LOW-RANK APPROXIMATION TO HETEROGENEOUS ELLIPTIC PROBLEMS

被引:3
|
作者
Li, Guanglian [1 ,2 ]
机构
[1] Univ Bonn, Inst Numer Simulat, D-53115 Bonn, Germany
[2] Imperial Coll London, London, England
来源
MULTISCALE MODELING & SIMULATION | 2018年 / 16卷 / 01期
关键词
low-rank approximation; heterogeneous elliptic problems; eigenvalue decays; asymptotic expansion; layer potential technique; DOMAIN DECOMPOSITION PRECONDITIONERS; FINITE-ELEMENT METHODS; HIGH-CONTRAST MEDIA; MULTISCALE FLOWS; COEFFICIENTS; OPERATOR; EQUATION;
D O I
10.1137/17M1120737
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In this work, we investigate the low-rank approximation of elliptic problems in heterogeneous media by means of Kolmogrov n-width and asymptotic expansion. This class of problems arises in many practical applications involving high-contrast media, and their efficient numerical approximation often relies crucially on certain low-rank structure of the solutions. We provide conditions on the permeability coefficient kappa that ensure a favorable low-rank approximation. These conditions are expressed in terms of the distribution of the inclusions in the coefficient kappa, e.g., the values, locations, and sizes of the heterogeneous regions. Further, we provide a new asymptotic analysis for high-contrast elliptic problems based on the perfect conductivity problem and layer potential techniques, which allows deriving new estimates on the spectral gap for such high-contrast problems. These results provide theoretical underpinnings for several multiscale model reduction algorithms.
引用
收藏
页码:477 / 502
页数:26
相关论文
共 50 条
  • [21] Projection Methods for Dynamical Low-Rank Approximation of High-Dimensional Problems
    Kieri, Emil
    Vandereycken, Bart
    COMPUTATIONAL METHODS IN APPLIED MATHEMATICS, 2019, 19 (01) : 73 - 92
  • [22] Iterative Concave Rank Approximation for Recovering Low-Rank Matrices
    Malek-Mohammadi, Mohammadreza
    Babaie-Zadeh, Massoud
    Skoglund, Mikael
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (20) : 5213 - 5226
  • [23] Preconditioned low-rank methods for high-dimensional elliptic PDE eigenvalue problems
    Kressner D.
    Tobler C.
    Computational Methods in Applied Mathematics, 2011, 11 (03) : 363 - 381
  • [24] Lower bounds for the low-rank matrix approximation
    Li, Jicheng
    Liu, Zisheng
    Li, Guo
    JOURNAL OF INEQUALITIES AND APPLICATIONS, 2017,
  • [25] Structured low-rank approximation for nonlinear matrices
    Fazzi, Antonio
    NUMERICAL ALGORITHMS, 2023, 93 (04) : 1561 - 1580
  • [26] The inertia of the symmetric approximation for low-rank matrices
    Casanellas, Marta
    Fernandez-Sanchez, Jesus
    Garrote-Lopez, Marina
    LINEAR & MULTILINEAR ALGEBRA, 2018, 66 (11): : 2349 - 2353
  • [27] Parameterized low-rank binary matrix approximation
    Fedor V. Fomin
    Petr A. Golovach
    Fahad Panolan
    Data Mining and Knowledge Discovery, 2020, 34 : 478 - 532
  • [28] Randomized algorithms for the low-rank approximation of matrices
    Liberty, Edo
    Woolfe, Franco
    Martinsson, Per-Gunnar
    Rolchlin, Vladimir
    Tyger, Mark
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2007, 104 (51) : 20167 - 20172
  • [29] Robust Structured Low-Rank Approximation on the Grassmannian
    Hage, Clemens
    Kleinsteuber, Martin
    LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION, LVA/ICA 2015, 2015, 9237 : 295 - 303
  • [30] Optimal low-rank approximation to a correlation matrix
    Zhang, ZY
    Wu, LX
    LINEAR ALGEBRA AND ITS APPLICATIONS, 2003, 364 : 161 - 187