Grad of a generalised scalar function

In summary, the homework statement is that Homework Equations: Vector identities? The Attempt at a Solution?
  • #1
Phyrrus
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Homework Statement



r=xi+yj+zk and r =[itex]\sqrt{x^2 + y^2 + z^2}[/itex] Let f(r) be a C2 scalar function

Prove that [itex]\nabla[/itex]f = [itex]\frac{1}{2}[/itex][itex]\frac{df}{dr}[/itex]r

Homework Equations



Vector identities?

The Attempt at a Solution



[itex]\nabla[/itex]f = ([itex]\frac{df}{dx}[/itex] , [itex]\frac{df}{dy}[/itex] , [itex]\frac{df}{dz}[/itex])
= df/dr]?
= [itex]\frac{df}{dr}[/itex][itex]\hat{r}[/itex] (unit vector of r)
= [itex]\frac{df}{dr}[/itex]r[itex]\frac{1}{r}[/itex]?

I'm pretty sure what I've attempted isn't mathematically correct in the slightest, though in my head it seems to make some geometric sense. Am I even close though?
 
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  • #2
Formally f(r) is the derivative with respect to vector r: f(r)=(df/dr)=(df/dr)˙r/r. To prove the statement in the problem expand f(r) with x, y, z, and see if it is equal to (df/dr)˙r/r, using that r=√(r2)

ehild
 
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  • #3
Sorry, I made a mistake in the OP, it's supposed to be

[itex]\nabla[/itex]f = [itex]\frac{1}{r}[/itex][itex]\frac{df}{dr}[/itex]r

Thanks for your reply, but I'm not really sure where the r=√(r^2) come into it? How do you arrive at the 1/r out the front? The only thing I can think of is that it's supposed to reduced it to a magnitude of only df/dr?
 
  • #4
Sorry, I also made a mistake, leaving out 1/r. df/dr is multiplied by the unit vector [itex]\hat{\vec r}[/itex], so [itex]\nabla f=\frac{df}{dr}\vec r /r. [/itex]

You get it by applying the chain rule: [tex]\frac{df(\sqrt{(\vec r)^2})}{d\vec r}=\frac{df}{dr} \frac{d \sqrt{(\vec r)^2}}{d \vec r}=\frac{df}{dr}\left(\frac{1}{2}\frac{1}{\sqrt{( \vec r)^2} }\right)2 \vec r[/tex].

ehild
 
  • #5
Thanks mate, finally got it in the end. Just a little bit stuck on part b of the question though, have to prove that:

[itex]\nabla[/itex]^2f = [itex]\frac{2}{r}[/itex][itex]\frac{df}{dr}[/itex]+[itex]\frac{d^2f}{dr^2}[/itex]

I've been using the vector identities so that I get:

= [itex]\frac{1}{r}[/itex][itex]\frac{df}{dr}[/itex]([itex]\nabla[/itex][itex]\bullet[/itex]r) +(r[itex]\bullet[/itex][itex]\nabla[/itex])[itex]\frac{1}{r}[/itex][itex]\frac{df}{dr}[/itex]

from here I get down to [itex]\frac{3}{r}[/itex][itex]\frac{df}{dr}[/itex]+r[itex]\bullet[/itex]([itex]\frac{1}{r}[/itex][itex]\nabla[/itex][itex]\frac{df}{dr}[/itex]+[itex]\frac{df}{dr}[/itex][itex]\nabla[/itex][itex]\frac{1}{r}[/itex])

This is where I am confused, what is [itex]\nabla\frac{df}{dr}[/itex] is it just [itex]\frac{d^2f}{dr^2}[/itex]?

And I keep getting [itex]\frac{df}{dr}[/itex][itex]\nabla[/itex][itex]\frac{1}{r}[/itex]=[itex]\nabla[/itex]f, which doesn't seem right.
 
  • #6
Phyrrus said:
from here I get down to [itex]\frac{3}{r}[/itex][itex]\frac{df}{dr}[/itex]+r[itex]\bullet[/itex]([itex]\frac{1}{r}[/itex][itex]\nabla[/itex][itex]\frac{df}{dr}[/itex]+[itex]\frac{df}{dr}[/itex][itex]\nabla[/itex][itex]\frac{1}{r}[/itex])

This is where I am confused, what is [itex]\nabla\frac{df}{dr}[/itex] is it just [itex]\frac{d^2f}{dr^2}[/itex]?

No, it is [itex]\frac{d^2f}{dr^2}\frac{\vec r}{r}[/itex]

ehild
 
  • #7
Ahhhh yes, got it, it all works out now. Thanks a lot mate.
But just how did you get that? I presume it's quite similar to the first question, chain rule again?
 
  • #8
Yes, it is the chain rule again.:smile:

ehild
 

FAQ: Grad of a generalised scalar function

What is the definition of grad of a generalised scalar function?

The grad of a generalised scalar function is a vector that represents the direction and magnitude of the maximum rate of change of the function at a given point. It is also known as the gradient of the function.

How is the grad of a generalised scalar function calculated?

The grad of a generalised scalar function is calculated by taking the partial derivatives of the function with respect to each variable and combining them into a vector.

What is the significance of the grad of a generalised scalar function?

The grad of a generalised scalar function is significant because it provides information about the direction in which a function changes the most rapidly at a given point. This can be useful in optimization problems and in understanding the behavior of a function.

Can the grad of a generalised scalar function be negative?

Yes, the grad of a generalised scalar function can have negative components. This indicates that the function is decreasing in that direction at the given point.

How is the grad of a generalised scalar function used in real-world applications?

The grad of a generalised scalar function is used in various fields such as physics, engineering, and economics to analyze and optimize systems. It can also be used in machine learning algorithms to find the optimal parameters for a given model.

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