A Globally Convergent Hybrid FR-PRP Conjugate Gradient Method for Unconstrained Optimization Problems

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dc.contributor.authorAdeleke, Olawale
dc.date.accessioned2022-03-21T08:37:04Z
dc.date.available2022-03-21T08:37:04Z
dc.date.issued2021
dc.description.abstractIn this paper, a new conjugate gradient (CG) parameter is proposed through the convex combination of the Fletcher-Reeves (FR) and Polak-RibiƩre-Polyak (PRP) CG update parameters such that the conjugacy condition of Dai-Liao is satisfied. The computational efficiency of the PRP method and the convergence profile of the FR method motivated the choice of these two CG methods. The corresponding CG algorithm satisfies the sufficient descent property and was shown to be globally convergent under the strong Wolfe line search procedure. Numerical tests on selected benchmark test functions show that the algorithm is efficient and very competitive in comparison with some existing classical methodsen_US
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/2080
dc.language.isoenen_US
dc.subjectUnconstrained optimizationen_US
dc.subjectConvex combinationen_US
dc.subjectConjugate gradient methoden_US
dc.subjectConjugacy conditionen_US
dc.subjectHybridizationen_US
dc.titleA Globally Convergent Hybrid FR-PRP Conjugate Gradient Method for Unconstrained Optimization Problemsen_US
dc.typeArticleen_US
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