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References in periodicals archive ?
The HPCG (High Performance Conjugate Gradient) benchmark measures how fast a computer can solve symmetric sparse linear system equations using the conjugate gradient method preconditioned with a multi-grid symmetric Gauss-Seidel smoother.
The authors cover iterative methods for the solution of systems of linear equations, conjugate gradient methods, and solutions of systems of linear equations, and many other related subjects.
In both the network, used the retropropagation algorithm of conjugate gradient scaling Figure As a function of learning, which through the updating of the W and b values of each of the neurons in the network, accelerating in this way the convergence of this.
For the first group of data is necessary to an algorithm or function of learning, which in this case is used retropropagacion algorithm of conjugate gradient scaling, which is responsible initially to train the multilayer networks with the algorithm of retropropagacion updating the values of W and b by means of the conjugate gradient scaling, which accelerates the convergence of the network, avoiding their dependence on external parameters by means of a rate of learning based on the equation 1, thus minimizing the falls of the network.
Conjugate gradient method was applied to reduce the complexity of data detection and precoding in the massive MIMO system with realistic antenna configurations [8].
Accordingly, the framework of adaptive proximal conjugate gradient (APCG) is proposed.
This algorithm needs more caculation in each iteration and more storage than the conjugate gradient methods, though it converges in less iteration.
A good choice in last case are iterative methods like Newton, Conjugate Gradient, Broyden, etc.
The classical LSQR algorithm is analytically equivalent to the conjugate gradient method applied to the associated normal equation.
Conjugate gradient (CG) methods have attracted special attention for solving large-scale unconstrained optimization problems in the form of [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], with the smooth nonlinear function f : [R.
1996) that they often converge faster than Conjugate Gradient Algorithms.
Bagnell, "Kernel Conjugate Gradient for Fast Kernel Machines", in Proc.