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: New family of nonlinear conjugate gradient methods for solving unconstrained optimization problems and its application
  name:
    name part: Norrlaili Shapiee
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Universiti Sultan Zainal Abidin
    name part: Faculty of Informatics and Computing
  type of resource: text
  origin info:
    place:
      place term: my
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xviii, 224 leaves : illustrations (some colour) ; 30cm.
  abstract: Conjugate gradient (CG) method is one of the most effective algorithms for solving unconstrained optimization problem. Some CG methods cycle infinitely without reaching the solution point and do not possess global convergence properties. Even if the method reaches a solution point, the process often results in high number of iterations. The new developments in CG method are sometimes difficult to implement. Furthermore, some particular conditions lead to failure hence making it unable to retain the global optimum solution. Nowadays CG method is less used in real life problems. To overcome such problems, two new CG methods for solving unconstrained problems have been proposed. They are denoted as NRM] (Norrlaili, Rivaie, Mustafa and Ismail) and NRM1+. The global convergence is established by using exact line search. These new CG methods are compared with some classical CG methods such as RMJL (Rivaie, Mustafa, Ismail and Leong) method, HS (Hestenes and Steifel) method as well as the FR (Fletcher and Reeves) method. The CG methods are tested with twenty-five standard optimization test problems by using MatlabR20 II b subroutine programming. For every problem, four initial points are used; ranging from the point that is nearest to the solution point, to one that is furthest away from it. The numerical results are based on the number of iterations and CPU time. The efficiency of the CG algorithms is analyzed by using performance profile. In addition, the new CG methods are used to solve determine the function between year and the index of road deaths. Theoretical proofs show that these new CG methods fulfill sufficient descent conditions and possess global convergence properties. From the results, FR method only solves 89.75% of the problems. The NRMI and NRMI+ methods successfully solve 100%. The HS method solves 95.5% while the RMIL method solves 97% of the problems. These new methods have been shown to outperform the FR, HS and RMIL methods. They are also successful …
  table of contents: 1. Introduction of optimization -- 2. Mathematical review in optimization -- 3. Conjugate gradient (CG) method -- 4. New conjugate gradient (CG) methods -- 5. Results and discussion -- 6. Implementation -- 7. Conclusion and future research
  note: Norrlaili binti Shapiee
  note: Thesis (Degree of Doctor of Philosophy, Informatics and Computing) - Universiti Sultan Zainal Abidin, 2017
  note: Includes bibliographical references (leaves 160-169)
  subject:
    name:
      name part: Universiti Sultan Zainal Abidin
    topic: Faculty of Informatics and Computing
    genre: Dissertations
  subject:
    topic: Conjugate gradient methods
  subject:
    topic: Conjugate gradient methods
    topic: Data processing
  subject:
    topic: Dissertations, Academic
  classification: QA218 .N67 2017
  record info:
    record content source: UniSZA
    record creation date: 180408
    record change date: 20200723093000.0
    record identifier: 0000094264