Eigenpairs and Hermitian matrices

In summary, the question is asking if you can find a hermitian matrix H with the given eigenpairs, and if so, what the eigenvalues are.
  • #1
Somefantastik
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0

Homework Statement



Choose [tex]\lambda_{1}, \lambda_{2}, \lambda_{3} [/tex] along with a set of vectors [tex]{v_{1},v_{2},v_{3}}[/tex] and construct an Hermitian matrix H with the eigenpairs [tex] (\lambda_{1},v_{1}),(\lambda_{2},v_{2}),(\lambda_{3},v_{3})[/tex]

Homework Equations





The Attempt at a Solution


[tex] \lambda_{1} = 2[/tex]
[tex]\lambda_{2} = -2[/tex]

[tex]v_{1} = \widehat{i}+2\widehat{j}[/tex]
[tex]v_{2} = \widehat{i}-2\widehat{j}[/tex]

[tex]u_{1} = \frac{1}{\sqrt{5}}\left(\widehat{i} + 2\widehat{j} \right)[/tex]
[tex]u_{2} = \frac{1}{\sqrt{5}}\left(\widehat{i} - 2\widehat{j}\right)[/tex]

[tex]v_{3} = u_{1} \times u_{2} = -\frac{4}{5}\widehat{k}[/tex]

[tex]u_{3} = -\widehat{k}[/tex]

[tex]Basis: \ U = \left\{ u_{1},u_{2},u_{3} \right}\}[/tex]

[tex]H = \left| \begin{array}{ccc} 2 & 0 & 0 \\ 0 & -2 & 0 \\ 0 & 0 & 1 \end{array} \right| [/tex]

How does this look?
 
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  • #2
can you choose any values here?

why not just
[tex] \lambda_{1} = 1 [/tex]
[tex] \lambda_{2} = 2 [/tex]
[tex] \lambda_{3} = 3 [/tex]

and
[tex] u_{1} = (1,0,0)[/tex]
[tex] u_{2} = (0,1,0)[/tex]
[tex] u_{3} = (0,0,1)[/tex]

then
[tex]H = \left| \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 2& 0 \\ 0 & 0 & 3 \end{array} \right| [/tex]
 
  • #3
If that's all this is, am I misunderstanding or is this a really trivial question?
 
  • #4
maybe it asking if you are given arbitrary
[tex] u_1, u_2, u_3 [/tex]
and corresponding eigenvalues
[tex] \lambda_1, \lambda_2, \lambda_3 [/tex]
can you find a hermitian matrix H, with the given eigenpairs?

one way you could do is assume you have found H and it can be diagonalised (which you know it can, from the eigenvectors), then the diagonal form of H is
[tex] D = P^{-1}.H.P[/tex]

where
[tex]D = \left| \begin{array}{ccc} \lambda_1 & 0 & 0 \\ 0 & \lambda_2& 0 \\ 0 & 0 & \lambda_3 \end{array} \right| [/tex]

now the matrix P is simply given by the eigenvectors
where
[tex]P = \left| \begin{array}{ccc} u_1, u_2, u_3 \end{array} \right| [/tex]

then H is
[tex] H = PDP^{-1}[/tex]

that gives the eigenpairs, however I doubt it would automatically be hermitian... for that you need, where the star means conjugate transpose
[tex] H = H^* [/tex]


so running that through
[tex] PDP^{-1} = (PDP^{-1})^* = P^{-1}^*D^*P^* = P^{-1}^*DP^* [/tex]

hmmm... not totally sure where to go now
 
Last edited:
  • #5
maybe a little left & right muliplication
[tex] D = P^{-1}(PDP^{-1})P = (P^{-1}P^{-1}^*)D(P^*P) [/tex]

this will be satisfied if P is unitary
[tex]P^*P = PP^* = I[/tex]
 
  • #6
from before P is given by the eigenvectors (choose up to some multiplicative constant)
[tex]P = \left| \begin{array}{ccc} u_1, u_2, u_3 \end{array} \right| [/tex]

so in subscript form if j is the eigenvector and i is it's component, write
[tex]P = P_{ij} = u_{j}^{(i)}[/tex]

now for the transpose conjugate of a matrix
[tex]P^* = (P^*)_{ij} = \bar{P}_{ji} = \bar{u}_{i}^{(j)} [/tex]

matrix multipication is given by (with summation over the repeated indice)
[tex]P^*P = (P^*)_{ij}P_{jk} = \bar{u}_{i}^{(j)} u_{k}^{(j)} [/tex]
which looks just like a complex inner product between eigenvectors

so to satisfy the unitary condition we were looking for
[tex]PP^* = I \rightarrow \bar{u}_{i}^{(j)} u_{k}^{(j)} = \delta_{ik} [/tex]

ie. all the columns of P must form an orthonormal basis, which is a property of unitary matricies in general. So the condition will be satisfied if you just normalise your eigenvectors to length 1.

got a bit carried away there... but from this you should be able to write the general form of H no worries
 
  • #7
Ok that helps. I guess I got confused and thought he was looking for D, but I think you're right; I think he's looking for PDP-1

Thanks!
 

1. What is an eigenpair?

An eigenpair is a pair of a scalar value (known as the eigenvalue) and a corresponding vector (known as the eigenvector) that satisfies a specific mathematical equation. In the context of matrices, an eigenpair is usually associated with a square matrix and represents a special relationship between the matrix and the vector.

2. What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. In simpler terms, it is a square matrix where the entries above and below the main diagonal are complex conjugates of each other. Hermitian matrices have special properties that make them useful in various mathematical and scientific fields, including quantum mechanics and statistics.

3. How do you find the eigenpairs of a Hermitian matrix?

To find the eigenpairs of a Hermitian matrix, you can use a variety of methods such as the power method, inverse iteration, or the Jacobi method. These methods involve solving a system of equations or performing matrix operations to find the eigenpairs, which can then be used for various applications.

4. What is the significance of eigenpairs and Hermitian matrices?

Eigenpairs and Hermitian matrices have many practical and theoretical applications in the field of mathematics and science. In linear algebra, they are used to represent and analyze linear transformations, and in quantum mechanics, they are used to describe the energy states of a system. In addition, Hermitian matrices play a crucial role in the analysis of real-world data, such as in principal component analysis and multivariate statistics.

5. Can a non-Hermitian matrix have eigenpairs?

Yes, a non-Hermitian matrix can have eigenpairs, but the eigenvalues and eigenvectors may not have the same properties as those of a Hermitian matrix. In general, non-Hermitian matrices do not have real eigenvalues and orthogonal eigenvectors, which are important properties of Hermitian matrices. However, they can still be useful for certain applications, such as in the study of complex systems and non-Hermitian quantum mechanics.

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