Question about computing Jacobians of transformations

In summary, there is a fast way to calculate the determinant jacobian for the given transformation without having to deal with the whole 3x3 determinant. By using a slight generalization and considering the denominator as a function, the determinant can be simplified to be the reciprocal of the sixth power of the radius of the sphere. The inverse transformation can also be used, but the determinant will not be equal to 1.
  • #1
Boorglar
210
10
Suppose I have the following transformation:

[tex]
u = \frac{x}{x^2+y^2+z^2}
[/tex]
[tex]
v = \frac{y}{x^2+y^2+z^2}
[/tex]
[tex]
w = \frac{z}{x^2+y^2+z^2}
[/tex]

Is there a fast way to calculate the determinant jacobian without having to deal with the whole 3x3 determinant?

I noticed that the inverse transformation is the same (switching x,y,z with u,v,w gives the equality again) but the determinant is not 1, so I don't really know if this can help.

Or would I really have to do it the long and boring way?
 
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  • #2
Your inverse is wrong.

Sure there is a fast way, consider the slight generalization where the denominator is a function R so your case is R=x^2+y^2+z^2

then we have

[tex]J=
\left|
\begin{array}{ccc}
u_x & u_y & u_z \\
v_x & v_y & v_z \\
w_x & w_y & w_z
\end{array} \right|
=\frac{1}{R^3}
\left|
\begin{array}{ccc}
1 - x R_x/R & -x R_y/R & -x R_z/R \\
-y R_x/R & 1 - y R_y/R & -y R_z/R \\
-z R_x/R & -z R_y/R & 1 - z R_z/R
\end{array} \right|
=\frac{1-(x R_x+y R_y+z R_z)/R}{R^3}[/tex]

The determinant is closely related to

[tex]\left|
\begin{array}{ccc}
a-U & -V & -W \\
-U & b-V & -W \\
-U & -V & c-W
\end{array} \right| =abc-bc U-ac V-abW[/tex]
 
Last edited:
  • #3
^The inverse is right.
 
  • #4
This function just divides each vector by the square of its length. A small cube alligned so that its base is tangent to a sphere centered at the origin will be shrunk in volume by a factor of the sixth power of the radius of the sphere. So the magnitude of the determinant is the reciprocal of the sixth power of the radius of the sphere.
 

1. What is a Jacobian in computing?

A Jacobian is a mathematical concept that is commonly used in computing to represent the linear transformation between two coordinate systems. It is a matrix of partial derivatives that provides information about how a function or transformation changes in response to changes in its input variables.

2. How is a Jacobian calculated in computing?

The Jacobian of a transformation can be calculated by taking the partial derivatives of the output variables with respect to the input variables and organizing them into a matrix. This matrix can then be used to represent the transformation and its effects on the input variables.

3. Why is the Jacobian important in computing?

The Jacobian is important because it provides a way to represent and understand how a transformation or function changes in response to changes in its input variables. This can be useful in many applications, such as optimization, machine learning, and computer graphics.

4. Can the Jacobian be used for non-linear transformations?

Yes, the Jacobian can be used to represent non-linear transformations as well. However, in these cases, the Jacobian is not a constant matrix and may vary at different points in the transformation. This is known as the generalized Jacobian.

5. Are there any limitations to using the Jacobian in computing?

One limitation of the Jacobian is that it only provides information about the local behavior of a transformation and does not account for global effects. Additionally, calculating the Jacobian can be computationally expensive for complex transformations, making it less practical for some applications.

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