Understanding transition from full derivative to partial

In summary, the conversation discusses the use of implicit differentiation to find the laplacian from cartesian to cylindrical and spherical coordinates. The approach seems to work in cases where the variables are independent, but may give different results in cases where the variables are dependent. This is because partial derivatives only consider the variables in question and do not take into account functional relationships between them.
  • #1
TheCanadian
367
13
I was looking over a derivation to find the laplacian from cartesian to cylindrical and spherical coordinates here: http://skisickness.com/2009/11/20/

Everything seems fine, but there is an instance (I have attached a screenshot) where implicit differentiation is done to find $$ \frac {\partial \phi}{\partial x} $$ by finding $$ \frac {d\phi}{dx} $$ implicitly and then equating this to $$ \frac {\partial \phi}{\partial x} $$ It seems like they are two separate equations. In this instance, x was independent of y, but in a future scenario, if x and y were dependent (e.g. y = kx), then the implicit differentiation would give a different result, right? In that case, would this approach work to still give $$ \frac {d\phi}{dx} = \frac {\partial \phi}{\partial x} {\text ?}$$

I understand the strategy based on the above equation (in the link) where they are trying to find each respective partial derivative to go from cartesian to cylindrical coordinates, but equating the full derivative to the partial derivative seems a little off. It looks like it works in this particular case because x and y are independent, but if they were dependent, would this approach work?
 

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  • #2
It might help to look at a really simple example, like ## f(x,y)=xy ##. Then implicit differentiation wrt ## x ## gives ## df = ydx ##. But this change in ## f ## assumes ## y ## is held constant, so a better way to write it might be ## (df)_y = y (dx) ##. Then "dividing through by ## dx ##" just yields ##\frac{\partial f}{\partial x} = y##. If ## y ## doesn't depend on ## x ## then ##\frac{dy}{dx}=0 ##, so that ## \frac{df}{dx}=\frac{\partial f}{\partial x}+\frac{\partial f}{\partial y}\frac{dy}{dx}=\frac{\partial f}{\partial x}=y##.

An important thing to keep in mind is that partial derivatives don't "care" about functional relationships that may exist between the arguments. Let's take the previous example but suppose that ## y ## depends on ## x ## this time as, say, ## y = x^3 ##. We would still have ## \frac{\partial f}{\partial x} = y ##. On the other hand, we would now write ## \frac{df}{dx} = \frac{\partial f}{\partial x}+\frac{\partial f}{\partial y}\frac{dy}{dx} = y + 3x^3 = 4x^3. ## Because ## \frac{\partial f}{\partial x} = y = x^3 ##, we no longer have ## \frac{\partial f}{\partial x}=\frac{df}{dx}##.
 
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1. What is a full derivative?

A full derivative is a mathematical term that refers to the rate of change of a function with respect to its independent variable. In other words, it measures how much a function changes when its input value changes.

2. How is a partial derivative different from a full derivative?

A partial derivative is similar to a full derivative in that it measures the rate of change of a function. However, it is specifically used to measure the rate of change of a function with respect to one of its variables while holding all other variables constant. In contrast, a full derivative considers the rate of change of a function with respect to all of its variables at once.

3. Why is understanding the transition from full derivative to partial important?

Understanding the transition from full derivative to partial is important because it allows us to analyze and optimize functions with multiple variables. By breaking down a function into its partial derivatives, we can identify which variables have the most impact on the overall output and make informed decisions based on that information.

4. What are some real-world applications of partial derivatives?

Partial derivatives have many applications in fields such as physics, economics, and engineering. For example, in physics, partial derivatives are used to analyze the rate of change of a physical quantity with respect to multiple variables, such as velocity with respect to both time and distance. In economics, partial derivatives are used to determine the impact of changing one variable, such as price, on the overall output or profit. In engineering, partial derivatives are used to optimize complex systems by identifying which variables have the most influence on the system's performance.

5. How can I improve my understanding of the transition from full derivative to partial?

The best way to improve your understanding of the transition from full derivative to partial is to practice solving problems and working with real-world applications. Additionally, reading textbooks or taking courses on calculus or multivariable calculus can provide a deeper understanding of the concepts and their applications. Finally, seeking help from a tutor or discussing the concepts with peers can also be helpful in clarifying any confusion or misconceptions.

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