Double projection algorithms for solving the split feasibility problems

We propose two new double projection algorithms for solving the split feasibility problem (SFP). Different from the extragradient projection algorithms, the proposed algorithms do not require fixed stepsize and do not employ the same projection region at different projection steps. We adopt flexible...

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Main Authors: Dang, Y.Z., Sun, Jie, Zhang, Su
Format: Journal Article
Language:English
Published: AMER INST MATHEMATICAL SCIENCES-AIMS 2019
Subjects:
Online Access:http://purl.org/au-research/grants/arc/DP160102819
http://hdl.handle.net/20.500.11937/90788
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author Dang, Y.Z.
Sun, Jie
Zhang, Su
author_facet Dang, Y.Z.
Sun, Jie
Zhang, Su
author_sort Dang, Y.Z.
building Curtin Institutional Repository
collection Online Access
description We propose two new double projection algorithms for solving the split feasibility problem (SFP). Different from the extragradient projection algorithms, the proposed algorithms do not require fixed stepsize and do not employ the same projection region at different projection steps. We adopt flexible rules for selecting the stepsize and the projection region. The proposed algorithms are shown to be convergent under certain assumptions. Numerical experiments show that the proposed methods appear to be more efficient than the relaxed- CQ algorithm.
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institution Curtin University Malaysia
institution_category Local University
language English
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publishDate 2019
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spelling curtin-20.500.11937-907882023-04-19T07:46:15Z Double projection algorithms for solving the split feasibility problems Dang, Y.Z. Sun, Jie Zhang, Su Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Operations Research & Management Science Mathematics, Interdisciplinary Applications Engineering Mathematics Armijo-type line search convergence analysis double projection algorithm optimization split feasibility problem EXTRAGRADIENT METHOD CQ ALGORITHM SETS We propose two new double projection algorithms for solving the split feasibility problem (SFP). Different from the extragradient projection algorithms, the proposed algorithms do not require fixed stepsize and do not employ the same projection region at different projection steps. We adopt flexible rules for selecting the stepsize and the projection region. The proposed algorithms are shown to be convergent under certain assumptions. Numerical experiments show that the proposed methods appear to be more efficient than the relaxed- CQ algorithm. 2019 Journal Article http://hdl.handle.net/20.500.11937/90788 10.3934/jimo.2018135 English http://purl.org/au-research/grants/arc/DP160102819 http://creativecommons.org/licenses/by/4.0/ AMER INST MATHEMATICAL SCIENCES-AIMS fulltext
spellingShingle Science & Technology
Technology
Physical Sciences
Engineering, Multidisciplinary
Operations Research & Management Science
Mathematics, Interdisciplinary Applications
Engineering
Mathematics
Armijo-type line search
convergence analysis
double projection algorithm
optimization
split feasibility problem
EXTRAGRADIENT METHOD
CQ ALGORITHM
SETS
Dang, Y.Z.
Sun, Jie
Zhang, Su
Double projection algorithms for solving the split feasibility problems
title Double projection algorithms for solving the split feasibility problems
title_full Double projection algorithms for solving the split feasibility problems
title_fullStr Double projection algorithms for solving the split feasibility problems
title_full_unstemmed Double projection algorithms for solving the split feasibility problems
title_short Double projection algorithms for solving the split feasibility problems
title_sort double projection algorithms for solving the split feasibility problems
topic Science & Technology
Technology
Physical Sciences
Engineering, Multidisciplinary
Operations Research & Management Science
Mathematics, Interdisciplinary Applications
Engineering
Mathematics
Armijo-type line search
convergence analysis
double projection algorithm
optimization
split feasibility problem
EXTRAGRADIENT METHOD
CQ ALGORITHM
SETS
url http://purl.org/au-research/grants/arc/DP160102819
http://hdl.handle.net/20.500.11937/90788