The University of Texas at Austin
Department of Electrical and Computer Engineering

EE381V - Large Scale Optimization

Fall Semester 2012



Some Basic Information

Instructors: Constantine Caramanis / Sujay Sanghavi

Email: caramanis/sanghavi AT mail DOT utexas DOT edu
Phone: (512) 471-9269
Office: ENS 427/429
Constantine's Office Hours: Tuesday 2:00 pm
Sujay's Office Hours: Monday 2:00 pm

TA: Dohyung Park

Email: dhpark AT utexas DOT edu
Office: ENS 138
Office Hours: Wednesday 2 - 4 pm

Lectures:

Time: Tuesday and Thursday, 12:30-2:00 PM,
Location: RLM 6.104


Course Overview

This is the first course in a two-course sequence on Large-Scale Optimization and Learning. While the first course will focus on optimization and the second on learning, we will draw examples from learning throughout the first course. The first course in the sequence will focus on Convex Optimization including basic material from convex geometry, convex analysis and convex optimization. It will cover basic modeling, and understanding how to find and exploit convexity, both for theoretical analysis, and also for developing algorithms.

Intended audience: This class is structured to be interesting and relevant to students who are using or plan to use optimization in their research, and are interested in solving large-scale optimization problems. The target audience is quite broad: graduate students from ECE, CS, OR, Math, DSSC, and related disciplines.

Course Prerequisites

This class does not assume previous exposure to optimization. (However, if you have previously taken courses such as Linear Programming, there should be minimal overlap with this class.) Good familiarity with Liner Algebra (at the level of, e.g., EE380K) is important, as we will freely use concepts, tools and techniques from linear algebra. Some basic knowledge of Matlab will also be needed, although basic familiarity with programming should be sufficient.

General Note: If you are concerned about the prerequisites or your background, or what the course will cover, please don't hesitate to contact us by e-mail, or come by office hours.

Grading: Homeworks, Exams and Scribing

The grade for the course will be determined by three components: Problem sets (15%), Scribing (5%), a Midterm Exam (35%) and a Final Exam (45%). Problem sets will be given out approximately weekly. The midterm and final will be in-class closed-book exams.

Scribing: Each student is expected to scribe a lecture in latex. The scribing will be done in groups of two or three students. The goal is to produce a high-quality, complete record of the material covered in class. Students that scribe a Tuesday lecture are expected to submit a high-quality, polished and complete draft to Constantine and Sujay by Friday of the same week. Students scribing a Thursday lecture should submit this by Monday. This leaves time for some iteration if required, with the goal of posting the scribed notes within a week of the class scribed. You can find the necessary scribing templates here. Please reference completely and fully, as if you were writing a paper to submit. Also, as with any paper, all the writing should be your own.

Policy on Collaboration: Discussion of homework questions is encouraged. Please be sure to submit your own independent homework solution. This includes any matlab code required for the assignments. Late homework assignments will not be accepted.

Text and References

The course will be taught from a collection of sources. The primary reference is the book: Convex Optimization by Stephen Boyd and Lieven Vandenberghe. Note that this book is available on line.

Additional References: Some additional references that may be helpful:



Lecture schedule (tentative)

Lecture No.

Date

Problem Sets

Problem Set Solutions

Assigned Reading

Scribed Notes

1

Thu August 30

problem set 0

solution set 0

B & V: Chapters 1 and 2

Lecture 1

2

Tue September 4

---

---

---

Lecture 2

3

Thu September 6

problem set 1

solution set 1

B & V: Chapters 9.1-9.3

Lecture 3

4

Tue September 11

---

---

---

Lecture 4

5

Thu September 13

problem set 2

solution set 2

---

Lecture 5

6

Tue September 18

---

---

B & V: Chapters 9.1 - 9.5

Lecture 6

7

Thu September 20

problem set 3

solution set 3

---

Lecture 7

8

Tue September 25

---

---

N & W: Chapters 5 and 6

Lecture 8

9

Thu September 27

problem set 4

solution set 4

N & W: Chapter 7.1

Lecture 9

10

Tue October 2

---

---

B & V: Chapters 4 and 5

Lecture 10

11

Thu October 4

---

---

See lecture on Blackboard

Lecture 11

12

Tue October 9

---

---

B & V: Chapters 4 and 5

Lecture 12

13

Thu October 11

problem set 5

solution set 5

---

Lecture 13

14

Tue October 16

---

---

---

Lecture 14

15

Thu October 18

problem set 6

solution set 6

B & V Chapters 7 and 8
paper 1
paper 2

Lecture 15

16

Tue October 23

---

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Lecture 16

17

Thu October 25

---

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paper 1

Lecture 17

MIDTERM

Tue October 30

---

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MIDTERM

18

Thu November 1

problem set 7

solution set 7

---

Lecture 18

19

Tue November 6

---

---

---

Lecture 19

20

Thu November 8

problem set 8

solution set 8

LV 4, LV 5
LV 6, SB 4

Lecture 20

21

Tue November 13

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Lecture 21

22

Thu November 15

---

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---

Lecture 22

23

Tue November 20

problem set 9
ps9.mat

solution set 9

---

Lecture 23

THANKSGIVING

Thu November 22

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24

Tue November 27

---

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Lecture 24

25

Thu November 29

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dual decomp
decomp apps
multplier methods
ADMM

Lecture 25

26

Tue December 4

---

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Lecture 26

27

Thu December 6

---

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Final Exam

Mon December 17
2 - 5 pm, UTC 3.104

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Final