MTH4333 · Discrete optimisation

Overview

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.

Areas of study: Master of Mathematics

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

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.

Workload

• 3 hours of seminars; • 1-hour applied session and • 8 hours of independent study per week.

Student experience, not official rules. Nothing here changes what the Handbook says.