Instances of Computational Optimal Recovery: Dealing with Observation Errors

DOI10.1137/20M1328476zbMath1483.41012arXiv2004.00192OpenAlexW3206740987MaRDI QIDQ5158927

Simon Foucart, Mahmood Ettehad

Publication date: 26 October 2021

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.00192

zbMATH Keywords

convex optimizationoptimal recoveryChebyshev centersapproximability models

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Semidefinite programming (90C22) Minimax problems in mathematical programming (90C47) Numerical methods involving duality (49M29) Abstract approximation theory (approximation in normed linear spaces and other abstract spaces) (41A65)

Related Items (3)

Learning from non-random data in Hilbert spaces: an optimal recovery perspective ⋮ Impact of physical model error on state estimation for neutronics applications ⋮ Optimal recovery from inaccurate data in Hilbert spaces: regularize, but what of the parameter?

Uses Software

• Basc

Cites Work

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• Instances of computational optimal recovery: refined approximability models
• Computation of Chebyshev polynomials for union of intervals
• Computing a quantity of interest from observational data
• Computation of Minimal Projections and Extensions
• Optimal Reduced Model Algorithms for Data-Based State Estimation
• Noisy Information and Computational Complexity
• Basc: constrained approximation by semidefinite programming
• Data Assimilation in Reduced Modeling
• A Stability Barrier for Reconstructions from Fourier Samples

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