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A Quasi Monte Carlo Method for Large-Scale Inverse Problems

Author(s): Polydorides, Nick; Wang, Mengdi; Bertsekas, Dimitri P.

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dc.contributor.authorPolydorides, Nick-
dc.contributor.authorWang, Mengdi-
dc.contributor.authorBertsekas, Dimitri P.-
dc.date.accessioned2020-02-24T21:13:48Z-
dc.date.available2020-02-24T21:13:48Z-
dc.date.issued2012en_US
dc.identifier.citationPolydorides, Nick, Mengdi Wang, and Dimitri P. Bertsekas. "A Quasi Monte Carlo Method for Large-Scale Inverse Problems." In Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Proceedings in Mathematics & Statistics, vol 23 (2012): 623-637. doi:10.1007/978-3-642-27440-4_36en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr15b59-
dc.descriptionIn Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Proceedings in Mathematics & Statistics, vol 23 (2012)en_US
dc.description.abstractWe consider large-scale linear inverse problems with a simulation-based algorithm that approximates the solution within a low-dimensional subspace. The algorithm uses Tikhonov regularization, regression, and low-dimensional linear algebra calculations and storage. For sampling efficiency, we implement importance sampling schemes, specially tailored to the structure of inverse problems. We emphasize various alternative methods for approximating the optimal sampling distribution and we demonstrate their impact on the reduction of simulation noise. The performance of our algorithm is tested on a practical inverse problem arising from Fredholm integral equations of the first kind.en_US
dc.format.extent623 - 637en_US
dc.language.isoen_USen_US
dc.relation.ispartofSpringer Proceedings in Mathematics and Statisticsen_US
dc.rightsAuthor's manuscripten_US
dc.titleA Quasi Monte Carlo Method for Large-Scale Inverse Problemsen_US
dc.typeBook Chapteren_US
dc.identifier.doi10.1007/978-3-642-27440-4_36-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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