This bootcamp explores the wide world of mathematical optimization, specifically for applications in machine learning, control theory, and inverse problems. This closely follows the textbook "Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control" (https://www.amazon.com/Optimization-Bootcamp-Machine-Learning-Problems/dp/1009755862)
Curated by: Steve Brunton (44 videos)
In this lecture, we introduce a definition for convexity of functions and explores some examples and useful properties of convex functions. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company. %%% CHAPTERS %%% 00:00 Intro & Definition 05:10 Example Functions 08:16 Norms 11:22 Linear & Quadratic Programming 13:05 Convexity & Local Optima 14:30 Convex Epigraphs 16:06 Outro