ELEC5213: Engineering Optimisation (2021 - Semester 1)
| Unit: | ELEC5213: Engineering Optimisation (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Postgraduate |
| Faculty/School: | School of Electrical & Information Engineering |
| Unit Coordinator/s: |
Dr Verbic, Gregor
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| Session options: | Semester 1 |
| Versions for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | None. |
| Brief Handbook Description: | The unit of study provides an introduction to engineering optimisation, focusing specifically on practical methods for formulating and solving linear, nonlinear and mixed-integer optimisation problems that arise in science and engineering. The course covers conventional optimisation techniques, including unconstrained and constrained single- and multivariable optimisation, convex optimisation, linear and nonlinear programming, mixed-integer programming, and sequential decision making using dynamic programming. The emphasis is on building optimisation models, understanding their structure and using off-the-shelf solvers to solve them. While the course is designed with engineers in mind, it provides sufficiently rigorous mathematical treatment to allow deeper study. The application focus is on the optimisation problems arising in electrical engineering, including power systems, communications, signal processing, control and computer engineering. The course will use Matlab and AMPL as modelling tools and a range of state-of-the-art solvers, including Cplex, Gurobi, Knitro and Ipopt. |
| Assumed Knowledge: | Linear algebra, differential calculus, and numerical methods. Competency at programming in a high-level language (such as Matlab or Python) |
| Timetable: | ELEC5213 Timetable | ||||||||||||||||||||
| Time Commitment: |
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Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.
(6) Communication and Inquiry/ Research (Level 3)
1. Ability to work in a team by assuming appropriate roles and balancing responsibilities to achieve the goals of a project in a timely manner.
2. Ability to make written and oral presentations using varied media aids and tools, to convey complex engineering material concisely and accurately.
3. Ability to undertake inquiry and knowledge development by identifying the limits of the available information on applied optimisation in the context of power systems by drawing on relevant research papers and articles on the topic.
(2) Engineering/ IT Specialisation (Level 4)
4. Ability to formulate engineering problems as optimisation problems and solve them using appropriate solvers.
5. Ability to demonstrate an understanding of applied optimisation to be able to solve practical power engineering problems.
6. Proficiency in using dedicated modelling tools and solvers for solving optimisation problems arising in power systems.
7. Ability to apply linear algebra and differential calculus to solve applied optimisation problems.
| Assessment Methods: |
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| Assessment Description: |
Final exam: a test of knowledge learned in lecture, tutorial and homework assignments (60%) Mid-semester exam: a test of knowledge learned in lecture and tutorial (10%) Homework: application of knowledge gained in lectures and tutorials to solve practical optimisation problems (30%) |
| Prescribed Text/s: |
Note: Students are expected to have a personal copy of all books listed.
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| Recommended Reference/s: |
Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
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Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
| Week | Description |
| Week 1 | Lecture: Introduction to optimisation. Statement of an optimisation problem. Classification of optimisation problems. |
| Week 2 | Lecture: Classical optimisation techniques. Single- and multivariable unconstrained optimisation. |
| Week 3 | Lecture: Multivariable optimisation with equality constraints. Solution by the method of Lagrange multipliers. |
| Week 4 | Lecture: Multivariable optimization with inequality constraints. Karush-Kuhn–Tucker optimality conditions. |
| Week 5 | Lecture: Convex optimisation. Duality. First and second-order optimality conditions. |
| Week 6 | Lecture: Linear programming. Geometry of linear programming problems. Duality in linear programming. |
| Week 7 | Lecture: Applications of linear programming. Economic dispatch. |
| Week 8 | Lecture: Nonlinear programming. Unconstrained optimisation techniques. Indirect search (descent) methods. Newton and Quasi-Newton method. |
| Week 9 | Lecture: Nonlinear programming. Constrained optimisation techniques. Interior point methods. |
| Assessment Due: Mid-Semester Exam | |
| Week 10 | Lecture: Mixed integer programming. Cutting plane method. Branch and bound methods. |
| Week 11 | Lecture: Applications of mixed integer linear programming. Unit commitment problem. |
| Week 12 | Lecture: Sequential decision making. Dynamic programming. |
| Week 13 | Lecture: Application of sequential decision making. Home energy management problem. |
| Exam Period | Assessment Due: Final exam |
Course Relations
The following is a list of courses which have added this Unit to their structure.
Course Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| (6) Communication and Inquiry/ Research (Level 3) | No | 6% |
| (2) Engineering/ IT Specialisation (Level 4) | No | 94% |
These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.
