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dc.contributor.authorDesmal, Abdulla
dc.contributor.authorBagci, Hakan
dc.date.accessioned2017-06-01T10:20:42Z
dc.date.available2017-06-01T10:20:42Z
dc.date.issued2014-01-06
dc.identifier.urihttp://hdl.handle.net/10754/623979
dc.description.abstractNewton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.
dc.subjectCEM
dc.titlePreconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.conference.dateJanuary 6-10, 2014
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
dc.conference.locationKAUST
kaust.personDesmal, Abdulla
kaust.personBagci, Hakan
refterms.dateFOA2018-06-13T15:55:22Z


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