A half thresholding projection algorithmfor sparse solutions of LCPs
A half thresholding projection algorithmfor sparse solutions of LCPs
In this paper, we aim to find sparse solutions of the linear complementarity problems (LCPs), which has many applications such as bimatrix games and portfolio selection. Mathematically, the underlying model is NP-hard in general. Thus, an ℓ1/2 regularized projection minimization model is proposed for relaxation. A half thresholding projection (HTP) algorithm is then designed for this regularization model, and the convergence of HTP algorithm is studied. Numerical results demonstrate that the HTP algorithm can effectively solve this regularization model and output very sparse solutions of LCPs with high quality.
1231-1245
Shang, Meijuan
170b322d-2478-4938-986a-09e778e597b7
Zhang, Chao
c99789f4-ce55-4a97-bfae-4a8b999fe14d
Peng, Dingtao
9d96447b-3c3b-42ae-8fca-d1d4fab70885
Zhou, Shenglong
d183edc9-a9f6-4b07-a140-a82213dbd8c3
2015
Shang, Meijuan
170b322d-2478-4938-986a-09e778e597b7
Zhang, Chao
c99789f4-ce55-4a97-bfae-4a8b999fe14d
Peng, Dingtao
9d96447b-3c3b-42ae-8fca-d1d4fab70885
Zhou, Shenglong
d183edc9-a9f6-4b07-a140-a82213dbd8c3
Shang, Meijuan, Zhang, Chao, Peng, Dingtao and Zhou, Shenglong
(2015)
A half thresholding projection algorithmfor sparse solutions of LCPs.
Optimization Letters, 9 (6), .
(doi:10.1007/s11590-014-0834-7).
Abstract
In this paper, we aim to find sparse solutions of the linear complementarity problems (LCPs), which has many applications such as bimatrix games and portfolio selection. Mathematically, the underlying model is NP-hard in general. Thus, an ℓ1/2 regularized projection minimization model is proposed for relaxation. A half thresholding projection (HTP) algorithm is then designed for this regularization model, and the convergence of HTP algorithm is studied. Numerical results demonstrate that the HTP algorithm can effectively solve this regularization model and output very sparse solutions of LCPs with high quality.
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e-pub ahead of print date: 3 December 2014
Published date: 2015
Identifiers
Local EPrints ID: 442378
URI: http://eprints.soton.ac.uk/id/eprint/442378
ISSN: 1862-4472
PURE UUID: cf319607-88c9-4f3e-9ef9-d9e36175f07c
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Date deposited: 14 Jul 2020 16:31
Last modified: 16 Mar 2024 08:32
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Author:
Meijuan Shang
Author:
Chao Zhang
Author:
Dingtao Peng
Author:
Shenglong Zhou
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