Outline

Overview

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 unit 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 unit 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 unit will use Matlab and AMPL as modelling tools and a range of state-of-the-art solvers, including Cplex, Gurobi, Knitro and Ipopt.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Linear algebra, differential calculus, and numerical methods. Competency at programming in a high-level language (such as Matlab or Python)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Gregor Verbic, gregor.verbic@sydney.edu.au
Laboratory supervisor(s)	Rui Chu, rui.chu@sydney.edu.au
Lecturer(s)	Gregor Verbic, gregor.verbic@sydney.edu.au
Tutor(s)	Mohsen Aldaadi, mohsen.aldaadi@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Skills-based evaluation	Practical exam Equivalent to final exam, Focus on programming, Open book	60%	STUVAC	2 hours
Outcomes assessed:
Assignment	Homework Assignment 1 Coding/computational assignment, Covers Modules 1-2	7.5%	Week 04 Due date: 17 Mar 2024 at 23:59	Homework
Outcomes assessed:
Assignment	Homework Assignment 2 Coding/computational assignment, Covers Modules 3-4	7.5%	Week 07 Due date: 14 Apr 2024 at 23:59	Homework
Outcomes assessed:
Tutorial quiz	Quiz Written exam with programming, Open book	10%	Week 08	50 minutes
Outcomes assessed:
Assignment	Homework Assignment 3 Coding/computational assignment, Covers Modules 5-7	7.5%	Week 10 Due date: 05 May 2024 at 23:59	Homework
Outcomes assessed:
Assignment	Homework Assignment 4 Coding/computational assignment, Covers Modules 8-12	7.5%	Week 13 Due date: 26 May 2024 at 23:59	Homework
Outcomes assessed:

Assessment summary

Assessment consists of four homework assignments (worth 30% of total mark), mid-semester exam (10%) and final exam (60%).

Assessment criteria

Result Name	Mark range	Description
Fail	<50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.
Pass	50-64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty
Credit	65-74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75-84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
High Distinction	85-100	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and motivation	Lecture (2 hr)
Week 02	Classical optimisation methods	Lecture (2 hr)
Week 03	Duality in optimisation	Lecture (2 hr)
Week 04	One-dimensional minimisation methods	Lecture (2 hr)
Week 05	Unconstrained optimisation techniques	Lecture (2 hr)
Week 06	Unconstrained optimisation techniques – Applications	Lecture (2 hr)
Week 07	Constrained optimisation methods	Lecture (2 hr)
Week 08	Constrained optimisation methods – Applications	Lecture (2 hr)
Week 09	Linear programming	Lecture (2 hr)
Week 10	Linear programming – Applications	Lecture (2 hr)
Week 11	Integer programming	Lecture (2 hr)
Week 12	Integer programming – Applications	Lecture (2 hr)
Week 13	Revision	Lecture (2 hr)
Weekly	Combined laboratory/tutorial workshop where students solve optimisation problems on a whiteboard, and model practical optimisation problems and solve them using optimisation solvers.	Workshop (39 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. Explain the fundamentals of mathematical optimisation and the need for numerical methods
LO2. Explain the mathematical foundation of optimisation algorithms
LO3. Explain the numerical methods required to solve optimisation problems
LO4. Identify the class of an optimisation problem along with suitable solver technology to solve it
LO5. Employ programming language MATLAB and MATLAB’s Optimization Toolbox to model and solve optimisation problems arising in different engineering disciplines
LO6. Write codes in a high-level programming language (such as Matlab or Python) to implement basic numerical solution techniques for solving constrained and unconstrained optimisation problems
LO7. Write a report to communicate complex project-specific information concisely and accurately and to the degree of specificity required by the project at hand

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 1. ENABLING SKILLS AND KNOWLEDGE DEVELOPMENT
	Engineers Australia Curriculum Performance Indicators - 1. ENABLING SKILLS AND KNOWLEDGE DEVELOPMENT 2. IN-DEPTH TECHNICAL COMPETENCE
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE 4. ENGINEERING APPLICATION EXPERIENCE
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE 4. ENGINEERING APPLICATION EXPERIENCE 5. PRACTICAL AND ‘HANDS-ON’ EXPERIENCE
	Engineers Australia Curriculum Performance Indicators - 2. IN-DEPTH TECHNICAL COMPETENCE 4. ENGINEERING APPLICATION EXPERIENCE 5. PRACTICAL AND ‘HANDS-ON’ EXPERIENCE
	Engineers Australia Curriculum Performance Indicators - 3. PERSONAL AND PROFESSIONAL SKILLS DEVELOPMENT

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The unit is offered for the first time.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ELEC5213: Engineering Optimisation

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect