2.1.6. sf_tools.signal package

2.1.6.1. Submodules

• 2.1.6.1.1. sf_tools.signal.cost module
• 2.1.6.1.2. sf_tools.signal.filter module
• 2.1.6.1.3. sf_tools.signal.gradient module
• 2.1.6.1.4. sf_tools.signal.linear module
• 2.1.6.1.5. sf_tools.signal.noise module
• 2.1.6.1.6. sf_tools.signal.optimisation module
• 2.1.6.1.7. sf_tools.signal.positivity module
• 2.1.6.1.8. sf_tools.signal.proximity module
• 2.1.6.1.9. sf_tools.signal.regression module
• 2.1.6.1.10. sf_tools.signal.reweight module
• 2.1.6.1.11. sf_tools.signal.svd module
• 2.1.6.1.12. sf_tools.signal.validation module
• 2.1.6.1.13. sf_tools.signal.wavelet module

2.1.6.2. Module contents

SIGNAL PROCESSING ROUTINES

This module contains submodules for signal processing.

Author:Samuel Farrens <samuel.farrens@gmail.com> Version:1.0 Date:06/04/2017

References

[Con201312]

Condat, A Primal-Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms, 2013, Journal of Optimization Theory and Applications, 158, 2, 460. [https://link.springer.com/article/10.1007/s10957-012-0245-9]

[B201112]

Bauschke et al., Fixed-Point Algorithms for Inverse Problems in Science and Engineering, 2011, Chapter 10. [http://rentals.springer.com/product/9781441995698]

[R201212]

Raguet et al., Generalized Forward-Backward Splitting, 2012, SIAM, v6. [https://arxiv.org/abs/1108.4404]

[CWB200712]

Candes, Wakin and Boyd, Enhancing Sparsity by Reweighting l1 Minimization, 2007, Journal of Fourier Analysis and Applications, 14(5):877-905. [https://arxiv.org/abs/0711.1612]