamental to the modelling of many practical problems in various fields such as economics, finance and industrial organization. Subgradient methods are practical iterative algorithms for solving large-scale quasi-convex optimization problems. In the present paper, focusing on quasi-convex optimization, we develop an abstract convergence theorem for a class of sequences, which satisfy a general basic inequality, under some suitable assumptions on parameters. The convergence properties in both function values and distances of iterates from the optimal solution set are discussed. The abstract convergence theorem covers relevant results of many types of subgradient methods studied in the literature, for either convex or quasi-convex optimization. Furthermore, we propose a new subgradient method, in which a perturbation of the successive direction is employed at each iteration. As an application of the abstract convergence theorem, we obtain the convergence results of the proposed subgradient method under the assumption of the Hölder condition of order p and by using the constant, diminishing or dynamic stepsize rules, respectively. A preliminary numerical study shows that the proposed method outperforms the standard, stochastic and primal-dual subgradient methods in solving the Cobb–Douglas production efficiency problem.     

A generalized cyclic iterative method for solving variational inequalities over the solution set of a split common fixed point problem

For solving the large-scale linear least-squares problem, we propose a block version of the randomized extended Kaczmarz method, called the two-subspace randomized extended Kaczmarz method, which does not require any row or column paving. Theoretical analysis and numerical results show that the two-subspace randomized extended Kaczmarz method is much more efficient than the randomized extended Kaczmarz method. When the coefficient matrix is of full column rank, the two-subspace randomized extended Kaczmarz method can also outperform the randomized coordinate descent method. If the linear system is consistent, we remove one of the iteration sequences in the two-subspace randomized extended Kaczmarz method, which approximates the projection of the right-hand side vector onto the orthogonal complement space of the range space of the coefficient matrix, and obtain the generalized two-subspace randomized Kaczmarz method, which is actually a generalization of the two-subspace randomized Kaczmarz method without the assumptions of unit row norms and full column rank on the coefficient matrix. We give the upper bound for the convergence rate of the generalized two-subspace randomized Kaczmarz method which also leads to a better upper bound for the convergence rate of the two-subspace randomized Kaczmarz method.

     

Faedo–Galerkin Approximation of Solution for a Nonlocal Neutral Fractional Differential Equation with Deviating Argument

In this work, we study a class of nonlocal neutral fractional differential equations with deviated argument in the separable Hilbert space. We obtain an associated integral equation and then, consider a sequence of approximate integral equations. We investigate the existence and uniqueness of the mild solution for every approximate integral equation by virtue of the theory of analytic semigroup theory via the technique of Banach fixed point theorem. Next we demonstrate the convergence of the solutions of the approximate integral equations to the solution of the associated integral equation. The Faedo–Galerkin approximation of the solution is studied and demonstrated some convergence results. Finally, we give an example.

     

Some variations of the Bohman–Korovkin theorem

In this paper we develop the main aspects of the Bohman–Korovkin theorem on approximation of continuous functions with the use of A-statistical convergence and matrix summability method which includes both convergence and almost convergence. Since statistical convergence and almost convergence methods are incompatible we conclude that these methods can be used alternatively to get some approximation results.     

单纯形调优法的收敛性质

在非线性规划中,单纯形调优法是一种可信用的算法,然而却缺乏理论分析,本文对单纯形调优法的理论进行了一些研究,所考虑的方法类似于Spendley,Hext,Himsworth的正规单纯形调优法,但采用了不同的反映条件,其中带有某种下山门槛或具有三点下降形式,这里的基本思想是证明:上述单纯形调优法是定步长下山法的特殊情形,所以,此研究紧密联系着作者关于定步长下山法收敛定理的工作。     

Disguised and new quasi-Newton methods for nonlinear eigenvalue problems

In this paper, we take a quasi-Newton approach to nonlinear eigenvalue problems (NEPs) of the type M(λ)v =?0, where \(M:\mathbb {C}\rightarrow \mathbb {C}^{n\times n}\) is a holomorphic function. We investigate which types of approximations of the Jacobian matrix lead to competitive algorithms, and provide convergence theory. The convergence analysis is based on theory for quasi-Newton methods and Keldysh’s theorem for NEPs. We derive new algorithms and also show that several well-established methods for NEPs can be interpreted as quasi-Newton methods, and thereby, we provide insight to their convergence behavior. In particular, we establish quasi-Newton interpretations of Neumaier’s residual inverse iteration and Ruhe’s method of successive linear problems.     

A consistency improving method in the analytic hierarchy process

In this paper, we propose a method to modify a given comparison matrix, by which the consistency ratio (CR) value of the modified matrix is less than that of the original one, and give an algorithm to derive a positive reciprocal matrix with acceptable consistency (i.e., CR < 0.1), then the convergence theorem for the given algorithm is established and its practicality is shown by some examples.     

Konvergenz mehrdimensionaler Interpolation

Summary This paper is concerned with convergence of interpolation polynomials in two variables. Some investigations have been made in the special cases of Lagrange and Fejér-Hermite interpolation. Here we give a general method to prove convergence of two-dimensional interpolation processes. The theory is based on the concept of tensor products of interpolation problems. Our main theorem shows that a two-dimensional interpolation process which is a tensor product of two one-dimensional processes converges for a functionh єC [a, b] C [, b] if both factors do for certain one-dimensional functions. In addition we prove how the order of convergence of the two-dimensional process depends on the one-dimensional processes.The converse theorem is not true. But we can give a sufficient condition which depends on the density of the interpolation nodes to assure the converse theorem in some cases.In an easy manner the convergence theorem may be extended to higher dimensions.

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Herrn Prof. Dr. Dr. h. c. L. Collatz zum 60. Geburtstag gewidmet     

Extension of Henrici's method to matrix sequences

In this paper we generalize the definition of linear convergence to matrix sequences. This new definition is used to establish some new results useful to study the new extension of Henrici's method. A convergence theorem, an algorithm for implementation of this method and some numerical examples are given.     

Boundary Behavior of Semigroups of Holomorphic Mappings on the Unit Ball in C n

We study the asymptotic behavior of semigroups generated by holomorphic mappings by using an infinitesimal version of the boundary Schwarz-Wolff Lemma. In particular, the best rate of exponential convergence is obtained. In addition, we establish a geometrical version of the implicit function theorem.     

Su talune disequazioni variazionali ellittiche non lineari del secondo ordine

Summary In this note, making use of a result of J. L. Lions, we examine some non linear elliptic variational inequalities defined on domains which may be unbounded. Such variational inequalities are associated to a uniformely second order elliptic operator. We start with the derivation of an existence theorem (on bounded domains) under non coerciveness assumptions. Next we examine the convergence for the solutions of a collection of variational inequalities. To this purpose we study convergence theorems for variational inequalities associated to operators belonging to a class of abstract mapping of pseudomonotone type between Banach spaces. The solvability of some variational inequalities on unbounded domains then follows directly.

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Entrata in Redazione il 6 aprile 1977.

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Lavoro eseguito nell'ambito del C.N.R., Laboratorio per la Matematica Applicata via L. B. Alberti 4, Genova.     

Inclusion results on statistical cluster points

We study the concepts of statistical cluster points and statistical core of a sequence for A _λ methods defined by deleting some rows from a nonnegative regular matrix A. We also relate A _λ-statistical convergence to A _μ-statistical convergence. Finally we give a consistency theorem for A-statistical convergence and deduce a core equality result.     

A note on rates of convergence in the multidimensional CLT for maximum partial sums

In this note we obtain rates of convergence in the central limit theorem for certain maximum of coordinate partial sums of independent identically distributed random vectors having positive mean vector and a nonsingular correlation matrix. The results obtained are in terms of rates of convergence in the multidimensional central limit theorem. Thus under the conditions of Sazonov (1968, Sankhya, Series A30 181–204, Theorem 2), we have the same rate of convergence for the vector of coordinate maximums. Other conditions for the multidimensional CLT are also discussed, c.f., Bhattachaya (1977, Ann. Probability 5 1–27). As an application of one of the results we obtain a multivariate extension of a theorem of Rogozin (1966, Theor. Probability Appl. 11 438–441).