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)
This lecture proves that all norms are convex and discusses the implications of norm complexity for optimization, especially in machine learning. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company. %%% CHAPTERS %%% 00:00 The l2 Norm 02:54 Example Convex Norms 04:45 Non-Convex Pseudonorms 05:55 The l2 Squared in Optimization 10:06 Gradient of l2 Squared 12:14 Proof: All Norms are Convex 16:34 Note on LP Norms & Outro