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 video explores the conditions for constraint equations that result in convex feasible sets for optimization problems. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company. %%% CHAPTERS %%% 00:00 Intro 02:20 Equality Constraint Linearity 03:57 Inequality Constraint Covexity: Geometric Intuition 07:29 Common Convex Constraints 08:57 Outro