Algorithm reveals upper bounds for Gagliardo-Nirenberg-Sobolev constants in numerical approximations, suggesting improved estimations.
Common proofs of the Gagliardo-Nirenberg-Sobolev (GNS) inequalities do not provide explicit bounds on the involved constants, unless a sharp constant is being determined. GNS inequalities naturally occur in error estimates for numerical approximations. In particular, bounds on GNS constants allow us to provide explicit a priori estimates. We provide an algorithm that determines explicit upper bounds on the non-endpoint homogeneous GNS inequalities in terms of explicit upper bounds for Young’s convolution inequality and parabolic estimates. Our method is based on the heat-kernel representation of the inverse Laplacian, from which we deduce interpolation estimates.
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Michael Hott (2025) studied this question.
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