A main result of this lecture is df = f_u \frac{du}{dx} + f_v \frac{dv}{dx}, where f is a function of two variables u,v which are both dependent on x, and f_u, f_v are partial derivatives. The quotient rule can be derived from this result as follows. Let g(x) = \frac{u}{v}, with u,v both dependent on x :
\begin{align*} df &=f_u \frac{du}{dx}+f_v \frac{dv}{dx} \\ &= \frac{1}{v}\frac{du}{dx}-\frac{u}{v^2}\frac{dv}{dx} \\ &= \frac{ v \frac{du}{dx}-u \frac{dv}{dx} }{v^2} \end{align*}
The last expression is the quotient rule for differentiation.
This lecture inspired me to some experimentation ( play ) with Mathematica's PolarPlot function. A polar coordinate is in fact a function of two variables x,y which are both dependent on r and \theta with x=r \cos(\theta), y=r \sin(\theta). By applying the theory above one suddenly gets control over geometric objects like this:
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Finally the concept of a gradient was mentioned which is merely a vector of partial derivatives. Gradients are the topic of lecture 12. I designed some problems and exercises ( and other experiments ) for functions in polar coordinates. I am delighted I feel more able in that regard.
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