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I'm not asking for df.
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Of course, they're related.
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And let's see if we can move this out of the way.
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Good.
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So what was I saying?
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Yes.
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So now, what I want to do is basically use
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this as the definition of a derivative, a more general
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definition of a derivative.
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So the linear algebra notion is that we have
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what's called a linear operator.
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So basically, the change in the output df
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is going to be a linear operator.
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Let me write that as times, which
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I'm going to call f prime of x times the change in the input.
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This is our-- whoops.
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Actually, but my input is in red, right?
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That's my color code.
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My color scheme is input is red and output is blue.
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So this is our dx.
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So I'm going to interpret this more generally.
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If x is going to be some kind of vector or matrix or whatever,
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this is just going to be a linear operation on this.
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And of course, for numbers, a linear operation,
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this is just a number.
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If dx is a number, the only linear operation you can do
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is multiply by a number.
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So let me just remind you if you haven't taken linear algebra
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for a while, a review.
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Let's talk about what a linear operator is.
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So suppose we have some given vectors, v,
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in some vector space.
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That would be capital V. And remember,
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a vector space is anything where you can basically
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add, subtract, and multiply by scalars.
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We have a plus or minus and times scalar operations
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that stay in our vector space.
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That's what the informal definition of a vector space
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is.
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You can write out axioms and so forth,
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but that's basically what it means.
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A linear operator, this is what we're
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going to mean by linearization in the derivative.
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It has to be linear.
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What does it mean to be linear in general?
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So we're going to call this--
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I'm going to denote this by, let's say, L of--
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let me denote it by square brackets or just by Lv.
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When it's clear enough, I'll just write it
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as if it were a multiplication.
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Often, that'll be clear enough.
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This is really acting on--
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when I write Lv, it's not necessarily
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an ordinary multiplication.
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This is just going to be acting on v.
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So a linear operator is a rule that basically takes a vector
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and gives you a vector out maybe in a different vector space.
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So this is L takes a vector in, v in,
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and it gives you a vector--
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that's a terrible L--
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Lv out maybe in a different vector space.
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And linearity means what you think it means.
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It means if you take, for example,
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L of v1 plus v2, if you take the sum of the inputs, that's
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the same thing as L of v1 plus L of v2.
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So if you add inputs, that's the same thing
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as adding the outputs or if you multiply by a scalar.
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And so as usual in linear algebra,
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Greek letters are going to denote scalars as you would.
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So that's equal to--
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you can pull out the scalar.
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And so the nice thing about linear operators
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is we can define them on lots of kinds of vector spaces.
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So let's just do a couple of examples
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just to make sure we're on the same page here.
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And so, for example, you could just
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have L is multiplication by a scalar.
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So you can have just L of v is just alpha v.
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That's a perfectly good linear operation.
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And if your vector space, if your vs are scalars,
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this is the only option.
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If vs are, say, real numbers, that's
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a perfectly good vector space.
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Another one that you're very familiar with
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is if L is a multiplication by a matrix.
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ALAN EDELMAN: Steven, maybe I'll just point out sometimes people
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like to ask me.
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Wait, I thought that the linear operators on scalars are,
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I think in high school notation, y equals mx plus b, right?
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It's scalar times--
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STEVEN G. JOHNSON: Yeah, yeah.
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ALAN EDELMAN: --plus an offset that may not be 0.
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So what's going on here?
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Is that linear or not linear?
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STEVEN G. JOHNSON: Yeah.
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So what about-- yeah, so let's do that.
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Let me call it a different thing.
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What letter should I use?
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O, let's use O. Ov equals 2v plus 1 for v is real numbers.
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ALAN EDELMAN: Is that linear or not linear?