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.
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 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.
Professional Skills Mapping:
- Assignment (50) [LO null]
- Final Exam (hurdle) (50) [LO null]
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Up to 60 hours of total face-time, which includes interactions with lectures and tutors. Up to 60 hours of total preparation, repeat, assignment and practical exercise time.
Requisite and Incompatibility
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
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Offerings, Dates and Class Summary Links
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