A hybrid HS-LS conjugate gradient algorithm for unconstrained optimization with applications in motion control and image recovery

Poom Kumam, Auwal Bala Abubakar, Maulana Malik, Abdulkarim Hassan Ibrahim, Nuttapol Pakkaranang*, Bancha Panyanak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This article presents a new hybrid conjugate gradient (CG) algorithm for solving unconstrained optimization problem. The search direction is defined as a combination of Hestenes–Stiefel (HS) and the Liu–Storey (LS) CG parameters and is close to the direction of the memoryless Broyden–Fletcher–Goldferb–Shanno (BFGS) quasi-Newton direction. In addition, the search direction is descent and bounded. The global convergence of the algorithm is obtained under the Wolfe-type and Armijo-type line searches. Numerical experiments on some benchmark test problems is carried out to depict the efficiency and robustness of the hybrid algorithm. Furthermore, a practical application of the algorithm in motion control of robot manipulator and image restoration is provided.

Original languageEnglish
Article number115304
JournalJournal of Computational and Applied Mathematics
Volume433
DOIs
Publication statusPublished - 1 Dec 2023
Externally publishedYes

Keywords

  • Global convergence
  • Line search
  • Three-term conjugate gradient method
  • Unconstrained optimization

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