Differentials and Implicit Differentiation

In summary, the conversation discusses implicit differentiation in physics, specifically in regards to Feynman's lectures. The concept is explained through an example and the rules for this operation are discussed. The conversation also touches on the idea of differentiating over functions and the notation for partial differentials. Finally, a mistake made by Feynman in the conversation is pointed out.
  • #1
Roo2
47
0

Homework Statement



I'm reviewing physics using Feynman's Lectures, and I'm finding that he frequently uses implicit differentiation in his lessons. This is unfortunate for me because I never got the hang of it beyond the simplest cases. I'm currently going through the proof that the gravitational potential inside a hollow sphere is zero. I almost got through it, but at one point (outlined below in red) he uses an implicit derivative and loses me. Could someone please explain how this works?

Implicit_zps7c650b1b.png


I'm used to two-variable implicit differentiation, where one variable (y) is dependent upon the independent variable (x). Thus, if you have an implicitly defined function such as

x^2 + y(x)^2 = 25

it can be solved by deriving with respect to this independent variable:

d(x^2)/dx + d(y^2)/dx = d(25)/dx

2x + 2y*dy/dx = 0

dy/dx = -x / y


Is this an incorrect way of thinking about it? I can't relate it to what Feynman is doing in the image above. First, he doesn't seem to be deriving with respect to anything; he's just taking the differential. What are the rules for this operation? Second, it's as if he's deriving with respect to r on the left side (r^2 ---> 2r dr) and with respect to x on the right side (a^2 + R^2 -2Rx ---> -2R dx). How is it "mathematically legal" to arbitrarily pick different variables to differentiate with respect to on the two sides of the equation?

On a hopefully more simple note, in the blue box within the red box, where did he hide the negative sign which appears in front of 2R dx?


Thanks to anyone who can help clear this up. I never really got implicit differentiation down in high school calculus and somehow I got through multivariate and diff eq without really understanding the concept.



Homework Equations



d/dx (F(y(x)) = dF/dy * dy/dx

The Attempt at a Solution



Stated above
 
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  • #2
It is just the differential
d(r^2)=2r dr
d(a^2+R^2-2Rx)=-2R dx

[tex]\text{d}F=\sum_{k=1}^n \dfrac{\partial F}{\partial x_k} \text{d}x_k[/tex]

for however many variable there are, in this case only one variable on each side.
 
  • #3
as an aside, whenever you differentiate over functions the d(whatever) is multiplied by the chain rule, quick example:

y=x^2
dy=2xdx
dy/dx=2x

a natural extension for multi variables.
 
  • #4
lurflurf said:
It is just the differential
d(r^2)=2r dr
d(a^2+R^2-2Rx)=-2R dx

[tex]\text{d}F=\sum_{k=1}^n \dfrac{\partial F}{\partial x_k} \text{d}x_k[/tex]

for however many variable there are, in this case only one variable on each side.

Thank you! I didn't notice that R is also held constant by the physical situation. So if R was also a variable in this problem, the differential would be 2r dr = 2R dR - 2xdR - 2Rdx?

joshmccraney said:
as an aside, whenever you differentiate over functions the d(whatever) is multiplied by the chain rule, quick example:

y=x^2
dy=2xdx
dy/dx=2x

a natural extension for multi variables.

That makes a lot of sense. Thanks for that explanation. So the differential is the total infinitesimal distance moved across all available degrees of freedom? Is there notation that specifies the differential across a subset of available dimensions? For example, for a 3D function F(x,y), how would you denote the differential only along x?

Finally, in the blue box from my image above, did Feynman make a mistake in dropping the negative sign, or am I missing something?

Thanks again for all the help!
 
  • #5
Roo2 said:
I didn't notice that R is also held constant by the physical situation. So if R was also a variable in this problem, the differential would be 2r dr = 2R dR - 2xdR - 2Rdx?
yes, but only if we assume a^2 is a constant (is this true? it seems x^2+y^2=a^2, and if this is the case we need to differentiate over that a^2. without more knowledge of this problem i can't say.


Roo2 said:
So the differential is the total infinitesimal distance moved across all available degrees of freedom? Is there notation that specifies the differential across a subset of available dimensions? For example, for a 3D function F(x,y), how would you denote the differential only along x?
not necessarily over all degrees of freedom. consider partial derivatives. lurflurf outlined general notation for the F(x,y). specifically, let n=2 recognizing x_2 is equivalent to y (this is natural at higher dimensions). it seems with your hypothetical R you understand, so not sure where the confusion is.

Roo2 said:
Finally, in the blue box from my image above, did Feynman make a mistake in dropping the negative sign, or am I missing something?
it seems so, though sometimes physicists are permitted to make assumptions mathematicians are not. nonetheless, what about the x that's in the a (assuming x^2+y^2=a^2)?
 
  • #6
Thank you! I didn't notice that R is also held constant by the physical situation. So if R was also a variable in this problem, the differential would be 2r dr = 2R dR - 2xdR - 2Rdx?
Yes that is right.


That makes a lot of sense. Thanks for that explanation. So the differential is the total infinitesimal distance moved across all available degrees of freedom? Is there notation that specifies the differential across a subset of available dimensions? For example, for a 3D function F(x,y), how would you denote the differential only along x?
I prefer to thing of it as the closest linear function. Often it is helpful to approximate differences of a function by small but not infinitesimal differentials. Also dealing with infinitesimals carelessly leads to logical errors. There is such a thing as a partial differential
[tex]d_{x_k}F=\dfrac{\partial F}{\partial x_k} \text{d}x_k[/tex]
so
[tex]\text{d}F=\sum_{k=1}^n d_{x_k}F=\sum_{k=1}^n \dfrac{\partial F}{\partial x_k} \text{d}x_k[/tex]

The total differential is invariant, meaning it is the same in any coordinate system. Partial differentials do not share this advantage, limiting their usefulness.


Finally, in the blue box from my image above, did Feynman make a mistake in dropping the negative sign, or am I missing something?
Yes a minus disappeared.

Some of these confusions become clear when we realize each (partial or total) derivative (or differential) we see depends upon how the variables are related. It is convenient to omit such information when obvious. It is a minor abuse to denote a function f(x) as y because F(x,y)=F(x,f(x)) for example.
 
  • #7
I think there was no reason to omit the minus sign..
 

Related to Differentials and Implicit Differentiation

1. What is a differential?

A differential is a mathematical concept that represents the instantaneous rate of change of a function at a given point. It is denoted by dy/dx and can be thought of as the slope of a tangent line at that point.

2. How is implicit differentiation different from regular differentiation?

Implicit differentiation is used when a function is not explicitly defined in terms of one variable. It involves taking the derivative with respect to one variable while treating all other variables as functions of that variable. Regular differentiation is used when a function is explicitly defined in terms of one variable.

3. What is the chain rule and how is it used in implicit differentiation?

The chain rule is a rule in calculus that is used to find the derivative of a composite function. In implicit differentiation, the chain rule is used to find the derivative of the dependent variable with respect to the independent variable.

4. Can implicit differentiation be used to solve for multiple variables?

Yes, implicit differentiation can be used to solve for the derivatives of multiple variables in a single equation. It is useful when solving for rates of change in complex systems that involve multiple variables.

5. What are some common applications of implicit differentiation?

Implicit differentiation is commonly used in physics, economics, and engineering to model and analyze real-world systems. It is also used in optimization problems, such as finding the maximum or minimum value of a function.

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