You are here: Home » Study Plan » Subject



master degree course in COMPUTER SCIENCE

Course year 1
Teaching units Unit Algoritmi di ottimizzazione
Related or Additional Studies (lesson)
  • TAF: Supplementary compulsory subjects SSD: MAT/09 CFU: 6
Teachers: Mauro DELL'AMICO
Exam type oral
Evaluation final vote
Teaching language Italiano
Contents download pdf download




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.

Admission requirements

Basics of linear and integer programming.
Basics of programming languages.

Course contents

Exact methods: column generation and Dantzg decomposition (hint )
Overview of Metaheursitic algorithms
Examples of mathematical modelling in python
Examples of applications

Teaching methods

Seminars, laboratory, project design in small groups

Assessment methods

The final examination consists in two parts: the design and implementation of a project dealing with an optimization problem; (80%) and in a discussion/written assesmnet on the main theoretical aspects presented during the course (20%).

Learning outcomes

Capability of designing a model for a complex problem.
Capability of choosing a proper algorithm and method to solve a complex problem
Capability of setting up a project for the development of a solution method for an optimization problem


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
XPRess Reference Manual