Hermite张量的秩R正Hermite逼近算法与正Hermite分解

被引:0
|
作者
杨博 [1 ]
李颖 [1 ]
倪谷炎 [1 ]
张梦石 [1 ]
机构
[1] 国防科技大学理学院数学系
基金
湖南省自然科学基金;
关键词
Hermite张量; 正Hermite分解; 秩R逼近; BFGS算法; 量子混合态;
D O I
暂无
中图分类号
O183.2 [张量分析];
学科分类号
070104 ;
摘要
Hermite张量是Hermite矩阵的高阶推广,可以用于表示量子混合态.在量子信息中,量子混合态的可分性判别和分解问题仍然是一个重要而棘手的问题.本文推导逼近函数的梯度,进而提出3种算法:Hermite张量的秩R正Hermite逼近的负梯度算法和BFGS (Broyden-Fletcher-Goldfarb-Shanno)算法,以及Hermite张量可分性判别和分解的BFGS算法.基于Taylor公式和凸分析,本文证明BFGS算法的有效性.数值算例进一步验证理论分析的正确性和算法的有效性.结果表明, BFGS算法可用于Hermite张量的可分性判别和正Hermite分解,并可得到其正Hermite秩分解.与半定松弛算法相比, BFGS算法能够分解高阶或高维Hermite张量且运行时间短.
引用
收藏
页码:1125 / 1144
页数:20
相关论文
共 22 条
  • [1] Ying Li,Guyan Ni.Separability discrimination and decomposition of m-partite quantum mixed states[J].Physical Review A,2020
  • [2] David Hong,Tamara G. Kolda,Jed A. Duersch.Generalized Canonical Polyadic Tensor Decomposition[J].SIAM Review,2020
  • [3] Michiel Vandecappelle,Nico Vervliet,Lieven De Lathauwer.A second-order method for fitting the canonical polyadic decomposition with non-least-squares cost[J].IEEE Transactions on Signal Processing,2020
  • [4] Stochastic Gradients for Large-Scale Tensor Decomposition
    Kolda, Tamara G.
    Hong, David
    [J]. SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE, 2020, 2 (04): : 1066 - 1095
  • [5] Jiawang Nie.Low Rank Symmetric Tensor Approximations[J].SIAM Journal on Matrix Analysis and Applications,2017
  • [6] Generating Polynomials and Symmetric Tensor Decompositions
    Jiawang Nie
    [J]. Foundations of Computational Mathematics, 2017, 17 : 423 - 465
  • [7] Spherical optimization with complex variablesfor computing US-eigenpairs
    Guyan Ni
    Minru Bai
    [J]. Computational Optimization and Applications, 2016, 65 : 799 - 820
  • [8] Chun-Feng Cui,Yu-Hong Dai,Jiawang Nie.All Real Eigenvalues of Symmetric Tensors[J].SIAM Journal on Matrix Analysis and Applications,2014
  • [9] Jiawang Nie,Li Wang.Semidefinite Relaxations for Best Rank-1 Tensor Approximations[J].SIAM Journal on Matrix Analysis and Applications,2014
  • [10] Guyan Ni,Liqun Qi,Minru Bai.Geometric Measure of Entanglement and U-Eigenvalues of Tensors[J].SIAM Journal on Matrix Analysis and Applications,2014