CRPC-TR98773-S						May 1998

Title: A Large-Scale Trust-Region Approach to the Regularization of 
Discrete Ill-Posed Problems

Author: Marielba Rojas

Submitted September 1999; Available as Rice Ph.D. Thesis and CAAM TR98-19

Abstract:
We consider the problem of  computing the solution of large-scale discrete 
ill-posed problems when there is noise in the data. These problems arise 
in important areas such as seismic inversion, medical imaging and signal 
processing. We pose the problem as a quadratically constrained least 
squares problem and develop a method for the solution of such problem. 
Our method does not require factorization of the coefficient matrix, it 
has very low storage requirements and handles the high degree of 
singularities arising in discrete ill-posed problems. We present 
numerical results on test problems and an application of the method to a 
practical problem with real data.

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Marielba Rojas
mrojas@caam.rice.edu
Department of Computational and Applied Mathematics
Rice University