PRECONDITIONED GAUSS-SEIDEL TYPE ITERATIVE METHOD FOR SOLVING LINEAR SYSTEMS

The preconditioned Gauss-Seidel type iterative method for solving linear systems, with the proper choice of the preconditioner, is presented. Convergence of the preconditioned method applied to Z-matrices is discussed. Also the optimal parameter is presented. Numerical results show that the proper choice of the preconditioner can lead to effective by the preconditioned Gauss-Seidel type iterative methods for solving linear systems.

CONVERGENCE OF PRECONDITIONED GAUSS-SEIDEL ITERATIVE METHODS

Let the linear system Ax=b where the coefficient matrix A=(aij)∈Rm,n is an L-ma-trix(that is,aij】0 （?） i and aij≤0 （?） i≠j),A=I-L-U,I is the identity matrix,-L and-U are,respectively,strictly lower and strictly upper triangular parts of A.In[1]theauthors considered two preconditioned linear systems?x=（?） and ?x=（?）

A Preconditioned Multigrid Method for Efficient Simulation of Three-dimensional Compressible and Incompressible Flows

To develop an efficient and robust aerodynamic analysis method for numerical optimization designs of wing and complex configuration, a combination of matrix preconditioning and multigrid method is presented and investigated. The time derivatives of three-dimensional Navier-Stokes equations are preconditioned by Choi-Merkle preconditioning matrix that is originally designed for two-dimensional low Mach number viscous flows. An extension to three-dimensional viscous flow is implemented, and a method improving the convergence for transonic flow is proposed. The space discretizaition is performed by employing a finite-volume cell-centered scheme and using a central difference. The time marching is based on an explicit Rtmge-Kutta scheme proposed by Jameson. An efficient FAS multigrid method is used to accelerate the convergence to steady-state solutions. Viscous flows over ONERA M6 wing and M100 wing are numerically simulated with Mach numbers ranging from 0.010 to 0.839. The inviscid flow over the DLR-F4 wing-body configuration is also calculated to preliminarily examine the performance of the presented method for complex configuration. The computed results are compared with the experimental data and good agreement is achieved. It is shown that the presented method is efficient and robust for both compressible and incompressible flows and is very attractive for aerodynamic optimization designs of wing and complex configuration.

High-efciency improved symmetric successive over-relaxation preconditioned conjugate gradient method for solving large-scale finite element linear equations

Fast solving large-scale linear equations in the finite element analysis is a classical subject in computational mechanics. It is a key technique in computer aided engineering （CAE） and computer aided manufacturing （CAM）. This paper presents a high-efficiency improved symmetric successive over-relaxation （ISSOR） preconditioned conjugate gradient （PCG） method, which maintains lelism consistent with the original form. Ideally, the by 50% as compared with the original algorithm. the convergence and inherent paralcomputation can It is suitable for be reduced nearly high-performance computing with its inherent basic high-efficiency operations. By comparing with the numerical results, it is shown that the proposed method has the best performance.

Greedy Randomized Gauss-Seidel Method with Oblique Direction

For the linear least squares problem with coefficient matrix columns being highly correlated, we develop a greedy randomized Gauss-Seidel method with oblique direction. Then the corresponding convergence result is deduced. Numerical examples demonstrate that our proposed method is superior to the greedy randomized Gauss-Seidel method and the randomized Gauss-Seidel method with oblique direction.

A Preconditioned Gridless Method for Solving Euler Equations at Low Mach Numbers

A preconditioned gridless method is developed for solving the Euler equations at low Mach numbers.The preconditioned system in a conservation form is obtained by multiplying apreconditioning matrix of the type of Weiss and Smith to the time derivative of the Euler equations,which are discretized using agridless technique wherein the physical domain is distributed by clouds of points.The implementation of the preconditioned gridless method is mainly based on the frame of the traditional gridless method without preconditioning,which may fail to converge for low Mach number simulations.Therefore,the modifications corresponding to the affected terms of preconditioning are mainly addressed.The numerical results show that the preconditioned gridless method still functions for compressible transonic flow simulations and additionally,for nearly incompressible flow simulations at low Mach numbers as well.The paper ends with the nearly incompressible flow over a multi-element airfoil,which demonstrates the ability of the method presented for treating flows over complicated geometries.

IMPROVED PRECONDITIONED CONJUGATE GRADIENT METHOD AND ITS APPLICATION IN F.E.A.FOR ENGINEERING

In this paper two theorems with theoretical and practical significance are given in respect to the preconditioned conjugate gradient method (PCCG). The theorems discuss respectively the qualitative property of the iterative solution and the construction principle of the iterative matrix. The authors put forward a new incompletely LU factorizing technique for non-M-matrix and the method of constructing the iterative matrix. This improved PCCG is used to calculate the ill-conditioned problems and large-scale three-dimensional finite element problems, and simultaneously contrasted with other methods. The abnormal phenomenon is analyzed when PCCG is used to solve the system of ill-conditioned equations, ft is shown that the method proposed in this paper is quite effective in solving the system of large-scale finite element equations and the system of ill-conditioned equations.

Preconditioned Iterative Methods for Algebraic Systems from Multiplicative Half-Quadratic Regularization Image Restorations

Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term. A regularized convex term can usually preserve the image edges well in the restored image. In this paper, we consider a class of convex and edge-preserving regularization functions, I.e., multiplicative half-quadratic regularizations, and we use the Newton method to solve the correspondingly reduced systems of nonlinear equations. At each Newton iterate, the preconditioned conjugate gradient method, incorporated with a constraint preconditioner, is employed to solve the structured Newton equation that has a symmetric positive definite coefficient matrix.The igenvalue bounds of the preconditioned matrix are deliberately derived, which can be used to estimate the convergence speed of the preconditioned conjugate gradient method. We use experimental results to demonstrate that this new approach is efficient,and the effect of image restoration is r0easonably well.

Formulation of a Preconditioned Algorithm for the Conjugate Gradient Squared Method in Accordance with Its Logical Structure

In this paper, we propose an improved preconditioned algorithm for the conjugate gradient squared method (improved PCGS) for the solution of linear equations. Further, the logical structures underlying the formation of this preconditioned algorithm are demonstrated via a number of theorems. This improved PCGS algorithm retains some mathematical properties that are associated with the CGS derivation from the bi-conjugate gradient method under a non-preconditioned system. A series of numerical comparisons with the conventional PCGS illustrate the enhanced effectiveness of our improved scheme with a variety of preconditioners. This logical structure underlying the formation of the improved PCGS brings a spillover effect from various bi-Lanczos-type algorithms with minimal residual operations, because these algorithms were constructed by adopting the idea behind the derivation of CGS. These bi-Lanczos-type algorithms are very important because they are often adopted to solve the systems of linear equations that arise from large-scale numerical simulations.

A Modified Precondition in the Gauss-Seidel Method

In recent years, a number of preconditioners have been applied to solve the linear systems with Gauss-Seidel method (see [1-7,10-12,14-16]). In this paper we use Sl instead of (S + Sm) and compare with M. Morimoto’s precondition [3] and H. Niki’s precondition [5] to obtain better convergence rate. A numerical example is given which shows the preference of our method.

A Note on Parameterized Preconditioned Method for Singular Saddle Point Problems

Recently, some authors (Li, Yang and Wu, 2014) studied the parameterized preconditioned HSS (PPHSS) method for solving saddle point problems. In this short note, we further discuss the PPHSS method for solving singular saddle point problems. We prove the semi-convergence of the PPHSS method under some conditions. Numerical experiments are given to illustrate the efficiency of the method with appropriate parameters.

The AOR Iterative Method for Preconditioned Linear Systems

The preconditioned methods for solving linear system are discussed. The convergence rate of accelerated overrelaxation (AOR) method can be enlarged by using the preconditioned method when the classical AOR method converges, and the preconditioned method is invalid when the classical iterative method does not converge. The results in corresponding references are improved and perfected.

PROPERTIES AND ALGORITHM ON PRECONDITIONED CONJUGATE GRADIENT METHOD

Some properties of the preconditioned gradient conjugate method are given.It may happen that loss of significant digits, when the norm of residual is very small. To avoid this, a variant algorithm which does not use the residual is put forward.

ALGEBRAIC MULTILEVEL RELAXED PRECONDITIONING METHODS

Some ways of multilevel relaxed preconditioning matrices for the stiffness matrix in the discretization of selfad joint second order elliptic boundary value problems are proposed. For reason-able assumptions of the relaxed factor ω, smaller relative condition numbers are given. The optimal relaxed factor ω is derived, too.

ON ALGEBRAIC MULTILEVEL PRECONDITIONINGMETHODS

In the present paper we extend the method presented by 0. Axelsson and P. Vassilevski called AMLP version (i) of recursively constructing preconditioner for the stiffness matrix in the discretization of selfadjoint second order elliptic boundary value problems. In our extended method the systems to be eliminated on each level containing the major block matrices of the given matrix can be solved approximately, while they must be solved exactly in the original method.

NONNESTED MULTILEVEL PRECONDITIONING METHODS WITH SIMPLE AND IDENTICAL COARSE MESH CORRECTION SPACES

In this paper, some simple and practical multilevel preconditioners for Hermite conforming and some well known nonconforming finite elements are constructed.

On the Monotonicity of Convergence Rate of Modified Gauss-Seidel Method

In this note, we prove that the convergence rate of the modified Gauss-Seidel (MGS) method with preconditional I+S α is a monotonic function of preconditioning parameter α. Based on this result, to achieve better convergence rate we suggest proforming twice preconditoning when applying the MGS method to solve a linear system whose coefficient matrix is an irreducible non-singular M-matrix.

Explicit Iterative Methods of Second Order and Approximate Inverse Preconditioners for Solving Complex Computational Problems

Explicit Exact and Approximate Inverse Preconditioners for solving complex linear systems are introduced. A class of general iterative methods of second order is presented and the selection of iterative parameters is discussed. The second order iterative methods behave quite similar to first order methods and the development of efficient preconditioners for solving the original linear system is a decisive factor for making the second order iterative methods superior to the first order iterative methods. Adaptive preconditioned Conjugate Gradient methods using explicit approximate preconditioners for solving efficiently large sparse systems of algebraic equations are also presented. The generalized Approximate Inverse Matrix techniques can be efficiently used in conjunction with explicit iterative schemes leading to effective composite semi-direct solution methods for solving large linear systems of algebraic equations.

求解多重线性系统的预条件张量分裂Gauss-Seidel迭代法

为了解决建立在强M-张量上的多重线性系统的预处理Gauss-Seidel迭代法,提出一个新的预条件子I+S'α,给出张量分裂,提出3种不同的Gauss-Seidel分裂方式,形成预处理迭代张量,并证明它们是收敛的。比较基于不同分裂形式的Gauss-Seidel迭代收敛速度,通过数值算例验证了所给算法是可行有效的。

求解绝对值方程组的广义SOR型方法

为了求解大规模的绝对值方程Ax-|x|=b,利用预处理技术及参数矩阵取代单参数的策略,文章提出了一类广义SOR型(GSOR)方法。通过选取适当的预处理矩阵或参数,GSOR方法能简化为已有的一种SOR型(NSOR)方法或导出更有效的SOR型方法。而且,基于Ax-|x|=b方程解的唯一性条件,建立了GSOR方法的收敛性定理并给出了该方法的拟最优参数。特别地,利用截断的Neumann展开构建了一个新的预处理矩阵,由此导出了一种特殊的GSOR方法,记为GSOR-1方法。文章进一步证明:GSOR-1方法具有比NSOR方法更小的拟最优收敛因子。数值测试进一步揭示:GSOR-1方法比NSOR方法具有更快的收敛速度且耗费更少的计算时间。