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.
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
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2° Year It will be activated in the A.Y. 2025/2026
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1 module among the following2 modules among the following1 module among the following
- A.A. 2024/2025 Complex systems and social physics - Network science and econophysics - Statistical methods for business intelligence not activated
- A.A. 2025/26 Network science and econophysics not activated1 module among the following2 modules among the followingLegend | 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.
Discrete optimization and decision making (2024/2025)
Teaching code
4S009081
Academic staff
Coordinator
Credits
6
Also offered in courses:
- Discrete Optimization of the course Master's degree in Artificial intelligence
- Mathematics for decisions of the course Master's degree in Mathematics
- Discrete optimization and decision making of the course Master's degree in Data Science
Language
English
Scientific Disciplinary Sector (SSD)
MAT/09 - OPERATIONS RESEARCH
Period
Semester 2 dal Mar 3, 2025 al Jun 13, 2025.
Courses Single
Authorized
Learning objectives
The course aims to introduce the basics of mathematical programming, in order to develop modeling skills to formulate and solve complex real problems in both deterministic and probabilistic domains. The course will cover topics of integer and continuous linear programming, also providing good knowledge in the field of stochastic programming and robust optimization, as methods in the field of decision theory. The lectures will focus on the computational aspects of the different approaches, as well as on the respective modeling and application features in concrete areas. At the end of the course the student has to show to have acquired the following skills: i) ability to deal with modeling, optimization and decision-making problems, ii) ability to develop computational tools for the application of theoretical solutions in the field of optimization of, e.g., routing, industrial production and financial processes, iii) ability to use specific software solutions to solve mathematical formulations, e.g., Gurobi, Cplex
Prerequisites and basic notions
this is an interfaculty course and we would like to keep the prerequisites to a minimum. The fundamental prerequisite is obviously interest and being assertive and autonomous.
Helpful:
- knowing how to solve systems of linear equations (linear algebra)
- having written/debugged some small program in some language (like python)
- curiosity
- interest in hopefully acquiring new skills and approaches
Program
- Basic notions on Problems, Models, Algorithms and Computational Complexity
- Recursion and Dynamica Programming
- Linear Programming (reference: Vanderbei chapters 2,3,4,5, but no need to read the proof concerning Bland's rule)
- the tableau and the simplex algorithm
- duality theory
- complementary slackness
- economic interpretation
- Modeling
- the art of resorting to a Solver (Gurobi)
- Integer Linear Programming
- simple enumeration and implicit enumeration algorithms
- branch & bound
- branch & cut
- compact formulations
- approximation algorithms
- heuristics and meta-heuristics
- Graphs as models and problems on graphs
- shortest paths
- maximum flows
- maximum bipartite matching
- TSP
Bibliography
Didactic methods
The lessons will take place in a traditional classroom but can be followed also from remote and will be recorded.
The Telegram Group https://t.me/DiscreteOptimization is a first reference for the course and keeps us all-2-all connected.
The list of bibliographic materials freely available through the university's Levanto service is https://univr.alma.exlibrisgroup.com/leganto/public/39UVR_INST/lists/5425495420005791?auth=SAML
Learning assessment procedures
homeworks proposed during the course will contribute to the grade
at the end of the course, on the same platform used for the homeworks, a project will be proposed
a final oral exam will be an opportunity to discuss in a broader way the skills learned, also verifying the possession of the skills demonstrated in the homeworks and with the project
Evaluation criteria
- homeworks: score obtained from the automatic feedback system and contextual verification (it is possible to work in a group but respecting the rules that allow for verification of authenticity)
- project: as for the exercises, but also modulated by other considerations
- oral: holistic evaluation on skills from the final program, active skills, and clarity of exposition
Criteria for the composition of the final grade
as sum of the points collected from the homeworks, the project, and the oral exam
Exam language
english
