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Unit outline_

COMP3530: Discrete Optimization

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit introduces students to the algorithmic theory and applications of discrete optimisation. The main aims of this unit are: Learn how to model various practical problems as abstract optimisation problems; Learn the theory underlying efficient algorithms for solving these problems; Learn how to use these tools in practice. Specific topics include: Linear and integer programming, polyhedral theory, and approximation algorithms.

Unit details and rules

Academic unit Computer Science
Credit points 6
COMP2123 OR COMP2823 OR COMP2007 OR COMP2907
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Julian Mestre,
Lecturer(s) Patrick Eades,
Type Description Weight Due Length
Supervised exam
hurdle task
Final exam
Final exam
60% Formal exam period 2.5 hours
Outcomes assessed: LO1 LO3 LO4
Small continuous assessment Quizzes
Bi-weekly quizzes
4% Multiple weeks 15 minutes
Outcomes assessed: LO1
Assignment Assignment 1
Assignment 1
9% Week 04
Due date: 24 Aug 2023 at 23:59

Closing date: 29 Aug 2023
10 working days
Outcomes assessed: LO2 LO1 LO3 LO4
Assignment Assignment 2
Assignment 2
9% Week 07
Due date: 14 Sep 2023 at 23:59

Closing date: 19 Sep 2023
10 working days
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment 3
Assignment 3
9% Week 09
Due date: 05 Oct 2023 at 23:59

Closing date: 10 Oct 2023
10 working days
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment 4
Assignment 4
9% Week 12
Due date: 26 Oct 2023 at 23:59

Closing date: 31 Oct 2023
10 working days
Outcomes assessed: LO1 LO2 LO3 LO4
hurdle task = hurdle task ?

Assessment summary

  • Assignments: There will be four individual assignments. These will consist of written problems, as well as implementation/experimental problems.
  • Final Exam: A written examination covering all the material covered in class. There is a 40% barrier for the final exam.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

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:

Late submissions are not accepted unless you are granted a special consideration arrangement.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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 to optimisation Lecture and tutorial (3 hr) LO1
Week 02 Simplex Lecture and tutorial (3 hr) LO1
Week 03 Modeling Lecture and tutorial (3 hr) LO3 LO4
Week 04 Duality theory Lecture and tutorial (3 hr) LO1 LO3
Week 05 Applications of linear programming Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 06 Integral polyhedra Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 07 Integer programming Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 08 Large scale optimisation Lecture and tutorial (3 hr) LO1 LO4
Week 09 Lagrangian relaxation Lecture and tutorial (3 hr) LO1 LO4
Week 10 Maximum submodular coverage Lecture and tutorial (3 hr) LO1 LO3 LO4
Week 11 Minimum submodular coverage Lecture and tutorial (3 hr) LO1 LO3
Week 12 LP rounding Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 13 Review Lecture and tutorial (3 hr) LO1

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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

  • Laurence A. Wolsey, Integer Programming.
  • Bertsimas, D., and J. Tsitsiklis., Linear Optimization.

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. understand fundamental algorithms for several optimisation problems, such as linear and integer programming, approximation algorithms, and fixed-parameter tractability
  • LO2. implement algorithms and use standard tools for solving real life problems
  • LO3. model application problems as optimisation problems
  • LO4. apply and tailor known algorithms for solving new, and challenging problems.

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

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

Taking into account the feedback received last year, we will feature programming tasks more prominently in the assignments.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.
In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.”
And then depending on whether we check for software similarity of text similarity in documents:
"Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see, or the similarity report available in ED ( These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.”
“All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.
There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.


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.