ConvexOptimizationII-Lecture15 -...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

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

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: ConvexOptimizationII-Lecture15 Instructor (Stephen Boyd) :All right, I think this means we are on. There is no good way in this room to know if you are when the lecture starts. Okay, well, we are down to a skeleton crew here, mostly because its too hot outside. So well continue with L_1 methods today. So last time we saw the basic idea. The most the simplest idea is this. If you want to minimize the cardinality of X, find the sparsest vector X thats in a convex set, the simplest heuristic and actually, today, well see lots of variations on it that are more sophisticated. But the simplest one, by far, is simply to minimize the one norm of X subject to X and Z. By the way, all of the thousands of people working on L_1, this is all they know. So the things we are going to talk about today, basically most people dont know. All right. We looked at that. Last time we looked at polishing, and now I want to interpret this I want to justify this L_1 norm heuristic. So here is one. We can turn this we can interpret this as a relaxation of we can make this a relaxation of a Boolean convex problem. So what we do is this. I am going to rewrite this cardinality problem this way. I am going to introduce some Boolean variables Z. And these are basically indicators that tell you whether or not each component is either zero or nonzero. And Ill enforce it this way. Ill say that the absolute value is XI is less than RZI. Now R is some number that bounds, for example like it could be just basically a bounding box for C, or it can be naturally part of the constraints. It really doesnt matter. The point is that any feasible point here has an infinity norm less than R. If we do this like this, we end up with this problem. This problem is a Boolean convex. And what that means is that it is everything is convex, and the variables, thats X and Z, except for one minor problem, and that is that these are 01. Okay? So this is a Boolean convex problem. And its absolutely equivalent to this one. It is just as hard, of course. So we are going to do the standard relaxation is if you have a 01 0, 1 variable, well change it into a left bracket 0,1 right bracket variable. And that means that its a continuous variable. This is a relaxation. And here, we have simply we have actually worked out, this is simply well, its obvious enough, but this is simply the convex hull of the Boolean points here. Now if you stare at this long enough, you realize something. You have seen this before. This is precisely the linear program that defines this is exactly the linear program that defines the L_1 norm. So here, for example, this is at norm X is its an upper bound on ZI is an upper bound on one over RXI. And so, in fact, this problem is absolutely the same as this one. And so now you see what you have. That Boolean problem is equivalent to this. By the way, this tells you something. It says that when you solve that L_1 problem, not only do you have tells you something....
View Full Document

Page1 / 18

ConvexOptimizationII-Lecture15 -...

This preview shows document pages 1 - 2. Sign up to view the full document.

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