LEARNING OUTCOMES

To learn classical data structures and algorithms, and to be able to analyze their correctness and efficiency. Base notions on complexity and elementary data structures are assumed as prerequisites. Topics include design and analisys techniques, sorting algorithms, advanced data structures, graph algorithms, NP-completeness.

TEACHING METHODS

Traditional.

SYLLABUS/CONTENT

Design and analysis techniques, asymptotic notations, correctness and complexity of recursive and iterative algorithms, divide-et-impera, dynamic programming, greedy algoritms.

Sorting: simple sorts, mergesort, quicksort, heapsort, lower bound for comparison-based sorting algorithms, linear sorts.

Advanced data structures: heaps, union-find structures.

Graphs: definitions, representations, visits, topological sorting, strongly-connected components, single-source shortest paths (Dijkstra algorithm), minimum spanning tree (Prim and Kruskal algorithms). 

Theory of NP-completeness: complexity classes, NP-complete problems, approximation algorithms

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes.

Introduction to algorithms and data structures. Cormen, Leiserson, Rivest, Stein. McGraw Hill.