Stable approximation of functions from equispaced samples via Jacobi frames
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In this paper, we study the Jacobi frame approximation with equispaced samples and derive an error estimation. We observe numerically that the approximation accuracy gradually decreases as the extended domain parameter γ increases in the uniform norm, especially for differentiable functions. In addition, we show that when the indexes of Jacobi polynomials α and β are larger (for example max{α,β} > 10), it leads to a divergence behavior on the frame approximation error decay.
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