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: Optimization for Decision Making
    sub title: Linear and Quadratic Models
  name:
    name part: Murty, Katta G.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXVI, 482 p. 47 illus. online resource.
  abstract: Optimization for Decision Making: Linear and Quadratic Models is a first-year graduate level text that illustrates how to formulate real world problems using linear and quadratic models; how to use efficient algorithms – both old and new – for solving these models; and how to draw useful conclusions and derive useful planning information from the output of these algorithms. While almost all the best known books on LP are essentially mathematics books with only very simple modeling examples, this book emphasizes the intelligent modeling of real world problems, and the author presents several illustrative examples and includes many exercises from a variety of application areas. Additionally, where other books on LP only discuss the simplex method, and perhaps existing interior point methods, this book also discusses a new method based on using the sphere which uses matrix inversion operations sparingly and may be well suited to solving large-scale LPs, as well as those that may not have the property of being very sparse. Individual chapters present a brief history of mathematical modeling; methods for formulating real world problems; three case studies that illustrate the need for intelligent modeling; classical theory of polyhedral geometry that plays an important part in the study of LP; duality theory, optimality conditions for LP, and marginal analysis; variants of the revised simplex method; interior point methods; sphere methods; and extensions of sphere method to convex and nonconvex quadratic programs and to 0-1 integer programs through quadratic formulations. End of chapter exercises are provided throughout, with additional exercises available online.
  table of contents: Linear Equations, Inequalities, Linear Programming: A Brief Historical Overview -- Formulation Techniques Involving Transformations of Variables -- Intelligent Modeling Essential to Get Good Results -- Polyhedral Geometry -- Duality Theory and Optimality Conditions for LPs -- Revised Simplex Variants of the Primal and Dual Simplex Methods and Sensitivity Analysis -- Interior Point Methods for LP -- Sphere Methods for LP -- Quadratic Programming Models.
  note: by Katta G. Murty.
  subject:
    topic: Mathematics
  subject:
    topic: Operations research
  subject:
    topic: Decision making
  subject:
    topic: Mathematical models
  subject:
    topic: Mathematical optimization
  subject:
    topic: Management science
  subject:
    topic: Industrial engineering
  subject:
    topic: Production engineering
  subject:
    topic: Mathematics
  subject:
    topic: Mathematical Modeling and Industrial Mathematics
  subject:
    topic: Operation Research/Decision Theory
  subject:
    topic: Operations Research, Management Science
  subject:
    topic: Optimization
  subject:
    topic: Industrial and Production Engineering
  classification: TA342-343
  classification: 003.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: International Series in Operations Research & Management Science, 137
  identifier: 9781441912916
  identifier: http://dx.doi.org/10.1007/978-1-4419-1291-6
  record info:
    record creation date: 100715
    record change date: 20151204144552.0
    record identifier: 978-1-4419-1291-6