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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.
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 work on a project which will give them hands-on experience solving real-world optimization problems. Topics include: 1. Mathematical 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: Identify, research and formulate optimisation problems in power systems, 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.
Either ENEL320 or ENMT301; and either ENEL321 or ENME303
Students must attend one activity from each section.
Jeremy Watson
Le Yang
J. Martins and A. Ning; Engineering Design Optimization ; Cambridge University Press, 2022 (Freely available from https://mdobook.github.io).
A. Beck; Introduction to Nonlinear Optimization ; SIAM, 2014.
J. Nocedal and S.J. Wright; Numerical Optimization ; Springer, 2000.
R. Baldick; Applied Optimization: Formulation and Algorithms for Engineering Systems ; Cambridge University Press, 2006.
S. Brunton; Optimization Bootcamp ; (https://faculty.washington.edu/sbrunton/OptimizationBootcamp.pdf).
Z. Michalewicz and D.B. Fogel; How to solve it: Modern Heuristics ; Springer, 1998.
Lateness PenaltiesFor the Project progress and final reports, a lateness penalty of 10% (in absolute terms) per day or part day late will be deducted from the original mark. For example, an assignment with a nominal mark of 83% submitted 0-24 hours late will receive a mark of 73%, and submitted 24-48 hours late will receive 63%.
Scaling of marks:In order to maintain consistency across courses and fairness for students, scaling of raw marks occurs. In the Faculty of Engineering, target course GPAs are calculated based on the performance of the cohort of students in their courses in the previous year. Scaling of the raw total course marks is normally performed so that when converted to grades (using UC Grade Scale) the outgoing GPA is in line with the target GPA for a course. Scaling up or down can occur.The Grading Scale for the University: https://www.canterbury.ac.nz/study/study-support-info/study-related-topics/grading-scaleArtificial Intelligence Tools:The use of Artificial Intelligence (AI) tools for each of the assessments in ENEL445 is summarised in the Table below. No AI use is allowed in the tests and exam because these are closed-book invigilated assessments. Students are always responsible for the accuracy of the submitted works, regardless of which tools are used.Assessment Item and Permitted use of AI: Project presentation: Generative AI Tools Are Permitted for Certain Parts of This Assessment Project reports: Generative AI Tools Are Permitted for Certain Parts of This Assessment Test: Generative AI tools cannot be used for this assessment. Exam: Generative AI tools cannot be used for this assessment. Generative AI Tools Are Permitted for Certain Parts of This Assessment:In the project-based assessments, you are permitted to use generative artificial intelligence (AI) for the purpose of proof reading and editing documents, for debugging code, and for gathering and summarising knowledge. No other use of generative AI is permitted, including the generation of substantial parts of your computer code or report. To assist with maintaining academic integrity, you must appropriately acknowledge any use of generative AI in your work. Please include a Statement of AI use (if no AI tool has been used, then this must also be stated) and a listing of all prompts provided to the AI tool, clearly indicating which AI tools were used and how they contributed to your assessment.
Contact HoursLectures: 30 hoursTutorials: 2 hoursWorkshops: 0 hoursLaboratories: 0 hours Independent studyReview of lectures: 34 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 .