Real Symmetric Endomorphism: Diagonalizability and Eigenvalues Explained

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In summary, a real symmetric endomorphism has real eigenvalues and is diagonalizable. All eigenvalues are real for a symmetric endomorphism because of the definition of an inner product.
  • #1
penguin007
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Hi,

We know that if u is a real symetric endomorphism, then u has a real eigenvalue and that u is diagonalizable.
But can we say that u is diagonalizable with only real eigenvalues?
 
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  • #2
Yes. Since you are talking about eigenvalues, I take it that u is an endomorphism on some vector space (a linear transformation from vector space V to itself). Specifically, if u is symmetric and [itex]\lambda[/itex] is an eigenvalue, then there exist non-zero x such that ux= \lambda x. Further, we can take x to have magnitude 1: <x, x>= 1. Then [itex]\lambda= \lambda<x, x>= <\lambda x, x>= <ux, x>= <x, ux>[/itex] (because u is symmetric). [itex]<x , ux>= \overline{<ux, x>}= \overline{\lambda x, x}= \overline{\lambda}\overline{<x, x>}= \overline{\lambda}[/itex].
(The overline indicates complex conjugation.)

Since [itex]\lambda= \overline{\lambda}[/itex], [itex]\lambda[/itex] is real. That is all eigenvalues are real for a symmetric endomorphism.
 
  • #3
I think you forgot an overline in the end, but I can't understand why we have:
<x,ux>=conjugate(<ux,x>) ?
 
  • #4
penguin007 said:
I think you forgot an overline in the end, but I can't understand why we have:
<x,ux>=conjugate(<ux,x>) ?
One of the requirements in the definition of "inner product" is that [itex]<u, v>= \overline{<v, u>}[/itex]. Of course, if the vector space is over the real numbers, that is the same as "<u, v>= <v, u>" but then your question is trivial.
 

What is a symmetric endomorphism?

A symmetric endomorphism is a linear transformation on a vector space that preserves the inner product of the space. This means that the image of any two vectors under the transformation will have the same inner product as the original vectors.

How is a symmetric endomorphism represented?

A symmetric endomorphism can be represented by a symmetric matrix. This means that the matrix is equal to its own transpose, and its diagonal elements are real numbers.

What is the significance of a symmetric endomorphism?

A symmetric endomorphism has many applications in physics and engineering, particularly in the study of systems that conserve energy or momentum. It can also be used to simplify calculations in linear algebra and differential equations.

What is the difference between a symmetric endomorphism and a symmetric matrix?

While a symmetric endomorphism is a type of linear transformation, a symmetric matrix is a specific representation of this transformation. A symmetric endomorphism can have different matrix representations depending on the basis chosen for the vector space, while a symmetric matrix is unique.

How is a symmetric endomorphism related to eigenvalues and eigenvectors?

A symmetric endomorphism has real eigenvalues and orthogonal eigenvectors. This property allows for easier analysis and diagonalization of the matrix representation of the endomorphism. Additionally, some important properties of symmetric endomorphisms, such as positive definiteness, can be determined from the eigenvalues and eigenvectors.

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