Use the Tab and Up, Down arrow keys to select menu items.
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 and linear matrix inequalities for power system optimisation, evolutionary algorithms, constraint satisfaction methods, Bayesian optimization, nonlinear least squares, adaptive filtering and backpropagation for deep neural networks.
Optimisation is at the core of many engineering disciplines, including power systems, signal processing, communications, control, and machine learning. This course will cover the theory as well as the practice of engineering optimisation, including the problem formulation, problem transformation, gradient-based and gradient-free optimisation algorithms, and nature-inspired methods. Besides in-class lectures, students will select a project which will give them hands-on experience solving real-world optimization problems. Topics include:1. Mathematics background and optimisation basics2. Gradient-based unconstrained optimisation 3. Constraint optimisation and KKT conditions4. Optimisation-based control5. Power system optimisation6. Nature-inspired optimisation 7. FIR filter design using optimisation8. Majorization-minorization (MM) algorithms9. Deep neural networks
At the conclusion of this course you should be able to:LO1: Apply mathematical modelling skills to 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: Apply optimisation software to solve formulated optimisation problems, analyse the convergence of the algorithms and interpret the results (WA3, WA4, WA5)LO4: Collaborate with other students and lecturers to formulate a real-world engineering problem as an optimisation problem and solve it 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.
EMTH210, . EMTH211, . ENEL320 and. ENEL321
Students must attend one activity from each section.
Jeremy Watson
Le Yang and Joe Chen
Note that University regulations state: “A student seeking course credit must engage satisfactorily in all required course-related activity, work and assessment specified in the course outline.”
J. Martins and A. Ning; Engineering Design Optimization ; Cambridge University Press, 2022 (Freely available from https://mdobook.github.io).
Baldick, Ross; Applied optimization : formulation and algorithms for engineering systems ; Cambridge University Press, 2006.
Michalewicz, Zbigniew. , Fogel, David B; How to solve it : modern heuristics ; Springer, 1999.
Nocedal, Jorge. , Wright, Stephen J; Numerical optimization ; Springer, 1999.
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 $1,059.00
International fee $6,000.00
* 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 .