Fast Multilevel Computation of Low-Rank Representation of H-Matrix Blocks

被引:23
|
作者
Brick, Yaniv [1 ]
Yilmaz, Ali E. [1 ]
机构
[1] Univ Texas Austin, Austin, TX 78712 USA
关键词
Compression algorithm; integral equations; moment methods; FAST DIRECT SOLVER; ALGORITHM; APPROXIMATION; OPERATORS;
D O I
10.1109/TAP.2016.2617376
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A physics-based algorithm for accelerating the computation of method of moments matrix blocks' low-rank approximation is presented. The algorithm relies on efficient sampling of phase-and amplitude-compensated interactions using nonuniform grids. Rank-revealing analysis is applied, in a multilevel fashion, to matrices of reduced column and row dimensions that describe subdomains' interactions with these coarse grids, rather than to the original matrix blocks. As a result, significant savings are achieved, especially for the inherently more compressible dynamic quasi-planar and quasi-static cases. The algorithm's reduced storage and computation time requirements are estimated analytically and verified numerically for representative examples.
引用
收藏
页码:5326 / 5334
页数:9
相关论文
共 50 条
  • [21] Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing
    Huang, Jie
    Huang, Ting-Zhu
    Deng, Liang-Jian
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (04): : 2419 - 2438
  • [22] Existence of a low rank or H-matrix approximant to the solution of a Sylvester equation
    Grasedyck, L
    NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 2004, 11 (04) : 371 - 389
  • [23] Robust and Low-Rank Representation for Fast Face Identification With Occlusions
    Iliadis, Michael
    Wang, Haohong
    Molina, Rafael
    Katsaggelos, Aggelos K.
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 26 (05) : 2203 - 2218
  • [24] Discriminative Orthonormal Dictionary Learning for Fast Low-Rank Representation
    Dong, Zhen
    Pei, Mingtao
    Jia, Yunde
    NEURAL INFORMATION PROCESSING, PT I, 2015, 9489 : 79 - 89
  • [25] Robust and Fast Measure of Information via Low-Rank Representation
    Dong, Yuxin
    Gong, Tieliang
    Yu, Shujian
    Chen, Hong
    Li, Chen
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 6, 2023, : 7450 - 7458
  • [26] Motion object tracking based on the low-rank matrix representation
    Kong, Xiaofang
    Chen, Qian
    Xu, Fuyuan
    Gu, Guohua
    Ren, Kan
    Qian, Weixian
    OPTICAL REVIEW, 2015, 22 (05) : 786 - 801
  • [27] Learning Structured Low-Rank Representation via Matrix Factorization
    Shen, Jie
    Li, Ping
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 51, 2016, 51 : 500 - 509
  • [28] Motion object tracking based on the low-rank matrix representation
    Xiaofang Kong
    Qian Chen
    Fuyuan Xu
    Guohua Gu
    Kan Ren
    Weixian Qian
    Optical Review, 2015, 22 : 786 - 801
  • [29] Robust Bilinear Matrix Recovery by Tensor Low-Rank Representation
    Zhang, Zhao
    Yan, Shuicheng
    Zhao, Mingbo
    Li, Fan-Zhang
    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 2945 - 2951
  • [30] Low-rank and sparse matrices fitting algorithm for low-rank representation
    Zhao, Jianxi
    Zhao, Lina
    COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2020, 79 (02) : 407 - 425