## ALGORITHMS AND DATA STRUCTURES ALGORITMI E STRUTTURE DATI

A.Y. Credits
2017/2018 12
Lecturer Email Office hours for students
Valerio Freschi
Teaching in foreign languages
Course with optional materials in a foreign language English
This course is entirely taught in Italian. Study materials can be provided in the foreign language and the final exam can be taken in the foreign language.

### Assigned to the Degree Course

Applied Informatics (L-31)
Curriculum: PERCORSO COMUNE
Date Time Classroom / Location
Date Time Classroom / Location

### Learning Objectives

The aim of the course is to illustrate the main techniques for algorithms design and to describe and analyze the most known basic algorithms together with respective used data structures, with particular focus on computational complexity facets.

### Program

01. Introduction to algorithms and data structures:
01.01 Algorithms and their typologies
01.02 Correctness of an algorithm with respect to a problem
01.03 Complexity of an algorithm with respect to resource usage
01.04 Data structures and their typologies

02. Classes of problems:
02.01 Decidable and undecidable problems
02.02 Tractable and intractable problems
02.03 Cook theorem
02.04 NP-completeness

03. Complexity of the algorithms:
03.01 Notations to express the asymptotic complexity
03.02 Computing the complexity of non-recursive algorithms
03.03 Computing the complexity of recursive algorithms

04. Array algorithms:
04.01 Arrays: basic definitions and classical problems
04.02 Traversal algorithm for arrays
04.03 Linear search algorithm for arrays
04.04 Binary search algorithm for sorted arrays
04.05 Comparison criteria for sorting algorithms for arrays
04.06 Insertsort
04.07 Selectsort
04.08 Bubblesort
04.09 Mergesort
04.10 Quicksort
04.11 Heapsort
04.12 Priority queues based on binary heaps

05. List algorithms:
05.01 Lists: basic definitions and classical problems
05.02 Traversal, search, insertion and removal algorithms for lists
05.03 Insertion and removal algorithms for queues
05.04 Insertion and removal algorithms for stacks

06. Tree algorithms:
06.01 Trees: basic definitions and classical problems
06.02 Traversal and search algorithms for binary trees
06.03 Search, insertion and removal algorithms for binary search trees
06.04 Balancing criteria for binary search trees
06.05 Search, insertion and removal algorithms for red-black binary search trees

07. Graph algorithms:
07.01 Graphs: basic definitions and classical problems
07.02 Traversal and search algorithms for graphs
07.03 Topological sorting algorithm for direct acyclic graphs
07.04 Strongly connected component algorithm for graphs
07.05 Kruskal algorithm
07.06 Prim algorithm
07.07 Properties of the shortest path
07.08 Bellman-Ford algorithm
07.09 Dijkstra algorithm
07.10 Floyd-Warshall algorithm

08. String algorithms:
08.01 Strings: basic definitions and classical problems
08.02 String matching naive algorithm
08.03 Edit distance computation algorithm
08.04 Longest common subsequence algorithm

09. Selection and order statistics:
09.01 Basic definitions and problems
09.02 Heapselect
09.03 Randomized selection
09.04 Deterministic selection

10. Algorithmic techniques:
10.01 Divide-and-conquer technique
10.02 Dynamic programming
10.03 Greedy technique
10.04 Backtracking technique

11. Laboratory activity:
11.01 C language fundamentals: recall, editing, compilation, debugging
11.02 Pseudorandom number generators: rand and srand functions
11.03 Algorithms complexity experimental evaluation: timing and counters
11.04 Experimental comparison of sorting algorithms for arrays
11.05 Experimental comparison of search algorithms for binary trees
11.06 Implementation of graph algorithms: breadth-first and depth-first traversal algorithms, Dijkstra algorithm
11.07 Implementation of string algorithms: Edit Distance and LC

### Bridging Courses

Although there are no mandatory prerequisites for this exam, students are strongly recommended to take it after Procedural and Logic Programming and Calculus.
It is also worth noticing that the topics covered by this course will be used in Operating Systems, Databases, Computer Networks and Object Oriented Programming and Software Engineering.

### Learning Achievements (Dublin Descriptors)

Knowledge and understanding:
At the end of the course, the student will learn: to be aware of the importance of efficient design of algorithms; the fundamental knowledge for analyzing the computational resources required by an algorithm; the main algorithms and data structures for solving basic computational problems.

Applying knowledge and understanding:
The student will learn the methodologies typical of algorithmic design and analysis. In particular she/he will be able to design a series of classic algorithms (sorting, search, etc.) working on different data structures (arrays, lists, trees, graphs) and to analyze their computational complexity. The ability to apply these techniques will be developed and sharpened during laboratory activities, where various algorithms will be analyzed and implementated in C language.

Making judgements:
The student will learn how to apply the algorithmic methodologies to understand and solve new problems of computational nature. Critical discussions during classes and laboratory activities will stimulate and develop the making judgements ability of the student.

Communication skills:
The student will acquire the ability to communicate the fundamental concepts of algorithms and data structures with an appropriate and rigorous terminology. She/he will learn to describe the problems related to the design and analysis of efficient algorithms, and the methodologies adopted to solve them.

Learning skills:
The student will acquire the ability to study and learn algoritmic techniques and fundamental data structures. She/he will learn to recognize the importance of computational resources (in particular space and time) in order to be able to autonomoulsy develop solutions for novel problems related to efficient program design.

### Teaching Material

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

### Teaching, Attendance, Course Books and Assessment

Teaching

Theory lectures and laboratory exercises, both face-to face and on-line.

Attendance

Although recommended, course attendance is not mandatory.

Course books

Cormen, Leiserson, Rivest, Stein, "Introduzione agli Algoritmi e alle Strutture Dati", McGraw-Hill, 2010.
(Cormen, Leiserson, Rivest, Stein, "Introduction to Algorithms", MIT Press, 2009.)
Demetrescu, Finocchi, Italiano, "Algoritmi e Strutture Dati", McGraw-Hill, 2008.
Crescenzi, Gambosi, Grossi, "Strutture di Dati e Algoritmi", Pearson/Addison-Wesley, 2012.
Sedgewick, "Algoritmi in C", Pearson, 2015.
(Sedgewick, "Algorithms in C", Addison-Wesley, 1998.)

Assessment

Learning achievements will be evaluated through three types of assessments: project (to be developed individually or by groups of two students), written exam, and oral exam. This procedure is aimed in particular at evaluating the achievement of applying knowledge and understanding, with respect to problem solving and efficient usage of computational resources and, at the same time, at assessing the student's capabilities of summarizing concepts and her/his communication skills.

The individual project, which changes at each exam session, has to be submitted at least seven days before the written exam. The project is passed if the mark (which is valid for all the exam calls of the same academic year) is at least 18/30. Should the project be resubmitted in a subsequent exam call, the mark of the previously submitted project is cancelled. If the resubmission takes place in the same exam session, a 5/30 penalty is applied to the mark of the newly submitted project.

The written exam, which can be taken only if the project has been passed, consists of seven questions to be answered in one hour and is passed if the mark (which is valid only for the exam call in which the written exam is taken) is at least 18/30.

The oral exam, which can be taken only if the project and the written exam have been passed, consists of further questions about the course program. If passed, it determines a spread between -5/30 and 5/30 of the average of the two previous marks, thus yielding the final mark.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

### Notes

The course offers additional e-learning facilities on the Moodle platform > elearning.uniurb.it

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