!Understanding Partial Derivatives of Coordinate Transformation

In summary, the equation for expressing the partial derivative with respect to x in terms of polar coordinates is ∂/∂x=cosθ*∂/∂r - (sinθ/r)*∂/∂θ. This is derived using the chain rule and taking into account that there are two variables, r and θ, in addition to x in the equation.
  • #1
onchoa
1
0
Hi Everyone,

I was studying coordinate transformation and I came across this equation, that I couldn't understand how it came up. Let me put it this way:
x = rcosθ

Then if I want to express the partial derivative (of any thing) with respect to x, what would be the expression? i.e. ∂/∂x=?
According to the text, answer would be
∂/∂x=cosθ*∂/∂r - (sinθ/r)*∂/∂θ

Please explain to me how to come up with this expression.

Thnaks
 
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  • #2
Hi.

$$\frac{\partial}{\partial x}=\frac{\partial}{\partial r}\frac{\partial r}{\partial x}+\frac{\partial}{\partial \theta}\frac{\partial \theta}{\partial x}$$

$$r^2=x^2+y^2$$

$$2rdr=2xdx+2ydy$$

$$\frac{\partial r}{\partial x}=\frac{x}{r}=cos \theta$$

$$tan \theta = \frac{y}{x}$$

$$\frac{d\theta}{cos^2 \theta}=-\frac{ydx}{x^2}+\frac{dy}{x}$$

$$\frac{\partial \theta}{\partial x}=-\frac{ cos^2 \theta y} {x^2} = -\frac{sin \theta} {r}$$

I hope these calculations are helpful.
 
  • #3
The chain rule:
[tex]\frac{\partial F}{\partial x}= \frac{\partial F}{\partial r}\frac{\partial r}{\partial x}+ \frac{\partial F}{\partial \theta}\frac{\partial \theta}{\partial x}[/tex]

But since there are two variable, r and [itex]\theta[/itex], just "[itex]x= r cos(\theta)[/itex]" is not enough. You also need [itex]y= r sin(\theta)[/itex]. Then we can write [itex]r= (x^2+ y^2)^{1/2}[/itex] and [itex]\theta= arctan(y/x)[/itex] so that
[tex]\frac{dr}{dx}= (1/2)(x^2+ y^2)^{-1/2}(2x)= \frac{x}{\sqrt{x^2+ y^2}}= \frac{rcos(\theta)}{r}= cos(\theta)[/tex]
and
[tex]\frac{d\theta}{dx}= \frac{1}{1+ y^2/x^2}\left(-\frac{y}{x^2}\right)= \frac{-y}{x^2+ y^2}= -\frac{r sin(\theta)}{r^2}= -\frac{1}{r}sin(\theta)[/tex]

So that
[tex]\frac{\partial F}{\partial x}= cos(\theta)\frac{\partial F}{\partial r}- \frac{sin(\theta)}{r}\frac{\partial F}{\partial \theta}[/tex]
 

Related to !Understanding Partial Derivatives of Coordinate Transformation

What is a partial derivative?

A partial derivative is a mathematical concept used in multivariable calculus to describe the rate of change of a function with respect to one of its variables, while holding all other variables constant. It is denoted by ∂ (pronounced "partial") and is often represented as ∂f/∂x, where f is the function and x is the variable.

Why do we need to understand partial derivatives of coordinate transformation?

Coordinate transformation is a fundamental concept in mathematics and physics, and understanding its partial derivatives is crucial for solving problems involving multiple variables. It allows us to analyze how a function changes as we move along different directions in a coordinate system, and is essential in fields such as engineering, economics, and statistics.

How are partial derivatives of coordinate transformation calculated?

To calculate the partial derivative of a function with respect to a specific variable, we hold all other variables constant and differentiate the function with respect to that variable. This process is similar to finding the derivative of a single variable function, except we treat the other variables as constants.

What is the chain rule and how does it apply to partial derivatives of coordinate transformation?

The chain rule is a rule in calculus that allows us to calculate the derivative of a composite function. In the context of partial derivatives of coordinate transformation, it helps us find the rate of change of a function with respect to a variable that is dependent on other variables. It is an essential tool for solving problems involving multiple variables and transformations.

What are some real-life applications of understanding partial derivatives of coordinate transformation?

Partial derivatives of coordinate transformation have various applications in real-life. They are used in fields such as economics to analyze supply and demand functions, in physics to study the motion of objects in different directions, and in engineering to optimize designs and systems. They are also used in data analysis and machine learning to understand how multiple variables affect a particular outcome.

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