Proof of the total differential of f(x,y)?

In summary, the conversation revolves around showing that the expression Δz ≈ (∂z/∂x)Δx + (∂z/∂y)Δy is a good approximation for a smooth, continuous function of 2 variables. The participants discuss different approaches, including using the chain rule and putting a tangent plane at a specific point. They also acknowledge that the demonstration of this approximation is fundamental in proving the chain rule.
  • #1
Curl
758
0
If I have a smooth, continuous function of 2 variables, z=f(x,y)

I want to show what Δz ≈ (∂z/∂x)Δx + (∂z/∂y)Δy

Most places I've seen call this a definition, but it's not really that obvious. I know that it makes perfect sense geometrically, but I want a little more.

One way I thought of approaching it is to put a tangent plane at the point x0 y0 and show that going along x then along y is like cutting diagonally across to x,y.
Basically I need to show that f(x+Δx ,y+Δy) = f(x+Δx, y) +f(x, y+Δy) - f(x,y).

Unfortunately I'm not good at math, not good at proofs, tired, and a bit busy/lazy :), so I'm calling in the troops. Thanks!
 
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  • #2
Hi Curl! :smile:

Try starting with f(x+Δx ,y+Δy) - f(x,y)

= f(x+Δx, y+∆y) - f(x+Δx, y) + f(x+Δx, y) - f(x, y). :wink:
 
  • #3
Another way of looking at it is this: suppose x and y were functions of some parameter, t.

Then f(t)= f(x(t),y(t)) and, by the chain rule,
[tex]\frac{df}{dt}= \frac{\partial f}{\partial x}\frac{dx}{dt}+ \frac{\partial f}{\partial y}\frac{dy}{dt}[/tex]
In terms of the differential, we can write that as
[tex]df= \frac{df}{dt}dt= \left(\frac{\partial f}{\partial x}\frac{dx}{dt}+ \frac{\partial f}{\partial y}\frac{dy}{dt}\right)dt= \frac{\partial f}{\partial x}dx+ \frac{\partial f}{\partial y}dy[/tex]
which is now independent of t.
 
  • #4
tiny-tim said:
Hi Curl! :smile:

Try starting with f(x+Δx ,y+Δy) - f(x,y)

= f(x+Δx, y+∆y) - f(x+Δx, y) + f(x+Δx, y) - f(x, y). :wink:
hehehe, clever! thanks.
And yes, I've thought of using the chain rule, but at this point we can't prove the chain rule without proving this. So it's like the chicken and the egg.
 
Last edited:
  • #5
Curl said:
If I have a smooth, continuous function of 2 variables, z=f(x,y)

I want to show what Δz ≈ (∂z/∂x)Δx + (∂z/∂y)Δy

Most places I've seen call this a definition, but it's not really that obvious. I know that it makes perfect sense geometrically, but I want a little more.
Just FYI, it sounds like what you are really asking for is a demonstration differential approximations are good approximations.
 

Related to Proof of the total differential of f(x,y)?

What is the total differential of a function?

The total differential of a function f(x,y) is a measure of how much the function changes when both x and y vary. It takes into account the partial derivatives of the function with respect to both variables.

How is the total differential of a function calculated?

The total differential of a function f(x,y) is calculated using the following formula:
df = ∂f/∂x * dx + ∂f/∂y * dy,
where ∂f/∂x and ∂f/∂y are the partial derivatives of the function with respect to x and y, and dx and dy are the changes in x and y, respectively.

Why is the total differential important in mathematics and science?

The total differential is important because it allows us to understand how a function changes when multiple variables are involved. This is useful in many areas of mathematics and science, such as optimization, economics, and physics.

Can the total differential be used to approximate the change in a function?

Yes, the total differential can be used to approximate the change in a function. This is especially useful when dealing with small changes in the variables, as it provides a more accurate estimate than using only the partial derivatives.

How does the concept of total differential relate to the concept of total derivative?

The total differential is closely related to the total derivative, as both concepts involve considering the change in a function with respect to multiple variables. However, the total derivative also takes into account the change in the independent variables, while the total differential only considers the changes in the dependent variables.

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