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In mathematics, in the field of control theory, a Sylvester equation is a matrix equation of the form: [1] A X + X B = C . {\displaystyle AX+XB=C.} It is named after English mathematician James Joseph Sylvester .
In numerical linear algebra, the Bartels–Stewart algorithm is used to numerically solve the Sylvester matrix equation =.Developed by R.H. Bartels and G.W. Stewart in 1971, [1] it was the first numerically stable method that could be systematically applied to solve such equations.
In numerical linear algebra, the alternating-direction implicit (ADI) method is an iterative method used to solve Sylvester matrix equations.It is a popular method for solving the large matrix equations that arise in systems theory and control, [1] and can be formulated to construct solutions in a memory-efficient, factored form.
The determinant of the Sylvester matrix of two polynomials is their resultant, which is zero when the two polynomials have a common root (in case of coefficients in a field) or a non-constant common divisor (in case of coefficients in an integral domain). Sylvester matrices are named after James Joseph Sylvester.
In matrix theory, Sylvester's formula or Sylvester's matrix theorem (named after J. J. Sylvester) or Lagrange−Sylvester interpolation expresses an analytic function f(A) of a matrix A as a polynomial in A, in terms of the eigenvalues and eigenvectors of A. [1] [2] It states that [3]
It is used to solve systems of linear differential equations. In the theory of Lie groups, the matrix exponential gives the exponential map between a matrix Lie algebra and the corresponding Lie group. Let X be an n×n real or complex matrix. The exponential of X, denoted by e X or exp(X), is the n×n matrix given by the power series = =!
Heather Locklear is opening up about her favorite memories from filming the sitcom Spin City — and sharing what was different about working with Michael J. Fox versus his replacement in the ...
In control theory, the cross Gramian (, also referred to by ) is a Gramian matrix used to determine how controllable and observable a linear system is. [1] [2]For the stable time-invariant linear system