Titlebook: Approximation Theory XIV: San Antonio 2013; Gregory E. Fasshauer,Larry L. Schumaker Conference proceedings 2014 Springer International Pub

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A Generalized Class of Hard Thresholding Algorithms for Sparse Signal Recovery,ur algorithms select a correct set of indices at each iteration, as long as the active support is smaller than the actual support of the vector to be recovered, with a proviso on the shape of the vector. Our theoretical findings are illustrated by numerical examples.

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,Suitability of Parametric Shepard?Interpolation for Nonrigid Image Registration, interpolation is stable under varying density and organization of data points as well as under highly varying data values. The suitability of parametric Shepard interpolation in nonrigid image registration is investigated and its speed and accuracy are compared with those of multiquadric, thin-plate spline, and moving least-squares.

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A Generalized Class of Hard Thresholding Algorithms for Sparse Signal Recovery,.. Our results generalize previous ones on hard thresholding pursuit algorithms. We show that uniform recovery of all .-sparse vectors . can be achieved under a certain restricted isometry condition. While these conditions might be unrealistic in some cases, it is shown that with high probability, o

BRAWL

On a New Proximity Condition for Manifold-Valued Subdivision Schemes, conditions for a manifold-valued subdivision scheme, based on a linear subdivision scheme, to share the same regularity as the linear scheme. This is called the .. In a companion paper, the authors introduced a . that solves the smoothness equivalence problem. In this paper, we review this conditio

Deject

Wachspress and Mean Value Coordinates,plications include surface parameterization in geometric modeling, curve and surface deformation in computer graphics, and their use as nodal shape functions for polygonal and polyhedral finite element methods.

Frequency-Range

Hermite and Bernstein Style Basis Functions for Cubic Serendipity Spaces on Squares and Cubes,recent work of Arnold and Awanou [.. (2011), 337–344]. The serendipity spaces are substantially smaller in dimension than the more commonly used bicubic and tricubic Hermite tensor product spaces—12 instead of 16 for the square and 32 instead of 64 for the cube—yet are still guaranteed to obtain cub

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