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Scaled conjugate gradient

Millam, J. M., Scuseria, G. E., 1997, Linear Scaling Conjugate Gradient Density Matrix Search as an Alternative to Diagonalization for First Principles Electronic Structure Calculations , J. Chem. Phys., 106, 5569. [Pg.295]

Neural network architectures and learning algorithms (see Table 9.1) GA = Genetic Algorithm SCG = Scaled Conjugate Gradient... [Pg.115]

Mailer, M. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6,525-33. [Pg.126]

Table 1. Confusion matrix of classification results based on Scaled Conjugate Gradient (SCG) Algorithm. Table 1. Confusion matrix of classification results based on Scaled Conjugate Gradient (SCG) Algorithm.
M.F. Moller, A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural networks, vol. 6, no. 4, pp. 525-533, 1993. [Pg.131]

The Scaled Conjugate Gradient (SCG) algorithm was chosen as the training algorithm. Table 1 shows the MLP parameter settings used. [Pg.545]

The advantage of a conjugate gradient minimizer is that it uses the minimization history to calculate the search direction, and converges faster than the steepest descent technique. It also contains a scaling factor, b, for determining step size. This makes the step sizes optimal when compared to the steepest descent technique. [Pg.59]

It is noted that the Rosenbrock function given by the next equation has been used to test the performance of various algorithms including modified Newton s and conjugate gradient methods (Scales, 1986)... [Pg.77]

The basic difficulty with the steepest descent method is that it is too sensitive to the scaling of/(x), so that convergence is very slow and what amounts to oscillation in the x space can easily occur. For these reasons steepest descent or ascent is not a very effective optimization technique. Fortunately, conjugate gradient methods are much faster and more accurate. [Pg.194]

For additional details concerning the application of conjugate gradient methods, especially to large-scale and sparse problems, refer to Fletcher (1980), Gill et al. (1981), Dembo et al. (1982), and Nash and Sofer (1996). [Pg.195]


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Conjugate gradient

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