Microkicking for Fast Convergence of Sparse Kaczmarz and Sparse LMS

Activity: Talk or presentation › Poster presentation › science-to-science

Description

Algorithms based on linearized Bregman iterations are able to perform sparse reconstruction at a low computational complexity. Especially the Least-Mean-Squares (LMS) and Kaczmarz variants of linearized Bregman iterations proved to be very feasible for fixed-point digital hardware implementation. We present a method that we call microkicking for improving the convergence speed of linearized Bregman based algorithms. This method can be implemented with only a negligible complexity overhead leading to significantly faster convergence for both variants of the linearized Bregman iterations. We furthermore show simulation results demonstrating the performance gains achievable by microkicking.
Period11 Dec 2017
Event title2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Event typeConference
LocationCuraçaoShow on map

Fields of science

  • 202017 Embedded systems
  • 202027 Mechatronics
  • 202015 Electronics
  • 202037 Signal processing
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202022 Information technology
  • 202041 Computer engineering

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing

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