ConvexOptimizationII-Lecture18

ConvexOptimizationII-Lecture18 - -Lecture18...

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Unformatted text preview: ConvexOptimizationII-Lecture18 Instructor (Stephen Boyd) :Well, let me – you can turn off all amplification in here. So yeah, so it’s still – you still have amplification on in here so you can – oh, well, we’ll let them figure that out. Let’s see, couple of announcements. It’s actually kind of irritating, frankly. I wonder if we can just cut that off? Oh, the advertisement went away, that’s good. All right, I’ll let them figure out how to turn off the amplification in a room that’s got, like, a depth of 15 feet. So yes! No? All right. Anyway, I’ll let them worry about it. Okay, first just a couple of announcements to – I keep getting hopefully. What do you think? Are we there? Getting better. It’s funny. You’d think you could just turn it off. But anyway, all right, okay. So a couple of announcements is, as you know, the final project reports are due tomorrow. So that’s one deadline. The other is on Monday; I think it’s been agreed. We’re gonna have our poster session. We haven’t fixed the time exactly yet but we’re thinking of something like 5:00-7:00 and we’re gonna figure out about getting some food. So we’ll – but we haven’t announced that. That’ll be on the website and all that kind of stuff when we figure everything out. And there’s already been some questions about what’s the format of the posters and we don’t know. So some people are gonna go ahead and do the fancy thing and make big posters which you’re more than welcome to do but you don’t have to. You can just print out 12 black and white slides, or whatever, and, like, you know, tack them to the poster board. And I don’t remember what the size of the poster board is but maybe that’ll also be forthcoming on the website or maybe be an announcement or something like that. So the format, which is 12 slides, is – that’s fixed and I’ve said it many times but, you know, please use our template. You’re welcome not to, you don’t have to, but then you better have better taste than we do. So, I mean, even – and there’s certain things you just can’t do because it’s not acceptable and it’s amateurish and stuff like that. And you all know what I’m talking about. So, okay, so we’re gonna finish up our absolutely last topic which is branch and bound methods. These are methods for global optimization. So in global optimization you have a non-convex problem but you’re going to give up – you’re not gonna give up a globalness. That’s not a word, I just made it up. But you’re not gonna give up globalness – globality? You’re gonna give up neither globalness nor globality but you are gonna give up speed. So what’s gonna happen is these are gonna be methods that are slow but they don’t lie. They will produce a – at any point you can stop them and exactly like a convex optimization method they will have a lower bound and they will have an upper bound. And they’re not approximate, they are correct and they terminate when the lower – when...
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ConvexOptimizationII-Lecture18 - -Lecture18...

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