DagSemProc.04401.5.pdf
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Information-Based Nonlinear Approximation: An Average Case Setting
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Dagstuhl Seminar Proceedings, Volume 4401
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2005-04-19
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Leszek Plaskota. Information-Based Nonlinear Approximation: An Average Case Setting. In Algorithms and Complexity for Continuous Problems. Dagstuhl Seminar Proceedings, Volume 4401, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)
https://doi.org/10.4230/DagSemProc.04401.5
https://doi.org/10.4230/DagSemProc.04401.5
Abstract
Nonlinear approximation has usually been studied
under deterministic assumption and complete
information about the underlying functions.
We assume only partial information and we are
interested in the average case error and
complexity of approximation. It turns out that
the problem can be essentially split into two
independent problems related to average case
nonlinear (restricted) approximation from
complete information, and average case
unrestricted approximation from partial
information. The results are then applied to
average case piecewise polynomial approximation,
and to average case approximation of real
sequences.
Keywords
- average case setting
- nonlinear approximation
- information-based comlexity
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