This content originally appeared on HackerNoon and was authored by Algorithmic Bias (dot tech)
Table of Links
1.1 ESPRIT algorithm and central limit error scaling
1.4 Technical overview and 1.5 Organization
2 Proof of the central limit error scaling
3 Proof of the optimal error scaling
4 Second-order eigenvector perturbation theory
5 Strong eigenvector comparison
5.1 Construction of the “good” P
5.2 Taylor expansion with respect to the error terms
5.3 Error cancellation in the Taylor expansion
C Deferred proofs for Section 2
D Deferred proofs for Section 4
E Deferred proofs for Section 5
F Lower bound for spectral estimation
F Lower bound for spectral estimation
\ To prove this theorem, we will employ the following lemma [AAL23, Thm. 1.8]:
\
\
:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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:::info Authors:
(1) Zhiyan Ding, Department of Mathematics, University of California, Berkeley;
(2) Ethan N. Epperly, Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA;
(3) Lin Lin, Department of Mathematics, University of California, Berkeley, Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, and Challenge Institute for Quantum Computation, University of California, Berkeley;
(4) Ruizhe Zhang, Simons Institute for the Theory of Computing.
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This content originally appeared on HackerNoon and was authored by Algorithmic Bias (dot tech)
Algorithmic Bias (dot tech) | Sciencx (2025-05-17T02:30:03+00:00) Lower Bound for Spectral Estimation in Noisy Super-Resolution. Retrieved from https://www.scien.cx/2025/05/17/lower-bound-for-spectral-estimation-in-noisy-super-resolution/
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