WEBVTT

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The months ahead of you hold within them a lot of hard work, some neat examples,

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some not-so-neat examples, beautiful connections to physics,

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not-so-beautiful piles of formulas to memorize,

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plenty of moments of getting stuck and banging your head into a wall,

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a few nice aha moments sprinkled in as well, and some genuinely lovely

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graphical intuition to help guide you through it all.

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But if the course ahead of you is anything like my first introduction to calculus,

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or any of the first courses I've seen in the years since,

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there's one topic you will not see, but which I believe stands to greatly accelerate

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your learning.

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You see, almost all of the visual intuitions from that first year are based on graphs.

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The derivative is the slope of a graph, the integral is a certain area under that graph.

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But as you generalize calculus beyond functions whose inputs and outputs are

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simply numbers, it's not always possible to graph the function you're analyzing.

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So if all your intuitions for the fundamental ideas, like derivatives,

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are rooted too rigidly in graphs, it can make for a very tall and largely

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unnecessary conceptual hurdle between you and the more quote-unquote advanced

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topics like multivariable calculus and complex analysis, differential geometry.

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What I want to share with you is a way to think about derivatives,

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which I'll refer to as the transformational view,

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that generalizes more seamlessly into some of those more general contexts

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where calculus comes up.

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And then we'll use this alternate view to analyze a fun puzzle about repeated fractions.

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But first off, I just want to make sure we're all

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on the same page about what the standard visual is.

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If you were to graph a function, which simply takes real numbers as inputs and outputs,

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one of the first things you learn in a calculus course is that the derivative gives

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you the slope of this graph, where what we mean by that is that the derivative of

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the function is a new function which for every input x returns that slope.

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Now I'd encourage you not to think of this derivative

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as slope idea as being the definition of a derivative.

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Instead think of it as being more fundamentally about how

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sensitive the function is to tiny little nudges around the input.

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And the slope is just one way to think about that sensitivity

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relevant only to this particular way of viewing functions.

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I have not just another video, but a full series on this

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topic if it's something you want to learn more about.

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The basic idea behind the alternate visual for the derivative is to

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think of this function as mapping all of the input points on the

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number line to their corresponding outputs on a different number line.

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In this context, what the derivative gives you is a measure of how

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much the input space gets stretched or squished in various regions.

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That is, if you were to zoom in around a specific input and take a look at some

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evenly spaced points around it, the derivative of the function of that input is

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going to tell you how spread out or contracted those points become after the mapping.

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Here, a specific example helps.

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Take the function x2, it maps 1 to 1, 2 to 4, 3 to 9, and so on.

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You can also see how it acts on all of the points in between.

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If you were to zoom in on a little cluster of points around the input 1,

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and see where they land around the relevant output,

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which for this function also happens to be 1, you'd notice that they tend

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to get stretched out.

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In fact, it roughly looks like stretching out by a factor of 2.

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The closer you zoom in, the more this local behavior looks just like multiplying by a

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factor of 2. This is what it means for the derivative of x2 at the input x equals 1 to be

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2.

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It's what that fact looks like in the context of transformations.

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If you looked at a neighborhood of points around the input 3,

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they would get stretched out by a factor of 6.

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This is what it means for the derivative of this function at the input 3 to equal 6.

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Around the input 1 fourth, a small region tends to get contracted specifically by a

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factor of 1 half, and that's what it looks like for a derivative to be smaller than 1.

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The input 0 is interesting.

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Zooming in by a factor of 10, it doesn't really

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look like a constant stretching or squishing.

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For one thing, all of the outputs end up on the right positive side of things.

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As you zoom in closer and closer, by 100x, or by 1000x,

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it looks more and more like a small neighborhood of points around 0 just

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gets collapsed into 0 itself. This is what it looks like for the derivative to be 0.

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The local behavior looks more and more like multiplying the whole number line by 0.

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It doesn't have to completely collapse everything to a point at a particular zoom level,

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instead it's a matter of what the limiting behavior is as you zoom in closer and closer.

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It's also instructive to take a look at the negative inputs here.

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Things start to feel a little cramped since they collide with where all the positive

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input values go, and this is one of the downsides of thinking of functions as

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transformations.

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But for derivatives, we only really care about the local behavior anyway,

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what happens in a small range around a given input.

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Here, notice that the inputs in a little neighborhood around, say,

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negative 2, don't just get stretched out, they also get flipped around.

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Specifically, the action on such a neighborhood looks more

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and more like multiplying by negative 4 the closer you zoom in.

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This is what it looks like for the derivative of a function to be negative.

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And I think you get the point, this is all well and good,

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but let's see how this is actually useful in solving a problem.

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A friend of mine recently asked me a pretty fun question about the infinite

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fraction 1 plus 1 divided by 1 plus 1 divided by 1 plus 1 divided by 1,

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and clearly you watch math videos online, so maybe you've seen this before,

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but my friend's question actually cuts to something you might not have

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thought about before, relevant to the view of derivatives we're looking at here.

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The typical way you might evaluate an expression like this is to set it equal to x,

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and then notice that there is a copy of the full fraction inside itself.

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So you can replace that copy with another x, and then just solve for x.

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That is, what you want is to find a fixed point of the function 1 plus 1 divided by x.

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But here's the thing, there are actually two solutions for x,

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two special numbers where 1 plus 1 divided by that number gives you back the same thing.

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One is the golden ratio, phi, around 1.618, and the other is negative 0.618,

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which happens to be negative 1 divided by phi.

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I like to call this other number phi's little brother,

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since just about any property that phi has, this number also has.

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And this raises the question, would it be valid to say that the infinite

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fraction we saw is somehow also equal to phi's little brother, negative 0.618?

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Maybe you initially say, obviously not, everything on the left hand side is positive,

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so how could it possibly equal a negative number?

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Well, first we should be clear about what we actually mean by an expression like this.

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One way you could think about it, and it's not the only way,

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there's freedom for choice here, is to imagine starting with some constant, like 1,

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and then repeatedly applying the function 1 plus 1 divided by x, and then asking,

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what is this approach as you keep going?

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I mean, certainly symbolically what you get looks more and more

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like our infinite fraction, so maybe if you wanted to equal a number,

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you should ask what this series of numbers approaches.

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And if that's your view of things, maybe you start off with a negative number,

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so it's not so crazy for the whole expression to end up negative.

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After all, if you start with negative 1 divided by phi,

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then applying this function 1 plus 1 over x, you get back the same number,

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negative 1 divided by phi, so no matter how many times you apply it,

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you're staying fixed at this value.

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But even then, there is one reason you should

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view phi as the favorite brother in this pair.

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Here, try this, pull up a calculator of some kind, then start with any random number,

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and plug it into this function, 1 plus 1 divided by x,

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and plug that number into 1 plus 1 over x, and again, and again, and again, and again.

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No matter what constant you start with, you eventually end up at 1.618.

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Even if you start with a negative number, even one that's really close to phi's

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little brother, eventually it shies away from that value and jumps back over to phi.

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So, what's going on here?

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Why is one of these fixed points favored above the other one?

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Maybe you can already see how the transformational understanding of derivatives

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is helpful for understanding this setup, but for the sake of having a point of contrast,

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I want to show you how a problem like this is often taught using graphs.

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If you were to plug in some random input to this function,

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the y value tells you the corresponding output, right?

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So to think about plugging that output back into the function,

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you might first move horizontally until you hit the line y equals x,

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and that's going to give you a position where the x value corresponds to your

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previous y value, right?

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So then from there, you can move vertically to see what output this new x value has,

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and then you repeat.

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You move horizontally to the line y equals x to find a point whose x value is the same

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as the output you just got, and then you move vertically to apply the function again.

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Now personally, I think this is kind of an awkward way

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to think about repeatedly applying a function, don't you?

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I mean, it makes sense, but you kind of have to pause

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and think about it to remember which way to draw the lines.

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And you can, if you want, think through what conditions make this spiderweb

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process narrow in on a fixed point, versus propagating away from it.

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In fact, go ahead, pause right now, and try to think it through as an exercise.

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It has to do with slopes.

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Or if you want to skip the exercise for something that I think gives a much more

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satisfying understanding, think about how this function acts as a transformation.

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So I'm going to go ahead and start here by drawing a bunch of

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arrows to indicate where the various sampled input points will go.

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And side note, don't you think this gives a neat emergent pattern?

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I wasn't expecting this, but it was cool to see it pop up when animating.

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I guess the action of 1 divided by x gives this nice emergent circle,

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and then we're just shifting things over by 1.

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Anyway, I want you to think about what it means to repeatedly apply some function,

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like 1 plus 1 over x, in this context.

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Well after letting it map all of the inputs to the outputs,

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you could consider those as the new inputs, and then just apply the same process again,

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and then again, and do it however many times you want.

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Notice, in animating this with a few dots representing the sample points,

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it doesn't take many iterations at all before all of those dots kind of clump in around 1.

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618.

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Now remember, we know that 1.618 and its little brother,

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negative 0.618 on and on, stay fixed in place during each iteration of this process.

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But zoom in on a neighborhood around phi.

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During the map, points in that region get contracted around phi,

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meaning that the function 1 plus 1 over x has a derivative with a magnitude less

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than 1 at this input.

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In fact, this derivative works out to be around negative 0.38.

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So what that means is that each repeated application scrunches the neighborhood

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around this number smaller and smaller, like a gravitational pull towards phi.

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So now tell me what you think happens in the neighborhood of phi's little brother.

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Over there, the derivative actually has a magnitude larger than 1,

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so points near the fixed point are repelled away from it.

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And when you work it out, you can see that they get

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stretched by more than a factor of 2 in each iteration.

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They also get flipped around, because the derivative is negative here,

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but the salient fact for the sake of stability is just the magnitude.

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Mathematicians would call this right value a stable fixed point,

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and the left one is an unstable fixed point.

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Something is considered stable if when you perturb it just a little bit,

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it tends to come back towards where it started, rather than going away from it.

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So what we're seeing is a very useful little fact,

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that the stability of a fixed point is determined by whether or not the magnitude of its

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derivative is bigger or smaller than 1.

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This explains why phi always shows up in the numerical play,

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where you're just hitting enter on your calculator over and over,

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but phi's little brother never does.

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As to whether or not you want to consider phi's little brother a

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valid value of the infinite fraction, well that's really up to you.

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Everything we just showed suggests that if you think of this expression

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as representing a limiting process, then because every possible seed

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value other than phi's little brother gives you a series converging to phi,

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it does feel silly to put them on equal footing with each other.

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But maybe you don't think of it as a limit, maybe the kind of math

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you're doing lends itself to treating this as a purely algebraic object,

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like the solutions of a polynomial, which simply has multiple values.

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Anyway, that's beside the point, and my point here is not that viewing derivatives

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as this change in density is somehow better than the graphical intuition on the whole.

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In fact, picturing an entire function this way can be

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kind of clunky and impractical as compared to graphs.

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My point is that it deserves more of a mention in most of the

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introductory calculus courses, because it can help make a

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student's understanding of the derivative a little more flexible.

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Like I mentioned, the real reason I'd recommend you carry this perspective

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with you as you learn new topics is not so much for what it does with your

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understanding of single variable calculus, it's for what comes after.
