ORIE 6326: Convex Optimization


  • Where: 571 Rhodes Hall and streamed to Cornell Tech

  • When: Monday and Wednesday 10:10am – 11:25am.

  • Discussion forum: Piazza. Sign up here.


Convex optimization generalizes least-squares, linear and quadratic programming, and semidefinite programming, and forms the basis of many methods for non-convex optimization. This course focuses on recognizing and solving convex optimization problems that arise in applications, and introduces a few algorithms for convex optimization. Topics include: Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Algorithms: interior-point, subgradient, proximal gradient, splitting methods such as ADMM. Applications to statistics and machine learning, signal processing, control and mechanical engineering, and finance.

Prerequisites: Strong working knowledge of linear algebra, a modern scripting language (such as Python, Matlab, Julia, R).


  • The final exam will take place on Wednesday 5-17-16 from 2pm – 5pm in Olin Hall room 165

  • Homework 11 is due Monday 5-8-17 at 5pm. Office hours are on Tuesday, Wednesday and Thursday.