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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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Passive Control of Linear Systems

TL;DR: This work proposes a method for designing optimal damping viscosities of dampers in order to calm down vibrations of a structure with given mass and stiffness parameters based on the minimization of the trace of the Lyapunov equation in the underlying phase space equipped with the energy norm.
Journal ArticleDOI

Smoothness Regularized Multiview Subspace Clustering With Kernel Learning

TL;DR: To capture the nonlinear relations between multiview data points, the proposed model maps the concatenatedMultiview observations into a high-dimensional kernel space, in which the linear relations reflect the non linear relations between multi-view data points in the original space.

A Sylvester-Arnoldi type method for the generalized eigenvalue problem with two-by-two operator determinants

TL;DR: In this paper, a generalized eigenvalue problem of the form (B1 ⊗A2 −A1⊗B2)z = μ(B1 ∈ C2 − C1 ⌈ B2 )z is solved by solving a Sylvester equation with shift-and-invert transformation.
Journal ArticleDOI

Analysis and optimization of multiple tuned mass dampers with coulomb dry friction

TL;DR: In this paper, the Coulomb dry friction force is incorporated as an energy dissipation mechanism to deal with the nonlinearity of the CDF force, which is a source of difficulty in the optimization process, and a statistical linearization that replaces the original nonlinear system with an equivalent linear system is adopted.
Proceedings ArticleDOI

LUDIA: an aggregate-constrained low-rank reconstruction algorithm to leverage publicly released health data

TL;DR: This paper introduces LUDIA, a novel low-rank approximation algorithm that utilizes aggregation constraints in addition to auxiliary information in order to estimate or "reconstruct" the original individual-level values from aggregate data.
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.