Proof of Eigenvector: A*v=λ*v

In summary: The left side becomes Acv. But since v is an eigenvector, Av= λv, so you have Acv= λcv. But that is just the definition of "cv is an eigenvector of A with eigenvalue λ". So you have proved "If v is an eigenvector of A with eigenvalue λ, then so is cv".
  • #1
JayTheGent
1
0
Hello PF, brand new member here.

A question about a proof:

If A*v=λ*v, then w = c*v is also an eigenvector of A.

This seems really simple to me, but perhaps I am doing it incorrectly:

A*c*v=λ*c*v, divide both sides by c and you are left with your original eigenvector of A. Am I missing something here?
 
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  • #2
JayTheGent said:
Hello PF, brand new member here.

A question about a proof:

If A*v=λ*v, then w = c*v is also an eigenvector of A.

This seems really simple to me, but perhaps I am doing it incorrectly:

A*c*v=λ*c*v, divide both sides by c and you are left with your original eigenvector of A. Am I missing something here?

What you've shown is that if cv is an eigenvector, then v is an eigenvector. You need to show the other way. In addition, you can't always divide by c.

Also, this should be in homework help.
 
  • #3
notice that subspaces are closed under scaling. Or just factor out the c as the matrix cId.
 
  • #4
JayTheGent said:
Hello PF, brand new member here.

A question about a proof:

If A*v=λ*v, then w = c*v is also an eigenvector of A.

This seems really simple to me, but perhaps I am doing it incorrectly:

A*c*v=λ*c*v, divide both sides by c and you are left with your original eigenvector of A. Am I missing something here?

Welcome to PF!:smile:
Do not use * when applying an operator on a vector. The operator A assigns a vector u to vector v. Write u=A(v). In case v is an eigenvector of operator A, A(v)= λ*v. The right side is a product - a vector multiplied by a scalar, but the left-hand side is not.

You can use the property of linear operators that A(cv)=c A(v) (c is a scalar).

ehild
 
Last edited:
  • #5
JayTheGent said:
Hello PF, brand new member here.

A question about a proof:

If A*v=λ*v, then w = c*v is also an eigenvector of A.

This seems really simple to me, but perhaps I am doing it incorrectly:

A*c*v=λ*c*v, divide both sides by c and you are left with your original eigenvector of A. Am I missing something here?
As you have been told, what you shown is that "If cv is an eigenvector of A with eigenvalue λ then so is v". To prove the other way, reverse your proof: from Av= λv, multiply both sides by c.
 

1. What is an eigenvector and why is it important in science?

An eigenvector is a vector that does not change direction when multiplied by a given matrix. In other words, the direction of an eigenvector remains the same even after it has been transformed by a matrix. Eigenvectors are important in science because they help us understand the behavior of complex systems and can be used to identify patterns and relationships within data.

2. How is the eigenvalue related to an eigenvector?

An eigenvalue is a scalar value that represents the amount by which an eigenvector is scaled during a transformation by a matrix. For example, if the eigenvalue is 2, the eigenvector will be scaled by a factor of 2 after being transformed by the matrix. This relationship is shown in the equation A*v=λ*v, where A is the matrix, v is the eigenvector, and λ is the eigenvalue.

3. How is the proof of eigenvector used in real-world applications?

The proof of eigenvector is used in a variety of fields, including physics, engineering, economics, and data analysis. It is used to solve problems involving linear transformations, such as finding the principal components in a dataset, calculating the stability of a physical system, and predicting the behavior of a financial market.

4. Can a matrix have multiple eigenvectors and eigenvalues?

Yes, a matrix can have multiple eigenvectors and eigenvalues. In fact, most matrices have more than one eigenvector and eigenvalue. The number of distinct eigenvectors and eigenvalues a matrix has is equal to its dimension.

5. How is the proof of eigenvector related to the concept of diagonalization?

The proof of eigenvector is closely related to the concept of diagonalization, which is the process of finding a diagonal matrix that is similar to a given matrix. The diagonal elements of the diagonal matrix are the eigenvalues of the original matrix, and the columns of the diagonal matrix are the corresponding eigenvectors. This allows us to simplify complex matrix operations, making it easier to analyze and understand the behavior of a system.

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