Jacobian for kronecker delta

In summary, the conversation discusses the use of the Kronecker delta in tensor calculus and how it relates to Jacobian matrices of coordinate transformations and their inverses. The equation ##|\frac{\partial y^i}{\partial x^j}| |\frac{\partial x^\alpha}{\partial y^\beta}|= 1## can be derived from the expansion of the Kronecker delta and the property of matrix multiplication. This is done by taking the determinant of both sides of the top equation and using the fact that the determinant of the Kronecker delta is 1.
  • #1
cr7einstein
87
2
Dear all,
I was revising on a bit of tensor calculus, when I stumbled upon this:

$$\delta^i_j = \frac{\partial y^i}{\partial x^\alpha} \frac{\partial x^\alpha}{\partial y^j}$$

And the next statement reads,

"this expression yields:

$$|\frac{\partial y^i}{\partial x^j}| |\frac{\partial x^\alpha}{\partial y^\beta}|= 1$$, ........(1)

With $ |\frac{\partial y^i}{\partial x^j}|$ being the jacobian for transformation $$y^i=y^i(x^1...x^n)$$, and $$|\frac{\partial x^\alpha}{\partial y^\beta}|$$ being the Jacobian of the INVERSE transformation."

My question is, how do you get eq. 1 from the Kronecker delta, as they are merely jacobians of coordinate transformatios, and being inverse of each other, are 1. But how do they follow from $$\delta^i_j$$'s expansion? *(i.e. DERIVE eq. 1 using the expansion for kronecker delta)*? I am most probably making a conceptual error, but this is the first time I have seen such a representation of the kronecker delta. Thanks in advance!
 
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  • #2
It's just matrix multiplication. You have the two Jacobian matrices
[tex] T_{i\alpha}=\frac{\partial y^i}{\partial x^\alpha} ,~~~~
S_{\alpha j}=\frac{\partial x^\alpha}{\partial y^j} . [/tex]
for the coordinate transformation and its inverse. When you multiply them together and use the first formula in your post, you get the matrix
[tex]
(TS)_{ij}=\frac{\partial y^i}{\partial x^\alpha}\frac{\partial x^\alpha}{\partial x^j}=\delta^i_j
[/tex]
so [itex]TS=\mathrm{id} [/itex]. By multiplicativity of the determinant the result now follows.
 
  • #3
Recall the equality ##\det(AB)=\det(A)\det(B)## for ##A,B## square matrices of equal size (as coordinate transformations and their inverses have to be). Equation one will follow directly when you take the determinant of both sides of the top equation. The determinant of the Kronecker delta is obviously 1 since it is the identity matrix.
 

1. What is the Jacobian for Kronecker delta?

The Jacobian for Kronecker delta is a mathematical concept that relates to the change in variables in a system of equations. It is often used in multivariate calculus and differential geometry to determine the transformation of variables in a given function.

2. How is the Jacobian for Kronecker delta calculated?

The Jacobian for Kronecker delta is calculated by taking the determinant of the partial derivatives of the variables in a given function. This can be done using various methods, such as the chain rule or the product rule, depending on the complexity of the function.

3. What is the significance of the Jacobian for Kronecker delta?

The Jacobian for Kronecker delta is significant because it allows us to understand the transformation of variables in a function and how they affect the overall behavior of the function. It is also used in various applications, such as optimization problems and solving systems of equations.

4. Can the Jacobian for Kronecker delta be negative?

Yes, the Jacobian for Kronecker delta can be negative. The sign of the Jacobian is dependent on the orientation of the coordinate system and the direction of the variables in the function. It can be positive, negative, or zero, and each has its own implications for the behavior of the function.

5. How is the Jacobian for Kronecker delta used in machine learning?

The Jacobian for Kronecker delta is used in machine learning to determine the gradient of a given function. This is important in training neural networks and other machine learning algorithms, as it allows us to update the parameters of the model in the direction of the steepest descent, ultimately improving its performance.

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