IND ENG 160 Lecture Notes - Lecture 2: Univariate, Maxima And Minima, Stationary Point

• School: Berkeley
• Department: Industrial Engineering And Operations Research
• Course: IND ENG 160
• Professor: Javad Lavaei

Document Summary

Unconstrained: there are no constraints. min z r x2 cos(x) + log(x) This can include problems like the generic min f (x). 1. 2 constrained univariate optimization max x3 + 5x2s. t. Now, we may have multiple decision variables min x2. 1. 4 constrained multivariate optimization max x1 + 5x2s. t. x2. We want to have well-behaved functions for our objective function: discontinuous. Discontinuous functions are bad for optimization, so we will not see them in this course. This could be a function that has a cusp" or sharp edge. They are easier than discontinuous functions, but not easy. In most of this course, we will not deal with these functions. As an example of one of those functions, consider |x|. It is not di eren- tiable at x = 0: continuous and di erentiable. 1. 6 types of minima and maxima of functions: local: Say x is local minimum if , > 0, such that for every x that satis es.