Cellular Computing and Least Squares for Partial Differential Problems Parallel Solving

被引:0
|
作者
Fressengeas, Nicolas [1 ,3 ]
Frezza-Buet, Herve [2 ,3 ]
机构
[1] Univ Lorraine, Lab Mat Opt Photon & Syst, EA 4423, F-57070 Metz, France
[2] Supelec, Team Informat Multimodal & Signal, F-57070 Metz, France
[3] Georgia Tech CNRS, Int Joint Res Lab, UMI 2958, F-57070 Metz, France
关键词
Partial differential equations; cellular automata; distributed memory; parallel architectures; LSFEM; finite elements; AUTOMATA; CNN; EQUATIONS; ENVIRONMENT; MODELS;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper shows how partial differential problems can be numerically solved on a parallel cellular architecture through a completely automated procedure. This procedure leads from a discrete differential problem to a Cellular Algorithm that efficiently runs on parallel distributed memory architectures. This completely automated procedure is based on a adaptation of the Least Square Finite Elements Method that allows local only computations in a discrete mesh. These local computations are automatically derived from the discrete differential problem through formal computing and lead automatically to a Cellular Algorithm which is efficiently coded for parallel execution on a dedicated distributed interactive platform.
引用
收藏
页码:1 / 21
页数:21
相关论文
共 50 条
  • [21] Partial least squares and compositional data: Problems and alternatives
    Hinkle, J
    Rayens, W
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1995, 30 (01) : 159 - 172
  • [22] Collocated Mixed Discrete Least Squares Meshless (CMDLSM) method for solving quadratic partial differential equations
    Gargari, S. Faraji
    Kolandoozan, M.
    Afshar, M. H.
    SCIENTIA IRANICA, 2018, 25 (04) : 2000 - 2011
  • [23] COMPUTING PARTIAL SPECTRA WITH LEAST-SQUARES RATIONAL FILTERS
    Xi, Yuanzhe
    Saad, Yousef
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2016, 38 (05): : A3020 - A3045
  • [24] Simplified neural networks for solving linear least squares and total least squares problems in real time
    Cichocki, Andrzej
    Unbehauen, Rolf
    1600, IEEE, Piscataway, NJ, United States (05):
  • [25] Solving Toeplitz least squares problems via discrete polynomial least squares approximation at roots of unity
    Van Barel, M
    Heinig, G
    Kravanja, P
    ADVANCED SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, AND IMPLEMENTATIONS X, 2000, 4116 : 167 - 172
  • [26] PLS1-MD: A partial least squares regression algorithm for solving missing data problems
    Gonzalez, Victor
    Giraldo, Ramon
    Leiva, Victor
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2023, 240
  • [27] A derivative free iterative method for solving least squares problems
    Hongmin Ren
    Ioannis K. Argyros
    Saïd Hilout
    Numerical Algorithms, 2011, 58 : 555 - 571
  • [28] A derivative free iterative method for solving least squares problems
    Ren, Hongmin
    Argyros, Ioannis K.
    Hilout, Said
    NUMERICAL ALGORITHMS, 2011, 58 (04) : 555 - 571
  • [29] AN ALGORITHM FOR SOLVING SPARSE NONLINEAR LEAST-SQUARES PROBLEMS
    MARTINEZ, JM
    COMPUTING, 1987, 39 (04) : 307 - 325
  • [30] SOLVING ELLIPSOID-CONSTRAINED INTEGER LEAST SQUARES PROBLEMS
    Chang, Xiao-Wen
    Golub, Gene H.
    SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2009, 31 (03) : 1071 - 1089