Basic doubts in vector and multi variable calculus

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Discussion Overview

The discussion revolves around understanding the concept of partial derivatives and their application in vector and multivariable calculus, particularly in relation to scalar functions like temperature. Participants explore how changes in a scalar function can be expressed in terms of its partial derivatives and the implications of these expressions in different directions.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant questions the presence of ##dy## in the equation for the change in a scalar function ##T(x,y,z)## when moving in the ##Y## direction, expressing confusion about how this relates to the change in the function.
  • Another participant suggests that the equation represents the total derivative and relates it to Taylor's theorem, indicating that the total change in ##T## can be approximated by the sum of changes in all three directions.
  • Several participants propose testing the equation with specific functions and small changes to verify its accuracy, particularly for functions of two variables.
  • There is a discussion about deriving the equation for a specific function, ##T(x,y) = x + y##, and how to approach proving the relationship between the changes in the function and its partial derivatives.
  • Participants emphasize the importance of understanding the concept of partial derivatives as rates of change and suggest that this understanding should be geometrically intuitive.
  • One participant references the definition of the derivative in single-variable calculus to draw parallels with the multivariable case.

Areas of Agreement / Disagreement

Participants express varying levels of understanding regarding the application and derivation of the equation involving partial derivatives. There is no consensus on a single method of proof, and some participants remain uncertain about the derivation process.

Contextual Notes

Some participants mention the need for numerical approximations to test the validity of the equations discussed. The discussion includes references to Taylor's theorem and the definition of derivatives, but these concepts are not universally agreed upon as straightforward or intuitive.

Hamiltonian
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dT.png

If say we have a scalar function ##T(x,y,z)## (say the temperature in a room). then the rate at which T changes in a particular direction is given by the above equation)
say You move in the ##Y##direction then ##T## does not change in the ##x## and ##z## directions hence ##dT = \frac{\partial T}{\partial y}dy##
I don't understand why there is a ##dy## in this equation.
basically, I don't understand how the above equation gives the change of the function ##T## in any particular direction. My books says a theorem on partial derivatives states the above equation but I am unable to find any such theorem(maybe because it is obvious? but if it is I don't see why).
 
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This is essentially the definition of the total derivative. I'll give a Physicsy explanation... you can think of it like, ##\frac{\partial T}{\partial x} \Delta x## is the approximate change in ##T## when moving a small distance ##\Delta x## in the ##x## direction, and the same for ##y## and ##z##. If that's not clear, then think back to this statement of Taylor's theorem in one-dimension, $$f(x + \Delta x) \approx f(x) + \Delta x f'(x) \implies \Delta f(x) \approx f'(x) \Delta x$$The total change in ##T## along a small displacement ##\Delta \vec{r} = \Delta x\hat{x} + \Delta y\hat{y} + \Delta z\hat{z}## just going to be the sum of the changes due to moving in all three directions, i.e. approximately ##\Delta T = \frac{\partial T}{\partial x} \Delta x + \frac{\partial T}{\partial y} \Delta y + \frac{\partial T}{\partial z} \Delta z##. The exact statement is$$dT = \frac{\partial T}{\partial x} dx + \frac{\partial T}{\partial y} dy + \frac{\partial T}{\partial z} dz = \nabla T \cdot d\vec{r}$$
 
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Hamiltonian299792458 said:
Summary:: -

View attachment 271587
If say we have a scalar function ##T(x,y,z)## (say the temperature in a room). then the rate at which T changes in a particular direction is given by the above equation)
say You move in the ##Y##direction then ##T## does not change in the ##x## and ##z## directions hence ##dT = \frac{\partial T}{\partial y}dy##
I don't understand why there is a ##dy## in this equation.
basically, I don't understand how the above equation gives the change of the function ##T## in any particular direction. My books says a theorem on partial derivatives states the above equation but I am unable to find any such theorem(maybe because it is obvious? but if it is I don't see why).
You could take an example function ##T## and a small change in ##x, y, z## and compute the change in ##T## and check the above equation is a good approximation for small ##dx, dy, dz##.

PS to keep things simple, do it for a function of two variables.
 
PeroK said:
You could take an example function ##T## and a small change in ##x, y, z## and compute the change in ##T## and check the above equation is a good approximation for small ##dx, dy, dz##.

PS to keep things simple, do it for a function of two variables.
I am a bit confused about how exactly I can prove the above equation using this method.

say I try to prove it for ##T(x,y) = x + y## are you suggesting I take numerical values and then prove the above equation?
 
Hamiltonian299792458 said:
say $$T(x, y) = x + y$$
$$\Delta T = \Delta x\hat x + \Delta y\hat y$$
how exactly should I proceed from here to prove the above equation?
First you have to understand what the equation in your OP is saying:

If we have ##T## at some point ##T(x_0, y_0)## and we look at ##T(x_0 + dx, y_0 + dy)##, where we will take ##dx, dy## to be small and finite. Then:
$$dT \equiv T(x_0 + dx, y_0 + dy) -T(x_0, y_0) \approx \frac{\partial T(x_0, y_0)}{\partial x} dx + \frac{\partial T(x_0, y_0)}{\partial y} dy$$ where that means the partial derivatives are evaluated at ##(x_0, y_0)##.

And, when ##dx, dy## are differentials, we have equality, rather than a finite approximation.
 
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$$T(x_0 + dx, y_0 + dy) -T(x_0, y_0) \approx \frac{\partial T(x_0, y_0)}{\partial x} dx + \frac{\partial T(x_0, y_0)}{\partial y} dy$$
how exactly are u getting this?
 
Hamiltonian299792458 said:
$$T(x_0 + dx, y_0 + dy) -T(x_0, y_0) \approx \frac{\partial T(x_0, y_0)}{\partial x} dx + \frac{\partial T(x_0, y_0)}{\partial y} dy$$
how exactly are u getting this?
I'm suggesting you test it out as a numerical approximation. And get a feel for why it works.
 
PeroK said:
I'm suggesting you test it out as a numerical approximation. And get a feel for why it works.
ok,
so if $$T(x,y) = x + y$$
##x_0 = 1## ##y_0 = 1##
$$T(1+dx, 1+dy) - T(1, 1) = 1 + dx + 1 + dy -1 -1 = dx + dy$$
$$\frac {\partial T}{\partial x}dx + \frac{\partial T}{\partial y} dy = dx + dy$$

ok so the above equation is true but how exactly did you derive it?
 
Hamiltonian299792458 said:
ok,
so if $$T(x,y) = x + y$$
##x_0 = 1## ##y_0 = 1##
$$T(1+dx, 1+dy) - T(1, 1) = 1 + dx + 1 + dy -1 -1 = dx + dy$$
$$\frac {\partial T}{\partial x}dx + \frac{\partial T}{\partial y} dy = dx + dy$$

ok so the above equation is true but how exactly did you derive it?
It's not much more than using the concept of a partial derivative as a rate of change. It should be geometrically intuitive.
 
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For example, the single variable case falls out of the definition of the derivative:
$$f'(x_0) = \lim_{\Delta x \rightarrow 0} \frac{f(x_0 + \Delta x) - f(x_0)}{\Delta x}$$ Which means that for small ##\Delta x## we have $$\Delta f \equiv f(x_0 + \Delta x) - f(x_0) \approx f'(x_0)\Delta x$$ Hence the definition of the single-variable differential: $$df = f'(x)dx$$
 
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