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dc.contributor.authorSuthep Suantaien_US
dc.contributor.authorSuparat Kesornpromen_US
dc.contributor.authorPrasit Cholamjiaken_US
dc.date.accessioned2019-09-16T12:55:41Z-
dc.date.available2019-09-16T12:55:41Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn22277390en_US
dc.identifier.other2-s2.0-85070448124en_US
dc.identifier.other10.3390/math7080708en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070448124&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/66703-
dc.description.abstract© 2019 by the authors. We investigate the split variational inclusion problem in Hilbert spaces. We propose efficient algorithms in which, in each iteration, the stepsize is chosen self-adaptive, and proves weak and strong convergence theorems. We provide numerical experiments to validate the theoretical results for solving the split variational inclusion problem as well as the comparison to algorithms defined by Byrne et al. and Chuang, respectively. It is shown that the proposed algorithms outrun other algorithms via numerical experiments. As applications, we apply our method to compressed sensing in signal recovery. The proposed methods have as a main advantage that the computation of the Lipschitz constants for the gradient of functions is dropped in generating the sequences.en_US
dc.subjectMathematicsen_US
dc.titleModified proximal algorithms for finding solutions of the split variational inclusionsen_US
dc.typeJournalen_US
article.title.sourcetitleMathematicsen_US
article.volume7en_US
article.stream.affiliationsUniversity of Phayaoen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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