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An advanced course on optimisation techniques and their engineering applications. The course first provides a review of mathematical background, and then covers the formulation of unconstrained/constrained optimisation problems, gradient descent, method of Lagrangian multipliers, and first-order KKT conditions. Other topics include model predictive control, power system optimisation, evolutionary algorithms, constraint satisfaction methods, Bayesian optimization, nonlinear least squares,adaptive filtering and backpropagation for deep neural networks.
Either ENEL320 or ENMT301; and either ENEL321 or ENME303
Students must attend one activity from each section.
J. Martins and A. Ning; Engineering Design Optimization ; Cambridge University Press, 2022 (Freely available from https://mdobook.github.io).
J. Nocedal and S.J. Wright; Numerical Optimization ; Springer, 2000.
R. Baldick; Applied Optimization: Formulation and Algorithms for Engineering Systems ; Cambridge University Press, 2006.
Z. Michalewicz and D.B. Fogel; How to solve it: Modern Heuristics ; Springer, 1998.
Contact HoursLectures: 33 hoursTutorials: 0 hoursWorkshops: 0 hoursLaboratories: 0 hours Independent studyReview of lectures: 33 hoursTest and exam preparation: 24 hoursAssignments: 60 hoursTutorial preparation: 0 hoursLaboratory calculations: 0 hours Total 150
Domestic fee $1,344.00
International fee $6,488.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Electrical and Computer Engineering .