Homework 6 is out, due by the end of the day on Thursday, May 1.
April 15
Homework 5 is out, due by the end of the day on Thursday, April 24.
April 4
Extra credit and the final project are posted. The extra credit problem can be completed by anyone in 3- or 4-hour sections. It will be worth 50 points that will count towards your homework total. The final project is only for those enrolled in the 4-hour section.
March 25
Midterm exam II will be held in class, on Thursday, April 3; midterm info.
March 13
Homework 4 is out, due by the end of the day on Thursday, March 27.
March 10
There will be no instructor office hours on Tuesday, March 11. The instructor and the TA office hours on Tuesday, March 11 will be held as usual.
March 4
Homework 3 is out, due by the end of the day on Tuesday, March 11.
Note the date change: Midterm II will be held in class on Thursday, April 3 (a week later than the original date).
February 13
Midterm exam I will be held in class, on Thursday, February 20; midterm info.
February 10
Another correction in Problem 3 of Homework 2; see update.
February 7
There was an important condition omitted in Problem 1 of Homework 2. It has been updated.
February 6
Homework 2 is out, due by the end of the day on Thursday, February 13.
January 28
Homework 1 is out, due by the end of the day on Tuesday, February 4.
January 20
Given the weather forecast, there will be no class tomorrow, Jan 21. Lectures will begin on Thursday, Jan 23.
Homework 0 is out. It will not be collected, but it should give you an idea of the material from multivariable calculus and linear algebra that will be needed in this course.
January 15
Welcome! Watch this space for all important course-related announcements.
About this course
What is this?
Introduction to Optimization is a senior/first year graduate-level course on optimization. Topics include necessary and sufficient conditions for local optima; characterization of convex sets and functions; unconstrained optimization, gradient descent and it variants; constrained optimization and the gradient projection method; optimization with equality and inequality constraints, Lagrange multipliers, KKT conditions; penalty and barrier function methods; weak and strong duality and Slater conditions; augmented Lagrangian methods; sub-gradient methods; proximal gradient descent; applications.
Materials
Required textbook:
Optimization for Data Analysis by Stephen J. Wright and Benjamin Recht, Cambridge University Press, 2022
Coursework
There will be regular problem sets (roughly, one problem set every two weeks) and two in-class midterm exams. Students enrolled for 4 credit hours will also have to complete a final project.