速度追踪问题中鞍点系统的新分裂迭代

 有限元离散一类速度追踪问题后得到具有鞍点结构的线性系统,针对该鞍点系统,本文提出了一种新的分裂迭代技术.证明了新的分裂迭代方法的无条件收敛性,详细分析了新的分裂预条件子对应的预处理矩阵的谱性质.数值结果验证了对于大范围的网格参数和正则参数,新的分裂预条件子在求解有限元离散速度追踪问题得到的鞍点系统时的可行性和有效性.     

The generalized HSS method for solving singular linear systems

For the singular, non-Hermitian, and positive semidefinite linear systems, we propose an alternating-direction iterative method with two parameters based on the Hermitian and skew-Hermitian splitting. The semi-convergence analysis and the quasi-optimal parameters of the proposed method are discussed. Moreover, the corresponding preconditioner based on the splitting is given to improve the semi-convergence rate of the GMRES method. Numerical examples are given to illustrate the theoretical results and the efficiency of the generalized HSS method either as a solver or a preconditioner for GMRES.     

广义鞍点问题基于PSS的约束预条件子

对于(1,1)块为非Hermitian阵的广义鞍点问题,本文给出了一种基于正定和反对称分裂(Positive definite andskew-Hermitian splitting, PSS)的约束预条件子.该预条件子的(1,1)块由求解非Hermitian正定线性方程组时的PSS迭代法所构造得到.文中分析了PSS约束预条件子的一些性质并证明了预处理迭代法的收敛性.最后用数值算例验证了该预条件子的有效性.     

On HSS-based constraint preconditioners for generalized saddle-point problems

For the generalized saddle-point problems with non-Hermitian (1,1) blocks, we present an HSS-based constraint preconditioner, in which the (1,1) block of the preconditioner is constructed by the HSS method for solving the non-Hermitian positive definite linear systems. We analyze the invertibility of the HSS-based constraint preconditioner and prove the convergence of the preconditioned iteration method. Numerical experiments are used to demonstrate the efficiency of the preconditioner as well as the corresponding preconditioned iteration method, especially when the (1,1) block of the saddle-point matrix is essentially non-Hermitian.     

A generalized variant of the deteriorated PSS preconditioner for nonsymmetric saddle point problems

Based on the variant of the deteriorated positive-definite and skew-Hermitian splitting (VDPSS) preconditioner developed by Zhang and Gu (BIT Numer. Math. 56:587–604, 2016), a generalized VDPSS (GVDPSS) preconditioner is established in this paper by replacing the parameter α in (2,2)-block of the VDPSS preconditioner by another parameter β. This preconditioner can also be viewed as a generalized form of the VDPSS preconditioner and the new relaxed HSS (NRHSS) preconditioner which has been exhibited by Salkuyeh and Masoudi (Numer. Algorithms, 2016). The convergence properties of the GVDPSS iteration method are derived. Meanwhile, the distribution of eigenvalues and the forms of the eigenvectors of the preconditioned matrix are analyzed in detail. We also study the upper bounds on the degree of the minimum polynomial of the preconditioned matrix. Numerical experiments are implemented to illustrate the effectiveness of the GVDPSS preconditioner and verify that the GVDPSS preconditioned generalized minimal residual method is superior to the DPSS, relaxed DPSS, SIMPLE-like, NRHSS, and VDPSS preconditioned ones for solving saddle point problems in terms of the iterations and computational times.     

Preconditioning of saddle point problems by the method of Hermitian and skew-Hermitian splitting iterations

For a nonsingular symmetric system of linear equations with a saddle point, a Hermitian and skew-Hermitian splitting (HSS) preconditioner is considered. For the preconditioned system, symmetrizability conditions are established under which estimates are derived for the spectrum and the convergence rate of Chebyshev-type algorithms and GMRes.     

A modified positive-definite and skew-Hermitian splitting preconditioner for generalized saddle point problems from the Navier-Stokes equation

In this paper, we extend the relaxed positive-definite and skew-Hermitian splitting preconditioner (RPSS) for generalized saddle-point problems in [J.-L. Zhang, C.-Q. Gu and K. Zhang, Appl. Math. Comput. 249(2014)468-479] by introducing an additional parameter. The spectral properties of the presented new preconditioned matrix for generalized saddle-point problem are investigated, meanwhile, the infinite termination merit of the iterative step is also discussed if the Krylov subspace method preconditioned by the modified positive-definite and skew-Hermitian splitting preconditioner (MPSS) is applied. Some numerical experiments illustrate that the efficiency of the proposed new preconditioner.     

On a type of matrix splitting preconditioners for a class of block two-by-two linear systems

Recently, Bai et al. (2013) proposed an effective and efficient matrix splitting iterative method, called preconditioned modified Hermitian/skew-Hermitian splitting (PMHSS) iteration method, for two-by-two block linear systems of equations. The eigenvalue distribution of the iterative matrix suggests that the splitting matrix could be advantageously used as a preconditioner. In this study, the CGNR method is utilized for solving the PMHSS preconditioned linear systems, and the performance of the method is considered by estimating the condition number of the normal equations. Furthermore, the proposed method is compared with other PMHSS preconditioned Krylov subspace methods by solving linear systems arising in complex partial differential equations and a distributed control problem. The numerical results demonstrate the difference in the performance of the methods under consideration.     

A generalized preconditioned HSS method for singular saddle point problems

Li et al. recently studied the generalized HSS (GHSS) method for solving singular linear systems (see Li et al., J. Comput. Appl. Math. 236, 2338–2353 (2012)). In this paper, we generalize the method and present a generalized preconditioned Hermitian and skew-Hermitian splitting method (GPHSS) to solve singular saddle point problems. We prove the semi-convergence of GPHSS under some conditions, and weaken some semi-convergent conditions of GHSS, moreover, we analyze the spectral properties of the corresponding preconditioned matrix. Numerical experiments are given to illustrate the efficiency of GPHSS method with appropriate parameters both as a solver and as a preconditioner.     

A new block preconditioner for complex symmetric indefinite linear systems

Using the equivalent block two-by-two real linear systems and relaxing technique, we establish a new block preconditioner for a class of complex symmetric indefinite linear systems. The new preconditioner is much closer to the original block two-by-two coefficient matrix than the Hermitian and skew-Hermitian splitting (HSS) preconditioner. We analyze the spectral properties of the new preconditioned matrix, discuss the eigenvalue distribution and derive an upper bound for the degree of its minimal polynomial. Finally, some numerical examples are provided to show the effectiveness and robustness of our proposed preconditioner.     

几乎各向同性的高维空间分数阶扩散方程的分块快速正则Hermite分裂预处理方法

一类空间分数阶扩散方程经过有限差分离散后所得到的离散线性方程组的系数矩阵是两个对角矩阵与Toeplitz型矩阵的乘积之和.在本文中,对于几乎各向同性的二维或三维空间分数阶扩散方程的离散线性方程组,采用预处理Krylov子空间迭代方法,我们利用其系数矩阵的特殊结构和具体性质构造了一类分块快速正则Hermite分裂预处理子.通过理论分析,我们证明了所对应的预处理矩阵的特征值大部分都聚集于1的附近.数值实验也表明,这类分块快速正则Hermite分裂预处理子可以明显地加快广义极小残量(GMRES)方法和稳定化的双共轭梯度(BiCGSTAB)方法等Krylov子空间迭代方法的收敛速度.     

Fast inexact subspace iteration for generalized eigenvalue problems with spectral transformation

We study inexact subspace iteration for solving generalized non-Hermitian eigenvalue problems with spectral transformation, with focus on a few strategies that help accelerate preconditioned iterative solution of the linear systems of equations arising in this context. We provide new insights into a special type of preconditioner with “tuning” that has been studied for this algorithm applied to standard eigenvalue problems. Specifically, we propose an alternative way to use the tuned preconditioner to achieve similar performance for generalized problems, and we show that these performance improvements can also be obtained by solving an inexpensive least squares problem. In addition, we show that the cost of iterative solution of the linear systems can be further reduced by using deflation of converged Schur vectors, special starting vectors constructed from previously solved linear systems, and iterative linear solvers with subspace recycling. The effectiveness of these techniques is demonstrated by numerical experiments.     

解线性方程组的预条件Gauss-Seidel型迭代法

给出了解线性方程组的预条件Gauss-Seidel型方法,提出了选取合适的预条件因子．并讨论了对Z-矩阵应用这种方法的收敛性,给出了收敛最快时的系数取值．最后给出数值例子,说明选取合适的预条件因子应用Gauss-Seidel方法求解线性方程组是有效的．     

The spectral properties of the Hermitian and skew-Hermitian splitting preconditioner for generalized saddle point problems

In this paper, we consider the Hermitian and skew-Hermitian splitting (HSS) preconditioner for generalized saddle point problems with nonzero (2, 2) blocks. The spectral property of the preconditioned matrix is studied in detail. Under certain conditions, all eigenvalues of the preconditioned matrix with the original system being non-Hermitian will form two tight clusters, one is near (0, 0) and the other is near (2, 0) as the iteration parameter approaches to zero from above, so do all eigenvalues of the preconditioned matrix with the original system being Hermitian. Numerical experiments are given to demonstrate the results.     

Multistep matrix splitting iteration preconditioning for singular linear systems

Multistep matrix splitting iterations serve as preconditioning for Krylov subspace methods for solving singular linear systems. The preconditioner is applied to the generalized minimal residual (GMRES) method and the flexible GMRES (FGMRES) method. We present theoretical and practical justifications for using this approach. Numerical experiments show that the multistep generalized shifted splitting (GSS) and Hermitian and skew-Hermitian splitting (HSS) iteration preconditioning are more robust and efficient compared to standard preconditioners for some test problems of large sparse singular linear systems.     

A SHIFT-SPLITTING PRECONDITIONER FOR NON-HERMITIAN POSITIVE DEFINITE MATRICES

A shift splitting concept is introduced and, correspondingly, a shift-splitting iteration scheme and a shift-splitting preconditioner are presented, for solving the large sparse system of linear equations of which the coefficient matrix is an ill-conditioned non-Hermitian positive definite matrix. The convergence property of the shift-splitting iteration method and the eigenvalue distribution of the shift-splitting preconditioned matrix are discussed in depth, and the best possible choice of the shift is investigated in detail. Numerical computations show that the shift-splitting preconditioner can induce accurate, robust and effective preconditioned Krylov subspace iteration methods for solving the large sparse non-Hermitian positive definite systems of linear equations.     

Suitable iterative methods for solving the linear system arising in the three fields domain decomposition method

There are two approaches for applying substructuring preconditioner for the linear system corresponding to the discrete Steklov–Poincaré operator arising in the three fields domain decomposition method for elliptic problems. One of them is to apply the preconditioner in a common way, i.e. using an iterative method such as preconditioned conjugate gradient method [S. Bertoluzza, Substructuring preconditioners for the three fields domain decomposition method, I.A.N.-C.N.R, 2000] and the other one is to apply iterative methods like for instance bi-conjugate gradient method, conjugate gradient square and etc. which are efficient for nonsymmetric systems (the preconditioned system will be nonsymmetric). In this paper, second approach will be followed and extensive numerical tests will be presented which imply that the considered iterative methods are efficient.     

An improved block splitting preconditioner for complex symmetric indefinite linear systems

In this paper, an improved block splitting preconditioner for a class of complex symmetric indefinite linear systems is proposed. By adopting two iteration parameters and the relaxation technique, the new preconditioner not only remains the same computational cost with the block preconditioners but also is much closer to the original coefficient matrix. The theoretical analysis shows that the corresponding iteration method is convergent under suitable conditions and the preconditioned matrix can have well-clustered eigenvalues around (0,1) with a reasonable choice of the relaxation parameters. An estimate concerning the dimension of the Krylov subspace for the preconditioned matrix is also obtained. Finally, some numerical experiments are presented to illustrate the effectiveness of the presented preconditioner.     

广义鞍点问题的松弛维数分解预条件子

本文将Benzi等提出的松弛维数分解(Relaxed dimensionalfactorization, RDF)预条件子进一步推广到广义鞍点问题上,并称为GRDF(Generalized RDF)预条件子.该预条件子可看做是用维数分裂迭代法求解广义鞍点问题而导出的改进维数分裂(Modified dimensional split, MDS)预条件子的松弛形式, 它相比MDS预条件子更接近于系数矩阵, 因而结合Krylov子空间方法(如GMRES)有更快的收敛速度.文中分析了GRDF预处理矩阵特征值的一些性质,并用数值算例验证了新预条件子的有效性.     

Continuous preconditioners for the mixed Poisson problem

In this note, we show how to apply preconditioners designed for piecewise linear finite element discretizations of the Poisson problem as preconditioners for the mixed problem. Our preconditioner can be applied both to the original and to the reduced Schur complement problem. Combined with a suitable iterative method, the number of iterations required to solve the preconditioned system will have the same dependency on the mesh size as for the preconditioner applied to the Poisson problem. The presented theory is demonstrated by numerical examples.