“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 "cause-effect" maps. The solution of these problems is important in real applications, such as signal processing and learning from examples.
The course aims to define the ill-posed problems resulting from the inversion of linear operators and to give an overview, both theoretical and applied, of the main regularization methods. The student will be able to identify the class of inverse problems associated with the inversion of linear operators and to apply to these problems the main numerical regularization methods, both analytical and stochastic. Together with lectures, computational laboratory activities are planned. Important examples of inverse problems in the application area are biomedical imaging methods (CT, Computerized Axial Tomography), satellite remote sensing in climatology, oceanographic acoustic tomography and non-destructive analysis in civil engineering, image reconstruction, automatic learning from examples.
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 ill-posed problems;
• to manage functional analysis tools, such as the theory of linear operators in infinite-dimensional 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 in-depth 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 42 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:
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:
Ricevimento: Students may contact the professor by e-mail.
The class will start according to the academic calendar, please see
https://corsi.unige.it/corsi/11907/studenti-orario
The timetable for this course is available here: EasyAcademy
The exam is oral. In some cases, a computational laboratory activity may be discussed.
Students with DSA certification ("Specific Learning Disorders"), 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. Such aids will be defined according to specific needs, together with the Referent for the School of the Committe for the inclusion of Students with Disabilites and with SLD. Students in such conditions are invited to get in touch (via e-mail) with the Teacher at least 7 days before the examination.
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.
Ask professors for other information not included in the teaching schedule.
