# COMPSCI 320 : Applied Algorithmics

## Science

### Course Prescription

Fundamental design techniques used for efficient algorithmic problem-solving and software development. Methods that yield algorithms that are both provably correct and efficient. Efficiency of algorithms to provide a basis for deciding which algorithm is best for the job. Limits on the power of computers and the theory of NP-completeness. An introduction to methods whose correctness or performance is not guaranteed.

### Course Overview

Fundamental design techniques used for efficient algorithmic problem-solving and software development. Methods that yield algorithms that are both provably correct and efficient. Efficiency of algorithms to provide a basis for deciding which algorithm is best for the job. Limits on the power of computers and the theory of NP-completeness. An introduction to methods whose correctness or performance is not guaranteed. Algorithmics is the systematic study of the design and analysis of algorithms. It deals with such fundamental questions as: How do we go about designing an algorithm for a given problem? Is the algorithm correct? Does it perform efficiently? Is it the best possible for the job? Is there any good algorithm for this problem? It has been said that algorithms form the soul of computer science. Certainly the study of algorithms is a fundamental activity of a computer scientist for society.

The skills developed in this course are particularly useful for those wishing to have a career involving efficient programming, problem solving and data science.  The class is considered essential for all students interested in continuing to graduate programmes in computer science, as it covers ACM curriculum core topics deemed required within the area of algorithms for every computer science undergraduate major.

### Course Requirements

Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255

### 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

### Learning Outcomes

By the end of this course, students will be able to:
1. Define clearly a computational problem (with input, output, and I/O constraint) and discuss how an efficient algorithm for solving the latter problem would help to resolve the original problem - when given a "real-world" problem. Consider social ramifications of your solution model. (Capability 1, 4 and 6)
2. Design an algorithm guaranteed to solve a computational problem by using a given design technique from: greedy, divide/conquer, dynamic programming, exhaustive search (Capability 2 and 3)
3. Estimate rigorously the big-Theta running time of a simple algorithm devised as above (Capability 2 and 4)
4. Determine whether a given computational problem can be solved by a standard algorithmic technique as above, and if so, choose the most promising technique (Capability 1, 2 and 3)
5. Determine whether a given difficult computational problem can be mapped by standard methods to an NP-hard algorithmic problem; formally communicate your problem reduction (Capability 2, 4 and 5)

### Assessments

Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Final Exam 60% Individual Coursework
Test 10% Individual Examination
1 2 3 4 5
Assignments
Final Exam
Test
There is both a theory (test+exam) and practical (assignments) component for this course. Both parts need to be passed. Furthermore, the assignments require you to seperately pass both the written and programming tasks.

### Key Topics

Algorithm design techniques: Greedy, Divide-Conquer, Dynamic Programming, Network Flow, Randomization
NP-completeness/hardness and intractability

### Special Requirements

See the assessment section above.

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, per week, you can expect 3 hours of lectures, a 1 hour tutorial, 5 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation

### Delivery Mode

#### Campus Experience

Attendance is encouraged for both lectures and tutorials.
Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The course may include live online events including group discussions/tutorials.
Attendance on campus is required for the test and exam, provided University is open.
The activities for the course are scheduled as a standard weekly timetable delivery.

### Learning Resources

Various textbooks on algorithms, lecture slides, automarker.cs.auckland.ac.nz

### 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.

Additional feedbeek is welcome anytime during the course, either directly to lecturers or anonymously via class representatives.

### 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 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.

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.

### 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

### 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.

### Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Under Covid-19 situations:
Level 1: Delivered normally as specified in delivery mode
Level 2: You will not be required to attend in person. All teaching and assessment will have a remote option.
Level 3 / 4: All teaching activities and assessments are delivered remotely

### 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 .

### Disclaimer

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 22/12/2020 12:02 p.m.