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: Feasibility and Infeasibility in Optimization
    sub title: Algorithms and Computational Methods
  name:
    name part: Chinneck, John W.
    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: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 274 p. online resource.
  abstract: Constrained optimization models are core tools in business, science, government, and the military with applications including airline scheduling, control of petroleum refining operations, investment decisions, and many others. Constrained optimization models have grown immensely in scale and complexity in recent years as inexpensive computing power has become widely available. Models now frequently have many complicated interacting constraints, giving rise to a host of issues related to feasibility and infeasibility. For example, it is sometimes difficult to find any feasible point at all for a large model, or even to accurately determine if one exists, e.g. for nonlinear models. If the model is feasible, how quickly can a solution be found? If the model is infeasible, how can the cause be isolated and diagnosed? Can a repair to restore feasibility be carried out automatically? Researchers have developed numerous algorithms and computational methods in recent years to address such issues, with a number of surprising spin-off applications in fields such as artificial intelligence and computational biology. Over the same time period, related approaches and techniques relating to feasibility and infeasibility of constrained problems have arisen in the constraint programming community. Feasibility and Infeasibility in Optimization is a timely expository book that summarizes the state of the art in both classical and recent algorithms related to feasibility and infeasibility in optimization, with a focus on practical methods. All model forms are covered, including linear, nonlinear, and mixed-integer programs. Connections to related work in constraint programming are shown. Part I of the book addresses algorithms for seeking feasibility quickly, including new methods for the difficult cases of nonlinear and mixed-integer programs. Part II provides algorithms for analyzing infeasibility by isolating minimal infeasible (or maximum feasible) subsets of constraints, or by fi…
  table of contents: Seeking Feasibility -- Preliminaries -- Seeking Feasibility in Linear Programs -- Seeking Feasibility in Mixed-Integer Linear Programs -- A Brief Tour of Constraint Programming -- Seeking Feasibility in Nonlinear Programs -- Analyzing Infeasibility -- Isolating Infeasibility -- Finding the Maximum Feasible Subset of Linear Constraints -- Altering Constraints to Achieve Feasibility -- Applications -- Other Model Analyses -- Data Analysis -- Miscellaneous Applications -- Epilogue.
  note: by John W. Chinneck.
  subject:
    topic: Mathematics
  subject:
    topic: Operations research
  subject:
    topic: Decision making
  subject:
    topic: Mathematical models
  subject:
    topic: Mathematical optimization
  subject:
    topic: Industrial engineering
  subject:
    topic: Production engineering
  subject:
    topic: Engineering economics
  subject:
    topic: Engineering economy
  subject:
    topic: Econometrics
  subject:
    topic: Mathematics
  subject:
    topic: Optimization
  subject:
    topic: Operation Research/Decision Theory
  subject:
    topic: Mathematical Modeling and Industrial Mathematics
  subject:
    topic: Engineering Economics, Organization, Logistics, Marketing
  subject:
    topic: Industrial and Production Engineering
  subject:
    topic: Econometrics
  classification: QA402.5-402.6
  classification: 519.6
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: International Series in Operations Research and Management Science, 118
  identifier: 9780387749327
  identifier: http://dx.doi.org/10.1007/978-0-387-74932-7
  record info:
    record creation date: 100301
    record change date: 20151204185141.0
    record identifier: 978-0-387-74932-7