Derivative of a potential function that depends on several position vectors

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Homework Help Overview

The discussion revolves around the application of the chain rule in the context of a potential function that depends on multiple position vectors. The original poster seeks to demonstrate a specific relationship involving partial derivatives of the potential function with respect to generalized coordinates.

Discussion Character

  • Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • The original poster attempts to apply the chain rule to a scalar function dependent on vectors, expressing uncertainty about how to handle derivatives with respect to vector components.
  • Some participants clarify that partial derivatives should be taken with respect to the components of the vectors, suggesting that the original poster's approach may need adjustment.
  • There is a discussion about the validity of taking derivatives with respect to vectors, with one participant questioning the original poster's understanding of the concept.

Discussion Status

The conversation includes attempts to clarify the application of the chain rule and the nature of derivatives involving vectors. Some guidance has been offered regarding the correct approach to take derivatives with respect to vector components, but the discussion remains open without a definitive resolution.

Contextual Notes

Participants are navigating the complexities of vector calculus and the implications of differentiating scalar functions that depend on multiple variables. The original poster's initial confusion highlights the challenges in understanding these concepts in a multi-dimensional context.

andresordonez
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Homework Statement


Show that:
[tex] \sum_i \vec{\nabla_i}V \cdot \frac{\partial \vec{r_i}}{\partial q_j} = \frac{\partial V}{\partial q_j}[/tex]

Homework Equations


[tex]V=V(\vec{r_1},\vec{r_2},\vec{r_3},...\vec{r_N},t)[/tex]
[tex]\vec{r_i}=\vec{r_i}(q_1,q_2,q_3,...,q_n); i=1,2,3,...,N; n<N[/tex]

The Attempt at a Solution


I'm not sure how to apply the chain rule when the function depends on vectors. I guess is something like this:
[tex] \frac{\partial V}{\partial q_j}=\sum_i \frac{\partial V}{\partial \vec{r_i}} \frac{\partial \vec{r_i}}{\partial q_j}[/tex]
However, the partial derivative of a scalar function with respect to a vector is something I'm not used to. Thanks in advance
 
Last edited:
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You don't take the partial derivatives with respect to a vector, you do it with respect to the the vector's components (because they are all independent variables), so the result will contain terms like

[tex]\frac{\partial V}{\partial (r_i)_k}\frac{\partial (r_i)_k}{\partial q_j}[/tex]

where [itex](r_i)_k[/itex] is component k of vector i.

By the way, I like to write the chain rule as

[tex](f\circ g)_{,i}(x)=\sum_j f_{,j}(g(x))g_{j,i}(x)[/tex]

where ",i" denotes partial differentiation with respect to the ith variable, and [itex]g_j[/itex] is the jth component of g. This is both easy to remember and easy to use.

Normally I would use Einstein's summation convention as well, and write component indices upstairs, so I'd write the chain rule as

[tex](f\circ g)_{,i}(x)=f_{,j}(g(x))g^j_{,i}(x)[/tex]

and if f is vector-valued, the same formula still holds for the components of f, so we only have to put an index on f as well.

[tex](f\circ g)^k_{,i}(x)=(f^k\circ g)_{,i}(x)=f^k_{,j}(g(x))g^j_{,i}(x)[/tex]
 
andresordonez said:
However, the partial derivative of a scalar function with respect to a vector is something I'm not used to.

That's because its nonsense.:wink:

Let's look at a simpler example. Let's say that [itex]V=V(\textbf{r}_1)[/itex] only and that [itex]\textbf{r}_1=\textbf{r}_1(q_1,q_2,q_3)[/itex]. You then have [itex]V=V(\textbf{r}_1)=V(\textbf{r}_1(q_1,q_,q_3))[/itex] which is the same thing as saying [itex]V=V(q_1,q_,q_3)[/itex]...what do you get when you apply the chain rule to that?
 
Thanks, I solved it.
 

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