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Suna Ma

Publications and source records attributed to Suna Ma.

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Theoretical analysis of the extended cyclic reduction algorithm

The extended cyclic reduction algorithm developed by Swarztrauber in 1974 was used to solve the block-tridiagonal linear system. The paper fills in the gap of theoretical results concerning the zeros of matrix polynomial $B_{i}^{(r)}$ with respect to a tridiagonal matrix which are computed by Newton's method in the extended cyclic reduction algorithm. Meanwhile, the forward error analysis of the extended cyclic reduction algorithm for solving the block-tridiagonal system is studied. To achieve the two aims, the critical point is to find out that the zeros of matrix polynomial $B_{i}^{(r)}$ are eigenvalues of a principal submatrix of the coefficient matrix.

math.NA

Generalised Hermite spectral methods for PDEs involving integral fractional Laplacian and Schrödinger operators

In this paper, we introduce two new families of generalised Hermite polynomials/functions (GHPs/GHFs) in arbitrary dimensions, and develop efficient and accurate generalised Hermite spectral algorithms for PDEs with integral fractional Laplacian (IFL) and/or Schrödinger operators in $\mathbb R^d.$ As a generalisation of the G. Szegö's family in 1D (1939), the first family of GHPs (resp. GHFs) are orthogonal with respect to $|\bx|^{2μ} \e^{-|\bx|^2}$ (resp. $|\bx |^{2μ}$) in $\mathbb R^d$. We further define adjoint generalised Hermite functions (A-GHFs) which have an interwoven connection with the corresponding GHFs through the Fourier transform, and which are orthogonal with respect to the inner product $[u,v]_{H^s(\mathbb R^d)}=((-Δ)^{s/ 2}u, (-Δ)^{s/2} v )_{\mathbb R^d}$ associated with the IFL of order $s>0$. Thus, the spectral-Galerkin method using A-GHFs as basis functions leads to a diagonal stiffness matrix for the IFL (which is known to be notoriously difficult and expensive to discretise). The new basis also finds efficient and accurate in solving PDEs with the fractional Schrödinger operator: $(-Δ)^s +|\bs x|^{2μ}$ with $s\in (0,1]$ and $μ>-1/2.$ Following the same spirit, we construct the second family of GHFs, dubbed as Müntz-type generalised Hermite functions (M-GHFs), which are orthogonal with respect to an inner product associated with the underlying Schrödinger operator, and are tailored to the singularity of the solution at the origin. We demonstrate that the Müntz-type GHF spectral method leads to sparse matrices and spectrally accurate to some Schrödinger eigenvalue problems.

math.NA

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

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 $M$ is constructed by approximating the variable coefficients with a ($T$+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 $M$ is approximately solved by a one-step iterative method based on the ILU(0) factorization. The ILU(0) factorization of $M\in \mathbb{R}^{(N-1)^d\times(N-1)^d}$ can be computed using $\mathcal{O}(T^{2d} N^d)$ operations, and the number of nonzeros in the factorization factors is of $\mathcal{O}(T^{d} N^d)$, $d=1,2,3$. 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 $\mathcal{O}(N^d (\log N)^2)$. As a result, the PCG method has a $\mathcal{O}(N^d (\log N)^2)$ total complexity for a $d$ dimensional domain with $(N-1)^d$ unknows, $d=1,2,3$. Numerical examples are given to demonstrate the efficiency of proposed preconditioners and the algorithm for fast matrix-vector multiplications.

math.NA