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Artificial Intelligence and Machine Learning

A note on computing approximations to the exponential function

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Two methods are discussed which result in near minimax rational approximations to the exponential function and at the same time retain the desirable property that the approximation for negative values of the argument is the reciprocal of the approximation for corresponding positive values. These methods lead to approximations which are much superior to the commonly used convergents of the Gaussian continued fraction for the exponential. Coefficients and errors are given for the intervals [-1/2 ln 2, 1/2 ln 2] and [-ln 2, ln 2]. Two methods are discussed which result in near minimax rational approximations to the exponential function and at the same time retain the desirable property that the approximation for negative values of the argument is the reciprocal of the approximation for corresponding positive values. These methods lead to approximations which are much superior to the commonly used convergents of the Gaussian continued fraction for the exponential. Coefficients and errors are given for the intervals [-1/2 ln 2, 1/2 ln 2] and [-ln 2, ln 2].

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