3 citations · 4 across the 11 of their papers we have counts for
7 papers · 1 filter
A Novel Block-Alternating Iterative Algorithm for Retrieving Top- Elements from Factorized Tensors
Chuanfu Xiao, Jiaxin Zeng
Tensors, especially higher-order tensors, are typically represented in low-rank formats to preserve the main information of the high-dimensional data while saving memory space. In…
Provable Low-Rank Tensor-Train Approximations in the Inverse of Large-Scale Structured Matrices
Chuanfu Xiao, Kejun Tang, Zhitao Zhu
This paper studies the low-rank property of the inverse of a class of large-scale structured matrices in the tensor-train (TT) format, which is typically discretized from different…
HaTT: Hadamard avoiding TT recompression
Zhonghao Sun, Jizu Huang, Chuanfu Xiao +1
The Hadamard product of tensor train (TT) tensors is a fundamental nonlinear operation in scientific computing and data analysis. However, due to its tendency to significantly incr…
APTT: An accuracy-preserved tensor-train method for the Boltzmann-BGK equation
Zhitao Zhu, Chuanfu Xiao, Kejun Tang +2
Solving the Boltzmann-BGK equation with traditional numerical methods suffers from high computational and memory costs due to the curse of dimensionality. In this paper, we propose…
HOSCF: Efficient decoupling algorithms for finding the best rank-one approximation of higher-order tensors
Chuanfu Xiao, Zeyu Li, Chao Yang
Best rank-one approximation is one of the most fundamental tasks in tensor computation. In order to fully exploit modern multi-core parallel computers, it is necessary to develop d…
A rank-adaptive higher-order orthogonal iteration algorithm for truncated Tucker decomposition
Chuanfu Xiao, Chao Yang
We propose a novel rank-adaptive higher-order orthogonal iteration (HOOI) algorithm to compute the truncated Tucker decomposition of higher-order tensors with a given error toleran…
Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors
Chuanfu Xiao, Chao Yang, Min Li
The low multilinear rank approximation, also known as the truncated Tucker decomposition, has been extensively utilized in many applications that involve higher-order tensors. Popu…