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

The 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.

CURRICULUM TIPO:

2° Year   It will be activated in the A.Y. 2027/2028

ModulesCreditsTAFSSD
6
A
MATH-02/A
6
B
MATH-02/B
6
C
ECON-01/A
6
C
ECON-01/A
English B2 level
6
E
-

3° Year   It will be activated in the A.Y. 2028/2029

ModulesCreditsTAFSSD
6
C
ECON-05/A
Final exam
6
E
-
It will be activated in the A.Y. 2027/2028
ModulesCreditsTAFSSD
6
A
MATH-02/A
6
B
MATH-02/B
6
C
ECON-01/A
6
C
ECON-01/A
English B2 level
6
E
-
It will be activated in the A.Y. 2028/2029
ModulesCreditsTAFSSD
6
C
ECON-05/A
Final exam
6
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
Further activities
6
F
-
Between the years: 1°- 2°- 3°

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S004793

Credits

6

Scientific Disciplinary Sector (SSD)

MATH-05/A - Analisi numerica

Learning objectives

The course will discuss, from both the analytic and computational points of view, the numerical solution of Mathematical problems such as: non linear systems, linear systems, matrix eigenvalues, interpolation and approximation, Gaussian quadrature. The objective therefore is to expand on the material introduced in Calcolo Numerico I and to introduce new and more sophisticated solution algorithms. In particular, we will present techniques that are fundamental for important modern problems of Applied Mathematics such as that of high dimensional datasets (SVD and PCoA) and optimization (conjugate gradient method). The course has a Laboratory component where the methods studied will be implemented using the MATLAB programming platform (using either the official Matlab from Mathworks or else the open source version GNU OCTAVE). At the end of the course the student will be expected to demonstrate that s/he has attained a level of competence in the computational and computer aspects of the course subject, as well as the ability to recognize which algorithms are appropriate for basic and advanced problems of numerical analysis.