CODE  38754 

ACADEMIC YEAR  2022/2023 
CREDITS 

SCIENTIFIC DISCIPLINARY SECTOR  MAT/08 
TEACHING LOCATION 

SEMESTER  1° Semester 
TEACHING MATERIALS  AULAWEB 
“Inverse problems" indicates a large class of problems in which the measurement of some effects allows to calculate their causes. The course deals with the mathematical theory of regularization methods for the solution of inverse problems, which are modelled by linear operators between Hilbert spaces, representative of the "causeeffect" maps. The solution of these problems is important in real applications, such as signal processing and learning from examples.
The course allows students to acquire basic knowledge, both theoretical and applicative, relating to the study of inverse problems. The student will in fact be able to theoretically model the class of illposed problems deriving from the inversion of linear operators and to apply the main numerical regularization methods, both analytical and stochastic, to these problems. For this purpose, together with lectures on the theory, computational laboratory activities are planned.
The course allows students to understand the basic mathematical tools for the solution of linear inverse problems. To this end, along with lectures on the regularization theory, computer lab activities are planned.
At the end of the course the student will have acquired sufficient theoretical knowledge:
• to identify the main mathematical models associated with illposed problems;
• to manage functional analysis tools, such as the theory of linear operators in infinitedimensional spaces, to solve inverse problems;
• to understand and classify Tikhonov regularization methods and iterative regularization methods;
• to understand the probabilistic approach to the regularization of inverse problems;
• to understand the optimality criteria for the best approximation;
• to use the techniques for estimating the optimal approximation, both deterministic and statistical;
• to solve linear inverse problems with the use of spectral regularization coupled with the optimal choice of the regularization parameter;
• to apply numerical methods to problems of image deconvolution and dynamic inverse problems, in which the unknown varies over time.
All the mathematical tools to understand the arguments are given in the course. For an indepth understanding it can be useful to have some basis of:
theory of linear operators in Hilbert spaces;
iterative methods for linear systems;
probability theory and statistic;
The teaching activity is in presence and consists of:
• traditional lectures, for a total of 50 hours, in which the subjects are introduced and explained in their classical theoretical setting;
• an additional 10 hours of computational laboratory activity, in which the theoretical tools are applied to the resolution of some applicative inverse problems.
Although attendance is optional, it is strongly recommended.
The program focuses on the following main topics:
• Linear operators on Hilbert spaces: operators with closed and nonclosed ranges, compact operators and spectral resolution of selfadjoint operators.
• Illposed problems, generalized inverse. Case of compact operators. Singular system.
• Regularization methods: regularization algorithms in the sense of Tikhonov, theoretical study by spectral resolution.
• Regularization iterative methods: LandweberFridman method and conjugate gradient.
• Problems of image reconstruction and deconvolution. The previously introduced regularization methods are analyzed in the context of Fourier analysis. Discrete Fourier transform.
• Statistical approach to inverse problems: Maximum Likelihood and Maximum APosteriori.
• Methods for the choice of the regularization parameter:
• Extension of the Tikhonov regularization to the dynamic case: the Kalman filter.
Laboratory numerical computation with MatLab language will be done during the course.
In general, the notes taken during class lessons and some downloadable materials from the course web page are sufficient. In addition, the following texts may be useful:
Office hours: By appointment via email.
Office hours: By appointment
CLAUDIO ESTATICO (President)
ALBERTO SORRENTINO
FEDERICO BENVENUTO (President Substitute)
The class will start according to the academic calendar.
All class schedules are posted on the EasyAcademy portal.
The exam is oral. In some cases, a computational laboratory activity may be discussed.
Students with DSA certification ("specific learning disabilities"), disability or other special educational needs are advised to contact the teachers at the beginning of the course to agree on teaching and examination methods that, in compliance with the teaching objectives, take account of individual learning arrangements and provide appropriate compensatory tools.
The oral exam focuses on the theory, and aims to ascertain its understanding, also through the discussion of the analytical concepts and the examples. In some cases it will also be possible to evaluate a written laboratory report.