Prove the chain rule for Jacobi determinants

In summary, the chain rule for Jacobi determinants states that the determinant of a product of two square matrices is equal to the product of their determinants. This can be proven using the definition of Jacobi determinant and working with partial derivatives. By substituting the partial derivatives of the multivariate function into the equations, the proof can be obtained.
  • #1
vst98
51
0

Homework Statement



Prove the chain rule for Jacobi determinants

[itex] \frac{d(f,g)}{d(u,v)} * \frac{d(u,v)}{d(x,y)}=\frac{d(f,g)}{d(x,y)}[/itex]

Homework Equations



Definition of Jacobi determinant

[itex] \frac{d(f,g)}{d(u,v)} = \frac{d(f,g)}{d(u,v)} = det \begin{bmatrix}
\frac{df}{du}&\frac{df}{dv} \\
\frac{dg}{du}&\frac{dg}{dv}
\end{bmatrix} [/itex]

The determinant of a matrix product of square matrices equals the product of their determinants:
det(AB)=det(A)det(B)

The Attempt at a Solution



[itex]\frac{d(f,g)}{d(u,v)} * \frac{d(u,v)}{d(x,y)} = det \begin{bmatrix}
\frac{df}{du}&\frac{df}{dv} \\
\frac{dg}{du}&\frac{dg}{dv}
\end{bmatrix} *
det \begin{bmatrix}
\frac{du}{dx}&\frac{du}{dy} \\
\frac{dv}{dx}&\frac{dv}{dy}
\end{bmatrix} = det (\begin{bmatrix}
\frac{df}{du}&\frac{df}{dv} \\
\frac{dg}{du}&\frac{dg}{dv}
\end{bmatrix} *
\begin{bmatrix}
\frac{du}{dx}&\frac{du}{dy} \\
\frac{dv}{dx}&\frac{dv}{dy}
\end{bmatrix}) = det\begin{bmatrix}
\frac{df}{du}\frac{du}{dx} + \frac{df}{dv}\frac{dv}{dx}&\frac{df}{du}\frac{du}{dy} + \frac{df}{dv}\frac{dv}{dy} \\
\frac{dg}{du}\frac{du}{dx} + \frac{dg}{dv}\frac{dv}{dx} & \frac{dg}{du}\frac{du}{dy} + \frac{dg}{dv}\frac{dv}{dy}
\end{bmatrix} = det \begin{bmatrix}
2\frac{df}{dx} & 2\frac{df}{dy} \\
2\frac{dg}{dx}&2\frac{dg}{dy}
\end{bmatrix} = 2*\frac{d(f,g)}{d(x,y)}[/itex]

which is obviously wrong but I don't see where is my error.
Is it wrong to cancel out those cross terms after multiplying matrices, but what else can I do ?
 
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  • #2
Proof

I know this is an old thread (and I hope you are not still stuck:tongue:), but I am just replying so other can use it for future reference.

The problem lies in the fact that you see the jacobian matrix as a matrix containing derivatives. As it is related to a multivariate function (a function depending on multiple variables), you should work with partial derivatives. As f is a function of u and v you can write

[itex]df=\left(\frac{\partial f}{\partial u}\right)_{v}du+\left(\frac{\partial f}{\partial v}\right)_{u}dv[/itex]

where the subscript [itex]\left(\frac{\partial f}{\partial u}\right)_{x}[/itex] means the partial derivative of f with respect to u holding x constant.

As u and v are a function of x and y you can write:
[itex]du=\left(\frac{\partial u}{\partial x}\right)_{y}dx+\left(\frac{\partial u}{\partial y}\right)_{x}dy[/itex]
[itex]dv=\left(\frac{\partial v}{\partial x}\right)_{y}dx+\left(\frac{\partial v}{\partial y}\right)_{x}dy[/itex]

If we will in these equations in the previous, we can obtain:
[itex]df=\left[\left(\frac{\partial f}{\partial v}\right)_{u}\left(\frac{\partial v}{\partial x}\right)_{y}+\left(\frac{\partial f}{\partial u}\right)_{v}\left(\frac{\partial u}{\partial x}\right)_{y}\right]dx+\left[\left(\frac{\partial f}{\partial u}\right)_{v}\left(\frac{\partial u}{\partial y}\right)_{x}+\left(\frac{\partial f}{\partial v}\right)_{u}\left(\frac{\partial v}{\partial y}\right)_{x}\right]dy[/itex]
As f is also a function of x and y we can also write:
[itex]df=\left(\frac{\partial f}{\partial x}\right)_{y}dx+\left(\frac{\partial f}{\partial y}\right)_{x}dy[/itex]

From the last two equations, we can solve:
[itex]\left(\frac{\partial f}{\partial x}\right)_{y}=\left(\frac{\partial f}{\partial v}\right)_{u}\left(\frac{\partial v}{\partial x}\right)_{y}+\left(\frac{\partial f}{\partial u}\right)_{v}\left(\frac{\partial u}{\partial x}\right)_{y}[/itex]
[itex]\left(\frac{\partial f}{\partial y}\right)_{x}=\left(\frac{\partial f}{\partial u}\right)_{v}\left(\frac{\partial u}{\partial y}\right)_{x}+\left(\frac{\partial f}{\partial v}\right)_{u}\left(\frac{\partial v}{\partial y}\right)_{x}[/itex]

You can see that this resembles the upper row of your second to last result. If you rework your proof using these rules, it should find the proof you need.

Good luck!
 

What is the chain rule for Jacobi determinants?

The chain rule for Jacobi determinants is a mathematical concept that explains the relationship between the partial derivatives of two functions and their resulting Jacobian determinant. It states that the determinant of the Jacobian matrix of a composite function can be calculated by multiplying the determinants of the individual functions involved.

Why is it important to prove the chain rule for Jacobi determinants?

Proving the chain rule for Jacobi determinants is important because it is a fundamental concept in multivariable calculus and is widely used in various fields of science and engineering. It allows for the efficient calculation of derivatives of composite functions, which is crucial in many practical applications.

How is the chain rule for Jacobi determinants derived?

The chain rule for Jacobi determinants can be derived using the properties of matrix determinants and the chain rule for partial derivatives. It involves expanding the determinant of the Jacobian matrix into a product of determinants and then applying the chain rule to each individual determinant.

Can you provide an example of using the chain rule for Jacobi determinants in real-life?

One example of using the chain rule for Jacobi determinants is in the field of fluid mechanics, where it is used to calculate the rate of change of a fluid property, such as temperature, at a specific point in a flowing fluid. This is important in designing efficient heat transfer systems, such as in power plants and heat exchangers.

What are the limitations of the chain rule for Jacobi determinants?

While the chain rule for Jacobi determinants is a powerful tool, it has limitations in certain cases, such as when the functions involved are not differentiable or when the Jacobian matrix is singular. In these cases, alternative methods may need to be used to calculate derivatives.

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