In this paper, motivated by the above results, we consider the problem of finding a common solution to the zero point problems involving two monotone operators and fixed point problems involving asymptotically strictly pseudocontractive mappings based on a one-step iterative method.
It is observed that in many respects a three-step iterative process is better than a two- and a one-step iterative process in giving numerical results under certain conditions (see [31 33]).
Recently Abbas et al. [8] introduced an one-step iterative process to approximate a common fixed point of two multi-valued nonexpansive mappings in uniformly convex Banach spaces.
A one-step iterative scheme is offered to determine plate spacings for simulation of weakly sheared flows, constrained by zero vertical pressure gradient in the downstream flow (representative of boundary layer conditions).
Recently, Abbas et al. [18], introduced a one-step iterative process as follows, x 1 ∈ K : x n + 1 = a n x n + b n y n + c n z n, n ≥ 1. (1.2).
In the third and final part of the article, alternative highly branched polyamide architectures will be discussed, which are not obtained in a one step reaction, but which are produced via iterative protocols based on the ring-opening polymerization of α-amino acid N-carboxyanhydrides.
This paper concerns a fast, one-step iterative technique of imaging extended perfectly conducting cracks with Dirichlet boundary condition.
We introduce a new one-step iterative process and use it to approximate the common fixed points of two asymptotically nonexpansive mappings through some weak and strong convergence theorems.
One-step iterative prediction based on CPRBF network has good prediction performance.
Figure 5 shows the one-step iterative predicted result on the last 100 points of Lorenz's time series by CPRBF network.
Figures 5 and 6 prove that: One-step iterative prediction based on CPRBF network has good prediction performance Comparing with RBF network, CPRBF network has better performance on iterative prediction, in terms of convergence and stability.
