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 search direction formula of steepest descent method for large scale unconstrained optimization
  title info:
    title: x
  name:
    name part: Siti Farhana binti Husin
    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
  genre: Dissertations, Academic
  origin info:
    place:
      place term: my
    date issued: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xviii, 253 leaves : illustrations (some color) ; 30 cm.
  abstract: One of the simplest optimization methods for solving unconstrained optimization problems is the steepest descent (SD) method. The main ad antage of the SD algorithm with exact line search is that it satisfies global convergence properties under suitable assumptions. This method requires only the first derivative to be solved for the search direction which leads to low computational cost and storage requirements. However, the method has a slow convergence rate based on the higher number of iterations and central processing unit (CPU) time. Therefore, a new SD method that possesses lower iterations and CPU time is needed. This research concerns on the development of the SD method for solving large-scale nonlinear unconstrained optimization problems by suggesting new search direction in SD algorithm. This study focuses on the modification f search direction for SD methods by adding new parameters to the clas ical SD direction with the first suggestion is in two-term direction. Secondly, this work has also developed a three-term search direction for the SD method with two different parameters. The proposed method is specifically designed for solving large scale optimization problems under exact line search procedures. A new formulation of search direction for SD method combined with the conjugate gradient coefficients has been suggested. Twenty-six test problems are tested under different initial points ranging from two to five thousand variables. Numerical results for all of these methods are compared with existing SD methods based on the number of iterations and CPU time in which each method is evaluated over the same set of test problems and are interpreted by using the performance profile. The applicability of the introduced methods is shown by applying in least square method to solve some chosen nonlinear ordinary differential equations and to be implemented on data fitting through regression analysis. A set of data regarding relationship between fin length and tot…
  table of contents: 1. Introduction of research -- 2. Elementary concept of unconstrained optimization -- 3. Steepest descentmethod -- 4. New search direction for steepest descent method -- 5. Three-term search direction for steeepest descent method -- 6. Application of new steepest descentmethod -- 7. Conclusion and recommendation
  note: Siti Farhana binti Husin
  note: x
  note: Theses (Degree of Doctor of Philosophy) - Universiti Sultan Zainal Abidin,2021
  note: Includes bibliographical references (pages 190-201)
  subject:
    name:
      name part: x
  subject:
    name:
      name part: Universiti Sultan Zainal Abidin
    topic: Dissertations
  subject:
    name:
      name part: Universiti Sultan Zainal Abidin
    topic: Faculty of Informatics and Computing
    genre: Dissertations
  subject:
    topic: Algorithms
  subject:
    topic: Mathematical analysis
  subject:
    topic: Mathematical optimization
  subject:
    geographic: on order
  classification: on order
  classification: QA402.5 .S582021
  classification: x
  identifier: UniSZA
  record info:
    record content source: UniSZA
    record creation date: 210927
    record change date: 20220610093000.0
    record identifier: 0000100427