Hasnain

Engineers at Google, Jane Street, and Two Sigma use this exact framework to make decisions worth $10,000,000,000+. Most of them learned it from one 80-minute Stanford lecture. His name is Stephen Boyd. Stanford. EE364A. Convex Optimization. Boyd spent 30 years making this course free for anyone on earth to watch. Then the geometry. He draws a set on the board. Asks one question: if you take any two points inside it, does the line between them stay inside? That's a convex set. That's the entire foundation. Then the halfspace. Every constraint in a real optimization problem - budget limits, engineering tolerances, risk parameters - is secretly just a halfspace. A line that divides the world into allowed and not allowed. Boyd shows you how to see this in 4 minutes. Then the ellipsoid. A covariance matrix defines an ellipsoid. Every portfolio optimization problem at Bridgewater, AQR, and Renaissance Technologies starts here. Boyd draws it on the board like it's obvious. It wasn't obvious until he made it obvious. Then the PSD cone. Positive semidefinite matrices form a cone. If you don't know what that means, you cannot do modern machine learning. Boyd explains it in two sentences. Watch the moment he describes "street fighting" methods - the heuristics engineers actually use when they're at a startup at 2am and the math won't cooperate. The room laughs. Then they write it down. A quant at Citadel told me he reviews this lecture every time he starts a new project. Base salary: $400,000. Bonus year: $1,200,000. Bookmark this and watch later - after this lecture every optimization problem you see will feel like a solved puzzle. Stanford EE364A Lecture 2 | Stephen Boyd | 2023