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For the purpose of developing sparse PCE, an iterative reweighted algorithm is proposed to construct the objective function.
This paper constitutes our initial effort in developing sparse grid discontinuous Galerkin (DG) methods for high-dimensional partial differential equations (PDEs).
Later, bandlet transforms that aim at developing sparse geometric representations of the images have been introduced and studied in the context of image coding and image denoising [29].
In this paper, a new method for developing sparse PCE is proposed based on the diffeomorphic modulation under observable response preserving homotopy (D-MORPH) algorithm.
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Exposed sandy coasts generally develop sparse populations, especially between the tide lines, while the few organisms inhabiting wave-swept rocky shores are generally firmly cemented or anchored to the substratum.
Finally, we provide the comparison of the DFTCOMM with the recently developed sparse fast Fourier transform (SFFT) algorithmic family.
First, we develop sparse abstractions to speed up the controller synthesis procedure by reducing the number of transitions.
In this paper, we develop sparse grid DG methods for elliptic equations with the aim of breaking the curse of dimensionality.
Plenoptic functions contain a rich geometric structure that we suggest could be exploited to develop sparse models for use in joint recovery algorithms.
In this work, a recently developed sparse signal reconstruction algorithm called orthogonal clustering (OC) is applied for the TDE problem [18].
In this paper, we apply Multiresolution Analysis (MRA) to develop sparse but accurate representations for the Multiscale Coarse-Graining (MSCG) approximation to the many-body potential of mean force.
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