Work on unit vector notation for matrices?

In summary, Artin uses this notation in first chapter of Algebra. So there you can find discussion and exercises, which I at least found challenging.
  • #1
dimension10
371
0
I would like to inquire whether there has been any recent work on representing matrices in unit vector notation?

Thanks in advance!
 
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  • #2
dimension10 said:
I would like to inquire whether there has been any recent work on representing matrices in unit vector notation?

Thanks in advance!

Hey dimension10.

I'm not exactly sure what you are getting at, but I'll throw a few comments in.

You can look at the level of how the matrix 'stretches' something in terms of the hyper-volume which is given by the determinant.

Matrices also have norms just like vectors, so you may want to look into this as well.

There are also a special group of matrices called the the orthogonal and special orthogonal group and if you make sure that the transpose equals the inverse, then you have what is called a rotation group which has determinant 1. These matrices preserve the distance of the point to the origin under all valid rotation transformations, so the length of the vector that is applied to the operator doesn't change it's length.
 
  • #3
chiro said:
Hey dimension10.

I'm not exactly sure what you are getting at, but I'll throw a few comments in.

You can look at the level of how the matrix 'stretches' something in terms of the hyper-volume which is given by the determinant.

Matrices also have norms just like vectors, so you may want to look into this as well.

There are also a special group of matrices called the the orthogonal and special orthogonal group and if you make sure that the transpose equals the inverse, then you have what is called a rotation group which has determinant 1. These matrices preserve the distance of the point to the origin under all valid rotation transformations, so the length of the vector that is applied to the operator doesn't change it's length.

Thanks for the info, but what I was really was asking is whether there is any way to represent a matrix as a linear or non-linear combination of unit vectors. For e.g.

[tex]\left[ \begin{array}{l}
a\\
b
\end{array} \right] = a{{\bf{\hat e}}_1} + b{{\bf{\hat e}}_2}[/tex]

I was wondering if there is any work on how to represent a matrix in a similar way?
 
  • #4
dimension10 said:
Thanks for the info, but what I was really was asking is whether there is any way to represent a matrix as a linear or non-linear combination of unit vectors. For e.g.

[tex]\left[ \begin{array}{l}
a\\
b
\end{array} \right] = a{{\bf{\hat e}}_1} + b{{\bf{\hat e}}_2}[/tex]

I was wondering if there is any work on how to represent a matrix in a similar way?

If the basis vectors are orthogonal, then is basically a rotation group in any dimension with determinant 1 where R_inverse = R^T (R transpose).

Also before I forget, if they are not orthonormal then find a transformation from cartesian co-ordinates to your basis (if it's a curved geometry use tensor theory), and then go from there.

Have you studied tensors?
 
  • #5
chiro said:
If the basis vectors are orthogonal, then is basically a rotation group in any dimension with determinant 1 where R_inverse = R^T (R transpose).

Also before I forget, if they are not orthonormal then find a transformation from cartesian co-ordinates to your basis (if it's a curved geometry use tensor theory), and then go from there.

Have you studied tensors?

Thanks, I got what you are saying. I know some basic tensor theory though not much.
 
  • #6
A linear operator is a function, not a vector. A particularly simple operator could be represented by [itex]\underline F(a)=(a\cdot f) g[/itex] for vectors [itex]a,f,g[/itex], but in general, they're more complicated.
 
  • #7
[tex]\left[ \begin{array}{l}
a & b\\
c & d
\end{array} \right] = a{{\bf{\hat e}}_{11}} + b{{\bf{\hat e}}_{12}} + c{{\bf{\hat e}}_{21}} + d{{\bf{\hat e}}_{22}}[/tex]
 
  • #8
I like Serena said:
[tex]\left[ \begin{array}{l}
a & b\\
c & d
\end{array} \right] = a{{\bf{\hat e}}_{11}} + b{{\bf{\hat e}}_{12}} + c{{\bf{\hat e}}_{21}} + d{{\bf{\hat e}}_{22}}[/tex]

Thanks.
 
  • #9
I like Serena said:
[tex]\left[ \begin{array}{l}
a & b\\
c & d
\end{array} \right] = a{{\bf{\hat e}}_{11}} + b{{\bf{\hat e}}_{12}} + c{{\bf{\hat e}}_{21}} + d{{\bf{\hat e}}_{22}}[/tex]

Artin uses this notation in first chapter of Algebra. So there you can find discussion and exercises, which I at least found challenging.

I was recently reading this in self-study, and I didn't think to relate this to idea of tensors and vectors, so I'm glad you brought this up to discussion.
 
  • #10
It might be helpful to read about Dyadic notation.

That Wikipedia article doesn't mention it, but the same idea can be written using Dirac notation for outer products. If
##
\{ |1 \rangle, \ldots, | N \rangle \}
##
is an orthonormal basis for an ##N## dimensional space, then any linear operator on that space can be written

##
\quad a_{11} | 1 \rangle \langle 1 | + a_{12} | 1 \rangle \langle 2 | + \cdots + a_{1N} | 1 \rangle \langle N |
##
##
+ a_{21} | 2 \rangle \langle 1 | + a_{22} | 2 \rangle \langle 2 | + \cdots + a_{2N} | 2 \rangle \langle N |
##
##
+ \cdots
##
##
+ a_{N1} | N \rangle \langle 1 | + a_{N2} | N \rangle \langle 2 | + \cdots + a_{NN} | N \rangle \langle N |
##

In the given basis, this is the operator represented by the matrix
##
A =
\left[\begin{array}{cccc}
a_{11} & a_{12} & \cdots & a_{1N} \\
a_{21} & a_{22} & \cdots & a_{2N} \\
\cdots \\
a_{N1} & a_{N2} & \cdots & a_{NN} \\
\end{array}\right]
##
 
  • #11
I like Serena said:
[tex]\left[ \begin{array}{l}
a & b\\
c & d
\end{array} \right] = a{{\bf{\hat e}}_{11}} + b{{\bf{\hat e}}_{12}} + c{{\bf{\hat e}}_{21}} + d{{\bf{\hat e}}_{22}}[/tex]

But isn't ##\mathbf{\hat{e}}_{nn}=\mathbf{\hat{e}}_n\wedge \mathbf{\hat{e}}_n=0##?
 
  • #12
dimension10 said:
But isn't ##\mathbf{\hat{e}}_{nn}=\mathbf{\hat{e}}_n\wedge \mathbf{\hat{e}}_n=0##?

Hmm, I presume you mean the outer product with your wedge.
But the result of an outer product would be a vector, not a matrix.
So no, that is not ##\mathbf{\hat{e}}_{nn}##.

##\mathbf{\hat{e}}_{ij}## is defined as the matrix with a 1 on position (i,j) and 0 everywhere else.
 
  • #13
Why would you want to represent matrices in vector notation anyway?

I like Serena said:
I presume you mean the outer product with your wedge.
I don't know why but that made me grin :redface:
 
  • #14
dimension10 said:
But isn't ##\mathbf{\hat{e}}_{nn}=\mathbf{\hat{e}}_n\wedge \mathbf{\hat{e}}_n=0##?

As I Like Serena says, in this context it's meant more like a dyad than as a multivector. I think I've seen that notation used for wedge products of unit vectors, too, though. I've also seen it used for the geometric product, in which case [itex]e_{nn} = 1[/itex].
 
  • #15
Ok, thanks everyone. I got it clear now. I was asking because I had submitted a paper to a journal about representing matrices in unit vector notation and it was accepted (yay! my 1st publication) and they had given me a comment to add more references and thus I was asking about that. In the end, I ignored the comment but at least I did learn something!
 

Related to Work on unit vector notation for matrices?

1. What is a unit vector notation for matrices?

A unit vector notation for matrices is a way to represent matrices using vectors that have a magnitude of 1. This notation is useful for simplifying calculations and understanding the geometric properties of matrices.

2. How do you convert a matrix into unit vector notation?

To convert a matrix into unit vector notation, you first need to find the magnitude of each column vector in the matrix. Then, divide each column vector by its magnitude to create a unit vector. Finally, arrange these unit vectors as columns to create the unit vector notation for the matrix.

3. Can unit vector notation be used for any type of matrix?

Yes, unit vector notation can be used for any type of matrix, whether it is a square matrix, rectangular matrix, or even a complex matrix. However, it is most commonly used for square matrices.

4. What are the advantages of using unit vector notation for matrices?

Unit vector notation has several advantages, including simplifying calculations, providing insights into the geometric properties of matrices, and making it easier to visualize and understand matrix operations.

5. How is unit vector notation related to vector spaces?

Unit vector notation is closely related to vector spaces, as it allows us to represent matrices as a combination of basis vectors. This is similar to how vector spaces are defined, with vectors being combined using scalar multiplication and addition to form a linear combination.

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