Implementation of the Jacobian-free Newton–Krylov method for solving the first-order ice sheet momentum balance

We have implemented the Jacobian-free Newton–Krylov (JFNK) method for solving the first-order ice sheet momentum equation in order to improve the numerical performance of the Glimmer-Community Ice Sheet Model (Glimmer-CISM), the land ice component of the Community Earth System Model (CESM). Our JFNK implementation is based on significant re-use of existing code. For example, our physics-based preconditioner uses the original Picard linear solver in Glimmer-CISM. For several test cases spanning a range of geometries and boundary conditions, our JFNK implementation is 1.8–3.6 times more efficient than the standard Picard solver in Glimmer-CISM. Importantly, this computational gain of JFNK over the Picard solver increases when refining the grid. Global convergence of the JFNK solver has been significantly improved by rescaling the equation for the basal boundary condition and through the use of an inexact Newton method. While a diverse set of test cases show that our JFNK implementation is usually robust, for some problems it may fail to converge with increasing resolution (as does the Picard solver). Globalization through parameter continuation did not remedy this problem and future work to improve robustness will explore a combination of Picard and JFNK and the use of homotopy methods.     

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.     

预条件广义极小残余新算法

研究Krylov子空间广义极小残余算法(GMRES(m))的基本理论，给出GMRES(m)算法透代求解所满足的代数方程组．深入探讨算法的收敛性与方程组系数矩阵的密切关系，提出一种改进GMRES(m)算法收敛性的新的预条件方法，并作出相关论证．     

求解二维三温能量方程的基于AMG预条件子的Krylov子空间迭代法

本文对一类二维三温能量方程的实际应用问题，建立了一种半粗化的代数多重网格法（SAMG），进而得到了以该SAMG方法为预条件子的Krylov子空间迭代法。数值实验结果表明，该方法对求解二维三温能量方程的实际问题是十分有效和健壮的。     

On the convergence behavior of the restarted GMRES algorithm for solving nonsymmetric linear systems

The solution of nonsymmetric systems of linear equations continues to be a difficult problem. A main algorithm for solving nonsymmetric problems is restarted GMRES. The algorithm is based on restarting full GMRES every s iterations, for some integer s>0. This paper considers the impact of the restart frequency s on the convergence and work requirements of the method. It is shown that a good choice of this parameter can lead to reduced solution time, while an improper choice may hinder or preclude convergence. An adaptive procedure is also presented for determining automatically when to restart. The results of numerical experiments are presented.     

Comparison of the GMRES and ORTHOMIN for the black oil model in porous media

This paper deals with the application of the GMRES algorithm to a three‐dimensional, three‐phase black oil model used in petroleum reservoir simulation. Comparisons between the GMRES and ORTHOMIN algorithms in terms of storage and total flops per restart step are given. Numerical results show that the GMRES is faster than the ORTHOMIN for large‐scale simulation problems. The GMRES uses only as much as 63% of the CPU time of the ORTHOMIN for some of the problems tested. Copyright © 2005 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.     

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.     

谱方法用于非定常流动计算的隐式求解

本文对谱方法用于周期性非定常流动的隐式求解方法进行了探讨,分析了影响计算稳定性和收敛速度的因素.提出了结合多重网格的隐式求解方法并对算法进行了验证,初步计算表明本文算法具有良好的稳定性和收敛速度.对于周期性非定常流动,结合本文提出的隐式求解的时域谱方法可以达到很高的精度且具有良好的计算效率.     

不可压缩流基于块预处理的并行有限元计算

发展了一个模拟非定常不可压缩粘性流的并行有限元求解器,时间离散使用具有二阶精度的隐式中点格式,基于三维非结构四面体网格剖分,使用高阶混合有限元离散速度场（P2）和压力场（P1）.全离散格式产生的代数方程组是大型、稀疏、非对称和病态的,基于修正的压力对流扩散预处理（PCD）和精心设计的子问题迭代执行策略,采用预处理的GMRES迭代法来高效求解线性方程组.利用相同的子问题迭代策略,同时给出基于最小二乘交换子（LSC）预处理的并行效率对比.大量数值算例验证了算法的精度、可扩展性和可靠性.三维驱动方腔流模拟结果（Re=3200.0）清晰地显示了方腔流中主涡（PE）、下游二次涡（DSE）、上游二次涡（USE）、侧壁涡（EWV）和TGL涡的存在.