1 citations · 2 across the 9 of their papers we have counts for
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Sublinear Time Eigenvector Approximation via Column Sampling
Rajarshi Bhattacharjee, Cameron Musco, Dominic Rutkowski
We study sublinear time sampling methods for approximating the outlying eigenvectors of large matrices. Our main result is an algorithm that uniformly samples just $\tilde{O}(\log…
Fast Length-Squared Sampling for Positive-Semidefinite Matrices
Rajarshi Bhattacharjee, Ethan N. Epperly, Cameron Musco +1
We describe a simple rejection-sampling-based algorithm to perform length-squared sampling on an positive-semidefinite (psd) matrix: that is, to sample a column with p…
Improved Spectral Density Estimation via Explicit and Implicit Deflation
Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco +2
We study algorithms for approximating the spectral density of a symmetric matrix that is accessed through matrix-vector product queries. By combining a previously studied Cheby…
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra
Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco +3
Let satisfy , where is the all ones matrix and is the spectral norm. It is well-kno…
Sublinear Time Eigenvalue Approximation via Random Sampling
Rajarshi Bhattacharjee, Gregory Dexter, Petros Drineas +2
We study the problem of approximating the eigenspectrum of a symmetric matrix with bounded entries (i.e., ).…