An Algebraic Multigrid Method for Higher-order Finite Element Discretizations

  • Authors:

  • S. Shu;D. Sun;J. Xu

  • Affiliations:

  • Department of Mathematics, Xiangtan University, 411105, Hunan, P.R. China;Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Sciences, P.O. Box 2719, Academia Sinica, 100080, Beijing, P.R. China;Institute for Computational and Applied Mathematics, Xiangtan Univ., P.O. Box 2719, Academia Sinica, 411105, Hunan, P.R. China and Ctr. for Computl. Math. and Appl., Dept. of Math., Penn. Ste. Uni ...

  • Venue:
  • Computing
  • Year:
  • 2006

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Abstract

In this paper, we will design and analyze a class of new algebraic multigrid methods for algebraic systems arising from the discretization of second order elliptic boundary value problems by high-order finite element methods. For a given sparse stiffness matrix from a quadratic or cubic Lagrangian finite element discretization, an algebraic approach is carefully designed to recover the stiffness matrix associated with the linear finite element disretization on the same underlying (but nevertheless unknown to the user) finite element grid. With any given classical algebraic multigrid solver for linear finite element stiffness matrix, a corresponding algebraic multigrid method can then be designed for the quadratic or higher order finite element stiffness matrix by combining with a standard smoother for the original system. This method is designed under the assumption that the sparse matrix to be solved is associated with a specific higher order, quadratic for example, finite element discretization on a finite element grid but the geometric data for the underlying grid is unknown. The resulting new algebraic multigrid method is shown, by numerical experiments, to be much more efficient than the classical algebraic multigrid method which is directly applied to the high-order finite element matrix. Some theoretical analysis is also provided for the convergence of the new method.