rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Approximate Solutions of Common Fixed-Point Problems
  name:
    name part: Zaslavski, Alexander J.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: IX, 454 p. online resource.
  abstract: This book presents results on the convergence behavior of algorithms which are known as vital tools for solving convex feasibility problems and common fixed point problems. The main goal for us in dealing with a known computational error is to find what approximate solution can be obtained and how many iterates one needs to find it. According to know results, these algorithms should converge to a solution. In this exposition, these algorithms are studied, taking into account computational errors which remain consistent in practice. In this case the convergence to a solution does not take place. We show that our algorithms generate a good approximate solution if computational errors are bounded from above by a small positive constant. Beginning with an introduction, this monograph moves on to study: · dynamic string-averaging methods for common fixed point problems in a Hilbert space · dynamic string methods for common fixed point problems in a metric space · dynamic string-averaging version of the proximal algorithm · common fixed point problems in metric spaces · common fixed point problems in the spaces with distances of the Bregman type · a proximal algorithm for finding a common zero of a family of maximal monotone operators · subgradient projections algorithms for convex feasibility problems in Hilbert spaces .
  table of contents: 1.Introduction -- 2. Dynamic string-averaging methods in Hilbert spaces -- 3. Iterative methods in metric spaces -- 4. Dynamic string-averaging methods in normed spaces -- 5. Dynamic string-maximum methods in metric spaces -- 6. Spaces with generalized distances -- 7. Abstract version of CARP algorithm -- 8. Proximal point algorithm -- 9. Dynamic string-averaging proximal point algorithm -- 10. Convex feasibility problems -- 11. Iterative subgradient projection algorithm -- 12. Dynamic string-averaging subgradient projection algorithm.– References.– Index. .
  note: by Alexander J. Zaslavski.
  subject:
    topic: Mathematics
  subject:
    topic: Operator theory
  subject:
    topic: Numerical analysis
  subject:
    topic: Calculus of variations
  subject:
    topic: Mathematics
  subject:
    topic: Calculus of Variations and Optimal Control; Optimization
  subject:
    topic: Numerical Analysis
  subject:
    topic: Operator Theory
  classification: QA315-316
  classification: QA402.3
  classification: QA402.5-QA402.6
  classification: 515.64
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Optimization and Its Applications, 112
  identifier: 9783319332550
  identifier: http://dx.doi.org/10.1007/978-3-319-33255-0
  record info:
    record creation date: 160630
    record change date: 20160630120030.0
    record identifier: 978-3-319-33255-0