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author | Preston Pan <preston@nullring.xyz> | 2023-07-23 09:12:03 -0700 |
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committer | Preston Pan <preston@nullring.xyz> | 2023-07-23 09:12:03 -0700 |
commit | 80da24887ac760a9d18936634d8d46c0643521ee (patch) | |
tree | 20c7846353ca983a10c724d965631e28d3fe0587 /mindmap/derivative.org | |
parent | c335c05f511a373681d8644500d7750a519f58fa (diff) |
add a lot of mindmap articles
Diffstat (limited to 'mindmap/derivative.org')
-rw-r--r-- | mindmap/derivative.org | 152 |
1 files changed, 152 insertions, 0 deletions
diff --git a/mindmap/derivative.org b/mindmap/derivative.org new file mode 100644 index 0000000..be84116 --- /dev/null +++ b/mindmap/derivative.org @@ -0,0 +1,152 @@ +:PROPERTIES: +:ID: 31d3944a-cddc-496c-89a3-67a56e821de3 +:END: +#+title: derivative +#+author: Preston Pan +#+html_head: <link rel="stylesheet" type="text/css" href="../style.css" /> +#+html_head: <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script> +#+html_head: <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script> +#+options: broken-links:t +#+OPTIONS: tex:dvipng + +* Derivation +Let's say we want to know the rate of change of the [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] \(f(x) = x^{2}\). Because this [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] is not +linear (the [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] is a parabola and is therefore curved), we can only take the rate of change by finding +the tangent line to a point on the curve. In other words, for any point \((x_{0}, y_{0})\), we want to find +the straight line that touches this point, and no other points on the parabola. + +The gameplan will look like this: first, we find the slope between two points \((x_{0}, y_{0})\) and \((x_{0} + dx, y_{0} + dy)\), +where \(dx\) and \(dy\) are the difference in x and y respectively, or in other words, just a small change in x and change in y. +Then, we make these points infinitely close to each other and see what the slope is. This resulting line will be infinitely close +to the tangent line. + +Now we want to find the equation for the slope between these two points: +\begin{align*} +m = \frac{y_{1} - y_{0}}{x_{1} - x_{0}} \\ +m = \frac{y_{0} + dy - y_{0}}{x_{0} + dx - x_{0}} \\ +m = \frac{dy}{dx} +\end{align*} + +Because both of these points need to satisfy the [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] \(f(x) = x^{2}\): +\begin{align*} +y_{0} = x_{0}^{2} \\ +y_{0} + dy = (x_{0} + dx)^{2} \\ +dy = (x_{0} + dx)^{2} - y_{0} \\ +dy = (x_{0} + dx)^{2} - x_{0}^{2} \\ +m = \frac{(x_{0} + dx)^{2} - x_{0}^{2}}{dx} +\end{align*} + +Then we use a binomial expansion: +\begin{align*} +m = \frac{x_{0}^{2} + 2x_{0}dx + dx^{2} - x_{0}^{2}}{dx} \\ += \frac{2x_{0}dx + dx^{2}}{dx} \\ += 2x_{0} + dx +\end{align*} +Now we see that since \(dx\) is infinitely close to zero (but not zero because otherwise that would be dividing by zero), we can say that +the tangent line of \(x^{2}\) at this point is: +\begin{align*} +2x_{0} +\end{align*} +And since this works for all points over \(f(x)\), we can simply say: +\begin{align*} +\frac{dy}{dx} = 2x \\ +f'(x) = 2x +\end{align*} +These two notations are both valid. The first is called Leibniz notation, and the second is called Lagrange notation. + +* Definition +Note that you can easily show that the process we did for \(x^{2}\) works for most functions, and is defined as follows: +\begin{align*} +\frac{d}{dx}f(x) = \lim_{h\to0}\frac{f(x + h) - f(x)}{h} +\end{align*} +This \(lim_{h\to0}\) notation is a limit. It broadly dictates that \(h\) is going infinitely close to zero but is not exactly zero. You +will also see \(\frac{d}{dx}\) used as an operator on a [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] much of the time, which also means you're taking a derivative with +whatever \(\frac{d}{dx}\) is multiplied with. + +** Higher Derivatives +The notation \( \frac{d^{n}}{dx^{n}}f(x) \) denotes taking \(n\) derivatives of \(f(x)\), one after the other. \(f''(x)\) works for second derivatives, and so on. +However, this gets annoying, so you can use \( f^{(n)}(x) \) as the \(n^{th}\) derivative of \( f(x) \) as well. + +* Derivative Rules +Usually, instead of using the definition in order to calculate derivatives, we use some simpler rules to do so. +We derive many of them here. +** Addition Rule +\begin{align*} +\frac{d}{dx}(f(x) + g(x)) = \lim_{h\to0}\frac{f(x + h) + g(x + h) - f(x) - g(x)}{h} = \lim_{h\to0}\frac{f(x + h) - f(x) + g(x + h) - g(x)}{h} = \lim_{h\to0}\frac{f(x + h) - f(x)}{h} + \frac{g(x + h) - g(x)}{h} \\ += \frac{d}{dx}f(x) + \frac{d}{dx}g(x) +\end{align*} +of course, subtraction works in the same way. +** Multiplication Rule +\begin{align*} +\frac{d}{dx}(f(x)g(x)) = \lim_{h\to0}\frac{f(x + h)g(x + h) - f(x)g(x)}{h} = \lim_{h\to0}\frac{f(x + h)g(x + h) - f(x)g(x + h) + f(x)g(x + h) - f(x)g(x)}{h} \\ += \lim_{h\to0}\frac{g(x + h)(f(x + h) - f(x)) + f(x)(g(x + h) - g(x))}{h} \\ += g(x)\lim_{h\to0}\frac{f(x + h) - f(x)}{h} + f(x)\frac{g(x + h) - g(x)}{h} = g(x)f'(x) + g'(x)f(x) +\end{align*} +And using the this rule as well as the chain rule and power rule which we will show later, the division rule is easily acquired. +** Chain Rule +:PROPERTIES: +:ID: ffd1bc3d-ab64-4916-9c09-0c89d2731b6d +:END: +The chain rule is a rule about nested functions in the form \( (f \circ g)(x) \). +Using Leibniz notation, it is easy to given an intuition on something called the chain rule: +\begin{align*} +\frac{dy}{dz}\frac{dz}{dx} = \frac{dy}{dx} +\end{align*} +Which, in other words, reads: if you have a [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] \(y\) which has a [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] \(z\) inside of it that is +dependent on \(x\), then \(y'(x) = y'(z(x))z'(x)\). We have manipulated things in the form \(dy\), \(dz\), \(dx\) +before all as regular variables, so although people say this is not rigorous, I would say that it in fact is. +You can treat these "differentials" as regular variables. +** Derivative Rules for Particular Functions +*** Power Rule +*** Sinusoidal Functions +*** Exponential Functions +By the definition of a derivative: +\begin{align*} +\lim_{h\to0}\frac{a^{x + h} - a^{x}}{h} = a^{x}\lim_{h\to0}\frac{a^{h} - 1}{h} +\end{align*} +The constant \(e\) is defined such that: +\begin{align*} +\lim_{h\to0}\frac{e^{h} - 1}{h} = 1; \\ +\frac{d}{dx}e^{x} = e^{x} +\end{align*} +Then by the chain rule: +\begin{align*} +\frac{d}{dx}a^{x} = \frac{d}{dx}(e^{\ln(a)})^{x} = \frac{d}{dx}e^{\ln(a)x}= \ln(a)e^{\ln(a)x} +\end{align*} +And therefore: +\begin{align*} +\lim_{h\to0}\frac{a^{h} - 1}{h} = \ln(a) +\end{align*} +* Implicit Differentiation +The equation of a circle centered at the origin is: +\begin{align*} +x^{2} + y^{2} = r^{2} +\end{align*} +This [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] is clearly dependent on \(y\), and no, we don't need to do algebra to isolate the y (yet, we can do that later). instead, +we can simply take the derivative of both sides: +\begin{align*} +\frac{d(x^{2} + y^{2})}{dx} = \frac{d(r^{2})}{dx} +\end{align*} +the right hand side is obviously going to reduce to zero because it is a constant inside a derivative. +Because we consider \(y = y(x)\), taking the derivative of \(y\) in terms of \(x\) means we have to apply +the chain rule. +\begin{align*} +2x + 2y(x) * y'(x) = 0 +\end{align*} +Remember, the [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] we are taking the derivative of here is \((y(x))^{2}\), which is why the \(y'(x)\) term +appears; you're doing the chain rule on an inner [[id:b1f9aa55-5f1e-4865-8118-43e5e5dc7752][function]] that you don't know the value of but that you can represent +nonetheless. + +Now, we move everything to the other side in order to find \(y'(x)\): +\begin{align*} +y'(x) = -\frac{x}{y(x)} +\end{align*} + +and then we finally find \(y(x)\) and substitute it in: +\begin{align*} +y(x) = (r^{2} - x^{2})^{\frac{1}{2}} \\ +y'(x) = -\frac{x}{(r^{2} - x^{2})^{\frac{1}{2}}} +\end{align*} +The benefit of this strategy is that you can find the derivative of a circle (or as we will see later, many other curves) in terms of \(y\), +which is useful for converting coordinate systems. Implicit differentiation is also useful for some other things, like: +** Derivative of [[id:4f088813-cf40-4194-9251-b2392a50dc1c][Inverse]] Function |