in this paper, he's highly prolic variational iteration method is applied ef-fectively for showing the existence, uniqueness and solving a class of singularsecond order two point boundary value problems. the process of nding solu-tion involves generation of a sequence of appropriate and approximate iterativesolution function equally likely to converge to the exact solution of the givenpr...

We propose a novel approach to the decomposition of large probabilistic models. The goal of our method is to avoid the evaluation of the subnetworks obtained by decomposition for all values of the state description vector, as would be necessary with a standard aggregation and decomposition approach. Instead, we propose a fixed-point iteration that requires the evaluation of the subnetwork for o...

in this paper, we represent an inexact inverse subspace iteration method for com- puting a few eigenpairs of the generalized eigenvalue problem ax = bx[q. ye and p. zhang, inexact inverse subspace iteration for generalized eigenvalue problems, linear algebra and its application, 434 (2011) 1697-1715 ]. in particular, the linear convergence property of the inverse subspace iteration is preserved.

The purpose of this paper is to introduce a new class of quasi-contractive operators and to show that the most used fixed point iterative methods, that is, the Picard and Mann iterations, are convergent to the unique fixed point. The comparison of these methods with respect to their convergence rate is obtained.

In this paper, we examine the stability of KirkIshikawa and Kirk-Mann iteration processes for nonexpansive and quasi-nonexpansive operators in uniformly convex Banach space. To the best of our knowledge, apart from the results of Olatinwo [19], stability of fixed point iteration processes has not been investigated in uniformly convex Banach space. Our results generalize, extend and improve some...

This paper presents an image resolution enhancement algorithm using spatially invariant point spread function. Point spread function is used to constrain the solution space. This parameter is computed at each iteration step using partially restored image at each iteration, and High pass filter is used to impose the degree of edge smoothness on the solution. The resulting iterative algorithm exh...

An algorithm for linear programming (LP) and convex quadratic programming (CQP) is proposed, based on an interior point iteration introduced more than ten years ago by J. Herskovits for the solution of nonlinear programming problems. Herskovits' iteration can be simpliied signiicantly in the LP/CQP case, and quadratic convergence from any initial point can be achieved. Interestingly the linear ...

Point orthogonal projection onto an algebraic surface is a very important topic in computer-aided geometric design and other fields. However, implementing this method currently extremely challenging difficult because it to achieve desired degree of robustness. Therefore, we construct polynomial, which the ninth formula, after inner product eighth formula itself. Additionally, use Newton iterati...

Anderson(m) is a method for acceleration of fixed point iteration which stores m + 1 prior evaluations of the fixed point map and computes the new iteration as a linear combination of those evaluations. Anderson(0) is fixed point iteration. In this paper we show that Anderson(m) is locally r-linearly convergent if the fixed point map is a contraction and the coefficients in the linear combinati...

In this paper, we draw upon connections between bilinear programming and the process of computing (post) fixed points in abstract interpretation. It is well-known that the data flow constraints for numerical domains are expressed in terms of bilinear constraints. Algorithms such as policy and strategy iteration have been proposed for the special case of bilinear constraints that arise from temp...