19 citations · 33 across the 7 of their papers we have counts for
7 papers
Superfast 1-Norm Estimation
Soo Go, Victor Y. Pan
A matrix algorithm is said to be superfast (that is, runs at sublinear cost) if it involves much fewer scalars and flops than the input matrix has entries. Such algorithms have bee…
Iterative Refinement and Oversampling for Low Rank Approximation
Victor Y. Pan, Qi Luan, Soo Go
Iterative refinement is particularly popular for numerical solution of linear systems of equations. We extend it to Low Rank Approximation of a matrix (LRA) and observe close link…
CUR Low Rank Approximation of a Matrix at Sublinear Cost
Soo Go, Qi Luan, Victor Y. Pan +2
Low rank approximation of a matrix (hereafter LRA) is a highly important area of Numerical Linear and Multilinear Algebra and Data Mining and Analysis. One can operate with an LRA…
QRP Variation of Cross--Approximation Iterations for Low Rank Approximation
Victor Y. Pan, John Svadlenka
We call matrix algorithms superfast if they use much fewer flops and memory cells than the input matrix has entries. Using such algorithms is indispensable for Big Data Mining and…
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
Liang Zhao, Siyu Liao, Yanzhi Wang +4
Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neura…
Highly Efficient Computation of Generalized Inverse of a Matrix
V. Y. Pan, F. Soleymani, Liang Zhao
We propose a hyperpower iteration for numerical computation of the outer generalized inverse of a matrix which achieves the 18th order of convergence by using only seven matrix mul…
Low-rank Approximation of a Matrix: Novel Insights, New Progress, and Extensions
Victor Y. Pan, Liang Zhao
Low-rank approximation of a matrix by means of random sampling has been consistently efficient in its empirical studies by many scientists who applied it with various sparse and st…