Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
dc.contributor.author | Desmal, Abdulla | |
dc.contributor.author | Bagci, Hakan | |
dc.date.accessioned | 2017-06-01T10:20:42Z | |
dc.date.available | 2017-06-01T10:20:42Z | |
dc.date.issued | 2014-01-06 | |
dc.identifier.uri | http://hdl.handle.net/10754/623979 | |
dc.description.abstract | Newton-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.subject | CEM | |
dc.title | Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging | |
dc.type | Poster | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Electrical Engineering Program | |
dc.conference.date | January 6-10, 2014 | |
dc.conference.name | Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014) | |
dc.conference.location | KAUST | |
kaust.person | Desmal, Abdulla | |
kaust.person | Bagci, Hakan | |
refterms.dateFOA | 2018-06-13T15:55:22Z |
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)