Generalized Successive Overrelaxation Research Articles

Computation of Green’s Function in a Strongly Heterogeneous Medium Using the Lippmann–Schwinger Equation: A Generalized Successive Over-Relaxion plus Preconditioning Scheme

The computation of Green’s function is a basic and time-consuming task in realizing seismic imaging using integral operators because the function is the kernel of the integral operators and because every image point functions as the source point of Green’s function. If the perturbation theory is used, the problem of the computation of Green’s function can be transformed into one of solving the Lippmann–Schwinger (L–S) equation. However, if the velocity model under consideration has large scale and strong heterogeneity, solving the L–S equation may become difficult because only numerical or successive approximate (iterative) methods can be used in this case. In the literature, one of these methods is the generalized successive over-relaxation (GSOR) iterative method, which can effectively solve the L–S equation and obtain the desired convergent iterative series. However, the GSOR iterative method may encounter slow convergence when calculating the high-frequency Green’s function. In this paper, we propose a new scheme that utilizes the GSOR iterative with a precondition to solve the complex wavenumber L–S equation in a slightly attenuated medium. The complex wavenumber with imaginary components localizes the energy of the background Green’s function and reduces its singularity by enabling exponential decay. Introduction of the preconditioning operator can further improve the convergence speed of the GSOR iterative series. Then, we provide a preconditioned generalized successive over-relaxation (pre-GSOR) iterative format. Our numerical results show that if an appropriate damping factor and a proper preconditioning operator are selected, the method presented here outperforms the GSOR iterative for the real wavenumber L–S equation in terms of the convergence speed, accuracy, and adaptation to high frequencies.

MN-PGSOR Method for Solving Nonlinear Systems with Block Two-by-Two Complex Symmetric Jacobian Matrices

For solving the large sparse linear systems with 2 × 2 block structure, the generalized successive overrelaxation (GSOR) iteration method is an efficient iteration method. Based on the GSOR method, the PGSOR method introduces a preconditioned matrix with a new parameter for the coefficient matrix which can enhance the efficiency. To solve the nonlinear systems in which the Jacobian matrices are complex and symmetric with the block two-by-two form, we try to use the PGSOR method as an inner iteration, with the help of the modified Newton method as an efficient outer iteration method. This new method is called the modified Newton-PGSOR (MN-PGSOR) method. Local convergence properties of the MN-PGSOR are analyzed under the Hölder condition. Finally, we give the comparison of our new method with some previous methods in the numerical results. The MN-PGSOR method is superior in both iteration steps and computing time.

On GSOR-based iteration methods for solving weakly nonlinear systems with complex symmetric coefficient matrices

In this paper, two methods based on generalized successive overrelaxation (GSOR) for solving weakly nonlinear systems with complex coefficient matrices are proposed: Picard-accelerated GSOR (P-AGSOR) and Picard-preconditioned GSOR (P-PGSOR) methods. Theoretical analysis demonstrates the local convergence properties of the two methods under appropriate assumptions. Numerical examples confirm the effectiveness and superiority of the two methods.

Modified Newton-AGSOR method for solving nonlinear systems with block two-by-two complex symmetric Jacobian matrices

In this paper, we modify the accelerated generalized successive overrelaxation (AGSOR) method for block two-by-two complex linear systems, and use the AGSOR method as an inner iteration for the modified Newton equations to solve the nonlinear system whose Jacobian matrix is a block two-by-two complex symmetric matrix. Our new method is named modified Newton AGSOR (MN-AGSOR) method. Because generalized successive overrelaxation (GSOR) method is a special form of the AGSOR method, the modified Newton GSOR (MN-GSOR) method is also analyzed in the discussion. Next, we use the Holder continuous condition instead of the Lipschitz assumption to analyze and prove the local convergence properties of the MN-AGSOR method. At last, numerical experiments verify the efficiency of the MN-AGSOR method, and it can be seen from the comparison of various aspects that the MN-AGSOR method is superior to some other recently proposed methods.

Generalized ASOR and Modified ASOR Methods for Saddle Point Problems

Recently, the accelerated successive overrelaxation- (SOR-) like (ASOR) method was proposed for saddle point problems. In this paper, we establish a generalized accelerated SOR-like (GASOR) method and a modified accelerated SOR-like (MASOR) method, which are extension of the ASOR method, for solving both nonsingular and singular saddle point problems. The sufficient conditions of the convergence (semiconvergence) for solving nonsingular (singular) saddle point problems are derived. Finally, numerical examples are carried out, which show that the GASOR and MASOR methods have faster convergence rates than the SOR-like, generalized SOR (GSOR), modified SOR-like (MSOR-like), modified symmetric SOR (MSSOR), generalized symmetric SOR (GSSOR), generalized modified symmetric SOR (GMSSOR), and ASOR methods with optimal or experimentally found optimal parameters when the iteration parameters are suitably chosen.

Two Efficient Inexact Algorithms for a Class of Large Sparse Complex Linear Systems

Recently Salkuyeh et al. (Int J Comput Math 92:802–815, 2015) studied the generalized SOR (GSOR) iterative method for a class of complex symmetric linear system of equations. In this paper, we present an inexact variant of the GSOR method in which the conjugate gradient and the preconditioned conjugate gradient methods are regarded as its inner iteration processes at each step of the GSOR outer iteration. Moreover, we construct a new method called shifted GSOR iteration method which is obtained from combination of a shift-splitting iteration scheme and the GSOR iteration method. The convergence analysis of the proposed methods are presented. Some numerical experiments are given to show the performance of the methods and are compared with those of the inexact MHSS method.

Preconditioned GSOR iterative method for a class of complex symmetric system of linear equations

SummaryIn this paper, we present a preconditioned variant of the generalized successive overrelaxation (GSOR) iterative method for solving a broad class of complex symmetric linear systems. We study conditions under which the spectral radius of the iteration matrix of the preconditioned GSOR method is smaller than that of the GSOR method and determine the optimal values of iteration parameters. Numerical experiments are given to verify the validity of the presented theoretical results and the effectiveness of the preconditioned GSOR method. Copyright © 2015 John Wiley & Sons, Ltd.

Generalized successive overrelaxation iterative method for a class of complex symmetric linear system of equations

In this paper, to solve a broad class of complex symmetric linear systems, we recast the complex system in a real formulation and apply the generalized successive overrelaxation (GSOR) iterative method to the equivalent real system. We then investigate its convergence properties and determine its optimal iteration parameter as well as its corresponding optimal convergence factor. In addition, the resulting GSOR preconditioner is used to precondition Krylov subspace methods such as the generalized minimal residual method for solving the real equivalent formulation of the system. Finally, we give some numerical experiments to validate the theoretical results and compare the performance of the GSOR method with the modified Hermitian and skew-Hermitian splitting iteration.

Variants of the Uzawa method for saddle point problem

In this paper, we first propose three variants of the Uzawa method for solving the saddle point problem, and then we provide convergence results for the three proposed methods. Numerical experiments show that our proposed methods with three parameters perform about twice as fast as the GSOR (Generalized SOR) method with two parameters since the proposed methods have less workload per iteration than the GSOR.

On Contraction and Semi-Contraction Factors of GSOR Method for Augmented Linear Systems

The generalized successive overrelaxation (GSOR) method was presented and studied by Bai, Parlett and Wang [Numer. Math. 102(2005), pp.1-38] for solving the augmented system of linear equations, and the optimal iteration parameters and the corresponding optimal convergence factor were exactly obtained. In this paper, we further estimate the contraction and the semi-contraction factors of the GSOR method. The motivation of the study is that the convergence speed of an iteration method is actually decided by the contraction factor but not by the spectral radius in flnite-step iteration computations. For the nonsingular augmented linear system, under some restrictions we obtain the contraction domain of the parameters involved, which guarantees that the contraction factor of the GSOR method is less than one. For the singular but consistent augmented linear system, we also obtain the semi-contraction domain of the parameters in a similar fashion. Finally, we use two numerical examples to verify the theoretical results and the efiectiveness of the GSOR method.

A variable preconditioned GCR( [formula omitted]) method using the GSOR method for singular and rectangular linear systems

The Generalized Conjugate Residual (GCR) method with a variable preconditioning is an efficient method for solving a large sparse linear system A x = b . It has been clarified by some numerical experiments that the Successive Over Relaxation (SOR) method is more effective than Krylov subspace methods such as GCR and ILU(0) preconditioned GCR for performing the variable preconditioning. However, SOR cannot be applied for performing the variable preconditioning when solving such linear systems that the coefficient matrix has diagonal entries of zero or is not square. Therefore, we propose a type of the generalized SOR (GSOR) method. By numerical experiments on the singular linear systems, we demonstrate that the variable preconditioned GCR using GSOR is effective.

Generalized AOR Algorithms for Linear Complementarity Problem

In this paper, we propose a class of generalized accelerated overrelaxation (GAOR) methods, in which one special case reduces to the generalized successive overrelaxation(GSOR) methods, for solving a linear complementarity problem LCP( M, q). Sufficient conditions for convergence of the GAOR and GSOR methods are presented for cases when the system matrix M is an H–matrix, M–matrix and a strictly or irreducible diagonally dominant matrix. Moreover, when M is an L–matrix, we examine the monotone convergence of the new methods. Finally a numerical example is used to validate the results proved in this paper.

Generalized AOR methods for linear complementarity problem

In this paper, we firstly establish a class of generalized AOR (GAOR) methods for solving a linear complementarity problem LCP( M, q), whose special case reduces to generalized SOR (GSOR) method. Then, some sufficient conditions for convergence of the GAOR and GSOR methods are presented, when the system matrix M is an H-matrix, M-matrix and a strictly or irreducible diagonally dominant matrix. Moreover, when M is an L-matrix, we discuss the monotone convergence of the new methods. Lastly, we report some computational results with the proposed methods.

On convergence of the generalized accelerated overrelaxation method

In this paper, we study the convergence of the generalized accelerated overrelaxation (GAOR) iterative method. That is an extension of the classical convergence result of the generalized successive overrelaxation (GSOR) iterative method. We proposed some theorems, which they obtain better results than similar works. By some numerical examples we show the goodness of our results.

On generalized successive overrelaxation methods for augmented linear systems

For the augmented system of linear equations, Golub, Wu and Yuan recently studied an SOR-like method (BIT 41(2001)71---85). By further accelerating it with another parameter, in this paper we present a generalized SOR (GSOR) method for the augmented linear system. We prove its convergence under suitable restrictions on the iteration parameters, and determine its optimal iteration parameters and the corresponding optimal convergence factor. Theoretical analyses show that the GSOR method has faster asymptotic convergence rate than the SOR-like method. Also numerical results show that the GSOR method is more effective than the SOR-like method when they are applied to solve the augmented linear system. This GSOR method is further generalized to obtain a framework of the relaxed splitting iterative methods for solving both symmetric and nonsymmetric augmented linear systems by using the techniques of vector extrapolation, matrix relaxation and inexact iteration. Besides, we also demonstrate a complete version about the convergence theory of the SOR-like method.

Preconditioned conjugate gradient method and generalized successive over relaxation method for the weighted least squares problems

In this paper, the connection between the generalized successive over relaxation (GSOR) method and the preconditioned conjugate gradient (PCG) method for the normal equations is investigated. It is shown that the PCG method is at least as fast as the GSOR method. Numerical examples demonstrates that the PCG method is much faster than the GSOR method. †Email: yuxiaoyun@21cn.com ‡Email: zhangruying@yahoo.com

Preconditioned Conjugate Gradient Method and Generalized Successive Overrelaxation Method for the Least Squares Problems

In a recent paper [4], Li et al . gave a generalized successive overrelaxation (GSOR) method for the least squares problems. In this paper, the connection between the GSOR method and the preconditioned conjugate gradient (PCG) method for the normal equations is investigated. It is shown that the PCG method is at least as fast as the GSOR method. Numerical examples demonstrates that the PCG method is much faster than the GSOR method.

GSOR Method for the Equality Constrained Least Squares Problems and the Generalized Least Squares Problems

In a recent paper [4], Li et al . gave a generalized successive overrelaxation (GSOR) method for the least squares problems. In this paper, we show that the GSOR method can be applied to the equality constrained least squares (LSE) problems and the generalized least squares (GLS) problems.

Convergence of the generalized AOR method

A generalized AOR (GAOR) method is proposed here to solve the linear system with some special structure. The convergence of the GAOR method is studied. The convergence result obtained here is an extension of the classical convergence result of the regular AOR method with weaker condition. In comparing with the generalized SOR (GSOR) method, we show that the GAOR method is better than the GSOR method under certain conditions. In some cases, the GAOR method is convergent even if the GSOR method does not converge. Finally, the GAOR method is applied to solve the generalized least-squares (LS) problems. Three numerical algorithms for the generalized LS problems are given.

A generalized successive overrelaxation method for least squares problems

In this paper a new iterative method is given for solving large sparse least squares problems and computing the minimum norm solution to underdetermined consistent linear systems. The new scheme is called the generalized successive overrelaxation (GSOR) method and is shown to be convergent ifA is full column rank. The GSOR method involves a parameter ρ and an auxiliary matrixP. One can choose matrix P so that the GSOR method only involves matrix and vector operations; therefore the GSOR method is suitable for parallel computations. Besides, the GSOR method can be combined with preconditioning techniques, and therefore can be expected to be more effective.