Help me prove something on eigenvectors?

  • Thread starter Rijad Hadzic
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    Eigenvectors
In summary: This is the induction step.For the base case, it is clear that ##A^0 x = x = \lambda^0x## and ##A^1x = \lambda x = \lambda^1 x##, so the statement is true for ##m = 0## and ##m = 1##.Now, assume that the statement is true for ##m = k##, i.e., ##A^k x = \lambda^k x##. Then, we can write ##A^{k + 1}x = A(A^k x) = A(\lambda^k x)##, using the induction hypothesis. Now, use the property of matrix multiplication, that is, ##(AB)x
  • #1
Rijad Hadzic
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Homework Statement


Prove that if the eigenvalues of a matrix A are [itex] \lambda_1 ... \lambda_n [/itex] with corresponding eigenvectors [itex] x_1...x_n [/itex] then [itex] \lambda^m_1...\lambda^m_n [/itex] are eigenvalues of A^m with corresponding eigenvectors x_1...x_n

Homework Equations


[itex] Ax= \lambda x [/itex]

The Attempt at a Solution


So I start with

[itex] Ax= \lambda x [/itex]

I think I am trying to prove

[itex] A^mx= \lambda^m x [/itex]

correct?

If so I proceed:

[itex] A^{m-1}Ax= \lambda^m x [/itex]

[itex] A^{m-1}\lambda x= \lambda^m x [/itex]

and basically this will continue... but I'm not sure how to write this out to get it to

[itex] \lambda^m x= \lambda^m x [/itex]

?

I don't get how I'm going to be able to set

[itex] A^{m-1}[/itex] = to [itex] \lambda^m [/itex]
 
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  • #2
Do you know how to use mathematical induction?

If so, prove the statement
$$P(m):\quad A^m x=\lambda^m x$$
by induction on the power ##m##.

##P(m)## is trivially true for ##m\in\{0,1\}##. So what you need to do is prove that if it is true for ##m=k##, with ##k\geq 0##, then it is true for ##m=k+1##.
 
  • #3
Ok guys I'm stupid. I actually plugged numbers into the exponents and its starting to make sense now. As A goes down, lambda goes up, and eventually it comes to lambda = lambda
 
  • #4
andrewkirk said:
Do you know how to use mathematical induction?

If so, prove the statement
$$P(m):\quad A^m x=\lambda^m x$$
by induction on the power ##m##.

##P(m)## is trivially true for ##m\in\{0,1\}##. So what you need to do is prove that if it is true for ##m=k##, with ##k\geq 0##, then it is true for ##m=k+1##.

I do not know how to use mathematical induction. Is it common to learn this before your first Linear Algebra course? I will read the wiki article right now though.
 
  • #5
You cannot start with ##A^m x_i = \lambda^m_i x_i## and proceed from there, if this is what you want to show. It must be your last line, not the first. So either you prove it by induction or you go with ##A^m x_i = A^{m-1}(Ax_i) = A^{m-1} (\lambda_i x_i) = \ldots = \lambda_i^m x_i ## and use properties of matrix multiplication. Since ##m## is a fixed finite number, an argument "and so on" is equally good.
 
  • #6
Rijad Hadzic said:
I do not know how to use mathematical induction. Is it common to learn this before your first Linear Algebra course? I will read the wiki article right now though.
Yes. Where I come from, mathematical induction is taught in high school, whereas linear algebra is not taught until uni, or in the most advanced late high school classes. Mathematical induction is one of the most commonly used types of proofs there is.
 
  • #7
fresh_42 said:
You cannot start with ##A^m x_i = \lambda^m_i x_i## and proceed from there, if this is what you want to show. It must be your last line, not the first. So either you prove it by induction or you go with ##A^m x_i = A^{m-1}(Ax_i) = A^{m-1} (\lambda_i x_i) = \ldots = \lambda_i^m x_i ## and use properties of matrix multiplication. Since ##m## is a fixed finite number, an argument "and so on" is equally good.

That makes sense. It must be the last line since that is what we are trying to prove.

Thanks for all the repsonses guys. I will continue my studies.
 
  • #8
Rijad Hadzic said:
I do not know how to use mathematical induction.
There are three parts in proving that ##A^mx = \lambda^m x## using induction.
1) Show that this statement ##Ax = \lambda x## is true, which is trivially true here.
2) Assume that ##A^kx = \lambda^k x##
3) Show that ##A^kx = \lambda^k x## being true implies that ##A^{k + 1}x = \lambda^{k + 1} x## is also true. In other words, use the statement in 2) to show that the statement in 3) must also be true.
 

FAQ: Help me prove something on eigenvectors?

What are eigenvectors and why are they important?

Eigenvectors are special vectors that represent the direction and magnitude of a linear transformation. They are important because they can help us understand the behavior of a system and make predictions about future states.

How do I prove that a vector is an eigenvector?

To prove that a vector is an eigenvector, you must show that it remains in the same direction after being transformed by a matrix. This can be done by multiplying the vector by the matrix and checking if the resulting vector is a scalar multiple of the original vector.

What is the difference between eigenvalues and eigenvectors?

Eigenvalues are the scalar values that represent the amount by which an eigenvector is scaled when transformed by a matrix. Eigenvectors, on the other hand, are the special vectors that remain in the same direction after being transformed by a matrix.

Can eigenvectors be complex numbers?

Yes, eigenvectors can be complex numbers. In fact, in some cases, complex eigenvectors are necessary to fully describe a system. However, when dealing with real-world applications, we typically use real eigenvectors.

How are eigenvectors used in data analysis?

Eigenvectors are used in data analysis to reduce the dimensionality of a dataset. By finding the eigenvectors of a covariance matrix, we can identify the most important features of the data and discard the less important ones. This can help us to better understand and interpret the data.

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