Chain rule when taking vector derivatives

In summary: Basically T is supposed to be a kinetic energy function of a scleronomic system of particles. Where q's represent generalized coordinates and the r-vectors represent the positions of the particles in a carthesian frame.
  • #1
Coffee_
259
2
Consider a function of several variables ##T=T(x_{1},...,x_{3N})## Let's say I have N vectors of the form ##\vec{r_{1}}=(x_1,x_{2},x_{3})## and ##x_j=x_j(q_1,...,q_n)##. Awkward inex usage but the point is just that the each variable is contained in exactly 1 vector.

Is it correct to in general use the chain rule in this way? :

##\frac{\partial T}{\partial q_j} = \sum\limits_{k=1}^N \frac{\partial T}{\partial \vec{r_k}}.\frac{\partial \vec{r_k}}{\partial q_j}##

Where the notation ##\frac{\partial T}{\partial \vec{r_k}}## is just ##(\frac{\partial T}{\partial x_k},..., \frac{\partial T}{\partial x_{k+2}})##
 
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  • #2
There's something wrong with your Latex, please edit your post to fix it.
 
  • #3
jedishrfu said:
There's something wrong with your Latex, please edit your post to fix it.

Done
 
  • #4
Coffee_ said:
Consider a function of several variables ##T=T(x_{1},...,x_{3n})##. Let's call vector ##\vec{r_{k}}=(x_{k},x_{k+1},x_{k+2})## in ##R^{3}##

Is it correct to in general use the chain rule in this way? :

##\frac{\partial T}{\partial x_j} = \sum\limits_{k=1}^n \frac{\partial T}{\partial \vec{r_k}}.\frac{\partial \vec{r_k}}{\partial x_j}##

Where the notation ##\frac{\partial T}{\partial \vec{r_k}}## is just ##(\frac{\partial T}{\partial x_k},..., \frac{\partial T}{\partial x_{k+2}})##

The way you've written this, I'd infer that the x's are all independent variables, so it makes no sense to differentiate one wrt another.
 
  • #5
PeroK said:
The way you've written this, I'd infer that the x's are all independent variables, so it makes no sense to differentiate one wrt another.

Yeah totally right. I've edited it, correctly this time I hope.
 
  • #6
Coffee_ said:
Consider a function of several variables ##T=T(x_{1},...,x_{3N})## Let's say I have N vectors of the form ##\vec{r_{1}}=(x_1,x_{2},x_{3})## and ##x_j=x_j(q_1,...,q_n)##. Awkward inex usage but the point is just that the each variable is contained in exactly 1 vector.

Is it correct to in general use the chain rule in this way? :

##\frac{\partial T}{\partial q_j} = \sum\limits_{k=1}^N \frac{\partial T}{\partial \vec{r_k}}.\frac{\partial \vec{r_k}}{\partial q_j}##

Where the notation ##\frac{\partial T}{\partial \vec{r_k}}## is just ##(\frac{\partial T}{\partial x_k},..., \frac{\partial T}{\partial x_{k+2}})##

Do you mean that you have ##\vec{r}_1 = (x_1,x_2,x_3)##, ##\vec{r}_2 = (x_4, x_5, x_6)##, etc? Using the notation ##\xi_i(q_1,q_2, \ldots, q_n)## instead of ##x_i(q_1, q_2, \ldots, q_n)## (just so as to keep straight the distinction between the variable ##x_i## and the function ##\xi_i## that delivers you the value of ##x_i##), it seems you are asking for the derivative of
[tex] T = {\cal T}(\xi_1(q_1, q_2, \ldots, q_n), \xi_2(q_1, q_2, \ldots, q_n) , \dots, \xi_{3N}(q_1, q_2, \ldots, q_n)), [/tex]
again being careful to distinguish between the variable ##T## and the function that delivers you ##T## (which I call ##{\cal T}##).
 
  • #7
Ray Vickson said:
Do you mean that you have ##\vec{r}_1 = (x_1,x_2,x_3)##, ##\vec{r}_2 = (x_4, x_5, x_6)##, etc? Using the notation ##\xi_i(q_1,q_2, \ldots, q_n)## instead of ##x_i(q_1, q_2, \ldots, q_n)## (just so as to keep straight the distinction between the variable ##x_i## and the function ##\xi_i## that delivers you the value of ##x_i##), it seems you are asking for the derivative of
[tex] T = {\cal T}(\xi_1(q_1, q_2, \ldots, q_n), \xi_2(q_1, q_2, \ldots, q_n) , \dots, \xi_{3N}(q_1, q_2, \ldots, q_n)), [/tex]
again being careful to distinguish between the variable ##T## and the function that delivers you ##T## (which I call ##{\cal T}##).

Yeah. Maybe I should have included the physics context since this is a physics forum. Would have messed up less and wasted less of people's time by converting it into math format wrongly.

Basically T is supposed to be a kinetic energy function of a scleronomic system of particles. Where q's represent generalized coordinates and the r-vectors represent the positions of the particles in a carthesian frame.

I was wondering how to formulate the chain rule this way when taking the partial derivative of T wrt. of one of the generalized coordinates. In case you are not familiar with Lagrangian mechanics just nevermind what I said and yes you seem to have stated my problem very well.
 

Related to Chain rule when taking vector derivatives

What is the chain rule when taking vector derivatives?

The chain rule is a rule used to find the derivative of a composite function. When taking vector derivatives, the chain rule is used to find the derivative of a function that is composed of other functions involving vectors.

Why is the chain rule important in vector calculus?

The chain rule is important in vector calculus because it allows us to find the derivatives of more complex functions involving vectors. This is useful in many scientific fields, such as physics and engineering, where vector calculus is used to model and solve problems.

How is the chain rule applied in vector calculus?

The chain rule is applied in vector calculus by first breaking down the composite function into its component functions. Then, the derivatives of each component function are calculated and multiplied together to find the overall derivative of the composite function.

What are some common mistakes when applying the chain rule in vector calculus?

One common mistake when applying the chain rule in vector calculus is forgetting to multiply the derivatives of the component functions together. Another mistake is not carefully keeping track of the variables and their corresponding derivatives.

Can the chain rule be applied to higher dimensions in vector calculus?

Yes, the chain rule can be applied to higher dimensions in vector calculus. In fact, the chain rule is crucial when dealing with functions involving multiple variables in higher dimensions, as it allows us to find the derivatives of these functions more easily.

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