Javascript must be enabled to continue!
A faster fixed point iterative algorithm and its application to optimization problems
View through CrossRef
<p>In this paper, we studied the AA-iterative algorithm for finding fixed points of the class of nonlinear generalized $ (\alpha, \beta) $-nonexpansive mappings. First, we proved weak convergence and then proved several strong convergence results of the scheme in a ground setting of uniformly convex Banach spaces. We gave a few numerical examples of generalized $ (\alpha, \beta) $-nonexpansive mappings to illustrate the major outcomes. One example was constructed over a subset of a real line while the other one was on the two dimensional space with a taxicab norm. We considered both these examples in our numerical computations to show that our iterative algorithm was more effective in the rate of convergence corresponding to other fixed point algorithms of the literature. Some 2D and 3D graphs were obtained that supported graphically our results and claims. As applications of our major results, we solved a class of fractional differential equations, 2D Voltera differential equation, and a convex minimization problem. Our findings improved and extended the corresponding results of the current literature.</p>
American Institute of Mathematical Sciences (AIMS)
Title: A faster fixed point iterative algorithm and its application to optimization problems
Description:
<p>In this paper, we studied the AA-iterative algorithm for finding fixed points of the class of nonlinear generalized $ (\alpha, \beta) $-nonexpansive mappings.
First, we proved weak convergence and then proved several strong convergence results of the scheme in a ground setting of uniformly convex Banach spaces.
We gave a few numerical examples of generalized $ (\alpha, \beta) $-nonexpansive mappings to illustrate the major outcomes.
One example was constructed over a subset of a real line while the other one was on the two dimensional space with a taxicab norm.
We considered both these examples in our numerical computations to show that our iterative algorithm was more effective in the rate of convergence corresponding to other fixed point algorithms of the literature.
Some 2D and 3D graphs were obtained that supported graphically our results and claims.
As applications of our major results, we solved a class of fractional differential equations, 2D Voltera differential equation, and a convex minimization problem.
Our findings improved and extended the corresponding results of the current literature.
</p>.
Related Results
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm ...
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm ...
A NEW MULTI-OBJECTIVE ARITHMETIC OPTIMIZATION ALGORITHM
A NEW MULTI-OBJECTIVE ARITHMETIC OPTIMIZATION ALGORITHM
Today, as engineering problems become more complex in terms of the effective variables in these problems and the range of their changes and their multidimensionality (in terms of n...
An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems
An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems
AbstractAiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value...
A new type bionic global optimization: Construction and application of modified fruit fly optimization algorithm
A new type bionic global optimization: Construction and application of modified fruit fly optimization algorithm
Fruit fly optimization algorithm, which is put forward through research on the act of foraging and observing groups of fruit flies, has some merits such as simplified operation, st...
On iterative methods to solve nonlinear equations
On iterative methods to solve nonlinear equations
Many of the problems in experimental sciences and other disciplines can be expressed in the form of nonlinear equations. The solution of these equations is rarely obtained in close...
Newton-SOR Iterative Method with Lagrangian Function for Large-Scale Nonlinear Constrained Optimization Problems
Newton-SOR Iterative Method with Lagrangian Function for Large-Scale Nonlinear Constrained Optimization Problems
With the rapid development of computer technology and the wide application of nonlinear constrained optimization problems, many researchers are committed to solve large-scale const...
Iterative convergent computation may not be a useful inductive bias for residual neural networks
Iterative convergent computation may not be a useful inductive bias for residual neural networks
Abstract
Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative recurrent computations. Iterative computations are usef...

