How Does the Total Derivative Sum Up Changes in Multiple Directions?

In summary: So, if you know how the change in a linear object changes when you increase one of its coordinates by a small amount, you can approximate the change of the function in the same way, by adding the change in the coordinates.
  • #1
Agent 47
10
0
##dz = \frac{\partial z}{\partial x} dx + \frac{\partial z}{\partial y} dy##

I'm confused as to how the total derivative represents the total change in a function.

My own interpretation, which I know is incorrect, is that ##\frac{\partial z}{\partial x} dx## represents change in the x direction and ##\frac{\partial z}{\partial y} dy## change in the y direction, and to get the total change you would need to square both and take the square root much like finding the magnitude of a vector.

Obviously this is not the case because, as the equation shows, you simply need to add the two terms together.

So my overarching question is: Could someone please intuitively explain what the total differential represents and why it's a simple sum?
 
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  • #2
The total derivative is the best linear approximation to the (local) change in values of a differentiable function.

The case you use in your post is that of a tangent plane. Given a 2-dimensional object Y:=f(X) embedded in R^n, if f is differentiable at a point y in f(X), this is equivalent to saying that , near y=f(x), the change ##f(x)-f(x_0) ## can be
(locally, unless f is itself linear) approximated to any degree of accuracy, in a delta-epsilon sense, by a linear function L(x_o, \epsilon); for Y 2-dimensional , L is the plane tangent to Y at x=x_o.
 
  • #3
Then my followup question would be how does the addition of the two tangent lines actually equate to the correct tangent plane. Is there a proof for tangent plane approximation that doesn't use the chain rule?
 
  • #4
I don't understand what you mean by the addition of tangent lines. How is this addition defined?
 
  • #5
Sorry for the vagueness. Here is what I'm referring to:
ocgulog.png

The way it's presented in my textbook makes it seem like the addition of two tangent lines lying on the same plane. What I'm wondering is how adding two lines lying in the same plane actually gives you that plane.
 
  • #6
But notice you are not really adding lines, because lines have a fixed slope; neither ## f_y(x_0,y_0)## nor ##f_x(x_0,y_0) ## are constant; they depend on the choice of ##(x_0,y_0)##.
 
  • #7
I would answer your original post by saying that z is a scalar function of (x,y). The change in z has no direction, so your vector anologue is inappropriate. The equation says that if you know how the scalar z changes when you increase x by dx, and you separately know what happens to z when you increase y by dy, then if you want to know what happens if you increase both x and y, you add the two effects (at least locally when dx and dy are very small).
 
  • #8
Agent 47 said:
##dz = \frac{\partial z}{\partial x} dx + \frac{\partial z}{\partial y} dy##

I'm confused as to how the total derivative represents the total change in a function.
<Snip>

So my overarching question is: Could someone please intuitively explain what the total differential represents and why it's a simple sum?

The (total) derivative does not represent the total change of a function. The differential is an approximation to the total change. The best intuition I can think of is that the change of a differentiable function looks locally like the change of a linear object ( line, plane, higher-dimensional equivalent ).
 

FAQ: How Does the Total Derivative Sum Up Changes in Multiple Directions?

What is the concept of total derivative intuition?

Total derivative intuition is a mathematical concept used in calculus to understand how a function changes with respect to its inputs. It involves taking partial derivatives of a function and combining them to find the total derivative, which represents the overall change in the function.

How is total derivative intuition applied in real-world situations?

Total derivative intuition is used in various fields such as physics, economics, and engineering to model and analyze how a system or process changes over time. It can be used to optimize processes, predict future outcomes, and understand the behavior of complex systems.

What is the difference between total derivative intuition and partial derivative intuition?

The main difference between total derivative intuition and partial derivative intuition is that total derivatives take into account changes in all of the function's input variables, while partial derivatives only consider changes in one input variable while holding the others constant. Total derivatives provide a more complete understanding of a function's behavior.

What are some common techniques for finding total derivatives?

There are several techniques for finding total derivatives, including the chain rule, product rule, quotient rule, and the total derivative formula. These techniques involve taking partial derivatives and combining them in different ways to find the total derivative of a function.

Can total derivative intuition be extended to multivariate functions?

Yes, total derivative intuition can be extended to multivariate functions, which involve multiple input variables. In this case, the total derivative is a vector that represents the change in each input variable. The same techniques used for single variable functions can be applied to multivariate functions.

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