3x3 Diagonalizable Matrices over GF(2)

In summary, there are 8 possible diagonalizable 3x3 matrices over GF(2), with entries being 0's and 1's. However, since the ordering of eigenvalues is not relevant, that leaves only 4 diagonalizable matrices. Additionally, according to the spectral theorem, a real matrix is diagonalizable if and only if it is symmetric, but this is not necessarily true for matrices over GF(2). Therefore, there may be more than 8 diagonalizable matrices.
  • #1
ArcanaNoir
779
4
Does anyone know where I can find or how I can compute (without checking all 512) the 8 diagonalizable 3x3 matrices over GF(2)? GF(2) means the entries are 0's and 1's. I'm working on some graph polynomial research and to check out a formula I'm working with I would have to take a sum over these 8 little guys but I don't know what they are. I'm not too up on my linear algebra, but I believe diagonalizable implies there will be 3 distinct eignenvalues. Not sure if that helps narrow the possible forms of the matrices down. Is there some way I can cut the possibilities down from 512 to something I could more reasonably check by hand? Or some maple code? Thanks for any help :)
 
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  • #2
ArcanaNoir said:
Does anyone know where I can find or how I can compute (without checking all 512) the 8 diagonalizable 3x3 matrices over GF(2)? GF(2) means the entries are 0's and 1's. I'm working on some graph polynomial research and to check out a formula I'm working with I would have to take a sum over these 8 little guys but I don't know what they are. I'm not too up on my linear algebra, but I believe diagonalizable implies there will be 3 distinct eignenvalues. Not sure if that helps narrow the possible forms of the matrices down. Is there some way I can cut the possibilities down from 512 to something I could more reasonably check by hand? Or some maple code? Thanks for any help :)

Hey Arcana!

A diagonal matrix has only non-zero entries on its main diagonal.
The main diagonal has 3 entries, each of which can either be 0 or 1.
That makes 8 possible diagonal matrices.
If the ordering of eigenvalues is not relevant, that leaves 4 diagonal matrices.
Note that the eigenvalues cannot be distinct, since you have 3 entries on the diagonal but only 2 possible eigenvalues.

According to the spectral theorem, a real matrix is diagonalizable if and only if it is symmetric.
I think it also holds for matrices over the field GF(2).
That means that you have a free choice for the upper triangular matrix for 2^6=64 choices. After that the remaining part of the lower triangle is fixed.
 
  • #3
I like Serena said:
According to the spectral theorem, a real matrix is diagonalizable if and only if it is symmetric.

No, this is false. There are plenty of matrices which are diagonalizable but not symmetric.
 
  • #4
micromass said:
No, this is false. There are plenty of matrices which are diagonalizable but not symmetric.

From the link I provided:
"Another way to phrase the spectral theorem is that a real n×n matrix A is symmetric if and only if there is an orthonormal basis of consisting of eigenvectors for A."
 
  • #5
I like Serena said:
From the link I provided:
"Another way to phrase the spectral theorem is that a real n×n matrix A is symmetric if and only if there is an orthonormal basis of consisting of eigenvectors for A."

A diagonalizable matrix need not have an orthonormal basis of eigenvectors.
 
  • #6
micromass said:
A diagonalizable matrix need not have an orthonormal basis of eigenvectors.

Good point. I'll take the time to digest that.
 
  • #7
There are definitely more than 8 diagonalizable matrices because there are 8 diagonal matrices. Because you're in F(2) the only eigenvalues are 0 or 1 - this is nice because it means that all diagonal matrices are projections (and hence all diagonalizable matrices are projections as well, and vice versa of course). This means that P is diagonalizable if and only if P = P2 which gives a ton of algebraic relations - it might be possible to solve directly but you only have 512 matrices so you could write some code to output all matrices such that P = P2 pretty fast if you know how to program

I Like Serena: All you need is a basis of eigenvectors, doesn't matter if they're orthogonal to each other.
 
  • #8
The spectral theorem states that a matrix is orthogonally diagonalizable (i.e. has an orthogonal eigenbasis) with real eigenvalues if and only if it is self-adjoint. However as noted above, there of course exist diagonalizable matrices whose eigenbasis is not orthogonal and whose eigenvalues are not all real.

Also, a matrix doesn't have to have distinct eigenvalues to be diagonalizable. The converse is true of course but the original statement is false (take the identity matrix for example). All you need is for the geometric multiplicities of the eigenspaces to add up to the dimension of the original space.
 
  • #9
So I just realized I have a bit of a disconnect with the notation in the OP. Do you want diaogonalizable matrices, or diagonalizable invertible matrices? Because there is only one of the latter
 
  • #10
I like Serena said:
Over GF(2) all 3x3 matrices that are invertible have determinant 1.
So all invertible matrices are orthogonal.

I'm not seeing the connection between orthogonal and having determinant 1.

Therefore each basis of independent eigenvectors has an associated matrix that is orthogonal.

How would you define "orthogonal" in ##\mathbb{F}_2## anyway. There is no inner product on that space. At most, there is a symmetric bilinear form.

[tex]\left(\begin{array} 1 & 0 & 1\\ 0 & 1 & 0\\ 1 & 0 & 1\end{array}\right)[/tex]
It means that in this particular case, each diagonalizable matrix is symmetric.

Counterexample:

[tex]\left(\begin{array} 1 & 0 & 1\\ 0 & 1 & 0\\ 0 & 0 & 0\end{array}\right)[/tex]
 
  • #11
I had already deleted my post.
Still, you make good points.


So I wrote a program to count the invertible matrices, and to count the diagonalizable matrices.
I got the surprising result that there are 168 invertible matrices and 58 diagonalizable matrices.

It turns out that for instance the following symmetric matrix is not diagonalizable:
$$\begin{bmatrix}0&1&0 \\ 1&0&0 \\0&0&0\end{bmatrix}$$
 
  • #12
Office_Shredder said:
So I just realized I have a bit of a disconnect with the notation in the OP. Do you want diaogonalizable matrices, or diagonalizable invertible matrices? Because there is only one of the latter

They need not be invertible.

I like Serena said:
Hey Arcana!

A diagonal matrix has only non-zero entries on its main diagonal.
The main diagonal has 3 entries, each of which can either be 0 or 1.
That makes 8 possible diagonal matrices.

I'm confused about this, first you say the main diagonal can only have non-zero entries, but then you say the main diagonal can have 0's or 1's. Also, diagonalizable is not the same as diagonal, right?
 
  • #13
I like Serena said:
I had already deleted my post.
Still, you make good points.


So I wrote a program to count the invertible matrices, and to count the diagonalizable matrices.
I got the surprising result that there are 168 invertible matrices and 58 diagonalizable matrices.

It turns out that for instance the following symmetric matrix is not diagonalizable:
$$\begin{bmatrix}0&1&0 \\ 1&0&0 \\0&0&0\end{bmatrix}$$

58? Crap! They must have started at n=0... :(:(:( Well in that case there is no way I'm going to do this sum over 58 matrices.
 
  • #14
ArcanaNoir said:
I'm confused about this, first you say the main diagonal can only have non-zero entries, but then you say the main diagonal can have 0's or 1's. Also, diagonalizable is not the same as diagonal, right?

Sorry for the confusion.
A diagonal matrix can have zeroes on its diagonal.
It's just that all other entries have to be zero.

And yes, diagonalizable is not the same as diagonal.
 
  • #15
I just checked the paper again and it is diagonal matrices, not diagonalizable. The world is saved again :)
 
  • #16
And thanks to everybody for all your input :)
 
  • #17
ArcanaNoir said:
I just checked the paper again and it is diagonal matrices, not diagonalizable. The world is saved again :)

ArcanaNoir said:
58? Crap! They must have started at n=0... :(:(:( Well in that case there is no way I'm going to do this sum over 58 matrices.

Ah well, just for the record, the sum of those 58 matrices is the identity matrix! :smile:
 
  • #18
Oh that's interesting :) I don't suppose you've seen my post in the precalc forum about an associative function... *my hero* ;)
 
  • #19
ArcanaNoir said:
Oh that's interesting :) I don't suppose you've seen my post in the precalc forum about an associative function... *my hero* ;)

Yeah, I saw it... but I do not consider it precalc. :wink:
With my first couple of attempts I couldn't prove associativity, although I could verify with a couple of examples that it does seem to be associative.
So for now I decided not to add to the zero-count of the thread.
Perhaps someone else is more creative than I am.
 
  • #20
I like Serena said:
Yeah, I saw it... but I do not consider it precalc. :wink:

Do you mean I'm allowed to post in the big-people sections now?
 
  • #21
I would say the post is precalculus, since it's just an algebraic manipulation.
 
  • #22
ArcanaNoir said:
Do you mean I'm allowed to post in the big-people sections now?

Dunno.
Until now no one has corrected me for posting in the big-people section.
So I guess that makes it okay.

As for your associativity problem, it seems to me that mere algebraic manipulation is not going to cut it.
Moreover, I'd consider associativity problems part of abstract algebra.
 
  • #23
I like Serena said:
As for your associativity problem, it seems to me that mere algebraic manipulation is not going to cut it.

It will. But it's tedious.
 
  • #24
micromass said:
I would say the post is precalculus, since it's just an algebraic manipulation.

That's what I thought too.

I like Serena said:
Moreover, I'd consider associativity problems part of abstract algebra.

It is, it's actually a group theory thing I'm working on.

micromass said:
It will. But it's tedious.

It will? Are you sure? Any hints would be greatly appreciated.
 
  • #25
micromass said:
It will. But it's tedious.

It appears to turn into a higher order polynomial expression.
It may not be possible to solve that.
At any rate, Wolfram doesn't.
 
  • #26
I like Serena said:
It appears to turn into a higher order polynomial expression.
It may not be possible to solve that.
At any rate, Wolfram doesn't.

Neither will maple. Although maple sucks at algebraic manipulation.
 
  • #27
I just noticed that this sub forum for abstract algebra is now moderated by Mark44 and Fredrik.
... but neither of those two is high up into abstract algebra.
It seems micromass has left some sort of vacuum here...
 
  • #28
Mark44 and Fredrik certainly know their abstract algebra. Mark44 has a masters in math (and maybe more), so he's more than capable. And I've seen Fredrik make some very good abstract algebra posts.

Besides, you don't need to be an expert in a subject to be able to moderate something well. I'm certainly no expert in abstract algebra related things, but I did feel like I had no trouble moderating it.
 
  • #29
micromass said:
Mark44 and Fredrik certainly know their abstract algebra. Mark44 has a masters in math (and maybe more), so he's more than capable. And I've seen Fredrik make some very good abstract algebra posts.

Besides, you don't need to be an expert in a subject to be able to moderate something well. I'm certainly no expert in abstract algebra related things, but I did feel like I had no trouble moderating it.

I'm sorry.
I didn't mean to criticize you or Mark44 or Fredrik.
My remark was more intended as a compliment to you, since I do consider you to be high up.
 
  • #30
I like Serena said:
I just noticed that this sub forum for abstract algebra is now moderated by Mark44 and Fredrik.
... but neither of those two is high up into abstract algebra.
It seems micromass has left some sort of vacuum here...

micromass said:
Mark44 and Fredrik certainly know their abstract algebra. Mark44 has a masters in math (and maybe more), so he's more than capable. And I've seen Fredrik make some very good abstract algebra posts.

Besides, you don't need to be an expert in a subject to be able to moderate something well. I'm certainly no expert in abstract algebra related things, but I did feel like I had no trouble moderating it.

Yeah I don't think who the moderator is makes much difference, it seems like everyone tries to answer whatever they can. It's not like only moderators answer questions and only in their own forums. Besides, I didn't even put it in the algebra forum. As micro said, it feels like I might be able to solve it with "the math we used to call algebra before we learned about abstract algebra and which now seems to lack an appropriate designation".
 
  • #31
ArcanaNoir said:
They need not be invertible.



I'm confused about this, first you say the main diagonal can only have non-zero entries, but then you say the main diagonal can have 0's or 1's. Also, diagonalizable is not the same as diagonal, right?
IlikeSerena made a slight grammatical error.

He/she said "the main diagonal can only have non-zero entries". What he/she meant to say was "only the main diagonal can have non-zero entries". The position of the word "only" is crucial!
 
  • #32
HallsofIvy said:
IlikeSerena made a slight grammatical error.

He/she said "the main diagonal can only have non-zero entries". What he/she meant to say was "only the main diagonal can have non-zero entries". The position of the word "only" is crucial!

thanks for the clarification :)
 

1. What is a 3x3 diagonalizable matrix over GF(2)?

A 3x3 diagonalizable matrix over GF(2) is a square matrix with dimensions of 3 rows and 3 columns, where all the entries are elements of the Galois field GF(2). This means that all the elements in the matrix can only take on values of 0 or 1.

2. How do you determine if a 3x3 matrix is diagonalizable over GF(2)?

A 3x3 matrix is diagonalizable over GF(2) if it can be transformed into a diagonal matrix through a similarity transformation. This means that the matrix must have 3 linearly independent eigenvectors, which can be used to form the diagonal matrix.

3. What is the significance of diagonalizable matrices over GF(2)?

Diagonalizable matrices over GF(2) have important applications in fields such as coding theory and cryptography. They can be used to efficiently encode and decode messages, and also play a role in error correction algorithms.

4. Can a 3x3 matrix over GF(2) be diagonalizable if it has repeated eigenvalues?

Yes, a 3x3 matrix over GF(2) can still be diagonalizable even if it has repeated eigenvalues. As long as the matrix has 3 linearly independent eigenvectors, it can be transformed into a diagonal matrix through a similarity transformation.

5. How do you find the eigenvalues and eigenvectors of a 3x3 diagonalizable matrix over GF(2)?

To find the eigenvalues, you can solve the characteristic equation det(A-λI) = 0, where A is the matrix and λ is the eigenvalue. To find the corresponding eigenvectors, you can solve the equation (A-λI)x = 0, where x is the eigenvector. Repeat this process for each eigenvalue to find all 3 eigenvectors.

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