ALGORITHM ANALYSIS AND DESIGN
The aim of the course is to learn classical data structures and algorithms, and to analyze their correctness and efficiency. Base notions on complexity and elementary data structures are assumed as prerequisites.
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.
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
Introduction to algorithms and data structures. Cormen, Leiserson, Rivest, Stein. McGraw Hill.
Ricevimento: On request. In addition, on aulaweb there will be a discussion forum for questions and answer of general interest for all students.
Ricevimento: Appointment by email
ELENA ZUCCA (President)
ALESSANDRO VERRI (President Substitute)
L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.
Written and oral exam.
The written exam checks the ability of the student to apply in practice learned notions.
The oral exam checks the understanding of concepts and the ability to appropriately illustrate learned notions.