Understanding the Inverse of Jacobian Matrices: A Brief Overview

In summary, the conversation discusses the use of partial differentiation to find 2X2 matrices A and B. The matrices are used to find the partial derivatives of x and y with respect to u and v, and the relation between these derivatives is discussed. The conversation concludes with an example of how this relation can be applied to find specific components of the matrices.
  • #1
parsesnip
9
0
Homework Statement
I want to prove that ##\left| \frac{\partial(x,y)}{\partial(u,v)} \right|=\frac{1}{\left|\frac{\partial(u,v)}{\partial(x,y)}\right|}## (If this is true)
Relevant Equations
##\frac{\partial(x,y)}{\partial(u,v)}=\begin{vmatrix} x_u & x_v\\ y_u&y_v \end {vmatrix}##
##\frac{\partial(u,v)}{\partial(x,y)}=\begin{vmatrix} u_x & u_y\\ v_x&v_y \end {vmatrix}##
I got that ##{x_u}{y_v}-{x_y}{y_u}=####\frac{1}{\frac{1}{{x_u}{y_v}}-\frac{1}{{y_u}{x_v}}}##. But this implies that ##{x_u}{x_v}{y_u}{y_v}=-1## and I don't see how that is true?
 
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  • #2
Very simple case of u=x, v=y obviously the statement stands. but
[tex]x_u x_v y_u y_v = 1*0*0*1=0[/tex]
So your result seems wrong.

Find 2X2 matrices A, B by partial differentiation
[tex]
\begin{pmatrix}
du \\
dv \\
\end{pmatrix}

= A

\begin{pmatrix}
dx \\
dy \\
\end{pmatrix}[/tex]

[tex]
\begin{pmatrix}
dx \\
dy \\
\end{pmatrix}

= B

\begin{pmatrix}
du \\
dv \\
\end{pmatrix}[/tex]

So
[tex]AB=BA=E[/tex]
[tex]det\ A\ \ det\ B = 1[/tex]
 
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  • #3
By the chain rule [tex]
1 = \frac{\partial x}{\partial x} = \frac{\partial x}{\partial u}\frac{\partial u}{\partial x} + \frac{\partial x}{\partial v}\frac{\partial v}{\partial x}[/tex] and [tex]
0 = \frac{\partial y}{\partial x} = \frac{\partial y}{\partial u}\frac{\partial u}{\partial x} + \frac{\partial y}{\partial v}\frac{\partial v}{\partial x}[/tex] and similarly [itex]\frac{\partial y}{\partial y} = 1[/itex] and [itex]\frac{\partial x}{\partial y} = 0[/itex]. Now I suspect that if you expand [tex]
1 = \frac{\partial x}{\partial x}\frac{\partial y}{\partial y} - \frac{\partial y}{\partial x}\frac{\partial x}{\partial y}[/tex] and rearrange it then you will obtain your result.
 
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  • #4
pasmith said:
By the chain rule [tex]
1 = \frac{\partial x}{\partial x} = \frac{\partial x}{\partial u}\frac{\partial u}{\partial x} + \frac{\partial x}{\partial v}\frac{\partial v}{\partial x}[/tex] and [tex]
0 = \frac{\partial y}{\partial x} = \frac{\partial y}{\partial u}\frac{\partial u}{\partial x} + \frac{\partial y}{\partial v}\frac{\partial v}{\partial x}[/tex] and similarly [itex]\frac{\partial y}{\partial y} = 1[/itex] and [itex]\frac{\partial x}{\partial y} = 0[/itex]. Now I suspect that if you expand [tex]
1 = \frac{\partial x}{\partial x}\frac{\partial y}{\partial y} - \frac{\partial y}{\partial x}\frac{\partial x}{\partial y}[/tex] and rearrange it then you will obtain your result.
I think I understand. But if ##\frac {\partial x}{\partial u}=\frac{1}{\frac {\partial u}{\partial x}}##, then doesn't ##\frac {\partial x}{\partial u}\frac {\partial u}{\partial x}=1##? Then how do you resolve the contradiction that ##1 = \frac{\partial x}{\partial x} = \frac{\partial x}{\partial u}\frac{\partial u}{\partial x} + \frac{\partial x}{\partial v}\frac{\partial v}{\partial x} = 2##?
 
  • #5
parsesnip said:
I think I understand. But if ##\frac {\partial x}{\partial u}=\frac{1}{\frac {\partial u}{\partial x}}##, then doesn't ##\frac {\partial x}{\partial u}\frac {\partial u}{\partial x}=1##? Then how do you resolve the contradiction that ##1 = \frac{\partial x}{\partial x} = \frac{\partial x}{\partial u}\frac{\partial u}{\partial x} + \frac{\partial x}{\partial v}\frac{\partial v}{\partial x} = 2##?

The relation [tex]\frac{\partial x}{\partial u} = \left(\frac{\partial u}{\partial x}\right)^{-1}[/tex] does not hold in general. For example, with plane polar coordinates we have [tex]\frac{\partial r}{\partial x} = \frac xr[/tex] and [tex]\frac{\partial x}{\partial r} = \cos \theta = \frac xr \neq \frac rx.[/tex]

Rather, if that relation does hold then it implies that [itex]x[/itex] is independent of [itex]v[/itex] so that [itex]\frac{\partial x}{\partial v} = 0[/itex].
 
  • #6
Further to my post #2

[tex]A=
\begin{pmatrix}
u_x & u_y \\
v_x & v_y \\
\end{pmatrix}[/tex]
[tex]B=
\begin{pmatrix}
x_u & x_v \\
y_u & y_v \\
\end{pmatrix}[/tex]
[tex]AB=
\begin{pmatrix}
u_x & u_y \\
v_x & v_y \\
\end{pmatrix}
\begin{pmatrix}
x_u & x_v \\
y_u & y_v \\
\end{pmatrix}
=
\begin{pmatrix}
1 & 0 \\
0 & 1 \\
\end{pmatrix}[/tex]

It meets with post #3.

[tex]A=B^{-1}[/tex]
[tex]
\begin{pmatrix}
u_x & u_y \\
v_x & v_y \\
\end{pmatrix}
=\frac{1}{x_uy_v-x_vy_u}
\begin{pmatrix}
y_v & -x_v \\
-y_u & x_u \\
\end{pmatrix}[/tex]

For an example (1,1) component says

[tex]u_x=\frac{1}{x_uy_v-x_vy_u}y_v[/tex]

You see in order ##u_x =\frac{1}{x_u}## as you expect, ##x_vy_u=0 ## and ##y_v \neq 0## is required.
 
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Related to Understanding the Inverse of Jacobian Matrices: A Brief Overview

1. What is the inverse of a Jacobian matrix?

The inverse of a Jacobian matrix is a matrix that, when multiplied with the original Jacobian matrix, results in the identity matrix. In other words, it is a matrix that "undoes" the effects of the Jacobian matrix.

2. Why is the inverse of a Jacobian matrix important?

The inverse of a Jacobian matrix is important because it allows for the calculation of partial derivatives in multidimensional systems. It is also used in solving systems of differential equations and in optimization problems.

3. How is the inverse of a Jacobian matrix calculated?

The inverse of a Jacobian matrix can be calculated using various methods, such as Gaussian elimination or LU decomposition. In some cases, it can also be calculated analytically by taking the inverse of each element in the matrix.

4. What is the relationship between the inverse of a Jacobian matrix and the determinant?

The determinant of a Jacobian matrix is related to its inverse through the following formula: det(J)^-1 = det(J^-1). This means that the inverse of a Jacobian matrix has the same determinant as the original matrix, but its value is inverted.

5. Can the inverse of a Jacobian matrix always be calculated?

No, the inverse of a Jacobian matrix can only be calculated if the matrix is invertible, which means that its determinant is non-zero. If the determinant is zero, the matrix is singular and its inverse does not exist.

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