Algorithm Engineering (MAE844)

Undergraduate Courses Outlines - Department of Mathematics

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Undergraduate
Course Code	MAE844
Semester	8
Course Title	Algorithm Engineering
Independent Teaching Activities	Lectures, laboratory exercises, tutorials, quiz (Weekly Teaching Hours: 3, Credits: 6)
Course Type	Special Background
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes
Course Website (URL)	-

Learning Outcomes

Learning outcomes

This course aims at introducing to students the concepts , techniques, properties, developments and applications of basic and advanced algorithms and data structures. Software development and software libraries that allow to easily develop and evaluate experimentally algorithms. Methodologies related to experimental research of efficient algorithms and data structures. After successfully passing this course the students will be able to: Understand basic algorithmic techniques Analyze complex algorithms Design and develop new algorithmic tools for experimental evaluation

General Competences

Search for, analysis and synthesis of data and information, with the use of the necessary technology Working independently Team work Project planning and management

Syllabus

• Introduction to algorithm engineering
• Methodology of Algorithm Engineering: motivation, applications, software systems
• System checking
• Software reliability and correctness
• STL and Generalized programming
• Experimental evaluation of algorithms

Teaching and Learning Methods - Evaluation

Delivery	Lectures
Use of Information and Communications Technology	Use of projector and interactive board during lectures. Course website maintenance. Announcements and posting of teaching material (lecture slides and notes, programs). Announcement of assessment marks via the ecourse platform by UOI.
Teaching Methods	Activity Semester Workload Lectures 39 Working independently 78 Team work 33 Course total 150
Student Performance Evaluation	Final written examination (70%) Exercises (30%)

Attached Bibliography

• K. Mehlhorn and S. Naeher, LEDA: A platform for combinatorial and geometric computing, Cambridge University Press, 1999.
• M. Mueller-Hannemanni and S. Schirra, Algorithm Engineering - Bridging the Gap between Algorithm Theory and Practice, Springer 2010.
• C.C. McGeoch, A Guide to Experimental Algorithmics, Cambridge University Press, 2012.
• J. Siek, L.Q. Lee, and A. Lumsdaine, The Boost Graph Library, Addison-Wesley, 2002.
• M.A. Weiss, Data structures and problem solving with C++, 2 Edition, Addison-Wesley, 2000.