Regularization of ill-posed linear inverse problems via $ell_1$ penalization has been proposed for cases where the solution is known to be (almost) sparse. One way to obtain the minimizer of such an $ell_1$ penalized functional is via an iterative so
ft-thresholding algorithm. We propose an alternative implementation to $ell_1$-constraints, using a gradient method, with projection on $ell_1$-balls. The corresponding algorithm uses again iterative soft-thresholding, now with a variable thresholding parameter. We also propose accelerat
