A NEW CLASS OF NONLINEAR CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION MODELS AND ITS APPLICATION IN PORTFOLIO SELECTION

Maulana Malik, Ibrahim Mohammed Sulaiman, Mustafa Mamat, Siti Sabariah Abas, Sukono

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a new conjugate gradient method for solving uncon-strained optimization models. By using exact and strong Wolfe line searches, the proposedmethod possesses the sufficient descent condition and global convergence properties. Numeri-cal results show that the proposed method is efficient at small, medium, and large dimensionsfor the given test functions. In addition, the proposed method was applied to solve practicalapplication problems in portfolio selection

Original languageEnglish
Pages (from-to)811-837
Number of pages27
JournalNonlinear Functional Analysis and Applications
Volume26
Issue number4
DOIs
Publication statusPublished - 2021

Keywords

  • Conjugate gradient method
  • Descent condition
  • Global convergence
  • Portfolio selection
  • Unconstrained optimization

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