# EE236B - Convex Optimization (Winter Quarter 2016-17)

## Homework

Exercise numbers with prefix ’T’ refer to the textbook. Exercise numbers with prefix ’A’ refer to the collection of additional exercises (last revised on 1/17).

• Homework 1 (due Friday 1/20). Exercises T2.7, T2.12 (d,e,g), T2.16, and two additional problems. The additional problems requires the MATLAB files circlefit.m and illumdata.m.
Note the typo in T2.16: should be .

• Homework 2 (due Thursday 1/26). Exercises T2.37 (b,c), T3.1, T3.2, A2.10, A5.8. Problem A5.8 requires the files spline_data.m and bsplines.m.

• Homework 3 (due Thursday 2/2). Exercises T3.18 (a), T3.19 (a), T3.22(c), A2.5 (a,b), A2.30, A2.31, A3.17.

• Homework 4 (due Thursday 2/9). Exercises T3.55, A3.5, T4.21 (b), T4.25, A7.9, A14.8. Problem A14.8 requires the file spacecraft_landing_data.m.

• Homework 5 (due 2/16). Exercise A3.21 (a,b), three additional problems, and exercise A3.11.

• Homework 6 (due 2/23). Exercise A12.6, A4.4, T4.43 (b,c), T5.19, T5.21 (a,b,c), A4.30.

• Homework 7 (due 3/2). Exercises T5.26, T5.29, T5.30, A4.10, A4.14, A4.17.

• Homework 8 (due 3/9). Exercise T5.18, A4.25, A4.26, A6.5, A7.1. Exercise A6.5 requires the file nonlin_meas_data.m.

• Homework 9 (due 3/16). Exercise T9.3, A8.1, A8.9. Exercise A8.9 requires one_bit_meas_data.m.

Homework is due at 4PM on the due date. It can be submitted at the start of the lecture or in the EE236B homework box in the TA meeting room (67-112 Engineering 4).

## Course information

Lectures: Kinsey 1200B, Tuesday & Thursday 16:00-17:50PM.

Discussion: Boelter 3400, Friday 15:00-15:50PM.

Office hours

• Prof. Vandenberghe: Wednesday 13:00-16:00, Rm 66-147L Eng. 4.

• Xin Jiang: Thursday 11:00-12:00, Rm 67-112 Eng. 4.

• Hemant Saggar: Monday 11:00-12:00, Rm 67-112 Eng. 4.

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

Course requirements. Weekly homework assignments; open-book final exam on Wednesday, March 22, 11:30AM-14:30 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. Python users are welcome to use CVXPY instead of MATLAB and CVX. Julia users are welcome to use Convex.jl.