An Efficient Modified AZPRP Conjugate Gradient Method for Large-Scale Unconstrained Optimization Problem

Ahmad Alhawarat, Thoi Trung Nguyen, Ramadan Sabra, Zabidin Salleh and Qingli Zhao

Journal of Mathematics, 2021, vol. 2021, 1-9

Abstract: To find a solution of unconstrained optimization problems, we normally use a conjugate gradient (CG) method since it does not cost memory or storage of second derivative like Newton’s method or Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. Recently, a new modification of Polak and Ribiere method was proposed with new restart condition to give a so-call AZPRP method. In this paper, we propose a new modification of AZPRP CG method to solve large-scale unconstrained optimization problems based on a modification of restart condition. The new parameter satisfies the descent property and the global convergence analysis with the strong Wolfe-Powell line search. The numerical results prove that the new CG method is strongly aggressive compared with CG_Descent method. The comparisons are made under a set of more than 140 standard functions from the CUTEst library. The comparison includes number of iterations and CPU time.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:6692024

DOI: 10.1155/2021/6692024

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