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dc.contributor.authorXu, Honglei
dc.contributor.authorTeo, Kok Lay
dc.contributor.authorJiang, C.
dc.date.accessioned2017-01-30T10:31:54Z
dc.date.available2017-01-30T10:31:54Z
dc.date.created2015-10-29T04:09:28Z
dc.date.issued2015
dc.identifier.citationXu, H. and Teo, K.L. and Jiang, C. 2015. Exponential H∞ stabilizing control of a class of uncertain impulsive switched systems. Pacific Journal of Optimization. 11 (3): pp. 549-556.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/3522
dc.description.abstract

Sparse tensor optimization has recently attracted much attention since it has many applications in areas such as biology, computer vision and information science. In this paper, we focus on the application of tensor optimization in surveillance video. Based on the static background of surveillance video, we introduce the new definition of rank-min-one tensor. Then we consider a rank-min-one and sparse tensor decomposition model for surveillance video. We establish the modified iterative reweighted l1algorithm (MIRL1), and give its convergence analysis. For synthetic and real surveillance data, numerical experiments are also presented to illustrate the efficiency of our proposed MIRL1.

dc.publisherYOKOHAMA PUBL
dc.relation.urihttp://www.ybook.co.jp/online2/oppjo/vol11/p549.html
dc.titleExponential H∞ stabilizing control of a class of uncertain impulsive switched systems
dc.typeJournal Article
dcterms.source.volume11
dcterms.source.number3
dcterms.source.startPage549
dcterms.source.endPage556
dcterms.source.issn1348-9151
dcterms.source.titlePACIFIC JOURNAL OF OPTIMIZATION
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available


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