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Spline functions and total positivity.

M. Gasca (1996)

Revista Matemática de la Universidad Complutense de Madrid

In this survey we show the close connection between the theory of Spline Functions and that of Total Positivity. In the last section we mention some recent results on totally positive bases which are optimal for shape preserving properties in Computer Aided Geometric Design.

Spline Subdivision Schemes for Compact Sets. A Survey

Dyn, Nira, Farkhi, Elza (2002)

Serdica Mathematical Journal

Dedicated to the memory of our colleague Vasil Popov January 14, 1942 – May 31, 1990 * Partially supported by ISF-Center of Excellence, and by The Hermann Minkowski Center for Geometry at Tel Aviv University, IsraelAttempts at extending spline subdivision schemes to operate on compact sets are reviewed. The aim is to develop a procedure for approximating a set-valued function with compact images from a finite set of its samples. This is motivated by the problem of reconstructing a 3D object from...

Splines and pseudo-inverses

F. J. Delvos (1978)

ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

Statistical estimates for generalized splines

Magnus Egerstedt, Clyde Martin (2003)

ESAIM: Control, Optimisation and Calculus of Variations

In this paper it is shown that the generalized smoothing spline obtained by solving an optimal control problem for a linear control system converges to a deterministic curve even when the data points are perturbed by random noise. We furthermore show that such a spline acts as a filter for white noise. Examples are constructed that support the practical usefulness of the method as well as gives some hints as to the speed of convergence.

Statistical Estimates for Generalized Splines

Magnus Egerstedt, Clyde Martin (2010)

ESAIM: Control, Optimisation and Calculus of Variations

In this paper it is shown that the generalized smoothing spline obtained by solving an optimal control problem for a linear control system converges to a deterministic curve even when the data points are perturbed by random noise. We furthermore show that such a spline acts as a filter for white noise. Examples are constructed that support the practical usefulness of the method as well as gives some hints as to the speed of convergence.

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