Course Information:
Course Information:
For prerequisites, evaluation, suggested materials and other policies, please check the syllabus.
Lecture Notes (to be released along with the lectures)
Lecture Notes (to be released along with the lectures)
Lec 1 - Introduction
Lec 2 - Convex Sets
Lec 3 - Convex Functions
Lec 4 - Unconstrained Optimization
Lec 5 - Gradient Descent, Convergence Analysis
Lec 6 - Accelerated First-order Methods
Lec 7 - Newton's method, Quasi-Newton methods
Lec 8 - Subgradient Algorithms
Lec 9 - Proximal Algorithms
Lec 10- Constrained Geometry
Lec 11- Karush-Kuhn-Tucker (KKT) Conditions
Lec 12- Lagrange Duality
Lec 13- Fenchel Duality
Lec 14- Constrained Algorithm Basics
Lec 15- ADMM and Saddle Points
Lec 16- Distributed
Lec 17- Decentralized
Lec 18- Convex Relaxation
Lec 19- Stochastic Gradient Algorithm
Lec 20- Minimax
Homeworks (released and submitted via Canvas)
Homeworks (released and submitted via Canvas)