Yuneisy Garcia Guzman, Michael Lunglmayr,
"Implementing Sparse Estimation: Cyclic Coordinate Descent vs Linearized Bregman Iterations"
: Proceedings of 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019), IEEE, Seite(n) 335-340, 10-2019, ISBN: 978-1-7281-3140-5
Implementing Sparse Estimation: Cyclic Coordinate Descent vs Linearized Bregman Iterations
Sprache des Titels:
Proceedings of 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Implementing sparse estimation efficiently in digital
hardware is crucial for real-time applications. For such an
implementation one typically favours lightweight iterative algorithms.
This not only keeps the complexity low, but also allows
a fine-granular tuning of the performance/complexity trade-off.
Recently, algorithms based on Linearized Bregman Iterations
(LBI) have shown to be very feasible for low complexity digital
hardware implementation. An alternative approach would be to
use cyclic coordinate descent (CCD) algorithms. However, the
state-of-the-art formulation of sparse cyclic coordinate descent
has properties preventing an efficient hardware implementation.
In this work, we propose variations of cyclic coordinate descent,
specifically tailored for digital efficient hardware implementation.
These modifications allow cyclic coordinate descent algorithms
to be competitive in a hardware implementation compared to
the implementation efficient Linearized Bregman iteration algorithms.
We show simulation results for different sparse estimation
use-cases demonstrating the capabilities of both methods. We also
identify scenarios where our CCD approach allows to obtain the
same performance with less complexity than LBI.