Show/hide contentOpenClose All
Curricular information is subject to change
1. Be able to formulate real world optimization problems and incorporate uncertainty.
2. Be capable of implementing optimization solvers.
3. Convert problems to their dual formulation.
4. Be able to prove basic results on convex optimization.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Specified Learning Activities | 12 |
Autonomous Student Learning | 80 |
Online Learning | 11 |
Total | 127 |
A good facility with computer scripting, as well as a solid grasp on numerical methods will ground a student well for this module. A good familiarity with MATLAB or C++ or Python, Julia or GAMS will be very helpful.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Not yet recorded. |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
Name | Role |
---|---|
Dr Barry Cardiff | Lecturer / Co-Lecturer |
Dr Deepu John | Lecturer / Co-Lecturer |
Professor Andrew Keane | Lecturer / Co-Lecturer |
Dr Alireza Nouri | Lecturer / Co-Lecturer |
Ms Rui Cai | Tutor |
Cliodhna Gartland | Tutor |