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Continuous linear multistep method for the general solution of first order initial value problems for Volterra integro-differential equations

机译:Volterra积分微分方程一阶初值问题一般解的连续线性多步法

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Purpose - The purpose of this paper is to develop a block method of order five for the general solution of the first-order initial value problems for Volterra integro-differential equations (VIDEs). Design/methodology/approach - A collocation approximation method is adopted using the shifted Legendre polynomial as the basis function, and the developed method is applied as simultaneous integrators on the first-order VIDEs. Findings - The new block method possessed the desirable feature of the Runge-Kutta method of being self-starting, hence eliminating the use of predictors. Originality/value - In this paper, some information about solving VIDEs is provided. The authors have presented and illustrated the collocation approximation method using the shifted Legendre polynomial as the basis function to investigate solving an initial value problem in the class of VIDEs, which are very difficult, if not impossible, to solve analytically. With the block approach, the non-self-starting nature associated with the predictor corrector method has been eliminated. Unlike the approach in the predictor corrector method where additional equations are supplied from a different formulation, all the additional equations are from the same continuous formulation which shows the beauty of the method. However, the absolute stability region showed that the method is A-stable, and the application of this method to practical problems revealed that the method is more accurate than earlier methods.
机译:目的-本文的目的是针对Volterra积分微分方程(VIDE)的一阶初值问题的一般解,开发一种五阶的块方法。设计/方法/方法-采用移位的Legendre多项式作为基函数的搭配近似方法,并将开发的方法用作一阶VIDE的同时积分器。发现-新的块方法具有Runge-Kutta方法具有自启动功能的理想功能,因此无需使用预测变量。原创性/价值-本文提供了一些有关解决VIDE的信息。作者提出并举例说明了以移位的勒让德多项式为基础函数的搭配近似方法,以研究解决VIDE类中的初值问题,这是非常困难的,即使不是不可能,也无法通过解析来解决。通过块方法,消除了与预测器校正器方法相关的非自启动特性。与预测器校正器方法中从其他公式提供其他方程式的方法不同,所有其他方程式都来自同一连续公式,这显示了该方法的优点。然而,绝对稳定性区域表明该方法是A稳定的,并且该方法在实际问题中的应用表明该方法比早期方法更准确。

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