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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|
|Specified Learning Activities||
|Autonomous Student Learning||
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|
|Assignment: Solving some optimisation problems , interpreting the results, coding||Varies over the Trimester||n/a||Graded||No||
|Multiple Choice Questionnaire: Online exam, Mix of theoretical and practical questions||Week 7||n/a||Standard conversion grade scale 40%||No||
|Examination: Online exam||2 hour End of Trimester Exam||Yes||Standard conversion grade scale 40%||No||
|Resit In||Terminal Exam|
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
|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|