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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

COMP3027: Algorithm Design

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.


Academic unit Computer Science
Unit code COMP3027
Unit name Algorithm Design
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

COMP2007 OR COMP2907 OR COMP3927
COMP2123 OR COMP2823 OR INFO1105 OR INFO1905
Assumed knowledge

MATH1004 OR MATH1904 OR MATH1064

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Seeun William Umboh,
Type Description Weight Due Length
Final exam Final exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO3 LO5 LO6 LO7 LO8 LO9
Assignment Assignment
25% Multiple weeks n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO9
Assignment Quiz
15% Multiple weeks n/a
Outcomes assessed: LO4 LO6 LO7 LO8

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

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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. Deduction of 20% of the maximum mark for each calendar day after the due date. After 2 calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Unit introduction, algorithms and complexity, motivation and course outline. Overview of graphs, motivating example problems (2 hr)  
Week 02 Greedy algorithms (4 hr)  
Week 03 Divide and conquer (4 hr)  
Week 04 Dynamic Programming (4 hr)  
Week 05 Dynamic Programming (4 hr)  
Week 06 Flow Networks (4 hr)  
Week 07 Flow Networks (4 hr)  
Week 08 Circulations and Reductions (4 hr)  
Week 09 NP-hardness (4 hr)  
Week 10 NP-hardness (4 hr)  
Week 11 Coping with hardness (4 hr)  
Week 12 Randomised Algorithms (4 hr)  
Week 13 Review of unit of study (4 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.

Required readings

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

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

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. produce a clear account of an algorithm, that would allow others to understand and implement it
  • LO2. learn about a novel algorithm, 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. draw from 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
No significant changes have been made since this unit was last offered.


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