An Efficient Randomized Fixed-Precision Algorithm for Tensor Singular Value Decomposition  

作　　者：Salman Ahmadi-Asl 

机构地区：[1]Skolkovo Institute of Science and Technology(SKOLTECH),Center for Artificial Intelligence Technology,Moscow,Russia

出　　处：《Communications on Applied Mathematics and Computation》2023年第4期1564-1583,共20页应用数学与计算数学学报（英文）

基　　金：the Ministry of Education and Science of the Russian Federation(Grant 075.10.2021.068).

摘　　要：The existing randomized algorithms need an initial estimation of the tubal rank to compute a tensor singular value decomposition.This paper proposes a new randomized fixed-precision algorithm which for a given third-order tensor and a prescribed approximation error bound,it automatically finds the tubal rank and corresponding low tubal rank approximation.The algorithm is based on the random projection technique and equipped with the power iteration method for achieving better accuracy.We conduct simulations on synthetic and real-world datasets to show the efficiency and performance of the proposed algorithm.

关 键 词：Tubal tensor decomposition RANDOMIZATION Fixed-precision algorithm 

分 类 号：O17[理学—数学]