Second order partial derivatives vanish?

In summary, the conversation discusses the principle that the second order partial derivative vanishes if one of the parts in the denominator is in the same reference frame as the numerator. However, this principle needs to be used with caution, as demonstrated by a counterexample. The speaker then revises their rule to state that the second order derivative of a coordinate by two other coordinates in the same reference frame vanishes.
  • #1
George Keeling
Gold Member
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At the end of a long proof I came across something in tensor calculus that seems too good to be true. And if something seems too good to be true ...

The something is that a second order partial derivative vanishes if one of the parts in the denominator is in the same reference frame as the numerator. That is for example
\begin{align}

\frac{{\partial }^2x^{\mu }}{\partial x^{\rho }\partial x^{{\mu }^{'}}}=\frac{{\partial }^2x^{\mu }}{\partial x^{{\mu }^{'}}\partial x^{\rho }}=0 \\

\end{align}The equality of the first two parts follows because partial derivatives commute. We have \begin{align}

\frac{{\partial }^2x^{\mu }}{\partial x^{{\mu }^{'}}\partial x^{\rho }}=\frac{\partial }{\partial x^{{\mu }^{'}}}\left(\frac{\partial x^{\mu }}{\partial x^{\rho }}\right) \\

\end{align}but\begin{align}

\frac{\partial x^{\mu }}{\partial x^{\rho }}={\delta }^{\mu }_{\rho } \\

\end{align}where ##{\delta }^{\mu }_{\rho }## is the Kronecker delta which is a constant. (3) seems very reasonable because when ##\mu \neq \rho ##, ##\partial x^{\mu }## and ##\partial x^{\rho }## are orthogonal so ##{\partial x^{\mu }}/{\partial x^{\rho }}## vanishes and when ##\mu =\rho ##, ##{\partial x^{\mu }}/{\partial x^{\rho }}=1##.

So equation (1) is the derivative of a constant which always vanishes. QED.

Have I made a very stupid mistake? Or am I stating something everybody knows?
 
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  • #2
George Keeling said:
The equality of the first two parts follows because partial derivatives commute.

This principle needs to be used with caution. For a fixed coordinate system, if ##x## and ##y## are two different coordinates, then it is definitely the case that ##\frac{\partial}{\partial x} \frac{\partial}{\partial y} f(x,y) = \frac{\partial}{\partial y} \frac{\partial}{\partial x} f(x,y)##

However, if ##x## and ##y## are from DIFFERENT coordinate systems, then that isn't necessarily true. Here's a trivial example. Consider one dimension, so there is only one coordinate, ##x##. We can switch to another coordinate system ##x' = \sqrt{x}##. Consider the function ##cos(x)##

Take the derivative with respect to ##x##. You get ##-sin(x)##.
Re-express it in terms of ##x'##. You get ##-sin((x')^2)##.
Take the derivative with respect to ##x'##. You get ##-2 x' cos((x')^2) = -2 \sqrt{x} cos(x)##.

Now do it in the opposite order.
Re-express ##cos(x)## in terms of ##x'##. You get ##cos((x')^2)##.
Take the derivative with respect to ##x'##. You get ##-2x' sin((x')^2)##.
Re-express it in terms of ##x##. You get ##-2\sqrt{x} sin(x)##.
Take the derivative with respect to ##x##. You get ##-\frac{1}{\sqrt{x}} sin(x) - 2\sqrt{x} cos(x)##.

Those aren't the same.
 
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  • #3
stevendaryl said:
This principle needs to be used with caution.
Oh dear. I'll have to think about cautions tomorrow.
 
  • #4
stevendaryl said:
This principle needs to be used with caution.
Thanks for taking the trouble to work the example. It teaches me two things:
1) Apply that caution
2) Try an example when I get another 'to good to be true result'
Following rule (1) we believe$$
\frac{{\partial }^2f}{\partial x^{\rho }\partial x^{{\nu }}}=\frac{{\partial }^2f}{\partial x^{{\nu }}\partial x^{\rho }}
$$Following rule (2) test$$
f\left(x,y\right)=x^2{\mathrm{sin} y\ }
$$then $$
\frac{\partial }{\partial x}\left(\frac{\partial }{\partial y}f\right)=\frac{\partial }{\partial x}\left(x^2{\mathrm{cos} y\ }\right)=2x{\mathrm{cos} y\ }
$$and$$
\frac{\partial }{\partial y}\left(\frac{\partial }{\partial x}f\right)=\frac{\partial }{\partial y}\left(2x{\mathrm{sin} y\ }\right)=2x{\mathrm{cos} y\ }
$$That works!
My question was really when ##f=x^\mu## which is a coordinate. So I should test ##f=y## and ##f=z##. The second order derivatives both rapidly vanish. I mow need to revise my rule to:
The second order derivative of a coordinate by two other coordinates in the same reference frame vanishes. Not so impressive.:frown:

I will now now have to rework my problem :hammer:
Once again stevendaryl, thanks for the help you have given with with this and other problems.
 

FAQ: Second order partial derivatives vanish?

1. What are second order partial derivatives?

Second order partial derivatives are a type of mathematical function that measures the rate of change of a function with respect to two independent variables. They are used in multivariable calculus to analyze the behavior of functions in multiple dimensions.

2. How do you calculate second order partial derivatives?

To calculate second order partial derivatives, you first take the partial derivative of the function with respect to one variable, and then take the partial derivative of that result with respect to the other variable. This can be done using the standard rules of differentiation, such as the product rule and chain rule.

3. What does it mean for second order partial derivatives to vanish?

When second order partial derivatives vanish, it means that the rate of change of the function with respect to two independent variables is equal to zero. This indicates that the function is not changing in that particular direction, and can be used to identify critical points or extrema of the function.

4. Why is it important for second order partial derivatives to vanish?

Second order partial derivatives can provide valuable information about the behavior of a function in multiple dimensions. When they vanish, it can indicate the presence of critical points or extrema, which are important for understanding the overall behavior of the function.

5. In what fields of science are second order partial derivatives commonly used?

Second order partial derivatives are commonly used in fields such as physics, engineering, economics, and statistics. They are particularly useful in analyzing systems with multiple variables, such as fluid dynamics, optimization problems, and statistical models.

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