Learning outcomes
- Be able to apply Linear Programming and Mixed-Integer Programming model to solve real-world problems.
- Be able to recognize and formulate convex optimization problems arising in practice.
- Demonstrate an understanding of theoretical foundations of convex optimization and be able to use it to characterize optimal solutions to general problems.
- Be able to define an appropriate local search neighbourhood for a given problem.
- Be able to use a variety of meta-heuristics to escape local minima in a neighbourhood
- Demonstrate an understanding of the propagation of a global constraint in a Constraint programming system.
Semester 2 2023 details
See prerequisites and requirements under programs and courses:
Assessments
Assessment | COMP4691 | COMP8691 |
---|---|---|
Final Exam | 50% (40% hurdle) | 50% (40% hurdle) |
Assignment 1 | 10% | 10% |
Assignment 2 | 10% | 10% |
5 Quizzes | 30% | 20% |
Seminar | – | 10% |
Schedule
The lecture and lab session times and locations can be found by searching for the course under timetabling. See activities and deliverables for more details.