EECE 7323 — Numerical Optimization Methods
4 semester hoursGraduateLectureusually offered: springtypical days: TBostonTraditional
Introduces fundamental theoretical and algorithmic concepts behind numerical optimization theory for objective functions with finite numbers of parameters. Optimization problems arise ubiquitously in all areas of engineering and science. Presents established numerical methods for iterative unconstrained and constrained optimization. Topics covered include line-search and trust-region strategies, gradient descent and Newton methods and their variations, linear and quadratic programming, penalty-augmented Lagrangian methods, sequential quadratic programming, and interior point methods. The course relies on the use of Matlab in projects. Requires a basic knowledge of calculus and linear algebra.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2024 | 1 | 16 | 40 | 40% | 24.0 |
| Spring 2025 | 1 | 23 | 40 | 57% | 17.0 |
| Spring 2026 | 1 | 21 | 40 | 53% | 19.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 100% · W 0% · Th 0% · F 27%
Common patterns: T (73% of sections), TF (27% of sections) — in patterns, R means Thursday
Professors
Spring
- Hessam Mahdavifar (73% of students) · reviews
- Edmund Yeh (27% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Unlocks
Courses that list EECE 7323 in their prerequisites.
Links
Official catalog (EECE course descriptions) · Student reviews on RateMyHusky · All EECE courses · Plan it at numap.app