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High Order Approximation by Multivariate Sublinear and Max-Product Operators Under Convexity

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Nonlinearity: Ordinary and Fractional Approximations by Sublinear and Max-Product Operators

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 147))

Abstract

Here we search quantitatively under convexity the approximation of multivariate function by general multivariate positive sublinear operators with applications to multivariate Max-product operators. These are of Bernstein type, of Favard-Szász-Mirakjan type, of Baskakov type, of sampling type, of Lagrange interpolation type and of Hermite-Fejér interpolation type. Our results are both: under the presence of smoothness and without any smoothness assumption on the function to be approximated which fulfills a convexity assumption. It follows (Anastassiou, Approximations by multivariate sublinear and max-product operators under convexity, submitted, 2017, [4]).

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References

  1. G. Anastassiou, Moments in Probability and Approximation Theory, Pitman Research Notes in Mathematics Series (Longman Group, New York, 1993)

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  2. G. Anastassiou, Approximation by Sublinear Operators (2017, submitted)

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  3. G. Anastassiou, Approximation by Max-Product Operators (2017, submitted)

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  4. G. Anastassiou, Approximations by Multivariate Sublinear and Max-product Operators under Convexity (2017, submitted)

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  5. B. Bede, L. Coroianu, S. Gal, Approximation by Max-Product type Operators (Springer, Heidelberg, 2016)

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Correspondence to George A. Anastassiou .

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Anastassiou, G.A. (2018). High Order Approximation by Multivariate Sublinear and Max-Product Operators Under Convexity. In: Nonlinearity: Ordinary and Fractional Approximations by Sublinear and Max-Product Operators. Studies in Systems, Decision and Control, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-89509-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-89509-3_12

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  • Publisher Name: Springer, Cham

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