iterative methods

This study presents the process of using extrapolation methods to solve the nonlinear Volterra–Fredholm integral equations of the second kind. To do this, by approximating the integral terms contained in equations by a quadrature rule, the nonlinear Volterra–Fredholm integral equations of the second kind are reduced to a set of nonlinear algebraic equations. Then, the solution of the corresponding system of nonlinear equations is approxi-mated by an iterative method, and finally, these iterations are accelerated by an extrapolation method. We demonstrate the effectiveness of the pro-posed approach by solving some numerical examples.

Nonlinear equations are important in engineering because they may simulate complicated real-world phenomena such as fluid dynamics, material stress, and electrical circuits, where linear assumptions fail. They allow engineers to more correctly estimate how a system will respond in certain conditions. The main aim of this effort is to develop an efficient higher-order simultaneous computer approach capable of computing all solutions concurrently. The convergence theorem analysis indicates that the scheme has a local convergence order of 10. Using a few engineering applications, we show that the order strategy surpasses the current approach in terms of residual error, stability, and consistency.

In this paper, we present a generalized adaptive Monte Carlo algorithm using the Diagonal and Off-Diagonal Splitting (DOS) iteration method to solve a system of linear algebraic equations (SLAE). The DOS method is a generalized iterative method with some known iterative methods such as Jacobi, Gauss-Seidel, and Successive Overrelaxation methods as its special cases. Monte Carlo algorithms usually use the Jacobi method to solve SLAE. In this paper, the DOS method is used instead of the Jacobi method which transforms the Monte Carlo algorithm into the generalized Monte Carlo algorithm. we establish theoretical results to justify the convergence of the algorithm. Finally, numerical experiments are discussed to illustrate the accuracy and efficiency of the theoretical results. Furthermore, the generalized algorithm is implemented to price options using the finite difference method. We compare the generalized algorithm with standard numerical and stochastic algorithms to show its efficiency.

In this paper, a novel optimal class of eighth-order convergence methods for finding simple roots of nonlinear equations is derived based on the Predictor-Corrector of Halley method. By combining weight functions and derivative approximations,  an optimal class of iterative methods with eighth-order convergence is constructed. In terms of computational cost, the proposed methods require three function evaluations, and the first derivative is evaluated once per iteration. Moreover, the methods have efficiency indices equal to 1.6817. The proposed methods have been tested with several numerical examples, as well as a comparison with existing methods for analyzing efficacy is presented.

Treating images as functions and using variational calculus,mathematical imaging offers to design novel and continuous methods, outperforming traditional methods based on matrices, for modelling real life tasks in image processing.Image segmentation is one of such fundamental tasks  as  many application areas demand a reliable segmentation method. Developing reliable selective segmentation algorithms isparticularly important in relation to training data preparation in modern machine learning as accurately isolating a specific object in an image with minimal user input is a valuable tool. When an image's intensity is consisted of mainly piecewise constants, convex models are available.Different from previous works, this paperproposes two convex models that are capable of segmenting local features defined by geometric constraints for images having intensity inhomogeneity.Our new, local, selective and convex variants are extended from the non-convex Mumford-Shah model intended for global segmentation.They have fundamentally improved on previous selective models that assume intensity  of piecewise constants. Comparisons with related models are conducted to illustrate the advantages of  our new models.

‎In the present paper‎, ‎a novel two-step iterative method‎, ‎based on real interval arithmetic‎, ‎is produced‎. ‎By using this method‎, ‎we obtain enclosures of roots of systems of nonlinear equations‎. ‎Discussion on the convergence analysis for the produced method is presented‎. ‎The efficiency‎, ‎accuracy‎, ‎and validity of this method are demonstrated by its application to four implemented examples with INTLAB and by comparing our results with the results obtained by other methods available in the literature‎.

In this paper, we introduce a new iteration method called inertial residual algorithm for finding a common fixed point of finite family of strictly pseudocontractive mappings in a real uniformly smooth Banach spaces. We also establish weak and strong convergence theorems for the scheme. Finally, we give numerical experiment to explain the proposed method. Our results generalize and improve many recent results in the literature.

In the realm of solving large linear systems of equations, multisplitting methods emerge as a prominent class of iterative techniques. This paper introduces two-step diagonal and off-diagonal multisplitting methods and evaluates their effectiveness in comparison to symmetric successive overrelaxation multisplitting and quasi-Chebyshev accelerated multisplitting techniques for solving linear systems of equations. Additionally, this study investigates convergence theorems when the system matrix is an $H$-matrix and demonstrates the effectiveness of the proposed methods by presenting numerical results.

In this paper we consider the semi-local convergence analysis of the Homeier method for solving nonlinear equation in Banach space. As far as we know no semi-local convergence has been given for the Homeier under Lipschitz conditions. Our goal is to extend the applicability of the Homeier method in the semi-local convergence under these conditions. We use majorizing sequences and conditions only on the first derivative which appear on the method for proving our results. Numerical experiments are provided in this study.

In this paper we consider the semi-local convergence analysis of the Homeier method for solving nonlinear equation in Banach space. As far as we know no semi-local convergence has been given for the Homeier under Lipschitz conditions. Our goal is to extend the applicability of the Homeier method in the semi-local convergence under these conditions. We use majorizing sequences and conditions only on the first derivative which appear on the method for proving our results. Numerical experiments are provided in this study.

System of linear equations plays an important role in science and engineering. One of the applications of this system occurs in the discretization of the partial differential equations. This paper aims to investigate an experimental comparison between two kinds of iterative models for solving the elliptic partial differential equations. Different tools of solution such as stationary and non-stationary iterative methods with preconditioning models have been studied. Two types of discretization schemes (centered and hybrid) have been also considered for the comparison of the solution.

A fast and efficient Newton-Shultz-type iterative method  is presented to compute the inverse of an invertible tensor. Analysis of the convergence error shows that the proposed method has the sixth order convergence. It is shown that the proposed algorithm can be used for finding the Moore-Penrose inverse of tensors. Computational complexities of the algorithm is presented to support the theoretical aspects of the paper. Using the  new method, we obtain a new preconditioner to solve the multilinear  system $mathcal{A}ast_Nmathcal{X}=mathcal{B}$. The effectiveness and accuracy  of this method are re-verified by several numerical examples. Finally, some conclusions are given.

In this paper, we propose to solve nonlinear functional equations given in an infinite-dimensional Banach space by linearizing first and then discretizing the linear iterative equations. We establish new sufficient conditions which provide new criteria for dealing with convergence results. These conditions define a class of discretization schemes. Some numerical examples confirm the theoretical results by treating an integro-differential equation.

The objective of this research is to propose a new multi-step method in tackling a system of nonlinear equations. The constructed iterative scheme achieves a higher rate of convergence whereas only one LU decomposition per cycle is required to proceed. This makes the efficiency index to be high as well in contrast to the existing solvers. The usefulness of the presented approach for tackling differential equations of nonlinear type with partial derivatives is also given.

In this paper, a mixed reproducing kernel function (RKF) is introduced. The kernel function consists of piecewise polynomial kernels and polynomial kernels. On the basis of the mixed RKF, a new numerical technique is put forward for solving non-linear boundary value problems (BVPs) with nonlocal conditions. Compared with the classical RKF-based methods, our method is simpler since it is unnecessary to convert the original equation to an equivalent equation with homogeneous boundary conditions. Also, it is not required to satisfy the homogeneous boundary conditions for the used RKF. Finally, the higher accuracy of the method is shown via several numerical tests.

In this paper, two adaptive methods with memory are improved based on Cordero- Torregrosa method. The technique of adaptive method increases the efficiency index as high as possible. The new derivative free methods have possessed the convergence order 7.46315 and 7.99315, and they only use the information from the last two iterations. Finally, we provide convergence analysis and numerical examples to illustrate the proposed methods.

We consider the eigenvalues of the fractional-order Sturm--Liouville equation of the form begin{equation*} -{}^{c}D_{0^+}^{alpha}circ D_{0^+}^{alpha} y(t)+q(t)y(t)=lambda y(t),quad 0<alphaleq 1,quad tin[0,1], end{equation*} with Dirichlet boundary conditions $$I_{0^+}^{1-alpha}y(t)vert_{t=0}=0quadmbox{and}quad I_{0^+}^{1-alpha}y(t)vert_{t=1}=0,$$ where $qin L^2(0,1)$ is a real-valued potential function. The method is used based on a Picard's iterative procedure. We show that the eigenvalues are obtained from the zeros of the Mittag-Leffler function and its derivatives.

In this paper, we introduce two iterative schemes by a modified Krasnoselskii-Mann algorithm for finding a common element of the set of solutions of equilibrium problems and the set of fixed points of multivalued nonexpansive mappings in Hilbert space. We prove that the sequence generated by the proposed method converges strongly to a common element of the set of solutions of equilibruim problems and the set of fixed points of multivalued nonexpansive mappings which is also the minimum-norm element of the above two sets. Finally, some applications of our results to optimization problems with constraint and the split feasibility problem are given. No compactness assumption is made. The methods in the paper are novel and different from those in early and recent literature.

In this paper, we  introduce and study a new iterative method which is based on viscosity general algorithm and forward-backward splitting method  for finding a common element of the set of common fixed points of multivalued demicontractive and quasi-nonexpansive mappings and the set of solutions of  variational inclusion with set-valued maximal monotone mapping and inverse strongly monotone mappings in  real Hilbert spaces. We prove that the sequence $x_n$ which is generated by the proposed iterative algorithm converges strongly to a common element of two sets above. Finally, our theorems are applied to approximate a common solution of fixed point problems with set-valued operators and the composite convex minimization problem. Our theorems are significant improvements on several important recent results.

A new iteration scheme for computing the sign of a matrix which has no pure imaginary eigenvalues is presented. Then, by applying a well-known identity in matrix functions theory, an algorithm for computing the geometric mean of two Hermitian positive definite matrices is constructed. Moreover, another efficient algorithm for this purpose is derived free from the computation of principal matrix square root. Finally, several experiments are collected.