Sylvester's formula

From testwiki
Jump to navigation Jump to search

Template:Short description In matrix theory, Sylvester's formula or Sylvester's matrix theorem (named after J. J. Sylvester) or Lagrange−Sylvester interpolation expresses an analytic function Template:Math of a matrix Template:Mvar as a polynomial in Template:Mvar, in terms of the eigenvalues and eigenvectors of Template:Mvar.[1][2] It states that[3]

f(A)=∑i=1kf(λi)Ai,

where the Template:Math are the eigenvalues of Template:Mvar, and the matrices

Ai≡∏j=1j≠ik1λi−λj(A−λjI)

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

Conditions

Template:Expert needed

Sylvester's formula applies for any diagonalizable matrix Template:Mvar with Template:Mvar distinct eigenvalues, Template:Mvar1, ..., Template:Mvark, and any function Template:Mvar defined on some subset of the complex numbers such that Template:Math is well defined. The last condition means that every eigenvalue Template:Math is in the domain of Template:Mvar, and that every eigenvalue Template:Math with multiplicity Template:Mvari > 1 is in the interior of the domain, with Template:Mvar being (Template:Math) times differentiable at Template:Math.[1]Template:Rp

Example

Consider the two-by-two matrix:

A=[1342].

This matrix has two eigenvalues, 5 and −2. Its Frobenius covariants are

A1=c1r1=[34][1717]=[37374747]=A+2I5−(−2)A2=c2r2=[17−17][4−3]=[47−37−4737]=A−5I−2−5.

Sylvester's formula then amounts to

f(A)=f(5)A1+f(−2)A2.

For instance, if Template:Mvar is defined by Template:Math, then Sylvester's formula expresses the matrix inverse Template:Math as

15[37374747]−12[47−37−4737]=[−0.20.30.4−0.1].

Generalization

Sylvester's formula is only valid for matrices with no repeated eigenvalues; an extension due to Arthur Buchheim, based on Hermite interpolating polynomials, covers the general case:[4]

f(A)=∑i=1s[∑j=0ni−11j!ϕi(j)(λi)(A−λiI)j∏j=1,j≠is(A−λjI)nj],

where ϕi(t):=f(t)/∏j≠i(t−λj)nj.

A concise form is further given by Hans Schwerdtfeger,[5]

f(A)=∑i=1sAi∑j=0ni−1f(j)(λi)j!(A−λiI)j,

where Template:Mvari are the corresponding generalized Frobenius covariants of Template:Mvar

Special case

Template:See also If a matrix Template:Mvar is both Hermitian and unitary, then it can only have eigenvalues of ±1, 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 Template:Mvar,

f(θA)=f(θ)A++f(−θ)A−=f(θ)I+A2+f(−θ)I−A2=f(θ)+f(−θ)2I+f(θ)−f(−θ)2A.

In particular, eiθA=(cos⁡θ)I+(isin⁡θ)A and A=eiπ2(I−A)=e−iπ2(I−A).

See also

References

Template:Reflist

  1. ↑ 1.0 1.1 / Roger A. Horn and Charles R. Johnson (1991), Topics in Matrix Analysis. Cambridge University Press, Template:ISBN
  2. ↑ Jon F. Claerbout (1976), Sylvester's matrix theorem, a section of Fundamentals of Geophysical Data Processing. Online version at sepwww.stanford.edu, accessed on 2010-03-14.
  3. ↑ Template:Cite journal
  4. ↑ Template:Cite journal
  5. ↑ Template:Cite book