** American Journal of Computational Mathematics ** Vol. 3 No. 2 (2013) , Article ID: 33544 , 10 pages DOI:10.4236/ajcm.2013.32021

A Wavelet Multigrid Method Using Symmetric Biorthogonal Wavelets

Department of Mathematics, California State University, Fresno, USA

Email: doreendl@csufresno.edu

Copyright © 2013 Doreen De Leon. 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 March 24, 2013; revised April 25, 2013; accepted May 23, 2013

**Keywords:** Multigrid; Wavelets; Biorthogonal Wavelets

ABSTRACT

In [1], the author introduced a wavelet multigrid method that used the wavelet transform to define the coarse grid, interpolation, and restriction operators for the multigrid method. In this paper, we modify the method by using symmetric biorthogonal wavelet transforms to define the requisite operators. Numerical examples are presented to demonstrate the effectiveness of the modified wavelet multigrid method for diffusion problems with highly oscillatory coefficients, as well as for advection-diffusion equations in which the advection is moderately dominant.

1. Introduction

It is well known that the multigrid method is very useful in increasing the efficiency of iterative methods used to solve systems of algebraic equations approximating partial differential equations. However, when confronted by certain problems, for example diffusion problems with discontinuous or highly oscillatory coefficients, the standard multigrid procedure converges slowly, with a rate dependent on the initial mesh size, or may even break down.

In [2], the wavelet transform, which uses both highand low-pass filter operators, is used to derive a new approach for the two-level multigrid method, under the assumption that the matrix on the fine grid is symmetric. Some one-dimensional examples are also examined in that paper. In [1,3], the author extended the results of this approach to two dimensions and to multiple-level multigrid, dropping the assumption of a symmetric fine grid operator. This approach was considered for several reasons. First, in [4], the authors investigate similarities between multiresolution analyses and multigrid methods, which similarities motivate investigation into waveletbased multigrid methods. In addition, in [5], for example, it is shown that a wavelet coarse grid operator defined by a Schur complement provides a good approximation to the homogenized coarse grid operator, and, as stated in [1,3], homogenization has been used to improve convergence of multigrid methods for diffusion problems with periodic coefficients (e.g., [6-9]) because the homogenized operator provides a very good approximation of the important properties (e.g., eigenvalues and eigenfunctions) of the original fine grid operator. Also a wavelet coarse grid operator defined by a Schur complement has a natural connection to the interpolation and restriction operators. Furthermore, wavelets can be applied to problems with periodic as well as non-periodic coefficients. Finally, the application of wavelet operators to vectors and matrices maintains the properties of the original problem.

Recently, symmetric biorthogonal wavelets have received attention for use in image compression (see, e.g., [10,11]), based on the lifting scheme developed by Sweldens in [12]. In fact, symmetric biorthogonal wavelets are the basis for lossless compression in the JPEG 2000 standard (see, e.g., [13]). In addition, using biorthogonal wavelets to define multigrid methods has been explored in other papers. For example, in [14], the authors use biorthogonal wavelets adapted to a differential operator with constant coefficients, from which they develop a system of linear equations, and then use a biorthogonal two-grid method to solve. In [15], the authors consider solving ill-conditioned symmetric Toeplitz systems by using a two-grid method in which the interpolation and restriction operators are defined using the Cohen, Daubechies, and Feauveau (CDF) 9/7 symmetric biorthogonal wavelets. In [16], the authors use biorthogonal wavelets as preconditioners for an algebraic multigrid method which they use to solve various partial differential equations.

In this paper, the wavelet multigrid method introduced in [1] is modified by using symmetric biorthogonal wavelet transforms to define the requisite operators. The organization of this paper is as follows. In Section 2, some background on multigrid methods and wavelets is given. Section 3 discusses the wavelet multigrid method developed from the application of the biorthogonal wavelet transform to a general second order partial differential equation in two dimensions. In Section 4, numerical considerations are discussed. Section 5 presents some numerical results of applying the modified wavelet multigrid method using symmetric biorthogonal wavelets to two diffusion problems with highly oscillatory coefficients and to three advection-diffusion problems with moderately dominant advection. The rapid convergence, relatively independent of the initial mesh size, of the modified wavelet multigrid method using biorthogonal wavelets is demonstrated for these problems. For all numerical results in this paper, the V-cycle multigrid method is used with one iteration of the smoother for the coarsening and the correcting phases.

2. Background

2.1. Multigrid Methods

The problem we are concerned with solving is the system of linear equations

(1)

where and arise from discretization of a (partial) differential equation on some grid, where is the step size.

We briefly describe the V-cycle method used in this paper. Given some interpolation operator, where the superscript refers to the fine grid and the subscript refers to the coarse grid, and a restriction operator, we can define a multigrid method recursively. For the two-level V-cycle method, we do the following.

Step 1: Relax a few (usually one or two) steps on the fine grid to get an initial guess.

Step 2: Compute the residual and restrict the residual to the coarse grid.

Step 3: Solve the error equation on the coarse grid.

Step 4: Set and again relax a few (usually one or two) steps on the fine grid.

Based on this two-level method, the V-cycle multigrid scheme is defined recursively. Some good references for multigrid methods are [17-19].

One type of multigrid scheme is algebraic multigrid, which only uses the structure of the matrix in the problem to determine the coarsening process (choice of coarse grid and definition of interpolation/restriction operators). This process is performed in order to ensure that the range of interpolation approximates the errors not sufficiently reduced via relaxation. For a more detailed description of algebraic multigrid methods, see, e.g., [19- 22]. Note that in [21] the relationship between algebraic multigrid and Schur complements is discussed. Algebraic multigrid methods are of particular interest, in that they are the nearest methods to the approach used for the wavelet multigrid method.

It is expected that the multigrid method should converge at a rate independent of the fine mesh size. However, for certain problems, including elliptic problems with highly oscillatory coefficients, such convergence does not occur for the standard multigrid method. One difficulty is that the small eigenvalues of A are not necessarily associated with smooth eigenfunctions, a key assumption for the standard multigrid method. For such problems, it is not as simple to approximate the smooth eigenfunctions on the coarse grids. New methods for restriction and interpolation, or for treating the entire problem, must be found. One such approach is the wavelet multigrid method discussed in [1,3] and the modified wavelet multigrid method discussed in this paper.

2.2. Wavelets and Biorthogonal Wavelets

For background, a brief description of wavelets, and then of biorthogonal wavelets, follows. For more details, the reader is referred to [10,23-25].

2.2.1. Wavelets

Wavelets basically separate data (or functions or operators) into different frequency components and analyze them by scaling. The wavelets can be chosen to form a complete orthonormal basis of. Due to the scaling of the wavelet functions, they have timeor spacewidths that are related to their frequency: at high frequencies, they are narrow, and at low frequencies, they are broader. Therefore, they provide good localization of functions in both the frequency domain and physical space, and representation by wavelets seems natural to apply to the analysis of fine and coarse scales.

A multiresolution analysis (MRA) consists of a sequence of closed subspaces of, the scaling spaces, that satisfy certain conditions. For every is defined as the orthogonal complement of in The are called the wavelet spaces. Define and to be the operators that transform the basis of the space to the bases of the spaces and, respectively. The properties of and (assuming and are real-valued) are

(i).

(ii).

(iii).

and can be thought of in terms of filter theory, with being a low-pass filter (i.e., allowing only low frequency values to pass) and being a high-pass filter (i.e., allowing only high frequency values to pass). The wavelet transform, , is defined by

Note that is orthogonal due to the properties of and.

In the discrete context, the wavelet operators are computationally efficient. With respect to the Haar multiresolution analysis, application of the low-frequency operator to a vector in involves only operations. The same holds for the high-frequency operator. So, the application of the wavelet transform requires only operations. In general, application of the wavelet transform requires operations, assuming a finite number of coefficients for the lowand high-frequency operators.

In two dimensions, the tensor product of one-dimensional multiresolution analyses is used. So is defined by. These spaces then form an MRA in. For each, is defined as the orthogonal complement of in So,

(2)

Then, analogous to the one dimensional case, define the operators and so that

and

and have the same properties as their onedimensional analogues. As in the one-dimensional case, the wavelet transform, , is defined by

Again, due to the properties of and, is orthogonal.

2.2.2. Biorthogonal Wavelets

A biorthogonal multiresolution analysis consists of two dual multiresolution analyses. In other words, there are two dual sequences of closed subspaces of,

both of which satisfy the conditions of a multiresolution analysis. For every, can be written as the direct sum of and, and can be written as the direct sum of and. and are defined so that their biorthogonal basis functions, the wavelets and, respectively, satisfy certain conditions including forming a Riesz basis of. For symmetric biorthogonal multiresolution analyses, these wavelet bases (and the bases for the scaling spaces) are symmetric. Define and to be the operators that transform the basis of the space to the bases of the spaces and, respectively; and define and to be the operators that transform the basis of the space to the bases of the spaces and, respectively. The properties of, , and are as follows:

(i).

(ii).

(iii).

The wavelet transform, and the dual transform are defined by

where we use to denote the direct sum for ease of notation. Note that is orthogonal to due to the properties of, , and. Also, note that in terms of filter theory, and are low-pass filters, and and are high-pass filters.

The discrete biorthogonal wavelet transforms are also computationally efficient, and application of these operators requires operations, assuming a finite number of coefficients for the lowand high-frequency operators.

In two dimensions, we use the tensor product in a similar way as for the standard multiresolution analysis. So, is defined by and is defined by. These spaces and then form dual MRAs in. and are defined so that their basis functions are the tensor products of the one-dimensional wavelets and scaling functions. For each, can be written as the direct sum of and, and can be written as the direct sum of and. So, we have

(3)

and similarly for, where we again use to denote the direct sum for ease of notation.

Then, analogous to the one-dimensional case, define the operators, , and so that:

and

have the same properties as their one-dimensional analogues. As in the one-dimensional case, the wavelet transform and the dual transform and, are defined by

Again, due to the properties of, , and, is orthogonal to.

3. The Modified Wavelet Multigrid Method Using Biorthogonal Wavelets

The two-dimensional wavelet multigrid method was introduced in [3] and [1]. Both of these works assumed orthogonal wavelet operators. Here, we describe the modified wavelet multigrid method using biorthogonal wavelet transforms. For the work in this paper, the biorthogonal wavelet transform is formed using symmetric biorthogonal wavelets.

Given the problem

(4)

where represents the operator obtained by discretizing a two-dimensional partial differential equation on the fine grid, apply the wavelet transform to both sides of the equation and use the orthogonality of with to obtain

(5)

where and

The main idea for the remainder of the method follows in a similar manner as the wavelet multigrid method using orthogonal wavelet transforms. First, is computed, and the resulting matrix, denoted by, is partitioned to obtain

(6)

Then, the block UDL decomposition of, where U is block upper triangular with unit diagonal, D is block diagonal, and L is block lower triangular with unit diagonal, is computed and is used to find as follows. The block UDL decomposition of is determined to be

(7)

The inverse of this factorization of is then computed, which after multiplication of the factors gives

where we observe that is the Schur complement of in. Define

Solving for gives

Since. So,

(8)

Motivated by the work in [1], we denote

(9)

and

(10)

as the interpolation and restriction operators, respectively. Plugging the interpolation and restriction operators defined in (9) and (10) into (8) gives,

(11)

In multigrid, the error correction on the coarse grid is sought, i.e., the equation being solved is (11) with u replaced by the error, e, and f replaced by the residual, r:

Assuming that is small, i.e., r is almost in the error can be approximated by

resulting in

The above assumption is good for most of the classical iterative methods, like Jacobi and Gauss-Seidel. Therefore, the coarse grid operator is defined by

(12)

which we again note is the Schur complement of in

Note: This coarse grid operator is the same as the operator obtained by solving for:

The above procedure may be repeatedly applied until the desired coarseness is reached. Notice that this method is applicable to problems which are not symmetric. However, regardless of the discretization scheme used, the operator matrix must be a square matrix whose row size is a multiple of four.

4. Numerical Considerations

The main numerical issue is fill-in in the matrices as the multigrid method involves increasing levels in the Vcycle. Thresholding is used to reduce fill-in in the computation of. Furthermore, although is not dense, its inverse is dense due to fillin. However, a significant amount of decay is observed, indicating that it is possible to increase the efficiency of the method in this area.

One step towards achieving this goal is to compute an approximate inverse using a factorized sparse approximate inverse. In this work, we use the approach suggested by Salkuyeh in [26]. Salkuyeh’s approach is based on Kolotilina and Yeremin’s factorized sparse approximate inverse algorithm, FSAI [27,28]. The goal is to determine a factorization of the form, where is upper triangular, is lower triangular, and D is diagonal. This approximation is determined by computing sparse approximate solutions to the systems of equations

(13)

(14)

where denotes the ith principal submatrix of, , and. In Salkuyeh’s paper, these systems are solved using a self-preconditioned minimum residual (MR) algorithm with dropping in the search direction (similar to that proposed by Chow and Saad in [29]), starting with an initial sparse guess for the solution and iterating while the solution has fewer than the specified number of nonzeros, denoted by the value lfil. In our work, lfil iterations of the MR algorithm are performed in order to avoid potential infinite loops. Then, structural requirements are enforced; namely, the inverse is required to have the same structure as the matrix. In addition, thresholding is used to further reduce the fill-in in the inverse.

4.1. Cost of Computing the Approximate Inverse

The bulk of the cost of the self-preconditioned MR algorithm with dropping in the search direction can be measured in terms of the sparse matrix-sparse vector products and the sparse vector-sparse vector products. One sparse matrix-sparse vector product is required to compute the initial residual, and each iteration of the algorithm requires one sparse matrix-sparse vector product and two sparse vector-sparse vector products.

The MR algorithm is used to compute sparse approximate solutions to the system in (13)-(14). For each i, these systems have coefficient matrices that are, where i ranges from 1 to, with the size of the matrix whose inverse is being computed. Thus, the MR algorithm is run times.

The final major contributions to the cost of the approximate inverse are the computation of (1), which requires scalar inverses, and (2), which requires scalar products, where is the number of nonzero elements in, followed by one sparse matrix-sparse matrix product.

4.2. Computational Complexity of the Approximate Inverse

The following briefly examines the actual computational complexity of the approximate inverse for the wavelet multigrid method. The value of lfil used to construct the approximate inverse of depends on the type of problem, the type of wavelet used, the fine grid size, and the level in the multigrid method. For all problems and wavelets, post-processing is done to ensure that has the same structure as, and thresholding is used on all matrices (except for for diffusion problems using Haar wavelets). Note that this is an improvement on the work in [1].

Thresholding values for the two diffusion examples in Section 5.1 are given in Tables 1 and 2. Note that the thresholding values for and for the 9/7 symmetric biorthogonal wavelets depend on the fine grid size and the level of the multigrid method. Similarly, the thresholding values for for Daubechies 4 wavelets depend on the fine grid size and the level of the multigrid method.

In terms of the lfil chosen for FSAI, for the diffusion problem with oscillations in the x-direction, for the modified wavelet multigrid method using 9/7 symmetric biorthogonal wavelets lfil ranges from 3 to 6, and using the 10/6 symmetric biorthogonal wavelets lfil ranges from 4 to 10. For that same problem, for the wavelet multigrid method using Haar wavelets lfil ranges from 9 to 11, and using Daubechies 4 wavelets lfil ranges from 3 to 10. For the diffusion problem with oscillation along diagonals, for the 9/7 symmetric biorthogonal wavelets lfil ranges from 1 to 6, and for the 10/6 symmetric biorthogonal wavelets lfil ranges from 1 to 9. For that same problem, using Haar wavelets lfil ranges from 4 to 11, and using Daubechies 4 wavelets lfil ranges from 3 to 10.

Thresholding values for the three advection-diffusion examples in Section 5.2 are given in Tables 3-5. Note that the thresholding values for (and in the case of parabolic flow (9/7) and recirculant flow (10/6)) for the 9/7 and 10/6 symmetric biorthogonal wavelets depend on the fine grid size and the level of the multigrid method. Similarly, the thresholding values for (and in the case of recirculant flow) for Daubechies 4 wavelets depend on the fine grid size and the level of the multigrid method.

For the advection-diffusion problems with moderately dominant advection, for parabolic flow, for the modified wavelet multigrid method using 9/7 symmetric biorthogonal wavelets lfil ranges from 2 to 6, depending on the fine grid size and on the level of the multigrid, and using 10/6 symmetric biorthogonal wavelets, lfil ranges from 2 to 7. Using the wavelet multigrid method with Haar wavelets, lfil ranges from 2 to 12 and using it with Daubechies 4 wavelets, lfil ranges from 3 to 6. For recirculant flow, lfil ranges from 2 to 5 for 9/7 symmetric biorthogonal wavelets and from 2 to 4 for 10/6 symmetric biorthogonal wavelets, again depending on the fine grid size and on the level. For Haar wavelets, lfil ranges from 3 to 8, and for Daubechies 4 wavelets, it ranges from 2 to 10. For the problem with skewed vorticity, lfil ranges from 4 to 7 for 9/7 symmetric biorthogonal wavelets and from 2 to 6 for 10/6 symmetric biorthogonal wavelets. Using Haar wavelets, lfil ranges from 2 to 10, and using Daubechies 4 wavelets, it ranges from 4 to 9.

4.3. Storage and Other Computational Issues

The bulk of the remaining computational work occurs in the construction of the intergrid transfer and coarse grid operators. The construction of the intergrid transfer and coarse grid operators each requires two sparse matrixsparse matrix products and one sparse matrix-sparse matrix difference.

The storage requirements of the coarse grid and intergrid transfer operators are minimized by using sparse matrix storage techniques, resulting in storage requirements of the order of the number of nonzero elements in each matrix.

Table 1. Thresholding values for horizontal diffusion example in Section 5.1.

Table 2. Thresholding values for diagonal diffusion example in Section 5.1.

Table 3. Thresholding values for parabolic advection-diffusion example in Section 5.2.

Table 4. Thresholding values for advection-diffusion example with recirculant flow in Section 5.2.

Table 5. Thresholding values for advection-diffusion example with skewed recirculant flow in Section 5.2.

5. Numerical Applications

This section describes the numerical results of applying the modified wavelet multigrid method using symmetric biorthogonal wavelets to two diffusion problems with highly oscillatory coefficients and to three advectiondiffusion problems with moderately dominant advection. We compare the convergence of the modified wavelet multigrid method using both 9/7 and 10/6 symmetric biorthogonal wavelets with the wavelet multigrid method using both Haar wavelets and Daubechies 4 wavelets. For all problems, numerical results are analyzed using, for the fine grid in the interior, a 32 × 32 grid, leading to a 1024 × 1024 matrix; a 64 × 64 grid, leading to a 4096 × 4096 matrix; and a 128 × 128 grid, leading to a 16384 × 16384 matrix. In all problems, the stopping criterion for all multigrid methods is, where is the residual obtained from the kth iteration of the method.

5.1. Diffusion Problems

First, we look at the diffusion problem

(15)

where is the unit square centered at and We will examine two cases where is a highly oscillatory function:

giving oscillation in the x-direction, and, giving oscillation along diagonals. Results are examined using both 9/7 and 10/6 symmetric biorthogonal wavelets. The standard Gauss-Seidel iterative method is used as the smoother.

Tables 6 and 7 compare the average convergence factor per cycle of the modified wavelet multigrid method using symmetric biorthogonal wavelets with that of the wavelet multigrid method. These tables demonstrate results using a fixed coarsest grid size of 8 × 8 for each fine mesh size.

For the diffusion problem with oscillation in x, as well as the diffusion problem with oscillation along diagonals, the convergence of the modified wavelet multigrid method using symmetric biorthogonal wavelets is relatively independent of the fine grid size, as it is for the wavelet multigrid method using orthogonal wavelets. Convergence results for the method using both the 9/7 symmetric biorthogonal wavelets and the 10/6 symmetric biorthogonal wavelets are comparable to the results using the orthogonal wavelets for both problems.

5.2. The Advection-Diffusion Problem

Here, we are investigating the problem

(16)

where is the unit square centered at and. In this problem, difficulties with multigrid methods are encountered due to the fact that some of the components of the solution oscillate along characteristics [30,31]. We apply the wavelet multigrid method to these problems to overcome this difficulty, since application of the wavelet operator preserves the characteristics of the original problem.

To discretize, we use the standard five-point centered discretization for the diffusion term and a first order upwind scheme for the advection part of the equation. Although using first order upwind introduces artificial diffusion into the solution of the order of the mesh size squared, it provides a convenient test of the effectiveness of the modified wavelet multigrid method. Symmetric Gauss-Seidel is used as the smoother in order to ensure that sweeps are performed in the direction of the characteristics over the entire flow field. Results shown use

First, we have a comparison of the methods for (16), where and is defined by

(17)

Note that the discontinuous boundary condition will give rise to a boundary layer near the left-hand boundary. Also, the characteristics are parabolic, resulting in flow entering and exiting through the left-hand boundary. For both the modified wavelet multigrid method with symmetric biorthogonal wavelets and the wavelet multigrid method, convergence appears to be relatively independent of the fine mesh size, as can be seen in Table 8, which compares the average convergence factor per cycle using a coarsest grid size of 8 × 8.

In a second example,

giving recirculant flow (i.e., closed characteristics), and is defined in (17). The modified wavelet multigrid method using symmetric biorthogonal wavelets and the wavelet multigrid method both have a convergence rate that is relatively independent of the fine grid size. For this problem, the modified wavelet multigrid method

Table 6. Average convergence factor per cycle for diffusion problem with oscillation in x.

Table 7. Average convergence factor per cycle for diffusion problem with oscillation along diagonals.

Table 8. Average convergence factor per cycle for the advection-diffusion problem with parabolic characteristics and discontinuous boundary conditions.

Table 9. Average convergence factor per cycle for the advection-diffusion problem with recirculant flow and discontinuous boundary conditions.

Table 10. Average convergence factor per cycle for the advection-diffusion problem with skewed recirculant flow and discontinuous boundary conditions.

using 10/6 symmetric biorthogonal wavelets outperforms the wavelet multigrid method using both Haar wavelets and Daubechies 4 wavelets. The results are shown in Table 9, which compares the average convergence factor per cycle using a coarsest grid size of 8 × 8.

The final example uses the boundary conditions given by (17), but the advection component has both closed characteristics and a skewed flow, so that it does not line up with the grid. Here,

where

Both the modified wavelet multigrid method using symmetric biorthogonal wavelets and the wavelet multigrid method converge rapidly, with a convergence rate that is relatively independent of the fine grid size, as shown in Table 10, which displays the average convergence factor per cycle for the modified wavelet multigrid method using both 9/7 and 10/6 symmetric biorthogonal wavelets and for the wavelet multigrid method using both Haar wavelets and Daubechies 4 wavelets. Again, the coarsest grid size for all trials is 8 × 8.

6. Conclusion

The modification of the wavelet multigrid method to use symmetric biorthogonal wavelets has been shown to be effective and utile. The results for the modified wavelet multigrid method using the 9/7 and 10/6 symmetric biorthogonal wavelets are comparable to those obtained by using the wavelet multigrid method with Haar and Daubechies 4 wavelets. The properties of biorthogonal wavelets should permit the modified wavelet multigrid method to be efficiently applied in many cases through the use of compression. The results shown in this paper have demonstrated that it was worthwhile to further explore applying biorthogonal wavelets to the wavelet multigrid method.

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