Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/27526
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dc.contributor.authorTausiesakul B.
dc.date.accessioned2022-12-14T03:17:33Z-
dc.date.available2022-12-14T03:17:33Z-
dc.date.issued2022
dc.identifier.issn1024123X
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124107846&doi=10.1155%2f2022%2f8711843&partnerID=40&md5=e2e5cb87a3a3e22476be333a600fd793
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/27526-
dc.description.abstractWe present a normalization of the p-norm. A compressive sensing criterion is proposed using the normalized zero norm. Based on the method of Lagrange multipliers, we derive the solution of the proposed optimization framework. It turns out that the new solution is a limit case of the least fractional norm solution for p=0, where its fixed-point iteration algorithm can readily follow an existing algorithm. The derivation of the minimal normalized zero norm solution herein gives a relation in the aspect of Lagrange multiplier method to existing works that invoke least fractional norm and least pseudo zero norm criteria. © 2022 Bamrung Tausiesakul.
dc.languageen
dc.subjectIterative methods
dc.subjectCompressive sensing
dc.subjectFixed-point iterations
dc.subjectIteration algorithms
dc.subjectLagrange multiplier method
dc.subjectMinimisation
dc.subjectNew solutions
dc.subjectNormalisation
dc.subjectOptimization framework
dc.subjectZero norms
dc.subjectLagrange multipliers
dc.titleMethod of Lagrange Multipliers for Normalized Zero Norm Minimization
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationMathematical Problems in Engineering. Vol 2022, No. (2022)
dc.identifier.doi10.1155/2022/8711843
Appears in Collections:Scopus 2022

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