|Unit||Credits||Academic sector||Period||Academic staff|
|Laboratorio||2||INF/01-INFORMATICS||1° Q, 2° Q||
|Teoria||8||INF/01-INFORMATICS||1° Q, 2° Q||
Alessandra Di Pierro
In this course students will learn how to use a computer for solving real life problems via the design of suitable algorithms. Possible solutions are compared in terms of their efficiency. To this purpose the course includes the study of the mathematical methods for the analysis and the evaluation of the various algorithms. Abstract mathematical stuctures are used to represent the concrete data of a given problem; they play a fundamental role in the encoding of the concrete setting in the virtual world of computers.
In the laboratory of Algorithms and Data Structures the expertise of students on object programming will be improved in order to implement advanced data structures and algorithms. Lectures will be focused on Java language, of which it is expected a knowledge at a basic level.
-Computational problems: indecidability, tractability, computational model and computational complexity.
- Analysis of algorithms and the asynthotic notation.
-Arrays and lists: binary search, recursion, linked lists.
-Trees: recursive algorithms on binary trees, insertion and deletion, breadth-first visit and tree representations.
-Dictionaries: binary search trees, AVL trees.
-Dynamic Programming and Greedy Algorithms.
-Graphs: problems on graphs, graph representations, shortest paths.
-Stacks and queues: implementation, breadth-first and depth-first search.
-Priority queues: heaps, implementation, building and sorting a heap.
-NP-completeness: Polynomial time, NP-completeness and reducibility, NP-complete problems.
Lecture 1: Use of interface mechanism. Example of application by implementation of ADT lists, codes, e piles.
Lecture 2: Use of comparable interface. Implementation of ordering algorithms by insertion (InsertionSort) and by decrementing (ShellSort).
Lecture 3: Techniques to compare implementation methods. Comparison between ordering algorithm implementations: QuickSort and MergeSort.
Lecture 4: Refresh of Javadoc and Exceptions concepts. Implementation of ADT Tree and binary search tree.
Lecture 5: Use of interface Iterator. Implementation of visit methods. Sketches on algorithms for construction of suffix trees.
Lecture 6: Implementation of a dynamic programming algorithm: search of maximum common subsequence (MaxSSC).
Lecture 7: Exact string matching algorithms.
Lecture 8: Implementation of a greedy algorithm: Kruskal algorithm.
Written test divided in two parts: Teoria and Laboratorio. The duration of the test is 3 hours: 2 hours for the theory part and 1 hour for the practical part. Candidates must score at least 18/30 for each part of the test. The overall final score is calculated as the weighted sum of the two individual scores with weights 4/5 for the theory part and 1/5 for the practical part.
Written exam divided in two parts: Teoria and Laboratorio. The time allowed for the test is 3 hours: 2 hours for the theory part and 1 hour for the laboratory part. Candidates must score at least 18/30 for each part of the test. The overall final score is calculated as the weighted sum of the two individual scores with weights 4/5 for the theory part and 1/5 for the practical part.
|T. Cormen, C. Leiserson, R. Rivest, C. Stein||Introduzione agli Algoritmi e Strutture Dati (Edizione 2)||McGraw-Hill||2005||88-386-6251-7|
|Crescenzi, P. - Gambosi, G. - Grossi, R.||Strutture di Dati e Algoritmi||Pearson Education Italia||2006||8871922735|