This course provides foundations and plenty of exercises in practical optimisation problems, while covering all basic elements of optimisation including forms of constraint programming as well as variations on linear programming and convex optimisation.
Additional source review: awaiting review. Published wording is shown below.
Saved requirements encoded for automatic checks. These are prototype interpretations, not ANU decisions or degree-completion checks.
Requisite and Incompatibility
To enrol in this course you must have completed COMP6320 or COMP3620. Incompatible with COMP4690.
Referenced entries (3)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP6320 · 2027
- COMP3620 · 2027 · saved source not linked
- COMP4690 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
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 and reflect on a variety of meta-heuristics to escape local minima in a neighbourhood.
Demonstrate an understanding and reflect on of the propagation of a global constraint in a Constraint programming system.
Published offerings
The page explicitly says there are no current offerings. This does not establish permanent discontinuation.
Complete offering text
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage. There are no current offerings for this course.
Source evidence (1 saved copy)
Matching copies share this record. Their original file locations and checksums are retained.
assets/Courses/Artificial Intelligence/8000/Optimisation/Optimisation - ANU.html
Published ANU page · saved page URL
SHA-256: 64d114529debfd7b2191510209757f398260e5383b2a936d117db28638608d60