Eigenvalues and Eigenvectors of exponential matrix

In summary: This is the statement I was going for, which is correct. More generally it's good to notice that for exponentiating, along with many other power series operations, you get the same eigenvectors as when you started.
  • #1
unscientific
1,734
13

Homework Statement



Part (a): Find the eigenvalues and eigenvectors of matrix A:

[tex]
\left(
\begin{array}{cc}
2 & 0 & -1\\
0 & 2 & -1\\
-1 & -1 & 3 \\
\end{array}
\right)
[/tex]Part(b): Find the eigenvalues and eigenvectors of matrix ##B = e^{3A} + 5I##.

Homework Equations


The Attempt at a Solution



Part (a)
[tex]\lambda = 1, 2, 4[/tex]
[tex] u_1 = \frac{1}{\sqrt3}(1,1,1)[/tex]
[tex]u_2 = \frac{1}{\sqrt 2}(1,-1,0)[/tex]
[tex]u_3 = \frac{1}{\sqrt 5}(1,1,-2)][/tex]

Part(b)

Realize A is a hermitian matrix.
Diagonalize A:
[tex] A'=
\left(
\begin{array}{cc}
1 & 0 & 0\\
0 & 2 & 0\\
0 & 0 & 4 \\
\end{array}
\right)
[/tex]

[tex] B' = exp(3A') + 5I[/tex]

Therefore, eigenvalues of ##B'= e+5, e^2 + 5, e^4+5##. Also, eigenvalues of B = B'.

How do I find the eigenvectors of B? Do I need to undiagonalize B' using the transformation matrix made up of eigenvectors of A?
 
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  • #2
unscientific said:
How do I find the eigenvectors of B? Do I need to undiagonalize B' using the transformation matrix made up of eigenvectors of A?

You can, but it's easier than that. You found the eigenvectors of A, then observed that doing a change of basis to the set of eigenvectors of A diagonalizes A (which is a general fact). You then got B', which is diagonal after doing a change of basis using the eigenvectors of A. That should instantly tip you off about what the eigenvectors of B are.
 
  • #3
Office_Shredder said:
You can, but it's easier than that. You found the eigenvectors of A, then observed that doing a change of basis to the set of eigenvectors of A diagonalizes A (which is a general fact). You then got B', which is diagonal after doing a change of basis using the eigenvectors of A. That should instantly tip you off about what the eigenvectors of B are.



I can instantly find out what the eigenvalues of B are, but I don't see the trick/link to find eigenvectors of B. I know it's related to eigenvectors of A.

I know the relation between B and A. I know how A is diagonalized using its eigenvectors. But it tells us nothing about how B is diagonalized!
 
  • #4
Can I simply say that since eigenvectors of A also diagonalize B, eigenvectors of B is also eigenvectors of A?
 
  • #5
unscientific said:
Can I simply say that since eigenvectors of A also diagonalize B, eigenvectors of B is also eigenvectors of A?
Are eigenvectors of A also eigenvectors of A2, A3, ...? Of a power series in A?
 
  • #6
haruspex said:
Are eigenvectors of A also eigenvectors of A2, A3, ...? Of a power series in A?

Ah, for a ##A^n##, to completely diagonalize it, we must apply matrix ##U## n times. Following this line of thought, I would say the eigenvectors of B are ##exp(v_1)##, ##exp(v_2)## and ##exp(v_3)##.
 
  • #7
unscientific said:
Ah, for a ##A^n##, to completely diagonalize it, we must apply matrix ##U## n times.
Why?
U-1A2U = U-1AUU-1AU.
 
  • #8
haruspex said:
Why?
U-1A2U = U-1AUU-1AU.

Ok, then I have no idea..
 
  • #9
unscientific said:
Ok, then I have no idea..

U-1A2U = U-1AU U-1AU. (Agreed?)
If U-1AU = D is diagonal, then what does that tell you for U-1A2U? For U-1AnU?
 
  • #10
haruspex said:
U-1A2U = U-1AU U-1AU. (Agreed?)
If U-1AU = D is diagonal, then what does that tell you for U-1A2U? For U-1AnU?

They are all diagonal! So each term in the ##exp(3A)## is diagonal. Meaning B is made diagonal by eigenvectors of A. Therefore, eigenvectors of B = eigenvectors of A. (The factor of 3 cancels out, because (3A)u = (3λ)u.
 
  • #11
unscientific said:
Can I simply say that since eigenvectors of A also diagonalize B, eigenvectors of B is also eigenvectors of A?

This is the statement I was going for, which is correct. More generally it's good to notice that for exponentiating, along with many other power series operations, you get the same eigenvectors as when you started. The only thing that can screw things up is the constant term of the power series.
 

FAQ: Eigenvalues and Eigenvectors of exponential matrix

What are eigenvalues and eigenvectors of an exponential matrix?

Eigenvalues and eigenvectors of an exponential matrix are special values and vectors that remain unchanged when multiplied by the exponential matrix. They are used to understand the behavior of the exponential matrix and its relationship to the original matrix.

How are eigenvalues and eigenvectors calculated for an exponential matrix?

The eigenvalues and eigenvectors for an exponential matrix can be calculated by solving the characteristic equation of the matrix, which is obtained by subtracting the identity matrix from the exponential matrix and finding the determinant. The eigenvectors can then be found by substituting the eigenvalues into the characteristic equation and solving for the corresponding vectors.

What is the significance of eigenvalues and eigenvectors in an exponential matrix?

The eigenvalues and eigenvectors of an exponential matrix provide important information about the behavior and properties of the matrix. They can be used to determine the rate of growth or decay of the matrix, the stability of the matrix, and the direction and magnitude of change in the matrix.

Can an exponential matrix have complex eigenvalues and eigenvectors?

Yes, an exponential matrix can have complex eigenvalues and eigenvectors. This is because the exponential function can take on complex values and therefore the exponential matrix can have complex elements. The eigenvalues and eigenvectors in this case will also be complex numbers.

How are eigenvalues and eigenvectors used in practical applications?

Eigenvalues and eigenvectors of an exponential matrix have various applications in fields such as physics, engineering, and economics. They can be used to model and understand systems that exhibit exponential growth or decay, such as population growth, radioactive decay, and financial investments. They are also used in the fields of computer graphics and image processing for tasks such as image compression and feature extraction.

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