﻿Finite Difference Preconditioners for Legendre Based Spectral Element Methods on Elliptic Boundary Value Problems

Applied Mathematics
Vol.4 No.5(2013), Article ID:31570,10 pages DOI:10.4236/am.2013.45115

Finite Difference Preconditioners for Legendre Based Spectral Element Methods on Elliptic Boundary Value Problems*

Seonhee Kim1, Amik St-Cyr2, Sang Dong Kim1#

1Department of Mathematics, Kyungpook National University, Daegu, South Korea

2Computation and Modeling, Shell Technology Center Houston (STCH), Houston, USA

Email: seonykim@knu.ac.kr, Amik.St-Cyr@shell.com, #skim@knu.ac.kr

Copyright © 2013 Seonhee Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received November 13, 2012; revised April 1, 2013; accepted April 8, 2013

Keywords: Finite Difference Preconditioner; Iterative Method; Spectral Element Method; Elliptic Operator

ABSTRACT

Finite difference type preconditioners for spectral element discretizations based on Legendre-Gauss-Lobatto points are analyzed. The latter is employed for the approximation of uniformly elliptic partial differential problems. In this work, it is shown that the condition number of the resulting preconditioned system is bounded independently of both of the polynomial degrees used in the spectral element method and the element sizes. Several numerical tests verify the h-p independence of the proposed preconditioning.

1. Introduction

Linear systems engendered by spectral element discretizations of a simple second-order elliptic boundary value problem have large condition numbers depending on the number of elements E and the polynomial degree N employed. Convergence of iterative solvers thus deteriorates as E and N increases. Regardless of these restrictions, the spectral element method is very popular, accurate and used in many engineering problems. However, it is widely known that an efficient preconditioner is necessary in order to improve the convergence of Krylov subspace methods traditionally used to solve the resulting linear system (see [1-7]). Since the work of [8], it is numerically known that finite difference preconditioning of the spectral element matrix leads to satisfactory results in terms of convergence rates. Multigrid methods are optimal in terms of convergence rate and have linear cost for finite difference problems. The Algebraic multigrid (=: AMG) method can be easily applied to finite difference discretizations of elliptic operators. If it is instead applied directly to high-order discretizations, such as spectral elements, some outstanding issues still need to be addressed. The idea of employing a low-order discretization combined with multigrid as the preconditioner of a high-order problem was investigated in [9] where P1 finite elements were employed instead of finite differences. Other efforts concerning the computational cost of P1 finite element based two levels additive Schwarz preconditioners can be found in [10,11]. In both approaches an intermediate problem for the Laplace equation was constructed using the high-order Legendre-Gauss-Lobatto (=: LGL) nodes. Furthermore, analytic work was performed in [12] for a second-order uniformly elliptic boundary value problem using LGL nodes and also the analytic research based on Chebyshev-Gauss-Lobatto (=: CGL) nodes was done in [13], in which the various node configurations (LGL and CGL nodes) were employed for the construction of the P1 finite element preconditioner. In the 1- and 2-dimensional case, the approach was proven optimal and scalable, respectively.

Thus an efficient and optimal algorithm, with linear cost, for solving problems based on spectral element discretizations, which guarantees the convergence of the overall preconditioning strategy, is readily available. The P1 finite element matrix lowers the complexity of the system to invert since the matrices, representing the Laplacian, are tri, penta or hepta diagonal and are easily solvable using the multigrid method.

Recently Canuto et al. [14,15] have discussed preconditioning with low order finite-elements methods. They show the equivalence between spectral elements and low order finite-elements. The proof is shown for finite-elements and so called numerical integration (NI). In our approach, following motivations found in [4], we propose another preconditioning approach using a simple finite difference scheme based on the LGL nodes in a pointwise approximation perspective. The latter is represented as tensor product matrices, and finite element related properties are employed to prove its optimality. Additionally, it is shown that the proposed preconditioners herein are optimal from the theoretical analysis standpoint and that optimality is confirmed with numerical experiments.

The paper is organized as follows. First, the problem is described in Section 2 and basic definitions are recalled in the same Section 2 also. In the same section, the finite difference preconditioner is introduced. In Section 3, a uniform bound for, the ratio of LGL weights to the distance between the adjacent LGL nodes, is derived which will be used for the 2D case. The optimality results are presented in Sections 4 and 5 with the numerical experiments which support the developed theories. Finally, conclusions are drawn.

2. Description of the Problem

Consider the classical boundary value problem of a second-order elliptic operator

(2.1)

where and are satisfying

(2.2)

The resulting matrix for the operator A, with variable coefficients, discretized using spectral element methods based on LGL nodes is represented as and in oneand two-dimensional case, respectively. Twodimensional matrix is the result of tensor products using the matrices obtained in the one-dimensional case. We shall consider the preconditioner and for the 1D and 2D case, respectively. Then the problem is to demonstrate the validity of the preconditioners for and for, respectively. Moreover, the goal is to show that the preconditioners are optimal in the sense that the condition numbers of the preconditioned systems and for each case are independent of mesh sizes of elements and degrees of the piecewise polynomials employed in spectral element discretization.

For the above goal, we will introduce some notations and definitions from now on. Let and be the reference LGL points in and quadrature weights associated with the -degree Legendre polynomial. With dimensionless notations t, let us denote as the knots in the interval such that For convenience, we denote as the degree of Legendre polynomial on each subinterval. will be the sum of the degrees of each element up to k:. In particular, the notation is used when k = E and. The translated LGL points and corresponding weights from and are

with

(2.3)

and, respectively,:

(2.4)

and for.

For simplicity, all LGL points are arranged as and the corresponding LGL weights are ordered as with

. Notice that from (2.3) for follows.

Let be the space of all polynomials defined on whose degrees are less than or equal to k and let be the subspace of, which consists of piecewise polynomials with support whose degrees are less than or equal to. Let be the space of all piecewise Lagrange linear functions with respect to on. Define as the space of all Lagrange piecewise linear functions with respect to. Let be the standard Sobolev space with zero boundary conditions. Let and

be the finite dimensional subspaces of

with the basis functions and

respectively, where. To communicate between the space of piecewise linear functions and the space of piecewise polynomial, we use the interpolation operator such that for.

For the two-dimensional case, the LGL points are reordered by horizontal lines or, more precisely, all the points are listed as, where

for,. For the two-dimensional high-order and piecewise linear space, let

and

whose basis functions are given by tensor products of one-dimensional piecewise Lagrange polynomials and linear functions, respectively. Let.

Note that, where, or.

To provide the preconditioner, using the global LGL points, we define the operator on

(2.5)

where,.

With this operator and components, , we define the one-dimensional finite difference operator as following:

where.

The two-dimensional finite difference operator is therefore defined as

(2.6)

where is the 2D version of (2.5) for or y, precisely,

(2.7)

and, for or y and.

Finally, the notation for any two real quantities a and b is a shorthand notation corresponding to the existence of two positive constants c and C, which do not depend on the mesh sizes and the degrees of polynomials such that The notation (U, V) stands for for any two vectors and where the superscript T denotes the transpose of a vector or matrix. The standard Sobolev space H1 and L2 will be used. And we will use, and as the standard Sobolev H1-norm, H1-seminorm and the usual L2-norm, respectively.

3. Basic Estimates

We begin by recalling the relations between the distances of LGL nodes and the LGL weights in the reference element found in [6].

Lemma 3.1. With LGL nodes and LGL weights, let

(3.1)

(3.2)

Then the are uniformly bounded for all . In particular,

(3.3)

where are constants independent of the polynomial degree N.

Proof. See Lemma 3.1 in [6].                   □

Since the goal is to develop preconditioners on spectral elements considering different polynomial degrees on each elements of which sizes are not identical, we need an advanced version or -version of Lemma 3.1. Hence the modifications of (3.1)-(3.2) for

are required, where

.

First, let, and be defined as

(3.4)

and

(3.5)

Lemma 3.2. The given in (3.4)-(3.5) are bounded for all independently of both degrees of polynomials and the mesh sizes of each element.

Proof. Since the LGL nodes in each subinterval are defined by an affine map transformation from the ones in reference interval, they depend on the mesh sizes of each element thus they are distinguished as follows

(3.6)

Since the first and second cases of (3.6) are

and

it is clear that, are uniformly bounded by Lemma 3.1.

For the case, , it can be easily shown that

Lastly for, , note that

Using (3.3), it comes

and

where are the absolute positive constants that appear in Lemma 3.1. Therefore,

which completes the proof.                       □

Define the two matrices W and H, which are made up of, respectively, the quadrature weights and the distances between the LGL points:

(3.7)

where and.

Notice that the quantities and are positive for all. By Lemma 3.2, defined in (3.5) are uniformly bounded. Thus we have the following corollary:

Corollary 3.3. For, it follows that

The next result is a clear consequence but we write down for convenience.

Lemma 3.4. For any diagonal matrix S with nonnegative entries whose size is the same as W (see definition in (3.7)),

where the equivalence depends on the minimum and maximum entries of S.

The matrix W will be replaced by for twodimensional problems.

Proposition 3.5. Let be symmetric and positive definite matrices such that

(3.8)

for any nonzero vector. Then for any symmetric and positive definite matrix, we have

(3.9)

Proof. Since all the matrices are symmetric and positive definite, it is enough to discuss (3.9) in terms of eigenvalues.

Consider the eigenvalue problem

It has a complete set of eigenpairs, . Let

(3.10)

where 1 is a vector consisting of element 1 with length.

Since

the vectors and eigenvalues in (3.10) are complete sets of eigenvectors and eigenvalues of the eigenvalue problem

From (3.8), since are positive, bounded and independent of, , so are. Therefore it follows

By the same reasoning, it follows

.        □

Finally, the known results stated in Theorem 3.3 and 3.5 from [12] are recalled here for completeness.

Theorem 3.6. It follows that for all,

4. One-Dimensional Case

Before going ahead, suppose that and satisfy and , where, , and are constants. Now consider the following elliptic boundary value problem with zero boundary condition:

(4.1)

Expanding and in (4.1) on the space as and, the matrix representation of the operator A in (4.1) by the spectral element discretization based on LGL points is now given by

(4.2)

where, , D is the differentiation matrix defined as and W is the matrix defined in (3.7).

Since P and Q are the diagonal matrices with nonnegative elements, by Lemma 3.4 we have for any vector U,

and

More precisely, it follows that

(4.3)

(4.4)

Besides, we can see that for

,

(4.5)

where.

Let be the matrix representation of, which is defined in (2.5). For, the easy calculation leads to

(4.6)

where.

Note that the constants such as, appeared in this and next sections are generic positive constants, independent of the number of elements and the degree of polynomials applied to spectral discretization.

Now to provide a finite difference type preconditioner, consider the bilinear form with a positive constant, defined on the space

where. This induces the matrix:

Thus it comes immediately

(4.7)

Lemma 4.1. For any vector, it follows that

Proof. Note that can be expressed as

, so that its piecewise polynomial interpolation becomes.

Since, it follows from (4.3)-(4.5), that

The last inequality is due to Poincare’s inequality. Therefore, (4.7), (4.6) and Theorem 3.6 complete the proof.                                        □

Remark 1. From the above lemma, one may notice that the upper bound for the condition numbers of the matrix does not depend on the mesh size hj of an element and the degree Ni of a polynomial. Further, it does not depend on. Hence, even if (4.1) has coefficient functions and, it is enough to take the preconditioner with in (4.7).

Next, we consider another bilinear form on the space with a constant

where and. The matrix representation corresponding to is

where

(4.8)

Now the goal is to analyze the validity of the matrix operator for the preconditioner to.

Theorem 4.2. For any vector, it follows that

where the equivalence depends only on, , , , and.

Proof. For such that, its piecewise polynomial interpolation is

. Using (4.3)-(4.5), we get

On the other hand, applying Corollary 3.3, (4.5) and Theorem 3.6 we have

(4.9)

so that

Hence, using Theorem 3.6 again, we have

To guarantee the positivity of the lower bound, if, we will take. Applying Poincare’s inequality with (4.9) and (4.6), we have for some positive constant C. Therefore, using (4.3)-(4.5) and Theorem 3.6, we get

so that

The latter, combined with the min-max theorem, yields the next result.

Corollary 4.3. The eigenvalues of are all positive real and bounded above and below. The bounds are independent of the mesh sizes hj and the degrees of the polynomials Nk. That is, there are absolute constants and such that

Remark 2. Theorem 4.2 reveals that the condition numbers of are bounded above by

or. Hence one may notice that the condition numbers do not depend on the degrees of polynomials and the mesh sizes.

According to Remark 2, we may summarize the behavior of the condition numbers of in the following corollary.

Corollary 4.4. The upper bound of the the condition numbers of behaves like, , or.

We will investigate the efficiency of the preconditioners and for several problems with constant and variable coefficients. Moreover, for a variable coefficients problem, we will compare the developed preconditioners with the finite element preconditioner (see [12]) in terms of iteration numbers using the preconditioned conjugate gradient method (=: PCG).

Example 1. Consider the operator with constant coefficients:

(4.10)

with homogeneous Dirichlet boundary conditions, where and are constants. Note that the spectral element discretized matrix corresponding to (4.2) becomes. Since the condition numbers are dependent on the ratios and by the definition, we take and focus mainly on the role of, where.

First, it is computed with. Figure 1 is shown to be ineffective for large values, but we can see that is effective for -value equal or less than 1 (see Table 1).

Second, to show that the preconditioning work is not independent of the polynomial degrees N and the numbers of element E, applied in spectral element method, it is tested for the cases with and . For the values, the condition numbers of go up to approximately when, but ones of the matrices preconditioned by are bounded above by approximately 4.5, independently of N, E (see Figure 2).

Third, We investigated the condition numbers of the matrices by preconditioned by with several -values for each -value (see Figure 3). When is taken to be equal to -value or, it gives good preconditioning results (see Figure 4).

Example 2. Now we consider the operator with the variable coefficients:

(4.11)

with homogeneous Dirichlet boundary conditions, where and, where and are constants. In this case, the spectral element discretization yield the matrix

, where P and are the matrices of and represented by spectral

Figure 1. Condition numbers of, where.

Table 1. Condition numbers of, where.

Figure 2. Condition numbers of and, where.

Figure 3. Behaviors of, where.

element discretization, respectively. Figure 5 shows that or give the good preconditioning results.

Example 3. For the variable coefficients problem of Example 2 with and:

Figure 4. Condition numbers of, where βH.

Figure 5. Condition numbers of, where βH.

(4.12)

we compute the iteration number using PCG preconditioned by. Also we compare the developed preconditioners with the finite element preconditioner (see [10,12] for example) in terms of iteration numbers using PCG.

The computational results are shown in Table 2 with various numbers of elements E and polynomial degrees N. While the number of iterations increases as N or E becomes large in case of CG iterations, the PCG preconditioned by gives relatively small. In particular, it can be predicted that the preconditioning effects become stronger as N or E are made larger. Furthermore we note that the results are comparable to the ones for finite element preconditioner.

5. Two-Dimensional Case

In this section the tensor notation is employed. It has the form and the superscripts denote the spatial dimension on which it acts. Their order will always be the same and thus we omit them. For the tensor product representation, we refer to [16].

Consider the elliptic operator corresponding to 2D case with zero boundary conditions:

(5.1)

where and are satisfying ,.

As in the 1D case, by expanding the variable coefficients and in terms of the basis

of, two coefficients matrices are obtained:

Table 2. Iteration numbers in 1D.

Then the matrix from the spectral element discretization, using one-dimensional matrices D and W, becomes

(5.2)

where is the 1D mass matrix, which becomes the identity for the Lagrange basis.

From the similar argument in 1D case such as (4.3)- (4.4), we have

for any nonzero vector.

In order to describe the preconditioner for the case, with the operator defined in (2.6), we consider a bilinear form on

which becomes

where

(5.3)

and is the matrix induced from (2.7) for each or.

Notice that in the proof of Lemma 4.1 it is shown that is for any nonzero vector. Using this fact and Proposition 3.5, we have the following:

Theorem 5.1. For any vector, we have

where the equivalence are dependent only on, , , , and.

Hence, the eigenvalues of are all positive and bounded. The bounds are independent of the mesh sizes and the degrees, where or.

Remark 3. This result reveals, as in case, that the upper bound for the condition numbers of the matrix is dependent only on the variable coefficients and, precisely the condition numbers are bounded above by or .

In the following example, the various eigenvalues and condition numbers of the preconditioned matrix are reported. They are compared with their finite element sibling. Also included is a comparison of the iteration counts, for a PCG solver, for the proposed preconditioners.

Example 1. Consider the homogeneous Dirichlet boundary value problem

where and

.

As in 1D case, we consider or to have a good preconditioning work, which are shown in Figure 6.

Example 2. For the homogeneous Dirichlet boundary value problem

we now compare the developed preconditioners with the finite element preconditioner in terms of iteration numbers using the PCG.

As in the 1D case, we compare the number of PCG iterations for the preconditioners and which is provided in [12]. In Table 3, we can see that the suggested finite difference preconditioner is more

Figure 6. Condition numbers of.

Table 3. Iteration numbers in 2D.

efficient as compared to the finite element preconditioner.

6. Conclusion

We have proposed finite differences preconditioners and for spectral element discretizations, and constructed on LGL nodes for uniformly elliptic problem in n-dimension, , respectively. The two preconditioners optimality for the corresponding spectral elements problems was demonstrated through the theoretical proofs and the computational results. The burden of the efficiency is now on the multigrid algorithm for solving finite-differences problems and not on MG for high-order elements.

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NOTES

*The third author (corresponding) was supported by basic science research program through the National Research Foundation of Korea (NRF) funded by the ministry of education, science and technology (grant number 2010-0008317) and Kyungpook National University Research Fund 2012.

#Corresponding author.