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dimension10
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I would like to inquire whether there has been any recent work on representing matrices in unit vector notation?
Thanks in advance!
Thanks in advance!
dimension10 said:I would like to inquire whether there has been any recent work on representing matrices in unit vector notation?
Thanks in advance!
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.
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?
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?
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]
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]
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]
dimension10 said:But isn't ##\mathbf{\hat{e}}_{nn}=\mathbf{\hat{e}}_n\wedge \mathbf{\hat{e}}_n=0##?
I don't know why but that made me grinI like Serena said:I presume you mean the outer product with your wedge.
dimension10 said:But isn't ##\mathbf{\hat{e}}_{nn}=\mathbf{\hat{e}}_n\wedge \mathbf{\hat{e}}_n=0##?
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.
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.
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.
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.
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.