100 citations · 217 across the 14 of their papers we have counts for
15 papers
Randomized Projection for Rank-Revealing Matrix Factorizations and Low-Rank Approximations
Jed A. Duersch, Ming Gu
Rank-revealing matrix decompositions provide an essential tool in spectral analysis of matrices, including the Singular Value Decomposition (SVD) and related low-rank approximation…
Superlinear Convergence of Randomized Block Lanczos Algorithm
Qiaochu Yuan, Ming Gu, Bo Li
The low rank approximation of matrices is a crucial component in many data mining applications today. A competitive algorithm for this class of problems is the randomized block Lan…
Spectrum-Revealing Cholesky Factorization for Kernel Methods
Jianwei Xiao, Ming Gu
Kernel methods represent some of the most popular machine learning tools for data analysis. Since exact kernel methods can be prohibitively expensive for large problems, reliable l…
Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations
Jianwei Xiao, Ming Gu, Julien Langou
Factorizing large matrices by QR with column pivoting (QRCP) is substantially more expensive than QR without pivoting, owing to communication costs required for pivoting decisions.…
Low-Rank Matrix Approximations with Flip-Flop Spectrum-Revealing QR Factorization
Yuehua Feng, Jianwei Xiao, Ming Gu
We present Flip-Flop Spectrum-Revealing QR (Flip-Flop SRQR) factorization, a significantly faster and more reliable variant of the QLP factorization of Stewart, for low-rank matrix…
Randomized Complete Pivoting for Solving Symmetric Indefinite Linear Systems
Yuehua Feng, Jianwei Xiao, Ming Gu
The Bunch-Kaufman algorithm and Aasen's algorithm are two of the most widely used methods for solving symmetric indefinite linear systems, yet they both are known to suffer from oc…