Smoothing and interpolation in a convex subset of a Hilbert space : II. The semi-norm case

DOI10.1051/m2an/1991250404251zbMath0741.65045OpenAlexW797193083MaRDI QIDQ3978385

Charles A. Micchelli, Florencio I. Utreras

Publication date: 25 June 1992

Published in: ESAIM: Mathematical Modelling and Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://eudml.org/doc/193634

zbMATH Keywords

interpolationsmoothingdecompositionHilbert spacebounded linear operatororthogonal projectionconvex constraintsmin-max problemleast norm solutionfinite dimensional kernelreduction of computational costconstrained best interpolationsmooth monotone interpolation

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) General theory of numerical analysis in abstract spaces (65J05) Numerical interpolation (65D05) Abstract approximation theory (approximation in normed linear spaces and other abstract spaces) (41A65) Approximation with constraints (41A29) Numerical solutions to equations with linear operators (65J10)

Related Items

Best interpolation in seminorm with convex constraints, Constrained data smoothing via optimal control, Positive definite dot product kernels in learning theory, Penalized maximum-likelihood estimation, the Baum-Welch algorithm, diagonal balancing of symmetric matrices and applications to training acoustic data

Cites Work

• Unnamed Item
• Unnamed Item
• Constrained \(L_ p\) approximation
• Constrained interpolation and smoothing
• Smoothing noisy data under monotonicity constraints existence, characterization and convergence rates
• On the characterization of non-negative volume-matching surface splines
• Constrained best approximation in Hilbert space
• Smoothing and Interpolation in a Convex Subset of a Hilbert Space
• Convex Analysis