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252-0002-00L 8 Credits BSC , DR , MSC D-CHAB , D-INFK , D-MATH
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Data Structures and Algorithms

Datenstrukturen & Algorithmen

Lecturers & Examiners: Dr. Felix Friedrich Wicker
VVZ CR n/a

Last Updated: 2026-02-05 16:07:06

Abstract

The course provides the foundations for the design and analysis of algorithms.Classical problems ranging from sorting up to problems on graphs are used to discuss common data structures, algorithms and algorithm design paradigms.The course also comprises an introduction to parallel and concurrent programming and the programming model of C++ is discussed in some depth.

Objective

An understanding of the analysis and design of fundamental and common algorithms and data structures. Deeper insight into a modern programming model by means of the programming language C++. Knowledge regarding chances, problems and limits of parallel and concurrent programming.

Content

Data structures and algorithms: mathematical tools for the analysis of algorithms (asymptotic function growth, recurrence equations, recurrence trees), informal proofs of algorithm correctness (invariants and code transformation), design paradigms for the development of algorithms (induction, divide-and-conquer, backtracking and dynamic programming), classical algorithmic problems (searching, selection and sorting), data structures for different purposes (linked lists, hash tables, balanced search trees, quad trees, heaps, union-find), further tools for runtime analysis (generating functions, amortized analysis. The relationship and tight coupling between algorithms and data structures is illustrated with graph algorithms (traversals, topological sort, closure, shortest paths, minimum spanning trees, max flow). Programming model of C++: correct and efficient memory handling, generic programming with templates, exception handling, functional approaches with functors and lambda expressions. Parallel programming: structure of parallel architectures (multicore, vectorization, pipelining) concepts of parallel programming (Amdahl's and Gustavson's laws, task/data parallelism, scheduling), problems of concurrency (data races, bad interleavings, memory reordering), process synchronisation and communication in a shared memory system (mutual exclusion, semaphores, monitors, condition variables), progress conditions (freedom from deadlock, starvation, lock- and wait-freedom). The concepts are underpinned with examples of concurrent and parallel programs and with parallel algorithms, implemented in C++. In general, the concepts provided in the course are motivated and illustrated with practically relevant algorithms and applications. Exercises are carried out in Code-Expert, an online IDE and exercise management system. All required mathematical tools above high school level are covered, including a basic introduction to graph theory.

Resources

Literature

Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text) Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012. B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013.

Learning Materials (Links)

General Information

Language
German
Levels
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 150 minutes
Aids
Sie dürfen maximal 4 A4-Blätter mit in die Prüfung nehmen. Inhaltliche und formale Anforderungen (Text, Bilder, ein-/doppelseitig, Ränder, Schriftgrösse, etc.) bestehen nicht.Elektronische Geräte bzw. digitale Unterlagen sind nicht erlaubt.
Digital
The exam takes place on devices provided by ETH Zurich.
Durch Bearbeitung der wöchentlichen Übungsserien kann ein Bonus von maximal 0.25 Notenpunkten erarbeitet werden, der an die Prüfung mitgenommen wird. Der Bonus ist proportional zur erreichten Punktzahl von speziell markierten Bonus-Aufgaben, wobei volle Punktzahl einem Bonus von 0.25 entspricht. Die Zulassung zu speziell markierten Bonusaufgaben kann von der erfolgreichen Absolvierung anderer Übungsaufgaben abhängen. Der erreichte Notenbonus verfällt, sobald die Vorlesung neu gelesen wird.Die Prüfung findet voraussichtlich in hybrider Form (auf Papier und am Computer) statt.

Course Components

Type Title Time & Place Hours
lecture Datenstrukturen & Algorithmen
  • Mon 10:15-12:00 (HG G 3)
  • Fri 08:15-10:00 (HG G 3)
4 h weekly
exercise Datenstrukturen & Algorithmen
  • Fri 10:15-12:00 (CAB G 59)
  • Fri 10:15-12:00 (LFW B 2)
  • Fri 10:15-12:00 (NO C 6)
  • Fri 10:15-12:00 (RZ F 21)
  • Fri 14:15-16:00 (CAB G 57)
  • Fri 14:15-16:00 (CHN D 42)
  • Fri 14:15-16:00 (CHN D 48)
  • Fri 14:15-16:00 (CHN G 22)
2 h weekly

Offered In