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PxCxlsl_YwY
thing. So, let's take the vector, OP.
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OK, so vector OP, of course, has components x,
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y, z. Now, we can think of this as
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actually a dot product between OP and a mysterious vector that
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won't remain mysterious for very long,
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namely, the vector one, two, three.
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OK, so, this condition is the same as OP.A equals zero,
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right? If I take the dot product
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OPdotA I get x times one plus y times two plus z times three.
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But now, what does it mean that the dot product between OP and A
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is zero? Well, it means that OP and A
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are perpendicular. OK, so I have this vector, A.
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I'm not going to be able to draw it realistically.
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Let's say it goes this way. Then, a point,
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P, solves this equation exactly when the vector from O to P is
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perpendicular to A. And, I claim that defines a
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plane. For example,
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if it helps you to see it, take a vertical vector.
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What does it mean to be perpendicular to the vertical
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vector? It means you are horizontal.
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It's the horizontal plane. Here, it's a plane that passes
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through the origin and is perpendicular to this vector,
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A. OK, so what we get is a plane
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through the origin perpendicular to A.
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And, in general, what you should remember is
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that two vectors have a dot product equal to zero if and
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only if that's equivalent to the cosine of the angle between them
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is zero. That means the angle is 90°.
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That means A and B are perpendicular.
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So, we have a very fast way of checking whether two vectors are
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perpendicular. So, one additional application
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I think we'll see actually tomorrow is to find the
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components of a vector along a certain direction.
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So, I claim we can use this intuition I gave about dot
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product telling us how much to vectors go in the same direction
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to actually give a precise meaning to the notion of
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component for vector, not just along the x,
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y, or z axis, but along any direction in
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space. So, I think I should probably
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stop here. But, I will see you tomorrow at
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2:00 here, and we'll learn more about that and about cross
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products.
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STEVEN G. JOHNSON: So I want to revisit the things that Alan
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talked about, but just a little bit more
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slowly and a bit more--
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just try and lay out the rules for you as clearly as I can.
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And what we're going to try and do
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is, again, just revisit the notion of a derivative
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to try and write it in a way that we can generalize
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to other kinds of objects.
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And so I'm going to start with 18.01
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and then go to 18.02 and so forth.
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So as Alan said, the key notion of a derivative,
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just, I think, it's easy to get so good at taking derivatives,
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like knowing the rule for the derivative
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of sine or cosine or x squared.
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You're so good at doing them that you forget what they are,
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right?
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And so the very first thing you learned about a derivative
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is that it's the slope of the tangent.
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But what that really is is linearization.
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So you have some arbitrary maybe nonlinear function f of x.
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And you're at a point x.
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And near that point, you're going
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to approximate the function with a straight line.
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That's the tangent.
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So it's really the linear approximation of f.
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And then if you move a little bit away from x-- so
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let me call that delta x, so not d.
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Delta is going to be a finite change.
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d is going to be infinitesimal pretty soon.
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But if you move it just a finite amount, a little finite amount
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delta x away, of course, the function value changes.
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But in a linear approximation, the new function value
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is the red dot here.
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So that linear approximation is, if you're
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taking the function f of x at x plus delta x,
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the new value is f of x.
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And then this linear thing is just
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the slope, which we call f prime of x times delta x.
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That's just the definition of the slope.
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It's the little change in y for a little change in x.
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And, of course, these two terms are not exact.
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This red dot doesn't exactly match where
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you are in the real function.
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So there are also corrections.
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But the corrections are higher order.
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They're terms look like delta x squared, delta x cubed maybe.
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Maybe if the function is not higher--
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it doesn't have higher derivatives,
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it might have square root of delta x, so delta x to the 1.1.
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But these are all terms that are going
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to be higher powers of delta x terms
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that, if delta x is sufficiently small,
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these terms will become more and more negligible
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compared to this linear term.
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And a nice notation for this that's