Course Unit Page

  • Teacher Paolo Guasoni

  • Credits 6

  • SSD SECS-S/06

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Statistics, Economics and Business (cod. 8876)

  • Teaching resources on Virtuale

  • Course Timetable from Apr 20, 2022 to May 19, 2022


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Decent work and economic growth Industry, innovation and infrastructure

Academic Year 2021/2022

Learning outcomes

By the course the student acquires fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. At the end of the course the student is able: - to define generalized linear regression models; - to estimate parameters and test hypotheses about them - to choose the most suitable model for the specific problem at hand.

Course contents

This is a course on applied optimization, which focuses on modeling and computational solutions. The course covers the main aspects of linear programming, integer programming, sensitivity analysis, and nonlinear programming. Students will learn how to formulate optimization problems, solve them through modern optimization software, and interpret the results.


No textbook is necessary. Reference texts include

Bradley, S.P., Hax, A.C. and Magnanti, T.L., 1977. Applied mathematical programming. Addison-Wesley.

Bertsimas, D. and Tsitsiklis, J.N., 1997. Introduction to linear optimization (Vol. 6, pp. 479-530). Belmont, MA: Athena Scientific.

Teaching methods

The course will include weekly theoretical and practical sessions. Students are encouraged to bring their laptops to class and to familiarize with optimization problems both on and off campus.

Assessment methods

The final exam consists in a practical test in which students are required to formulate an optimization problem, solve it with the tools learned in the course, and interpret the results.

Teaching tools

The course will implement optimization models through Excel, R, and possibly Julia.

Office hours

See the website of Paolo Guasoni