Core Concepts
Eigenmatrix offers a new approach for unstructured sparse recovery problems.
Abstract
The content discusses unstructured sparse recovery problems in various fields.
Challenges include noise in sample values and unstructured sample locations.
Proposed solution: Eigenmatrix construction for approximate eigenvalues and eigenvectors.
Applications include rational approximation, spectral function estimation, Fourier inversion, Laplace inversion, and sparse deconvolution.
Numerical results demonstrate the efficiency of the eigenmatrix approach.
Related work includes Prony's method and ESPRIT algorithm.
Future work includes error estimates and combining eigenmatrix with other algorithms.
Stats
수학 주제 분류에서의 희소 복구 문제에 대한 새로운 접근 방식을 제공합니다.
Quotes
"Eigenmatrix offers a new way for these sparse recovery problems."
"Numerical results are provided to demonstrate the efficiency of the proposed method."