Emphasis on large, sparse ill-conditioned linear and nonlinear problems. Description of the most common techniques and computer laboratory experiments with some real world data from applications in tomography, meteorology, and engineering. Background in Linear Algebra and Numerical Analysis, and computing skills are assumed.


Bibliography to be used:

[1] Björck, Å.; "Numerical Methods for Least Squares Problems"; SIAM, 1996.
[2] Golub, G. and C.Van Loan; "Matrix Computations"; 3d ed., John Hopkins, 1996.
[3] Hansen, P.H.;"Rank-deficient and Discrete Ill-posed Problems"; SIAM;1998.