Sparsity-Enabled Step Width Adaption for Linearized Bregman based Algorithms

Description

Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hard- and software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.

Period	12 Jun 2018
Event title	IEEE Statistical Signal Processing Workshop (SSP 2018)
Event type	Conference
Location	GermanyShow on map