Multidimensional unstructured sparse recovery via eigenmatrix
Numerical Analysis
2024-02-28 v1 Machine Learning
Numerical Analysis
Abstract
This note considers the multidimensional unstructured sparse recovery problems. Examples include Fourier inversion and sparse deconvolution. The eigenmatrix is a data-driven construction with desired approximate eigenvalues and eigenvectors proposed for the one-dimensional problems. This note extends the eigenmatrix approach to multidimensional problems. Numerical results are provided to demonstrate the performance of the proposed method.
Cite
@article{arxiv.2402.17215,
title = {Multidimensional unstructured sparse recovery via eigenmatrix},
author = {Lexing Ying},
journal= {arXiv preprint arXiv:2402.17215},
year = {2024}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2311.16609