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

A.A. 2026/2027 A.A. 2025/2026

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:

1° Year 

ModulesCreditsTAFSSD
12
B
MATH-03/A
6
B
MATH-04/A

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

ModulesCreditsTAFSSD
Final exam
30
E
-
ModulesCreditsTAFSSD
12
B
MATH-03/A
6
B
MATH-04/A
It will be activated in the A.Y. 2027/2028
ModulesCreditsTAFSSD
Final exam
30
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following: area algebra and geometry + analysis
- A.A. 2026/2027 Applied algebra not delivered
6
B
MATH-02/B
6
B
MATH-02/A ,MATH-02/B
6
B
MATH-02/A
6
B
MATH-02/B
6
B
MATH-02/A
6
B
MATH-03/A
6
B
MATH-02/A
6
B
MATH-03/A
6
B
MATH-03/A
Between the years: 1°- 2°
3 modules among the following: area modeling and computational mathematics
6
B
MATH-04/A
6
B
MATH-05/A
6
B
MATH-03/B
6
B
MATH-06/A
6
B
MATH-05/A
6
B
MATH-05/A
6
B
MATH-05/A
6
B
MATH-03/B
6
B
MATH-03/B
6
B
MATH-03/B
Between the years: 1°- 2°
24 credits among the following modules:
- A.A. 2026/2027 Applied algebra not delivered
6
B/C
MATH-01/A
6
B/C
MATH-02/B
6
B/C
MATH-02/A ,MATH-02/B
6
B/C
MATH-04/A
6
B/C
MATH-02/A
6
B/C
MATH-02/A
6
B/C
INFO-01/A
6
B/C
INFO-01/A
6
B/C
MATH-02/B
6
B/C
MATH-05/A
6
B/C
MATH-02/A
6
B/C
ECON-07/A
6
B/C
INFO-01/A
6
B/C
MATH-03/B
6
B/C
MATH-01/A
6
B/C
MATH-03/A
6
B/C
MATH-06/A
12
B/C
MATH-01/B
6
B/C
MATH-02/A
6
B/C
PHYS-03/A
6
B/C
MATH-05/A
6
B/C
MATH-05/A
6
B/C
MATH-05/A
6
B/C
MATH-03/A
6
B/C
MATH-03/A
6
B/C
PHYS-03/A
6
B/C
MATH-03/B
6
B/C
MATH-03/B
6
B/C
MATH-03/B
Between the years: 1°- 2°
Further activities
6
F
-
Between the years: 1°- 2°
12
D
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Optimization  (2026/2027)

Teaching code

4S001106

Teacher

Mauro Bonafini

Coordinator

Mauro Bonafini

Credits

6

Also offered in courses:

  • Optimization of the course Master's degree in Mathematics

Language

English en

Scientific Disciplinary Sector (SSD)

MATH-03/A - Mathematical Analysis

Period

II semestre dal Mar 1, 2027 al Jun 11, 2027.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

In this course we will provide an introduction to Convex Analysis in finite and infinite-dimensional spaces. We will show also some applications to problems of nonlinear optimizations and control theory arising from physics and economics. At the end of the course, the student should be able to: - understand the deep link between this and the previous courses (in particular, Functional Analysis); - use the main tools of convex analysis to solve convex optimization problems; - formalize and analyze simple control system coming from physical and economics models, in the framework of optimal control theory; - be autonomous in the use of the textbook suggested for the course.

Prerequisites and basic notions

Multivariable calculus, linear algebra and basic topology.

Program

Convex sets and functions, convex and affine hulls, recession cones, hyperplanes, hyperplane separation, conjugate functions.
Polyhedral sets and functions, extreme points, polar cones.
Convex optimization, existence of optimal solutions. Duality for linear programming, convex programming, linear-conic programming and second-order cone programming, subgradients and optimality conditions, nonconvex problems. Existence of dual optimal solutions.
Descent methods for convex/nondifferentiable optimization, steepest descent method, subgradient methods, approximation methods, proximal methods, primal-dual approximation methods.

Didactic methods

In-room lectures, team working, homeworks.

Learning assessment procedures

The exam consists of a written and an oral test, both of which must be passed in the same session. Assessment criteria are identical for both attending and non-attending students.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

- Knowledge and understanding: both the written and oral exams will assess your comprehension of the core course content.
- Applying knowledge and understanding: you will be required to solve practical problems in both exams to demonstrate your ability to apply course concepts.
- Making judgments: some exam questions will require you to analyze and solve problems through a critical synthesis of the concepts, moving beyond their simple mechanical application.
- Communication skills: across both exams, priority will be given to solutions that are presented clearly, completely, and concisely.

Criteria for the composition of the final grade

The final grade is based on a qualitative evaluation of the student's overall performance across both the written and oral exams.

Exam language

Inglese