Multidimensional unstructured sparse recovery via eigenmatrix
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916140895174656 |
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| author | Ying, Lexing |
| author_facet | Ying, Lexing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multidimensional unstructured sparse recovery via eigenmatrix Ying, Lexing Numerical Analysis Machine Learning 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. |
| title | Multidimensional unstructured sparse recovery via eigenmatrix |
| topic | Numerical Analysis Machine Learning |
| url | https://arxiv.org/abs/2402.17215 |