# EE236C - Optimization Methods for Large-Scale Systems (Spring 2013-14)

## Lecture notes

Gradient method

Quasi-Newton methods

Conjugate gradient method

Subgradients

Subgradient method

Proximal gradient method

Fast proximal gradient methods

Conjugate functions

The proximal mapping

Proximal point method

Dual decomposition

Dual proximal gradient method

Douglas-Rachford splitting and ADMM

Conic optimization

Barrier functions

Path-following methods

Symmetric cones

Primal-dual interior-point methods

**Additional lectures** (from previous editions of the course)

## Homework and project

Homework solutions and grades are posted on the
EEweb course website. (Follow the links to “Assignments” or “Grades”.)

## Course information

**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%.