Course Description
Formulation and analysis of algorithms for continuous and discrete optimization problems; linear, nonlinear, network, dynamic, and integer optimization; large-scale problems; software packages and their implementation; duality theory and sensitivity.
Average difficulty
Average quality
unfriendly for students without a strong background in linear algebra. lots of mistakes on slides, and no practice problems are available for study before exams.
| Difficulty: | 4.5 | |
| Quality: | 1.5 |
Great course with Prof. Friedlander. Although you should only take it if you are into Numerical Computation stuff.