Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Mathematics - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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1 module between the following
1 module between the following
3 modules among the following
Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Numerical modelling and optimization (2019/2020)
Teaching code
4S008275
Credits
6
Coordinator
Language
English
Also offered in courses:
- Research and modelling seminar (seminar course) of the course Master's degree in Mathematics
The teaching is organized as follows:
Learning outcomes
The aim of the first module is to deepen the knowledge and skills especially in the modern theory of dynamical systems and give the student a solid appreciation of the deep connections between mathematics and other scientific disciplines, both in terms of the mathematical problems that they inspire and the important role that mathematics plays in scientific research and industry. Mathematical software tools, and others, will be used to implement algorithms for the solution of the real world problems studied during the course. At the end of the course the student is expected to be able to complete professional and technical tasks of a high level in the context of mathematical modelling and computation, both working alone and in groups. In particular the student will be able to write a model of a real problem, to recognise the effective parameters and analyse the model and its possible implications. The second module wants to provide sufficient theoretical and numerical background for the optimal control of dynamical systems. Such problems will be developed by means of real application examples, and recent research studies. At the end of the course students will be able to decide which numerical method is suitable for the solution of some specific optimal control problems. He/She will be able to provide theoretical results on the controllability and stability of certain optimal control problem and numerical methods. He/She will be able to develop his/her own code, and capable choose the appropriate optimization method for each application shown during the course.
Program
The course presents different differential models with application in biology, economics and robotics.
The analysis of these models will be enached by the study of theoretical aspects, and the development of several computational methods.
PART I: (Numerical Optimization)
* Linear and Nonlinear optimization, KKT conditions, gradient methods, quasi-Newton and Newton methods. Convex optimization.
* Optimal control: direct methods (shooting, collocation), indirect methods (forward-backward) and dynamic programming, Model-Predictive Control.
* Inverse problems and parameters estimation.
Examples and exercise with software ( Matlab/Octave and CVX).
PARTE II: (Modelling)
* Modelling of complex and multi-agent systems (swarming, opinion formaton, Network, and (non-)holonomic systems).
* Bifurcation analysis, and geometric control.
The programme is in accordance with the ECMI standards (European Consortium for Mathematics in Industry, https://ecmiindmath.org/)
Bibliography
Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|
L. T. Biegler | Nonlinear Programming | SIAM | 2010 | ||
Nocedal, Jorge, Stephen Wright | Numerical optimization | Springer Science & Business Media | 2006 | ||
Betts, J. | Practical Methods for Optimal Control and Estimation Using Nonlinear Programming | SIAM | 2010 | ||
S. Strogatz | Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering | Westview Press | 2010 |
Examination Methods
The student is expected to demonstrate the ability to mathematically formalize and solve models used in several scientific discipline, using, adapting and developing the models and advanced methods discussed during the lectures. To that end the final evaluation will consist in a written and oral exam.
Written exam: One question/exercise for each part of the course (Part I and Part II), the solution will possible require the use of computer.
Oral exam: Subject of student choice and discussion of the written exam with questions.
The subject of student choice can be substituted with the development of a small-project to be decided together with the teacher.