CME 252: Introduction to Optimization
Welcome to the course website for CME 252: Introduction to Optimization, brought to you by the Institute for Computational and Mathematical Engineering (ICME) at Stanford University.
Announcements
- 10/18: Homework 3 is posted!
- 10/11: Homework 2 is posted!
- 10/5: Typo fixed in HW1 (Prob 1 should be
Ax = b, not Ax <= b)
- 9/29: Office hours will be on Thursdays, 5:30-7:30pm, in the Huang building basement, in front of ICME
- 9/29: Homework 0 is posted!
- 9/27: First class on Monday, September 28
Schedule
- Lectures are on Mondays and Wednesdays, 3:30-4:50pm, in McCullough 115
- Class meets for a total of 8 sessions: September 28, 30 and October 5, 7, 12, 14, 19, 21
Piazza
Instructor
- AJ Friend,
ajfriend at stanford dot edu
- Please include “CME 252” in email subject line
- Office hours: Thursdays, 5:30-7:30pm, in the Huang building basement, in front of ICME
Course Requirements
- The course is given for 1 unit, and graded as Satisfactory / No Credit
- To receive credit, students must complete 3-4 short homework assignments
- Assignments will consist of writing Python scripts to solve optimization problems with CVXPY
Course Description
This course introduces mathematical optimization and modeling, with a focus on convex optimization. Topics include: varieties of mathematical optimization, convexity of functions and sets, convex optimization modeling with CVXPY, gradient descent and basic distributed optimization, in-depth examples from machine learning, statistics and other fields and applications of bi-convexity and non-convex gradient descent.
Prerequisites
Familiarity with linear algebra, differential multivariable calculus, and basic probability and statistics. Experience with Python will be helpful, but not required.