AIMS AND CONTENT

LEARNING OUTCOMES

The teaching introduces the main optimization problems (constrained, unconstrained, convex) and illustrates their main solving techniques, with the aim of initiating the student into their use in the various fields of electrical engineering.

AIMS AND LEARNING OUTCOMES

The aim of the teaching unit is to introduce the basic principles and methods of resolution for optimization problems. Different classes of optimization problems will be considered: (1) unconstrained optimization, (2) constrained optimization, (3) convex optimization, (4) linear programming, (5) quadratic optimization problems, (6) nonlinear problems, (7) mixed-integer programming. Then, the resolution methods will be introduced and the a specific software tool able to solve optimization problems will be described. Once the teaching unit is completed, students will be able to recognize the class of an optimization problem and to identify and implement its resolution.

TEACHING METHODS

The lessons are equally divided into:

• Theoretical lessons in which the mathematical requirements related to the modeling of optimization problems and their resolution are provided.
• Classroom exercises in which software environment implementations and solutions for application problems related to electrical systems (optimal dispatch, unit commitment, microgrid energy management, etc.) are carried out

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Federico Scarpa (federico.scarpa@unige.it ), the School's disability liaison.

SYLLABUS/CONTENT

1. Unconstrained optimization
2. Constrained optimization:
   • First order optimality conditions
   • Second order optimality conditions
3. Convex optimization problems
4. Active Set Methods:
   • Projected Gradient Method
5. Classification of optimization problems:
   • Linear programming
   • Quadratic programming
   • Non-linear problems (outline)
   • Mixed-integer optimization (outline)
6. MATLAB/GAMS implementation of optimization problems

RECOMMENDED READING/BIBLIOGRAPHY

• J. Nocedal, S. J. Wright, “Numerical Optimization”, Springer, 1999
• Matlab-Manual: https://it.mathworks.com/help/
• GAMS-Manual: User's Guide (gams.com)

TEACHERS AND EXAM BOARD

Exam Board

MARIO NERVI (President)

FABIO D'AGOSTINO

PAOLA GIRDINIO

DANIELE MESTRINER

PAOLO MOLFINO

GIORGIO MOLINARI

GABRIELE MOSAICO

MANSUETO ROSSI

EUGENIA TORELLO

MASSIMO BRIGNONE (President Substitute)

MATTEO SAVIOZZI (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/8731/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral exam.

ASSESSMENT METHODS

Verification of the acquisition of theoretical and practical knowledge of the calculation methodologies in a software environment related to the optimization problems addressed in the lessons. The oral exam will verify the student's ability to reproduce and discuss the theoretical and applied methods covered during the course. The quality of the presentation and the correct use of specialized terminology will also be evaluated, as well as the student's reasoning ability, autonomy, and recall of the previously defined cultural prerequisites.

Exam schedule

FURTHER INFORMATION

Ask the professor for other information not included in the teaching schedule.