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
MAT/05
6
B
MAT/07

2° Year   It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
Final exam
30
E
-
ModulesCreditsTAFSSD
12
B
MAT/05
6
B
MAT/07
It will be activated in the A.Y. 2026/2027
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. 2025/2026 Homological Algebra not activated
- A.A. 2026/2027 Applied algebra not activated
6
B
MAT/03
6
B
MAT/02 ,MAT/03
6
B
MAT/02
6
B
MAT/03
6
B
MAT/02
6
B
MAT/05
6
B
MAT/02
6
B
MAT/05
6
B
MAT/05
Between the years: 1°- 2°
3 modules among the following: area modeling and computational mathematics
6
B
MAT/07
6
B
MAT/08
6
B
MAT/06
6
B
MAT/09
6
B
MAT/08
6
B
MAT/08
6
B
MAT/08
6
B
MAT/06
6
B
MAT/06
6
B
MAT/06
Between the years: 1°- 2°
24 credits among the following courses
- A.A. 2025/2026 Homological Algebra not activated.
- A.A. 2025/2026 Physics education laboratory not activated
- A.A. 2026/2027 Applied algebra not activated
6
C
MAT/01
6
C
MAT/03
6
C
MAT/02 ,MAT/03
6
C
MAT/07
6
C
MAT/02
6
C
MAT/02
6
C
INF/01
6
C
INF/01
6
C
MAT/03
6
C
MAT/08
6
C
MAT/02
6
C
SECS-P/08
6
C
INF/01
6
C
MAT/06
6
C
MAT/01
6
C
MAT/05
6
C
MAT/09
12
C
MAT/04
6
C
MAT/02
6
C
FIS/01
6
C
MAT/08
6
C
MAT/08
6
C
MAT/08
6
C
MAT/05
6
C
MAT/05
6
C
FIS/01
6
C
MAT/06
6
C
MAT/06
6
C
MAT/06
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  (2025/2026)

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)

MAT/05 - MATHEMATICAL ANALYSIS

Period

2nd semester dal Mar 2, 2026 al Jun 12, 2026.

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

Finite-dimensional calculus, linear algebra, 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 is divided into a written and an oral test, the two tests must be passed in the same exam session. There are no difference between the assessment of attending or 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: a part of the written and the oral tests will be devoted to verify the effective knowledge and understanding of the course's contents.
- Applying knowledge and understanding: both during the written and the oral tests, the student will be required to solve problems based on the course's contents.
- Making judgements: during the tests, the student can be asked to solve problems requiring a contribution basing on the material of the course assigned for personal study.
- Communication skills: during the written and the oral tests, the solutions expressed in a clear, complete and short way will be preferred.

Criteria for the composition of the final grade

Overall assessment of written and oral tests (on the Italian ranking system from 18 to 30).

Exam language

Inglese