KKT

This is a personal lecture notes from a TUM course Nonlinear Optimization in WS23-24. This isn’t a full course recap—just a way for me to organize the core ideas I want to remember. I’ve picked out the topics that I found most important and useful. 1. Problem Setting and Definitions The general NLP (nonlinear program) is: $$ \text{min}_{x \in \mathbb{R}^n}f(x) \quad \text{s.t.} \quad g(x) \leq 0, h(x) = 0 $$ ...