Algorithms (2018/2019)

Course code
Romeo Rizzi
Teaching is organised as follows:
Unit Credits Academic sector Period Academic staff

Learning outcomes

We refer to the web pages of the modules for the complete and precise list of goals of the course as divided into its two parts. We limit ourselves to focus here on the overall targets of the Algorithm course, as a whole, which 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. Here, we can only touch upon the nature of the privileged relationship of these two disciplines, and their actual unity (like the Yin and Yang sides of a single one art), hoping, nonetheless, that this will help orienting the student in tackling this adventurous path with the right enthusiasm and perspective.

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. For non-trivial problems, the process of algorithm design rests on the theory of complexity not only to identify on which questions, and subproblems, it may make sense to concentrate efforts, but also as a dialectical counterpart providing the right language to disclose the subtle nuances of the problem and guiding towards the appropriate way of addressing its solution. A goal of the course is to highlight and illustrate the symbiosis between the competences (algorithm design and the study of problem complexity) which are addressed in the two modules.

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.


See the sheets for the two separate modules of this course.

Assessment methods and criteria

When the student has collected, in its own mark wallet, both a positive mark for the Complexity module (at least 18) and a positive mark for the Algorithms module (at least 18), then he can ask for the recording of its final mark obtained as the rounded-up average of the above two marks, where 30+lode = 33. In order to get 30+lode as your final mark you need to get at least one lode and both marks should be at least 30. When you think your time has come to ask for registering this final mark, you send a mail to making precise:
1. mark for the Algorithms module: specify the session of the exam at which you got your best mark, this best mark, and the bonus points. (Specify the file in the mark wallet).
2. mark for the Complexity module: specify the last session of the exam where you delivered your elaborate for correction.
3. specify your generalities (student code VRxxxxxx) and the expected mark.

The whole workflow for obtaining your mark is described at:

At the same page you can find the wallet of your marks for both the "Algorithms" and the "Computationl Complexity" modules comprising the course (if any), plus your extra scores for Algorithms in case you have collected any of them with projects. (For the Algorithms module, you will also find here the problems given at previous exam sessions,
and more detailed instructions on the procedures for the exam and for the composition and registration of your mark.)
For the module-specific informations we redirect to the Didactic Dashboard sheets of the single module.

Reference books
Author Title Publisher Year ISBN Note
Sanjoy Dasgupta, Christos Papadimitriou, Umesh Vazirani Algorithms (Edizione 1) McGraw-Hill Higher Education 2007 978-0-07-352340-8
S. Arora, B. Barak Computational Complexity. A modern approach (Edizione 1) Cambridge University Press 2009 9780521424264
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
J. Kleinberg, É. Tardos Algorithm Design (Edizione 1) Addison Wesley 2006 978-0321295354
Ingo Wegener Complexity Theory Springer 2005
Cristopher Moore, Stephan Mertens The Nature of Computation Oxford 2011