The effectiveness of using model sparsity as a priori information when
solving linear inverse problems is studied. We investigate the reconstruction
quality of such a method in the non-idealized case and compute some typical
recovery errors (depending on the sparsity of the desired solution, the number
of data, the noise level on the data, and various properties of the measurement
matrix); they are compared to known theoretical bounds and illustrated on a
magnetic tomography example.Comment: 11 pages, 5 figure