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 activated in the A.Y. 2023/2024
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1 module among the following (a.a. 2023/24: Data protection in business organizations not activated)2 modules among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)2 modules among the following (a.a. 2023/24: Complex systems and social physics not activated)2 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 (2022/2023)
Teaching code
4S009081
Academic staff
Coordinator
Credits
6
Also offered in courses:
- Discrete Optimization of the course Master's degree in Artificial intelligence
Language
English
Scientific Disciplinary Sector (SSD)
MAT/09 - OPERATIONS RESEARCH
Period
Semester 2 dal Mar 6, 2023 al Jun 16, 2023.
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
rudiments of analysis, calculus and linear algebra
Program
- Basic notions on Problems, Models, Algorithms and Computational Complexity
- 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 on 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.
Learning assessment procedures
To pass the exam, students must demonstrate:
- to have understood the principles underlying discrete optimization techniques
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Evaluation criteria
Discussed and agreed with the aim that they can be both fair and reasonable.
Exam language
English is fine. Italiano va benissimo.
