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Discontinuous information in the worst case and randomized settings

By Aicke Hinrichs and Erich Novak

Abstract

Dedicated to Hans Triebel on the occasion of his 75th birthday We believe that discontinuous linear information is never more powerful than continuous linear information for approximating continuous operators. We prove such a result in the worst case setting. In the randomized setting we consider compact linear operators defined between Hilbert spaces. In this case, the use of discontinuous linear information in the randomized setting cannot be much more powerful than continuous linear information in the worst case setting. These results can be applied when function evaluations are used even if function values are defined only almost everywhere.

Year: 1969
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.5058
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