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• Analysis of non-linear extensions of the Lane-Riesenfeld algorithm. • Using the Laurent polynomial representation for analyzing non-uniform subdivision. • Analyzing non-linear subdivision as perturbations of non-uniform linear schemes. We investigate some variants of the linear Lane-Riesenfeld algorithm in the functional setting, generated by replacing the standard binary arithmetic averages between real numbers by non-linear, binary, symmetric averages between real numbers. For certain classes of non-linear averages we show that such generalized r -th order Lane-Riesenfeld algorithms generate limit functions with the same smoothness as the linear r -th order Lane-Riesenfeld algorithm. The analysis is based on the observation that a non-linear subdivision scheme can be regarded as a non-uniform linear scheme with mask coefficients depending on the initial data, and on ideas from a recent investigation of smoothly varying non-uniform linear subdivision schemes (Dyn et al., 2014). Our main result is motivated by an example in Duchamp et al. (2016). It extends a result in Duchamp et al. (2018) , derived for subdivision schemes on smooth manifolds, and applied to the non-linear Lane-Riesenfeld algorithm in the functional setting, by allowing different non-linear averages at different locations in a smoothing step. [ABSTRACT FROM AUTHOR]

Abstract: We show that the generalized Bernstein bases in Müntz spaces defined by Hirschman and Widder (1949) and extended by Gelfond (1950) can be obtained as pointwise limits of the Chebyshev–Bernstein bases in Müntz spaces with respect to an interval as the positive real number a converges to zero. Such a realization allows for concepts of curve design such as de Casteljau algorithm, blossom, dimension elevation to be transferred from the general theory of Chebyshev blossoms in Müntz spaces to these generalized Bernstein bases that we termed here as Gelfond–Bernstein bases. The advantage of working with Gelfond–Bernstein bases lies in the simplicity of the obtained concepts and algorithms as compared to their Chebyshev–Bernstein bases counterparts. [Copyright &y& Elsevier]