What is the General Solution for the Gradient of r^n?

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Discussion Overview

The discussion revolves around finding the general expression for the gradient of the function \( r^n \), where \( r \) is the magnitude of the separation vector in three-dimensional space. Participants explore different methods of calculating this gradient, including Cartesian and spherical coordinates, and discuss the implications of their approaches.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents a solution for \( \nabla(r^n) \) as \( n(r^2)^{\frac{2n-2}{2}}\vec r \) and questions its correctness.
  • Another participant suggests that the expression should involve the unit vector \( \hat{r} \) instead of \( r^2 \) and recommends using spherical coordinates for simplification.
  • A different participant proposes verifying the solution by computing the gradient of \( r \) and checking if it aligns with the initial result.
  • Some participants express differing opinions on whether spherical coordinates or Cartesian coordinates are easier for this calculation, with one arguing that spherical coordinates are harder to remember.
  • One participant acknowledges a typographical error in their previous post and corrects their expression for \( \nabla(r^n) \) to \( n(r^2)^{\frac{n-2}{2}}\vec r \), confirming it matches the case for \( n=2 \).
  • Another participant asserts that the expression for \( \nabla \) in spherical coordinates is a specific solution and emphasizes that \( \vec r \) can be represented in any coordinate system.

Areas of Agreement / Disagreement

Participants express differing views on the best coordinate system to use for calculating the gradient, with no consensus reached on a single preferred method. Some participants agree on the need to verify results through specific cases, while others focus on the general expression.

Contextual Notes

Participants mention the importance of checking special cases, such as \( n=1 \), to validate their formulas. There is also a discussion about the memorization of formulas in different coordinate systems, highlighting the challenges and preferences of participants.

Reshma
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I worked out this problem from Griffith's book. The problem is to find the general expression for [tex]\nabla(r^n)[/tex]. This is how I worked it out:

If [tex]\vec r = \hat x x+\hat y y+\hat z z[/tex]

r is the separation vector whose magnitude is given by [tex]\sqrt{x^2+y^2+z^2}[/tex]

Hence [tex]r^n = (x^2+y^2+z^2)^\frac{n}{2}[/tex]

I applied the [tex]\nabla[/tex] operator to it and this is solution I got:

[tex]\nabla(r^n) = n(r^2)^\frac{2n-2}{2}\vec r[/tex]

Is this the right way to find the solution or is there another generalised solution?
 
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Reshma said:
I worked out this problem from Griffith's book. The problem is to find the general expression for [tex]\nabla(r^n)[/tex]. This is how I worked it out:

If [tex]\vec r = \hat x x+\hat y y+\hat z z[/tex]

r is the separation vector whose magnitude is given by [tex]\sqrt{x^2+y^2+z^2}[/tex]

Hence [tex]r^n = (x^2+y^2+z^2)^\frac{n}{2}[/tex]

I applied the [tex]\nabla[/tex] operator to it and this is solution I got:

[tex]\nabla(r^n) = n(r^2)^\frac{2n-2}{2}\vec r[/tex]

Is this the right way to find the solution or is there another generalised solution?

It seems to me that you shouldn't have r^2 in the parenthesis, that should be the unit vector at the end [tex]\hat{r}[/tex].. also you can reduce the fraction in your exponent. It is much easier to do something like that in spherical coordinates, see http://mathworld.wolfram.com/SphericalCoordinates.html
 
To see whether your solution is correct or not,follow this
[tex]\nabla (r^{n})=n r^{n-1} \nabla r[/tex]

Compute the gradient of "r" and see whether the whole result matches yours...

Daniel.
 
Reshma,just an advice,at the end of your calculation try to see whether your formula fits special cases.For example,n=1.
Check whether your formula "delivers the goods" for this simple case...

Daniel.
 
Biology, this is a thread about computing:

[tex]\nabla(r^n)[/tex]

Don't hijack other people's threads with random stuff! Post it in General Discussion.

Reshma,

Since the function you have been given is a function of r, it may be easier to compute the gradient in spherical coordinates. See the inside front cover of Griffiths for the formula. That way, you can at least check your answer using another method.
 
I think it's much easier in CARTESIAN COORDINATES...

Daniel.
 
First of all, I just re-read the thread, and realized that kanato already made the claim that it's easier in spherical coords. Second of all, I agree because the derivatives wrt phi and theta are zero, and you're only calculating one derivative!
 
Okay.Have it your way.Though i think the expression for "nabla" in spherical coordinates is much harder to remember (in an exam,for instance) than the one in cartesian coordinates...

Daniel.
 
dextercioby said:
Okay.Have it your way.Though i think the expression for "nabla" in spherical coordinates is much harder to remember (in an exam,for instance) than the one in cartesian coordinates...

Daniel.
If Jackson is good for anything it is this.
 
  • #10
And the inside covers of Cohen-Tannoudji.And the first 100 pages from Griffiths.

Daniel.
 
  • #11
dextercioby said:
Okay.Have it your way.Though i think the expression for "nabla" in spherical coordinates is much harder to remember (in an exam,for instance) than the one in cartesian coordinates...

Daniel.

It can be, but it's really useful to remember the gradiant and divergence for special situations where you have no angular dependence, because then they are really simple, and they take a problem that would be a pain in cartesian coordinates and make it an easy problem in one variable.
 
  • #12
I see no point in memorizing only the "r" derivative-thing from any of the traditional diff.operators...That's why i don't know them and have to use Cohen-Tannoudji every time...Which i don't mind at all...

Daniel.
 
  • #13
dextercioby said:
I see no point in memorizing only the "r" derivative-thing from any of the traditional diff.operators.
Come on. There's no memorizing involved in remembering the radial components of the gradient in spherical/cylindrical co-ords. It's the other components that need memorizing.

And this problem is clearly an example where knowing just the radial component (instead of having to do it in cartesian coords) make things a lot easier on yourself.
 
  • #14
dextercioby said:
To see whether your solution is correct or not,follow this
[tex]\nabla (r^{n})=n r^{n-1} \nabla r[/tex]

Compute the gradient of "r" and see whether the whole result matches yours...

Daniel.

Daniel,

I am EXTREMELY sorry for my typographical error. I reviewed my result yesterday and it is actually

[tex]\nabla(r^n) = n(r^2)^\frac{n-2}{2}\vec r[/tex]

I applied the result for n=2 and I got the result as

[tex]\nabla(r^2) = 2\vec r[/tex]

which tallies with the general solution.
Thanks for your help.

Regards,
Reshma
 
  • #15
kanato said:
It seems to me that you shouldn't have r^2 in the parenthesis, that should be the unit vector at the end [tex]\hat{r}[/tex].. also you can reduce the fraction in your exponent. It is much easier to do something like that in spherical coordinates, see http://mathworld.wolfram.com/SphericalCoordinates.html

Well if you are referring to the spherical coordinates, it would rather be a "specific solution''. I've mentioned [tex]\vec r[/tex] is just a separation vector which can belong to any coordinate system.
 
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