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Sparsity and Optimization

ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding


The alternating direction method of multipliers (ADMM) has been widely used for a very wide variety of imaging inverse problems. One of the disadvantages of this method, however, is the need to select an algorithm parameter, the penalty parameter, that has a significant effect on the rate of convergence of the algorithm. Although a number of heuristic methods have been proposed, as yet there is no general theory providing a good choice of this parameter for all problems.

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Authors:
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov
Submitted On:
3 October 2017 - 6:45pm
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[1] Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2254. Accessed: Oct. 24, 2017.
@article{2254-17,
url = {http://sigport.org/2254},
author = {Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov },
publisher = {IEEE SigPort},
title = {ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding},
year = {2017} }
TY - EJOUR
T1 - ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding
AU - Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2254
ER -
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. IEEE SigPort. http://sigport.org/2254
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, 2017. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. Available at: http://sigport.org/2254.
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding." Web.
1. Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2254

Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm


Embedding the l1 norm in gradient-based adaptive filtering is a popular solution for sparse plant estimation. Supported on the modal analysis of the adaptive algorithm near steady state, this work shows that the optimal sparsity tradeoff depends on filter length, plant sparsity and signal-to-noise ratio. In a practical implementation, these terms are obtained with an unsupervised mechanism tracking the filter weights. Simulation results prove the robustness and superiority of the novel adaptive-tradeoff sparsity-aware method.

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Authors:
Abdullah Alshabilli, Shihab Jimaa
Submitted On:
19 March 2016 - 12:32pm
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icassp2016-poster.pdf

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[1] Abdullah Alshabilli, Shihab Jimaa, "Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/825. Accessed: Oct. 24, 2017.
@article{825-16,
url = {http://sigport.org/825},
author = {Abdullah Alshabilli; Shihab Jimaa },
publisher = {IEEE SigPort},
title = {Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm},
year = {2016} }
TY - EJOUR
T1 - Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm
AU - Abdullah Alshabilli; Shihab Jimaa
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/825
ER -
Abdullah Alshabilli, Shihab Jimaa. (2016). Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm. IEEE SigPort. http://sigport.org/825
Abdullah Alshabilli, Shihab Jimaa, 2016. Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm. Available at: http://sigport.org/825.
Abdullah Alshabilli, Shihab Jimaa. (2016). "Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm." Web.
1. Abdullah Alshabilli, Shihab Jimaa. Adaptive Sparsity Tradeoff for L1-Constraint NLMS Algorithm [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/825