Symmetries play a vital role in the study of physical systems. For example, microworld and quantum physics problems are modeled on principles symmetry. These then formulated as equations defined suitable abstract spaces. Most these studies reduce to solving nonlinear spaces iteratively. In particular, convergence sixth-order Cordero type iterative method for was studied using Taylor expansion a...

In this paper we deal with iterative numerical methods to solve linear systems arising in continuous-time Markov chain (CTMC) models. We develop an algorithm to dynamically tune the relaxation parameter of the successive over-relaxation method. We give a sufficient condition for the Gauss-Seidel method to converge when computing the steady-state probability vector of a finite irreducible CTMC, ...

We consider the task of computing an approximate minimizer of the sum of a smooth and non-smooth convex functional, respectively, in Banach space. Motivated by the classical forward-backward splitting method for the subgradients in Hilbert space, we propose a generalization which involves the iterative solution of simpler subproblems. Descent and convergence properties of this new algorithm are...

In this paper, the method of upper and lower solutions and monotone iterative technique are employed to the study of nonlinear three-point boundary value problems for a class of first order impulsive integro-differential equations of mixed type. Sufficient conditions for the existence of extreme solutions are obtained. Mathematics Subject Classification: 34B37

The explicit implicit domain decomposition methods are a non iterative types of methods for non overlapping domain decomposition. In comparison with the classical Schwarz algorithm for parabolic problem the former methods are computationally and communicationally more efficient for each time step but due to the use of the explicit step for the interface prediction the methods suffer from the ac...

Results on stability of both local and global metric regularity under set-valued perturbations are presented. As an application, we study (super)linear convergence of a Newtontype iterative process for solving generalized equations. We investigate several iterative schemes such as the inexact Newton’s method, the nonsmooth Newton’s method for semismooth functions, the inexact proximal point alg...

Some matrix-splitting iterative methods for solving systems of linear equations contain parameters that need to be specified in advance, and the choice these directly affects efficiency corresponding methods. This paper uses a Bayesian inference-based Gaussian process regression (GPR) method predict relatively optimal some HSS-type iteration provide extensive numerical experiments compare predi...