10:00 AM-12:00 PM
Room: Ballroom 2
Many problems in science and engineering are concerned with the determination of the internal structure of a system from exterior measurements. These problems typically are ill-posed. Their discretization can give rise to large severely ill-conditioned linear or nonlinear systems of equations. The computation of a meaningful approximate solution in the presence of errors in the data requires regularization. Recently, the development of special iterative methods for determining a regularized approximate solution of large-scale discrete ill-posed problems has received considerable attention, in particular for image processing applications. The purpose of this minisymposium to present an overview of state-of-the-art methods for the numerical solution of ill-posed problems with particular emphasis on the linear algebra involved.
Organizer: Daniela Calvetti,
Stevens Institute of Technology;
and Lothar Reichel,
Kent State University