EE 231: CONVEX OPTIMIZATION IN ENGINEERING APPLICATIONS
University of California, Riverside
4 Units, Lecture, 3 hours; term paper, 3 hours. Prerequisite(s): EE 230, may be taken concurrently; graduate standing; or consent of instructor. Covers recognizing and solving convex optimization problems in engineering applications. Explores convex sets, functions, and optimization problems. Includes basics of convex analysis, least-squares, linear and quadratic programs, semidefinite programming, minimax, and other problems. Addresses optimality conditions, duality theory, theorems of alternative and applications, interior-point methods, and applications in engineering.
Average GPA: 3.74
Grade distribution records: 148 students across 6 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 14 | 9.5% |
| A | 87 | 58.8% |
| A- | 17 | 11.5% |
| B+ | 8 | 5.4% |
| B | 13 | 8.8% |
| B- | 3 | 2.0% |
| C+ | 5 | 3.4% |
| C | 1 | 0.7% |
Based on 148 student grade records across 6 terms and 1 professor.
Instructors
- Amir-Hamed Mohsenian-Rad 148 students, Average GPA 3.74