1 research outputs found

    Preconditioned Legendre spectral Galerkin methods for the non-separable elliptic equation

    Full text link
    The Legendre spectral Galerkin method of self-adjoint second order elliptic equations usually results in a linear system with a dense and ill-conditioned coefficient matrix. In this paper, the linear system is solved by a preconditioned conjugate gradient (PCG) method where the preconditioner MM is constructed by approximating the variable coefficients with a (TT+1)-term Legendre series in each direction to a desired accuracy. A feature of the proposed PCG method is that the iteration step increases slightly with the size of the resulting matrix when reaching a certain approximation accuracy. The efficiency of the method lies in that the system with the preconditioner MM is approximately solved by a one-step iterative method based on the ILU(0) factorization. The ILU(0) factorization of M∈R(Nβˆ’1)dΓ—(Nβˆ’1)dM\in \mathbb{R}^{(N-1)^d\times(N-1)^d} can be computed using O(T2dNd)\mathcal{O}(T^{2d} N^d) operations, and the number of nonzeros in the factorization factors is of O(TdNd)\mathcal{O}(T^{d} N^d), d=1,2,3d=1,2,3. To further speed up the PCG method, an algorithm is developed for fast matrix-vector multiplications by the resulting matrix of Legendre-Galerkin spectral discretization, without the need to explicitly form it. The complexity of the fast matrix-vector multiplications is of O(Nd(log⁑N)2)\mathcal{O}(N^d (\log N)^2). As a result, the PCG method has a O(Nd(log⁑N)2)\mathcal{O}(N^d (\log N)^2) total complexity for a dd dimensional domain with (Nβˆ’1)d(N-1)^d unknows, d=1,2,3d=1,2,3. Numerical examples are given to demonstrate the efficiency of proposed preconditioners and the algorithm for fast matrix-vector multiplications
    corecore