Linear convergence of iterative soft-thresholding
Provides theoretical convergence guarantees for a widely used sparse recovery algorithm, benefiting researchers in signal processing and inverse problems.
The paper proves linear convergence of iterative soft-thresholding for linear inverse problems in Hilbert spaces under finite basis injectivity or strict sparsity pattern, with explicit constants for compact operators.
In this article a unified approach to iterative soft-thresholding algorithms for the solution of linear operator equations in infinite dimensional Hilbert spaces is presented. We formulate the algorithm in the framework of generalized gradient methods and present a new convergence analysis. As main result we show that the algorithm converges with linear rate as soon as the underlying operator satisfies the so-called finite basis injectivity property or the minimizer possesses a so-called strict sparsity pattern. Moreover it is shown that the constants can be calculated explicitly in special cases (i.e. for compact operators). Furthermore, the techniques also can be used to establish linear convergence for related methods such as the iterative thresholding algorithm for joint sparsity and the accelerated gradient projection method.