A new class of conjugate gradient coefficient with global convergence properties

Published on Sep 27, 2012
· DOI :10.1063/1.4757519
Mohd Rivaie7
Estimated H-index: 7
Muhammad Fauzi3
Estimated H-index: 3
+ 1 AuthorsIsmail Mohd5
Estimated H-index: 5
Conjugate gradient (CG) methods play a significant and important role in solving unconstrained optimization. This paper presents, a new modification of the conjugate gradient coefficient (βκ) that has global convergence properties. The global convergence result is established using exact line searches. Numerical results show that the proposed formula is superior when compared to other CG coefficients. Numerical results also suggest that this method possesses global convergence properties.
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#2Xuetao Xie (Sichuan University)
#3Tao Gao (Beihang University)H-Index: 3
Abstract Elman recurrent network is a representative model with feedback mechanism. Although gradient descent method has been widely used to train Elman network, it frequently leads to slow convergence. According to optimization theory, conjugate gradient method is an alternative strategy in searching the descent direction during training. In this paper, an efficient conjugate gradient method has been presented to reach the optimal solution in two ways: (1) constructing a more effective conjugat...
Aug 15, 2018 in ICIC (International Conference on Intelligent Computing)
#1Mingyue Zhu (China University of Petroleum)H-Index: 1
#2Tao Gao (China University of Petroleum)H-Index: 3
Last. Jian Wang (China University of Petroleum)H-Index: 16
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Elman neural network is a typical class of recurrent network model. Gradient descent method is the popular strategy to train Elman neural networks. However, the gradient descent method is inefficient owing to its linear convergence property. Based on the Generalized Armijo search technique, we propose a novel conjugate gradient method which speeds up the convergence rate in training Elman networks in this paper. A conjugate gradient coefficient is proposed in the algorithm, which constructs conj...
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