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2011
DOI: 10.4236/am.2011.23035
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Three New Hybrid Conjugate Gradient Methods for Optimization

Abstract: In this paper, three new hybrid nonlinear conjugate gradient methods are presented, which produce sufficient descent search direction at every iteration. This property is independent of any line search or the convexity of the objective function used. Under suitable conditions, we prove that the proposed methods converge globally for general nonconvex functions. The numerical results show that all these three new hybrid methods are efficient for the given test problems.

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Cited by 11 publications
(12 citation statements)
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“…Hal yang sama juga dilakukan oleh [14] dengan mengusulkan metode baru metode konjugat gradien yaitu metode hibrid LS-CD, MCD dan NH3 [14]. Adapun metode NH3 diperoleh sebagai berikut:…”
Section: Pendahuluanunclassified
“…Hal yang sama juga dilakukan oleh [14] dengan mengusulkan metode baru metode konjugat gradien yaitu metode hibrid LS-CD, MCD dan NH3 [14]. Adapun metode NH3 diperoleh sebagai berikut:…”
Section: Pendahuluanunclassified
“…The nonlinear conjugate gradient method is an attractive method for solving large-scale unconstrained optimization problem due to its simplicity and low memory requirements [9,10]. For the previous work, Nagaiah et.…”
Section: Optimization Algorithmmentioning
confidence: 99%
“…where 0 < µ < σ < 1. More information on these line search methods and other line search methods can be found in the literature [9,14,25,31,34,37,39,41]. In this paper, we suggest another approach to get a new hybrid nonlinear conjugate gradient method.…”
Section: Introductionmentioning
confidence: 99%