Stabilization Techniques for Iterative Algorithms in Compressed Sensing

TL;DR

This paper reviews and compares stabilization techniques like damping and fractional approaches in iterative algorithms for compressed sensing, demonstrating their impact on convergence and steady-state performance through simulations.

Contribution

It provides a comprehensive introduction and interpretation of various stabilization methods, highlighting the benefits of combining multiple procedures.

Findings

Stabilization techniques improve convergence in compressed sensing algorithms.

Combining multiple stabilization procedures enhances steady-state performance.

Numerical simulations confirm the effectiveness of the proposed approaches.

Abstract

Algorithms for signal recovery in compressed sensing (CS) are often improved by stabilization techniques, such as damping, or the less widely known so-called fractional approach, which is based on the expectation propagation (EP) framework. These procedures are used to increase the steady-state performance, i.e., the performance after convergence, or assure convergence, when this is otherwise not possible. In this paper, we give a thorough introduction and interpretation of several stabilization approaches. The effects of the stabilization procedures are examined and compared via numerical simulations and we show that a combination of several procedures can be beneficial for the performance of the algorithm.