- Docente: Paolo Guasoni
- Credits: 6
- SSD: SECS-S/06
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Statistics, Economics and Business (cod. 8876)
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.
Readings/Bibliography
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
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.