An efficient hybrid conjugate gradient method for unconstrained optimization

Abdulkarim Hassan Ibrahim*, Poom Kumam, Ahmad Kamandi, Auwal Bala Abubakar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


In this paper, we propose a hybrid conjugate gradient method for unconstrained optimization, obtained by a convex combination of the LS and KMD conjugate gradient parameters. A favourite property of the proposed method is that the search direction satisfies the Dai–Liao conjugacy condition and the quasi-Newton direction. In addition, this property does not depend on the line search. Under a modified strong Wolfe line search, we establish the global convergence of the method. Numerical comparison using a set of 109 unconstrained optimization test problems from the CUTEst library show that the proposed method outperforms the Liu–Storey and Hager–Zhang conjugate gradient methods.

Original languageEnglish
Pages (from-to)1370-1383
Number of pages14
JournalOptimization Methods and Software
Issue number4
Publication statusPublished - 2022
Externally publishedYes


  • CUTEst
  • Dai–Liao conjugacy
  • Quasi-Newton direction
  • Unconstrained optimization
  • conjugate gradient method
  • hybrid conjugate gradient method


Dive into the research topics of 'An efficient hybrid conjugate gradient method for unconstrained optimization'. Together they form a unique fingerprint.

Cite this