We present a novel linear solver that works well for large systems obtained from discretizing PDEs. It is robust and, for the examples we studied, the computational eeort scales linearly with the number of equations. The algorithm is based on a wavelength decomposition that combines conjugate gradient, multi-scaling and iterative splitting methods into a single approach. On the surface, the alg...

A hybrid global optimization method, the coevolutionary global optimization algorithm, is proposed which utilizes the self-organized critical state as the mean of diversification of search and the traditional conjugate gradient local minimization method as the mean of intensification of search. The former has been recently used by Boettcher and Percus (Artificial Intelligence 119 (2000) 275) to...

A two-dimensional shape identification problem, i.e. inverse geometry problem, of estimating simultaneously two interfacial configurations in a multiple (three) region domain is solved in this study by using the Conjugate Gradient Method (CGM) and Boundary Element Method (BEM)-based inverse algorithm. Two over-utilized conditions should be applied in determining two gradient equations, this dif...

Occupying more than 70% of the concrete’s volume, aggregates play a vital role as the raw feed for construction materials; particularly in the production of concrete and concrete products. Often, the characteristics such as shape, size and surface texture of aggregates significantly affect the quality of the construction materials produced. This article discusses a novel method for automatic cl...

Conjugate gradient methods are efficient for smooth optimization problems, while there are rare conjugate gradient based methods for solving a possibly nondifferentiable convex minimization problem. In this paper by making full use of inherent properties of Moreau-Yosida regularization and descent property of modified conjugate gradient method we propose a modified Fletcher-Reeves-type method f...

Much is already known about when a conjugate gradient method can be implemented with short recursions for the direction vectors. The work done in 1984 by Faber and Manteuuel 3] gave necessary and suucient conditions on the iteration matrix A, in order for a conjugate gradient method to be implemented with a single recursion of a certain form. However, this form does not take into account all po...

The genetic algorithm (GA) have good global search characteristics and local optimizing algorithm (LOA) have good local search characteristics. In the present work, best characteristics of GA and LOA are combined to develop a hybrid genetic algorithm (HGA). A bank of GA s are used to get a good starting solution for a conjugate gradient algorithm. The number of GA banks is selected using an aut...

In a recent paper [2] we introduced the greedy Gradient Pursuit framework. This is a family of algorithms designed to find sparse solutions to underdetermined inverse problems. One particularly powerful member of this family is the (approximate) conjugate gradient pursuit algorithm, which was shown to be applicable to very large data sets and which was found to perform nearly as well as the tra...

In non-cooperative frequency hopping communication system, the network station sorting of received hybrid signals plays an important role and becomes active research area in recent years. order to solve problem that currently widely used clustering algorithm cannot achieve satisfactory accuracy. this paper, we propose a signal method for stations by applying neural classify description words si...

Conjugate gradient methods are important for large-scale unconstrained optimization. This paper proposes an acceleration of these methods using a modification of steplength. The idea is to modify in a multiplicative manner the steplength k α , computed by Wolfe line search conditions, by means of a positive parameter k η , in such a way to improve the behavior of the classical conjugate gradien...