FACULTY OF ARTS AND SCIENCES

Department of Mathematics

CE 380 | Course Introduction and Application Information

Course Name
Computational Geometry
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 380
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives The objective of this course is to teach the students techniques of solving geometric problems using algorithmic methods.
Learning Outcomes The students who succeeded in this course;
  • formally define the primitive computational geometric objects.
  • develop polynomial time algorithms for computational geometry problems where such an algorithm exists.
  • compute the convex hull of a given point set.
  • construct the Voronoi diagram of a given point set.
  • calculate the Delaunay triangulation of a given point set.
  • triangulate a given polygon.
  • partition a given polygon into convex or monotone polygons.
Course Description Well-known computational geometry problems, their algorithmic solutions and computational geometry problem solving techniques.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
X
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Background & Introduction
2 Polygon Triangulation I Chapter 1, Computational Geometry in C (2nd Edition), Joseph O'Rourke
3 Polygon Triangulation II Chapter 1, Computational Geometry in C (2nd Edition), Joseph O'Rourke
4 Polygon Partitioning Chapter 2, Computational Geometry in C (2nd Edition), Joseph O'Rourke
5 Convex Hulls in Two Dimensions I Chapter 3, Computational Geometry in C (2nd Edition), Joseph O'Rourke
6 Convex Hulls in Two Dimensions II Chapter 3, Computational Geometry in C (2nd Edition), Joseph O'Rourke
7 Review
8 Midterm
9 Convex Hulls in Three Dimensions I Chapter 4, Computational Geometry in C (2nd Edition), Joseph O'Rourke
10 Convex Hulls in Three Dimensions II Chapter 4, Computational Geometry in C (2nd Edition), Joseph O'Rourke
11 Voronoi Diagrams Chapter 5, Computational Geometry in C (2nd Edition), Joseph O'Rourke
12 Delaunay Triangulations Chapter 5, Computational Geometry in C (2nd Edition), Joseph O'Rourke
13 Search and Intersection I Chapter 7, Computational Geometry in C (2nd Edition), Joseph O'Rourke
14 Search and Intersection II Chapter 7, Computational Geometry in C (2nd Edition), Joseph O'Rourke
15 Review of Semester
16 Final Exam

 

Course Notes/Textbooks Computational Geometry in C (2nd Edition), Joseph O'Rourke, Cambridge University Press
Suggested Readings/Materials Computational Geometry Algorithms and Applications (3rd Edition), Mark De Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars, SpringerVerlag Publishing

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
40
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
25
Final Exam
1
35
Total

Weighting of Semester Activities on the Final Grade
2
65
Weighting of End-of-Semester Activities on the Final Grade
1
35
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
4
6
24
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
16
16
Final Exam
1
20
20
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.

2

To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.

3

To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.

4

To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.

X
5

To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.

X
6

To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.

7

To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.

8

To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.

9

To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.

X
10

To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.

11

To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To be able to relate the knowledge accumulated throughout the human history to their field of expertise.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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