共查询到20条相似文献,搜索用时 15 毫秒
1.
Congying Duan 《Journal of Computational and Applied Mathematics》2010,234(3):845-860
The global Arnoldi method can be used to compute exterior eigenpairs of a large non-Hermitian matrix A, but it does not work well for interior eigenvalue problems. Based on the global Arnoldi process that generates an F-orthonormal basis of a matrix Krylov subspace, we propose a global harmonic Arnoldi method for computing certain harmonic F-Ritz pairs that are used to approximate some interior eigenpairs. We propose computing the F-Rayleigh quotients of the large non-Hermitian matrix with respect to harmonic F-Ritz vectors and taking them as new approximate eigenvalues. They are better and more reliable than the harmonic F-Ritz values. The global harmonic Arnoldi method inherits convergence properties of the harmonic Arnoldi method applied to a larger matrix whose distinct eigenvalues are the same as those of the original given matrix. Some properties of the harmonic F-Ritz vectors are presented. As an application, assuming that A is diagonalizable, we show that the global harmonic Arnoldi method is able to solve multiple eigenvalue problems both in theory and in practice. To be practical, we develop an implicitly restarted global harmonic Arnoldi algorithm with certain harmonic F-shifts suggested. In particular, this algorithm can be adaptively used to solve multiple eigenvalue problems. Numerical experiments show that the algorithm is efficient for the eigenproblem and is reliable for quite ill-conditioned multiple eigenproblems. 相似文献
2.
Miloud Sadkane 《Numerische Mathematik》1993,64(1):195-211
Summary We present two methods for computing the leading eigenpairs of large sparse unsymmetric matrices. Namely the block-Arnoldi method and an adaptation of the Davidson method to unsymmetric matrices. We give some theoretical results concerning the convergence and discuss implementation aspects of the two methods. Finally some results of numerical tests on a variety of matrices, in which we compare these two methods are reported. 相似文献
3.
We discuss a class of deflated block Krylov subspace methods for solving large scale matrix eigenvalue problems. The efficiency of an Arnoldi-type method is examined in computing partial or closely clustered eigenvalues of large matrices. As an improvement, we also propose a refined variant of the Arnoldi-type method. Comparisons show that the refined variant can further improve the Arnoldi-type method and both methods exhibit very regular convergence behavior. 相似文献
4.
A block Arnoldi-Chebyshev method for computing the leading eigenpairs of large sparse unsymmetric matrices 总被引:1,自引:0,他引:1
Miloud Sadkane 《Numerische Mathematik》1993,64(1):181-193
Summary In this paper we describe a block version of Arnoldi's method for computing a few eigenvalues with largest or smallest real parts. The method is accelerated via Chebyshev iteration and a procedure is developed to identify the optimal ellipse which encloses the spectrum. A parallel implementation of this method is investigated on the eight processor Alliant FX/80. Numerical results and comparisons with simultaneous iteration on some Harwell-Boeing matrices are reported. 相似文献
5.
This paper introduces a general method for the numerical derivation of a minimum distance (MD) estimator for the parameters of an unknown distribution. The approach is based on an active sampling of the space in which the random sample takes values and on the optimization of the parameters of a suitable approximating model. This allows us to derive the MD estimator function for any given distribution, by which we can immediately obtain the MD estimate of the unknown parameters in correspondence to any observed random sample. Convergence of the method is proved when mild conditions on the sampling process and on the involved functions are satisfied, and it is shown that favorable rates can be obtained when suitable deterministic sequences are employed. Finally, simulation results are provided to show the effectiveness of the proposed algorithm on two case studies. 相似文献
6.
Daniel Kressner 《Numerische Mathematik》2006,103(3):461-483
Stewart's recently introduced Krylov-Schur algorithm is a modification of the implicitly restarted Arnoldi algorithm which
employs reordered Schur decompositions to perform restarts and deflations in a numerically reliable manner. This paper describes
a variant of the Krylov-Schur algorithm suitable for addressing eigenvalue problems associated with products of large and
sparse matrices. It performs restarts and deflations via reordered periodic Schur decompositions and, by taking the product
structure into account, it is capable of achieving qualitatively better approximations to eigenvalues of small magnitude.
Supported by DFG Research Center Matheon, Mathematics for key technologies, in Berlin. 相似文献
7.
Implementation of a variable block Davidson method with deflation for solving large sparse eigenproblems 总被引:3,自引:0,他引:3
The Davidson method is a preconditioned eigenvalue technique aimed at computing a few of the extreme (i.e., leftmost or rightmost)
eigenpairs of large sparse symmetric matrices. This paper describes a software package which implements a deflated and variable-block
version of the Davidson method. Information on how to use the software is provided. Guidelines for its upgrading or for its
incorporation into existing packages are also included. Various experiments are performed on an SGI Power Challenge and comparisons
with ARPACK are reported.
This revised version was published online in June 2006 with corrections to the Cover Date. 相似文献
8.
Shu-Tian Liu 《Linear algebra and its applications》2010,432(7):1851-1863
Recently, a continuous method has been proposed by Golub and Liao as an alternative way to solve the minimum and interior eigenvalue problems. According to their numerical results, their method seems promising. This article is an extension along this line. In this article, firstly, we convert an eigenvalue problem to an equivalent constrained optimization problem. Secondly, using the Karush-Kuhn-Tucker conditions of this equivalent optimization problem, we obtain a variant of the Rayleigh quotient gradient flow, which is formulated by a system of differential-algebraic equations. Thirdly, based on the Rayleigh quotient gradient flow, we give a practical numerical method for the minimum and interior eigenvalue problems. Finally, we also give some numerical experiments of our method, the Golub and Liao method, and EIGS (a Matlab implementation for computing eigenvalues using restarted Arnoldi’s method) for some typical eigenvalue problems. Our numerical experiments indicate that our method seems promising for most test problems. 相似文献
9.
The harmonic block Arnoldi method can be used to find interior eigenpairs of large matrices. Given a target point or shift τ to which the needed interior eigenvalues are close, the desired interior eigenpairs are the eigenvalues nearest τ and the associated eigenvectors. However, it has been shown that the harmonic Ritz vectors may converge erratically and even may fail to do so. To do a better job, a modified harmonic block Arnoldi method is coined that replaces the harmonic Ritz vectors by some modified harmonic Ritz vectors. The relationships between the modified harmonic block Arnoldi method and the original one are analyzed. Moreover, how to adaptively adjust shifts during iterations so as to improve convergence is also discussed. Numerical results on the efficiency of the new algorithm are reported. 相似文献
10.
We present methods for computing a nearby partial Jordan-Schur form of a given matrix and a nearby partial Weierstrass-Schur
form of a matrix pencil. The focus is on the use and the interplay of the algorithmic building blocks – the implicitly restarted
Arnoldi method with prescribed restarts for computing an invariant subspace associated with the dominant eigenvalue, the clustering
method for grouping computed eigenvalues into numerically multiple eigenvalues and the staircase algorithm for computing the
structure revealing form of the projected problem. For matrix pencils, we present generalizations of these methods. We introduce
a new and more accurate clustering heuristic for both matrices and matrix pencils. Particular emphasis is placed on reliability
of the partial Jordan-Schur and Weierstrass-Schur methods with respect to the choice of deflation parameters connecting the
steps of the algorithm such that the errors are controlled. Finally, successful results from computational experiments conducted
on problems with known canonical structure and varying ill-conditioning are presented.
This revised version was published online in June 2006 with corrections to the Cover Date. 相似文献
11.
Kensuke Aishima Takayasu MatsuoKazuo Murota Masaaki Sugihara 《Journal of Computational and Applied Mathematics》2012
In 1989, Bai and Demmel proposed the multishift QR algorithm for eigenvalue problems. Although the global convergence property of the algorithm (i.e., the convergence from any initial matrix) still remains an open question for general nonsymmetric matrices, in 1992 Jiang focused on symmetric tridiagonal case and gave a global convergence proof for the generalized Rayleigh quotient shifts. In this paper, we propose Wilkinson-like shifts, which reduce to the standard Wilkinson shift in the single shift case, and show a global convergence theorem. 相似文献
12.
In this paper, we consider solving the least squares problem minx‖b-Tx‖2 by using preconditioned conjugate gradient (PCG) methods, where T is a large rectangular matrix which consists of several square block-Toeplitz-Toeplitz-block (BTTB) matrices and b is a column vector. We propose a BTTB preconditioner to speed up the PCG method and prove that the BTTB preconditioner is a good preconditioner. We then discuss the construction of the BTTB preconditioner. Numerical examples, including image restoration problems, are given to illustrate the efficiency of our BTTB preconditioner. Numerical results show that our BTTB preconditioner is more efficient than the well-known Level-1 and Level-2 circulant preconditioners. 相似文献
13.
K. Jbilou 《Linear algebra and its applications》2010,432(10):2473-1398
In the present paper, we propose preconditioned Krylov methods for solving large Lyapunov matrix equations AX+XAT+BBT=0. Such problems appear in control theory, model reduction, circuit simulation and others. Using the Alternating Direction Implicit (ADI) iteration method, we transform the original Lyapunov equation to an equivalent symmetric Stein equation depending on some ADI parameters. We then define the Smith and the low rank ADI preconditioners. To solve the obtained Stein matrix equation, we apply the global Arnoldi method and get low rank approximate solutions. We give some theoretical results and report numerical tests to show the effectiveness of the proposed approaches. 相似文献
14.
In this paper a new iterative method is given for solving large sparse least squares problems and computing the minimum norm
solution to underdetermined consistent linear systems. The new scheme is called the generalized successive overrelaxation
(GSOR) method and is shown to be convergent ifA is full column rank. The GSOR method involves a parameter ρ and an auxiliary matrixP. One can choose matrix P so that the GSOR method only involves matrix and vector operations; therefore the GSOR method is
suitable for parallel computations. Besides, the GSOR method can be combined with preconditioning techniques, and therefore
can be expected to be more effective.
This author's work was supported by Natural Science Foundation of Liaoning Province, China. 相似文献
15.
Summary Continuation methods compute paths of solutions of nonlinear equations that depend on a parameter. This paper examines some aspects of the multicomputer implementation of such methods. The computations are done on a mesh connected multicomputer with 64 nodes.One of the main issues in the development of concurrent programs is load balancing, achieved here by using appropriate data distributions. In the continuation process, many linear systems have to be solved. For nearby points along the solution path, the corresponding system matrices are closely related to each other. Therefore, pivots which are good for theLU-decomposition of one matrix are likely to be acceptable for a whole segment of the solution path. This suggests to choose certain data distributions that achieve good load balancing. In addition, if these distributions are used, the resulting code is easily vectorized.To test this technique, the invariant manifold of a system of two identical nonlinear oscillators is computed as a function of the coupling between them. This invariant manifold is determined by the solution of a system of nonlinear partial differential equations that depends on the coupling parameter. A symmetry in the problem reduces this system to one single equation, which is discretized by finite differences. The solution of the discrete nonlinear system is followed as the coupling parameter is changed.This material is based upon work supported by the NSF under Cooperative Agreement No. CCR-8809615. The government has certain rights in this material. 相似文献
16.
We develop a discontinuous mixed covolume method for elliptic problems on triangular meshes. An optimal error estimate for the approximation of velocity is obtained in a mesh-dependent norm. First-order L2-error estimates are derived for the approximations of both velocity and pressure. 相似文献
17.
Maria Grazia Gasparo Alessandra Papini Aldo Pasquali 《Journal of Computational and Applied Mathematics》2007
In this work we are interested in the solution of nonlinear inverse problems of the form F(x)=y. We consider a two-stage method which is third order convergent for well-posed problems. Combining the method with Levenberg–Marquardt regularization of the linearized problems at each stage and using the discrepancy principle as a stopping criterion, we obtain a regularization method for ill-posed problems. Numerical experiments on some parameter identification and inverse acoustic scattering problems are presented to illustrate the performance of the method. 相似文献
18.
Axel Ruhe 《BIT Numerical Mathematics》1994,34(1):165-176
A new algorithm for the computation of eigenvalues of a nonsymmetric matrix pencil is described. It is a generalization of the shifted and inverted Lanczos (or Arnoldi) algorithm, in which several shifts are used in one run. It computes an orthogonal basis and a small Hessenberg pencil. The eigensolution of the Hessenberg pencil, gives Ritz approximations to the solution of the original pencil. It is shown how complex shifts can be used to compute a real block Hessenberg pencil to a real matrix pair.Two applicationx, one coming from an aircraft stability problem and the other from a hydrodynamic bifurcation, have been tested and results are reported.Dedicated to Carl-Erik Fröberg on the occasion of his 75th birthday. 相似文献
19.
Achiya Dax 《BIT Numerical Mathematics》1993,33(2):262-276
This paper presents a row relaxation method for solving the doubly regularized minimax problem
相似文献
20.
We show that the continuation method can be used to solve aweakly elliptic two-parameter eigenvalue problem. We generalizethe continuation method for a nonsymmetric eigenvalue problemAx = x by T. Y. Li, Z. Zeng and L. Cong (1992 SIAM J. Numer.Anal. 29, 229248) to two-parameter problems. 相似文献
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