Jacobian Matrix for Polar Coordinates

In summary, the conversation discussed the Jacobian Matrix for (x,y)T and (r, θ)T and how it can be derived using the equations x = r.cos(θ), y = r.sin(θ), r = x2 + y2, and theta = arctan(y/x). The correct form of the Jacobian matrix for (r, θ)T was provided and the mistake in the original equation of r = x^2 + y^2 was pointed out.
  • #1
adamwitt
25
0
Hi,

I need some help understanding the solution to a problem.

Equations:

x = r.cos(θ)
y = r.sin(θ)

r = x2 + y2
theta = arctan(y/x)Question:
Determine the Jacobian Matrix for (x,y)T and for (r, θ)T

SOLUTION:

I understand and can compute by myself the Jacobian for (x,y)T, but the solution to for J(r, θ) i don't understand.

J(r,θ) = ( (@r/@x, @r/@y) , (@θ/@x, @θ,@y) )T

Ok so that makes sense...

Then they gave me this...

(@r/@x, @r/@y) =( x / sqrt( x2 + y2 ) , y / sqrt(x2 + y2) )

Why isn't it just 2x and 2y respectively? why does it resemble something similar to a magnitude?

thanks for your help.
 
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  • #2
No, a "Jacobian" is not at all what you say. The Jacobian of a transformation is a matrix but what you wrote has only two components. If [itex]x= f(r,\theta)[/itex], [itex]y= g(r\theta)[/itex] then the Jacobian is
[tex]\frac{\partial x, y}{\partial r,\theta}= \begin{bmatrix}\frac{\partial x}{\partial r} & \frac{\partial x}{\partial \theta} \\ \frac{\partial y}{\partial r} & \frac{\partial y}{\partial \theta}\end{bmatrix}[/tex]

In this case, that would be
[tex]\begin{bmatrix}cos(\theta) & sin(\theta) \\ r sin(\theta) & r cos(\theta)\end{bmatrix}[/tex]

The Jacobian the other way would be
[tex]\begin{bmatrix}\frac{\partial r}{\partial x} & \frac{\partial \theta}{\partial x} \\ \frac{\partial r}{\partial y} & \frac{\partial \theta}{\partial y}\end{bmatrix}[/tex]
Here, we have [itex]r= (x^2+ y^2)^{1/2}[/itex] and [itex]\theta= tan^{-1}(y/x)[/itex] so that the Jacobian is
[tex]\begin{bmatrix}cos(\theta) & sin(\theta) \\ -\frac{1}{r} sin(\theta) & \frac{1}{r} cos(\theta)\end{bmatrix}[/tex]
which is, of course, the inverse matrix to the previous matrix.
 
  • #3
HallsofIvy said:
No, a "Jacobian" is not at all what you say. The Jacobian of a transformation is a matrix but what you wrote has only two components. If [itex]x= f(r,\theta)[/itex], [itex]y= g(r\theta)[/itex] then the Jacobian is
[tex]\frac{\partial x, y}{\partial r,\theta}= \begin{bmatrix}\frac{\partial x}{\partial r} & \frac{\partial x}{\partial \theta} \\ \frac{\partial y}{\partial r} & \frac{\partial y}{\partial \theta}\end{bmatrix}[/tex]

In this case, that would be
[tex]\begin{bmatrix}cos(\theta) & sin(\theta) \\ r sin(\theta) & r cos(\theta)\end{bmatrix}[/tex]

The Jacobian the other way would be
[tex]\begin{bmatrix}\frac{\partial r}{\partial x} & \frac{\partial \theta}{\partial x} \\ \frac{\partial r}{\partial y} & \frac{\partial \theta}{\partial y}\end{bmatrix}[/tex]
Here, we have [itex]r= (x^2+ y^2)^{1/2}[/itex] and [itex]\theta= tan^{-1}(y/x)[/itex] so that the Jacobian is
[tex]\begin{bmatrix}cos(\theta) & sin(\theta) \\ -\frac{1}{r} sin(\theta) & \frac{1}{r} cos(\theta)\end{bmatrix}[/tex]
which is, of course, the inverse matrix to the previous matrix.


Ok, so my understanding of Jacobian Matrix is how you have described. I've got no idea how to post matrices etc on here so my descriptions above may have been a bit off, but I'll try suss it out for this post...

Ok so, my understanding is fine all the way up until you talk about the Jacobian "the other way around"

Why isn't it the case that...

[tex]\frac{\partial r, \theta}{\partial x, y}= \begin{bmatrix}\frac{\partial r}{\partial x} & \frac{\partial r}{\partial y} \\ \frac{\partial \theta}{\partial x} & \frac{\partial \theta}{\partial y}\end{bmatrix}[/tex]

?



Just getting back to my original question, and I'm sorry if I have missed a larger point you were trying to make, but I'm still trying to work out how


[tex]\frac{\partial r}{\partial x}= [/tex] x / √(x2 + y2)


Since the original equation is...

r = x2 + y2

I would have expected dr/dx = 2x


Thanks very much for your help
My answers for the lower half of the matrix match the solutions given, just not the top half.
 
  • #4
[itex]r = \sqrt{x^2 + y^2}[/itex], not [itex]x^2 + y^2[/itex].
 
  • #5
Muphrid said:
[itex]r = \sqrt{x^2 + y^2}[/itex], not [itex]x^2 + y^2[/itex].

Yes, that makes much more sense. But I've been given r = x^2 + y^2... must have been a mistake in the question itself.


Thanks for your help guys.
 
  • #6
the first row of inverse Jacobian matrix should be <cosθ , -sinθ/r>
and the second row is <sinθ, cosθ/r>.
 

Related to Jacobian Matrix for Polar Coordinates

What is the Jacobian matrix for polar coordinates?

The Jacobian matrix for polar coordinates is a 2x2 matrix that relates the partial derivatives of a function in Cartesian coordinates to the partial derivatives in polar coordinates. It is given by J(r,θ) = [∂x/∂r ∂x/∂θ; ∂y/∂r ∂y/∂θ], where x and y are the Cartesian coordinates and r and θ are the polar coordinates.

How is the Jacobian matrix used in polar coordinates?

The Jacobian matrix is used to convert integrals and differential equations from Cartesian coordinates to polar coordinates. It allows for a simpler representation of certain functions and makes it easier to solve certain problems in polar coordinates.

How is the Jacobian matrix calculated?

The Jacobian matrix is calculated by taking the partial derivatives of the Cartesian coordinates with respect to the polar coordinates. For example, ∂x/∂r is calculated by taking the derivative of x with respect to r, while holding θ constant.

What is the purpose of the Jacobian matrix in polar coordinates?

The purpose of the Jacobian matrix is to provide a mathematical tool for converting between different coordinate systems. In polar coordinates, it allows for a more efficient representation of certain functions and simplifies the process of solving certain problems.

Why is the Jacobian matrix important in multivariable calculus?

The Jacobian matrix is important in multivariable calculus because it allows for the conversion of integrals and differential equations in different coordinate systems. This is essential for solving problems in physics, engineering, and other fields that involve multiple variables and coordinate systems.

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