Symplectic Householder transformations for a QR-like decomposition,a geometric and algebraic approaches

The aim of this paper is to show how geometric and algebraic approaches lead us to a new symplectic elementary transformations: the 2-D symplectic Householder transformations. Their features are studied in details. Their interesting properties allow us to construct a new algorithm for computing a SR factorization. This algorithm is based only on these 2-D symplectic Householder transformations. Its new features are highlighted. The study shows that, in the symplectic case, the new algorithm is the corresponding one to the classical QR factorization algorithm, via the Householder transformations. Some numerical experiments are given.     

A generalized unitary Hessenberg matrix

This paper describes the use of a generalized isometric Arnoldi algorithm to reduce a unitary matrix, via unitary similarity, to a product of elementary reflectors and permutations. The computation is analogous to the reduction of a unitary matrix to a unitary Hessenberg matrix using the isometric Arnoldi algorithm. In the case in which A is a shift matrix, the reduction provides a novel recurrence for the factor R in the QR factorization of a Toeplitz-like matrix.     

Fast block Toeplitz orthogonalization

Summary Algorithms are presented which compute theQR factorization of a block-Toeplitz matrix inO(n) ² block-operations, wheren is the block-order of the matrix and a block-operation is a multiplication, inversion or a set of Householder operations involving one or two blocks. The algorithms are in general analogous to those presented in the scalar Toeplitz case in a previous paper, but the basic operation is the Householder transform rather than the Givens transform, and the computation of the Householder coefficients and other working matrices requires special treatment. Two algorithms are presented-the first computes onlyR explicitly and the second computes bothQ andR.     

A block Cholesky-LU-based QR factorization for rectangular matrices

The Householder method provides a stable algorithm to compute the full QR factorization of a general matrix. The standard version of the algorithm uses a sequence of orthogonal reflections to transform the matrix into upper triangular form column by column. In order to exploit (level 3 BLAS or structured matrix) computational advantages for block-partitioned algorithms, we develop a block algorithm for the QR factorization. It is based on a well-known block version of the Householder method which recursively divides a matrix columnwise into two smaller matrices. However, instead of continuing the recursion down to single matrix columns, we introduce a novel way to compute the QR factors in implicit Householder representation for a larger block of several matrix columns, that is, we start the recursion at a block level instead of a single column. Numerical experiments illustrate to what extent the novel approach trades some of the stability of Householder's method for the computational efficiency of block methods.     

On symbolic factorization of partitioned sparse symmetric matrices

Partitioning a sparse matrix A is a useful device employed by a number of sparse matrix techniques. An important problem that arises in connection with some of these methods is to determine the block structure of the Cholesky factor L of A, given the partitioned A. For the scalar case, the problem of determining the structure of L from A, so-called symbolic factorization, has been extensively studied. In this paper we study the generalization of this problem to the block case. The problem is interesting because an assumption relied on in the scalar case no longer holds; specifically, the product of two nonzero scalars is always nonzero, but the product of two nonnull sparse matrices may yield a zero matrix. Thus, applying the usual symbolic factorization techniques to a partitioned matrix, regarding each submatrix as a scalar, may yield a block structure of L which is too full. In this paper an efficient algorithm is provided for determining the block structure of the Cholesky factor of a partitioned matrix A, along with some bounds on the execution time of the algorithm.     

Lanczos,Householder transformations,and implicit deflation for fast and reliable dominant singular subspace computation

Many applications, such as subspace‐based models in information retrieval and signal processing, require the computation of singular subspaces associated with the k dominant, or largest, singular values of an m×n data matrix A, where k?min(m,n). Frequently, A is sparse or structured, which usually means matrix–vector multiplications involving A and its transpose can be done with much less than ??(mn) flops, and A and its transpose can be stored with much less than ??(mn) storage locations. Many Lanczos‐based algorithms have been proposed through the years because the underlying Lanczos method only accesses A and its transpose through matrix–vector multiplications. We implement a new algorithm, called KSVD, in the Matlab environment for computing approximations to the singular subspaces associated with the k dominant singular values of a real or complex matrix A. KSVD is based upon the Lanczos tridiagonalization method, the WY representation for storing products of Householder transformations, implicit deflation, and the QR factorization. Our Matlab simulations suggest it is a fast and reliable strategy for handling troublesome singular‐value spectra. Copyright © 2001 John Wiley & Sons, Ltd.     

A fast algorithm for subspace state-space system identification via exploitation of the displacement structure

Two recent approaches (Van Overschee, De Moor, N4SID, Automatica 30 (1) (1994) 75; Verhaegen, Int. J. Control 58(3) (1993) 555) in subspace identification problems require the computation of the R factor of the QR factorization of a block-Hankel matrix H, which, in general has a huge number of rows. Since the data are perturbed by noise, the involved matrix H is, in general, full rank. It is well known that, from a theoretical point of view, the R factor of the QR factorization of H is equivalent to the Cholesky factor of the correlation matrix H^TH, apart from a multiplication by a sign matrix. In Sima (Proceedings Second NICONET Workshop, Paris-Versailles, December 3, 1999, p. 75), a fast Cholesky factorization of the correlation matrix, exploiting the block-Hankel structure of H, is described. In this paper we consider a fast algorithm to compute the R factor based on the generalized Schur algorithm. The proposed algorithm allows to handle the rank–deficient case.     

Multifrontal QR Factorization in a Multiprocessor Environment

We describe the design and implementation of a parallel QR decomposition algorithm for a large sparse matrix A . The algorithm is based on the multifrontal approach and makes use of Householder transformations. The tasks are distributed among processors according to an assembly tree which is built from the symbolic factorization of the matrix A ^T A . We first address uniprocessor issues and then discuss the multiprocessor implementation of the method. We consider the parallelization of both the factorization phase and the solve phase. We use relaxation of the sparsity structure of both the original matrix and the frontal matrices to improve the performance. We show that, in this case, the use of Level 3 BLAS can lead to very significant gains in performance. We use the eight processor Alliant˜FX/80 at CERFACS to illustrate our discussion.     

Fast algorithms for placing large entries along the diagonal of a sparse matrix

Solving a sparse system of linear equations Ax=b is one of the most fundamental operations inside any circuit simulator. The equations/rows in the matrix A are often rearranged/permuted before factorization and applying direct or iterative methods to obtain the solution. Permuting the rows of the matrix A so that the entries with large absolute values lie on the diagonal has several advantages like better numerical stability for direct methods (e.g., Gaussian elimination) and faster convergence for indirect methods (such as the Jacobi method). Duff (2009) [3] has formulated this as a weighted bipartite matching problem (the MC64 algorithm). In this paper we improve the performance of the MC64 algorithm with a new labeling technique which improves the asymptotic complexity of updating dual variables from O(|V|+|E|) to O(|V|), where |V| is the order of the matrix A and |E| is the number of non-zeros. Experimental results from using the new algorithm, when benchmarked with both industry benchmarks and UFL sparse matrix collection, are very promising. Our algorithm is more than 60 times faster (than Duff’s algorithm) for sparse matrices with at least a million non-zeros.     

Nonnegative rank factorization—a heuristic approach via rank reduction

Given any nonnegative matrix $A \in \mathbb{R}^{m \times n}$ , it is always possible to express A as the sum of a series of nonnegative rank-one matrices. Among the many possible representations of A, the number of terms that contributes the shortest nonnegative rank-one series representation is called the nonnegative rank of A. Computing the exact nonnegative rank and the corresponding factorization are known to be NP-hard. Even if the nonnegative rank is known a priori, no simple procedure exists presently that is able to perform the nonnegative factorization. Based on the Wedderburn rank reduction formula, this paper proposes a heuristic approach to tackle this difficult problem numerically. Starting with A, the idea is to recurrently extrat, whenever possible, a rank-one nonnegative portion from the previous matrix while keeping the residual nonnegative and lowering its rank by one. With a slight modification for symmetry, the method can equally be applied to another important class of completely positive matrices. No convergence can be guaranteed, but repeated restart might help alleviate the difficulty. Extensive numerical testing seems to suggest that the proposed algorithm might serve as a first-step numerical means for exploring the intriguing problem of nonnegative rank factorization.     

Factorization of symmetric singular M-matrices

Suppose A is a symmetric, singular M-matrix. A sufficient condition for A to have a triangular, singular M-matrix factorization is given, and it is shown that PAP^T always has such a factorization for a particular permutation matrix P.     

Upper eigenvalue bounds for pencils of matrices

Eigenvalue bounds are obtained for pencils of matrices A ? vB where A is a Stieltjes matrix and B is positive definite, under assumptions suitable for the estimation of asymptotic convergence rates of factorization iterative methods, where B represents the approximate factorization of A. The upper bounds obtained depend on the “connectivity” structure of the matrices involved, which enters through matrix graph considerations; in addition, a more classical argument is used to obtain a lower bound. Potential applications of these results include a partial confirmation of Gustafsson's conjecture concerning the nonnecessity of Axelsson's perturbations.     

A sparse counterpart of Reichel and Gragg’s package QRUP

We describe how to maintain the triangular factor of a sparse QR factorization when columns are added and deleted and Q cannot be stored for sparsity reasons. The updating procedures could be thought of as a sparse counterpart of Reichel and Gragg’s package QRUP. They allow us to solve a sequence of sparse linear least squares subproblems in which each matrix B_k is an independent subset of the columns of a fixed matrix A, and B_k+1 is obtained by adding or deleting one column. Like Coleman and Hulbert [T. Coleman, L. Hulbert, A direct active set algorithm for large sparse quadratic programs with simple bounds, Math. Program. 45 (1989) 373-406], we adapt the sparse direct methodology of Björck and Oreborn of the late 1980s, but without forming A^TA, which may be only positive semidefinite. Our Matlab 5 implementation works with a suitable row and column numbering within a static triangular sparsity pattern that is computed in advance by symbolic factorization of A^TA and preserved with placeholders.     

关于k-广义酉矩阵

本文研究了k-广义酉矩阵的性质及其与酉矩阵、辛矩阵、Householder矩阵之间的联系,取得了许多新的结果,推广了酉矩阵及Householder矩阵的相应结果,特别将正交矩阵的广义Cayley分解推广到了广义酉矩阵上;并将各类酉矩阵及辛矩阵统一了起来.     

On the Symplectic Eigenvalues of Positive Definite Matrices

A simple proof of Williamson’s theorem is given. This theorem states that a real symmetric positive definite matrix A of even order can be brought to diagonal form Λ by a symplectic congruence transformation. The diagonal entries of Λ are called symplectic eigenvalues of A. The problem of calculating these values is also discussed.     

Some results on symplectic matrices

The principal results are that if A is an integral matrix such that AA^T is symplectic then A = CQ, where Q is a permutation matrix and C is symplectic; and that if A is a hermitian positive definite matrix which is symplectic, and B is the unique hermitian positive definite pth.root of A, where p is a positive integer, then B is also symplectic.     

Inverse NO-matrices

Results similar to those of Thomas L. Markham concerning inverse M-matrices are shown to hold for inverse N_Omatrices. A more general result and an alternate method of proof using Schur complements are given for one of Markham's theorems. Conditions are given to transform a nonpositive matrix to an N_O-amtrix by multiplication by Householder transformations. Also, an LU factorization of an inverse N_O-matrix is given.     

Indefinite QR Factorization

Let A be a Hermitian positive definite matrix given by its rectangular factor G such that A=G^*G. It is well known that the Cholesky factorization of A is equivalent to the QR factorization of G. In this paper, an analogue of the QR factorization for Hermitian indefinite matrices is constructed. This problem has been considered by many authors, but the problem of zero diagonal elements has not been solved so far. Here we show how to overcome this difficulty. AMS subject classification (2000) 65F25, 46C20, 65F15     

A class of preconditioners based on matrix splitting for nonsymmetric linear systems

Given a general matrix splitting A=M-N where M is nonsingular, a new factorization scheme in terms of factorized and splitting matrices is given using the Sherman-Morrison formula. Theoretical analysis shows that the factorization can give an LDU decomposition of A under some special choices. We propose and implement a class of preconditioners based on this factorization combining with dropping rules. A number of numerical experiments from discrete convection diffusion equation and some practical problems show that the new preconditioner is efficient, and is comparable to existing preconditioners in term of storage requirement and computational cost.     

Accurate downdating of a modified Gram-Schmidt QR decomposition

A new algorithm for downdating a QR decomposition is presented. We show that, when the columns in the Q factor from the Modified Gram-Schmidt QR decomposition of a matrixX are exactly orthonormal, the Gram-Schmidt downdating algorithm for the QR decomposition ofX is equivalent to downdating the full Householder QR decomposition of the matrixX augmented by ann ×n zero matrix on top. Using this relation, we derive an algorithm that improves the Gram-Schmidt downdating algorithm when the columns in the Q factor are not orthonormal. Numerical test results show that the new algorithm produces far more accurate results than the Gram-Schmidt downdating algorithm for certain ill-conditioned problems.This work was partially supported in part by the National Science Foundation grants CCR-9209726 and CCR-9509085.