Eigenvalues and Eigenvectors of Invertible Linear Operators and Matrices

In summary, if L is an invertible linear operator with an eigenvalue lambda and associated eigenvector x, then 1/lambda is also an eigenvalue of L^-1 with the same associated eigenvector x. This statement also applies to matrices, where L(x) corresponds to Ax and L is invertible. If an eigenvalue is 0, then L is not invertible.
  • #1
hkus10
50
0
Let L : V>>>V be an invertible linear operator and let lambda be an eigenvalue of L with associated eigenvector x.
a) Show that 1/lambda is an eigenvalue of L^-1 with associated eigenvector x.
For this question, the things I know are that L is onto and one to one. Therefore, how to prove this question?

b) state the analogous statement for matrices. What does "state" the analogous statement mean?

Thanks
 
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  • #2
hkus10 said:
Let L : V>>>V be an invertible linear operator and let lambda be an eigenvalue of L with associated eigenvector x.
a) Show that 1/lambda is an eigenvalue of L^-1 with associated eigenvector x.
For this question, the things I know are that L is onto and one to one. Therefore, how to prove this question?
What does it mean to say that λ is an eigenvalue, and x an eigenvector of L?
hkus10 said:
b) state the analogous statement for matrices. What does "state" the analogous statement mean?
L(x) corresponds to Ax, where A is a matrix representation of the operator L. L is invertible, so what can you say about A?
 
  • #3
Mark44 said:
What does it mean to say that λ is an eigenvalue, and x an eigenvector of L?

a) This is what I get for part(a)let n=lambda.
Since r is an eigenvalue of L, Lx=nx.
Since the transformation is invertible, (L^-1)Lx=(L^-1)nx.
==> Ix=r(L^-1)x, where I=indentity matrix
At this point, I want to divide both sides by r. However, how can I be sure r is not equal to zero?

Thanks
 
  • #4
hkus10 said:
a) This is what I get for part(a)let n=lambda.
Since r is an eigenvalue of L, Lx=nx.
Since the transformation is invertible, (L^-1)Lx=(L^-1)nx.
==> Ix=r(L^-1)x, where I=indentity matrix
At this point, I want to divide both sides by r. However, how can I be sure r is not equal to zero?

Thanks

You seem to be using both n and r for lambda. But if the eigenvalue were 0, would L be invertible?
 
  • #5
Dick said:
You seem to be using both n and r for lambda. But if the eigenvalue were 0, would L be invertible?

Yes, this is a typo. It seems to me that if the eigenvalue is 0, L is not invertible. However, I cannot conceptualize it.

Could you please explain it ?

Thanks
 
  • #6
hkus10 said:
Yes, this is a typo. It seems to me that if the eigenvalue is 0, L is not invertible. However, I cannot conceptualize it.

Could you please explain it ?

Thanks

If the eigenvalue is 0, then L can't be invertible. If x is an eigenvector (hence not the zero vector) and has eigenvalue 0, then L(x)=0*x=0. But L^(-1)(0) must be 0, it can't be x since L^(-1) is linear. So if L is invertible, you can assume all eigenvalues are not zero.
 

Related to Eigenvalues and Eigenvectors of Invertible Linear Operators and Matrices

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts used in linear algebra to analyze the behavior of linear transformations. Eigenvalues are scalar values that represent how a transformation stretches or compresses a vector. Eigenvectors are the corresponding vectors that are only scaled by the transformation, not rotated.

2. How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated by finding the solutions to a system of equations known as the characteristic equation. This involves finding the values of lambda (λ) that satisfy the equation (A-λI)x = 0, where A is the transformation matrix and I is the identity matrix.

3. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important because they provide information about the behavior of linear transformations. They can be used to understand the stretching and compressing properties of a transformation, as well as its orientation or direction.

4. How are eigenvalues and eigenvectors used in data analysis?

Eigenvalues and eigenvectors are commonly used in data analysis and machine learning to reduce the dimensionality of data. This is done by projecting the data onto the eigenvectors corresponding to the largest eigenvalues, which allows for more efficient analysis and visualization of the data.

5. Can a matrix have multiple eigenvalues and eigenvectors?

Yes, a matrix can have multiple eigenvalues and eigenvectors. In fact, a square matrix will have the same number of eigenvalues and eigenvectors as its dimensions. However, not all matrices will have distinct eigenvalues or linearly independent eigenvectors.

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