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 eigenvalues λ1, ..., λk.

The Frobenius covariant Ai, for i = 1,..., k, is the matrix

A i j = 1 j i k 1 λ i λ j ( A λ j I )   . {\displaystyle A_{i}\equiv \prod _{j=1 \atop j\neq i}^{k}{\frac {1}{\lambda _{i}-\lambda _{j}}}(A-\lambda _{j}I)~.}

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

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 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

Consider the two-by-two matrix:

A = [ 1 3 4 2 ] . {\displaystyle 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 = [ 3 1 / 7 4 1 / 7 ] [ 5 0 0 2 ] [ 3 1 / 7 4 1 / 7 ] 1 = [ 3 1 / 7 4 1 / 7 ] [ 5 0 0 2 ] [ 1 / 7 1 / 7 4 3 ] . {\displaystyle 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

A 1 = c 1 r 1 = [ 3 4 ] [ 1 / 7 1 / 7 ] = [ 3 / 7 3 / 7 4 / 7 4 / 7 ] = A 1 2 A 2 = c 2 r 2 = [ 1 / 7 1 / 7 ] [ 4 3 ] = [ 4 / 7 3 / 7 4 / 7 3 / 7 ] = A 2 2   , {\displaystyle {\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 , A 1 + A 2 = I   . {\displaystyle A_{1}A_{2}=0,\qquad A_{1}+A_{2}=I~.}

Note tr A1 = tr A2 = 1, as required.

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