Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

Handle URI:
http://hdl.handle.net/10754/624925
Title:
Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
Authors:
Desmal, Abdulla ( 0000-0003-0861-8908 ) ; Bagci, Hakan ( 0000-0003-3867-5786 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
SHAXC-2 Workshop 2014
Issue Date:
4-May-2014
Type:
Poster
Appears in Collections:
Posters; Scalable Hierarchical Algorithms for eXtreme Computing (SHAXC-2) Workshop 2014

Full metadata record

DC FieldValue Language
dc.contributor.authorDesmal, Abdullaen
dc.contributor.authorBagci, Hakanen
dc.date.accessioned2017-06-12T10:24:00Z-
dc.date.available2017-06-12T10:24:00Z-
dc.date.issued2014-05-04-
dc.identifier.urihttp://hdl.handle.net/10754/624925-
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.en
dc.titlePreconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imagingen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateMay 4-6, 2014en
dc.conference.nameSHAXC-2 Workshop 2014en
dc.conference.locationKAUSTen
kaust.authorDesmal, Abdullaen
kaust.authorBagci, Hakanen
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