Optimisation
Optimisation is about finding the "best" way to do a task, subject to any restrictions. Research in optimisation includes model development, analysis, numerical techniques and applications.
Research interests
- Nonlinear programming
- Nonsmooth analysis and optimisation
- Global optimisation
- Convex optimisation
- Applied nonlinear functional analysis
- Stochastic programming
- Semidefinite programming
- Numerical methods for nonsmooth problems
- Integer programming
- Polynomial optimisation
- Robust optimisation
- Application of optimisation techniques to: Approximation; biology; finance; engineering; statistics; data mining; fluid mixing; lung cancer radiotherapy; open pit mining; minimise aircraft delays; efficiently recover disrupted airline schedules; schedule single track rail freight; scheduling port operations.
Relevant courses
- MATH2301 Mathematical Computing
- MATH2601 Higher Linear Algebra
- MATH3611 Higher Analysis
- MATH3161 Optimisation
- MATH3171 Linear and Discrete Optimization Modelling
- MATH3191 Mathematical Optimization for Data Science
- MATH3311 Mathematical Computing for Finance
- MATH3701 Higher Topology and Differential Geometry
- MATH3801 or MATH3901 Probability and Stochastic Processes/Higher version