一种加权的Simpler GMRES算法

GMRES方法是求解大规模非对称稀疏线性方程组最常用的方法,实际应用中存在着许多对标准GMRES进行改进的算法,比如Simpler GMRES和Weighted GMRES.Simpler GMRES通过改进GMRES中基的生成过程来减小计算量,同时保持较好的收敛性,Weighted GMRES是采用加权技术来加快GMRES方法的收敛速度,但是增加了计算量.本文提出了一种新称为Weighted Simpler GMRES的方法,它以Simpler GMRES方法为基础,结合Weighted GMRES方法得到.实验表明,对某些问题,Weighted Simpler GMRES方法的收敛性优于Simpler GMRES和GMRES,计算量小于Weighted GMRES.     

A note on ℝ‐linear GMRES for solving a class of ℝ‐linear systems

We prove that ?‐linear GMRES for solving a class of ?‐linear systems is faster than GMRES applied to the related ?‐linear systems in terms of matrix–vector products. Numerical examples are given to demonstrate the theoretical result. Copyright © 2011 John Wiley & Sons, Ltd.     

一类涡流控制问题的新的预处理子

构造了一类多调和涡流最优化控制问题(MECOC)的新的预处理子.结合新的预处理子对系数矩阵进行预处理后使用Krylov子空间方法,如GMRES方法求解,并分析了预处理矩阵的特征值分布情况.数值实验验证了理论结果的正确性,并说明了新的预处理子的有效性.     

Implicit solution of time spectral method for periodic unsteady flows

The present paper investigates the implicit solution of time spectral model for periodic unsteady flows. In the time spectral model, the physical time derivative is approximated using spectral method. The robustness issues associated with implicit solution of time spectral model are analyzed and validated by numerical results. It is found that spectral approximation of the time derivative weakens the diagonal dominance property of the Jacobian matrix, resulting in the deterioration of stability and convergence speed. In this paper we propose to solve the coupled governing equations implicitly using multigrid preconditioned generalized minimal residual (GMRES) method, which demonstrates favorable convergence speed. Also it is demonstrated that the current method is insensitive to the variations of frequency and number of harmonics. Comparison of computation results with dual time step unsteady computation validates the high efficiency of the current method. Copyright © 2009 John Wiley & Sons, Ltd.     

Steepest descent preconditioning for nonlinear GMRES optimization

Steepest descent preconditioning is considered for the recently proposed nonlinear generalized minimal residual (N‐GMRES) optimization algorithm for unconstrained nonlinear optimization. Two steepest descent preconditioning variants are proposed. The first employs a line search, whereas the second employs a predefined small step. A simple global convergence proof is provided for the N‐GMRES optimization algorithm with the first steepest descent preconditioner (with line search), under mild standard conditions on the objective function and the line search processes. Steepest descent preconditioning for N‐GMRES optimization is also motivated by relating it to standard non‐preconditioned GMRES for linear systems in the case of a standard quadratic optimization problem with symmetric positive definite operator. Numerical tests on a variety of model problems show that the N‐GMRES optimization algorithm is able to very significantly accelerate convergence of stand‐alone steepest descent optimization. Moreover, performance of steepest‐descent preconditioned N‐GMRES is shown to be competitive with standard nonlinear conjugate gradient and limited‐memory Broyden–Fletcher–Goldfarb–Shanno methods for the model problems considered. These results serve to theoretically and numerically establish steepest‐descent preconditioned N‐GMRES as a general optimization method for unconstrained nonlinear optimization, with performance that appears promising compared with established techniques. In addition, it is argued that the real potential of the N‐GMRES optimization framework lies in the fact that it can make use of problem‐dependent nonlinear preconditioners that are more powerful than steepest descent (or, equivalently, N‐GMRES can be used as a simple wrapper around any other iterative optimization process to seek acceleration of that process), and this potential is illustrated with a further application example. Copyright © 2012 John Wiley & Sons, Ltd.     

Krylov subspace methods with deflation and balancing preconditioners for least squares problems

For solving least squares problems, the CGLS method is a typical method in the point of view of iterative methods. When the least squares problems are ill-conditioned, the convergence behavior of the CGLS method will present a deteriorated result. We expect to select other iterative Krylov subspace methods to overcome the disadvantage of CGLS. Here the GMRES method is a suitable algorithm for the reason that it is derived from the minimal residual norm approach, which coincides with least squares problems. Ken Hayami proposed BAGMRES for solving least squares problems in [\emph{GMRES Methods for Least Squares Problems, SIAM J. Matrix Anal. Appl., 31(2010)}, pp.2400-2430]. The deflation and balancing preconditioners can optimize the convergence rate through modulating spectral distribution. Hence, in this paper we utilize preconditioned iterative Krylov subspace methods with deflation and balancing preconditioners in order to solve ill-conditioned least squares problems. Numerical experiments show that the methods proposed in this paper are better than the CGLS method.     

Krylov subspace methods to solve a class of tensor equations via the Einstein product

This paper is concerned with some of the well‐known iterative methods in their tensor forms to solve a class of tensor equations via the Einstein product. More precisely, the tensor forms of the Arnoldi and Lanczos processes are derived and the tensor form of the global GMRES method is presented. Meanwhile, the tensor forms of the MINIRES and SYMMLQ methods are also established. The proposed methods use tensor computations with no matricizations involved. Numerical examples are provided to illustrate the efficiency of the proposed methods and testify the conclusions suggested in this paper.     

基于GMRES算法的弹性结构强耦合小变形流激振动分析

使用混合广义变分原理,将基于Lagrange表述的小位移变形结构振动问题与基于Euler描述的不可压缩粘性流动问题,统一在功率平衡的框架下建立流固系统的耦合控制方程．用有限元格式做空间离散后,再用广义梯形法将有限元控制方程转化为增量型的线性方程组,该方程组的系数矩阵具有非对称性,其中元素含对流效应和时间因子．将GMRES算法与振动分析的Newmark法和流动分析的Hughes预测多修正法结合,发展成一种基于GMRES-Hughes-Newmark的稳定算法,用于计算具有复杂几何边界的强耦合流激振动问题．以混流式水轮机叶道为数值算例的计算表明,模拟结果与试验实测结果吻合较好．     

Expressions and Bounds for the GMRES Residual

Expressions and bounds are derived for the residual norm in GMRES. It is shown that the minimal residual norm is large as long as the Krylov basis is well-conditioned. For scaled Jordan blocks the minimal residual norm is expressed in terms of eigenvalues and departure from normality. For normal matrices the minimal residual norm is expressed in terms of products of relative eigenvalue differences.     

Necessary and sufficient conditions for GMRES complete and partial stagnation

In this paper we give necessary and sufficient conditions for the complete or partial stagnation of the GMRES iterative method for solving real linear systems. Our results rely on a paper by Arioli, Pták and Strakoš (1998), characterizing the matrices having a prescribed convergence curve for the residual norms. We show that we have complete stagnation if and only if the matrix A is orthonormally similar to an upper or lower Hessenberg matrix having a particular first row or column or a particular last row or column. Partial stagnation is characterized by a particular pattern of the matrix Q in the QR factorization of the upper Hessenberg matrix generated by the Arnoldi process.