[CvGmt News] [Notiziario] [Settimanale] avviso seminario di Matematica, prof. James Nagy (12.04.2019)

[Valeria Giuliani]

SEMINARIO DI MATEMATICA



*venerdì 12 aprile 2019*

ore 11:00



*Scuola Normale Superiore*

Pisa

Aula Mancini



*James Nagy*
(Department of Mathematics, Emory University)



Terrà un seminario dal titolo:

*“MATLAB Tools for Large-Scale Linear Inverse Problems**”*



*Abstract:*

Inverse problems arise in a variety of applications: image processing, finance,
mathematical biology, and more. Mathematical models for these applications
may involve integral equations, partial differential equations, and
dynamical systems, and solution schemes are formulated by applying
algorithms that incorporate regularization techniques and/or statistical
approaches. In most cases these solutions schemes involve the need to solve
a large-scale ill-conditioned linear system that is corrupted by noise and
other errors.  In this talk we describe
and demonstrate capabilities of a new MATLAB software package that consists
of state-of-the-art iterative methods for solving such systems, which
includes approaches that can automatically estimate regularization
parameters, stopping iterations, etc., making them very simple to use.
Thus, the package allows users to easily incorporate
into their own applications (or simply experiment with) different iterative
methods and regularization strategies with very little programming effort.
On the other hand, sophisticated users can also easily access various
options to tune the algorithms for certain applications.  Moreover, the
package includes several test problems and examples to illustrate how the
iterative methods can be used on a variety of large-scale inverse problems.
The talk will begin with a brief introduction to inverse problems, discuss
considerations that are needed to compute an approximate solution, and
describe some details about new efficient hybrid Krylov subspace methods
that are implemented in our package. These methods can guide users in
automatically choosing regularization parameters,
and can be used to enforce various regularization schemes, such as sparsity.
We will use imaging examples that arise in medicine and astronomy to
illustrate the performance of the methods.

This is joint work with Silvia Gazzola (University of Bath) and
Per Christian Hansen (Technical University of Denmark).


Valeria Giuliani
Scuola Normale Superiore
Servizio alla Didattica e Allievi
tel. 050 509260
Piazza dei Cavalieri, 7
56126 Pisa
E-mail: valeria.giuliani at sns.it
E-mail: classi at sns.it

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