MTH5333 - Discrete optimisation - 2019

This unit provides an introduction to optimisation over discrete domains using integer programming and combinatorial methods. Discrete optimisation is frequently used to model decision problems in business and industry. This unit covers some of the mathematical tools required to solve these types of problems in practice. Building on linear programming, the unit will cover dynamic programming, branch-and-bound, polyhedral analysis, decomposition methods and an introduction to heuristic search for combinatorial optimisation problems.

On completion of this unit students will be able to:

1. Develop specialised mathematical knowledge in discrete optimisation.
2. Understand the profound connections between discrete optimisation, continuous optimisation and combinatorics.
3. Apply sophisticated combinatorial optimisation and integer programming methods to a variety of practical optimisation problems.
4. Translate practical problem descriptions into mathematical formulations as discrete optimisation problems and communicate the results to non-technical audiences.
5. Apply critical thinking in the field of operations research.

Examination (3 hours): 60% (Hurdle)

Continuous assessment: 40%

Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for the end-of-semester exam.

This unit is offered at both Level 4 and Level 5, differentiated by the level of the assessment. Students enrolled in MTH5333 will be expected to demonstrate a higher level of learning in this subject than those enrolled in MTH4333Not offered in 2019. The assignments and exam in this unit will use some common items from the MTH4333Not offered in 2019 assessment tasks, in combination with several higher level questions and tasks.

• 3 hours of lectures
• 1-hour tutorial and
• 10 hours of independent study per week

See also Unit timetable information

Master of Mathematics