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abstract:
This talk is concerned with two important topics in the context of
sparse recovery in inverse and ill-posed problems. The focus is on the incomplete data scenario. We discuss extensions
of compressed sensing for specific infinite dimensional ill-posed measurement
regimes. We are able to establish recovery error estimates when adequately
relating the isometry constant of the sensing operator, the ill-posedness of the underlying
model operator and the regularization parameter. Finally, we very briefly
sketch how projected steepest descent iterations can be applied to retrieve the sparse
solution. |
