Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89218
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Title: Iteratively reweighted ℓ 1 algorithms with extrapolation
Authors: Yu, P 
Pong, TK 
Issue Date: Jun-2019
Source: Computational optimization and applications, Jun 2019, v. 73, no. 2, p. 353-386
Abstract: Iteratively reweighted ℓ 1 algorithm is a popular algorithm for solving a large class of optimization problems whose objective is the sum of a Lipschitz differentiable loss function and a possibly nonconvex sparsity inducing regularizer. In this paper, motivated by the success of extrapolation techniques in accelerating first-order methods, we study how widely used extrapolation techniques such as those in Auslender and Teboulle (SIAM J Optim 16:697–725, 2006), Beck and Teboulle (SIAM J Imaging Sci 2:183–202, 2009), Lan et al. (Math Program 126:1–29, 2011) and Nesterov (Math Program 140:125–161, 2013) can be incorporated to possibly accelerate the iteratively reweighted ℓ 1 algorithm. We consider three versions of such algorithms. For each version, we exhibit an explicitly checkable condition on the extrapolation parameters so that the sequence generated provably clusters at a stationary point of the optimization problem. We also investigate global convergence under additional Kurdyka–Łojasiewicz assumptions on certain potential functions. Our numerical experiments show that our algorithms usually outperform the general iterative shrinkage and thresholding algorithm in Gong et al. (Proc Int Conf Mach Learn 28:37–45, 2013) and an adaptation of the iteratively reweighted ℓ 1 algorithm in Lu (Math Program 147:277–307, 2014, Algorithm 7) with nonmonotone line-search for solving random instances of log penalty regularized least squares problems in terms of both CPU time and solution quality.
Keywords: Extrapolation
Iteratively reweighted ℓ 1 algorithm
Kurdyka–Łojasiewicz property
Publisher: Springer
Journal: Computational optimization and applications 
ISSN: 0926-6003
EISSN: 1573-2894
DOI: 10.1007/s10589-019-00081-1
Rights: © Springer Science+Business Media, LLC, part of Springer Nature 2019
This is a post-peer-review, pre-copyedit version of an article published in Computational Optimization and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10589-019-00081-1
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