It is well known that conjugate gradient methods are useful for solving large-scale unconstrained nonlinear optimization problems. In this paper, we consider combining the best features of two methods. particular, give a new method, based on hybridization DY (Dai-Yuan), and HZ (Hager-Zhang) The hybrid parameters chosen such proposed method satisfies conjugacy sufficient descent conditions. show...

This paper presents a new algorithm for the parallel in time (PiT) numerical simulation of dependent partial/ordinary differential equations. We propose reliable alternative to well know parareal algorithm, by formulating problem algebraically and solve it using an adapted Bi-Conjugate gradient stabilized method. The proposed Parallel Stable (PiTSBiCG short) has great potential stabilizing reso...

The performance of CGLS, a basic iterative method whose main idea is to organize the computation of conjugate gradient method applied to normal equations for solving least squares problems. On modern architecture is always limited because of the global communication required for inner products. Inner products often therefore present a bottleneck, and it is desirable to reduce or even eliminate ...

Classification is one of the most important task in application areas of artificial neural networks (ANN).Training neural networks is a complex task in the supervised learning field of research. The main difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training function for the classification task. We compared the performances of three types of tr...