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Convergence of linear barycentric rational interpolation for analytic functions

机译:解析函数的线性重心有理插值的收敛性

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Polynomial interpolation to analytic functions can be very accurate, depending on the distribution of the interpolation nodes. However, in equispaced nodes and the like, besides being badly conditioned, these interpolants fail to converge even in exact arithmetic in some cases. Linear barycentric rational interpolation with the weights presented by Floater and Hormann can be viewed as blended polynomial interpolation and often yields better approximation in such cases. This has been proven for differentiable functions and indicated in several experiments for analytic functions. So far, these rational interpolants have been used mainly with a constant parameter usually denoted by d, the degree of the blended polynomials, which leads to small condition numbers but to merely algebraic convergence. With the help of logarithmic potential theory we derive asymptotic convergence results for analytic functions when this parameter varies with the number of nodes. Moreover, we present suggestions for how to choose d in order to observe fast and stable convergence, even in equispaced nodes where stable geometric convergence is provably impossible. We demonstrate our results with several numerical examples.
机译:取决于插值节点的分布,对分析函数进行多项式插值可能非常准确。但是,在等间隔的节点等中,除了条件差以外,这些插值即使在某些情况下也无法以精确的算术收敛。可以将Floater和Hormann提出的具有权重的线性重心有理插值视为混合多项式插值,并且在这种情况下通常可以产生更好的近似值。这已被证明可以用于微分函数,并在针对分析函数的多个实验中得到了证明。到目前为止,这些有理插值已主要用于通常由d表示的常数,即混合多项式的阶数,这会导致较小的条件数,但仅导致代数收敛。借助对数势理论,当该参数随节点数变化时,我们可以得出解析函数的渐近收敛结果。此外,我们提出了关于如何选择d以便观察快速稳定的收敛的建议,即使在等距节点中也无法证明稳定的几何收敛。我们通过几个数值示例来证明我们的结果。

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