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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.
At the conclusion of this course you should be able to:LO1: Identify, research and formulate optimisation problems in powersystems, signal processing, machine learning, and control (WA1, WA2, WA4)LO2: Classify the formulated optimisation problems, design and derive practical algorithms to search for the optimal solution (WA3, WA4, WA5)LO3: Using optimisation software to solve formulated optimisation problems, analyse the convergence of the algorithms and investigate the results (WA3, WA4, WA5)LO4: Collaborate with other students and lecturers to formulate a real-world engineering problem as an optimisation problem and design a solution using optimisation software (WA3, WA4, WA9, WA10, WA11)
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
1.EMTH210 2. EMTH211 3. ENEL320 4. ENEL321 or ENME301.
Students must attend one activity from each section.
Jeremy Watson
Joe Chen and Le Yang
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 $1,268.00
International fee $6,238.00
* 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 .