Sublinear time spectral density estimation

Author:

Braverman Vladimir1,Krishnan Aditya1,Musco Christopher2

Affiliation:

1. Johns Hopkins University, USA

2. New York University, USA

Publisher

ACM

Reference43 articles.

1. Cucheb: A GPU implementation of the filtered Lanczos procedure

2. Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix

3. Jess Banks , Jorge Vargas , Archit Kulkarni , and Nikhil Srivastava . 2019. Pseudospectral Shattering , the Sign Function, and Diagonalization in Nearly Matrix Multiplication Time. In 2020 . Jess Banks, Jorge Vargas, Archit Kulkarni, and Nikhil Srivastava. 2019. Pseudospectral Shattering, the Sign Function, and Diagonalization in Nearly Matrix Multiplication Time. In 2020.

4. Mark Braverman , Elad Hazan , Max Simchowitz , and Blake Woodworth . 2020. The Gradient Complexity of Linear Regression. In 2020 . 125, 627–647. Mark Braverman, Elad Hazan, Max Simchowitz, and Blake Woodworth. 2020. The Gradient Complexity of Linear Regression. In 2020. 125, 627–647.

5. Vladimir Braverman Aditya Krishnan and Christopher Musco. 2021. Linear and sublinear time spectral density estimation. arXiv preprint arXiv:2104.03461. Vladimir Braverman Aditya Krishnan and Christopher Musco. 2021. Linear and sublinear time spectral density estimation. arXiv preprint arXiv:2104.03461.

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