Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
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I semestre | Oct 1, 2020 | Jan 29, 2021 |
II semestre | Mar 1, 2021 | Jun 11, 2021 |
Session | From | To |
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Sessione invernale d'esame | Feb 1, 2021 | Feb 26, 2021 |
Sessione estiva d'esame | Jun 14, 2021 | Jul 30, 2021 |
Sessione autunnale d'esame | Sep 1, 2021 | Sep 30, 2021 |
Session | From | To |
---|---|---|
Sessione Estiva | Jul 15, 2021 | Jul 15, 2021 |
Sessione Autunnale | Oct 15, 2021 | Oct 15, 2021 |
Sessione Invernale | Mar 15, 2022 | Mar 15, 2022 |
Period | From | To |
---|---|---|
Festa dell'Immacolata | Dec 8, 2020 | Dec 8, 2020 |
Vacanze Natalizie | Dec 24, 2020 | Jan 3, 2021 |
Epifania | Jan 6, 2021 | Jan 6, 2021 |
Vacanze Pasquali | Apr 2, 2021 | Apr 5, 2021 |
Santo Patrono | May 21, 2021 | May 21, 2021 |
Festa della Repubblica | Jun 2, 2021 | Jun 2, 2021 |
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Academic staff
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
2° Year activated in the A.Y. 2021/2022
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Problems solving: Algorithms and Complexity (2020/2021)
Teaching code
4S008896
Academic staff
Coordinator
Credits
12
Language
Italian
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
II semestre dal Mar 1, 2021 al Jun 11, 2021.
Learning outcomes
The overall targets of the course is to expose some aspects of the deep and important dialectic exchange between the search for algorithmic solutions and the study of the complexity of problems. Algorithms are the backbone and the substance of information technologies, but at the same time their study goes beyond the "mere" computer science and is pervasive to all the disciplines that are problem-bearers. The design of an algorithm starts from the study of the structure of the problem to be solved and it usually represents the highest achievement of this process. The study of algorithms requires and offers methodologies and techniques of problem solving, logical and mathematical skills. The course therefore aims to provide students with fundamental skills and methodologies for the analysis of problems and the design of the algorithms for solving them. Particular emphasis is given to the efficiency of the algorithms themselves, and the theory of computational complexity plays a profound methodological role in the analysis of problems. With reference to the overall didactic aims of the Master program, the course leads students to deepen and expand the three-year training in the field of analysis and evaluation of problems, algorithms, and computational models, providing a wealth of advanced tools to address non-trivial problems in different IT fields. The students will acquire logical-mathematical skills, techniques, experience and methodologies useful in the analysis of algorithmic problems, from detecting their structure and analyzing their computational complexity to designing efficient algorithms, and then planning and conducting their implementation. Besides that, The course provides the foundations of computational complexity theory, focussing on: the theory of NP-completeness; approximation algorithms and basic approaches for the analysis of the approximation guarantee of algorithms for hard problems; and parameterized approaches to hard problems. The student will apply the main algorithmic techniques: recursion, divide and conquer, dynamic programming, some data structures, invariants and monovariants. The student will develop sensitivity about which problems can be solved efficiently and with which techniques, acquiring also dialectical tools to place the complexity of an algorithmic problem and identify promising approaches for the same, looking at the problem to grasp its structure. She will learn to produce, discuss, evaluate, and validate conjectures, and also independently tackle the complete path from the analysis of the problem, to the design of a resolver algorithm, to the coding and experimentation of the same, even in research contexts either in the private sector or at research institutions. Based on the basic notions acquired of computational complexity, the students will be able to employ reductions, standard techniques in complexity theory, to analyze the structural properties of computational problems and identify possible alternative approaches (approximation, parameterization) in the absence of (provably) efficient solutions After attending the course the students will be able to: classify intractable computational problems; understand and verify a formal proof; read and understand a scientific article where a new algorithm is presented together with the analysis of its computational complexity.
Program
The Yin and the Yang of problem solving are the general methods for the solutions of instances of the problem (the Algorithms) on one side, and a detached aesthetic eye on the richness of the problem (Complexity) on the other. Two passionate enthusiasts will guide you in the exploration of these two synergistic arts with the hope that in you they might merge into just one.
ALGORITHMS SIDE:
1. The workflow of problem solving: analysis and comprehension of the problem, conception of an algorithmic solution, design of an efficient algorithm, planning the implementation, conducting the implementation, testing and debugging.
2. Methodology in analyzing a problem:
The study of special cases. Particularization and generalization.
Building a dialog with the problem. Conjectures. Simplicity assumptions.
Solving a problem by reducing it to another. Reductions among problems to organize them into classes. Reducing problems to isolate the most fundamental questions. The role of complexity theory in classifying problems into classes. The role of complexity theory in analyzing problems. Counterexamples and NP-hardness proofs. Good conjectures and good characterizations. The belief can make conjectures true. Decomposing problems and inductive thinking.
3. Algorithm design general techniques.
Recursion. Divide et impera. Recursion with memoization. Dynamic programming (DP). Greedy.
DP on sequences. DP on DAGs. More in depth: good characterization of DAGs and scheduling, composing partial orders into new ones.
DP on trees. More in depth: adoption of the children one by one; advantages of an edge-centric vision over the node-centric one.
The asymptotic eye on worst case performance guides the design of algorithms:
the binary search example; negligible improvements; amortized analysis.
Some data structures: binary heaps; prefix-sums; Fenwick trees; range trees.
4. Algorithms on graphs and digraphs.
Bipartite graphs: recognition algorithms and good characterizations.
Eulerian graphs: recognition algorithms and good characterizations.
Shortest paths. Minimum spanning trees.
Maximum flows and minimum cuts.
Bipartite matchings and node covers.
The kernel of a DAG. Progressively finite games. Sums of games.
5. General hints on implementing, coding, testing and debugging.
Plan your implementation. Anticipate the important decisions, and realize where the obscure points are. Try to go round the most painful issues you foresee. Code step by step. Verify step by step. Use the assert. Testing and debugging techniques. Self-certifying algorithms.
COMPLEXITY SIDE:
Relationships among computational problems. Polynomial reductions of one problem into another. The classes P, NP, co-NP. Notion of completeness. Proofs od NP-completeness: Cook's theorem; proofs of completeness using appropriate reductions. Search and Decision Problems. Self-Reducibility of NP-complete problems and existence of non-selfreducible problems.
Space Complexity. Models and fundamental difference between the use of time resource and the space resource. Completeness for space complexity classes. The classes PSPACE and NPSPACE. Examples of reductions to prove PSPACE-completeness.
Introduction to the approximation algorithms for optimisation problems. Examples of approximation algorithms. Classification of problems with respect to their approximabuility. The classes APX, PTAS, FPTAS. Notion of inapproximability; the gap technique to prove inapproximability results; examples of approximation preserving reductions.
Examples of simple randomised algorithms in solving hard problems. . Examples of parameterized approaches to solve hard problems.
Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|
J. Kleinberg, É. Tardos | Algorithm Design (Edizione 1) | Addison Wesley | 2006 | 978-0321295354 | |
Ingo Wegener | Complexity Theory | Springer | 2005 | ||
Garey, M. R. and Johnson, D. S. | Computers intractability: a guide to the theory of NP-completeness | Freeman | 1979 | 0-7167-1045-5 | |
Michael Sipser | Introduction to the Theory of Computation | PWS | 1997 | 053494728X | |
T. Cormen, C. Leiserson, R. Rivest, C. Stein | Introduzione agli Algoritmi e Strutture Dati (Edizione 2) | McGraw-Hill | 2005 | 88-386-6251-7 | |
Cristopher Moore, Stephan Mertens | The Nature of Computation | Oxford | 2011 |
Examination Methods
The exam consists of a written test and a programming test, and the final grade is determined by the sum of the points in both tests. Both tests will include both topics of Algorithms and Complexity to verify that the student has acquired competence in both subjects and can exploit their complementarity.
Examples and additional information on the lab test can be found at:
https://rizzi.olinfo.it/algo-simula-prove
https://github.com/romeorizzi/esami-algo-public
Examples of the type of exercises included in the written test can be found at the repo:
https://github.com/romeorizzi/prove_scritte_AlgoComp
Type D and Type F activities
Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.
years | Modules | TAF | Teacher |
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1° 2° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinator)
|
1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Introduction to 3D printing | D |
Franco Fummi
(Coordinator)
|
1° 2° | Python programming language | D |
Vittoria Cozza
(Coordinator)
|
1° 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
1° 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
1° 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Roberto Giacobazzi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | The fashion lab (1 ECTS) | D |
Maria Caterina Baruffi
(Coordinator)
|
1° 2° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Nicola Fausto Spoto
(Coordinator)
|
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.
Tutoring faculty members
Graduation
Deadlines and administrative fulfilments
For deadlines, administrative fulfilments and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.
Need to activate a thesis internship
For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.
Final examination regulations
List of thesis proposals
Attendance modes and venues
As stated in the Teaching Regulations, attendance at the course of study is not mandatory.
Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.
The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus.
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.