Research article

A decent three term conjugate gradient method with global convergence properties for large scale unconstrained optimization problems

  • Received: 04 May 2021 Accepted: 14 July 2021 Published: 26 July 2021
  • MSC : 49M37, 65K05, 90C3

  • The conjugate gradient (CG) method is a method to solve unconstrained optimization problems. Moreover CG method can be applied in medical science, industry, neural network, and many others. In this paper a new three term CG method is proposed. The new CG formula is constructed based on DL and WYL CG formulas to be non-negative and inherits the properties of HS formula. The new modification satisfies the convergence properties and the sufficient descent property. The numerical results show that the new modification is more efficient than DL, WYL, and CG-Descent formulas. We use more than 200 functions from CUTEst library to compare the results between these methods in term of number of iterations, function evaluations, gradient evaluations, and CPU time.

    Citation: Ibtisam A. Masmali, Zabidin Salleh, Ahmad Alhawarat. A decent three term conjugate gradient method with global convergence properties for large scale unconstrained optimization problems[J]. AIMS Mathematics, 2021, 6(10): 10742-10764. doi: 10.3934/math.2021624

    Related Papers:

  • The conjugate gradient (CG) method is a method to solve unconstrained optimization problems. Moreover CG method can be applied in medical science, industry, neural network, and many others. In this paper a new three term CG method is proposed. The new CG formula is constructed based on DL and WYL CG formulas to be non-negative and inherits the properties of HS formula. The new modification satisfies the convergence properties and the sufficient descent property. The numerical results show that the new modification is more efficient than DL, WYL, and CG-Descent formulas. We use more than 200 functions from CUTEst library to compare the results between these methods in term of number of iterations, function evaluations, gradient evaluations, and CPU time.



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