A new conjugate gradient algorithms using conjugacy condition for solving unconstrained optimization

Qasim, Aseel M.; Salih, Zinah F.; Hassan, Basim A.

doi:10.11591/ijeecs.v24.i3.pp1647-1653

Cited by 4 publications

(5 citation statements)

References 15 publications

(17 reference statements)

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“…Now subs (F) of ( 20) in (25) we get: And that is the final form of our new coefficient. If the direction is exact line search, then 𝐵 𝑖 ^ will be reduced to Hesten Stefile method.…”

Section: New Formula Of 𝛽 𝒊 ^mentioning

confidence: 99%

“…then: (ᶊ−1) and 0 < κ < 1 < is negative, then (1 + κ) = m is a positive number then (31) satisfies 𝑡 𝑖+1 𝑇 𝑔 𝑖+1 ≤ −𝑚 ‖𝑔 𝑖+1 ‖ 2 , which completes the proof. To state the global convergence for the new algorithms, you should see [13], [25] which is containing the Zoutindijk condition.…”

Section: The Sufficient Descent and Globalmentioning

confidence: 99%

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New memoryless self-scaling quasi Newton strategy on large scale unconstrained optimization problems

Qasim

Salih

2022

IJEECS

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In unconstrained optimization algorithms, we employ the memoryless quasi Newton procedure to construct a new conjugacy coefficient for the conjugate gradient approaches. This newer updating formula was adapted by scaling the well-known broyden fletcher glodfarb shanno (BFGS) formula by a selfscaling factor in order to reach to the new form of the conjugacy coefficient which makes a satisfactory result in the descent direction and satisfies the globally convergent features when compared the proposed method to HS standard conjugate gradient approach. The theorems are studied in detail and moreover the numerical results of this paper is depend on a Fortran programming which are extremely stable.

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“…Now subs (F) of ( 20) in (25) we get: And that is the final form of our new coefficient. If the direction is exact line search, then 𝐵 𝑖 ^ will be reduced to Hesten Stefile method.…”

Section: New Formula Of 𝛽 𝒊 ^mentioning

confidence: 99%

Section: The Sufficient Descent and Globalmentioning

confidence: 99%

New memoryless self-scaling quasi Newton strategy on large scale unconstrained optimization problems

Qasim

Salih

2022

IJEECS

Self Cite

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“…Where the function 𝑓: 𝑅 𝑛 → 𝑅 and its gradient 𝑔 𝑖 , also conjugate gradient methods (C-G) have played special roles in solving large dimension nonlinear problems. C-G method is an iterative scheme that usually generated a sequence {𝑥 𝑖 } of an approximated solution for (1), [1]- [4] usually we use the repetition:…”

Section: Introductionmentioning

confidence: 99%

Two new classes of conjugate gradient method based on logistic mapping

Jahwar,

Ibrahim,

Ajeel

et al. 2023

TELKOMNIKA

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Following the standard methods proposed by Polak-Ribiere-Polyak (P-R), in this work we introduce two new non-linear conjugate gradient methods for solving unconstraint optimization problem, our new methods based on P-R. Standard method (P-R) have performance well in numerical result but does not satisfy global convergency condition. In this paper we modified double attractive and powerful parameters that have better performance and good numerical result than P-R method, also each of our robust method can satisfies the descent condition and global convergency condition by using wolf condition. More over the second method modified by logistic mapping form, the main novelty is their numerical results and demonstrate performance well with compare to a standard method.

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“…Furthermore, the design of CG-techniques had been studied by many researchers; for more details see, [13]- [15]. The three-term CG method is other important class of the CG methods [16], [17] presented a proposal of the three-term CG method by considering a descent modified (PRP and HS) CG method as (10).…”

Section: Introductionmentioning

confidence: 99%

A new three-term conjugate gradient method for training neural networks with global convergence

Ibrahim

Mohammed

2022

IJEECS

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Conjugate gradient methods (CG) constitute excellent neural network training methods that are simplicity, flexibility, numerical efficiency, and low memory requirements. In this paper, we introduce a new three-term conjugate gradient method, for solving optimization problems and it has been tested on artificial neural networks (ANN) for training a feed-forward neural network. The new method satisfied the descent condition and sufficient descent condition. Global convergence of the new (NTTCG) method has been tested. The results of numerical experiences on some wellknown test function shown that our new modified method is very effective, by relying on the number of functions evaluation and number of iterations, also included the numerical results for training feed-forward neural networks with other well-known method in this field.

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A new conjugate gradient algorithms using conjugacy condition for solving unconstrained optimization

Cited by 4 publications

References 15 publications

New memoryless self-scaling quasi Newton strategy on large scale unconstrained optimization problems

New memoryless self-scaling quasi Newton strategy on large scale unconstrained optimization problems

Two new classes of conjugate gradient method based on logistic mapping

A new three-term conjugate gradient method for training neural networks with global convergence

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