6 citations · 22 across the 10 of their papers we have counts for
9 papers · 1 filter
CUR Low Rank Approximation at Deterministic Sublinear Cost
Qi Luan, Victor Y. Pan, John Svadlenka
A matrix algorithm runs at {\em sublinear cost} if it uses much fewer memory cells and arithmetic operations than the input matrix has entries. Such algorithms are indispensable fo…
Superfast iterative refinement of low rank approximation of a matrix based on ALS method and random sampling
Qi Luan, Victor Y. Pan
A matrix algorithm runs superfast (aka at sublinear cost) if it involves much fewer flops and memory cells than an input matrix has entries. Big Data are frequently represented by…
Low Rank Approximation at Sublinear Cost
Qi Luan, Victor Y. Pan, John Svadlenka +1
Low Rank Approximation (LRA) of a matrix is a hot research subject, fundamental for Matrix and Tensor Computations and Big Data Mining and Analysis. Computations with low rank matr…
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…
Superfast Approximate Linear Least Squares Solution of a Highly Overdetermined Linear System of Equations
Qi Luan, Victor Y. Pan
With a high probability the Sarlos randomized algorithm of 2006 outputs a nearly optimal least squares solution of a highly overdeterminedlinear system of equations. We propose its…
Superfast Low Rank Approximation
Soo Go, Qi Luan, Victor Y. Pan +2
Low rank approximation of a matrix (LRA) is a highly important area of Numerical Linear and Multilinear Algebra and Data Mining and Analysis. One can operate with an LRA superfast…
Superfast CUR Matrix Algorithms, Their Pre-Processing and Extensions
Victor Y. Pan, Qi Luan, John Svadlenka +1
We study superfast algorithms that computes low rank approximation of a matrix (hereafter referred to as LRA) that use much fewer memory cells and arithmetic operations than the in…
Superfast Low-Rank Approximation and Least Squares Regression
Victor Y. Pan, Qi Luan, John Svadlenka +1
Low Rank Approximation is among most fundamental subjects of numerical linear algebra having important applications to various areas of modern computing and %they range from machin…