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: Practical Mathematical Optimization
    sub title: Basic Optimization Theory and Gradient-Based Algorithms
  name:
    name part: Snyman, Jan A.
    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: Wilke, Daniel N.
    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: 2018
    edition: 2nd ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXVI, 372 p. 81 illus., 17 illus. in color. online resource.
  abstract: This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be able to develop systematic and scientific numerical investigative skills. .
  table of contents: 1.Introduction -- 2.Line search descent methods for unconstrained minimization.-3. Standard methods for constrained optimization.-4. Basic Example Problems -- 5. Some Basic Optimization Theorems -- 6. New gradient-based trajectory and approximation methods -- 7. Surrogate Models -- 8. Gradient-only solution strategies -- 9. Practical computational optimization using Python -- Appendix -- Index.
  note: by Jan A Snyman, Daniel N Wilke.
  subject:
    topic: Mathematical optimization
  subject:
    topic: Algorithms
  subject:
    topic: Operations research
  subject:
    topic: Management science
  subject:
    topic: Numerical analysis
  subject:
    topic: Computer software
  subject:
    topic: Functions of real variables
  subject:
    topic: Optimization
  subject:
    topic: Algorithms
  subject:
    topic: Operations Research, Management Science
  subject:
    topic: Numerical Analysis
  subject:
    topic: Mathematical Software
  subject:
    topic: Real Functions
  classification: QA402.5-402.6
  classification: 519.6
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Optimization and Its Applications, 133
  identifier: 9783319775869
  identifier: https://doi.org/10.1007/978-3-319-77586-9
  record info:
    record creation date: 180502
    record change date: 20191023182351.0
    record identifier: 978-3-319-77586-9