NDAK14006U  Algorithm Engineering

Volume 2014/2015
MSc Programme in Computer Science

Algorithm engineering is a discipline between algorithm theory and computing practice. Theoretical algorithmics supplies us with a rich set of algorithms and data structures that, in principle, enable us to solve complex and hard real-world problems. Often the algorithms are
designed having the classical random-access machine in mind and the resource requirements of the developed algorithms are analysed in the worst-case or average-case scenario.

In algorithm engineering we design and analyse algorithms for more realistic machine models that take into account the existence of branch predictors, caches, disks, multi-cores, and clusters. In our analysis we take into account the constant factors in the leading terms of the resource bounds. We treat programs as first-class citizens and investigate how algorithms can be turned into reliable and efficient implementations and how these programs can be packaged into easy-to-use software libraries. We do experiments with real-world data and investigate how to solve typical problem instances efficiently.

To summarize, algorithm engineering can be seen as a general methodology for algorithmics. Its heart is an interwoven cycle of design, analysis, implementation, and experimentation. We will design algorithms and prove theorems about them, we will implement our algorithms and do experiments with the implementations. We will learn best practices of experimentation and library design.

Table of contents

- Introduction
- Modelling real applications
- Realistic models of computation
- Algorithm design hierarchy
- Meticulous analysis
- Implementation aspects
- Experimentation
- Library design
- Case studies

Learning Outcome


In the course the student will learn
- key concepts found in the literature on algorithm engineering
- best practices in algorithm engineering
- different models of computation used to predict program performance
- tools used in a meticulous analysis of programs
- how to use of scientific method in the area of empirical algorithmics
- architectural details of a modern program library on algorithms and data structures.


After the course the student should be able to
- model computational problems that appear in real-world applications
- design algorithms and data structures for different models of computation
- describe algorithms using pseudo-code
- analyse the key performance characteristics of algorithms and data structures
- implement algorithms efficiently using a concrete programming language
- carry out computational experiments that yield correct, general, informative, and useful results.


The student will get a deep understanding of how to
- fill in the gap between algorithm theory and computing practice
- transform theoretical designs into efficient programs.

Catherine C. McGeorg, A Guide to Experimental Algorithmics, Cambridge (2012)

Matthias Müller-Hannemann and Stefan Schirra (Eds.), Algorithm Engineering: Bridging the Gap between Algorithm Theory and Practice, Springer (2010)

- B.Sc.-level course on algorithms and data structures
- B.Sc.-level course on programming
- This course (AE) replaces the course Data Structures: Theory and Practice (DS:TP) in our M.Sc. programme so a student who has previously passed DS:TP cannot take AE.
- Additionally, AE replaces DS:TP in the specialization profile Algorithms and Data Structures
The course requires a solid foundation in algorithmics. Since there will be hands-on programming exercises, experience in one or more imperative programming languages (preferably C++) is necessary.
- lectures
- seminar presentations
- assignments
- project
- project workshop
- written test
7,5 ECTS
Type of assessment
Continuous assessment, 9 weeks
The exam consists of 4 elements:
- Written test (4 hours) in the second examination week
- Active participation and presentation
- 4 assignments
- 3-weeks project that results in a project workshop (4 hours) in the first examination week

In the final grade the weight of the different criteria is as follows: Assignments 25%, participation and presentations 20%, project 25%, and final 4-hours written test 30%.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Grading is based on the same evaluation criteria as the ordinary exam; selected course elements can be reused or remade completely.
Criteria for exam assesment

See Learning Outcome

  • Category
  • Hours
  • Lectures
  • 35
  • Seminar
  • 20
  • Exercises
  • 60
  • Project work
  • 60
  • Colloquia
  • 10
  • Exam
  • 21
  • Total
  • 206