Michael Lunglmayr, Mario Huemer,
"Efficient Linearized Bregman Iteration for Sparse Adaptive Filters and Kaczmarz Solvers"
: Proceedings of IEEE 9th Sensor Array and Multichannel Signal Processing Workshop (SAM 2016), IEEE, 7-2016, ISBN: 2151-870X
Efficient Linearized Bregman Iteration for Sparse Adaptive Filters and Kaczmarz Solvers
Sprache des Titels:
Proceedings of IEEE 9th Sensor Array and Multichannel Signal Processing Workshop (SAM 2016)
Linearized Bregman iterations are low complexity and high precision approaches for solving the combined l1 /l2 minimization problem. In this work we give a derivation of the linearized Bregman iteration and show the links to Kaczmarz?s algorithm as well as to sparse least mean squares (LMS) filters. We present a novel extension allowing to perform combined l1 /l2 minimization either in an LMS based adaptive filter or in a Kaczmarz based batch solution. By means of simulations we demonstrate that the performance of our extension is comparable to the original linearized Bregman approaches. Furthermore, we show that with this extension l1/l2 minimization can be performed with less complexity than the corresponding l2 minimization.