Proof problem(Linear Algebra- Eigenvalues/Eigenvectors)

However, "diagonalizable" does not necessarily imply "orthogonally diagonalizable".In summary, the conversation discusses the geometric and algebraic multiplicities of an eigenvalue of a symmetric matrix, and whether a symmetric matrix is necessarily diagonalizable. The conclusion is that a symmetric matrix is indeed diagonalizable, but not necessarily orthogonally diagonalizable.
  • #1
foxofdesert
22
0

Homework Statement


True/False
The geometric multiplicity of an eigenvalue of a symmetric matrix necessarily equals to its algebric multiplicity.

Homework Equations




The Attempt at a Solution


True.
If a matrix is symmetric, then the matrix is diagonalizable. Since the matrix is diagonalizable, there must be eigenvectors correspond to each eigenvalues.


So, I did the proof, but I'm not so sure if it sounds right. I think there could be something more tricky or missing. Would you guys check if this sounds right to you?
 
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  • #2
Sound right to me.
 
  • #3
Thanks for checking. Just quick checking tho,
'A matrix is symmetric if and only if the matrix is diagonalizable.'

Is this a right statement?

or 'orthogonally diagonalizable'
 
  • #4
foxofdesert said:
Thanks for checking. Just quick checking tho,
'A matrix is symmetric if and only if the matrix is diagonalizable.'

Is this a right statement?

or 'orthogonally diagonalizable'

No, it's not iff. Is the matrix [[1,1],[0,0]] diagonalizable? Is it symmetric?
 
  • #5
oh, thanks!
 
  • #6
foxofdesert said:
oh, thanks!

Well, it is true that "orthogonally diagonalizable" iff symmetric.
 

1. What is a proof problem in linear algebra?

A proof problem in linear algebra is a mathematical problem that requires a logical and systematic approach to prove a certain concept or theorem. It involves using techniques and properties of linear algebra to demonstrate the validity of a statement or equation.

2. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are important concepts in linear algebra. Eigenvalues represent the scalar values that, when multiplied with an eigenvector, result in a scaled version of the original vector. Eigenvectors, on the other hand, are non-zero vectors that are only scaled by the corresponding eigenvalue when multiplied by a matrix.

3. How do you find eigenvalues and eigenvectors?

To find eigenvalues and eigenvectors, we need to solve the characteristic equation det(A-λI)=0, where A is a square matrix and λ is the eigenvalue. The resulting values of λ are the eigenvalues, and the corresponding eigenvectors can be found by solving the equation (A-λI)x=0.

4. What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many applications in mathematics, physics, and engineering. They allow us to understand and analyze complex systems by reducing them to simpler forms. They also play a crucial role in solving systems of differential equations, diagonalizing matrices, and analyzing the stability of a system.

5. What are some common techniques used to solve proof problems in linear algebra?

There are various techniques used to solve proof problems in linear algebra, including matrix operations, Gaussian elimination, determinants, and vector space methods. Other useful tools include eigenvalues and eigenvectors, eigen-decomposition, and the Gram-Schmidt process. It is essential to have a solid understanding of these techniques and their properties to effectively solve proof problems in linear algebra.

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