Characteristic Polynomial of Matrix

In summary, the conversation discusses finding the minimal polynomial and characteristic polynomial of a nxn matrix J with all entries equal to 1. Methods such as computing the determinant and using induction are suggested, as well as considering the eigenvalues and trace of J. The final goal is to prove a relationship between J2 and nJ and determine the multiplicity of its eigenvalues.
  • #1
iamalexalright
164
0

Homework Statement


Let J be the nxn matrix all of whose entries are equal to 1. Find the minimal polynomial and characteristic polynomial of J and the eigenvalues.


Well, I figure the way I'm trying to do it is more involved then other methods but this is the easiest method for me to start with.

We can find the characteristic equation of J (and thus the eigenvalues) by computing:
[tex]det(J - \lambda I)[/tex]

We also know that the determinant of a nxn matrix can be found by computing:
[tex]det(A) = \sum_{\sigma \in S_{n}} sgn(\sigma) \prod_{i = 1}^{n}A_{i,\sigma_{i}}[/tex]
where S_n is the set of all permutations of (1,2,3,...n). sgn is the signature of the permutation.

Now we want to compute [tex]det(J - \lambda I)[/tex]
I know there will be n! terms after applying Leibniz's formula, half of which will have a positive signature and half that will have an odd signature.

Now, I don't really know how to prove this but with some investigation we can see that there will only be one term with degree n, ie:
[tex]det(J - \lambda I) = (1 - \lambda)^{n} + ...[/tex]

There shouldn't be any terms with degree n - 1...

I'm not sure where to go after this... I realize that this probably isn't the best method to go about this (it's late and I've been working on homework for awhile now...) so any other suggestions would be great (along with suggestions on how to continue with this path if possible).
 
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  • #2
hi iamalexalright! :smile:

(have a lambda: λ :wink:)

hint: subtract the first row from the other rows :wink:
 
  • #3
Hrmph, could have sworn I thought of that...

so then we are left with a new J with all 1's in the first row and 0's everywhere else

the determinant of this new matrix would then be (since only diagonal terms contribute):
[tex](-1)^{n + 1}(1 - \lambda)\lambda^{n - 1}[/tex]

Here is my issue... that is the charecteristic equation for the new matrix but there should be a coefficient of n on the smaller degree:

example: n = 3
the formula above gives:
[tex]-x^{3} + x^{2}[/tex]
but I should have:
[tex]-x^{3} + 3x^{2}[/tex]

Don't know how to resolve that.

Also, if n = 3 would the minimal polynomial be:
x(3 - x) = 3x - x²
?
 
  • #4
iamalexalright said:
so then we are left with a new J with all 1's in the first row and 0's everywhere else

no, you need to do it for J - λI :redface:
 
  • #5
Okay, so that will leave me with a matrix that has:
(1 - λ) in the first diagonal position
-λ for all other diagonals
1's in the rest of the first row
λ's in the rest of the first column
zero's elsewhere

Not sure where to go from here... I can see with small examples that it works out but proving for all n seems tricky for me.
 
  • #6
have you tried induction?
 
  • #7
Check out Avodyne's reply in this (https://www.physicsforums.com/showthread.php?t=314070&page=3) thread for another way to pursue this. You can check to see that J2 = nJ, which imposes a condition on the possible values for J's eigenvalues. Then look at the trace tr(J) to find the multiplicity of each of these values.
 

Related to Characteristic Polynomial of Matrix

What is the characteristic polynomial of a matrix?

The characteristic polynomial of a matrix is a polynomial function that is used to find the eigenvalues of a square matrix. It is obtained by taking the determinant of the matrix minus a variable, usually denoted by lambda. The resulting polynomial is then solved for lambda, and the solutions are the eigenvalues of the matrix.

How do you find the characteristic polynomial of a matrix?

To find the characteristic polynomial of a matrix, you first need to subtract lambda from the diagonal entries of the matrix. Then, take the determinant of the resulting matrix. This will give you a polynomial in terms of lambda, which is the characteristic polynomial. You can then solve this polynomial to find the eigenvalues of the matrix.

Why is the characteristic polynomial important?

The characteristic polynomial is important because it allows us to find the eigenvalues of a matrix, which are crucial in many applications of linear algebra. Eigenvalues have many uses, such as determining stability in systems of differential equations, finding the principal components in data analysis, and solving systems of linear differential equations.

What is the relationship between the characteristic polynomial and the determinant of a matrix?

The characteristic polynomial and the determinant of a matrix are closely related. The characteristic polynomial is obtained by taking the determinant of the matrix minus a variable, and the determinant of a matrix can be calculated by evaluating the characteristic polynomial at a specific value of lambda (usually 0). This means that the determinant of a matrix is equal to the constant term of the characteristic polynomial.

Can a matrix have multiple characteristic polynomials?

No, a matrix can only have one characteristic polynomial. This is because the characteristic polynomial is defined by the matrix itself and is unique to that matrix. However, a matrix can have multiple eigenvalues, which are the solutions to the characteristic polynomial, and each eigenvalue can have its own algebraic and geometric multiplicities.

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