The Numerical Study of the S = 1/2 XXZ Chain

We adopt the Lanczos method combined with the quantum conformal field theory to investigate the S = 1/2XXZ chain in detail. The bulk-limit ground state energy, the anomalous scaling dimension of the spin operators, theFermi velocity and the zero-temperature susceptibility are numerically calculated. The results agree to the exact solution well.``     

A numerical solution of the constrained weighted energy problem

A numerical algorithm is presented to solve the constrained weighted energy problem from potential theory. As one of the possible applications of this algorithm, we study the convergence properties of the rational Lanczos iteration method for the symmetric eigenvalue problem. The constrained weighted energy problem characterizes the region containing those eigenvalues that are well approximated by the Ritz values. The region depends on the distribution of the eigenvalues, on the distribution of the poles, and on the ratio between the size of the matrix and the number of iterations. Our algorithm gives the possibility of finding the boundary of this region in an effective way.We give numerical examples for different distributions of poles and eigenvalues and compare the results of our algorithm with the convergence behavior of the explicitly performed rational Lanczos algorithm.     

Matrix-free iterative solution strategies for large dense linear systems

A purely algebric approach to solving very large general unstructured dense linear systems, in particular, those that arise in 3D boundary integral applications is suggested. We call this technique the matrix-free approach because it allows one to avoid the necessity of storing the whole coefficient matrix in any form, which provides significant memory and arithmetic savings. We propose to approximate a non-singular coefficient matrix A by a block low-rank matrix à and to use the latter when performing matrix–vector multiplications in iterative solution algorithms. Such approximations are shown to be easily computable, and a reliable a posteriori accuracy estimate of ‖A − Ã‖₂ is derived. We prove that block low-rank approximations are sufficiently accurate for some model cases. However, even in the absence of rigorous proof of the existence of accurate approximations, one can apply the algorithm proposed to compute a block low-rank approximation and then make a decision on its practical suitability. We present numerical examples for the 3D CEM and CFD integral applications, which show that, at least for some industrial applications, the matrix-free approach is robust and cost-effective. © 1997 John Wiley & Sons, Ltd.     

Extending the eigCG algorithm to nonsymmetric Lanczos for linear systems with multiple right‐hand sides

The technique that was used to build the eigCG algorithm for sparse symmetric linear systems is extended to the nonsymmetric case using the BiCG algorithm. We show that, similar to the symmetric case, we can build an algorithm that is capable of computing a few smallest magnitude eigenvalues and their corresponding left and right eigenvectors of a nonsymmetric matrix using only a small window of the BiCG residuals while simultaneously solving a linear system with that matrix. For a system with multiple right‐hand sides, we give an algorithm that computes incrementally more eigenvalues while solving the first few systems and then uses the computed eigenvectors to deflate BiCGStab for the remaining systems. Our experiments on various test problems, including Lattice QCD, show the remarkable ability of eigBiCG to compute spectral approximations with accuracy comparable with that of the unrestarted, nonsymmetric Lanczos. Furthermore, our incremental eigBiCG followed by appropriately restarted and deflated BiCGStab provides a competitive method for systems with multiple right‐hand sides. Copyright © 2013 John Wiley & Sons, Ltd.     

Dealing with linear dependence during the iterations of the restarted block Lanczos methods

The Lanczos method can be generalized to block form to compute multiple eigenvalues without the need of any deflation techniques. The block Lanczos method reduces a general sparse symmetric matrix to a block tridiagonal matrix via a Gram–Schmidt process. During the iterations of the block Lanczos method an off-diagonal block of the block tridiagonal matrix may become singular, implying that the new set of Lanczos vectors are linearly dependent on the previously generated vectors. Unlike the single vector Lanczos method, this occurrence of linearly dependent vectors may not imply an invariant subspace has been computed. This difficulty of a singular off-diagonal block is easily overcome in non-restarted block Lanczos methods, see [12,30]. The same schemes applied in non-restarted block Lanczos methods can also be applied in restarted block Lanczos methods. This allows the largest possible subspace to be built before restarting. However, in some cases a modification of the restart vectors is required or a singular block will continue to reoccur. In this paper we examine the different schemes mentioned in [12,30] for overcoming a singular block for the restarted block Lanczos methods, namely the restarted method reported in [12] and the Implicitly Restarted Block Lanczos (IRBL) method developed by Baglama et al. [3]. Numerical examples are presented to illustrate the different strategies discussed.     

PRECONDITIONING BLOCK LANCZOS ALGORITHM FOR SOLVING SYMMETRIC EIGENVALUE PROBLEMS

1. Introduction;The Lanczos process is an effective method [1, 2, 14, 21] for computing a feweigenValues and corresponding eigenvectors of a large sparse symmetric matrix A ERnxn. If it is practical to factor the matrix A -- PI for one or more values of p near thedesired eigenvalues, the Lanczos method can be used with the inverted operator andconvergence will be very rapid[5,10,22]. In practical applications, however, the matrixA is usually large and sparse, so factoring A is either impos…     

A symmetry exploiting Lanczos method for symmetric Toeplitz matrices

Several methods for computing the smallest eigenvalues of a symmetric and positive definite Toeplitz matrix T have been studied in the literature. Most of them share the disadvantage that they do not reflect symmetry properties of the corresponding eigenvector. In this note we present a Lanczos method which approximates simultaneously the odd and the even spectrum of T at the same cost as the classical Lanczos approach.     

TWO ALGORITHMS FOR SYMMETRIC LINEAR SYSTEMS WITH MULTIPLE RIGHT-HAND SIDES

1　ＩｎｔｒｏｄｕｃｔｉｏｎＩｎｍａｎｙａｐｐｌｉｃａｔｉｏｎｓｗｅｎｅｅｄｔｏｓｏｌｖｅｍｕｌｔｉｐｌｅｓｙｓｔｅｍｓｏｆｌｉｎｅａｒｅｑｕａｔｉｏｎｓＡｘ(ｉ) =ｂ(ｉ) ,ｉ=1,… ,ｓ (1)ｗｉｔｈｔｈｅｓａｍｅｎ×ｎｒｅａｌｓｙｍｍｅｔｒｉｃｃｏｅｆｆｉｃｉｅｎｔｍａｔｒｉｘＡ ,ｂｕｔｓｄｉｆｆｅｒｅｎｔｒｉｇｈｔ ｈａｎｄｓｉｄｅｓｂ(ｉ) (ｉ=1,… ,ｓ) .Ｉｆａｌｌｏｆｔｈｅｒｉｇｈｔ ｈａｎｄｓｉｄｅｓａｒｅａｖａｉｌａｂｌｅｓｉｍｕｌｔａｎｅｏｕｓｌｙ ,ｔｈｅｎｔｈｅｓｅｓｌｉｎｅａｒｓｙｓｔｅ…     

基于双边Lanczos算法的声子晶体复模态重分析

针对声子晶体拓扑结构修改时模态计算效率低的问题,提出基于双边Lanczos算法的复模态重分析。与全分析不同,本方法利用声子晶体原始结构的模态分析结果,通过双边Lanczos算法构建投影向量矩阵,将广义特征值方程映射进子空间来压缩矩阵规模,求解方程后再利用近似模态关系得到最终解。通过对尺寸和形状发生修改的声子晶体进行分析,验证了方法所求结果具有高精度,与全分析相比缩减了约35%的计算时间,在处理拓扑修改变化量大和计算规模大的声子晶体模态分析问题上有很大潜力。     

Changing poles in the rational Lanczos method for the Hermitian eigenvalue problem

Applications such as the modal analysis of structures and acoustic cavities require a number of eigenvalues and eigenvectors of large‐scale Hermitian eigenvalue problems. The most popular method is probably the spectral transformation Lanczos method. An important disadvantage of this method is that a change of pole requires a complete restart. In this paper, we investigate the use of the rational Krylov method for this application. This method does not require a complete restart after a change of pole. It is shown that the change of pole can be considered as a change of Lanczos basis. The major conclusion of this paper is that the method is numerically stable when the poles are chosen in between clusters of the approximate eigenvalues. Copyright © 2001 John Wiley & Sons, Ltd.