Regularization Methods for large discrete ill-posed problems
Discretization of linear inverse problems generally gives rise to very
ill-conditioned linear systems of algebraic equations. Typically, the linear
systems obtained have to be regularized to make the computation of a
meaningful approximate solution possible. Tikhonov regularization is one of
the most popular regularization methods. A regularization parameter
specifies the amount of regularization and, in general, an appropriate
value of this parameter is not known a priori. This research focuses on
iterative methods for the determination of a suitable value of the regularization
parameter by the L-curve criterion and
the solution of regularized systems of algebraic equations.
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