That’s all. One factor, two back solves. Okay. Now we need to get to something very important. A lot of you probably haven’t seen it. It’s probably – it’s one of the most important topics, which I believe is basically not covered because it falls between the cracks. It’s covered somewhere deep into some class on the horrible fine details of numerical computing or something like that, I guess. I don’t think it’s well enough covered, at least from the people I hang out with – not enough of them know about it. And it has to do with them exploiting sparsity in numerical algebra. So if a matrix A is sparse, you can factor it as P1LUP2. ...

See the whole transcript at Convex Optimization I - Lecture 14