Fluid simulation has been an active research field in computer graphics for the last 30 years. Stam’s stable fluids method, among others, is used for solving equations that govern fluids. This method solves a sparse linear system during the diffusion and move steps, using either relaxation methods (Jacobi, Gauss-Seidel, etc), Conjugate Gradient (and its variants), or others (not subject of stud...

The conjugate gradient methods are outstanding by choosing a suitable for coefficient conjugate. In this paper, modified version of the algorithm suggested Hideaki and Yasushi [4] is proposed in order to show that new method globally convergent, under standard assumptions. To exemplify efficiency method, its performance examined impulse noise removal images.

Model based iterative reconstruction (MBIR) algorithms have been used in clinical studies to allow significant dose reduction in CT scans while maintaining the diagnostic image quality. Simultaneous-update algorithms, which can take advantage of massively parallel computer architectures, are promising to significantly improve the speed of MBIR. To achieve this goal, we also need to improve the ...

In this paper we derive two second-order algorithms, based on conjugate gradient, for on-line training of recurrent neural networks. These azgorithms use two different techniques to extract second-order information on the Hessian matrix without calculating or storing it and without making numericaz approximations. Several simulation results for non-linear system identzjication tests by locally’...

1. Introduction. We consider the resolution of large-scale nonlinear problems arising from the finite-element discretization of geometrically non-linear structural analysis problems. We use a classical Newton Raphson algorithm to handle the non-linearity which leads to the resolution of a sequence of linear systems with non-invariant matrices and right hand sides. The linear systems are solved ...

The trust region step problem, by solving a sphere constrained quadratic programming, plays a critical role in the trust region Newton method. In this paper, we propose an efficient Multi-Stage Conjugate Gradient (MSCG) algorithm to compute the trust region step in a multi-stage manner. Specifically, when the iterative solution is in the interior of the sphere, we perform the conjugate gradient...

Variational inference is computationally challenging in models that contain both conjugate and non-conjugate terms. Methods specifically designed for conjugate models, even though computationally efficient, find it difficult to deal with non-conjugate terms. On the other hand, stochastic-gradient methods can handle the nonconjugate terms but they usually ignore the conjugate structure of the mo...

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate ...

This paper introduces a new algorithm which solves nonsymmetric sparse linear systems of equations, by the conjugate gradient squared (CGS) method combined with some preconditionings. The algorithm is dramatically superior to biconjugate gradient (BCG) algorithm, although it is a variant of the BCG algorithm. The algorithm is also amenable for implementing on parallel computers. We report on so...

In this paper we describe a parallel implementation of a preconditioned conjugate gradient algorithm on the Meiko CS The parallel implemen tation is based on the use of a global reduction operator this allows to obtain a simple and e cient code whose performance is easily and pre cisly predictable We describe and validate a performance model and we show and comment experimental results