Course Information: 

For prerequisites, evaluation, suggested materials and other policies, please check the syllabus.

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)