Mathematical Optimization
Durham
Engineering&Physical Sciences::Mathematics&Statistics
Credits: 3.0
Class Size: 5
Term:
Spring 2025
-
Full Term (01/21/2025
-
05/05/2025)
CRN:
52858
Grade Mode:
Letter Grading
This course introduces the foundations of mathematical optimization and reinforces them via applications. The content includes convex optimization, first and second-order methods, constrained problems, duality, linear and quadratic programming, as well as discrete and non-convex optimization. Applications will focus on machine learning methods but also include problems from engineering and operations research. Students are required to have a mastery of Calculus II and programming proficiency in MATLAB, R, Java, C, Python, or equivalent.
Equivalent(s):
CS 857
Instructors:
Marek Petrik
Times & Locations
| Start Date | End Date | Days | Time | Location |
|---|---|---|---|---|
| 1/21/2025 | 5/5/2025 | MWF | 10:10am - 11:00am | KING N113 |
Booklist
| Book | Details |
|---|---|
|
CONVEX OPTIMIZATION
04
by BOYD
Recommended
|
|
|
NONLINEAR PROGRAMMING
3RD 16
by BERTSEKAS
Required
|
|
