View : 294 Download: 0

Full metadata record

DC Field Value Language
dc.contributor.author김선영-
dc.date.accessioned2016-08-28T11:08:21Z-
dc.date.available2016-08-28T11:08:21Z-
dc.date.issued2010-
dc.identifier.isbn9781424453542-
dc.identifier.otherOAK-13503-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/229487-
dc.description.abstractSensor network localization (SNL) has been an important subject of research in recent years for a wide variety of applications. Among the solution methods proposed for SNL problems, semidefinite programming (SDP) approach is known for its effectiveness of obtaining solutions. In particular, the full SDP (FSDP) relaxation by Biswas and Ye was shown to be successful for solving small to medium-sized SNL problems. We present a sparse version of FSDP (SFSDP) for larger-sized problems by exploiting the sparsity of the problem. This method finds the same quality of solutions as the FSDP in a shorter amount of time. The performance of the SFSDP is measured with randomly generated test problems and compared with other methods. Numerical results suggest that exploiting the sparsity of the problem improve the efficiency of solving largersized problems. © 2010 IEEE.-
dc.languageEnglish-
dc.titleSemidefinite programming relaxations for sensor network localization-
dc.typeConference Paper-
dc.relation.indexSCOPUS-
dc.relation.startpage19-
dc.relation.lastpage23-
dc.relation.journaltitleProceedings of the IEEE International Symposium on Computer-Aided Control System Design-
dc.identifier.doi10.1109/CACSD.2010.5612817-
dc.identifier.scopusid2-s2.0-78649842262-
dc.author.googleKim S.-
dc.author.googleKojima M.-
dc.contributor.scopusid김선영(57221275622)-
dc.date.modifydate20210915112050-
Appears in Collections:
자연과학대학 > 수학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE