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: Nonlinear Optimization
  name:
    name part: Aragón, Francisco J.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Goberna, Miguel A.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: López, Marco A.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Rodríguez, Margarita M.L.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 350 p. 181 illus., 106 illus. in color. online resource.
  abstract: This textbook on nonlinear optimization focuses on model building, real world problems, and applications of optimization models to natural and social sciences. Organized into two parts, this book may be used as a primary text for courses on convex optimization and non-convex optimization. Definitions, proofs, and numerical methods are well illustrated and all chapters contain compelling exercises. The exercises emphasize fundamental theoretical results on optimality and duality theorems, numerical methods with or without constraints, and derivative-free optimization. Selected solutions are given. Applications to theoretical results and numerical methods are highlighted to help students comprehend methods and techniques.
  table of contents: 1. Preliminaries -- Part I. Analytical Optimization -- 2. Convexity -- 3. Unconstrained Optimization -- 4. Convex Optimization -- Part II. Numerical Optimization -- 5. Unconstrained Optimization Algorithms -- 6. Constrained Optimization -- Solutions to selected exercises -- References -- Index.
  note: by Francisco J. Aragón, Miguel A. Goberna, Marco A. López, Margarita M.L. Rodríguez.
  subject:
    topic: Mathematical optimization
  subject:
    topic: Optimization
  classification: QA402.5-402.6
  classification: 519.6
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Undergraduate Texts in Mathematics and Technology
  identifier: 9783030111847
  identifier: https://doi.org/10.1007/978-3-030-11184-7
  record info:
    record creation date: 190227
    record change date: 20190619131556.0
    record identifier: 978-3-030-11184-7