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.

A.A. 2016/2017

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.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I sem. Oct 3, 2016 Jan 31, 2017
II sem. Mar 1, 2017 Jun 9, 2017
Exam sessions
Session From To
Sessione invernale Appelli d'esame Feb 1, 2017 Feb 28, 2017
Sessione estiva Appelli d'esame Jun 12, 2017 Jul 31, 2017
Sessione autunnale Appelli d'esame Sep 1, 2017 Sep 29, 2017
Degree sessions
Session From To
Sessione estiva Appelli di Laurea Jul 20, 2017 Jul 20, 2017
Sessione autunnale Appelli di laurea Nov 23, 2017 Nov 23, 2017
Sessione invernale Appelli di laurea Mar 22, 2018 Mar 22, 2018
Period From To
Festa di Ognissanti Nov 1, 2016 Nov 1, 2016
Festa dell'Immacolata Concezione Dec 8, 2016 Dec 8, 2016
Vacanze di Natale Dec 23, 2016 Jan 8, 2017
Vacanze di Pasqua Apr 14, 2017 Apr 18, 2017
Anniversario della Liberazione Apr 25, 2017 Apr 25, 2017
Festa del Lavoro May 1, 2017 May 1, 2017
Festa della Repubblica Jun 2, 2017 Jun 2, 2017
Vacanze estive Aug 8, 2017 Aug 20, 2017

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.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff


Albi Giacomo +39 045 802 7913

Angeleri Lidia 045 802 7911

Baldo Sisto 045 802 7935

Bos Leonard Peter +39 045 802 7987

Caliari Marco +39 045 802 7904

Chignola Roberto 045 802 7953

Cordoni Francesco Giuseppe

Daffara Claudia +39 045 802 7942

Daldosso Nicola +39 045 8027076 - 7828 (laboratorio)

De Sinopoli Francesco 045 842 5450

Di Persio Luca +39 045 802 7968

Gregorio Enrico 045 802 7937

Malachini Luigi 045 8054933

Marigonda Antonio +39 045 802 7809

Mariotto Gino +39 045 8027031

Mariutti Gianpaolo 045 802 8241

Mazzuoccolo Giuseppe +39 0458027838

Orlandi Giandomenico

giandomenico.orlandi at 045 802 7986
Foto,  September 29, 2016

Rinaldi Davide

Rizzi Romeo +39 045 8027088

Schuster Peter Michael +39 045 802 7029

Solitro Ugo +39 045 802 7977

Zuccher Simone

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 enrolment year.


2° Year


3° Year

Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
Between the years: 1°- 2°- 3°
Altre attività formative

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.

SPlacements in companies, public or private institutions and professional associations

Teaching code



Romeo Rizzi


Romeo Rizzi



Scientific Disciplinary Sector (SSD)





II sem. dal Mar 1, 2017 al Jun 9, 2017.

Learning outcomes

The student of mathematics (L40, Verona) will encounter in concrete the concepts of: problems, models, formulations of operations research, but also of instances, algorithms, reductions and mappings among problems of the computer science field. The course will propose some models of operations research, at least the following: linear programming (LP), integer linear programming (ILP), max-flows and min-cuts, bipartite matchings and node covers, minimum spanning trees, shortest paths, Eulerian paths, and some models resorting on dynamic programming among which some knapsack variants. For all these models/problems, except PLI, the student will learn the solving algorithms, the properties on which they hinge, and how to conduct their execution.
However, besides and beyond this, the course aims at building a good and active relationship, practice, and acquaintance, with general mathematical methodologies and techniques (more typical of discrete math and for this reason not yet fully assimilated from our students) and some basic underpinnings of computer science. In particular, we insist on the dialog with problems and with the art/technique of conjecturing, no occasion is lost to spotlight where invariants and monovariants play a role in proofs, algorithms and data structures. We build up confidence with mathematical induction as an active tool for problem solving, and introducing the dialects of induction most voted to efficiency (divide et impera, recursion with memoization, dynamic programming). Some basic principles of informatics are underlined, like coding, algorithms, data structures, recursion as a counterpart of mathematical induction and of computability. (In some editions of the course first scratch introductions to numerability and computability have been offered). Coming to efficiency, our central perspective, the use of asymptotic notation is justified and adopted, the classes P, NP, coNP are introduced, and the concepts of good characterizations, good conjectures and good theorems are illustrated in length and complexity theory is advertised as a lively source of new methodologies in the art of facing problems and enquiry their intrinsic structural properties. Several aspects of the role and importance of the art of reducing one problem to another are discussed and clarified. The life cycle of a good conjecture, the workflow linking good conjectures and algorithms, the production and interpretation of counterexamples as a means of dialog with the problem, and the possible use of them in obtaining NP-completeness proofs, are all discussed, investigated and exemplified in action.
Explicit emphasis is constantly given to the role and use of certificates. Meanwhile these transversal and high competences of methodological interest and imprinting are delivered, the students is asked to learn and develop several concrete procedural competences, in particular within LP, and in an algorithmic treatment of graph theory, introduced as a versatile model and an intuitive and expressive language for the formulation of problems.
For a complete and detailed list of all these procedural competences delivered and requested, see the past exams and corrections over the various editions of the course.


Operations Research offers quantitative methods and models for the optimal management of resources, and optimization of profits, services, strategies, procedures.
This course of Operations Research gets to Mathematical Programming
moving from Algorithmics and Computational Complexity.
After revisiting mathematical induction, recursion, divide et impera, with a curiosity driven problem solving approach, we insist on dynamic programming thinking which gets then exemplified in a few classical models of Operations Research and Computational Biology.
With emphasis on method and techniques, we get involved in formulating, encoding and modeling problems, conjecturing about them, reducing one to the other,
and well characterizing them.
The course offers an in-depth introduction to linear programming.
Following the historical path, we introduce graphs as for modeling,
and explore the basic fundamental results in combinatorial optimization and graph theory.


1. Basic Notions

2. Introduction to Algorithms and Complexity
analysis of a few algorithms
design techniques (recursion, divede et impera, recursion with memoization, dynamic programming, greedy)
complexity theory (P, NP, co-NP, good characterizations, good conjectures, examples of NP-completeness proofs)

3. Combinatorial Optimization Models
knapsack problems
Problems on sequences
Problems on DAGs

4. Introduction to Graph Theory
graphs and digraphs as models
a few good characterizations (bipartite, Eulerian, acyclic, planar graphs)
a few NP-hard models (Hamilton cycles, cliques, colorability)
shortest paths
minimum spanning trees
maximum flows
bipartite matchings

5. Linear Programming (LP)
the LP and the ILP models (definition, motivations, complexity, role)
geometric method and view (feasibility space,
pivot, duality, dual variables, degeneracy, complementary slackness)
standard and canonical form
simplex method
duality theory
complementary slackness
economic interpretation of the dual variables
sensitivity analysis


Reference texts
Author Title Publishing house Year ISBN Notes
T. Cormen, C. Leiserson, R. Rivest Introduction to algorithms (Edizione 1) MIT Press 1990 0262031418
Robert J. Vanderbei Linear Programming: Foundations and Extensions (Edizione 4) Springer 2001 978-1-4614-7630-6 DOI: 10.1007/978-1-4614-7630-6 eBook ISBN: 978-1-4614-7630-6 Hardcover ISBN: 978-1-4614-7629-0 Softcover ISBN: 978-1-4899-7376-4

Examination Methods

At the end of the course, a written exam with various types of exercises and questions, and several opportunities to gather points to test and prove your preparation.
In preparing yourself for this exam,
take profit of the material (text and correction for each previous exam) available at the website of the course:

Testing your preparation on these tasks and comparing your solutions against the ones given as reference (pay attention not only to the answers but also to the way you offer them to the examiner/verifier, to the quality of your certificates) will allow you to check your comprehension of the topics covered and of the algorithms and methodologies illustrated during the course. Furthermore, it will help
you in tuning your preparation to the exam and refine your procedures and approaches, making clear what kinds of evidences the examiner is expecting you to produce with clarity.

During the exam, be prepared to work for 4 or more hours to what I call "a paper chromatography test". I hope it serves the purpose to recognize in reasonable enough confidence the amount of work you have put in and how much you got for yourself from the course. And make a mark proposal out of it.
There are far more points available than needed to go in saturation. Indeed, your goal should be to prove the competences you have collected: what counts is what you know and/or can solve, not what you don't.
Rather, pay attention to this: only the answers given with clarity, providing the due certificates, count.
All the rest is of null measure to my correction.
In this, the spirit of the correction is in line with the methodologies from computational complexity advertised by the course.

Type D and Type F activities

Modules not yet included

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.


As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, except for specific practical or lab activities, attendance is not mandatory. Regarding these activities, please see the web page of each module for information on the number of hours that must be attended on-site.
Please refer to the Crisis Unit's latest updates for the mode of teaching.



List of theses and work experience proposals

theses proposals Research area
Formule di rappresentazione per gradienti generalizzati Mathematics - Analysis
Formule di rappresentazione per gradienti generalizzati Mathematics - Mathematics
Mathematics Bachelor and Master thesis titles Various topics
Stage Research area
Internship proposals for students in mathematics Various topics

Gestione carriere

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.