Abstract

This paper addresses the problem of computing the minimizers for Tikhonov functionals associated with inverse problems with sparsity constraints in general Banach spaces. We present, based on splitting the Tikhonov functional into a smooth and a non-smooth part, a general iterative procedure for the Banach-space setting. In case of sparsity constraints, this algorithm yields a successive application of thresholding-like functions which generalizes the well-known iterative soft-thresholding procedure. The convergence properties of the proposed method are studied. Depending on the smoothness and convexity of the underlying spaces, convergence of asymptotic rate is obtained with the help of Bregman and Bregman–Taylor distance estimates. In particular, strong convergence can be achieved for a large class of linear inverse problems with sparsity constraints in Banach space.