A hybrid fr-dy conjugate gradient algorithm for unconstrained optimization with application in portfolio selection

Auwal Bala Abubakar, Poom Kumam, Maulana Malik, Parin Chaipunya, Abdulkarim Hassan Ibrahim

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a new hybrid conjugate gradient (CG) approach for solving unconstrained optimization problem. The search direction is a hybrid form of the Fletcher-Reeves (FR) and the Dai-Yuan (DY) CG parameters and is close to the direction of the memoryless Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton approach. Independent of the line search, the search direction of the new approach satisfies the descent condition and possess the trust region. We establish the global convergence of the approach for general functions under the Wolfe-type and Armijo-type line search. Using the CUTEr library, numerical results show that the propose approach is more efficient than some existing approaches. Furthermore, we give a practical application of the new approach in optimizing risk in portfolio selection.

Original languageEnglish
Pages (from-to)6506-6527
Number of pages22
JournalAIMS Mathematics
Volume6
Issue number6
DOIs
Publication statusPublished - 2021

Keywords

  • Global convergence
  • Hybrid three-term conjugate gradient method
  • Portfolio selection
  • Unconstrained optimization

Fingerprint

Dive into the research topics of 'A hybrid fr-dy conjugate gradient algorithm for unconstrained optimization with application in portfolio selection'. Together they form a unique fingerprint.

Cite this