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Journal ArticleDOI

Solution of the matrix equation AX + XB = C [F4]

R. H. Bartels, +1 more
- 01 Sep 1972 - 
- Vol. 15, Iss: 9, pp 820-826
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TLDR
The algorithm is supplied as one file of BCD 80 character card images at 556 B.P.I., even parity, on seven ~rack tape, and the user sends a small tape (wt. less than 1 lb.) the algorithm will be copied on it and returned to him at a charge of $10.O0 (U.S.and Canada) or $18.00 (elsewhere).
Abstract
and Canada) or $18.00 (elsewhere). If the user sends a small tape (wt. less than 1 lb.) the algorithm will be copied on it and returned to him at a charge of $10.O0 (U.S. only). All orders are to be prepaid with checks payable to ACM Algorithms. The algorithm is re corded as one file of BCD 80 character card images at 556 B.P.I., even parity, on seven ~rack tape. We will supply the algorithm at a density of 800 B.P.I. if requested. The cards for the algorithm are sequenced starting at 10 and incremented by 10. The sequence number is right justified in colums 80. Although we will make every attempt to insure that the algorithm conforms to the description printed here, we cannot guarantee it, nor can we guarantee that the algorithm is correct.-L.D.F. Descdption The following programs are a collection of Fortran IV sub-routines to solve the matrix equation AX-.}-XB = C (1) where A, B, and C are real matrices of dimensions m X m, n X n, and m X n, respectively. Additional subroutines permit the efficient solution of the equation ArX + xa = C, (2) where C is symmetric. Equation (1) has applications to the direct solution of discrete Poisson equations [2]. It is well known that (1) has a unique solution if and only if the One proof of the result amounts to constructing the solution from complete systems of eigenvalues and eigenvectors of A and B, when they exist. This technique has been proposed as a computational method (e.g. see [1 ]); however, it is unstable when the eigensystem is ill conditioned. The method proposed here is based on the Schur reduction to triangular form by orthogonal similarity transformations. Equation (1) is solved as follows. The matrix A is reduced to lower real Schur form A' by an orthogonal similarity transformation U; that is A is reduced to the real, block lower triangular form.

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Citations
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Journal ArticleDOI

On solutions of the matrix equations XF − AX = C and XF-AX¯=C

TL;DR: With the help of the concept of Kronecker map, an explicit solution for the matrix equation XF − AX = C is established and is neatly expressed by a symmetric operator matrix, a controllability matrix and an observability matrix.
Proceedings ArticleDOI

Balanced approximation of two-dimensional and delay-differential systems

TL;DR: In this article, a generalized balanced approximation method for reducing two-dimensional (2-D) and delay differential models is given, which is possible because of the form the Gramians of such systems take, and the reachability and observability Gramians can be expressed as integrals of a certain system related function along the imaginary axis or unit circle of the complex plane.
Journal ArticleDOI

Robust Design of Tuned Mass Damper Systems for Seismic Protection of Multistory Buildings

TL;DR: In this article, a method is proposed for the robust design of tuned mass damper (TMD) systems for seismic protection of multistory buildings, where uncertainties in the properties of both the building and the input seismic excitation are explicitly accounted for in the robust TMD system, in particular, the uncertain parameters considered are stiffness and damping of the structure, and the Kanai-Tajimi model used for representing the surface ground filter of the white noise process acting at the bedrock.
Journal ArticleDOI

Approximate solution of large sparse Lyapunov equations

TL;DR: The method is based on the power method and matrix-vector multiplications and is particularly suitable for problems where those multiplications can be done efficiently, such as where the coefficient matrices are large and sparse or low-rank.
Journal ArticleDOI

Tensor modelling of MIMO communication systems with performance analysis and Kronecker receivers

TL;DR: A new alternating least squares (ALS)-based method for estimating the matrix factors of a Kr onecker product, the so-called Kronecker ALS (KALS) method is presented, and the design of multiple-input multiple-output (MIMO) wireless communication systems using tensor modelling is considered.
References
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Book

The algebraic eigenvalue problem

TL;DR: Theoretical background Perturbation theory Error analysis Solution of linear algebraic equations Hermitian matrices Reduction of a general matrix to condensed form Eigenvalues of matrices of condensed forms The LR and QR algorithms Iterative methods Bibliography.
Journal ArticleDOI

The Direct Solution of the Discrete Poisson Equation on a Rectangle

Fred W. Dorr
- 01 Apr 1970 - 
TL;DR: In this article, the authors provide a survey of direct methods for solving finite difference equations with rectilinear domains. But the authors do not discuss whether the methods are easily adaptable to more general regions, and to general elliptic partial differential equations.
Journal ArticleDOI

TheQ R algorithm for real hessenberg matrices

TL;DR: The volume of work involved in a QR step is far less if the matrix is of Hessenberg form, and since there are several stable ways of reducing a general matrix to this form, the QR algorithm is invariably used after such a reduction.
Journal ArticleDOI

Matrix and other direct methods for the solution of systems of linear difference equations

TL;DR: In this paper, the problem of boundary problems arising in the approximate solutions of linear PDEs was investigated. But the work was conducted on desk machines, and the operations involved are, however, capable of being handled efficiently and simply by modern high-speed digital computers.