Sylvester's formula explained

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
λij

\left(A-λjI\right)

are the corresponding Frobenius covariants of, which are (projection) matrix Lagrange polynomials of .

Conditions

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]

Example

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}.

Generalization

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

}^\left(A - \lambda_j I\right)^ \right],where

\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
iI)
,where i are the corresponding Frobenius covariants of

Special case

See also: Euler's formula. If a matrix is both Hermitian and unitary, then it can only have eigenvalues of

\plusmn1

, and therefore

A=A+-A-

, where

A+

is the projector onto the subspace with eigenvalue +1, and

A-

is the projector onto the subspace with eigenvalue

-1

; By the completeness of the eigenbasis,

A++A-=I

. Therefore, for any analytic function,

\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

and

A

i\pi(I-A)
2
=e
-i\pi(I-A)
2
=e
.

See also

References

Notes and References

  1. / Roger A. Horn and Charles R. Johnson (1991), Topics in Matrix Analysis. Cambridge University Press,
  2. [Jon Claerbout|Jon F. Claerbout]
  3. Sylvester. J.J.. 1883. XXXIX. On the equation to the secular inequalities in the planetary theory. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. en. 16. 100. 267–269. 10.1080/14786448308627430. 1941-5982.
  4. Buchheim. Arthur. 1884. On the Theory of Matrices. Proceedings of the London Mathematical Society. en. s1-16. 1. 63–82. 10.1112/plms/s1-16.1.63. 0024-6115.
  5. Book: Schwerdtfeger, Hans. Les fonctions de matrices: Les fonctions univalentes. I, Volume 1. Hermann. 1938. Paris, France.