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pro vyhledávání: '"Luan, Qi"'
We approximate the d complex zeros of a univariate polynomial p(x) of a degree d or those zeros that lie in a fixed region of interest on the complex plane such as a disc or a square. Our divide and conquer algorithm of STOC 1995 supports solution of
Externí odkaz:
http://arxiv.org/abs/2301.11268
Publikováno v:
In Theoretical Computer Science 19 February 2025 1027
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 for Big Data Mining and Analysis. Quite typically in that area the input
Externí odkaz:
http://arxiv.org/abs/1907.10481
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 LRA at sublinear cost, that is, by using much fewer memory cells and flops th
Externí odkaz:
http://arxiv.org/abs/1906.04112
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 of the resulting algorithm to oversampling techniques, commonly used i
Externí odkaz:
http://arxiv.org/abs/1906.04223
Autor:
Luan, Qi, Pan, Victor Y.
We call a matrix algorithm superfast (aka running at sublinear cost) if it involves much fewer flops and memory cells than the matrix has entries. Using such algorithms is highly desired or even imperative in computations for Big Data, which involve
Externí odkaz:
http://arxiv.org/abs/1906.04929
Low Rank Approximation (LRA) of an m-by-n matrix is a hot research subject, fundamental for Matrix and Tensor Computations and Big Data Mining and Analysis. Computations with LRA can be performed at sublinear cost -- by using much fewer than mn memor
Externí odkaz:
http://arxiv.org/abs/1906.04327
Autor:
Luan, Qi, Pan, Victor Y.
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 simple deterministic variation which computes such a solution for a r
Externí odkaz:
http://arxiv.org/abs/1906.03784
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 input matrix has entries. We first specify a family of 2mn matrices of s
Externí odkaz:
http://arxiv.org/abs/1710.07946
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 machine learning theory and %neural networks to data mining and analysis. Th
Externí odkaz:
http://arxiv.org/abs/1611.01391