Although quasi-Newton algorithms generally converge in fewer iterations than conjugate gradient algorithms, they have the disadvantage of requiring substantially more storage. An algorithm will be described which uses an intermediate (and variable) amount of storage and which demonstrates convergence which is also intermediate, that is, generally better than that observed for conjugate gradient...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...

There have been some conjugate gradient methods with strong convergence but numerical instability and conversely‎. Improving these is an interesting idea to produce new both and‎‏ ‎numerical stability‎. ‎In this paper‎, ‎a hybrid method introduced based on the Fletcher ‎formula (CD) Liu Storey formula (LS) good results‎. ‎New directions satisfy sufficient descent property‎, ‎independent of line...

 Abstract: This study deals with modeling of heat flux at the external surface of combustion chamber wall in an internal combustion (IC) engine as a function of crank angle. This investigation results in an inverse heat conduction problem in the cylinder wall. Alifanov regularization method is used for solving this inverse problem. This problem study as an optimization problem in which a square...

s at ICCAM 2008 CR Variants of Hybrid Bi-CG Methods for Solving Linear Systems with Nonsymmetric Matrices Kuniyoshi Abe Gifu Shotoku University 1-38, Nakauzura, Gifu, 500-8288 Japan [email protected] Joint work with: S. Fujino By Krylov subspace methods, we are solving a large sparse linear system Ax = b, where A stand for an n-by-n matrix, and x and b are n-vectors, respectively. The Bi-C...

Abstract: The conjugate gradient method is an algorithm for the numerical solution of systems of linear equations whose matrices are positive-definite and symmetric. It is an iterative method that can even be applied to solve a sparse system of equations. In this paper, we applied the conjugate gradient method of higher order to wave propagation control problems. The algorithm of this method wa...

We combine linear algebra techniques with finite element techniques to obtain a reliable stopping cri-terion for Krylov method based algorithms. The Conjugate Gradient method has for a long time beensuccessfully used in the solution of the symmetric and positive definite systems obtained from thefinite-element approximation of self-adjoint elliptic partial differential equations...

A new method used to prove the global convergence of the nonlinear conjugate gradient methods, the spectral method, is presented in this paper, and it is applied to a new conjugate gradient algorithm with sufficiently descent property. By analyzing the descent property, several concrete forms of this algorithm are suggested. Under standard Wolfe line searches, the global convergence of the new ...

NEURBT, a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. NEURBT is based on Møller’s scaled conjugate gradient algorithm which is a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the imple­ mentation are discussed such as the efficient computation ...