A weird eigenvalue problem

In summary: There is no easy way to find a numeric solution for this problem. However, by using eigenvalue logic, you can find a solution.
  • #1
ahmethungari
2
0
Hi,

Is there any solution for the following problem:

[tex]Ax = \lambda x + b[/tex]

Here [tex]x[/tex] seems to be an eigenvector of [tex]A[/tex] but with an extra translation vector [tex]b[/tex].
I cannot say whether [tex]b[/tex] is parallel to [tex]x[/tex] ([tex]b = cx[/tex]).

Thank you in advance for your help...

Birkan
 
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  • #2
ahmethungari said:
Hi,

Is there any solution for the following problem:

$Ax = \lambda x + b$

Here $x$ seems to be an eigenvector of $A$ but with an extra translation vector $b$.
I cannot say whether $b$ is parallel to $x$ ($b = cx$).

Thank you in advance for your help...

Birkan

You didn't say what vector space you are working with, so I will assume a complex finite-dimensional vector space.

We can rewrite your problem as

[tex](A - \lambda I)x = b[/tex]

If [tex]b = 0[/tex], this has a solution if and only if lambda is an eigenvalue of A. Every map in a complex finite-dimensional vector space has an eigenvalue, so a solution exists in this case.

If [tex]b \neq 0[/tex], then this is equivalent to

[tex]b \in image(A - \lambda I)[/tex]

For this to happen, it suffices that [tex]A - \lambda I[/tex] be surjective. This is true if and only if [tex]A - \lambda I[/tex] is invertible, which is true if and only if lambda is NOT an eigenvalue of A. Thus there are plenty of solutions in this case!
 
  • #3
jbunniii said:
You didn't say what vector space you are working with, so I will assume a complex finite-dimensional vector space.

We can rewrite your problem as

[tex](A - \lambda I)x = b[/tex]

If [tex]b = 0[/tex], this has a solution if and only if lambda is an eigenvalue of A. Every map in a complex finite-dimensional vector space has an eigenvalue, so a solution exists in this case.

If [tex]b \neq 0[/tex], then this is equivalent to

[tex]b \in image(A - \lambda I)[/tex]

For this to happen, it suffices that [tex]A - \lambda I[/tex] be surjective. This is true if and only if [tex]A - \lambda I[/tex] is invertible, which is true if and only if lambda is NOT an eigenvalue of A. Thus there are plenty of solutions in this case!


I got it. By the way, vector space is actually finite-dimensional (d=9000) Euclidean Space.

Since I do not know the [tex]\lambda[/tex], (only [tex]A[/tex] and [tex]b[/tex] are known) how can I find an numeric solution for that? Is there any way using eigenvalue logic here?
Such as
-- find eigenvalues of [tex]A[/tex],
-- check if [tex]b[/tex] is parallel to any
-- select the appropriate eigenvector etc.
 

What is an eigenvalue problem?

An eigenvalue problem is a mathematical problem that involves finding the values (known as eigenvalues) and corresponding vectors (known as eigenvectors) of a linear transformation or matrix. It is a fundamental concept in linear algebra and has applications in various areas of science and engineering.

Why is the eigenvalue problem important?

The eigenvalue problem is important because it allows us to understand and analyze complex systems by breaking them down into simpler components. It is used in many fields, including physics, chemistry, and engineering, to model and solve real-world problems.

What makes a "weird" eigenvalue problem different?

A "weird" eigenvalue problem is one that exhibits unusual or unexpected behavior, often due to non-traditional or non-standard conditions. This can include complex or imaginary eigenvalues, non-orthogonal eigenvectors, or non-hermitian matrices.

How do scientists approach solving a weird eigenvalue problem?

Scientists use a variety of mathematical techniques and numerical methods to solve weird eigenvalue problems, depending on the specific characteristics of the problem. These may include diagonalization, eigendecomposition, or iterative algorithms such as the power method.

What are some real-world applications of the eigenvalue problem?

The eigenvalue problem has numerous applications in science and engineering, including analyzing vibrations in structures, modeling chemical reactions, and understanding quantum mechanics. It is also used in data analysis and machine learning for tasks such as dimensionality reduction and clustering.

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