The monotonicity of two-stage iterative methods

Two-stage iterative methods for the solution of linear systems are analyzed when the coefficient matrix is Hermitian positive definite. Comparison theorems, based on the number of inner iterations performed, are given.     

New eighth-order iterative methods for solving nonlinear equations

In this paper, three new families of eighth-order iterative methods for solving simple roots of nonlinear equations are developed by using weight function methods. Per iteration these iterative methods require three evaluations of the function and one evaluation of the first derivative. This implies that the efficiency index of the developed methods is 1.682, which is optimal according to Kung and Traub’s conjecture [7] for four function evaluations per iteration. Notice that Bi et al.’s method in [2] and [3] are special cases of the developed families of methods. In this study, several new examples of eighth-order methods with efficiency index 1.682 are provided after the development of each family of methods. Numerical comparisons are made with several other existing methods to show the performance of the presented methods.     

Some linear stability results for iterative schemes for implicit Runge-Kutta methods

This article examines stability properties of some linear iterative schemes that have been proposed for the solution of the nonlinear algebraic equations arising in the use of implicit Runge-Kutta methods to solve a differential systemx =f(x). Each iteration step requires the solution of a set of linear equations, with constant matrixI –hJ, whereJ is the Jacobian off evaluated at some fixed point. It is shown that the stability properties of a Runge-Kutta method can be preserved only if is an eigenvalue of the coefficient matrixA. SupposeA has minimal polynomial (x – ) ^m p(x),p() 0. Then stability can be preserved only if the order of the method is at mostm + 2 (at mostm + 1 except for one case).This work was partially supported by a grant from the Science and Engineering Research Council.     

Superlinear convergence theorems for Newton-type methods for nonlinear systems of equations

In this paper, someQ-order convergence theorems are given for the problem of solving nonlinear systems of equations when using very general finitely terminating methods for the solution of the associated linear systems. The theorems differ from those of Dembo, Eisenstat, and Steihaug in the different stopping condition and in their applicability to the nonlinear ABS algorithm.Lecture presented at the University of Bergamo, Bergamo, Italy, October 1989.     

On the convergence of the method for indefinite integration of oscillatory and singular functions

We consider the problem of convergence and error estimation of the method for computing indefinite integrals proposed in Keller [8]. To this end, we have analysed the properties of the difference operator related to the difference equation for the Chebyshev coefficients of a function that satisfies a given linear differential equation with polynomial coefficients. Properties of this operator were never investigated before. The obtained results lead us to the conclusion that the studied method is always convergent. We also give a rigorous proof of the error estimates.     

A new family of eighth-order iterative methods for solving nonlinear equations

A family of eighth-order iterative methods with four evaluations for the solution of nonlinear equations is presented. Kung and Traub conjectured that an iteration method without memory based on n evaluations could achieve optimal convergence order 2^n-1. The new family of eighth-order methods agrees with the conjecture of Kung-Traub for the case n=4. Therefore this family of methods has efficiency index equal to 1.682. Numerical comparisons are made with several other existing methods to show the performance of the presented methods.     

Accelerated iterative methods for finding solutions of a system of nonlinear equations

In this paper, we present a technique to construct iterative methods to approximate the zeros of a nonlinear equation F(x)=0, where F is a function of several variables. This technique is based on the approximation of the inverse function of F and on the use of a fixed point iteration. Depending on the number of steps considered in the fixed point iteration, or in other words, the number of evaluations of the function F, we obtain some variants of classical iterative processes to solve nonlinear equations. These variants improve the order of convergence of classical methods. Finally, we show some numerical examples, where we use adaptive multi-precision arithmetic in the computation that show a smaller cost.     

Three-step iterative methods with optimal eighth-order convergence

In this paper, based on Ostrowski’s method, a new family of eighth-order methods for solving nonlinear equations is derived. In terms of computational cost, each iteration of these methods requires three evaluations of the function and one evaluation of its first derivative, so that their efficiency indices are 1.682, which is optimal according to Kung and Traub’s conjecture. Numerical comparisons are made to show the performance of the new family.     

On the convergence of general stationary iterative methods for range‐Hermitian singular linear systems

General stationary iterative methods with a singular matrix M for solving range‐Hermitian singular linear systems are presented, some convergence conditions and the representation of the solution are also given. It can be verified that the general Ortega–Plemmons theorem and Keller theorem for the singular matrix M still hold. Furthermore, the singular matrix M can act as a good preconditioner for solving range‐Hermitian linear systems. Numerical results have demonstrated the effectiveness of the general stationary iterations and the singular preconditioner M. Copyright © 2009 John Wiley & Sons, Ltd.     

Numerical methods for singular optimal control

Methods are described for the numerical solution of singular optimal control problems. A simple method is given for solving a class of problems which form a transition from nonsingular to singular cases. A procedure is given for determining the structure of a singular problem if it is initially unknown. Several numerical examples are presented.This work is based on the author's PhD Dissertation at The Hatfield Polytechnic, Hatfield, Hertfordshire, England.     

Necessary and sufficient conditions for the convergence of iterative methods for the linear complementarity problem

Necessary and sufficient conditions are established for the convergence of various iterative methods for solving the linear complementarity problem. The fundamental tool used is the classical notion of matrix splitting in numerical analysis. The results derived are similar to some well-known theorems on the convergence of iterative methods for square systems of linear equations. An application of the results to a strictly convex quadratic program is also given.This research was based on work supported by the National Science Foundation under Grant No. ECS-81-14571.The author gratefully acknowledges several comments by K. Truemper on the topics of this paper.     

Convergence of multi-level iterative aggregation-disaggregation methods

This paper introduces an error propagation formula of a certain class of multi-level iterative aggregation-disaggregation (IAD) methods for numerical solutions of stationary probability vectors of discrete finite Markov chains. The formula can be used to investigate convergence by computing the spectral radius of the error propagation matrix for specific Markov chains. Numerical experiments indicate that the same type of the formula could be used for a wider class of the multi-level IAD methods. Using the formula we show that for given data there is no relation between convergence of two-level and of multi-level IAD methods.     

Convergence and quotient convergence of iterative methods for solving singular linear equations with index one

Singular systems with index one arise in many applications, such as Markov chain modelling. In this paper, we use the group inverse to characterize the convergence and quotient convergence properties of stationary iterative schemes for solving consistent singular linear systems when the index of the coefficient matrix equals one. We give necessary and sufficient conditions for the convergence of stationary iterative methods for such problems. Next we show that for the stationary iterative method, the convergence and the quotient convergence are equivalent.     

On the convergence of basic iterative methods for convection–diffusion equations

In this paper we analyze convergence of basic iterative Jacobi and Gauss–Seidel type methods for solving linear systems which result from finite element or finite volume discretization of convection–diffusion equations on unstructured meshes. In general the resulting stiffness matrices are neither M‐matrices nor satisfy a diagonal dominance criterion. We introduce two newmatrix classes and analyse the convergence of the Jacobi and Gauss–Seidel methods for matrices from these classes. A new convergence result for the Jacobi method is proved and negative results for the Gauss–Seidel method are obtained. For a few well‐known discretization methods it is shown that the resulting stiffness matrices fall into the new matrix classes. Copyright © 1999 John Wiley & Sons, Ltd.     

The “Gauss-Seidelization” of iterative methods for solving nonlinear equations in the complex plane

In this paper we introduce a process we have called “Gauss-Seidelization” for solving nonlinear equations. We have used this name because the process is inspired by the well-known Gauss-Seidel method to numerically solve a system of linear equations. Together with some convergence results, we present several numerical experiments in order to emphasize how the Gauss-Seidelization process influences on the dynamical behavior of an iterative method for solving nonlinear equations.     

A note on the singular linear system of the generalized finite element methods

We characterize the kernel of the global stiffness matrix in the singular linear system of the generalized finite element methods (GFEM) which uses the classical finite element (FE) shape functions and local approximation space of harmonic polynomials.     

On the local convergence of an iterative approach for inverse singular value problems

The purpose of this paper is to provide the convergence theory for the iterative approach given by M.T. Chu [Numerical methods for inverse singular value problems, SIAM J. Numer. Anal. 29 (1992), pp. 885–903] in the context of solving inverse singular value problems. We provide a detailed convergence analysis and show that the ultimate rate of convergence is quadratic in the root sense. Numerical results which confirm our theory are presented. It is still an open issue to prove that the method is Q-quadratic convergent as claimed by M.T. Chu.     

On the global convergence of Chebyshev's iterative method

In [A. Melman, Geometry and convergence of Euler's and Halley's methods, SIAM Rev. 39(4) (1997) 728–735] the geometry and global convergence of Euler's and Halley's methods was studied. Now we complete Melman's paper by considering other classical third-order method: Chebyshev's method. By using the geometric interpretation of this method a global convergence theorem is performed. A comparison of the different hypothesis of convergence is also presented.