Prof. L. Vandenberghe, UCLA

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

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

`illumdata.m`.Homework 2 (due 1/22). Exercises T2.12 (d,e,g), T2.37 (b,c), A5.8, and two additional problems. Problem A5.8 requires the files

`spline_data.m`and`bsplines.m`.Homework 3 (due 1/29). Exercises T3.19(a), A2.3, A2.10, A2.20, A2.26, A5.4. (Note that the 3rd sentence in problem A2.26 is incomplete: ‘where ’ should be: ‘where is the identity matrix’.)

Homework 4 (due 2/5). Exercises T3.55, A2.17, A2.21, A3.17, A7.9.

Homework 5 (due 2/12). Exercises A3.21, A5.9, T4.27, A12.6, and an additional problem.

Homework 6 (due 2/19). Exercises A3.5, A3.11 (a,b,c), A3.13, A7.2, A4.3, T5.21 (a,b,c).

Homework 7 (due 2/26). Exercises T5.29, A4.4, A4.14, A4.17, A4.22, T5.30.

Homework 8 (due 3/5). Exercises A4.10, A4.18, A4.20, A6.5, A7.1, A7.7. Problem A6.5 requires the file

`nonlin_meas_data.m`.Homework 9 (practice problems; you don’t need to submit solutions). Exercises A8.1, A8.9, A8.8, A9.6. Problem A8.9 requires the file

`one_bit_meas_data.m`.

Homework is due at 4PM on the due date. It can be submitted in the 236B homework 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”.)

**Lectures**: Boelter 5440, Tuesday & Thursday 10:00-11:50AM.

**Textbook**
The textbook is *Convex
Optimization*, available online and in hard copy at the UCLA bookstore.
The following books are useful as 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 16, 8:00-11:00 AM.
The weights in the final grade are: homework 20%, final exam 80%.

**Software**.
We will use CVX,
a MATLAB software package for convex optimization.
Python users are welcome to use CVXPY instead of MATLAB
and CVX.