In matrix theory, Sylvester's formula or Sylvester's matrix theorem (named after J. J. Sylvester) or Lagrange−Sylvester interpolation expresses an analytic function of a matrix as a polynomial in, in terms of the eigenvalues and eigenvectors of .[1] [2] It states that[3]
f(A)=
k | |
\sum | |
i=1 |
f(λi)~Ai~,
where the are the eigenvalues of, and the matrices
Ai\equiv
k | |
\prod | |
j=1\atopj\nei |
1 | |
λi-λj |
\left(A-λjI\right)
Sylvester's formula applies for any diagonalizable matrix with distinct eigenvalues, 1, ..., k, and any function defined on some subset of the complex numbers such that is well defined. The last condition means that every eigenvalue is in the domain of, and that every eigenvalue with multiplicity i > 1 is in the interior of the domain, with being times differentiable at .[1]
Consider the two-by-two matrix:
A=\begin{bmatrix}1&3\ 4&2\end{bmatrix}.
This matrix has two eigenvalues, 5 and −2. Its Frobenius covariants are
\begin{align} A1&=c1r1=\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+2I | |
5-(-2) |
\\ A2&=c2r2=\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-5I | |
-2-5 |
. \end{align}
Sylvester's formula then amounts to
f(A)=f(5)A1+f(-2)A2.
For instance, if is defined by, then Sylvester's formula expresses the matrix inverse as
1 | |
5 |
\begin{bmatrix}
3 | |
7 |
&
3 | \ | |
7 |
4 | |
7 |
&
4 | |
7 |
\end{bmatrix}-
1 | |
2 |
\begin{bmatrix}
4 | |
7 |
&-
3 | \ - | |
7 |
4 | |
7 |
&
3 | |
7 |
\end{bmatrix}=\begin{bmatrix}-0.2&0.3\ 0.4&-0.1\end{bmatrix}.
Sylvester's formula is only valid for diagonalizable matrices; an extension due to Arthur Buchheim, based on Hermite interpolating polynomials, covers the general case:[4]
f(A)=
s | |
\sum | |
i=1 |
\left[
ni-1 | |
\sum | |
j=0 |
1 | |
j! |
(j) | |
\phi | |
i |
(λi)\left(A-λiI\right)j\prod{j=1,j\ne
\phii(t):=f(t)/\prodj\ne\left(t-
nj | |
λ | |
j\right) |
A concise form is further given by Hans Schwerdtfeger,[5]
s | |
f(A)=\sum | |
i=1 |
Ai
ni-1 | |
\sum | |
j=0 |
f(j)(λi) | |
j! |
j | |
(A-λ | |
iI) |
See also: Euler's formula. If a matrix is both Hermitian and unitary, then it can only have eigenvalues of
\plusmn1
A=A+-A-
A+
A-
-1
A++A-=I
\begin{align}f(\thetaA)&=f(\theta)A+1+f(-\theta)A-1\\ &=f(\theta)
I+A | +f(-\theta) | |
2 |
I-A | \\ &= | |
2 |
f(\theta)+f(-\theta) | I+ | |
2 |
f(\theta)-f(-\theta) | |
2 |
A\\ \end{align}.
In particular,
ei\theta=(\cos\theta)I+(i\sin\theta)A
A
| |||||
=e |
| |||||
=e |