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 breaks down the components of an optimization problem while exploring how variations in each component lead to the problem categories and challenges that will be explored over the course of the lecture series. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company. %%% CHAPTERS %%% 00:00 Intro 01:26 The Objective Function 03:18 Constraint Equations 04:50 Min vs ArgMin 06:53 Convex vs Non-Convex Objectives 09:56 The Gradient of f 12:02 Convex vs Non-Convex Feasible Regions 13:00 Matrix Systems of Linear Inequalities 14:43 Convex Polytopes 16:01 Slack Variable Form 17:16 Non-Linear Constraints 18:07 Challenges in Optimization 22:32 Linear & Quadratic Programming 24:56 Outro