Frobenius covariant

In matrix theory, the Frobenius covariants of a square matrix A are special polynomials of it, namely projection matrices Ai associated with the eigenvalues and eigenvectors of A.[1]:pp.403,437-8 They are named after the mathematician Ferdinand Frobenius.

Each covariant is a projection on the eigenspace associated with the eigenvalue λi. Frobenius covariants are the coefficients of Sylvester's formula, which expresses a function of a matrix f(A) as a matrix polynomial, namely a linear combination of that function's values on the eigenvalues of A.

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

 A_i \equiv  \prod_{j=1 \atop j \ne i}^k \frac{1}{\lambda_i-\lambda_j} (A - \lambda_j I)~.

It is essentially the Lagrange polynomial with matrix argument. As an idempotent projection matrix to a one-dimensional subspace, it has a unit trace.

Computing the covariants

Ferdinand Georg Frobenius (1849–1917), German mathematician. His main interests were elliptic functions differential equations, and later group theory.

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 = ci ri.

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

Example

Consider the two-by-two matrix:

 A = \begin{bmatrix} 1 & 3 \\ 4 & 2 \end{bmatrix}.

This matrix has two eigenvalues, 5 and −2; hence (A−5)(A+2)=0.

The corresponding eigen decomposition is

 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}.

Hence the Frobenius covariants, manifestly projections, are

 \begin{array}{rl}
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_2^2 ~,
\end{array}

with

A_1 A_2 =0 , \qquad A_1+A_2= I ~.

Note trA1=trA2=1, as required.

References

  1. 1 2 Roger A. Horn and Charles R. Johnson (1991), Topics in Matrix Analysis. Cambridge University Press, ISBN 978-0-521-46713-1
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