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
[edit]Let A be a diagonalizable matrix with eigenvalues λ1, ..., λk.
The Frobenius covariant Ai, for i = 1,..., k, is the matrix
It is essentially the Lagrange polynomial with matrix argument. If the eigenvalue λi is simple, then as an idempotent projection matrix to a one-dimensional subspace, Ai has a unit trace.
Computing the covariants
[edit]![](http://upload.wikimedia.org/wikipedia/commons/thumb/9/99/GeorgFrobenius.jpg/180px-GeorgFrobenius.jpg)
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 right eigenvector of A, that is, the ith column of S; and let ri be the ith left eigenvector of A, namely the ith row of S−1. Then Ai = ci ri.
If A has an eigenvalue λi appearing multiple times, then Ai = Σj cj rj, where the sum is over all rows and columns associated with the eigenvalue λi.[1]: p.521
Example
[edit]Consider the two-by-two matrix:
This matrix has two eigenvalues, 5 and −2; hence (A − 5)(A + 2) = 0.
The corresponding eigen decomposition is
Hence the Frobenius covariants, manifestly projections, are
with
Note tr A1 = tr A2 = 1, as required.
References
[edit]- ^ a b Roger A. Horn and Charles R. Johnson (1991), Topics in Matrix Analysis. Cambridge University Press, ISBN 978-0-521-46713-1