COMPSCI 717 : Fundamentals of Algorithmics


2020 Semester One (1203) (30 POINTS)

Course Prescription

Fundamental techniques are covered for the design of algorithms such as greedy algorithms, divide-and-conquer, and dynamic programming. Data structures are explored that help implement algorithms. Essential tools are taught for analysing algorithms, for example worst- and average-case analyses of space and time. Recommended preparation: 15 points from COMPSCI 120 or equivalent and 15 points from COMPSCI 130 or equivalent

Course Overview

The course provides a comprehensive introduction to the design and analysis of algorithms at the postgraduate level. It prepares students, who have not had an introduction to this topic, for advanced studies of most computer science subjects, such as artificial intelligence and data science. The ultimate aim of this course is to give students the skill to create computer code that can provably and efficiently solve every instance of a given problem. For this purpose, an in-depth understanding is required why the algorithm works correctly and does the best job it can. Proof techniques, such as direct proofs, proofs by contraposition or contradiction, and induction principles are discussed. Algorithmic design paradigms such as greedy, divide-and-conquer, and dynamic programming are explained in general and on concrete examples. An elementary toolbox is established for analysing the worst-case and average-case resources in terms of time and space required by an algorithm. Data structures, such as records, lists, graphs, hash tables, priority queues, and trees, will be used to implement algorithms efficiently. An opportunity is provided to delve into deeper topics of algorithmics, including probabilistic, parallel, or approximate algorithms, or algorithmic complexity theory. The latter provides principled methods in showing the infeasibility of algorithmic solutions, or the likely intractability of computational problems.

Course Requirements

Prerequisite: Approval of Academic Head or nominee Restriction: COMPSCI 220, 320, SOFTENG 250

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Master of Data Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Explain three fundamental programming paradigms, that is, greedy algorithms, divide-and-conquer algorithms, and dynamic programming, and describe examples of algorithms from each class (Capability 1 and 2)
  2. Solve an elementary computational problem by modelling, designing, and implementing an efficient algorithm (Capability 1, 2 and 3)
  3. Analyse algorithms with respect to their asymptotic worst-case behaviour in terms of time and space (Capability 1, 2 and 3)
  4. Reason about the correctness of elementary algorithms (Capability 1, 2 and 3)
  5. Distinguish between problems and their algorithmic solutions, and between efficient and inefficient solutions; relate these distinctions to the basic complexity classes of problems, namely P, NP, and NP-complete problems (Capability 1, 2 and 3)
  6. Implement selected algorithms in the context of motivating applications using a standard programming language (Capability 1, 2 and 3)
  7. Communicate their work on an implementation of an algorithm and an algorithm analysis via a written or oral report, including any implications on the fair use of this algorithm in business or society (Capability 1, 2, 3, 4, 5 and 6)


Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Test 10% Individual Test
Reports 20% Individual Coursework
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Final Exam

Learning Resources

Recommended books:
  • G. Brassard and P. Bratley: Fundamentals of Algorithmics, Prentice-Hall, 1996. 
  • S. Dasgupta, C. Papadimitriou and U. Vazirani: Algorithms (1st Ed), 2008. 
  • J. Kleinberg and E. Tardos: Algorithm Design, (Addison Wesley 2006) 
  • T.H. Cormen: Algorithms Unlocked (ebook) , 2013 
  • J. MacCormick: Nine algorithms that changed the future: The ingenious ideas that drive today's computers, 2012. 
  • S. Skiena: The Algorithm Design Manual (ebook), 2009 
  • M. J. Dinneen, G. Gimel'farb and M. C. Wilson: Introduction to Algorithms and Data Structures (4th electronic edition, ebook), 2018  
  • B. Khoussainov, N. Khoussainova: Lectures on Discrete Mathematics for Computer Science (Algebra and Discrete Mathematics), World Scientific, 2012 

Special Requirements

Not applicable

Workload Expectations

This course is a 30 point course and students are expected to spend 20 hours per week involved in a 30 point course that they are enrolled in.

For this course, you can expect 48 hours of lectures, 24 hours of tutorials, 150 hours of reading and thinking about the content and 75 hours of work on assignments and/or test preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 13/07/2020 04:55 p.m.