18-01F07-L14

18-01F07-L14 - MIT OpenCourseWare http:/ocw.mit.edu 18.01...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
MIT OpenCourseWare http://ocw.mit.edu 18.01 Single Variable Calculus, Fall 2007 Please use the following citation format: David Jerison, 18.01 Single Variable Calculus, Fall 2007 . (Massachusetts Institute of Technology: MIT OpenCourseWare). http://ocw.mit.edu (accessed MM DD, YYYY). License: Creative Commons Attribution-Noncommercial-Share Alike. Note: Please use the actual date you accessed this material in your citation. For more information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
MIT OpenCourseWare http://ocw.mit.edu 18.01 Single Variable Calculus, Fall 2007 Transcript – Lecture 14 The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation, or to view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: What we're going to talk about today is a continuation of last time. I want to review Newton's method because I want to talk to you about its accuracy. So if you remember, the way Newton's method works is this. If you have a curve and you want to know whether it crosses the axis. And you don't know where this point is, this point which I'll call x here, what you do is you take a guess. Maybe you take a point x0 here. And then you go down to this point on the graph. and you draw the tangent line. I'll draw these in a couple of different colors so that you can see the difference between them. So here's a tangent line. It's coming out like that. And that one is going to get a little closer to our target point. But now the trick is, and this is rather hard to see because the scale gets small incredibly fast, is that if you go right up from that, and you do this same trick over again. That is, this is your second guess, x1, and now you draw the second tangent line. Which is going to come down this way. That's really close. You can see here on the chalkboard, it's practically the same as the dot of x. So that's the next guess. Which is x2. And I want to analyze, now, how close it gets. And just describe to you how it works. So let me just remind you of the formulas, too. It's worth having them in your head. So the formula for the next one is this. And then the idea is just to repeat this process. Which has a fancy name, which is in algorithms, which is to iterate, if you like. So we repeat the process. And that means, for example, we generate x2 from x1 by the same formula. And we did this last time. And, more generally, the n + 1st is generated from the nth guess, by this formula here. So what I'd like to do is just draw the picture of one step a little bit more closely. So I want to blow up the picture, which is above me there. That's a little too high. Where are my erasers? Got to get it a little lower than that, since I'm going to depict everything above the line here. So here's my curve coming down. And suppose that x1 is here, so this is
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/18/2012 for the course MATH 18.01 taught by Professor Brubaker during the Fall '08 term at MIT.

Page1 / 10

18-01F07-L14 - MIT OpenCourseWare http:/ocw.mit.edu 18.01...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online