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Regularization Theory for Ill-posed Problems : Selected Topics

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Title: Regularization Theory for Ill-posed Problems : Selected Topics
Description: This monograph is a valuable contribution to the highly topical and extremly productive field of regularisation methods for inverse and ill-posed problems. The author is an internationally outstanding and accepted mathematician in this field. In his book he offers a well-balanced mixture of basic and innovative aspects. He demonstrates new, differentiated viewpoints, and important examples for applications. The book demontrates the current developments in the field of regularization theory, such as multiparameter regularization and regularization in learning theory. The book is written for graduate and PhD students and researchers in mathematics, natural sciences, engeneering, and medicine.
Authors: Shuai Lu, Sergei V. Pereverzev
Resource Type: eBook.
Subjects: Numerical analysis--Improperly posed problems, Numerical differentiation
Categories: MATHEMATICS / Applied, MATHEMATICS / Differential Equations / General, MATHEMATICS / Research, MATHEMATICS / Numerical Analysis
Database: eBook Collection (EBSCOhost)
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  – Type: ebook-pdf
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  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 641765
RelevancyScore: 1051
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1050.81640625
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  Label: Title
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  Data: Regularization Theory for Ill-posed Problems : Selected Topics
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This monograph is a valuable contribution to the highly topical and extremly productive field of regularisation methods for inverse and ill-posed problems. The author is an internationally outstanding and accepted mathematician in this field. In his book he offers a well-balanced mixture of basic and innovative aspects. He demonstrates new, differentiated viewpoints, and important examples for applications. The book demontrates the current developments in the field of regularization theory, such as multiparameter regularization and regularization in learning theory. The book is written for graduate and PhD students and researchers in mathematics, natural sciences, engeneering, and medicine.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shuai+Lu%22">Shuai Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Sergei+V%2E+Pereverzev%22">Sergei V. Pereverzev</searchLink>
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  Data: eBook.
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  Data: <searchLink fieldCode="DE" term="%22Numerical+analysis--Improperly+posed+problems%22">Numerical analysis--Improperly posed problems</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+differentiation%22">Numerical differentiation</searchLink>
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RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 518.53
        Scheme: ddc
        Type: prePub
      – Code: 518.53
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Numerical analysis--Improperly posed problems
        Type: general
      – SubjectFull: Numerical differentiation
        Type: general
    Titles:
      – TitleFull: Regularization Theory for Ill-posed Problems : Selected Topics
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shuai Lu
      – PersonEntity:
          Name:
            NameFull: Sergei V. Pereverzev
      – PersonEntity:
          Name:
            NameFull: Shuai Lu
      – PersonEntity:
          Name:
            NameFull: Sergei V. Pereverzev
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2013
            – D: 04
              M: 02
              Type: profile
              Y: 2014
          Identifiers:
            – Type: isbn-print
              Value: 9783110286465
            – Type: isbn-electronic
              Value: 9783110286496
          Titles:
            – TitleFull: Regularization Theory for Ill-posed Problems : Selected Topics
              Type: main
ResultId 1