6 citations · 37 across the 17 of their papers we have counts for
11 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…
Counting Roots of a Polynomial in a Convex Compact Region by Means of Winding Number Calculation via Sampling
Vitaly Zaderman, Liang Zhao
In this paper we propose a novel efficient algorithm for calculating winding numbers, aiming at counting the number of roots of a given polynomial in a convex region on the complex…
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…
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 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…
Fast Derandomized Low-rank Approximation and Extensions
Victor Pan, John Svadlenka, Liang Zhao
Low-rank approximation of a matrix by means of structured random sampling has been consistently efficient in its extensive empirical studies around the globe, but adequate formal s…
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…
Polynomial Root Isolation by Means of Root Radii Approximation
Victor Y. Pan, Liang Zhao
Univariate polynomial root-finding is a classical subject, still important for modern computing. Frequently one seeks just the real roots of a real coefficient polynomial. They can…
New Studies of Randomized Augmentation and Additive Preprocessing
Victor Y. Pan, Liang Zhao
1. A standard Gaussian random matrix has full rank with probability 1 and is well-conditioned with a probability quite close to 1 and converging to 1 fast as the matrix deviates fr…