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The Kramer sampling theorem revisited

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URI: https://hdl.handle.net/10016/35961
ISSN: 0167-8019
DOI: https://doi.org/10.1007/s10440-013-9860-1
UXXI: AR/0000015645
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kramer_APM_2014_ps.pdf (4.05 MB)
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2014-10-01
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Springer
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Abstract
The classical Kramer sampling theorem provides a method for obtaining orthogonal sampling formulas. Besides, it has been the cornerstone for a significant mathematical literature on the topic of sampling theorems associated with differential and difference problems. In this work we provide, in an unified way, new and old generalizations of this result corresponding to various different settings; all these generalizations are illustrated with examples. All the different situations along the paper share a basic approach: the functions to be sampled are obtaining by duality in a separable Hilbert space through an -valued kernel K defined on an appropriate domain.
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Sampling formulas, Kramer kernels, Reproducing kernel Hilbert spaces, Lagrange-type interpolation series, Zero-removing property, Semi-inner products, Reproducing kernel Banach spaces, Reproducing distributions
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Acta Applicandae Mathematicae, (2014), 133(1), pp.: 87–111.
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