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VARIABLE TRANSFORMATIONS AND GAUSS-LEGENDRE QUADRATURE FOR INTEGRALS WITH ENDPOINT SINGULARITIES

机译:具有端点奇异性的积分的变量变换和Gauss-Legendre正交

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Gauss-Legendre quadrature formulas have excellent convergence properties when applied to integrals f~1_0 f(x) dx with f __ C~__ [0, 1]. However, their performance deteriorates when the integrands f(x) are in C~__(0, 1) but are singular at x = 0 and/or x = 1.One way of improving the performance of Gauss-Legendre quadrature in such cases is by combining it with a suitable variable transformation such that the transformed integrand has weaker sin_gularities than those of f(x). Thus, if x = _×(t) is a variable transformation that maps [0, 1] onto itself, we apply Gauss-Legendre quadrature to the trans_formed integral f~1_O f(_×(t))_×'(t) dt, whose singularities at t = 0 and/or t = 1 are weaker than those of f (x) at x = 0 and/or x = 1. In this work, we first de_fine a new class of variable transformations we denote S_(p,q), where p and q are two positive parameters that characterize it. We also give a simple and easily computable representative of this class. Next, by invoking some recent results by the author concerning asymptotic expansions of Gauss_Legendre quadra_ture approximations as the number of abscissas tends to infinity, we present a thorough study of convergence of~the combined approximation procedure, with variable transformations from S_(p,q). We show how optimal results can be obtained by adjusting the parameters p and q of the variable transformation in an appropriate fashion. We also give numerical examples that confirm the theoretical results.
机译:高斯-勒让德勒(Gauss-Legendre)正交公式在f __ C〜__ [0,1]时应用到积分f〜1_0 f(x)dx时具有出色的收敛性。但是,当被积数f(x)处于C〜__(0,1),但在x = 0和/或x = 1时为奇数时,它们的性能会下降。在这种情况下,提高Gauss-Legendre正交性能的一种方法通过将其与适当的变量转换组合在一起,使转换后的被积数比f(x)的sin_gularity弱。因此,如果x = _×(t)是将[0,1]映射到其自身的变量变换,则对变换后的积分f〜1_O f(_×(t))_×'(t )dt,其在t = 0和/或t = 1的奇异性比在x = 0和/或x = 1的f(x)的奇异性弱。在本文中,我们首先定义新的一类变量转换S_(p,q),其中p和q是表征它的两个正参数。我们还为此类提供了一个简单易计算的代表。接下来,通过引用作者最近关于Gauss_Legendre Quadra_ture逼近的渐近展开的一些结果,随着横坐标的数量趋于无穷大,我们对组合逼近过程的收敛进行了透彻的研究,并从S_(p,q)进行了变量转换。我们展示了如何通过以适当的方式调整变量转换的参数p和q来获得最佳结果。我们还提供了数值示例来证实理论结果。

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