Prove the eigenvectors are linearly independent

In summary, the homework equation is U+irU= (a-br)U+i(ar+b)U. This is consistent with what we get if we multiply everything out in terms of (a+bi)(U+irU).
  • #1
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Homework Statement


Suppose that a matrix A has real entries (which we always assume) and a complex
(non-real) eigenvalue  [tex]\lambda[/tex]= a + ib , with b not equal to 0. Let W = U + iV be the corresponding
complex eigenvector, having real and imaginary parts U and V , respectively. Show that
U and V are necessarily linearly independent (meaning that one vector is not a scalar
multiple of the other).
HINT: Argue by contradiction: suppose that U and V are dependent, say, V = rU for
some scalar r, and derive a contradiction (that is, a statement that follows logically from
the supposition, but which is false, such as 0 > 1).



Homework Equations



--

The Attempt at a Solution



This question confuses me because I don't even know how to go about it or where to start. How should I do this? Where do I begin??

Thanks!
 
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  • #2
U+irU being an eigenvector of A with eigenvalues a+bi means that

[tex]A(U+irU)=(a+bi)(U+irU)[/tex]

Work out the parantheses on both sides and try to reach a contradiction...
 
  • #3
Sorry to revive this question, but I have the same assignment and I'm drawing a blank as to the solution.

I see no contradiction. Our teacher gave us that

A(W) = A(U) + iA(V) = (aU - bV) + i(aV + bU),

so, applying this to micromass's equation, I get

A(U + irU) = (a-br)U + i(ar+b)U,

which is consistent with what I get if I multiply everything out in

(a+bi)(U+irU).

What am I doing wrong?
 
  • #4
sabrathos said:
Sorry to revive this question, but I have the same assignment and I'm drawing a blank as to the solution.

I see no contradiction. Our teacher gave us that

A(W) = A(U) + iA(V) = (aU - bV) + i(aV + bU),

so, applying this to micromass's equation, I get

A(U + irU) = (a-br)U + i(ar+b)U,

which is consistent with what I get if I multiply everything out in

(a+bi)(U+irU).

What am I doing wrong?

Keep going. So AU+irAU=(a-br)U+i(ar+b)U. Equate real and imaginary parts. So AU=(a-br)U and rAU=(b+ar)U. Can you finish up?
 

1. What are eigenvectors and why are they important?

Eigenvectors are special vectors that have the property of remaining in the same direction when a linear transformation is applied to them. They are important in many fields of science, including physics, engineering, and computer science, because they allow us to simplify complex systems and make predictions about their behavior.

2. What does it mean for eigenvectors to be linearly independent?

Linear independence means that no eigenvector in a set of eigenvectors can be written as a linear combination of the others. In other words, each eigenvector in the set is unique and cannot be created by combining other eigenvectors. This is important because it allows us to fully describe a system using only its eigenvectors.

3. How do you prove that eigenvectors are linearly independent?

To prove that eigenvectors are linearly independent, we must show that the only solution to the equation c1v1 + c2v2 + ... + cnvn = 0, where c1, c2, ..., cn are constants and v1, v2, ..., vn are the eigenvectors, is when all the constants are equal to 0. This can be done by setting up a system of equations and using techniques such as Gaussian elimination to solve for the constants.

4. Can eigenvectors be linearly dependent?

Yes, it is possible for eigenvectors to be linearly dependent. This means that at least one eigenvector in a set can be written as a linear combination of the others. In this case, the set of eigenvectors does not fully describe the system and additional vectors would be needed.

5. Why is it important to prove that eigenvectors are linearly independent?

Proving that eigenvectors are linearly independent is important because it allows us to use them as a basis to describe a system. This means that by knowing the eigenvalues and eigenvectors of a system, we can easily calculate the behavior of the system without needing to know all of its individual components. It also allows us to simplify complex systems and make predictions about their behavior.

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