On the numerical modeling of convection-diffusion problems by finite element multigrid preconditioning methods

During the last decades, multigrid methods have been extensively used in order to solve large scale linear systems derived from the discretization of partial differential equations using the finite difference method. The effectiveness of the multigrid method can be also exploited by using the finite element method. Finite Element Approximate Inverses in conjunction with Richardon’s iterative method could be used as smoothers in the multigrid method. Thus, a new class of smoothers based on approximate inverses can be derived. Effectiveness of explicit approximate inverses relies in the fact that they are close approximants to the inverse of the coefficient matrix and are fast to compute in parallel. Furthermore, the proposed class of finite element approximate inverses in conjunction with the explicit preconditioned Richardson method yield improved results against the classic smoothers such as Jacobi method. Moreover, a dynamic relaxation scheme is proposed based on the Dynamic Over/Under Relaxation (DOUR) algorithm. Furthermore, results for multigrid preconditioned Krylov subspace methods, such as GMRES(res), IDR(s) and BiCGSTAB based on approximate inverse smoothing and a dynamic relaxation technique are presented for the steady-state convection-diffusion equation.     

Block preconditioners for elliptic PDE-constrained optimization problems

For the structured systems of linear equations arising from the Galerkin finite-element discretizations of the distributed control problems, we construct block-counter-diagonal and block-counter-tridiagonal preconditioning matrices to precondition the Krylov subspace methods such as GMRES. We derive explicit expressions for the eigenvalues and eigenvectors of the corresponding preconditioned matrices. Numerical implementations show that these structured preconditioners may lead to satisfactory experimental results of the preconditioned GMRES methods when the regularization parameter is suitably small.     

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 Uzawa-PPS iteration methods for nonsingular and singular non-Hermitian saddle point problems

In this paper, based on the positive-definite and positive-semidefinite splitting (PPS) iteration scheme, we establish a class of Uzawa-PPS iteration methods for solving nonsingular and singular non-Hermitian saddle point problems with the (1,1) part of the coefficient matrix being non-Hermitian positive definite. Theoretical analyses show that the convergence and semi-convergence properties of the proposed methods can be guaranteed under suitable conditions. Furthermore, we consider acceleration of the Uzawa-PPS methods by Krylov subspace (like GMRES) methods and discuss the spectral properties of the corresponding preconditioned matrix. Numerical experiments are given to confirm the theoretical results which show that the feasibility and effectiveness of the proposed methods and preconditioners.     

Some preconditioners for elliptic PDE-constrained optimization problems

For the structured systems of linear equations arising from the Galerkin finite element discretizations of elliptic PDE-constrained optimization problems, some preconditioners are proposed to accelerate the convergence rate of Krylov subspace methods such as GMRES for both cases of the Tikhonov parameter $\beta$ not very small (equal or greater than 1e?6) and sufficiently small (less than 1e?6), respectively. We derive the explicit expressions for the eigenvalues and eigenvectors of the corresponding preconditioned matrices. Numerical results show that the corresponding preconditioned GMRES methods perform and match well with the theoretical results.     

Determination of a good value of the time step and preconditioned Krylov subspace methods for the Navier-Stokes equations

The main purpose of this paper is to develop stable versions of some Krylov subspace methods for solving the linear systems of equations Ax = b which arise in the difference solution of 2-D nonstationary Navier-Stokes equations using implicit scheme and to determine a good value of the time step. Our algorithms are based on the conjugate-gradient method with a suitable preconditioner for solving the symmetric positive definite system and preconditioned GMRES, Orthomin(K), QMR methods for solving the nonsymmetric and (in)definite system. The performance of these methods is compared. In addition, we show that by using the condition number of the first nonsymmetric coefficient matrix, it is possible to determine a good value of the time step.     

Parallel Performance of Block ILU Preconditioners for a Block-tridiagonal Matrix

The parallelizable block ILU (incomplete LU) factorization preconditioners for a block-tridiagonal matrix have been recently proposed by the author. In this paper, we describe a parallelization of Krylov subspace methods with the block ILU factorization preconditioners on distributed-memory computers such as the Cray T3E, and then parallel performance results of a preconditioned Krylov subspace method are provided to evaluate the effectiveness and efficiency of the block ILU preconditioners on the Cray T3E.     

GMRES implementations and residual smoothing techniques for solving ill-posed linear systems

There are verities of useful Krylov subspace methods to solve nonsymmetric linear system of equations. GMRES is one of the best Krylov solvers with several different variants to solve large sparse linear systems. Any GMRES implementation has some advantages. As the solution of ill-posed problems are important. In this paper, some GMRES variants are discussed and applied to solve these kinds of problems. Residual smoothing techniques are efficient ways to accelerate the convergence speed of some iterative methods like CG variants. At the end of this paper, some residual smoothing techniques are applied for different GMRES methods to test the influence of these techniques on GMRES implementations.     

基于解空间分解的GMRES算法及其在图像处理中的应用

提出一种基于解空间分解的加速GMRES算法来求解不适定问题,该算法将解空间分解为Krylov子空间和一个辅助子空间,其中一部分解用一种加速GMRES法迭代得到,另一部分解用直接求解的方法得到。数值实验和分析表明这种算法是行之有效的,在达到相同的估计精度的条件下,迭代速度大大提高,求解时间只有普通GMRES算法的五分之一,甚至更少;而且在迭代次数相同的情况下,解的精度更高,如解的均方误差平均是普通GMRES算法的五分之三。最后将该方法应用到光学图像复原,实验结果表明该方法能够明显改善光学图像的质量。     

A Matrix-free Two-grid Preconditioner for Solving Boundary Integral Equations in Electromagnetism

In this paper, we describe a matrix-free iterative algorithm based on the GMRES method for solving electromagnetic scattering problems expressed in an integral formulation. Integral methods are an interesting alternative to differential equation solvers for this problem class since they do not require absorbing boundary conditions and they mesh only the surface of the radiating object giving rise to dense and smaller linear systems of equations. However, in realistic applications the discretized systems can be very large and for some integral formulations, like the popular Electric Field Integral Equation, they become ill-conditioned when the frequency increases. This means that iterative Krylov solvers have to be combined with fast methods for the matrix-vector products and robust preconditioning to be affordable in terms of CPU time. In this work we describe a matrix-free two-grid preconditioner for the GMRES solver combined with the Fast Multipole Method. The preconditioner is an algebraic two-grid cycle built on top of a sparse approximate inverse that is used as smoother, while the grid transfer operators are defined using spectral information of the preconditioned matrix. Experiments on a set of linear systems arising from real radar cross section calculation in industry illustrate the potential of the proposed approach for solving large-scale problems in electromagnetism.     

A relaxed block-triangular splitting preconditioner for generalized saddle-point problems

For the generalized saddle-point problems, based on a new block-triangular splitting of the saddle-point matrix, we introduce a relaxed block-triangular splitting preconditioner to accelerate the convergence rate of the Krylov subspace methods. This new preconditioner is easily implemented since it has simple block structure. The spectral property of the preconditioned matrix is analysed. Moreover, the degree of the minimal polynomial of the preconditioned matrix is also discussed. Numerical experiments are reported to show the preconditioning effect of the new preconditioner.     

A Fast Preconditioned Iterative Algorithm for the Electromagnetic Scattering from a Large Cavity

In this paper, a fast preconditioned Krylov subspace iterative algorithm is proposed for the electromagnetic scattering from a rectangular large open cavity embedded in an infinite ground plane. The scattering problem is described by the Helmholtz equation with a nonlocal artificial boundary condition on the aperture of the cavity and Dirichlet boundary conditions on the walls of the cavity. Compact fourth order finite difference schemes are employed to discretize the bounded domain problem. A much smaller interface discrete system is reduced by introducing the discrete Fourier transformation in the horizontal and a Gaussian elimination in the vertical direction, presented in Bao and Sun (SIAM J. Sci. Comput. 27:553, 2005). An effective preconditioner is developed for the Krylov subspace iterative solver to solve this interface system. Numerical results demonstrate the remarkable efficiency and accuracy of the proposed method.     

Krylov子空间方法解线性方程组的并行性能分析及应用

许多并行计算问题,在结合并行机的特有体系结构时,要对算法的并行性能及其可扩展性进行分析。它决定了该算法解决有关问题是否有效,并进一步判断所用的并行计算系统是否符合求解问题的要求。文章通过对Ｋｒｙｌｏｖ子空间中两种有效算法－ＰＣＧ算法和ＧＭＲＥＳ（ｍ）算法在一类并行系统中形成的并行算法的性能进行了分析,给出了其求解问题规模与处理机数与加速比的关系结果表明。ＧＭＲＥＳ（ｍ）算法比ＰＣＧ算法更适合于并行。     

A real‐time algorithm for nonlinear receding horizon control using multiple shooting and continuation/Krylov method

In this paper, we propose a real‐time algorithm for nonlinear receding horizon control using multiple shooting and the continuation/GMRES method. Multiple shooting is expected to improve numerical accuracy in calculations for solving boundary value problems. The continuation method is combined with a Krylov subspace method, GMRES, to update unknown quantities by solving a linear equation. At the same time, we apply condensing, which reduces the size of the linear equation, to speed up numerical calculations. A numerical example shows that both numerical accuracy and computational speed improve using the proposed algorithm by combining multiple shooting with condensing. Copyright © 2008 John Wiley & Sons, Ltd.     

三维热传导方程的Krylov子空间方法并行分析

热传导方程在地下水流动数值模拟、油藏数值模拟等工程计算中有着广泛应用,其并行实现是加速问题求解速度、提高问题求解规模的重要手段,因此热传导方程的并行求解具有重要意义。对Krylov子空间方法中的CG和GMRES算法进行并行分析,并对不同的预处理CG算法作了比较。在Linux集群系统上,以三维热传导模型为例进行了数值实验。实验结果表明,CG算法比GMRES算法更适合建立三维热传导模型的并行求解。此外,CG算法与BJACOBI预条件子的整合在求解该热传导模型时,其并行程序具有良好的加速比和效率。因此,采用BJACOBI预处理技术的CG算法是一种较好的求解三维热传导模型的并行方案。     

Distributed control and constraint preconditioners

Optimization problems with constraints that involve a partial differential equation arise widely in many areas of the sciences and engineering, in particular in problems of design. The solution of such PDE-constrained optimization problems is usually a major computational task. Here we consider simple problems of this type: distributed control problems in which the 2- and 3-dimensional Poisson problem is the PDE. Large dimensional linear systems result from the discretization and need to be solved: these systems are of saddle-point type. We introduce an optimal preconditioner for these systems that leads to convergence of symmetric Krylov subspace iterative methods in a number of iterations which does not increase with the dimension of the discrete problem. These preconditioners are block structured and involve standard multigrid cycles. The optimality of the preconditioned iterative solver is proved theoretically and verified computationally in several test cases. The theoretical proof indicates that these approaches may have much broader applicability for other partial differential equations.     

On parameterized matrix splitting preconditioner for the saddle point problems

In this paper, we present a parameterized matrix splitting (PMS) preconditioner for the large sparse saddle point problems. The preconditioner is based on a parameterized splitting of the saddle point matrix, resulting in a fixed-point iteration. The convergence theorem of the new iteration method for solving large sparse saddle point problems is proposed by giving the restrictions imposed on the parameter. Based on the idea of the parameterized splitting, we further propose a modified PMS preconditioner. Some useful properties of the preconditioned matrix are established. Numerical implementations show that the resulting preconditioner leads to fast convergence when it is used to precondition Krylov subspace iteration methods such as generalized minimal residual method.     

预条件的平方Smith法求解大型Sylvester矩阵方程

提出了一种预条件的平方Smith算法求解大型连续Sylvester矩阵方程,该算法利用交替方向隐式迭代(ADI)来构造预条件算子,将原方程转换为非对称Stein方程,并在Krylov子空间中应用平方Smith法迭代产生低秩逼近解。数值实验表明,与已知的Jacobi迭代法等算法相比,该算法有更好的迭代效率和收敛精度。     

Application of an element-by-element BiCGSTAB iterative solver to a monotonic finite element model

This paper deals with the advection-diffusion equation in adaptive meshes. The main feature of the present finite element model is the use of Legendre-polynomials to span finite element spaces. The success that this model gives good resolutions to solutions in regions of boundary and interior layers lies in the use of M-matrix theory. In the monotonic range of Peclet numbers, the Petrov-Galerkin method performs well in the sense that oscillatory solutions are not present in the flow. With proper stabilization, finite element matrix equations can be iteratively solved by the Lanczos method, used concurrently with local minimization provided by GMRES(1). The resulting BiCGSTAB iterative solver, supplemented with the Jacobi preconditioner, is implemented in an element-by-element fashion. This gives solutions which are computationally feasible for large-scale flow simulations. The results of two computations are presented in support of the ability of the present finite element model to resolve sharp gradients in the solution. As is apparent from this study is that considerable savings in computer storage and execution time are achieved in adaptive meshes through use of the preconditioned BiCGSTAB iterative solver.     

A Krylov‐subspace technique for the simulation of integrated RF/microwave subsystems driven by digitally modulated carriers

This article introduces a new approach to the analysis of nonlinear RF/microwave systems or subsystems described at the circuit level and excited by sinusoidal carriers modulated by arbitrary baseband signals. The circuit is simulated by a sequence of harmonic‐balance analyses based on a Krylov‐subspace method driven by an inexact Newton loop, and suitably modified to account for coupling with a finite number of preceding time instants. The Jacobian matrix of the nonlinear solving system is computed by an exact algorithm, and its structure is shown to be very well suited for the application of Krylov‐subspace techniques such as the GMRES method. In this way, problems with many millions of nodal unknowns may be efficiently tackled at the workstation level. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 490–505, 1999.