Partial derivatives of composition

In summary, use the chain rule to find the partial derivatives of \bar{U}(\bar{x}(u,v)), and remember that in general terms, the partial derivative of U with respect to u (or v) is \partial U/\partial x * \partial x/\partial u (or v). Alternatively, you can find the partial derivatives of U with respect to u and v separately and then substitute in the values of x(u,v) to get the final result. Best of luck!
  • #1
cantgetright
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Homework Statement



Find the partial derivatives with respect to \(\displaystyle u,v\) of \(\displaystyle \bar{U}(\bar{x}(u,v))\), where \(\displaystyle \bar{U}\) is the unit normal to a surface given by the parametrization \(\displaystyle \bar{x}(u,v)\). (This, of course, is part of a larger problem, but I just am looking for advice with the calculus.)


Homework Equations



Chain rule?



The Attempt at a Solution



I am showing some properties of the shape operator, so everything is in "general terms" (that's why there's no explicit expression for the unit normal or the parametrization). I'm used to explicit expressions--e.g., where I could evaluate what the composition looked like explicitly and take partial derivatives. Here, I am at a loss. I would like to take derivative of U evaluated at x(u,v) with respect to u and then multiply by derivative of x(u,v) with respect to u (and then repeat the process for the partial derivative with respect to v), but in "general terms", how do I express the partial derivative of U?

I am sorry if this is so elementary, but I have not seen calculus in almost 20 years. Be gentle.
 
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  • #2


Thank you for your question. It seems like you are on the right track with using the chain rule to find the partial derivatives of \bar{U}(\bar{x}(u,v)). In general terms, the partial derivative of U evaluated at x(u,v) with respect to u (or v) can be expressed as \partial U/\partial x * \partial x/\partial u (or v), where \partial U/\partial x is the gradient of U and \partial x/\partial u (or v) is the partial derivative of x with respect to u (or v).

Another way to think about it is to first find the partial derivative of U with respect to each variable (u and v) separately, and then plug in the values of x(u,v) to get the final result. This might be easier to visualize and work with in "general terms".

I hope this helps. Good luck with your problem!
 

Related to Partial derivatives of composition

1. What is the definition of a partial derivative of composition?

A partial derivative of composition refers to the derivative of a function with respect to one of its variables, while holding the other variables constant. It is commonly denoted as ∂f/∂x, where f is the function and x is the variable of interest.

2. How is the chain rule applied in finding partial derivatives of composition?

The chain rule is applied by taking the derivative of the outer function, then multiplying it by the derivative of the inner function with respect to the variable of interest. This process is repeated for each variable in the function.

3. Can partial derivatives of composition be used to find the rate of change in multivariable functions?

Yes, partial derivatives of composition can be used to find the rate of change in multivariable functions. They allow us to see how much a function changes in response to a change in one of its variables, while holding the others constant.

4. What are some applications of partial derivatives of composition in real-world problems?

Partial derivatives of composition have various applications in fields such as physics, economics, and engineering. They can be used to optimize functions, analyze the behavior of systems, and solve problems involving multiple variables.

5. Are there any limitations to using partial derivatives of composition?

One limitation of using partial derivatives of composition is that they only provide information about the rate of change at a single point, rather than the entire function. Additionally, they can only be used for differentiable functions, which may not be applicable in all scenarios.

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