In addition to the PDF versions of the lecture notes below, the notes are available in an HTML version on the instructor’s website.
Lecture 1: Introduction (PDF) | (HTML)
Lecture 2: First-Order Optimality Conditions (PDF) | (HTML)
Lecture 3: More on Normal Cones, and a First Taste of Duality (PDF) | (HTML)
Lecture 4: The Special Case of Convex Functions (PDF) | (HTML)
Lecture 5: The Driver of Duality: Separation (PDF) | (HTML)
Lecture 6: Separation as a Proof System (PDF) | (HTML)
Lecture 7: Lagrange Multipliers and KKT Conditions (PDF) | (HTML)
Lecture 8: Lagrangian Duality (PDF) | (HTML)
Lecture 9: Conic Optimization (PDF) | (HTML)
Lecture 10: Polynomial Optimization (PDF) | (HTML)
Lecture 11: Polarity and Oracle Equivalence (PDF) | (HTML)
Lecture 12: Gradient Descent and Descent Lemmas (PDF) | (HTML)
Lecture 13: Acceleration and Momentum (PDF) | (HTML)
Lecture 14: Projected Gradient Descent and Mirror Descent (PDF) | (HTML)
Lecture 15: Stochastic Gradient Descent (PDF) | (HTML)
Lecture 16: Distributed Optimization and ADMM (PDF) | (HTML)
Lecture 17: Hessians, Preconditioning, and Newton’s Method (PDF) | (HTML)
Lecture 18: Adaptive Preconditioning: AdaGrad and ADAM (PDF) | (HTML)
Lecture 19: Self-Concordant Functions (PDF) | (HTML)
Lecture 20: Central Path and Interior-Point Methods (PDF) | (HTML)
