htw saar Piktogramm QR-encoded URL
Back to Main Page Choose Module Version:
emphasize objectives XML-Code

flag

Algorithms and Complexity

Module name (EN):
Name of module in study programme. It should be precise and clear.
Algorithms and Complexity
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Computer Science and Communication Systems, Master, ASPO 01.04.2016
Module code: KI745
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4V (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 1
Mandatory course: no
Language of instruction:
English
Assessment:
Written examination

[updated 08.05.2008]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

KI745 Computer Science and Communication Systems, Master, ASPO 01.04.2016 , semester 1, optional course
PIM-WI10 Applied Informatics, Master, ASPO 01.10.2011 , semester 1, optional course, course inactive since 29.07.2015
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dave Swayne
Lecturer:
Prof. Dave Swayne


[updated 10.07.2007]
Learning outcomes:
The students are capable of classifying algorithmic problems with respect to time and space complexity. The algorithmic tools of this course enable the student to find effective approaches to many problems. Consequently, they are able to propose efficient solutions – these may be approximate if the problem is NP-hard.

[updated 08.05.2008]
Module content:
. Mathematical tools
-        order calculus
-        difference equations
-        logarithms
 
2. Brute force
 
3. Divide and conquer
-        large integers and the Strassen algorithm
-        fundamental theorem of divide and conquer
-        convex hull and closest pair case studies
 
4. Decrease and conquer, transform and conquer
 
5. Auxiliary techniques
-        Precomputation
-        Time and space tradeoffs
-        String processing algorithms
 
6. Hierarchies of computational complexity
7. Approximation algorithms
 
8. Case studies in approximation algorithms
-        branch and bound
-        routing
-        pipe flow and its applications


[updated 08.05.2008]
Recommended or required reading:
To be announced

[updated 08.05.2008]
Module offered in:
WS 2007/08 
[Thu Apr 18 17:36:59 CEST 2024, CKEY=kaac, BKEY=kim, CID=KI745, LANGUAGE=en, DATE=18.04.2024]