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Unit outline_

ELEC5213: Engineering Optimisation

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

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
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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
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Final exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Homework Assignment 1
Covers Modules 1-2
7.5% Week 04 Homework
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Homework Assignment 2
Covers Modules 3-4
7.5% Week 07 Homework
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Online task Mid-semester exam
Mid-semester exam
10% Week 09 1 hour
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Homework Assignment 3
Covers Modules 5-7
7.5% Week 10 Homework
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Homework Assignment 4
Covers Modules 8-12
7.5% Week 13 Homework
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Type C final exam = Type C final exam ?

Assessment summary

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

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Any assessment submitted after the due time and date will incur a late penalty of 5% of the total marks per day.

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction and motivation Online class (2 hr) LO1
Week 02 Classical optimisation methods. Online class (2 hr) LO1 LO2
Week 03 Duality in optimisation. Online class (2 hr) LO1 LO2 LO3
Week 04 One-dimensional minimisation methods. Online class (2 hr) LO1 LO2 LO3
Week 05 Unconstrained optimisation techniques, Online class (2 hr) LO1 LO2 LO3
Week 06 Unconstrained optimisation techniques – Applications. Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 07 Constrained optimisation methods Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 Constrained optimisation methods – Applications Online class (2 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Linear programming Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 10 Linear programming – Applications Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 11 Integer programming Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 12 Integer programming – Applications Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 13 Revision Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6

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 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. Employprogramming language MATLAB and MATLAB’s Optimization Toolbox to model and solve optimisation problems arising in different engineering disciplines
  • LO6. Write the code 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

Alignment with Competency standards

Outcomes Competency standards
LO1
Engineers Australia Curriculum Performance Indicators - EAPI
1. ENABLING SKILLS AND KNOWLEDGE DEVELOPMENT
LO2
Engineers Australia Curriculum Performance Indicators - EAPI
1. ENABLING SKILLS AND KNOWLEDGE DEVELOPMENT
2. IN-DEPTH TECHNICAL COMPETENCE
LO3
Engineers Australia Curriculum Performance Indicators - EAPI
2. IN-DEPTH TECHNICAL COMPETENCE
LO4
Engineers Australia Curriculum Performance Indicators - EAPI
2. IN-DEPTH TECHNICAL COMPETENCE
4. ENGINEERING APPLICATION EXPERIENCE
LO5
Engineers Australia Curriculum Performance Indicators - EAPI
2. IN-DEPTH TECHNICAL COMPETENCE
4. ENGINEERING APPLICATION EXPERIENCE
5. PRACTICAL AND ‘HANDS-ON’ EXPERIENCE
LO6
Engineers Australia Curriculum Performance Indicators - EAPI
2. IN-DEPTH TECHNICAL COMPETENCE
4. ENGINEERING APPLICATION EXPERIENCE
5. PRACTICAL AND ‘HANDS-ON’ EXPERIENCE
LO7
Engineers Australia Curriculum Performance Indicators - EAPI
3. PERSONAL AND PROFESSIONAL SKILLS DEVELOPMENT

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

The unit is offered for the first time.

Disclaimer

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