Understanding the Proof of the Jacobian

In summary, the conversation discusses the idea of the proof of the Jacobian and how the surface is parametrized by the vector \vec T(u,v). The image of a small rectangle in the domain can be approximated by the parallelogram formed by two vectors, \vec T(u_0+\Delta u,v_0)-\vec T(u_0,v_0) and \vec T(u_,v_0+\Delta v)-\vec T(u_0,v_0). These vectors are in turn approximated by \frac{\partial}{\partial u}\vec T(u_0,v_0)\Delta u and \frac{\partial}{\partial v}\vec T(u_0,v_0)\Delta v
  • #1
leospyder
9
0
Can someone explain to me this part of the proof of the jacobian?

Idea of the Proof

As usual, we cut S up into tiny rectangles so that the image under T of each rectangle is a parallelogram.



We need to find the area of the parallelogram. Considering differentials, we have

T(u + Du,v) @ T(u,v) + (xuDu,yuDu)

T(u,v + Dv) @ T(u,v) + (xvDv,yvDv)


Thus the two vectors that make the parallelogram are

P = guDu i + huDu j

Q = gvDv i + hvDv j

I don't know what they're talking about...I can follow the rest (the cross product bla bla bla bla bla) but I don't know how they're getting these two vectors...I figured it has something to do with partial differentials but I am still confused. If anyone could provide any insight Id be appreciative. :eek:
 
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  • #2
The notation is a bit screwy, but here's what I think they're doing.

So suppose the surface is parametrised by [itex]\vec T(u,v)[/itex].
Take a small rectangle in the domain with dimensions [itex]\Delta u, \Delta v[/itex], the bottom left corner being the point [itex](u_0,v_0)[/itex].
The image of this rectangle is a patch of area, which can be approximated by the parallelogram formed by the vectors:

[tex]\vec T(u_0+\Delta u,v_0)-\vec T(u_0,v_0)[/tex]
and
[tex]\vec T(u_,v_0+\Delta v)-\vec T(u_0,v_0)[/tex] (a picture helps here).

These vectors are in turn approximated by
[tex]\frac{\partial}{\partial u}\vec T(u_0,v_0)\Delta u[/tex]
and
[tex]\frac{\partial}{\partial v}\vec T(u_0,v_0)\Delta v[/tex]
respectively.

So area patch is about [itex]|\vec T_u \times \vec T_v|\Delta u \Delta v[/itex] and you can figure out the rest.

Hope that helps.
 
Last edited:
  • #3
It might help (me, anyways) if you would say what you're trying to prove. The Jacobian is a number associated with a matrix; it doesn't make any more sense to ask about a proof of the Jacobian than it does to ask about a proof of the number 2.
 
  • #4
Galileo said:
The notation is a bit screwy, but here's what I think they're doing.
So suppose the surface is parametrised by [itex]\vec T(u,v)[/itex].
Take a small rectangle in the domain with dimensions [itex]\Delta u, \Delta v[/itex], the bottom left corner being the point [itex](u_0,v_0)[/itex].
The image of this rectangle is a patch of area, which can be approximated by the parallelogram formed by the vectors:
[tex]\vec T(u_0+\Delta u,v_0)-\vec T(u_0,v_0)[/tex]
and
[tex]\vec T(u_,v_0+\Delta v)-\vec T(u_0,v_0)[/tex] (a picture helps here).
These vectors are in turn approximated by
[tex]\frac{\partial}{\partial u}\vec T(u_0,v_0)\Delta u[/tex]
and
[tex]\frac{\partial}{\partial v}\vec T(u_0,v_0)\Delta v[/tex]
respectively.
So area patch is about [itex]|\vec T_u \times \vec T_v|\Delta u \Delta v[/itex] and you can figure out the rest.
Hope that helps.

Oh, I see it better now. THanks a lot, just wanted to say that before I go to bed. If i need further clarification Ill post the fool proof. THakns a lot guys
 

FAQ: Understanding the Proof of the Jacobian

What is the Jacobian in mathematics?

The Jacobian is a matrix of partial derivatives used to represent the rate of change of a set of variables with respect to another set of variables. It is commonly used in multivariate calculus and plays a significant role in determining the behavior of a system.

What is the importance of the Jacobian in differential equations?

The Jacobian is essential in solving systems of differential equations as it helps to determine the stability and behavior of the system. It can also be used to transform a complex system into a simpler form, making it easier to solve.

How is the Jacobian used in optimization problems?

In optimization problems, the Jacobian is used to find the critical points, or the points where the gradient of the function is equal to zero. These points correspond to the maximum or minimum values of the function.

Can the Jacobian be used in non-linear systems?

Yes, the Jacobian can be used in both linear and non-linear systems. In non-linear systems, the Jacobian is a matrix of partial derivatives, while in linear systems, it is a constant matrix.

How is the Jacobian related to the change of variables in multiple integrals?

The Jacobian is used to transform the coordinates in multiple integrals. It is incorporated into the integrand to compensate for the change in variables and ensure the correct integration over the new coordinate system.

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