# EE236B - Convex Optimization (Winter Quarter 2013-14)

## Lectures notes

Introduction

Convex sets

Convex functions

Convex optimization problems

Duality

Approximation and fitting

Statistical estimation

Geometric problems

Numerical linear algebra background

Unconstrained minimization

Equality constrained minimization

Interior-point methods

Conclusions

## Homework

Exercise numbers with prefix ’T’ refer to the
textbook.
Exercise numbers with prefix ’A’ refer to the collection of
additional exercises on the textbook page.

Homework 1 (due 1/15). The problem requires
the MATLAB file `illumdata.m`.

Homework 2 (due Thursday 1/23). Exercises T2.7, T2.12 (d,e,g),
A2.10, A5.8, and an additional problem.
Problem A5.8 requires the files `spline_data.m`
and `bsplines.m`.

Homework 3 (due 1/30). Exercises T3.2, T3.19 (a), A2.5 (a,b), A2.21,
A2.23, A5.4.

Homework 4 (due 2/6). Exercises T4.21 (a), A3.21, A7.9, A12.6, A2.28.

Homework 5 (due 2/13). Exercises T4.25, A5.9, A14.4, A14.3, A3.13,
and two additional problems.

Homework 6 (due Wednesday 2/26).
Exercises T5.21 (a,b,c), T5.26, T5.29, A4.4, A4.14, A4.20, A4.22.

Homework 7 (due 3/5). Exercises A4.10, T5.30, A4.17, A6.1, A6.5, A7.1.
Problem A6.5 requires the file
`nonlin_meas_data.m`.

Homework 8 (due 3/12). Exercise A8.9 (requires the file
`one_bit_meas_data.m`).

Homework is due at 4PM on the due date. It can be submitted in class or
using the box in the TA meeting room (67-112 Engineering 4).

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

## Course information

**Lectures**: Boelter 2444,
Monday & Wednesday 16:00-17:50PM.

**Textbook**
The textbook is *Convex
Optimization*, available online and in hard copy at the UCLA bookstore.
The following books are useful as (optional) reference texts.

A. Ben-Tal and A. Nemirovski,
*Lectures on Modern Convex Optimization* (SIAM).

D. Bertsekas, A. Nedic, A.E. Ozdaglar,
*Convex Analysis and Optimization* (Athena Scientific).

D. Bertsekas, *Convex Optimization Theory* (Athena Scientific).

J. M. Borwein and A. S. Lewis,
*Convex Analysis and Nonlinear Optimization* (Springer).

J.B. Hiriart-Urruty and C. Lemarechal, *Convex Analysis and Minimization
Algorithms* (Springer).

D. Luenberger and Y. Ye,
*Linear and Nonlinear Programming* (Springer).

Y. Nesterov, *Introductory Lectures on Convex Optimization: A Basic
Course* (Kluwer).

J. Nocedal and S. Wright,
*Numerical Optimization* (Springer).

**Course requirements**. Weekly homework assignments; open-book final
exam on Monday, March 17, 3:00-6:00 PM.
The weights in the final grade are: homework 20%, final exam 80%.

**Software**.
We will use CVX,
a MATLAB software package for convex optimization.