Additional lectures (from previous editions of the course)
Homework solutions and grades are posted on the EEweb course website. (Follow the links to “Assignments” or “Grades”.)
Lectures: Boelter Hall 5420. Monday and Wednesday 10:00 AM-11:50 AM.
Description. The course continues EE236B and covers several advanced and current topics in optimization, with an emphasis on large-scale algorithms for convex optimization. The following subjects will be discussed.
First-order methods for large-scale optimization: gradient and subgradient method, conjugate gradient method, proximal gradient method, accelerated gradient methods.
Decomposition and splittng methods: dual decomposition, augmented Lagrangian method, alternating direction method of multipliers.
Interior-point algorithms for conic optimization.
Textbook and lecture notes. The lecture notes will be posted on this website. The material is largely based on the following books, and on the notes of the course EE364b (Convex Optimization II) at Stanford University.
D. Bertsekas, Nonlinear Programming, Athena Scientific.
D. Bertsekas and J. Tsitsiklis, Parallel and Distributed Computation, Athena Scientific.
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press.
L. Lasdon, Optimization Theory for Large Systems, Dover.
Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Kluwer.
B. T. Polyak, Introduction to Optimization, Optimization Software.
Course requirements. Several homework assignments and a project.
Grading. Approximate weights in the final grade: homework 20%, project 80%.