The use of high-order generalized Rayleigh quotient shifting strategies is advocated as a means of improving the parallel performance of the double-shift qr algorithm for nonsymmetric eigenvalue problems.
The use of high-order generalized Rayleigh quotient shifting strategies is advocated as a means of improving the parallel performance of the double-shift qr algorithm for nonsymmetric eigenvalue problems.
This paper presents a variant $qr$ algorithm for calculating a Hamiltonian–Schur decomposition [10]. It is defined for Hamiltonian matrices that arise from single input control systems. Numerical stability and Hamilt...
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This paper presents a variant $qr$ algorithm for calculating a Hamiltonian–Schur decomposition [10]. It is defined for Hamiltonian matrices that arise from single input control systems. Numerical stability and Hamiltonian structure are preserved by using unitary symplectic similarity transformations. Following a finite step reduction to a Hessenberg-like condensed form, a sequence of similarity transformations yields a permuted triangular matrix. As the iteration converges, it deflates into problems of lower dimension. Convergence is accelerated by varying a scalar shift. When the Hamiltonian matrix is real, complex arithmetic can be avoided by using an implicit double shift technique. The Hamiltonian-Schur decomposition yields the same invariant subspace information as a Schur decomposition but requires significantly less work and storage for problems of size greater than about 20.
In applying the qr algorithm to compute the eigenvalues of a unitary Hessenberg matrix, a projected Wilkinson shift of unit modulus is proposed and proved to give global convergence with (at least) a quadratic asympto...
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A generalization of the qr algorithm proposed by Francis [2] for square matrices is introduced for the singular values decomposition of arbitrary rectangular matrices. Geometrically the algorithm means the subsequent ...
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The implementation on vector computers of the qr algorithm and of iterative schemes based on obtaining the determinant and its derivatives by Hymans method are presented. It is shown that iterative schemes based on Hy...
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We introduce the two-sided Rayleigh quotient shift to the qr algorithm for non-Hermitian matrices to achieve a cubic local convergence rate. For the singly shifted case, the two-sided Rayleigh quotient iteration is in...
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We introduce the two-sided Rayleigh quotient shift to the qr algorithm for non-Hermitian matrices to achieve a cubic local convergence rate. For the singly shifted case, the two-sided Rayleigh quotient iteration is incorporated into the qr iteration. A modified version of the method and its truncated version are developed to improve the efficiency. Based on the observation that the Francis double-shift qr iteration is related to a 2D Grassmann-Rayleigh quotient iteration, A doubly shifted qr algorithm with the two-sided 2D Grassmann-Rayleigh quotient double-shift is proposed. A modified version of the method and its truncated version are also developed. Numerical examples are presented to show the convergence behavior of the proposed algorithms. Numerical examples also show that the truncated versions of the modified methods outperform their counterparts including the standard Rayleigh quotient single-shift and the Francis double-shift.
MIMO-OFDM has become the core of future communication technologies for its great excellence, the detection algorithms are various, but only nonlinear algorithms are extensive used in practice. First, the ZF and MMSE c...
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ISBN:
(纸本)9781479913909
MIMO-OFDM has become the core of future communication technologies for its great excellence, the detection algorithms are various, but only nonlinear algorithms are extensive used in practice. First, the ZF and MMSE criteria are briefly introduced, the nonlinear detection algorithms of V-BLAST algorithm and qr algorithm are analyzed in detail. Then the algorithms based on ZF criterion and MMSE criterion named ZF-OSIC, MMSE-OSIC, SqrD-ZF and SqrD-MMSE are studied. Finally the BER performance of them are compared, through MATLAB simulation.
The qr algorithm is one of the three phases in the process of computing the eigenvalues and the eigenvectors of a dense nonsymmetric matrix. This paper describes a task-based qr algorithm for reducing an upper Hessenb...
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The qr algorithm is one of the three phases in the process of computing the eigenvalues and the eigenvectors of a dense nonsymmetric matrix. This paper describes a task-based qr algorithm for reducing an upper Hessenberg matrix to real Schur form. The task-based algorithm also supports generalized eigenvalue problems (QZ algorithm) but this paper concentrates on the standard case. The task-based algorithm adopts previous algorithmic improvements, such as tightly-coupled multi-shifts and Aggressive Early Deflation (AED), and also incorporates several new ideas that significantly improve the performance. This includes, but is not limited to, the elimination of several synchronization points, the dynamic merging of previously separate computational steps, the shortening and the prioritization of the critical path, and experimental GPU support. The task-based implementation is demonstrated to be multiple times faster than multi-threaded LAPACK and ScaLAPACK in both single-node and multi-node configurations on two different machines based on Intel and AMD CPUs. The implementation is built on top of the StarPU runtime system and is part of the open-source StarNEig library.
An efficient version of the parallel two-sided block-Jacobi algorithm for the singular value decomposition of an m x n matrix A includes the pre-processing step, which consists of the qr factorization of A with column...
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An efficient version of the parallel two-sided block-Jacobi algorithm for the singular value decomposition of an m x n matrix A includes the pre-processing step, which consists of the qr factorization of A with column pivoting followed by the optional LQ factorization of the R-factor. Then the iterative two-sided block-Jacobi algorithm is applied in parallel to the R-factor (or L-factor). For the efficient computation of the parallel qr (or LQ) factorization with (or without) column pivoting implemented in the ScaLAPACK, some matrix block cyclic distribution on a process grid r x c with p = r x c, r,c >= 1, and block size n(b) x n(b) is required so that all processors remain busy during the whole parallel qr (or LQ) factorization. Optimal values for parameters r, c and nb are estimated experimentally using matrices of order n = 4000 and 8000, and the number of processors p = 8 and 16, respectively. It turns out that the optimal values are about n(b) = 100 and r <= c with both r, c near to root p. These parameters are then used in numerical experiments for six various distributions of singular values combined with well- (k = 10(1)) and ill-conditioned matrices (K = 108). It is shown that using optimal parameters in the pre-processing step, the parallel two-sided block-Jacobi SVD algorithm performs better (or equally well) than the ScaLAPACK routine PDGESVD for matrices with a multiple minimal/maximal singular value regardless to the condition number. For other distributions of singular values, our algorithm is slower than the ScaLAPACK. The un-pivoted qrLQ pre-processing step is then re-formulated and extended to the qr iteration, and its connection to the qr algorithm applied to specific symmetric, positive definite matrices is shown. This connection helps to explain observations in another set of experiments with a variable number of qr iteration steps. In general, the best results for all six distributions of singular values are achieved by using about six qr iter
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ISBN:
(数字)9781611978377
ISBN:
(纸本)9781611978360
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Matrix eigenvalue problems arise in a wide variety of fields in science and engineering, so it is important to have reliable and efficient methods for solving them. Of the methods devised, bulge-chasing algorithms, such as the famous qr and QZ algorithms, are the most important. This book focuses on pole-swapping algorithms, a new class of methods that are generalizations of bulge-chasing algorithms and a bit faster and more accurate owing to their inherent flexibility. The pole-swapping theory developed by the authors sheds light on the functioning of the whole class of algorithms, including qr and QZ.
Pole-Swapping algorithms for the Eigenvalue Problem
is the only book on the topic,
describes the state of the art on eigenvalue methods, and
provides an improved understanding and explanation of why these important algorithms work.
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