Showing papers by "Zhong-Zhi Bai published in 2005"

On generalized successive overrelaxation methods for augmented linear systems

Abstract: 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.

Block Triangular and Skew-Hermitian Splitting Methods for Positive-Definite Linear Systems

Abstract: By further generalizing the concept of Hermitian (or normal) and skew-Hermitian splitting for a non-Hermitian and positive-definite matrix, we introduce a new splitting, called positive-definite and skew-Hermitian splitting (PSS), and then establish a class of PSS methods similar to the Hermitian (or normal) and skew-Hermitian splitting (HSS or NSS) method for iteratively solving the positive-definite systems of linear equations. Theoretical analysis shows that the PSS method converges unconditionally to the exact solution of the linear system, with the upper bound of its convergence factor dependent only on the spectrum of the positive-definite splitting matrix and independent of the spectrum of the skew-Hermitian splitting matrix as well as the eigenvectors of all matrices involved. When we specialize the PSS to block triangular (or triangular) and skew-Hermitian splitting (BTSS or TSS), the PSS method naturally leads to a BTSS or TSS iteration method, which may be more practical and efficient than the HSS and NSS iteration methods. Applications of the BTSS method to the linear systems of block two-by-two structures are discussed in detail. Numerical experiments further show the effectiveness of our new methods.

Structured preconditioners for nonsingular matrices of block two-by-two structures

Abstract: For the large sparse block two-by-two real nonsingular matrices, we establish a general framework of practical and efficient structured preconditioners through matrix transformation and matrix approximations. For the specific versions such as modified block Jacobi-type, modified block Gauss-Seidel-type, and modified block unsymmetric (symmetric) Gauss-Seidel-type preconditioners, we precisely describe their concrete expressions and deliberately analyze eigenvalue distributions and positive definiteness of the preconditioned matrices. Also, we show that when these structured preconditioners are employed to precondition the Krylov subspace methods such as GMRES and restarted GMRES, fast and effective iteration solvers can be obtained for the large sparse systems of linear equations with block two-by-two coefficient matrices. In particular, these structured preconditioners can lead to efficient and high-quality preconditioning matrices for some typical matrices from the real-world applications.

Best Approximate Solution of Matrix Equation AXB+CYD=E

Abstract: We derive an analytical expression of the best approximate solution in the least-squares solution set ${\mathbb S}_E$ of the matrix equation $AXB+CYD=E$ to a given matrix pair $(X_f, Y_f)$, where $A$, $B$, $C$, $D$, and $E$ are given matrices of suitable sizes. Our work is based on the projection theorem in the finite-dimensional inner product space, and we use the generalized singular value decomposition and the canonical correlation decomposition. Moreover, we establish a direct method for computing this best approximate solution. An algorithm for finding the best approximate solution is described in detail, and an example is used to show the feasibility and effectiveness of our algorithm.

On Inexact Preconditioners for Nonsymmetric Matrices

Abstract: Inexact versions of the block-triangular preconditioners for nonsymmetric matrices of block two-by-two structures in [M. F. Murphy, G. H. Golub, and A. J. Wathen, SIAM J. Sci. Comput., 21 (2000), pp. 1969--1972] and [I. C. F. Ipsen, SIAM J. Sci. Comput., 23 (2001), pp. 1050--1051] are presented, and the two preconditioners for symmetric block two-by-two matrices in [C. Durazzi and V. Ruggiero, Numer. Linear Algebra Appl., 10 (2003), pp. 673--688] are extended to general nonsymmetric matrices. Moreover, we precisely describe the spectral properties of the preconditioned matrices and the finite-step termination properties of the preconditioned Krylov subspace iteration methods with an optimal or Galerkin property, with respect to these preconditioners. Several numerical examples are performed to illustrate the effectiveness of the proposed preconditioners.

Alternating splitting waveform relaxation method and its successive overrelaxation acceleration

Abstract: For the large sparse implicit linear initial value problem, we present a block successive overrelaxation scheme for the alternating direction implicit waveform relaxation method to further accelerate its convergence speed, and discuss the convergence property of the resulting iteration method in detail. Numerical implementations about several non-Hermitian implicit linear initial value problems show that the alternating direction implicit waveform relaxation method is very effective, and the block successive overrelaxation technique really accelerates its convergence speed.