Handbook

This unit covers the theory, techniques and applications of optimisation, with a focus on applications in data analytics. The emphasis is on advanced methods for nonlinear continuous optimisation. In addition to its theoretical description of optimisation algorithms, the unit also has a strong practical focus with students required to solve problems computationally through programming. Topics covered include a selection from quasi-Newton methods, augmented Lagrangian methods, and stochastic gradient descent methods, with applications to machine learning and neural networks. Furthermore, the unit will cover constrained optimisation methods that may include quadratic programming, interior point methods, as well as stochastic meta-heuristics for nonlinear optimisation. Applications of these methods may include support vector machines and other classification methods.