Sciences
Subject: ALGORITMI DI OTTIMIZZAZIONE (A.A. 2022/2023)
master degree course in COMPUTER SCIENCE
| Course year | 1 |
|---|---|
| CFU | 6 |
| Teaching units |
Unit Algoritmi di ottimizzazione
Related or Additional Studies (lesson)
|
| Moodle portal | |
| Exam type | oral |
| Evaluation | final vote |
| Teaching language | Italiano |
Teachers
Overview
To develop capability of solving complex problems by means of some of the most updated and effective exact and heuristic techniques: namely column generation, decomposition, metaeuristic.
Introduce to the main mathematical/algorithmic methods to deal with computationally challenging problems.
Develop the capability of implementing algorithms to solve complex problems.
Present some recent techniques for integrating Machine Learning (ML) and Mathematical Optimization (MO)
Admission requirements
Basics of linear and integer programming.
Basics of programming languages.
Course contents
Exact methods: column generation and constraint generation (hint ) 2CFU
Overview of Metaheursitic algorithms 1CFU
Overview of Machine Learning methods for classification and forecasting 1CFU
Examples of mathematical modelling in python 1CFU
Examples of applications 1CFU
Teaching methods
Seminars, laboratory, project design in small groups
Assessment methods
The final examination consists in three parts: one/two homeworks to be developed during the course (20%), the design and implementation of a project dealing with an optimization problem; (60%), a discussion/written assesment on the main theoretical aspects presented during the course (20%).
Learning outcomes
1. Knowledge and understanding: know and understand the use of some of the main techniques for solving decision-making and optimization problems
2. Ability to apply knowledge and understanding: be able to apply optimization techniques to problems of medium complexity
3. Autonomy of judgment: ability to collect and interpret data and results of application of algorithmic methods
4. Communication skills: be able to communicate information, ideas, problems and solutions to specialist and non-specialist interlocutors;
5. Learning skills: having developed those learning skills that are necessary to extend one's knowledge of other optimization techniques and undertake subsequent studies with a high degree of autonomy
Readings
Dispense dell'docente
"Column Generation", Desaulniers, Guy, Desrosiers, Jacques, Solomon, Marius M. (Eds.) , Springer
Martin, Richard Kipp (1999), "Large Scale Linear and Integer Optimization: A Unified Approach", Springer
Ronald L. Rardin (2017, second ed.), "Optimization in Operations Research", Pearson.
Gurobi Reference manual