Lecture 5: Optimal And Locally Optimal Points

Am I gonna discuss generalized inequalities? No. That was the question. That was my answer, too. No. No, it’s clear enough in the book. And when we get to something where it’s relevant, like experiment design – it’ll also be relevant in detection and estimation, then I’ll go back over it. Also in multi-criterion optimization which we’re gonna do later. So I’ll go back over it. Okay. So today we’re gonna go through optimization problems. The first part is a bit boring. It’s just setting down basic terminology like what does it mean to be feasible, what are the constraint sets, actually what is a convex optimization problem, but then it’ll transition to actually useful, so you actually find out about what a linear program is, what a quadratic program is, second order cone program, and these types of things. ... See the whole transcript at [[http://see.stanford.edu/materials/lsocoee364a/transcripts/ConvexOptimizationI-Lecture05.pdf|Convex Optimization I - Lecture 05]]