Proving Nilpotency and Nonnegativity of Eigenvalues of Symmetric Matrices

In summary, to show that every eigenvalue of A is zero if and only if A is nilpotent, we can use a proof by contradiction and show that if A has a nonzero eigenvalue, A^k will not equal 0. For the second problem, we can use diagonalization to show that every eigenvalue of A is nonnegative if and only if A can be written as the square of a symmetric matrix B.
  • #1
stunner5000pt
1,463
3
Show that every eigenvalue of A is zero iff A is nilpotent (A^k = 0 for k>=1)
i m having trouble with going from right to left (left to right i got)

we know that det A = product of the eignevalues = 0
when we solve for the eigenvalues and put hte characteristic polynomial = 0
then
[tex] det (\lambda I - A) = det (-A) = 0 [/tex]
but i have a feeling taht i am not allowed to do that last step because the characteristic polynomial need not be equal to zero.

(Also i do not know the Cayley Hamilton theorem.)

If A is symmetric show taht every eigenvalue of A is nonnegaitve iff A = B^2 for some symmetric matrix B

im not even quite sure where to start with this one! Please help

thank you for any help!
 
Last edited:
Physics news on Phys.org
  • #2
For the first one, what if there were an eigenvalue that were nonzero? There would be an eigenvector v for that eigenvalue. And then what would A * A * A * A * A * ... * v equal?

For the second one, do you know that if an nxn matrix A is symmetric then A has n eigenvalues? Then simply factor A as PDP^-1, and you should be able to find B. (if you are having trouble with that then think, with A factored into PDP^-1, what is A^2?)
 
Last edited:
  • #3
then that A A ... v = 0
and since the eigenvector is not zero (not sure) then A^ n = 0
 
  • #4
No if there were an eigenvector v for a nonzero eigenvalue k then A * A * A * A * ... * v = k * k * k * k * ... * v
 
  • #5
0rthodontist said:
No if there were an eigenvector v for a nonzero eigenvalue k then A * A * A * A * ... * v = k * k * k * k * ... * v
but here the eignevalue is zero
so something like taht , the right hand side is zero
that leaves A A A A A ... v = 0

what does this lead to , though?
 
Last edited:
  • #6
What I was trying to show you was a proof by contradiction. What you do is, given that A^k = 0 for some k, assume that A has an eigenvalue that is NOT zero. THEN, you try to show that there is a contradiction, so you can conclude A can't have any nonzero eigenvalues.
 
  • #7
ok for the second one
[tex] A = PDP^{-1} [/tex]
[tex] A^2 = PDDP^{-1} [/tex]
[tex] A^2 = P D^2 P^{-1} [/tex]
not sure what happens now though
i tried removing one of As but that just leads back to the first step
 
  • #8
But what actually is D^2? Are you familiar with diagonalization? D is a diagonal matrix whose diagonal entries are the eigenvalues of A, and P is a matrix whose columns are the corresponding eigenvectors of A. You can calculate the entries of D^2 easily.

Don't bother looking for a direct way from A^2 to the formula you are looking for, I was only saying compute A^2 as an example that would give you insight on B.
 

FAQ: Proving Nilpotency and Nonnegativity of Eigenvalues of Symmetric Matrices

What is the significance of proving nilpotency and nonnegativity of eigenvalues of symmetric matrices?

Proving nilpotency and nonnegativity of eigenvalues of symmetric matrices is important because it allows us to understand the behavior and properties of these matrices. It also helps us to solve various mathematical problems involving symmetric matrices, such as finding the eigenvalues and eigenvectors.

How is nilpotency defined in the context of eigenvalues of symmetric matrices?

Nilpotency refers to the property of a matrix where its powers, when multiplied by itself multiple times, eventually become equal to the zero matrix. In the context of eigenvalues of symmetric matrices, this means that the eigenvalues of a nilpotent symmetric matrix are all equal to zero.

What is the relationship between nilpotency and nonnegativity of eigenvalues of symmetric matrices?

The relationship between nilpotency and nonnegativity of eigenvalues of symmetric matrices is that a symmetric matrix is nilpotent if and only if all of its eigenvalues are nonnegative. This means that if a symmetric matrix has at least one negative eigenvalue, it cannot be nilpotent.

How can the nilpotency and nonnegativity of eigenvalues of symmetric matrices be proven?

The nilpotency and nonnegativity of eigenvalues of symmetric matrices can be proven using various methods, such as the Cayley-Hamilton theorem, the spectral theorem, and the Gershgorin circle theorem. These methods involve using the properties and characteristics of symmetric matrices to determine the eigenvalues and their properties.

Are there any practical applications of proving nilpotency and nonnegativity of eigenvalues of symmetric matrices?

Yes, there are many practical applications of proving nilpotency and nonnegativity of eigenvalues of symmetric matrices. For example, in physics and engineering, these properties are used in the study of vibrations and oscillations of systems, as well as in the analysis of stability and control of systems. They are also used in fields such as computer science, statistics, and economics.

Back
Top