All eigenvalues zero => zero map

In summary, the conversation discusses how to prove that if all the eigenvalues of a linear transformation T: V --> V are zero, then T = 0. The conversation includes various strategies for proving this theorem, including using a basis where the coordinate representation of T is the zero matrix, and working directly with the diagonalized form of T. The conclusion is that the theorem is valid for finite-dimensional linear algebra and can be proven by showing that the coordinate representation is faithful.
  • #1
noospace
75
0
I want to prove that if all the eigenvalues of a linear transformation T : V --> V are zero, then T = 0. I think this is obvious but I'm having difficulty putting it into words.

If all the eigenvalues of T are zero, then there exists a basis B for V in which [itex][T]_B [/itex] is the zero matrix. Thus [itex][T(v)]_B=[T]^B_B[v]_B = 0\; \forall v \in V[/itex].

I think I should show that the matrix representation of T in an arbitrary basis D is zero. We have from second year linear algebra [itex][id]^B_D [T]^B_B [id]^D_B = [T]^D_D[/itex].
Thus

[itex][T(v)]_D = [T]^D_D [v]_D = [id]^B_D [T]^B_B [id]^D_B [v]_B = 0[v]_D = 0[/itex].

But [itex]v \in V[/itex] is abritrary so [itex][T]^D_D [v]_D = 0 \; \forall [v]_D \in \mathbb{F}^n[/itex] where [itex]\mathbb{F}[/itex] is the base field. Hence T = 0.

Is this correct?
 
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  • #2
noospace said:
I want to prove that if all the eigenvalues of a linear transformation T : V --> V are zero, then T = 0.
This is not a theorem! There are at least two ways this can fail:

This matrix over the reals has only one eigenvalue, 0. (It has 3 over the complexes, of course)
[tex]
\left(
\begin{array}{ccc}
0 & 0 & 0 \\
0 & 0 & -1 \\
0 & 1 & 0
\end{array}
\right)
[/tex]

This matrix over any field has only one eigenvalue, 0, of (algebraic) multiplicity 2.
[tex]
\left(
\begin{array}{cc}
0 & 1 \\
0 & 0
\end{array}
\right)
[/tex]
 
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  • #3
By the way, you don't have to deal with that "arbitrary basis" stuff.

The zero linear transformation from V to W is the linear transformation such that, for every v in V,
Tv = 0.​

If you can find a basis so that the coordinate representation of T is the zero matrix, then it's easy to show that T is the zero transformation.

(Recall that two functions f and g are equal if and only if f(x) = g(x) for every input x)
 
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  • #4
noospace said:
[itex][T]^D_D [v]_D = [id]^B_D [T]^B_B [id]^D_B [v]_B[/itex]
That doesn't look right: [itex][v]_D[/itex] and [itex][v]_B[/itex] are usually different.

Oh, but I think it's just a typo, because you switched back to [itex][v]_D[/itex] in the next expression.


You could have done the basis calculation in a shorter way:
[tex]
[T(v)]_D = [id]_D^B [T(v)]_B = [id]_D^B 0 = 0.
[/tex]
Other than your typo, I think you did the basis manipulations correctly.
 
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  • #5
Hi Hurkyl,

You're right, my theorem is a little bit too general.

Let T be an Hermitian linear operator.

Now I think it's okay.

And yes, that's a typo.

Btw, I don't see any other "easy" way to the proof based on what you said about equal functions.
 
  • #6
(caveat: this is only for finite-dimensional linear algebra. I'm not sure what generalizes properly to infinite dimensions)


That sounds fine; Hermitian matrices are diagonalizable over the reals, and I believe that is sufficient to prove your theorem. (BTW, you can do the proof by working directly with the diagonalized form!)
 
  • #7
noospace said:
Btw, I don't see any other "easy" way to the proof based on what you said about equal functions.

Starting from
For every v, [itex][T(v)]_B = 0 = [0]_B[/itex]​
it follows that
For every v, T(v) = 0
and thus
T = 0.​
 
  • #8
Yeah, but to get from assumption to therefore you need what I said right? Like l said it's obvious.
 
  • #9
noospace said:
Yeah, but to get from assumption to therefore you need what I said right? Like l said it's obvious.
It follows simply because the coordinate representation is faithful:
[tex]v = w \text{\ if and only if\ } [v]_B = [w]_B.[/tex]
Of course, your way is correct too.
 
  • #10
Hurkyl said:
(BTW, you can do the proof by working directly with the diagonalized form!)
*bonks self* I just realized this is exactly what you are doing when you said you could find a basis relative to which T's coordiante representation is the zero matrix!
 
  • #11
Right, like saying [itex][\cdot]_B: V \to \mathbb{F}^n[/itex] is injective. Thanks.
 

What does it mean for all eigenvalues to be zero?

When all eigenvalues of a matrix are equal to zero, it means that the matrix is singular and has no inverse. This means that the matrix cannot be inverted and therefore, all solutions to the associated system of equations are equal to zero.

What is the significance of a zero map?

A zero map is a linear transformation that maps all vectors in a vector space to the zero vector. This means that the output of the transformation is always zero, regardless of the input. The significance of a zero map lies in its ability to simplify calculations and proofs in linear algebra.

Can a matrix have all eigenvalues equal to zero?

Yes, a matrix can have all eigenvalues equal to zero. This is known as a singular matrix and it means that the matrix is not invertible. A singular matrix can also be referred to as a degenerate matrix.

How can you determine if a matrix has all eigenvalues equal to zero?

To determine if a matrix has all eigenvalues equal to zero, you can calculate the determinant of the matrix. If the determinant is equal to zero, then all eigenvalues of the matrix are also equal to zero. Alternatively, you can also calculate the trace of the matrix, which is the sum of all eigenvalues. If the trace is equal to zero, then all eigenvalues are also equal to zero.

What are some real-world applications of a zero map?

A zero map may be used in various fields such as physics, engineering, and computer science. In physics, a zero map can represent a state of equilibrium or no change. In engineering, a zero map can represent a system with no output or no response to input. In computer science, a zero map can be used to represent empty data structures or null pointers.

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