On some structured inverse eigenvalue problems

This work deals with various finite algorithms that solve two special Structured Inverse Eigenvalue Problems (SIEP). The first problem we consider is the Jacobi Inverse Eigenvalue Problem (JIEP): given some constraints on two sets of reals, find a Jacobi matrix J (real, symmetric, tridiagonal, with positive off-diagonal entries) that admits as spectrum and principal subspectrum the two given sets. Two classes of finite algorithms are considered. The polynomial algorithm which is based on a special Euclid–Sturm algorithm (Householder's terminology) and has been rediscovered several times. The matrix algorithm which is a symmetric Lanczos algorithm with a special initial vector. Some characterization of the matrix ensures the equivalence of the two algorithms in exact arithmetic. The results of the symmetric situation are extended to the nonsymmetric case. This is the second SIEP to be considered: the Tridiagonal Inverse Eigenvalue Problem (TIEP). Possible breakdowns may occur in the polynomial algorithm as it may happen with the nonsymmetric Lanczos algorithm. The connection between the two algorithms exhibits a similarity transformation from the classical Frobenius companion matrix to the tridiagonal matrix. This result is used to illustrate the fact that, when computing the eigenvalues of a matrix, the nonsymmetric Lanczos algorithm may lead to a slow convergence, even for a symmetric matrix, since an outer eigenvalue of the tridiagonal matrix of order n − 1 can be arbitrarily far from the spectrum of the original matrix. This revised version was published online in August 2006 with corrections to the Cover Date.     

New breakdown-free variant of AINV method for nonsymmetric positive definite matrices

This paper proposes a new breakdown-free preconditioning technique, called SAINV-NS, of the AINV method of Benzi and Tuma for nonsymmetric positive definite matrices. The resulting preconditioner which is an incomplete factorization of the inverse of a nonsymmetric matrix will be used as an explicit right preconditioner for QMR, BiCGSTAB and GMRES(m) methods. The preconditoner is reliable (pivot breakdown can not occur) and effective at reducing the number of iterations. Some numerical experiments on test matrices are presented to show the efficiency of the new method and comparing to the AINV-A algorithm.     

An ILU preconditioner for nonsymmetric positive definite matrices by using the conjugate Gram–Schmidt process

The present paper investigates the approach mentioned, but not developed, in [Benzi, Tuma, Numer. Linear Algebra Appl. 10 (2003)]. In this approach, using a conjugate Gram–Schmidt algorithm, an ILU preconditioner whose existence is guaranteed for any nonsingular (symmetric and nonsymmetric) real positive definite matrix is obtained. Numerical results on some nonsymmetric positive definite matrices demonstrate the efficiency of the method.     

解非对称矩阵特征值问题的一种并行分治算法

１引言考虑矩阵特征值问题其中Ａ是非对称矩阵．通过正交变换（如Ｈｏｕｓｅｈｏｌｄｅｒ变换或Ｇｉｖｅｎｓ变换）,Ａ可化为上Ｈｅｓｓｅｎｂｅｒｇ形．因而,本文假设Ａ为上Ｈｅｓｓｅｎｂｅｒｇ矩阵,表示如下：不失一般性,进一步假设所有的（ｊ＝２,…,ｎ）,即认为Ａ是不可约的关于如何求解上述问题,人们进行了不懈的努力,提出了许多行之有效的算法［１－８］．其中分治算法因具有良好的并行性而引人注目．分治算法的典型代表是基于同伦连续的分治算法［２,３,４］和基于Ｎｅｗｔｏｎ迭代的分治算法［１］．本文提出一种新的分…     

两个矩阵问题的并行算法

本文讨论在阵列机上两个矩阵问题的并行算法.一个是用高斯-约当法求逆矩阵的并行实现;另一个是确定矩阵特征值的个数的并行送代法.这些算法已在IBM PC/XT微型机上模拟实现.     

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.     

Parallel sparse Gaussian elimination with partial pivoting

In this article, we consider the factorization of a sparse nonsymmetric matrix using Gaussian elimination with partial pivoting on a multiprocessor having a globally-shared memory. The parallel algorithm makes use of a static data structure developed by George, Liu and Ng in [17]. Some numerical experiments on a Sequent Balance 8000 are presented to demonstrate the efficiency of the parallel implementation.Research supported in part by the Applied Mathematical Sciences Research Program, Office of Energy Research, U.S. Department of Energy under contract DE-AC05-84OR21400 with Martin Marietta Energy Systems, Inc.     

Matching is as easy as matrix inversion

We present a new algorithm for finding a maximum matching in a general graph. The special feature of our algorithm is that its only computationally non-trivial step is the inversion of a single integer matrix. Since this step can be parallelized, we get a simple parallel (RNC ²) algorithm. At the heart of our algorithm lies a probabilistic lemma, the isolating lemma. We show other applications of this lemma to parallel computation and randomized reductions. Work done while visiting MSRI, Berkeley, in Fall 1985. Supported by NSF Grant BCR 85-03611 and an IBM Faculty Development Award.     

A comparison of some domain decomposition and ILU preconditioned iterative methods for nonsymmetric elliptic problems

In recent years, competitive domain-decomposed preconditioned iterative techniques of Krylov-Schwarz type have been developed for nonsymmetric linear elliptic systems. Such systems arise when convection-diffusion-reaction problems from computational fluid dynamics or heat and mass transfer are linearized for iterative solution. Through domain decomposition, a large problem is divided into many smaller problems whose requirements for coordination can be controlled to allow effective solution on parallel machines. A central question is how to choose these small problems and how to arrange the order of their solution. Different specifications of decomposition and solution order lead to a plethora of algorithms possessing complementary advantages and disadvantages. In this report we compare several methods, including the additive Schwarz algorithm, the classical multiplicative Schwarz algorithm, an accelerated multiplicative Schwarz algorithm, the tile algorithm, the CGK algorithm, the CSPD algorithm, and also the popular global ILU-family of preconditioners, on some nonsymmetric or indefinite elliptic model problems discretized by finite difference methods. The preconditioned problems are solved by the unrestarted GMRES method. A version of the accelerated multiplicative Schwarz method is a consistently good performer.     

Block filtering decomposition

This paper introduces a new preconditioning technique that is suitable for matrices arising from the discretization of a system of PDEs on unstructured grids. The preconditioner satisfies a so‐called filtering property, which ensures that the input matrix is identical with the preconditioner on a given filtering vector. This vector is chosen to alleviate the effect of low‐frequency modes on convergence and so decrease or eliminate the plateau that is often observed in the convergence of iterative methods. In particular, the paper presents a general approach that allows to ensure that the filtering condition is satisfied in a matrix decomposition. The input matrix can have an arbitrary sparse structure. Hence, it can be reordered using nested dissection, to allow a parallel computation of the preconditioner and of the iterative process. We show the efficiency of our preconditioner through a set of numerical experiments on symmetric and nonsymmetric matrices. Copyright © 2014 John Wiley & Sons, Ltd.     

PARALLEL CHAOTIC MULTISPLITTING ITERATIVE METHODS FOR THE LARGE SPARSE LINEAR COMPLEMENTARITY PROBLEM

1. IntroductionWe consider the linear complementarity problem LCP(M,q): Find a z E m such thatwhere M = (mij) E boxs and q ~ (qi) 6 m are given real matriX and vector, respectively.This problem axises in various scientific computing areas such as the Nash equilibritun poillt ofa bimatrir game (e.g., Cottle and Dantzig[4] and Lelnke[12j) and the free boundary problems offluid mechedcs (e.g., Cryer[8]). There have been a lot of researches on the approximate solutionof the linear complemeat…     

Skew-symmetric methods for nonsymmetric linear systems with multiple right-hand sides

By transforming nonsymmetric linear systems to the extended skew-symmetric ones, we present the skew-symmetric methods for solving nonsymmetric linear systems with multiple right-hand sides. These methods are based on the block and global Arnoldi algorithm which is formed by implementing orthogonal projections of the initial matrix residual onto a matrix Krylov subspace. The algorithms avoid the tediously long Arnoldi process and highly reduce expensive storage. Numerical experiments show that these algorithms are effective and give better practical performances than global GMRES for solving nonsymmetric linear systems with multiple right-hand sides.     

有限元解三维弹性波方程的并行算法

研究弹性固体中波的传播问题由来已久,由于地震学、石油勘探、地震层析成像等方面的重要应用,三维弹性波方程的求解问题在工业应用中显得日益重要.自七十年代末由石油勘探界首先引入有限元方法求解二维弹性波方程以来,这一公认精度较高的方法在求解三维弹性波方程方面研究成果尚不多见,其主要原因是该问题需要较大的计算机存贮量和计算量.对三维问题使用一般的有限元方法求解,常常需要几十万次反复递推求解高达10~7阶以上的大型线性代数方程组.对于这类超大型的计算课题,目前的     

An effective hybrid algorithm for computing symbolic determinants

A hybrid algorithm for computing the determinant of a matrix whose entries are polynomials is presented. It is based on the dimension-decreasing algorithm [22] and the parallel algorithm for computing a symbolic determinant of [19]. First, through the dimension-decreasing algorithm, a given multivariate matrix can be converted to a bivariate matrix. Then, the parallel algorithm can be applied to effectively compute the determinant of the bivariate matrix. Experimental results show that the new algorithm can not only reduce enormously the intermediate expression swell in the process of symbolic computation, but also achieve higher degree of parallelism, compared with the single parallel algorithm given in [19].     

Solving the algebraic Riccati equation with the matrix sign function

This paper presents some improvements to the matrix-sign-function algorithm for the algebraic Riccati equation. A simple reorganization changes nonsymmetric matrix inversions into symmetric matrix inversions. Scaling accelerates convergence of the basic iteration and yields a new quadratic formula for certain 2-by-2 algebraic Riccati equations. Numerical experience suggests the algorithm be supplemented with a refinement strategy similar to iterative refinement for systems of linear equations. Refinement also produces an error estimate. The resulting procedure is numerically stable. It compares favorably with current Schur vector-based algorithms.     

Matrix inversion algorithm for linear array processor

This paper presents a parallel algorithm for computing the inversion of a dense matrix based on Gauss-Jordan elimination. The algorithm is proposed for the implementation on the linear array at a processor level which operates in a pipeline fashion. Two types of architecture are considered—one which uses serial data transfer (AP/S) and another which uses parallel data transfer (AP/P) between neighboring processors. The speed up of AP/S and AP/P are O(n/2) and O(4/5n), respectively.     

A Lanczos-type method for multiple starting vectors

Given a square matrix and single right and left starting vectors, the classical nonsymmetric Lanczos process generates two sequences of biorthogonal basis vectors for the right and left Krylov subspaces induced by the given matrix and vectors. In this paper, we propose a Lanczos-type algorithm that extends the classical Lanczos process for single starting vectors to multiple starting vectors. Given a square matrix and two blocks of right and left starting vectors, the algorithm generates two sequences of biorthogonal basis vectors for the right and left block Krylov subspaces induced by the given data. The algorithm can handle the most general case of right and left starting blocks of arbitrary sizes, while all previously proposed extensions of the Lanczos process are restricted to right and left starting blocks of identical sizes. Other features of our algorithm include a built-in deflation procedure to detect and delete linearly dependent vectors in the block Krylov sequences, and the option to employ look-ahead to remedy the potential breakdowns that may occur in nonsymmetric Lanczos-type methods.

     

The rational Krylov algorithm for nonsymmetric eigenvalue problems. III: Complex shifts for real matrices

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.     

A unitary joint diagonalization algorithm for nonsymmetric higher‐order tensors based on Givens‐like rotations

Based on Givens‐like rotations, we present a unitary joint diagonalization algorithm for a set of nonsymmetric higher‐order tensors. Each unitary rotation matrix only depends on one unknown parameter which can be analytically obtained in an independent way following a reasonable assumption and a complex derivative technique. It can serve for the canonical polyadic decomposition of a higher‐order tensor with orthogonal factors. Furthermore, based on cross‐high‐order cumulants of observed signals, we show that the proposed algorithm can be applied to solve the joint blind source separation problem. The simulation results reveal that the proposed algorithm has a competitive performance compared with those of several existing related methods.