Iterated smoothing for accelerated gradient convex minimization in signal processing

In this paper, we consider the problem of minimizing a non-smooth convex problem using first-order methods. The number of iterations required to guarantee a certain accuracy for such problems is often excessive and several methods, e.g., restart methods, have been proposed to speed-up the convergence. In the restart method a smoothness parameter is adjusted such that smoother approximations of the original non-smooth problem are solved in a sequence before the original, and the previous estimate is used as the starting point each time. Instead of adjusting the smoothness parameter after each restart, we propose a method where we modify the smoothness parameter in each iteration. We prove convergence and provide simulation examples for two typical signal processing applications, namely total variation denoising and 1-norm minimization. The simulations demonstrate that the proposed method require fewer iterations and show lower complexity compared to the restart method.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Tobias Lindstrøm Jensen, Jan Østergaard, Søren Holdt Jensen
• Keywords: ICASSP 2010, Non-smooth Convex Problem, Original Non-smooth Problem, Signal Processing, Smoothness Parameter