Numerical Methods for Large-Scale Ill-Posed Inverse Problems

Berkeley Lab - Computing Sciences Seminar
Date: Thursday, January 29, 2009
Time:10:00am - 11:30am
Location: Bldg. 50F, Room 1647

 

Speaker:
Julianne Chung
Department of Mathematics & Computer Science
Emory University

 

 

Abstract:
Ill-posed inverse problems arise in a variety of scientific
applications. Regularization methods exist for computing stable solution
approximations, but many are inadequate or insufficient for large-scale
problems. This work addresses these limitations by developing advanced
numerical methods to solve ill-posed inverse problems and by
implementing high-performance parallel code for large-scale
applications. Hybrid methods are developed for regularization of linear
and nonlinear least squares problems, and an efficient parallel
implementation based on the Message Passing Interface (MPI) library is
described for use on state-of-the-art computer architectures.
Regularization for nonlinear Poisson based models, such as those arising
from digital tomosynthesis reconstruction, is significantly more
challenging. However, reconstruction algorithms for polyenergetic
tomosynthesis will be discussed, and numerical experiments illustrate
the effectiveness and efficiency of the proposed methods.

Host of Seminar:

    Arie Shoshani