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Computation of Optimal Linear Strong Stability Preserving Methods Via Adaptive Spectral Transformations of Poisson–Charlier Measures
Journal of Scientific Computing ( IF 2.8 ) Pub Date : 2021-07-28 , DOI: 10.1007/s10915-021-01582-0
Rachid Ait-Haddou 1
Affiliation  

Strong stability preserving (SSP) coefficients govern the maximally allowable step-size at which positivity or contractivity preservation of integration methods for initial value problems is guaranteed. In this paper, we show that the task of computing optimal linear SSP coefficients of explicit one-step methods is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson–Charlier measures. Using this equivalence, we provide sharp upper and lower bounds for the optimal linear SSP coefficients in terms of the zeros of generalized Laguerre orthogonal polynomials. This in particular provides us with a sharp upper bound for the optimal SSP coefficients of explicit Runge–Kutta methods. Also based on this equivalence, we propose a highly efficient and stable algorithm for computing these coefficients, and their associated optimal linear SSP methods, based on adaptive spectral transformations of Poisson–Charlier measures. The algorithm possesses the remarkable property that its complexity depends only on the order of the method and thus is independent of the number of stages. Our results are achieved by adapting and extending an ingenious technique by Bernstein (Acta Math 52:1–66, 1928) in his seminal work on absolutely monotonic functions. Moreover, the techniques introduced in this work can be adapted to solve the integer quadrature problem for any positive discrete multi-parametric measure supported on \({\mathbb {N}}\) under some mild conditions on the zeros of the associated orthogonal polynomials.



中文翻译:

通过泊松-查理尔测度的自适应谱变换计算最优线性强稳定性保持方法

强稳定性保持 (SSP) 系数控制着最大允许步长,在该步长处,初始值问题的积分方法的正性或收缩性得以保证。在本文中,我们表明,计算显式一步法的最优线性 SSP 系数的任务在一定程度上等同于关于 Poisson-Charlier 测度表征具有整数节点的正正交的问题。使用这种等价性,我们根据广义拉盖尔正交多项式的零点为最佳线性 SSP 系数提供了清晰的上限和下限。这特别为我们提供了显式 Runge-Kutta 方法的最佳 SSP 系数的尖锐上限。同样基于这个等价,我们提出了一种高效且稳定的算法来计算这些系数,以及它们相关的最佳线性 SSP 方法,基于泊松-查理尔度量的自适应谱变换。该算法具有显着的特性,即其复杂度仅取决于方法的阶数,因此与阶段数无关。我们的结果是通过改编和扩展伯恩斯坦 (Acta Math 52:1–66, 1928) 在他关于绝对单调函数的开创性工作中的一项巧妙技术而获得的。此外,这项工作中引入的技术可以适用于解决任何支持的正离散多参数测度的整数正交问题。该算法具有显着的特性,即其复杂度仅取决于方法的阶数,因此与阶段数无关。我们的结果是通过改编和扩展伯恩斯坦 (Acta Math 52:1–66, 1928) 在他关于绝对单调函数的开创性工作中的一项巧妙技术而获得的。此外,这项工作中引入的技术可以适用于解决任何支持的正离散多参数测度的整数正交问题。该算法具有显着的特性,即其复杂度仅取决于方法的阶数,因此与阶段数无关。我们的结果是通过改编和扩展伯恩斯坦 (Acta Math 52:1–66, 1928) 在他关于绝对单调函数的开创性工作中的一项巧妙技术而获得的。此外,这项工作中引入的技术可以适用于解决任何支持的正离散多参数测度的整数正交问题。\({\mathbb {N}}\)在相关正交多项式的零点的一些温和条件下。

更新日期:2021-07-29
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