On the minimal cost of approximating linear problems based on information with deterministic noise

DOI10.1080/01630569008816386zbMath0696.65052OpenAlexW2047001044MaRDI QIDQ3474221

Bolesław Kacewicz, Leszek Plaskota

Publication date: 1990

Published in: Numerical Functional Analysis and Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01630569008816386

zbMATH Keywords

perturbationsnoisy informationfinite precision arithmeticminimal information costfunction recoveringbest informationfixed floating point arithmeticoptimal precisions

Mathematics Subject Classification ID

Numerical solutions to equations with linear operators (65J10) Equations and inequalities involving linear operators, with vector unknowns (47A50)

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Optimal pointwise approximation of SDE's from inexact information, Perspectives on information-based complexity, Euler scheme for approximation of solution of nonlinear ODEs under inexact information, Randomized derivative-free Milstein algorithm for efficient approximation of solutions of SDEs under noisy information, Exact orders of computational (numerical) diameters in problems of reconstructing functions and sampling solutions of the Klein-Gordon equation from Fourier coefficients, Optimal approximation of stochastic integrals in analytic noise model, Termination Conditions for Approximating Linear Problems with Noisy Information, Randomized Runge-Kutta method -- stability and convergence under inexact information

Cites Work

• Optimality of central and projection algorithms for bounded uncertainty
• A note on the trade-off between sampling and quantization in signal processing
• On adaption with noisy information
• On average case complexity of linear problems with noisy information
• Optimal Estimation of Linear Operators in Hilbert Spaces from Inaccurate Data
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