Convergence of the reweighted sub(1) minimization algorithm for sub(2)- sub( )pminimization

The iteratively reweighted sub(1) minimization algorithm (IRL1) has been widely used for variable selection, signal reconstruction and image processing. In this paper, we show that any sequence generated by the IRL1 is bounded and any accumulation point is a stationary point of the sub(2)- sub( )pmi...

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Published in:Computational optimization and applications 2014-10, Vol.59 (1-2), p.47-61
Main Authors: Chen, Xiaojun, Zhou, Weijun
Format: Article
Language:English
Subjects:
Algorithms
Construction
Convergence
Image processing
Mathematical models
Minimization
Optimization
Signal reconstruction
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description The iteratively reweighted sub(1) minimization algorithm (IRL1) has been widely used for variable selection, signal reconstruction and image processing. In this paper, we show that any sequence generated by the IRL1 is bounded and any accumulation point is a stationary point of the sub(2)- sub( )pminimization problem with 0
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subjects Algorithms
Construction
Convergence
Image processing
Mathematical models
Minimization
Optimization
Signal reconstruction
title Convergence of the reweighted sub(1) minimization algorithm for sub(2)- sub( )pminimization
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