Computing the Magnitude of F Using Gradient

In summary: I am trying to get the encircled step using formula on #4 but don't get it. May you show it me?You can find it on Page 148 of your textbook.
  • #1
JD_PM
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Homework Statement


Screenshot (148).png


Homework Equations



$$F = \nabla \phi$$

$$| F | = \sqrt{F \cdot F}$$

The Attempt at a Solution



I want to compute ##| F | = \sqrt{F \cdot F}##

$$| F | = \sqrt{F \cdot F} = \sqrt{\frac{(km)^2 (r-r_0)^2}{|r-r_0|^6}} = \sqrt{\frac{(km)^2}{(r-r_0)}} = \frac{km}{(r-r_0)^{1/2}} = \frac{km}{|r-r_0|} $$

That seems to be OK. I have one doubt here; would the following equality hold?

$$(r-r_0)^2 = ((x-x_0) - (y-y_0) - (z-z_0))^2$$

I do not need it for getting | F | but I want to be sure of this.

Thanks.
 

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  • #2
No, (r-r0)2 = (x-x0)2 + (y-y0)2 + (z-z0)2
There also seems to be a mistake in your algebra. (r-r0)2/(r-r0)6 = 1/(r-r0)4, so you should end up with (r-r0)2 in the denominator (any other power would be dimensionally wrong).
 
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  • #3
mjc123 said:
No, (r-r0)2 = (x-x0)2 + (y-y0)2 + (z-z0)2
There also seems to be a mistake in your algebra. (r-r0)2/(r-r0)6 = 1/(r-r0)4, so you should end up with (r-r0)2 in the denominator (any other power would be dimensionally wrong).

OK I see. Now I get:

$$| F | = \sqrt{F \cdot F} = \sqrt{\frac{(km)^2 (r-r_0)^2}{|r-r_0|^6}} = \sqrt{\frac{(km)^2}{(r-r_0)^4}} = \frac{km}{(r-r_0)^{2}} = \frac{km}{|r-r_0|^2}$$

But the solution is ##\frac{km}{|r-r_0|}## so I must be missing something...
 
  • #4
JD_PM said:
OK I see. Now I get:

$$| F | = \sqrt{F \cdot F} = \sqrt{\frac{(km)^2 (r-r_0)^2}{|r-r_0|^6}} = \sqrt{\frac{(km)^2}{(r-r_0)^4}} = \frac{km}{(r-r_0)^{2}} = \frac{km}{|r-r_0|^2}$$

But the solution is ##\frac{km}{|r-r_0|}## so I must be missing something...

Basically, I want to understand how to compute the derivative of the length of a vector function with respect to one variable, using:

Screenshot (149).png


The stated solution is:

Screenshot (150).png


NOTE: The scalar field is:

Screenshot (151).png
 

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  • #5
JD_PM said:
OK I see. Now I get:

$$| F | = \sqrt{F \cdot F} = \sqrt{\frac{(km)^2 (r-r_0)^2}{|r-r_0|^6}} = \sqrt{\frac{(km)^2}{(r-r_0)^4}} = \frac{km}{(r-r_0)^{2}} = \frac{km}{|r-r_0|^2}$$

But the solution is ##\frac{km}{|r-r_0|}## so I must be missing something...
That solution is the scalar field ##\phi##, not ##F##.
 
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  • #6
DrClaude said:
That solution is the scalar field ##\phi##, not ##F##.

I was computing the gradient of the wrong field! Actually, I have to compute the gradient of ##\phi##.Thanks for pointing that out.

Now we have two ways of computing it:

1):

Screenshot (152).png


I get that one.

What I do not get is the second method:

Screenshot (150).png


I am trying to get the encircled step using formula on #4 but don't get it. May you show it me?
 

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  • #7
That comes from the rule
$$
\frac{d}{dx} f(x)^{n} = n f(x)^{n-1} \frac{d}{dx} f(x)
$$
with ##n=-1##.
 
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1. What is the purpose of computing the magnitude of F using gradient?

The purpose of computing the magnitude of F using gradient is to determine the rate of change or slope of a function at a specific point. This can be useful in various fields such as physics, engineering, and economics, where understanding the rate of change is important for making predictions and analyzing data.

2. How is the magnitude of F calculated using gradient?

The magnitude of F is calculated by taking the square root of the sum of the squared partial derivatives of the function with respect to each variable. This can be represented mathematically as √(∂F/∂x)^2 + (∂F/∂y)^2 + (∂F/∂z)^2, where x, y, and z are the variables in the function.

3. What is the significance of the gradient in computing the magnitude of F?

The gradient is a vector that represents the direction and magnitude of the steepest ascent of a function. By using the gradient, we can determine the direction in which the function is changing the most rapidly, and the magnitude of this change.

4. Can the magnitude of F using gradient be negative?

No, the magnitude of F using gradient cannot be negative. This is because the square root of a negative number is undefined. Therefore, the magnitude of F is always a positive value.

5. How is the concept of gradient used in real-life applications?

The concept of gradient is used in various real-life applications, such as image processing, machine learning, and optimization problems. In image processing, gradient is used to detect edges and boundaries in images. In machine learning, gradient descent is a popular algorithm used for optimizing models. In optimization problems, gradient is used to find the minimum or maximum value of a function.

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