• Offered by School of Computing
  • ANU College ANU College of Engineering Computing & Cybernetics
  • Course subject Computer Science
  • Areas of interest Computer Science, Algorithms and Data, Computational Foundations
  • Academic career PGRD
  • Mode of delivery In Person
  • Co-taught Course
  • STEM Course

This course covers a large spectrum of algorithmic topics such as approximation algorithms, randomized algorithms, parallel/distributed algorithms, and online algorithms. Students will learn how to analyze the most fundamental algorithms in these areas using various techniques from graph theory, probability theory, and algebra. After successful completion of the course, students will be able to design efficient and effective algorithms for similar problems leveraging these techniques. Furthermore, students will learn about the most recent research advancements in these areas by studying current research publications.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Display a deep understanding of classical approximation and graph algorithms and their analysis.
  2. Analyse the efficiency and correctness of randomized algorithms and data structures.
  3. Demonstrate a good knowledge of parallel, distributed, and online algorithms.
  4. Design and analyse efficient algorithms using various advanced algorithmic techniques such as linear programming, graph theory, probability theory, and algebra.
  5. Understand, analyse, summarize, and present research publications in the area of algorithm design.

Indicative Assessment

  1. Midsemester Test (40) [LO 1,2,4]
  2. Assignment (20) [LO 5]
  3. Final Exam (40) [LO 1,2,3,4]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.


130 hours including lectures, tutorials and independent study.

Inherent Requirements


Requisite and Incompatibility

To enrol in this course, you must have completed COMP6466 or COMP3600. Incompatible with COMP4600.

Prescribed Texts

See Class Summary

Preliminary Reading

- Vijay V Vazirani. Approximation algorithms, volume 1. Springer, 2001.

- David P Williamson and David B Shmoys. The design of approximation algorithms. Cambridge university press, 2011.

- Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. Introduction to algorithms. MIT press, 2022.

- Rajeev Motwani and Prabhakar Raghavan. Randomized algorithms. Cambridge university press, 1995.

- Allan Borodin and Ran El-Yaniv. Online computation and competitive analysis. Cambridge university press, 2005.

- David Peleg. Distributed computing: a locality-sensitive approach. SIAM, 2000.


Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

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There are no current offerings for this course.

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