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Example: projections
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This matrix has two eigenvalues, 5 and −2. The corresponding eigen decomposition is
This matrix has two eigenvalues, 5 and −2. The corresponding eigen decomposition is
:<math> A = \begin{bmatrix} 3 & 1/7 \\ 4 & -1/7 \end{bmatrix} \begin{bmatrix} 5 & 0 \\ 0 & -2 \end{bmatrix} \begin{bmatrix} 3 & 1/7 \\ 4 & -1/7 \end{bmatrix}^{-1} = \begin{bmatrix} 3 & 1/7 \\ 4 & -1/7 \end{bmatrix} \begin{bmatrix} 5 & 0 \\ 0 & -2 \end{bmatrix} \begin{bmatrix} 1/7 & 1/7 \\ 4 & -3 \end{bmatrix}. </math>
:<math> A = \begin{bmatrix} 3 & 1/7 \\ 4 & -1/7 \end{bmatrix} \begin{bmatrix} 5 & 0 \\ 0 & -2 \end{bmatrix} \begin{bmatrix} 3 & 1/7 \\ 4 & -1/7 \end{bmatrix}^{-1} = \begin{bmatrix} 3 & 1/7 \\ 4 & -1/7 \end{bmatrix} \begin{bmatrix} 5 & 0 \\ 0 & -2 \end{bmatrix} \begin{bmatrix} 1/7 & 1/7 \\ 4 & -3 \end{bmatrix}. </math>
Hence the Frobenius covariants are
Hence the Frobenius covariants, manifestly projections, are
:<math> \begin{align}
:<math> \begin{align}
A_1 &= c_1 r_1 = \begin{bmatrix} 3 \\ 4 \end{bmatrix} \begin{bmatrix} 1/7 & 1/7 \end{bmatrix} = \begin{bmatrix} 3/7 & 3/7 \\ 4/7 & 4/7 \end{bmatrix} \\
A_1 &= c_1 r_1 = \begin{bmatrix} 3 \\ 4 \end{bmatrix} \begin{bmatrix} 1/7 & 1/7 \end{bmatrix} = \begin{bmatrix} 3/7 & 3/7 \\ 4/7 & 4/7 \end{bmatrix} = A_1^2\\
A_2 &= c_2 r_2 = \begin{bmatrix} 1/7 \\ -1/7 \end{bmatrix} \begin{bmatrix} 4 & -3 \end{bmatrix} = \begin{bmatrix} 4/7 & -3/7 \\ -4/7 & 3/7 \end{bmatrix}.
A_2 &= c_2 r_2 = \begin{bmatrix} 1/7 \\ -1/7 \end{bmatrix} \begin{bmatrix} 4 & -3 \end{bmatrix} = \begin{bmatrix} 4/7 & -3/7 \\ -4/7 & 3/7 \end{bmatrix}=A_2^2 ~.
\end{align} </math>
\end{align} </math>



Revision as of 15:43, 17 December 2013

In matrix theory, the Frobenius covariants of a square matrix A are matrices Ai associated with the eigenvalues and eigenvectors of A.[1] Each covariant is a projection on the eigenspace associated with λi.

Frobenius covariants are the coefficients of Sylvester's formula, that expresses a function of a matrix f(A) as a linear combination of its values on the eigenvalues of A. They are named after the mathematician Ferdinand Frobenius.

Formal definition

Let A be a diagonalizable matrix with k distinct eigenvalues, λ1, …, λk. The Frobenius covariant Ai, for i = 1,…, k, is the matrix

Computing the covariants

The Frobenius covariants of a matrix A can be obtained from any eigendecomposition A = SDS−1, where S is non-singular and D is diagonal with Di,i = λi. If A has no multiple eigenvalues, then let ci be the ith left eigenvector of A, that is, the ith column of S; and let ri be the ith right eigenvector of A, namely the ith row of S−1. Then Ai = ciri.

If A has multiple eigenvalues then Ai = Σj cjrj, where the sum is over all rows and columns associated with the eigenvalue λi.[1]: p.521 

Example

Consider the two-by-two matrix:

This matrix has two eigenvalues, 5 and −2. The corresponding eigen decomposition is

Hence the Frobenius covariants, manifestly projections, are

References

  1. ^ a b Roger A. Horn and Charles R. Johnson (1991), Topics in Matrix Analysis. Cambridge University Press, ISBN 978-0-521-46713-1