Qigang Liang
School of Mathematical Science, Tongji University, Shanghai 200092, China, qigang[email protected]Xuejun Xu
School of Mathematical Science, Tongji University, Shanghai 200092, China, qigang[email protected]Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China, [email protected]Shangyou Zhang
Department of Mathematical Sciences, University of Delaware, Newark, DE 19716, USA, [email protected]
On a Sharp Estimate of Overlapping Schwarz Methods in and
Qigang Liang
School of Mathematical Science, Tongji University, Shanghai 200092, China, qigang[email protected]Xuejun Xu
School of Mathematical Science, Tongji University, Shanghai 200092, China, qigang[email protected]Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China, [email protected]Shangyou Zhang
Department of Mathematical Sciences, University of Delaware, Newark, DE 19716, USA, [email protected]
Abstract:ย ย The previous proved-bound is
for the condition number of the
overlapping domain decomposition
and methods, where and are the sizes of
subdomains and overlaps respectively.
But all numerical results indicate that the best bound is .
In this work, we solve this long-standing open problem by proving that
is indeed the best bound.
Overlapping Schwarz method is one of the most important methods for computing the large-scale discrete problems arising from partial differential equations (PDE). This domain decomposition (DD) method is essentially parallel and has been extensively studied in the literature (see, e.g., [8, 6, 5, 4, 17, 10, 2, 12, 13] and the references therein). Generally speaking, the iterative convergence rate (e.g., PCG, preconditioned GMRES) depends on the condition number of the discrete system. Therefore, it is very important to obtain the sharp estimate of the preconditioned algebraic systems resulting from overlapping Schwarz methods.
For a long time, the best bound of the condition number of the overlapping domain decomposition method is for the second order elliptic boundary value problems, cf. [7, 18], where is the overlapping size and is the diameter of subdomains. In 1994, Dryja and Widlund [8] first improved the bound to . Subsequently, Brenner [6] proved the best bound is . The same techniques used are applied to the fourth order elliptic boundary value problems and high-frequency Helmholtz problems (see [6, 5, 4, 10]).
The same situation happens to the analysis of the two-level overlapping domain decomposition method in .
Toselli [17] proved an upper bound of for the condition number. For many years, people wonder if the best bound should be . Numerical results [17] indicate that the best bound should be . Bonazzoli et al. [2] posed this open problem if the best bound is . In this paper, we close this open problem and prove that is the best bound.
The key ideas in obtaining the sharp estimate of overlapping Schwarz methods in and are as follows. Firstly, the functions are limited to one element of the coarse triangulation where all functions are locally. This way get sharper estimates than the results in [17]. Secondly, by the Helmholtz decomposition, we get a solenoidal subspace in and an irrotational subspace in which is also in when is convex. After the decomposition we can utilize the techniques from the domain decomposition method for problems.
The rest of this paper is organized as follows: In section 2, we introduce model problems and some preliminaries. We introduce the overlapping Schwarz method and give a stable spacial decomposition for -elliptic problems in Section 3. An extension to -elliptic problems is introduced in Section 4. Finally, we present a conclusion in Section 5.
2 Model problems and preliminaries
Throughout this paper, we use standard notations for Sobolev spaces and with their associated norms and semi-norms . We denote by , and use to represents the -inner product and represents the corresponding -norm. For vector field space, we use bold font and to represent and , respectively (), and still use the notations of the norms , and with its inner product . If , we drop the subscript in associated norms or semi-norms or inner products. Let
equipped with the norm . represents a subspace where the vector valued functions have a vanishing tangential trace at domain boundary. represents another supspace where the vector valued functions have a vanishing . We also let
equipped with the norm . Let be a subspace where the vector valued functions are divergence-free. For convenience of notations in this paper, we define
(2.1)
In this paper, we assume is convex, bounded and simple connected. Then we know that the kernel of operator in is . Moreover, the Helmholtz decomposition holds
where is an orthogonal decomposition under .
For the theoretical analysis in the following, we define an operator (called as Hodge operator in [11]) for the above decomposition,
(2.2)
where and . Therefore, is -preserving that
(2.3)
Using the Sobolev embedding theorem (see, e.g., [1, 11, 9]), we known that
(2.4)
Consider the model problem
(2.5)
where represents the outward unit normal vector on . The variational form for (2.5) is as follows:
(2.6)
where
(2.7)
for all . By the Lax-Milgram theorem, it is easy to see that the solutions with respect to (2.6) are well-posed.
Let be a shape-regular and quasi-uniform triangulation. We consider the -th Nรฉdรฉlec element in as follows
(2.8)
where is the -th order local Nรฉdรฉlec space on element (see, e.g., [14, 15, 11]).
The discrete variational form of (2.6) may be written as:
(2.9)
Define an operator such that for all We denote the discrete divergence-free space by
(2.10)
with being a continuous and piecewise polynomial space on with vanishing trace on . Therefore, we have the discrete Helmholtz decomposition
(2.11)
It is easy to see that , where
is defined in (2.1).
Let a subspace be
where is defined in (2.2). Define another operator
such that
(2.12)
Due to the fact that the Poincarรฉ inequality holds in , we know that the operator is well-defined in (2.12). Further, we extend the operator to by
The following lemma holds (see Lemma 10.6 in [18]).
Lemma 2.1
Let be convex. Then the following error estimate holds,
with independent of and ,
where is defined in (2.12),ย (2.13) and
is defined in (2.10).
3 Overlapping Schwarz methods in
Let be a shape-regular and quasi-uniform coarse triangular or tetrahedral mesh on , where .
We let domain be subdivided into subdomains where
The fine shape-regular and quasi-uniform triangulation is obtained by subdividing and we denote it by . We may construct the edge element spaces on and but it is well-known that . To get overlapping subdomains , we enlarge a subdomain by adding a size- layer of fine elements, where .
We define, see the gray region in Figure 1 in 2D (The 3D case is same),
the overlapping region inside by
(3.1)
Figure 1: The diagrammatic presentation of for triangular mesh
We decompose the finite element space in (2.8) into overlapping subspaces,
For describing the overlapping domain decomposition preconditioner for , we define such that
Similarly, we also define such that
We denote by a -orthogonal projector and -orthogonal projectors. So the preconditioner is
(3.2)
Next, we introduce an assumption on overlapping domain decomposition (see [18]).
Assumption 1
The partition may be colored using at most colors, in such a way that subdomains with the same color are disjoint. The integer is independent of .
According to Assumption 1, we obtain a partition of unity, and then there exists a family continuous and piecewise linear polynomials
, which satisfy the following properties
(3.3)
where .
Next, we first present the main result in this paper, and delay its proof.
with the constants and independent of and , but not , where is defined in (3.2) and is defined in
Assumption 1.
Remark 3.1
By the Assumption 1, the upper bound in (3.4) is standard,
cf. [18]. We will prove the lower bound in (3.4).
For convenience of theoretical analysis, using the โlocalโ argument (see [3]), we may denote by , a local -orthogonal projector.
We have
(3.5)
In order to prove the main result, we first give some technical lemmas. In the following theoretical analysis, we also take advantage of the global -orthogonal projector . The following lemma holds (see [17, 18]).
Lemma 3.2
Let be shape-regular and quasi-uniform. Then for , we have
(3.6)
with the constant independent of and , where is the -orthogonal projection to .
Lemma 3.3
Let be a piecewise function, i.e. on each . It holds that
(3.7)
where is the layer of small elements around the boundary
of , defined in (3.1),
and are the neighbor subdomains which
have nonempty intersection
in the definition (3.1) of .
Proof.ย
A similar proof is given in [18] for Lemma 3.10 there, except we have a
piecewise function while it is global in [18].
For simplicity, we illustrate the proof in 2D case. The 3D case is same.
Figure 2: The definition of and the stripe .
We claim all triangles in belong to at least one of the following
stripes, cf. Figures 2 and 3
where ,
(3.8)
We note that inside each stripe is .
We will prove (3.7) on one stripe first, then get (3.7) by summing over
all strips in (3.8).
Figure 3: All related to the estimation on
We separate one stripe into finite patches
of triangles from , cf. Figure 2,
By using the Ponicarรฉ-Friedrichs inequality on , because , we have
Summing over the patches ,
we get, using the trace theorem in
Summing over all strips
in (3.8), by finite covering, we get (3.7).
This completes the proof of the lemma.
For any , we decompose it as
, where
(3.9)
where and are defined in (2.11),
is the H-curl interpolation operator to ,
is defined in (3.3), is defined in (2.12),
is defined in (3.6), is the nodal value interpolation to
defined in (2.10) and is the Scott-Zhang interpolation
operator (see [16]) to . In (3.9), the operation is well-defined, because and .
We check the decomposition,
In order to prove the lower bound in (3.4), we only need to give a stable decomposition (see Chapter 2 in [18]), and then prove that the stable parameter can be bounded by .
For the second term in (3.15), by Lemma 2.1, we deduce
For the third term in (3.15), we have,
because of (3.13),
By using (3.15) and the estimates of three terms and , we obtain
(3.19)
(4)
For the terms in (3.12), we have, by (3.9), the fact ,
triangle inequality, finite overlapping and (3.14),
(3.20)
Finally, combining (3.13), (3.14) (3.19) and (3.20), we complete the proof of (3.12).
By (3.9), adding (3.11) and (3.12), we get (3.10), noting that
the decomposition in last equation of (3.9) is orthogonal under .
Now we are in a position to give a proof of the main result.
Proof of Theorem 3.1:ย ย Based on Lemma 2.5 in Chapter 2 in [18] and Theorem 3.4 above, we obtain
which completes the proof of this theorem.
4 Extension to
In this section, we extend the theoretical techniques to the overlapping Schwarz method in . For convenience of theoretical analysis, we first define some notations:
with the constant independent of and , where is defined in (4.5), (4.6) and is defined in (4.4).
Lemma 4.2
[18] Let be shape-regular and quasi-uniform and be a -orthogonal projector. Then for , we have
(4.8)
with the constant independent of and .
Similar to the overlapping Schwarz method in in Section 3, we have the notations and . We denote by local subspaces. As for the analysis in , we only need to prove following Theorem 4.3.
For any , we decompose it as
, where
(4.9)
where and are defined in (4.3),
is the H-div interpolation operator to ,
is defined in (3.3),
is defined in (4.5),
is defined in (4.8),
is the H-curl interpolation operator to ,
is defined in (2.12) and
is defined in (3.6).
For the second term in (4.14), by Lemma 4.1, we deduce
For the third term in (4.14), we have, because of (4.13),
By using (4.14) and the estimates of three terms and , we obtain
(4.17)
For the -norm estimate, we have, by (4.9), the fact , finite overlapping and (4.13),
(4.18)
Combining (4.13), (4.17) and (4.18), we completes the proof of (4.12). Finally, by (4.9), (4.11) and (4.12), we get (4.10), noting that
the decomposition (4.3) is also orthogonal under .
5 Conclusions
In this paper, we prove that the estimates of the condition numbers of the overlapping Schwarz methods in and are bounded by , which is similar as the case in . We emphasize that the previous bound is . So we close this open problem for overlapping Schwarz methods in and .
References
[1]
Chรฉrif Amrouche, Christine Bernardi, Monique Dauge, and Vivette Girault.
Vector potentials in three-dimensional non-smooth domains.
Mathematical Methods in the Applied Sciences, 21(9):823โ864,
1998.
[2]
Marcella Bonazzoli, Victorita Dolean, Ivan Graham, Euan Spence, and Pierre
Tournier.
Domain decomposition preconditioning for the high-frequency
time-harmonic Maxwell equations with absorption.
Mathematics of Computation, 88(320):2559โ2604, 2019.
[3]
James Bramble and Jinchao Xu.
Some estimates for a weighted projection.
Mathematics of Computation, 56(194):463โ476, 1991.
[4]
Susanne Brenner.
A two-level additive Schwarz preconditioner for nonconforming plate
elements.
Numerische Mathematik, 72(4):419โ447, 1996.
[5]
Susanne Brenner.
Two-level additive Schwarz preconditioners for nonconforming finite
element methods.
Mathematics of Computation, 65(215):897โ921, 1996.
[6]
Susanne Brenner.
Lower bounds for two-level additive Schwarz preconditioners with
small overlap.
SIAM Journal on Scientific Computing, 21(5):1657โ1669, 2000.
[7]
Maksymilian Dryja and Olaf Widlund.
An additive variant of the Schwarz alternating method for the case
of many subregions, Technical Report TR-339, also Ultracomputer
Note 131, Department of Computer Science, Courant Institute.
1987.
[8]
Maksymilian Dryja and Olof Widlund.
Domain decomposition algorithms with small overlap.
SIAM Journal on Scientific Computing, 15(3):604โ620, 1994.
[9]
Vivette Girault and Pierre-Arnaud Raviart.
Finite Element Methods for Navier-Stokes Equations,
volumeย 5 of Springer Series in Computational Mathematics.
Springer-Verlag, Berlin, 1986.
Theory and algorithms.
[10]
Ivan Graham, Euan Spence, and Eero Vainikko.
Domain decomposition preconditioning for high-frequency Helmholtz
problems with absorption.
Mathematics of Computation, 86(307):2089โ2127, 2017.
[11]
Ralf Hiptmair.
Finite elements in computational electromagnetism.
Acta Numerica, 11:237โ339, 2002.
[12]
Ralf Hiptmair and Andrea Toselli.
Overlapping and multilevel Schwarz methods for vector valued
elliptic problems in three dimensions.
In Parallel solution of partial differential equations
(Minneapolis, MN, 1997), volume 120 of The IMA Volumes in
Mathematics and its Applications, pages 181โ208. Springer, New York, 2000.
[13]
Qigang Liang and Xuejun Xu.
A two-level preconditioned Helmholtz-Jacobi-Davidson method for
the Maxwell eigenvalue problem.
Mathematics of Computation, 91(334):623โ657, 2022.
[14]
Jean-Claude Nรฉdรฉlec.
Mixed finite elements in .
Numerische Mathematik, 35(3):315โ341, 1980.
[15]
Jean-Claude Nรฉdรฉlec.
A new family of mixed finite elements in .
Numerische Mathematik, 50(1):57โ81, 1986.
[16]
Ridgway Scott and Shangyou Zhang.
Finite element interpolation of nonsmooth functions satisfying
boundary conditions.
Mathematics of Computation, 54(190):483โ493, 1990.
[17]
Andrea Toselli.
Overlapping Schwarz methods for Maxwellโs equations in three
dimensions.
Numerische Mathematik, 86(4):733โ752, 2000.
[18]
Andrea Toselli and Olof Widlund.
Domain Decomposition MethodsโAlgorithms and Theory,
volumeย 34 of Springer Series in Computational Mathematics.
Springer-Verlag, Berlin, 2005.