A new method for interpolating in a convex subset of a Hilbert space
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Publication:2401024
DOI10.1007/s10589-017-9906-9zbMath1406.90127OpenAlexW2217793091MaRDI QIDQ2401024
Xavier Bay, Hassan Maatouk, Laurence Grammont
Publication date: 31 August 2017
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-017-9906-9
Convex programming (90C25) Approximation methods and heuristics in mathematical programming (90C59) Programming in abstract spaces (90C48)
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Generalization of the Kimeldorf-Wahba correspondence for constrained interpolation, Gaussian process emulators for computer experiments with inequality constraints, Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints, Finite-dimensional approximation of Gaussian processes with linear inequality constraints and noisy observations
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Cites Work
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- A numerically stable dual method for solving strictly convex quadratic programs
- Generalization of the Kimeldorf-Wahba correspondence for constrained interpolation
- Monotone interpolation of scattered data in \({\mathbb{R}}^ s\)
- Shape-preserving interpolation and smoothing for options market implied volatility
- Interpolation and approximation by monotone cubic splines
- An energy-minimization framework for monotonic cubic spline interpolation
- Convergence rates for monotone cubic spline interpolation
- Accurate Monotonicity Preserving Cubic Interpolation
- Nonnegativity-, Monotonicity-, or Convexity-Preserving Cubic and Quintic Hermite Interpolation
- Smoothing and Interpolation in a Convex Subset of a Hilbert Space
- Monotone Piecewise Cubic Interpolation
- A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
- A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures
- Theory of Reproducing Kernels
- Convergence of Newton's method for convex best interpolation