AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at providing knowledge on the use of mathematical methods to describe real-world phenomena, such as heat diffusion and wave propagation, as well as to take optimal decisions. More specifically, the students will be able to classify and manage the main analytical solution methods for linear partial differential equations. In addition, the students will learn how to manage multistage optimization problems by means of dynamic programming, and how to find the solution to nonlinear programming problems.

AIMS AND LEARNING OUTCOMES

The course focuses on three different topics: dynamic optimization, nonlinear programming, and partial differential equations.

In more detail, students will learn how to formalize and solve dynamic optimization problem via dynamic programming. Then, they will analyze the formalization and solution of static decision problem using nonlinear programming. Lastly, they will investigate the use of mathematical methods to describe real-world phenomena, such as heat diffusion and wave propagation, and the main analytical solution methods.

For all arguments, the focus will be on both methodological concepts and application examples. The various concepts are explained via traditional lessons.

TEACHING METHODS

Traditional lessons.

SYLLABUS/CONTENT

- Dynamic programming for the solution of dynamic optimization problems.

- Basic notions of nonlinear programming.

- Analytical solution of linear partial differential equations describing real-world phenomena.

This course, by covering topics of scientific-technological interest such as mathematical methods, contributes to the achievement of the following Sustainable Development Goals of the UN 2030 Agenda:

- 8.2 (Achieve higher standards of economic productivity through diversification, technological progress and innovation, including with a focus on high value-added and labor-intensive sectors);

- 9.5 (Increase scientific research, improve the technological capacities of the industrial sector in all states-particularly in developing states-as well as encourage innovations and substantially increase, by 2030, the number of employees per million people, in research and development and research spending-both public and private).

RECOMMENDED READING/BIBLIOGRAPHY

Handouts in electronic format provided by the lecturer.

Books for additional details:

[1] D.P. Bertsekas, “Dynamic Programming and Optimal Control”, Athena Scientific, 2005.

[2] F.S. Hillier, G.J. Lieberman, “Introduction to Operations Research”, McGraw-Hill, 2001.

[3] R. Courant, D. Hilbert, “Methods of Mathematical Physics”, Interscience Publishers, 1973.

[4] R. Bracewell, “The Fourier Transform and its Applications”, McGraw Hill, 1999.

[5] P.V. O’Neil, “Advanced Engineering Mathematics”, Brooks Cole, 2003.

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

TEACHERS AND EXAM BOARD

Exam Board

MAURO GAGGERO (President)

MARCELLO SANGUINETI

MASSIMO PAOLUCCI (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/10378/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists of an oral interview to ensure learning of the course content.

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

ASSESSMENT METHODS

Learning will be assessed by a number of oral questions regarding the various topics addressed in the course.

Exam schedule

FURTHER INFORMATION

None.