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: Conjugate Gradient Algorithms in Nonconvex Optimization
  name:
    name part: Pytlak, Radosław.
    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: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXVI, 478 p. online resource.
  abstract: This up-to-date book is on algorithms for large-scale unconstrained and bound constrained optimization. Optimization techniques are shown from a conjugate gradient algorithm perspective. Large part of the book is devoted to preconditioned conjugate gradient algorithms. In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradient algorithms. The special attention is paid to the methods of shortest residuals developed by the author. Several effective optimization techniques based on these methods are presented. Because of the emphasis on practical methods, as well as rigorous mathematical treatment of their convergence analysis, the book is aimed at a wide audience. It can be used by researches in optimization, graduate students in operations research, engineering, mathematics and computer science. Practitioners can benefit from numerous numerical comparisons of professional optimization codes discussed in the book.
  table of contents: Conjugate Direction Methods for Quadratic Problems -- Conjugate Gradient Methods for Nonconvex Problems -- Memoryless Quasi-Newton Methods -- Preconditioned Conjugate Gradient Algorithms -- Limited Memory Quasi-Newton Algorithms -- The Method of Shortest Residuals and Nondifferentiable Optimization -- The Method of Shortest Residuals for Differentiable Problems -- The Preconditioned Shortest Residuals Algorithm -- Optimization on a Polyhedron -- Conjugate Gradient Algorithms for Problems with Box Constraints -- Preconditioned Conjugate Gradient Algorithms for Problems with Box Constraints -- Preconditioned Conjugate Gradient Based Reduced-Hessian Methods.
  note: by Radosław Pytlak.
  subject:
    topic: Mathematics
  subject:
    topic: Operations research
  subject:
    topic: Decision making
  subject:
    topic: Calculus of variations
  subject:
    topic: Quality control
  subject:
    topic: Reliability
  subject:
    topic: Industrial safety
  subject:
    topic: Mathematics
  subject:
    topic: Calculus of Variations and Optimal Control; Optimization
  subject:
    topic: Operation Research/Decision Theory
  subject:
    topic: Quality Control, Reliability, Safety and Risk
  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: Nonconvex Optimization and Its Applications, 89
  identifier: 9783540856344
  identifier: http://dx.doi.org/10.1007/978-3-540-85634-4
  record info:
    record creation date: 100301
    record change date: 20151204173104.0
    record identifier: 978-3-540-85634-4