Outline

Overview

This unit provides an introduction to the design techniques that are used to find efficient algorithmic solutions for given problems. The techniques covered include greedy, divide-and-conquer, dynamic programming, and adjusting flows in networks. Students will extend their skills in algorithm analysis. The unit also provides an introduction to the concepts of computational complexity and reductions between problems.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	Distinction level results in COMP2123 or COMP2823
Corequisites ?	None
Prohibitions ?	COMP2007 or COMP2907 or COMP3027
Assumed knowledge ?	Discrete mathematics and probability (e.g. MATH1064 or equivalent)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Andre van Renssen, andre.vanrenssen@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Final Exam	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Quiz Canvas Quizzes	10%	Multiple weeks	-	AI allowed
Outcomes assessed:
Written work	Assignment 1 Written assignment	7.5%	Week 05 Due date: 25 Mar 2026 at 23:59 Closing date: 30 Mar 2026	-	AI allowed
Outcomes assessed:
Written work	Assignment 2 Written assignment	7.5%	Week 07 Due date: 15 Apr 2026 at 23:59 Closing date: 20 Apr 2026	-	AI allowed
Outcomes assessed:
Written work	Assignment 3 Written assignment	7.5%	Week 10 Due date: 06 May 2026 at 23:59 Closing date: 11 May 2026	-	AI allowed
Outcomes assessed:
Written work	Assignment 4 Written assignment	7.5%	Week 12 Due date: 20 May 2026 at 23:59 Closing date: 25 May 2026	-	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

It is a requirement 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.

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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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:

These penalties apply when written work is submitted after 11:59pm on the due date. Submissions made after the closing date will receive 0 marks.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 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. 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	Unit introduction, algorithms and complexity, motivation and course outline	Lecture (2 hr)
	Unit introduction, algorithms and complexity, motivation and course outline	Lecture (1 hr)
	Algorithms and complexity	Tutorial (2 hr)
Week 02	Greedy algorithms	Lecture (2 hr)
	Greedy algorithms	Lecture (1 hr)
	Greedy algorithms	Tutorial (2 hr)
Week 03	Divide and conquer	Lecture (2 hr)
	Divide and conquer	Lecture (1 hr)
	Divide and conquer	Tutorial (2 hr)
Week 04	Dynamic programming	Lecture (2 hr)
	Dynamic programming	Lecture (1 hr)
	Dynamic programming	Tutorial (2 hr)
Week 05	Dynamic programming	Lecture (2 hr)
	Dynamic programming	Lecture (1 hr)
	Dynamic programming	Tutorial (2 hr)
Week 06	Flow networks	Lecture (2 hr)
	Flow networks	Lecture (1 hr)
	Flow networks	Tutorial (2 hr)
Week 07	Flow networks	Lecture (2 hr)
	Flow networks	Lecture (1 hr)
	Flow networks	Tutorial (2 hr)
Week 08	Circulations and reductions	Lecture (2 hr)
	Circulations and reductions	Lecture (1 hr)
	Circulations and reductions	Tutorial (2 hr)
Week 09	NP-hardness	Lecture (2 hr)
	NP-hardness	Lecture (1 hr)
	NP-hardness	Tutorial (2 hr)
Week 10	NP-hardness	Lecture (2 hr)
	NP-hardness	Lecture (1 hr)
	NP-hardness	Tutorial (2 hr)
Week 11	Coping with hardness	Lecture (2 hr)
	Coping with hardness	Lecture (1 hr)
	Coping with hardness	Tutorial (2 hr)
Week 12	Dealing with uncertainty	Lecture (2 hr)
	Dealing with uncertainty	Lecture (1 hr)
	Dealing with uncertainty	Tutorial (2 hr)
Week 13	Review	Lecture (2 hr)
	Review	Lecture (1 hr)
	Review	Tutorial (2 hr)

Attendance and class requirements

Attendance to lectures and tutorials is strongly encouraged.

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.

Jon Kleinberg and Eva Tardos, Algorithm Design, Addison Wesley.

Jeff Erickson, Algorithms, available at http://jeffe.cs.illinois.edu/teaching/algorithms/

Tim Roughgarden, Algorithms Illuminated, Cambridge University Press.

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.

Outcomes
Graduate qualities

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

LO1. produce a clear account of an algorithm, that would allow others to understand and implement it
LO2. learn about new algorithms by searching for descriptions in textbooks or online
LO3. read, understand, analyze and modify a given algorithm, as well as design efficient algorithmic solutions for given problems and evaluate the proposal
LO4. demonstrate basic experience of implementing algorithms
LO5. analyze the complexity of a given algorithm
LO6. demonstrate knowledge of fundamental algorithms for several problems, especially graph problems, testing graph properties and solving optimization problems on graphs, as well as knowledge of fundamental general algorithmic design techniques, such as greedy, dynamic programming and divide-and-conquer
LO7. understand the fundamental concepts of computational hardness
LO8. understand NP-hardness and the ways of dealing with hardness as well as demonstrate knowledge of randomized algorithms and approximation algorithms
LO9. demonstrate knowledge of basic complexity classes and understanding of reductions between 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
Learning outcomes

Responding to student feedback

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

The assignment structure has been redesigned to emphasize feedback on the task, making some portions of the assignments feedback-only to encourage engagement with the unit material.

Additional information

IMPORTANT: School guidelines 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.

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.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

COMP3927: Algorithm Design (Adv)

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links