All eigenvalues 0 implies nilpotent

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In summary, the conversation discusses how to prove that a linear operator with all eigenvalues equal to 0 is nilpotent. The Cayley-Hamilton theorem can be used to prove this, but it is not allowed. The conversation explores other methods, such as showing that every vector in V is also in the kernel of some power of T. It is also noted that the restricted map T must be bijective and have an eigenvector. However, this may not hold true if the vector space is not over an algebraically closed field. The concept of "all eigenvalues equal to 0" is also clarified, as it may not necessarily mean that all eigenvalues are real.
  • #1
altcmdesc
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Homework Statement


How would I go about proving that if a linear operator [tex]T\colon V\to V[/tex] has all eigenvalues equal to 0, then [tex]T[/tex] must be nilpotent?

The Attempt at a Solution



I know that this follows trivially from the Cayley-Hamilton theorem (the characteristic polynomial is [tex]x^n[/tex] and hence [tex]T^n=0[/tex]), but, unfortunately, I'm not allowed to use that theorem. How else could I go about proving this? I've been trying to show that any vector in [tex]V[/tex] is also in the kernel of some power of [tex]T[/tex] and hence [tex]T[/tex] is nilpotent, but I'm getting stuck.
 
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  • #2
T^(k+1)(V) is a subset of T^k(V), right? If T isn't nilpotent then you eventually have a k such that T^(k+1)(V)=T^k(V) with T^k(V) not {0}. Consider the restricted map T:T^k(V)->T^k(V). What can you say about it?
 
  • #3
Is it that the restricted map doesn't have any eigenvalues/eigenvectors and is bijective? Am I on the right track?
 
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  • #4
You are on the right track. Yes, the restricted T is bijective. It must have an eigenvector. What can you say about the corresponding eigenvalue?
 
  • #5
Why must the restricted map have an eigenvector? I know where to go from there, but I can't seem to understand why that must be true since there isn't any assumption made that the base field of the vector space V is algebraically closed.
 
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  • #6
The restricted T maps the vector space T^m(V) to itself. Look at it's characteristic polynomial. It must have a root r, mustn't it? That means r*I-T is singular.
 
  • #7
My point is that if the vector space is over the reals, for example, not every characteristic polynomial has roots (e.g. x^2+1), so if T^m(V) isn't over an algebraically closed field, we don't necessarily have that the restricted map has an eigenvalue...right?
 
  • #8
altcmdesc said:
My point is that if the vector space is over the reals, for example, not every characteristic polynomial has roots (e.g. x^2+1), so if T^m(V) isn't over an algebraically closed field, we don't necessarily have that the restricted map has an eigenvalue...right?

Right. But then what exactly do you mean by "T has all eigenvalues equal to zero"? Take the matrix T=[[0,0,0],[0,0,-1],[0,1,0]]. T has one REAL eigenvalue and that's 0. T is not nilpotent. I don't think I would describe T as having all eigenvalues equal to 0.
 

What does it mean for all eigenvalues to be 0?

When all eigenvalues of a matrix are 0, it means that the matrix is singular and has no inverse.

What is a nilpotent matrix?

A nilpotent matrix is a square matrix where some power of the matrix is equal to the zero matrix.

Why does having all eigenvalues equal to 0 imply nilpotent?

This is because the characteristic polynomial of a nilpotent matrix only has 0 as a root, making all eigenvalues equal to 0.

Can a non-square matrix have all eigenvalues equal to 0?

No, all eigenvalues being equal to 0 is a property unique to square matrices.

How is the nilpotency index related to the eigenvalues of a nilpotent matrix?

The nilpotency index of a nilpotent matrix is equal to the highest power of the matrix that results in the zero matrix, which is also the number of times 0 appears as an eigenvalue.

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