Listed in: Mathematics and Statistics, as MATH-294
Tanya L. Leise (Section 01)
Optimization is a branch of applied mathematics focused on algorithms to determine maxima and minima of functions, often under constraints. Applications range from economics and finance to machine learning and information retrieval. This course will first develop advanced linear algebra tools, and then will study methods of convex optimization. Possible topics include linear, quadratic, second-order cone, and semidefinite models. Several applications will be explored, and algorithms will be implemented using mathematical software to aid numerical experimentation.
Requisite: MATH 211 and 271 or 272, or consent of the instructor. Limited to 30 students. Omitted 2021-22.
How to handle overenrollment: Preference is given to math majors.
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: Problem sets, Use of computational software, In-class or take-home exams, May include quizzes, group projects, or in-class group work.
Section 01
M 2:00 PM - 2:50 PM SMUD 014
Tu 2:30 PM - 3:20 PM SMUD 207
Th 2:30 PM - 3:20 PM SMUD 207
| ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|
| Linear and Nonlinear Programming, Fifth Edition | Springer, 2021 | Luenberger and Ye | TBD |