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Multidimensional unstructured sparse recovery via eigenmatrix

arXiv 2402.17215 ↗First posted 27 Feb 2024math.NA cs.LG cs.NAcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 18 references that are in the map. First posted on arXiv 27 Feb 2024.

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References · 2

  1. 2022Efficient Algorithms for Sparse Moment Problems without Separationcited by 3
  2. 2023Eigenmatrix for unstructured sparse recoverycited by 1

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