ENEL445-24S1 (C) Semester One 2024

Special Topic: Applied Engineering Optimisation

15 points

Details:
Start Date: Monday, 19 February 2024
End Date: Sunday, 23 June 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 3 March 2024
  • Without academic penalty (including no fee refund): Sunday, 12 May 2024

Description

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 basics
2. Gradient-based unconstrained optimisation
3. Constraint optimisation and KKT conditions
4. Optimisation-based control
5. Power system optimisation
6. Nature-inspired optimisation
7. FIR filter design using optimisation
8. Majorization-minorization (MM) algorithms
9. Deep neural networks

Learning Outcomes

  • At the conclusion of this course you should be able to:

  • LO1: Apply mathematical modelling skills to 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: 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)
    • University Graduate Attributes

      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.

Prerequisites

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 09:00 - 10:00 Karl Popper 508
19 Feb - 31 Mar
22 Apr - 2 Jun
02 Tuesday 09:00 - 10:00 Online Delivery
19 Feb - 31 Mar
22 Apr - 2 Jun
Lecture B
Activity Day Time Location Weeks
01 Monday 12:00 - 13:00 Rehua 103 Project Workshop
19 Feb - 31 Mar
22 Apr - 2 Jun
02 Monday 12:00 - 13:00 Online Delivery
19 Feb - 31 Mar
22 Apr - 2 Jun
Lecture C
Activity Day Time Location Weeks
01 Thursday 09:00 - 10:00 Jack Erskine 315
19 Feb - 31 Mar
22 Apr - 2 Jun
02 Thursday 09:00 - 10:00 Online Delivery
19 Feb - 31 Mar
22 Apr - 2 Jun

Examination and Formal Tests

Test A
Activity Day Time Location Weeks
01 Wednesday 19:00 - 20:30 Meremere 526
22 Apr - 28 Apr

Course Coordinator

Jeremy Watson

Lecturers

Le Yang and Joe Chen

Assessment

Assessment Due Date Percentage  Description
Term Test 20% Term Test
Project Progress Report 10% Project Progress Report
Oral Presentation 10%
Project Final Report 30% Project Final Report
Final Exam 30% Final Exam


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.”

Textbooks / Resources

Required Texts

J. Martins and A. Ning; Engineering Design Optimization ; Cambridge University Press, 2022 (Freely available from https://mdobook.github.io).

Recommended Reading

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.

Additional Course Outline Information

Mahi ā-Ākonga | Workload (expected distribution of student hours, note 15 points = 150 hours):

Contact Hours

Lectures: 33 hours
Tutorials: 0 hours
Workshops: 0 hours
Laboratories: 0 hours

Independent study

Review of lectures: 33 hours
Test and exam preparation: 24 hours
Assignments: 60 hours
Tutorial preparation: 0 hours
Laboratory calculations: 0 hours

Total 150

Indicative Fees

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 .

All ENEL445 Occurrences

  • ENEL445-24S1 (C) Semester One 2024