Is there a way to obtain generalized eigenvectors in case of high multiplicity of eigenvalues with a single one or at least very few commands ? In case of multiplicity 1 for each eigenvalue I can use [V,D] = eig(A)
, but this command doesn't work for multiple eigenvalues.
According to Matlab documentation, [V,D] = eig(A,B) produces a diagonal matrix D of generalized eigenvalues and a full matrix V whose columns are the corresponding eigenvectors so that A*V = B*V*D
Here an example how to do it yourself... First we enter a sample matrix A:
A = [ 35 -12 4 30 ;
22 -8 3 19 ;
-10 3 0 -9 ;
-27 9 -3 -23 ];
Then we explore its characteristic polynomial, eigenvalues, and eigenvectors.
poly(A)
ans =
1.0000 -4.0000 6.0000 -4.0000 1.0000
These are the coefficients of the characteristic polynomial, which hence is (λ − 1)^4 Then
[V, D] = eigensys(A)
V =
[ 1, 0]
[ 0, 1]
[-1, 3]
[-1, 0]
D =
[1]
[1]
[1]
[1]
Thus MATLAB finds only the two independent eigenvectors
w1 = [1 0 -1 -1]';
w2 = [0 1 3 0]';
associated with the single multiplicity 4 eigenvalue λ=1 , which therefore has defect 2.
So we set up the 4x4 identity matrix and the matrix B=A-λI
Id = eye(4);
B = A - L*Id;
with L=1, When we calculate B^2 and B^3
B2 = B*B
B3 = B2*B
We find that B2 ≠ 0, but B3 = 0, so there should be a length 3 chain associated with
the eigenvalue λ = 1 . Choosing the first generalized eigenvector
u1 = [1 0 0 0]';
we calculate the further generalized eigenvectors
u2 = B*u1
u2 =
34
22
-10
-27
and
u3 = B*u2
u3 =
42
7
-21
-42
Thus we have found the length 3 chain {u3, u2, u1} based on the (ordinary) eigenvector u3. (To reconcile this result with MATLAB's eigensys calculation, you can check that u3-42w1=7w2)
eigensys
does not exist anymore. eig(sym(A))
can be used as a substitute, though the outputs won't match exactly. Also, an alternative to poly
is charpoly
, which returns symbolic results. –
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