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: Stochastic Optimization Methods
  name:
    name part: Marti, Kurt.
    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: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 314 p. 14 illus. online resource.
  abstract: Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.
  table of contents: Basic Stochastic Optimization Methods -- Decision/Control Under Stochastic Uncertainty -- Deterministic Substitute Problems in Optimal Decision Under Stochastic Uncertainty -- Differentiation Methods -- Differentiation Methods for Probability and Risk Functions -- Deterministic Descent Directions -- Deterministic Descent Directions and Efficient Points -- Semi-Stochastic Approximation Methods -- RSM-Based Stochastic Gradient Procedures -- Stochastic Approximation Methods with Changing Error Variances -- Technical Applications -- Approximation of the Probability of Failure/Survival in Plastic Structural Analysis and Optimal Plastic Design.
  note: by Kurt Marti.
  subject:
    topic: Business
  subject:
    topic: Operations research
  subject:
    topic: Decision making
  subject:
    topic: Mathematical optimization
  subject:
    topic: Computational intelligence
  subject:
    topic: Business and Management
  subject:
    topic: Operation Research/Decision Theory
  subject:
    topic: Optimization
  subject:
    topic: Computational Intelligence
  classification: HD30.23
  classification: 658.40301
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783540268482
  identifier: http://dx.doi.org/10.1007/b138181
  record info:
    record creation date: 100301
    record change date: 20151204171909.0
    record identifier: 978-3-540-26848-2