Eigen Vector Proofs: Proving Real Symmetric Matrix M is Positive Definite

In summary, a symmetric matrix M can be diagonalized using an orthogonal matrix S, where the diagonal elements of the resulting diagonal matrix D are the eigenvalues of M. Additionally, M is positive definite if and only if its eigenvalues are positive. This can be proven by verifying that the transpose of the orthogonal matrix multiplied by the matrix M multiplied by the inverse of the orthogonal matrix equals the diagonal matrix D. This follows from the definition of eigenvectors, where S^-1 is the eigenvectors of M.
  • #1
sdevoe
21
0

Homework Statement



Let M be a symmetric matrix. The eigenvalues of M are real and further M can be
diagonalized using an orthogonal matrix S; that is M can be written as

M = S^-1*D*S

where D is a diagonal matrix.
(a) Prove that the diagonal elements of D are the eigenvalues of M.
(b) Prove that the real symmetric matrix M is positive defi nite if and only if its eigen-
values are positive.

Homework Equations



Mx=λx


The Attempt at a Solution



a) So i know that S is the eigen vectors of M and D is the eigen values but I do not know how to prove that.

b)Does this have something to do with the gradient. I know positive definite means that transpose(x)*M*x must be greater than zero where x is x(1) through x(n) and M is the matrix in question.
 
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  • #2
S is not the eigenvectors of M, S^{-1} is, the rest is straightforward verification.
 
  • #3
With what equation would I begin that proof?
 
  • #4
sdevoe said:
With what equation would I begin that proof?

first you claim that S^-1 are the eigenvectors, then you prove your claim by definition of eigenvectors
 
  • #5
note that M = S-1DS means that

S-1D = MS-1.

express both matrix products above in terms of the column vectors of the matrix on the right in each product. compare your results, what do they say?
 
  • #6
Ok I have that now what about the positive definite aspect?
 

FAQ: Eigen Vector Proofs: Proving Real Symmetric Matrix M is Positive Definite

What is an eigen vector?

An eigen vector is a non-zero vector that, when multiplied by a square matrix, gives a scalar multiple of itself. In other words, an eigen vector is a special vector that does not change direction when multiplied by a matrix.

What is a real symmetric matrix?

A real symmetric matrix is a square matrix in which the entries above and below the diagonal are reflections of each other. In other words, the matrix is equal to its own transpose.

What does it mean for a matrix to be positive definite?

A matrix is positive definite if all of its eigenvalues are positive. This means that when the matrix is multiplied by any non-zero vector, the result is always a positive value. In other words, the matrix has a positive effect on any vector it operates on.

How do you prove that a real symmetric matrix is positive definite?

To prove that a real symmetric matrix M is positive definite, you need to show that all of its eigenvalues are positive. This can be done by using the Cholesky decomposition method, where M is decomposed into a lower triangular matrix L and its transpose L^T. If all of the diagonal entries of L are positive, then M is positive definite.

Why is it important to prove that a matrix is positive definite?

It is important to prove that a matrix is positive definite because it allows us to determine the behavior and properties of the matrix. Positive definite matrices have many useful properties and are used in various fields such as physics, engineering, and data analysis. Additionally, positive definite matrices are essential in optimization problems as they guarantee the existence of a unique minimum point.

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