Note To The Mixed-Type Splitting Iterative Method For The Positive Real Linear System

In [5] a new iterative method is given for the linear system of equations Au=b , where A is large, sparse and nonsymmetrical and A^{\rm T}+A is symmetric and positive definite (SPD) or equivalently A is positive real. The new iterative method is based on a mixed-type splitting of the matrix A and is called the mixed-type splitting iterative method. The iterative method contains an auxiliary matrix D_1 that is restricted to be symmetric. In this note, the auxiliary matrix is allowed to be more general and it is shown that by proper choice of D 1 , the new iterative method is still convergent. It is also shown that by special choice of D_{1} , the new iterative method becomes the well-known (point) accelerated overrelaxation (AOR) [1] method. Hence, it is shown that the (point) AOR method applied to the positive real system is convergent under the proper choice of the overrelaxation parameters y and .     

A note on parallel multisplitting TOR method for H-matrices

In 2001, Chang studied the convergence of parallel multisplitting TOR method for H-matrices [D.W. Chang, The parallel multisplitting TOR(MTOR) method for linear systems, Comput. Math. Appl. 41 (2001), pp. 215–227]. In this paper, we point out some gaps in the proof of Chang's main results solving them. Moreover, we improve some of Chang's convergence results. A numerical example is presented in order to illustrate the improvement of Chang's convergence region.     

New gauss–seidel like block iterative methods to solve discrete boundary value problems

The concept of new Gauss–Seidel like iterative methods, which was introduced in [3], will be extended so as to obtain a class of convergent Gauss–Seidel like block iterative methods to solve linear matrix equations Ax=b with an M-Matrix A. New block iterative methods will be applied to finite difference approximations of the Laplace's equation on a square (“model problem” [8]) which surpass even the block successive overrelaxation iterative method with optimum relaxation factor in this example.     

On Factorization of Analytic Functions and Its Verification

An interval method for finding a polynomial factor of an analytic function f(z) is proposed. By using a Samelson-like method recursively, we obtain a sequence of polynomials that converges to a factor p*(z) of f(z) if an initial approximate factor p(z) is sufficiently close to p*(z). This method includes some well known iterative formulae, and has a close relation to a rational approximation. According to this factoring method, a fixed point relation for p*(z) is derived. Based on this relation, we obtain a polynomial with complex interval coefficients that includes p*(z).     

The determination of optimum accelerating factors for successive overrelaxation on an equidistant and non-equidistant rectangular net

A modification to the successive overrelaxation iterative procedure for solving elliptic partial differential equations is presented. The modified method is based on an extension of Brazier's nodal overrelaxation method in one dimension, characterised by the use of a different overrelaxation factor for each point in the net. The extension to several dimensions make use of the separability of the variables for the error distribution. Thus the optimum one dimensional results are directly used in the several dimensional problem.The present method has been examined in one and two dimensions, for equidistant and non-equidistant nets. The computational time required to obtain a given accuracy for a solution was found for all two dimensional cases to be half (or less) of that required by conventional methods.     

Parallel iterative solvers involving fast wavelet transforms for the solution of BEM systems

This paper describes the parallelization of a strategy to speed up the convergence of iterative methods applied to boundary element method (BEM) systems arising from problems with non-smooth boundaries and mixed boundary conditions. The aim of the work is the application of fast wavelet transforms as a black box transformation in existing boundary element codes. A new strategy was proposed, applying wavelet transforms on the interval, so it could be used in case of non-smooth coefficient matrices. Here, we describe the parallel iterative scheme and we present some of the results we have obtained.     

The convergence conditions of the psd iterative method for linear systems

In this paper, the PSD iterative method was proposed by Evans and Missirlis [4], for solving a large nonsingular system of linear equations Ax=b A general necessary condition for con-vergence of the PSD iterative method is obtained. The convergence theorems of the PSD iterative method are established under the condition that the coefficient matrix A is an H-matrix, our theorems improve and extend some known results.     

Optimization of a parameterized inexact Uzawa method for saddle point problems

For large sparse saddle point problems, Cao et al. studied a modified generalized parameterized inexact Uzawa (MGPIU) method (see [Y. Cao, M.Q. Jiang, L.Q. Yao, New choices of preconditioning matrices for generalized inexact parameterized iterative methods, J. Comput. Appl. Math. 235 (1) (2010) 263–269]). For iterative methods of this type, the choice of the relaxation parameter is crucial for the methods to achieve their best performance. In this paper, for an example of 2D Stokes equations, we derive the optimal relaxation parameter for the continuous version of the MGPIU method, by minimizing the corresponding convergence factor that is obtained using Fourier analysis. In addition, we find that the MGPIU method is mesh parameter independent, however, it depends asymptotically linearly on the viscosity ν, which suggests that the numerical methods for Stokes equations should be investigated with the presence of the viscosity ν, though it can be scaled out from the equations in advance. We use numerical experiments to validate our theoretical findings.     

Convergence of alternating direction method for minimizing sum of two nonconvex functions with linear constraints

The efficiency of the classic alternating direction method of multipliers has been exhibited by various applications for large-scale separable optimization problems, both for convex objective functions and for nonconvex objective functions. While there are a lot of convergence analysis for the convex case, the nonconvex case is still an open problem and the research for this case is in its infancy. In this paper, we give a partial answer on this problem. Specially, under the assumption that the associated function satisfies the Kurdyka–?ojasiewicz inequality, we prove that the iterative sequence generated by the alternating direction method converges to a critical point of the problem, provided that the penalty parameter is greater than 2L, where L is the Lipschitz constant of the gradient of one of the involved functions. Under some further conditions on the problem's data, we also analyse the convergence rate of the algorithm.     

Convergence of splittings of matrices

In order to investigate the convergence of splittings of matrices, we introduce a concept, where a splitting is (generalized) diagonally dominant. On this basis we prove some necessary and sufficient conditions for the convergence of general splittings of matrices. Much attention is also paid to special iterative methods concerning blockJacobi (BJ), blockGauss-Seidel (BGS), BJOR, BSOR andBAOR iterations, as well as generalizedJacobi (GJ), generalizedGauss-Seidel (GGS), GJOR, GSOR andGAOR iterations. Finally, some sufficient conditions for divergence are given, and we also provide a definition for a splitting to be diagonally weak.     

Robust H∞ control of uncertain stochastic Markovian jump systems with mixed time-varying delays

In this paper, robust H_∞ control for a class of uncertain stochastic Markovian jump systems (SMJSs) with interval and distributed time-varying delays is investigated. The jumping parameters are modelled as a continuous-time, finite-state Markov chain. By employing the Lyapunov-Krasovskii functional and stochastic analysis theory, some novel sufficient conditions in terms of linear matrix inequalities are derived to guarantee the mean-square asymptotic stability of the equilibrium point. Numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results.     

A modified symmetric successive overrelaxation method for augmented systems

In this paper, we establish a modified symmetric successive overrelaxation (MSSOR) method, to solve augmented systems of linear equations, which uses two relaxation parameters. This method is an extension of the symmetric SOR (SSOR) iterative method. The convergence of the MSSOR method for augmented systems is studied. Numerical examples show that the new method is an efficient method.     

Explicit and Iterative Methods for Solving the Matrix Equation AV + BW = EVF + C

In this paper, we consider explicit and iterative methods for solving the Generalized Sylvester matrix equation AV + BW = EVF + C. Based on the use of Kronecker map and Sylvester sum some lemmas and theorems are stated and proved where explicit and iterative solutions are obtained. The proposed methods are illustrated by numerical example. The obtained results show that the methods are very neat and efficient.     

Scientific applications of iterative Toeplitz solvers

Recent research on using the preconditioned conjugate gradient method as an iterative method for solving Toeplitz systems has brought much attention. One of the main important results of this methodology is that the complexity of solving a large class of Toeplitz systems can be reduced toO (n logn) operations as compared to theO(n log² n) operations required by fast direct Toeplitz solvers, provided that a suitable preconditioner is chosen under certain conditions on the Toeplitz operator. In this paper, we survery some applications of iterative Toeplitz solvers to Toeplitz-related problems arising from scientific applications. These applications include partial differential equations, queueing networks, signal and image processing, integral equations, and time series analysis. Research supported by the Cooperative Research Centre for Advanced Computational Systems. Research supported in part by HKRGC grants no. CUHK 316/94E.     

The eigenvalue problem (A − λB)x = 0 for symmetric matrices of high order

Vibrational problems of complex structures treated by the method of finite elements lead to the general eigenvalue problem $\text{(A ? λB)x = 0}$, where $\text{A}$ and $\text{B}$ are symmetric and sparse matrices of high order. The smallest eigenvalues and corresponding eigenvectors of interest are usually computed by a variant of the inverse vector iteration. Instead of this, the smallest eigenvalue can be computed as the minimum of the corresponding Rayleigh quotient for instance by the method of the coordinate relaxation of Faddejew/Faddejewa. The slow convergence of this simple algorithm can however be sped up considerably in analogy to the successive overrelaxation method by a systematic overrelaxation. Numerical experiments indicate indeed a rate of convergence of this coordinate overrelaxation as a function of the relaxation parameter which is comparable to that of the usual seccessive overrelaxation for linear equations. In comparison with known procedures for the solution of the general eigenvalue problem there result some important computational advantages with regard to the amount of work. Finally, the higher eigenvalues can be computed successively by minimizing the Rayleigh quotient of a modified eigenvalue problem based on a deflation process.     

Quotient convergence and multi-splitting methods for solving singular linear equations

Abstract   In this paper, we use the group inverse to characterize the quotient convergence of an iterative method for solving consistent singular linear systems, when the matrix index equals one. Next, we show that for stationary splitting iterative methods, the convergence and the quotient convergence are equivalent, which was first proved in [7]. Lastly, we propose a (multi-)splitting iterative method A=F–G, where the splitting matrix F may be singular, endowed with group inverse, by using F ^# as a solution tool for any iteration. In this direction, sufficient conditions for the quotient convergence of these methods are given. Then, by using the equivalence between convergence and quotient convergence, the classical convergence of these methods is proved. These latter results generalize Cao’s result, which was given for nonsingular splitting matrices F. Keywords: Group inverse, singular linear equations, iterative method, P-regular splitting, Hermitian positive definite matrix, multi-splitting, quotient convergence AMS Classification: 15A09, 65F35     

Multipoint iterative parallel methods for solving equations

In this paper we show the results of some research carried out on parallel iterative methods to solve equations. In particular we study general classes of one point parallel methods and multipoint ones without memory, and we point out the convergence order of these methods and the conditions which are both necessary and sufficient for them to be optimal. In addition we prove that the convergence order for multipoint parallel procedures without memory cannot be more thenr(r+) ^m−1 , wherer indicates the number of the parallel processor used andm the number of the functions and eventual derivatives, calculated not simultaneously in every iteration.

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Sommario In questo articolo presentiamo alcuni risultati concernenti i metodi iterativi paralleli per risolvere equazioni. In particolare analizziamo alcune classi generali di procedimenti ad un punto ed a più punti senza memoria, il loro ordine di convergenza e le condizioni necessarie e sufficienti per ottenere l'ottimalità. Inoltre dimostriamo che l'ordine di convergenza di un procedimento iterativo senza memoria non può eccedere:r(r+1) ^m−1 , dover indica il numero di processor in parallelo usati edm indica il numero di funzioni ed eventuali derivate calcolate non simultaneamente in ogni iterazione.

     

Strong convergence theorems for a new iterative method of -strictly pseudo-contractive mappings in Hilbert spaces

In this paper, we introduce a new iterative method of a k-strictly pseudo-contractive mapping for some 0≤k<1 and prove that the sequence {x_n} converges strongly to a fixed point of T, which solves a variational inequality related to the linear operator A. Our results have extended and improved the corresponding results of Y.J. Cho, S.M. Kang and X. Qin [Some results on k-strictly pseudo-contractive mappings in Hilbert spaces, Nonlinear Anal. 70 (2008) 1956–1964], and many others.     

Block iterative methods for the numerical solution of three dimensional mildly non-linear biharmonic problems of first kind

In this article, we discuss two sets of new finite difference methods of order two and four using 19 and 27 grid points, respectively over a cubic domain for solving the three dimensional nonlinear elliptic biharmonic problems of first kind. For both the cases we use block iterative methods and a single computational cell. The numerical solution of (?u/?n) are obtained as by-product of the methods and we do not require fictitious points in order to approximate the boundary conditions. The resulting matrix system is solved by the block iterative method using a tri-diagonal solver. In numerical experiments the proposed methods are compared with the exact solutions both in singular and non-singular cases.     

Jungck-Khan iterative scheme and higher convergence rate

In this paper, we continue the theme of analytical and numerical treatment of Jungck-type iterative schemes. In particular, we focus on a special case of Jungck-Khan iterative scheme introduced by Khan et al. [Analytical and numerical treatment of Jungck-type iterative schemes, Appl. Math. Comput. 231 (2014) 521–535] to get an insight in the strong convergence and data dependence results obtained therein. Our investigations show that this special case under different control conditions on parametric sequences provides higher convergence rate and better data dependence estimates as compared to the Jungck-Khan iterative scheme itself.