Symmetry in second order partial derivatives and chain rule

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SUMMARY

The discussion centers on the conditions under which the equality of mixed partial derivatives can be applied when dealing with functions of multiple variables, specifically in the context of the chain rule. The participants clarify that the expression involving the partial derivatives of a function \( f(h_1, ..., h_n) \) with respect to \( h_i \) and \( x_k \) is not well-defined without a proper transformation of variables. It is established that if \( f \) is expressed in terms of \( h_i \), substituting back to \( x_j \) can lead to ambiguities, particularly when bijections between the variables do not exist. A specific example using \( f = h_1h_2 \) illustrates the lack of a unique solution when calculating mixed partial derivatives.

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  • Familiarity with the chain rule in multivariable calculus
  • Knowledge of functional forms and variable transformations
  • Experience with bijections in mathematical functions
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When can I do the following where ##h_{i}## is a function of ##(x_{1},...,x_{n})##?

[itex]\frac{\partial}{\partial x_{k}}\frac{\partial f(h_{1},...,h_{n})}{\partial h_{m}}\overset{?}{=}\frac{\partial}{\partial h_{m}}\frac{\partial f(h_{1},...,h_{n})}{\partial x_{m}}\overset{\underbrace{chain\ rule}}{=}\frac{\partial}{\partial h_{m}} \sum_{l=1}^{n} \frac{\partial f(h_{1},...,h_{n})}{\partial h_{l}}\frac{\partial h_{l}}{\partial x_{k}}[/itex]

And why? Thank you for your time.
 
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I'm pretty sure this won't work.

I don't think the middle bit is well-defined. Partial derivatives operate on a functional form, not on a value. In this problem ##f## is given as a function of the ##h_i##, so the partial derivatives ##\frac{\partial f(h_{1},...,h_{n})}{\partial h_i}## are well-defined. But if you want to take partial derivatives of ##f## with respect to an ##x_k## you need to change the functional form of ##f## and it will become a new function ##g##. The most natural interpretation is to replace each ##h_i## by its expression in terms of the ##x_j##s. But when you've done that you no longer have any ##h_i##s in the functional form, which is ##\frac{\partial g(x_{1},...,x_{n})}{\partial x_k}##, so ##\frac{\partial}{\partial h_i}## will be zero.

In some cases you might be able to turn the ##x_j##s back into ##h_i##s, and then do the, ##\frac{\partial}{\partial h_i}##, but to do that you'd need there to be a bijection between ##x_1,...,x_n## and ##h_1,...,h_n## and in general there won't be.

Even if there is a bijection, the sought equality may not hold or be meaningless.

eg consider the case ##f=h_1h_2## where ##h_1=x_1+x_2, h_2=x_1-x_2##. Then the LHS of your equation is ##\frac{\partial^2 f}{\partial x_1 \partial h_1}=1##. To calculate the middle bit, we first express ##f## in terms of the ##x##s as ##f(h_1,h_2)=g(x_1,x_2)=(x_1+x_2)(x_1-x_2)=x_1^2-x_2^2##. Then ##\frac{\partial}{\partial x_1}## is ##2x_1##. How are we now going to take a partial derivative of this with respect to ##h_1##? We could write ##2x_1=2(h_1 -y)##, in which case the answer is 2, or we could write ##2x_1=2(h_2+y)## in which case the answer is zero. So there is not a unique answer.
 

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