Spectral approximated superconvergent method for nonlinear Volterra Hammerstein integral equations with weakly singular kernels

IF 2.1 2区 数学 Q1 MATHEMATICS, APPLIED
Samiran Chakraborty , Shivam Kumar Agrawal , Gnaneshwar Nelakanti
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引用次数: 0

Abstract

In this paper, we apply Jacobi spectral Galerkin and multi-Galerkin methods using Kumar–Sloan technique for obtaining approximations of weakly singular Volterra integral equation of Hammerstein type and obtain superconvergence results. We derive the enhanced superconvergence results for the Kumar–Sloan approximation based on Galerkin and multi-Galerkin methods in both cases: when the exact solution is smooth and when the exact solution is non-smooth, in both infinity and weighted-L2 norms. We conclude that without the need for the iterated versions, we achieve superconvergence rates as high as the superconvergence rates of iterated Galerkin and iterated multi-Galerkin methods. The numerical results are presented to demonstrate the theoretical ones.
弱奇异核非线性Volterra Hammerstein积分方程的谱逼近超收敛方法
本文应用Jacobi谱Galerkin和多重Galerkin方法,利用Kumar-Sloan技术得到了Hammerstein型弱奇异Volterra积分方程的逼近,得到了超收敛结果。我们得到了基于Galerkin和多重Galerkin方法的Kumar-Sloan逼近在两种情况下的增强超收敛结果:当精确解是光滑的和当精确解是非光滑的,在无穷范数和加权l2范数下。我们得出结论,在不需要迭代版本的情况下,我们实现了与迭代Galerkin方法和迭代多重Galerkin方法一样高的超收敛速度。用数值结果验证了理论结果。
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来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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