252-0002-AAL  Data Structures and Algorithms

SemesterFrühjahrssemester 2021
DozierendeF. Friedrich Wicker
Periodizitätjedes Semester wiederkehrende Veranstaltung
KommentarBelegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.

KurzbeschreibungThe 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.
LernzielAn 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.
InhaltData 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.
LiteraturCormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text)

B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013.

Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012.
Voraussetzungen / BesonderesPrerequisites:
Lecture Series 252-0856-00L Computer Science or equivalent knowledge in programming with C++.

Please note that this is a self study (virtual) course, which implies that (in the autumn semester) there are no physical lectures or exercise sessions offered. If you want to attend the real course, please go to 252-0002-00L in the spring semester.