6 citations · 37 across the 17 of their papers we have counts for
5 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…
Convergence and Applications of ADMM on the Multi-convex Problems
Junxiang Wang, Liang Zhao
In recent years, although the Alternating Direction Method of Multipliers (ADMM) has been empirically applied widely to many multi-convex applications, delivering an impressive per…