有界多连通区域数值保角变换的GMRES(m)法北大核心CSCD

求解复杂多连通区域的保角变换函数是困难的.针对这一问题,该文将求解保角变换函数转化为利用模拟电荷法求解一对定义在问题区域上的共轭调和函数,再根据边界条件建立约束方程,并利用GMRES(m)(the generalized minimal residual method)算法求解约束方程,获得了模拟电荷,进而构造了高精度的近似保角变换函数,将有界多连通区域映射为三种无界正则狭缝域.数值实验验证了该文算法的有效性.     

基于LSQR法的外部数值保角逆变换计算法

本文提出了基于模拟电荷法的外部数值保角逆变换计算法.利用LSQR (Least Square QR-factorization)方法来求解基于模拟电荷法的外部数值保角逆变换中的约束方程,得到了电荷量和逆变换半径,进而构造了近似保角逆变换函数.数值实验证明本文提出的算法是有效的.     

多连通区域到狭缝圆环域的共形映射计算法

基于模拟电荷法,研究了将具有高连通度的有界区域映射到带有对数螺旋狭缝单位圆环域的共形映射计算法.提出利用BiCR(bi-conjugate residual)算法求解由Dirichlet边界条件建立的约束方程组,得到模拟电荷,进而构造出高精度的近似共形映射函数.数值实验验证了该文算法的有效性,并成功将该共形映射计算法应用到绕流仿真模拟中,模拟了有界高连通度区域内螺旋点涡的绕流.     

双连通区域上的电磁波散射问题数值解

对于双连通区域上的电磁波散射问题,通过位势理论将其转化为边界积分方程组问题,然后采用Nystrom法和配置法对其离散求解,针对不同形状的障碍散射体,给出远场模式的数值解.     

保结构算法的相位误差分析及其修正

辛算法和保能量算法是应用最为广泛的两种保结构算法.本文从相位误差的角度给出了他们的比较结果.我们针对线性动力系统,分别分析了基于Pade对角逼近给出的辛算法和基于平均向量场法得到的能量守恒算法的相位误差,并通过数值验证了分析结果.文章还给出了保结构算法相位误差的改进方法,并通过数值例子验证了方法的有效性.     

计及边缘效应的平行板电容器单位长度电容的计算

将保角变换法与格林函数法相结合,研究计及边缘效应的平行板电容器的电场,得到其电势和场强分布,利用软件MATLAB对场分布进行数值模拟,给出其单位长度电容量的计算公式,并与忽略边缘效应的电容量的计算公式进行比较,得到两计算公式产生的相对误差随其宽与板间距之比变化的函数关系.     

一类非凸区域的拟法锥构造方法及其在非凸规划求解中的应用

本文给出基于球形的一类满足拟法锥条件区域的拟法锥构造方法,基于该可行域的拟法锥,建立求解在该类非凸区域上的规划问题的K-K-T点的部分凝聚同伦组合方程,并证明了该同伦内点法的整体收敛性,给出实现同伦内点法的具体数值跟踪算法步骤,并通过数值例子证明算法是可行的和有效的．     

基于Lanczos双A-正交的一种修正的QMR算法

本文研究了基于Lanczos双正交过程的拟极小残量法(QMR).将QMR算法中的Lanczos双正交过程用Lanczos双A-正交过程代替,利用该算法得到的近似解与最后一个基向量的线性组合来作为新的近似解,使新近似解的残差范数满足一个一维极小化问题,从而得到一种基于Lanczos双A-正交的修正的QMR算法.数值试验表明,对于某些大型线性稀疏方程组,新算法比QMR算法收敛快得多.     

基于混合编码的混合遗传算法

本文研究了神经网络优化问题.利用混合编码的方法,结合遗传算法与共轭梯度法的优点,得到一种基于混合编码的混合遗传算法.数值模拟结果表明,混合算法既具有较快的收敛速度,又能够收敛到全局最优解.     

单调迭代结合虚拟区域法求解非线性障碍问题

讨论了二阶半线性椭圆方程障碍问题的数值求解问题.用单调迭代算法求解障碍问题,并用改进的虚拟区域法求解相关的不规则区域上具有Dirichlet边界条件的椭圆方程.在计算过程中,传统的有限元离散会导致用扩展区域规则网格计算不规则物体边界上积分的困难.为了克服此困难,给出了一种新的基于有限差分的算法,从而使得偏微分快速算法可用.算法结构简单,易于编程实现.对有扩散和增长障碍的logistic人口模型数值模拟说明算法可行且高效.     

Using successive approximations for improving the convergence of GMRES method

In this paper, our attention is concentrated on the GMRES method for the solution of the system (I–T)x=b of linear algebraic equations with a nonsymmetric matrix. We perform m pre-iterations y _l+1 =T _yl +b before starting GMRES and put y _m for the initial approximation in GMRES. We derive an upper estimate for the norm of the error vector in dependence on the mth powers of eigenvalues of the matrix T Further we study under what eigenvalues lay-out this upper estimate is the best one. The estimate shows and numerical experiments verify that it is advisable to perform pre-iterations before starting GMRES as they require fewer arithmetic operations than GMRES. Towards the end of the paper we present a numerical experiment for a system obtained by the finite difference approximation of convection-diffusion equations.     

A new adaptive GMRES algorithm for achieving high accuracy

GMRES(k) is widely used for solving non-symmetric linear systems. However, it is inadequate either when it converges only for k close to the problem size or when numerical error in the modified Gram–Schmidt process used in the GMRES orthogonalization phase dramatically affects the algorithm performance. An adaptive version of GMRES(k) which tunes the restart value k based on criteria estimating the GMRES convergence rate for the given problem is proposed here. This adaptive GMRES(k) procedure outperforms standard GMRES(k), several other GMRES-like methods, and QMR on actual large scale sparse structural mechanics postbuckling and analog circuit simulation problems. There are some applications, such as homotopy methods for high Reynolds number viscous flows, solid mechanics postbuckling analysis, and analog circuit simulation, where very high accuracy in the linear system solutions is essential. In this context, the modified Gram–Schmidt process in GMRES, can fail causing the entire GMRES iteration to fail. It is shown that the adaptive GMRES(k) with the orthogonalization performed by Householder transformations succeeds whenever GMRES(k) with the orthogonalization performed by the modified Gram–Schmidt process fails, and the extra cost of computing Householder transformations is justified for these applications. © 1998 John Wiley & Sons, Ltd.     

Convergence conditions for a restarted GMRES method augmented with eigenspaces

We consider the GMRES(m,k) method for the solution of linear systems Ax=b, i.e. the restarted GMRES with restart m where to the standard Krylov subspace of dimension m the other subspace of dimension k is added, resulting in an augmented Krylov subspace. This additional subspace approximates usually an A‐invariant subspace. The eigenspaces associated with the eigenvalues closest to zero are commonly used, as those are thought to hinder convergence the most. The behaviour of residual bounds is described for various situations which can arise during the GMRES(m,k) process. The obtained estimates for the norm of the residual vector suggest sufficient conditions for convergence of GMRES(m,k) and illustrate that these augmentation techniques can remove stagnation of GMRES(m) in many cases. All estimates are independent of the choice of an initial approximation. Conclusions and remarks assessing numerically the quality of proposed bounds conclude the paper. Copyright © 2004 John Wiley & Sons, Ltd.     

Strang-Type Preconditioners for Solving Linear Systems from Delay Differential Equations

We consider the solution of delay differential equations (DDEs) by using boundary value methods (BVMs). These methods require the solution of one or more nonsymmetric, large and sparse linear systems. The GMRES method with the Strang-type block-circulant preconditioner is proposed for solving these linear systems. We show that if a P _{k 1},k ₂-stable BVM is used for solving an m-by-m system of DDEs, then our preconditioner is invertible and all the eigenvalues of the preconditioned system are clustered around 1. It follows that when the GMRES method is applied to solving the preconditioned systems, the method may converge fast. Numerical results are given to illustrate the effectiveness of our methods.     

Krylov sequences of maximal length and convergence of GMRES

In most practical cases, the convergence of the GMRES method applied to a linear algebraic systemAx=b is determined by the distribution of eigenvalues ofA. In theory, however, the information about the eigenvalues alone is not sufficient for determining the convergence. In this paper the previous work of Greenbaum et al. is extended in the following direction. It is given a complete parametrization of the set of all pairs {A, b} for which GMRES(A, b) generates the prescribed convergence curve while the matrixA has the prescribed eigenvalues. Moreover, a characterization of the right hand sidesb for which the GMRES(A, b) converges exactly inm steps, wherem is the degree of the minimal polynomial ofA, is given. This work was supported by AS CR Grant A2030706. Part of the work was performed while the third author visited Instituto di Analisi Numerica (IAN CNR).     

A geometric view of Krylov subspace methods on singular systems

We give a geometric framework for analysing iterative methods on singular linear systems A x = b and apply them to Krylov subspace methods. The idea is to decompose the method into the ?(A) component and its orthogonal complement ?(A)^?, where ?(A) is the range of A. We apply the framework to GMRES, GMRES(k) and GCR(k), and derive conditions for convergence without breakdown for inconsistent and consistent singular systems. The approach also gives a geometric interpretation and different proofs of the conditions obtained by Brown and Walker for GMRES. We also give examples arising in the finite difference discretization of two‐point boundary value problems of an ordinary differential equation. Copyright © 2010 John Wiley & Sons, Ltd.     

BCCB preconditioners for systems of BVM‐based numerical integrators

Boundary value methods (BVMs) for ordinary differential equations require the solution of non‐symmetric, large and sparse linear systems. In this paper, these systems are solved by using the generalized minimal residual (GMRES) method. A block‐circulant preconditioner with circulant blocks (BCCB preconditioner) is proposed to speed up the convergence rate of the GMRES method. The BCCB preconditioner is shown to be invertible when the BVM is A_k1,k2‐stable. The spectrum of the preconditioned matrix is clustered and therefore, the preconditioned GMRES method converges fast. Moreover, the operation cost in each iteration of the preconditioned GMRES method by using our BCCB preconditioner is less than that required by using block‐circulant preconditioners proposed earlier. In numerical experiments, we compare the number of iterations of various preconditioners. Copyright © 2003 John Wiley & Sons, Ltd.     

Fast conformal mapping of multiply connected regions

An iterative method is presented which constructs for an unbounded region G with m holes and sufficiently smooth boundary a circular region H and a conformal mapping Φ from H to G. With the usual normalization both H and Φ are uniquely determined by G. With a few modifications the method can also be applied to a bounded region G with m holes. The canonical region H is then the unit disc with m circular holes. The proposed method also determines the centers and radii of the boundary circles of H and requires, at each iterative step, the solution of a Riemann–Hilbert (RH) problem, which has a unique solution. Numerically, the RH problem can be treated efficiently by the method of successive conjugation using the fast Fourier transform (FFT). The iteration for the solution of the RH problem converges linearly. The conformal mapping method converges quadratically. The results of some test calculations exemplify the performance of the method.     

Weighted FOM and GMRES for solving nonsymmetric linear systems

This paper presents two new methods called WFOM and WGMRES, which are variants of FOM and GMRES, for solving large and sparse nonsymmetric linear systems. To accelerate the convergence, these new methods use a different inner product instead of the Euclidean one. Furthermore, at each restart, a different inner product is chosen. The weighted Arnoldi process is introduced for implementing these methods. After describing the weighted methods, we give the relations that link them to FOM and GMRES. Experimental results are presented to show the good performances of the new methods compared to FOM(m) and GMRES(m). This revised version was published online in August 2006 with corrections to the Cover Date.