Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1 hour / session

Course Description

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.

Software

We’ll use CVX throughout the course.

CVX: Matlab Software for Disciplined Convex Programming

Calendar

Homework assignments were due at the end of the week noted.

WEEK # KEY DATES
1  
2 Homework 1 due
3 Homework 2 due
4 Homework 3 due
5 Homework 4 due
6 Homework 5 due
7 Homework 6 due
8 Midterm exam
9 Homework 7 due
10 Homework 8 due
11 Homework 9 due
12  
13 Homework 10 due
14 Final exam

Course Info

Learning Resource Types

grading Exams
notes Lecture Notes
assignment Programming Assignments