Set of eigenvectors is linearly independent

In summary, a set of eigenvectors corresponding to different eigenvalues can be proven to be linearly independent if any two of the eigenvectors are independent. This can be shown by proving that any linear combination of the eigenvectors will also be independent.
  • #1
Fermat1
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I know eigenvectors corresponding to different eigenvalues are linearly independent but what about a set ${e_{1},...,e_{n}}$ of eigenvectors corresponding to different eigenvalues?
 
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  • #2
I don't understand your question because I don't see how the two parts of your question are different.

When you say
Fermat said:
I know eigenvectors corresponding to different eigenvalues are linearly independent
Do you mean that two eigenvectors corresponding to two different eigenvalues
but what about a set ${e_{1},...,e_{n}}$ of eigenvectors corresponding to different eigenvalues?
but asking, "what if there are more than two?". One can show generally, "if, in a set of vectors, any two are independent (au+ bv= 0 only if a= b= 0 which is the same as saying that b is NOT a multiple of a and vice-versa) then all the vectors are independent."
One can prove that by first proving 'if [tex]u_1, u_2, ... u_n[/tex] are each independent of v, then so is [tex]a_1u_1+ a_2u_2+ ...+ a_nu_n[/tex] is independent of v for any numbers [tex]a_1[/tex], [tex]a_2[/tex], ..., [tex]a_n[/tex].
 

FAQ: Set of eigenvectors is linearly independent

What is a set of eigenvectors?

A set of eigenvectors is a collection of vectors that are associated with a specific linear transformation. Each eigenvector corresponds to a specific eigenvalue, which represents the scale factor by which the vector is stretched or compressed by the transformation.

What does it mean for a set of eigenvectors to be linearly independent?

A set of eigenvectors is linearly independent if no vector in the set can be written as a linear combination of the other vectors. In other words, each eigenvector in the set is unique and cannot be expressed as a combination of the others.

How do you determine if a set of eigenvectors is linearly independent?

To determine if a set of eigenvectors is linearly independent, you can use the following criteria:

  • Check if the number of eigenvectors in the set is equal to the dimension of the vector space.
  • Calculate the determinant of the matrix formed by the eigenvectors. If the determinant is non-zero, the eigenvectors are linearly independent.
  • Check if the eigenvectors span the entire vector space. If they do, they are linearly independent.

Why is it important for a set of eigenvectors to be linearly independent?

A set of linearly independent eigenvectors is important because it allows for easier analysis and understanding of a linear transformation. It also allows for simpler and more efficient calculations in certain applications, such as diagonalization of matrices.

Can a set of eigenvectors be linearly dependent?

Yes, a set of eigenvectors can be linearly dependent. This means that at least one vector in the set can be expressed as a linear combination of the other vectors. In this case, the set does not span the entire vector space and is not considered linearly independent.

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