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)
The key result in convex optimization is that a local minimum is a global minimum for a convex function over a convex set. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company. %%% CHAPTERS %%% 00:00 Intro 01:46 Strict Convexity 03:46 Converse: Maximum of a Concave Function 08:33 Proof: Defining Local Minimum & Contradiction 13:28 Proof: Applying Convexity 19:17 Summary & Outro