Singular Saddle Point Problems Research Articles

Preconditioned iterative method for nonsymmetric saddle point linear systems

In this paper, a new preconditioned iterative method is presented to solve a class of nonsymmetric nonsingular or singular saddle point problems. The implementation of the proposed preconditioned Krylov subspace method avoids solving inverse of Schur complement and only needs to solve one linear sub-system at each step, which implies that it may save considerable costs. Theoretical convergence analysis, including the bounds of eigenvalues and eigenvectors, the degree of the minimal polynomial of the preconditioned matrix, are discussed in details. Moreover, a novel algebraic estimation technique for finding a practical iteration parameter is presented, which is very effective and practical even for large scale problems. At last, some numerical examples are carried, showing that the theoretical results are valid and convincing.

Class of Uzawa-NPHSS iteration method for solving nonsingular and singular saddle point problems

ABSTRACT Based on the non-alternating preconditioned HSS iteration scheme, a class of the Uzawa-NPHSS iteration method for solving nonsingular and singular non-Hermitian saddle point problems with the (1,1) part of the coefficient matrix being non-Hermitian positive definite is established. The convergence properties for the nonsingular saddle point problems and the semi-convergence properties for the singular ones of the proposed method are carefully discussed under suitable conditions. Meanwhile, the distribution of eigenvalues and the forms of the eigenvectors of the preconditioned matrix are analysed in detail. Additionally, the parameter selection strategy for the Uzawa-NPHSS iteration method is provided. Numerical experiments are implemented to confirm the theoretical results, which show the feasibility and effectiveness of the proposed method.

Regularized DPSS Preconditioners for Singular Saddle Point Problems

Recently, Cao proposed a regularized deteriorated positive and skewHermitian splitting (RDPSS) preconditioner for the non-Hermitian nonsingular saddle point problem. In this paper, we consider applying RDPSS preconditioner to solve the singular saddle point problem. Moreover, we propose a two-parameter accelerated variant of the RDPSS (ARDPSS) preconditioner to further improve its efficiency. Theoretical analysis proves that the RDPSS and ARDPSS methods are semi-convergent unconditionally. Some spectral properties of the corresponding preconditioned matrices are analyzed. Numerical experiments indicate that better performance can be achieved when applying the ARDPSS preconditioner to accelerate the GMRES method for solving the singular saddle point problem.

On equivalence of three-parameter iterative methods for singular symmetric saddle-point problem

There have been a couple of papers for the solution of the nonsingular symmetric saddle-point problem using three-parameter iterative methods. In most of them, regions of convergence for the parameters are found, while in three of them, optimal parameters are determined, and in one of the latter, many more cases, than in all the others, are distinguished, analyzed, and studied. It turns out that two of the optimal parameters coincide making the optimal three-parameter methods be equivalent to the optimal two-parameter known ones. Our aim in this work is manifold: (i) to show that the iterative methods we present are equivalent, (ii) to slightly change some statements in one of the main papers, (iii) to complete the analysis in another one, (iv) to explain how the transition from any of the methods to the others is made, (v) to extend the iterative method to cover the singular symmetric case, and (vi) to present a number of numerical examples in support of our theory. It would be an omission not to mention that the main material which all researchers in the area have inspired from and used is based on the one of the most cited papers by Bai et al. (Numer. Math. 102:1–38, 2005).

New modified shift-splitting preconditioners for non-symmetric saddle point problems

Zhou et al. and Huang et al. have proposed the modified shift-splitting (MSS) preconditioner and the generalized modified shift-splitting (GMSS) for non-symmetric saddle point problems, respectively. They have used symmetric positive definite and skew-symmetric splitting of the (1, 1)-block in a saddle point problem. In this paper, we use positive definite and skew-symmetric splitting instead and present new modified shift-splitting (NMSS) method for solving large sparse linear systems in saddle point form with a dominant positive definite part in (1, 1)-block. We investigate the convergence and semi-convergence properties of this method for nonsingular and singular saddle point problems. We also use the NMSS method as a preconditioner for GMRES method. The numerical results show that if the (1, 1)-block has a positive definite dominant part, the NMSS-preconditioned GMRES method can cause better performance results compared to other preconditioned GMRES methods such as GMSS, MSS, Uzawa-HSS and PU-STS. Meanwhile, the NMSS preconditioner is made for non-symmetric saddle point problems with symmetric and non-symmetric (1, 1)-blocks.

ON THE UPSS METHOD FOR NON-HERMITIAN SINGULAR SADDLE POINT PROBLEMS

Recently, a new Uzawa-type method, referred as the UPSS method, is proposed for solving the non-Hermitian nonsingular saddle point problems, see Dou, Yang and Wu (2017). In this paper, we give the semi-convergence analysis of the UPSS method when it is used to solve non-Hermitian singular saddle point problems. An example is given to verify the effectiveness of this method for solving non-Hermitian singular saddle point problems.

Convergence analysis of modified PGSS methods for singular saddle-point problems

Recently, variants of shift-splitting iteration method have been proposed for solving singular saddle-point problems. However, these methods can only be proved to converge to one of the solutions of the consistent singular linear system, not knowing any further information about this solution. In this work, we consider a modified preconditioned generalized shift-splitting (MPGSS) iteration method for solving both consistent and inconsistent singular saddle-point problems. This method is proved to converge to the best least squares solution. Moreover, based on the iteration form, a preconditioner is obtained to accelerate Krylov subspace methods. Theoretical analysis shows that the preconditioned GMRES method also converges to the best least squares solution of the consistent singular saddle-point problem. In addition, numerical results are presented to show the effectiveness and robustness of the proposed iteration method and preconditioner.

The improvements of the generalized shift-splitting preconditioners for non-singular and singular saddle point problems

ABSTRACTTo solve the saddle point problems with symmetric positive definite (1,1) parts, the improved generalized shift-splitting (IGSS) preconditioner is established in this paper, which yields the IGSS iteration method. Theoretical analysis shows that the IGSS iteration method is convergent and semi-convergent unconditionally. The choices of the iteration parameters are discussed. Moreover, some spectral properties, including the eigenvalue and eigenvector distributions of the preconditioned matrix are also investigated. Finally, numerical results are presented to verify the robustness and the efficiency of the proposed iteration method and the corresponding preconditioner for solving the non-singular and singular saddle point problems.

Semi-Convergence Analysis of Uzawa Splitting Iteration Method for Singular Saddle Point Problems

Semi-Convergence Analysis of Uzawa Splitting Iteration Method for Singular Saddle Point Problems

On the semi-convergence of regularized HSS iteration methods for singular saddle point problems

Recently, Bai and Benzi proposed a class of regularized Hermitian and skew-Hermitian splitting (RHSS) iteration methods for solving the nonsingular saddle point problem. In this paper, we apply this method to solve the singular saddle point problem. In the process of the semi-convergence analysis, we get that the RHSS method and the HSS method are unconditionally semi-convergent, which has improved the previous results. Then some spectral properties of the corresponding preconditioned matrices and a class of improved preconditioned matrices are analyzed. Finally, some numerical experiments on linear systems arising from the discretization of the Stokes equation are presented to illustrate the feasibility and effectiveness of this method and the corresponding preconditioners.

Modified PHSS iterative methods for solving nonsingular and singular saddle point problems

For large sparse saddle point problems, we establish a new version of the preconditioned Hermitian and skew-Hermitian splitting (PHSS) iteration method, called the modified PHSS (MPHSS) method in this paper. Then, we theoretically study its convergence and semi-convergence properties and determine its optimal iteration parameter and corresponding optimal convergence factor. Furthermore, the spectral properties of the MPHSS preconditioned matrix are discussed in detail. Numerical experiments show that the MPHSS iteration method is effective and robust when it is used either as a solver or as a matrix splitting preconditioner for the generalized minimal residual (GMRES) method.

The Semi-convergence of GSOR-like Methods for Singular Saddle Point Problems

In this paper, we prove the semi-convergence of the class of GSOR-like methods with two real functions applied to solve the singular saddle point problems, and discuss the optimal iteration parameters and the corresponding optimal semi-convergence factor for methods in the class.

GSTS-Uzawa method for a class of complex singular saddle point problems

In this paper, we propose GSTS–Uzawa method for solving a class of complex singular saddle point problems based on generalized skew-Hermitian triangular splitting (GSTS) iteration method and classical Uzawa method. We research on its semi-convergence properties and the eigenvalues distributions of its preconditioned matrix. The resulting GSTS–Uzawa preconditioner is used to precondition Krylov subspace methods such as the restarted generalized minimal residual (GMRES) method for solving the equivalent formulation of the complex singular saddle point problems. The theoretical results and effectiveness of the GSTS–Uzawa method are supported by a numerical example.

On the semi-convergence of preconditioned GLHSS iteration method for non-Hermitian singular saddle point problem

Recently, Fan and Zheng studied the preconditioned generalized local Hermitian and skew-Hermitian splitting (GLHSS) iteration method for non-Hermitian singular saddle point problem, and given its semi-convergence conditions; see Fan and Zheng (2014). In this note, we prove the semi-convergence of preconditioned GLHSS method by another method. The obtained result shows that the conditions for guaranteeing its semi-convergence are easy to check and more weaker.

A modification of the generalized shift-splitting method for singular saddle point problems

A modification of the generalized shift-splitting (GSS) method is presented for solving singular saddle point problems. In this kind of modification, the diagonal shift matrix is replaced by a block diagonal matrix which is symmetric positive definite. Semi-convergence of the proposed method is investigated. The induced preconditioner is applied to the saddle point problem and the preconditioned system is solved by the restarted generalized minimal residual method. Eigenvalue distribution of the preconditioned matrix is also discussed. Finally some numerical experiments are given to show the effectiveness and robustness of the new preconditioner. Numerical results show that the modified GSS method is superior to the classical GSS method.

Semi-convergence analysis of preconditioned deteriorated PSS iteration method for singular saddle point problems

In this paper, we propose a two-parameter preconditioned variant of the deteriorated PSS iteration method (J. Comput. Appl. Math., 273, 41–60 (2015)) for solving singular saddle point problems. Semi-convergence analysis shows that the new iteration method is convergent unconditionally. The new iteration method can also be regarded as a preconditioner to accelerate the convergence of Krylov subspace methods. Eigenvalue distribution of the corresponding preconditioned matrix is presented, which is instructive for the Krylov subspace acceleration. Note that, when the leading block of the saddle point matrix is symmetric, the new iteration method will reduce to the preconditioned accelerated HSS iteration method (Numer. Algor., 63 (3), 521–535 2013), the semi-convergence conditions of which can be simplified by the results in this paper. To further improve the effectiveness of the new iteration method, a relaxed variant is given, which has much better convergence and spectral properties. Numerical experiments are presented to investigate the performance of the new iteration methods for solving singular saddle point problems.

Semi-convergence analysis of the GSS iteration methods for singular saddle point problems

In this paper, we use the generalized shift-splitting (GSS) iteration method to solve nonsymmetric singular saddle point systems with the symmetric positive (semi-)definite (1,1) sub-block and symmetric positive semi-definite (2,2) sub-block, and prove the semi-convergence of the GSS iteration method. Numerical experiments are presented to investigate the correctness of the theoretical analyses and the feasibility of the GSS iteration method.

Spectral Analysis, Properties and Nonsingular Preconditioners for Singular Saddle Point Problems

Abstract We first derive some explicit bounds on the spectra of generalized non-symmetric singular or nonsingular saddle point matrices. Then we propose two new nonsingular preconditioners for solving generalized singular saddle point problems, and show that GMRES determines a solution without breakdown when applied to the resulting preconditioned systems with any initial guess. Furthermore, the detailed spectral properties of the preconditioned systems are analyzed. The nonsingular preconditioners are also applied to solve the singular finite element saddle point systems arising from the discretization of the Stokes problems to test their performance.

Semi-convergence analysis of parameterized ULT splitting iteration methods for singular saddle point problems

In this paper, we propose the parameterized upper and lower triangular (denoted by PULT) splitting iteration methods for solving singular saddle point problems. The eigenvalues and eigenvectors of iteration matrix of the new methods are studied. It is shown that the proposed methods are semi-convergent under certain conditions. Besides, the pseudo-optimal iteration parameter and corresponding convergence factor can be obtained in some special cases of the PULT iteration methods. Numerical example is presented to confirm the theoretical results, which implies that PULT iteration methods are effective and feasible for solving singular saddle point problems.

On equivalence of optimal relaxed block iterative methods for the singular nonsymmetric saddle point problem

There exist many classes of relaxed block iterative methods for the solution of the nonsingular and singular saddle point problems. Recently, the singular nonsymmetric saddle point problem has been optimally solved by means of a stationary linear second-order iterative method using the Manteuffel algorithm [Hadjidimos (2016) [19]]. The main purpose of this work is to extend, analyze and study a number of classes of stationary iterative methods based on generalizations of SOR-like methods, determine their optimal parameters, via the optimal parameters in the aforementioned work, and show the equivalence of the optimal methods studied. Finally, a computational comparison of the performances of the above optimal methods and their nonstationary counterparts shows the superiority of the latter methods.