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In this study, we develop first- and second-order time-accurate energy stable methods for the phase-field crystal equation and the Swift–Hohenberg equation with quadratic-cubic non-linearity. Based on a new Lagrange multiplier approach, the first-order time-accurate schemes dissipate the original energy in a time-discretized version, which are different from the modified energy laws obtained by the invariant energy quadratization (IEQ) and the scalar auxiliary variable (SAV) methods. Moreover, the proposed schemes do not require the bounded-from-below restriction which is necessary in the IEQ or SAV approach. We rigorously prove the energy dissipations of first- and second-order accurate methods with respect to the original energy and pseudo-energy in the time-discretized versions, respectively. An efficient algorithm is used to decouple the resulting weakly coupled systems. In one time iteration, only two linear systems with constant coefficients and one non-linear algebraic equation need to be solved. Finally, the accuracy, stability and practicability of the proposed methods are validated by intensive numerical tests.

The main aim of this research study is to present a new and efficient numerical method based on the second kind Chebyshev wavelets for solving the general form of distributed order fractional differential equations in the time domain with the Caputo fractional derivatives. For the first time, based on the second kind Chebyshev wavelets, an exact formula for the operational vector of the Riemann-Liouville fractional integral operator is derived by using the unit step function and Laplace transform method. Applying this operational vector via the collocation method in our approach provides an approximate solution by converting the problem under consideration into a system of algebraic equations which can be solved by the Newton method. Discussion on the error bound and convergence analysis for the proposed method is presented. Finally, five test problems are considered to confirm the reliability and effectiveness of the proposed method.

We propose a spectral collocation method, based on the generalized Jacobi wavelets along with the Gauss-Jacobi quadrature formula, for solving a class of third-kind Volterra integral equations. To do this, the interval of integration is first transformed into the interval [-1,1], by considering a suitable change of variable. Then, by introducing special Jacobi parameters, the integral part is approximated using the Gauss-Jacobi quadrature rule. An approximation of the unknown function is considered in terms of Jacobi wavelets functions with unknown coefficients, which must be determined. By substituting this approximation into the equation, and collocating the resulting equation at a set of collocation points, a system of linear algebraic equations is obtained. Then, we suggest a method to determine the number of basis functions necessary to attain a certain precision. Finally, some examples are included to illustrate the applicability, efficiency, and accuracy of the new scheme., Comment: This is a preprint of a paper whose final and definite form is with 'Numer. Algorithms', Print ISSN 1017-1398, Electronic ISSN 1572-9265, available at [https://www.springer.com/journal/11075]. Submitted 12-Oct-2019; Revised 17-Dec-2019; Accepted 11-Feb-2020

This paper presents a computational method based on least squares method and block pulse functions for solving Ito-Volterra integral equation. The Ito-Volterra integral equation is converted to a linear system of algebraic equations by the least squares method on the block pulse functions. The error analysis of the proposed method is investigated by providing theorems. Numerical examples show the accuracy and reliability of the presented method. The numerical results confirm that the presented method is more accurate than the block pulse functions operational matrix method.

In this paper, we propose an efficient numerical method based on two-dimensional hybrid of block-pulse functions and Legendre polynomials for numerically solving an inverse reaction diffusion equation. The main idea of the present method is based upon some of the important benefits of the hybrid functions such as high accuracy, wide applicability, and adjustability of the orders of the block-pulse functions and Legendre polynomials to achieve highly accurate numerical solutions. By using the spectral method, inverse reaction diffusion equation with initial and boundary conditions would reduce to a system of nonlinear algebraic equations. Due to the ill-posed system of nonlinear algebraic equations, a regularization scheme is employed to obtain a numerical stable solution. Finally, some numerical examples are presented to show the accuracy and effectiveness of this method.

This paper focuses on studying a general form of pantograph type Volterra integro-differential equations (PVIDEs). We apply a new collocation spectral approach, based on shifted Chebyshev polynomials, for converting such PVIDEs into systems of algebraic equations. In addition, we apply the new spectral approach for systems of pantograph type Volterra integro-differential equations (SPVIDEs). We investigate the error analysis of the proposed numerical approach. Also, we present some comparisons with other spectral approaches for clarifying the superiority of the new spectral approach.

This paper presents a new computational technique for solving fractional pantograph differential equations. The fractional derivative is described in the Caputo sense. The main idea is to use Muntz-Legendre wavelet and its operational matrix of fractional-order integration. First, the Muntz-Legendre wavelet is presented. Then a family of piecewise functions is proposed, based on which the fractional order integration of the Muntz-Legendre wavelets are easy to calculate. The proposed approach is used this operational matrix with the collocation points to reduce the under study problem to a system of algebraic equations. An estimation of the error is given in the sense of Sobolev norms. The efficiency and accuracy of the proposed method are illustrated by several numerical examples.

The purpose of this paper is to propose a computational method for the approximate solution of linear and nonlinear two-point boundary value problems. In order to approximate the solution, the expansions in terms of the Bernstein polynomial basis have been used. The properties of the Bernstein polynomial basis and the corresponding operational matrices of integration and product are utilized to reduce the given boundary value problem to a system of algebraic equations for the unknown expansion coefficients of the solution. On this approach, the problem can be solved as a system of algebraic equations. By considering a special case of the problem, an error analysis is given for the approximate solution obtained by the present method. At last, five examples are examined in order to illustrate the efficiency of the proposed method.

In this research, a Bernoulli wavelet operational matrix of fractional integration is presented. Bernoulli wavelets and their properties are employed for deriving a general procedure for forming this matrix. The application of the proposed operational matrix for solving the fractional delay differential equations is explained. Also, upper bound for the error of operational matrix of the fractional integration is given. This operational matrix is utilized to transform the problem to a set of algebraic equations with unknown Bernoulli wavelet coefficients. Several numerical examples are solved to demonstrate the validity and applicability of the presented technique.

The ź-smoothing property of a one-dimensional Volterra integral operator and some projectors (Liang and Brumer, SIAM J. Numer. Anal. 51, 2238---2259 (2013)) are extended for two-dimensional integral-algebraic equations (TIAEs). Using these concepts, we decompose the given general TIAEs into mixed systems of two-dimensional Volterra integral equations (TVIEs) consisting of second- and first-kind TVIEs. Numerical technique based on the Chebyshev polynomial collocation methods is presented for the solution of the mixed TVIE system. Global convergence results are established and the performance of the numerical scheme is illustrated by means of some test problems.

In this report, we consider two kind of general fractional variational problem depending on indefinite integrals include unconstrained problem and isoperimetric problem. These problems can have multiple dependent variables, multiorder fractional derivatives, multiorder integral derivatives and boundary conditions. For both problems, we obtain the Euler-Lagrange type necessary conditions which must be satisfied for the given functional to be extremum. Also, we apply the Rayleigh-Ritz method for solving the unconstrained general fractional variational problem depending on indefinite integrals. By this method, the given problem is reduced to the problem for solving a system of algebraic equations using shifted Legendre polynomials basis functions. An approximate solution for this problem is obtained by solving the system. We discuss the analytic convergence of this method and finally by some examples will be showing the accurately and applicability for this technique.

We consider nonlinear algebraic systems of the form F ( x ) = Ax + p , x ? ? + n $F(x)= Ax+p, x\in \mathbb {R}^{n}_{+}$ , where A is a positive matrix and p a non-negative vector. They are involved quite naturally in many applications. For such systems we prove that a positive solution x ? exists and is unique. Moreover, we prove that x ? is an attraction point for three Newton-type iterations. A numerical experiment, concerning the computing times for such iterations, is presented. Previously known results, involving existence and uniqueness of solution for particular functions F and matrices A, are extended and generalized.

This paper investigates a numerical method for solving two-dimensional nonlinear Fredholm integral equations of the second kind on non-rectangular domains. The scheme utilizes the shape functions of the moving least squares (MLS) approximation constructed on scattered points as a basis in the discrete collocation method. The MLS methodology is an effective technique for approximating unknown functions which involves a locally weighted least square polynomial fitting. The proposed method is meshless, since it does not need any background mesh or cell structures and so it is independent of the geometry of the domain. The scheme reduces the solution of two-dimensional nonlinear integral equations to the solution of nonlinear systems of algebraic equations. The error analysis of the proposed method is provided. The efficiency and accuracy of the new technique are illustrated by several numerical examples.

We consider the solution of large-scale Lyapunov and Stein equations. For Stein equations, the well-known Smith method will be adapted, with $A_k = A^{2^k}$ not explicitly computed but in the recursive form $A_k = A_{k-1}^{2}$ , and the fast growing but diminishing components in the approximate solutions truncated. Lyapunov equations will be first treated with the Cayley transform before the Smith method is applied. For algebraic equations with numerically low-ranked solutions of dimension n, the resulting algorithms are of an efficient O(n) computational complexity and memory requirement per iteration and converge essentially quadratically. An application in the estimation of a lower bound of the condition number for continuous-time algebraic Riccati equations is presented, as well as some numerical results.

Optimal homotopy analysis method is a powerful tool for nonlinear differential equations. In this method, the convergence of the series solutions is controlled by one or more parameters which can be determined by minimizing a certain function. There are several approaches to determine the optimal values of these parameters, which can be divided into two categories, i.e. global optimization approach and step-by-step optimization approach. In the global optimization approach, all the parameters are optimized simultaneously at the last order of approximation. However, this process leads to a system of coupled, nonlinear algebraic equations in multiple variables which are very difficult to solve. In the step-by-step approach, the optimal values of these parameters are determined sequentially, that is, they are determined one by one at different orders of approximation. In this way, the computational efficiency is significantly improved, especially when high order of approximation is needed. In this paper, we provide extensive examples arising in similarity and non-similarity boundary layer theory to investigate the performance of these approaches. The results reveal that with the step-by-step approach, convergent solutions of high order of approximation can be obtained within much less CPU time, compared with the global approach and the traditional HAM.

We propose a simple and effective hybrid (multiplicative) Schwarz precondtioner for solving systems of algebraic equations resulting from the mortar finite element discretization of second order elliptic problems on nonmatching meshes. The preconditioner is embedded in a variant of the classical preconditioned conjugate gradient (PCG) for an effective implementation reducing the cost of computing the matrix-vector multiplication in each iteration of the PCG. In fact, it serves as a framework for effective implementation of a class of hybrid Schwarz preconditioners. The preconditioners of this class are based on solving a sequence of non-overlapping local subproblems exactly, and the coarse problems either exactly or inexactly (approximately). The classical PCG algorithm is reformulated in order to make reuse of the results of matrix-vector multiplications that are already available from the preconditioning step resulting in an algorithm which is cost effective. An analysis of the proposed preconditioner, with numerical results, showing scalability with respect to the number of subdomains, and a convergence which is independent of the jumps of the coefficients are given.

Solving systems of nonlinear equations is a relatively complicated problem for which a number of different approaches have been proposed. In this paper, we employ the Homotopy Analysis Method (HAM) to derive a family of iterative methods for solving systems of nonlinear algebraic equations. Our approach yields second and third order iterative methods which are more efficient than their classical counterparts such as Newton’s, Chebychev’s and Halley’s methods.

In this paper we will present the family of Newton algorithms. From the computer algebra point of view, the most basic of them is well known for the local analysis of plane algebraic curves f(x,y)=0 and consists in expanding y as Puiseux series in the variable x. A similar algorithm has been developped for multi-variate algebraic equations and for linear differential equations, using the same basic tools: a “regular” case, associated with a “simple” class of solutions, and a “simple” method of calculus of these solutions; a Newton polygon; changes of variable of type ramification; changes of unknown function of two types y=ctμ+ϕ or y=exp (c/tμ)ϕ.

We present the first of two different algorithms for the explicit computation of Hermite–Pade forms (HPF) associated with the exponential function. Some roots of the algebraic equation associated with a given HPF are good approximants to the exponential in some subsets of the complex plane: they are called Hermite–Pade approximants (HPA) to this function. Our algorithm is recursive and based upon the expression of HPF as divided differences of the function t→exp (xt) at multiple integer nodes. Using this algorithm, we find again the results obtained by Borwein and Driver for quadratic HPF. As an example, we give an interesting family of quadratic HPA to the exponential.

Many problems of practical interest can be modeled by differential systems where the solution lies on an invariant manifold defined explicitly by algebraic equations. In computer simulations, it is often important to take into account the invariant's information, either in order to improve upon the stability of the discretization (which is especially important in cases of long time integration) or because a more precise conservation of the invariant is needed for the given application. In this paper we review and discuss methods for stabilizing such an invariant. Particular attention is paid to post-stabilization techniques, where the stabilization steps are applied to the discretized differential system. We summarize theoretical convergence results for these methods and describe the application of this technique to multibody systems with holonomic constraints. We then briefly consider collocation methods which automatically satisfy certain, relatively simple invariants. Finally, we consider an example of a very long time integration and the effect of the loss of symplecticity and time-reversibility by the stabilization techniques.

The splitting-integrating method is proposed to normalize digital images and patterns inn dimensions under inverse transformation. This method is much simpler than other approaches because no solutions of nonlinear algebraic equations are required. Also, the splitting-integrating method produces images free from superfluous holes and blanks, which often occur in transforming digitized images by other methods.

Multistep methods for the differential/algebraic equations (DAEs) in the form of $$F_1 (x) = 0, F_2 (x,x',z) = 0$$ are presented, whereF 1 maps from ℝ n to ℝ ′ ,F 2 from ℝ n x ℝ n x ℝ m to ℝ s andr