Outline

Overview

In many areas of computer science- robotics, computer graphics, virtual reality, and geographic information systems are some examples- it is necessary to store, analyse, and create or manipulate spatial data. This course deals with the algorithmic aspects of these tasks: we study techniques and concepts needed for the design and analysis of geometric algorithms and data structures. Each technique and concept will be illustrated on the basis of a problem arising in one of the application areas mentioned above.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	COMP4445
Assumed knowledge ?	Experience with data structures and algorithms as covered in COMP9003 or COMP9103 or COMP9123 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Andre van Renssen, andre.vanrenssen@sydney.edu.au
Lecturer(s)	Andre van Renssen, andre.vanrenssen@sydney.edu.au

Type	Description	Weight	Due	Length
Oral exam ?	Final exam Oral exam	30%	Formal exam period	20 minutes (oral)
Outcomes assessed:
Assignment	Assignment 1 Problem solving assignment	14%	Week 04 Due date: 13 Mar 2024 at 23:59	n/a
Outcomes assessed:
Assignment	Assignment 2 Problem solving assignment	14%	Week 06 Due date: 27 Mar 2024 at 23:59	n/a
Outcomes assessed:
Assignment	Assignment 3 Problem solving assignment	14%	Week 08 Due date: 17 Apr 2024 at 23:59	n/a
Outcomes assessed:
Assignment	Assignment 4 Problem solving assignment	14%	Week 11 Due date: 08 May 2024 at 23:59	n/a
Outcomes assessed:
Assignment	Assignment 5 Problem solving assignment	14%	Week 13 Due date: 22 May 2024 at 23:59	n/a
Outcomes assessed:
= hurdle task ?

Assessment summary

Assignments can be found on Ed and Gradescope.

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. It is a policy 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.

For more information see guide to grades.

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:

Late submission is not accepted. Special consideration up to 7 days are accepted without any changes to the assignment. Special consideration for longer than 7 days are handled by alternative assignments.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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	Art Gallery Problem	Lecture (2 hr)
Week 02	Sweepline Algorithms 1: Segment Intersection and Polygon Triangulation	Lecture and tutorial (3 hr)
Week 03	Sweepline Algorithms 2: Convex Hull	Lecture and tutorial (3 hr)
Week 04	Linear Programming	Lecture and tutorial (3 hr)
Week 05	Orthogonal Range Searching 1: kd-Trees and Range Trees	Lecture and tutorial (3 hr)
Week 06	Orthogonal Range Searching 2: Interval Trees and Segment Trees	Lecture and tutorial (3 hr)
Week 07	Voronoi Diagrams and Delaunay Triangulations	Lecture and tutorial (3 hr)
Week 08	Duality	Lecture and tutorial (3 hr)
Week 10	Planar Point Location	Lecture and tutorial (3 hr)
Week 11	Approximation Algorithms	Lecture and tutorial (3 hr)
Week 12	Curve Similarity and the Fréchet Distance	Lecture and tutorial (3 hr)
Week 13	Summary and research topic	Lecture and tutorial (3 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

Recommended reading and the book that this unit is based on:

M. de Berg, O. Cheong, M. van Kreveld and M. Overmars., Computational Geometry: Algorithms and Application (3rd edition). SpringerVerlag, Heidelberg, 2008. 978-3-540-77973-5.

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. Argue the correctness and efficiency of a proposed solution. Mainly in writing but also orally.
LO2. Demonstrate knowledge of fundamental algorithms for several problems, for example algorithms to compute convex hulls, triangulate polygons, low-dimensional linear programming and Voronoi diagrams, knowledge of fundamental general algorithmic design techniques, such as greedy, dynamic programming and divide-and-conquer.
LO3. Read, understand, analyze and modify a given algorithm. Ability to design algorithmic solutions for given geometric problems.
LO4. Attack theoretical and practical problems in various application domains.
LO5. Understand and apply important techniques and results in computational geometry.
LO6. Analyze the complexity of a given algorithm.
LO7. Demonstrate knowledge of fundamental geometric data structures, such as data structures for range searching, point location, and segment intersection. Demonstrate knowledge of fundamental general design techniques for data structures, such as multi-level trees, duality and divide-and-conquer.

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.

No major changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

COMP5045: Computational Geometry

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links