# Analysis of the SORAS domain decomposition preconditioner for non-self-adjoint or indefinite problems

We analyze the convergence of the one-level overlapping domain decomposition preconditioner SORAS (Symmetrized Optimized Restricted Additive Schwarz) applied to a general linear system whose matrix is not necessarily symmetric/self-adjoint nor positive definite. By generalizing the theory for the Helmholtz equation developed in [I.G. Graham, E.A. Spence, and J. Zou, preprint arXiv:1806.03731, 2019], we identify a list of assumptions and estimates that are sufficient to obtain an upper bound on the norm of the preconditioned matrix, and a lower bound on the distance of its field of values from the origin. As an illustration of this framework, we prove new estimates for overlapping domain decomposition methods with Robin-type transmission conditions for the heterogeneous reaction-convection-diffusion equation.

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• ### Domain decomposition preconditioners for high-order discretisations of the heterogeneous Helmholtz equation

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04/08/2020 ∙ by Shihua Gong, et al. ∙ 0

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Classically transmission conditions between subdomains are optimized for...
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• ### Overlapping Schwarz methods with GenEO coarse spaces for indefinite and non-self-adjoint problems

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• ### Convergence of parallel overlapping domain decomposition methods for the Helmholtz equation

We analyse parallel overlapping Schwarz domain decomposition methods for...
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## 1. Introduction

The discretization of several partial differential equations relevant in applications, such as the Helmholtz equation, the time-harmonic Maxwell equations or the reaction-convection-diffusion equation, yields linear systems whose matrices are not symmetric/self-adjoint or indefinite. The rigorous analysis of the convergence of preconditioned iterative methods for such problems is harder than for symmetric positive definite (SPD) problems. Indeed, in the SPD problem case, Hilbert space theorems such as the Fictitious Space lemma (see

[23, 16]) yield a powerful general framework of spectral analysis for domain decomposition preconditioners. In addition, in the general problem case the conjugate gradient method cannot be used, and the analysis of the spectrum of the preconditioned matrix is not sufficient for iterative methods such as GMRES suited for non-self-adjoint matrices. In fact, as stated in [15]

, “any nonincreasing convergence curve can be obtained with GMRES applied to a matrix having any desired eigenvalues”. In the literature, GMRES convergence estimates are based for instance on the field of values

[11, 10, 3] or on the pseudo-spectrum (see [26] and references therein) of the preconditioned operator. For example, field of values bounds were derived for overlapping domain decomposition preconditioners for the high-frequency Helmholtz [13, 14] and time-harmonic Maxwell [4] equations.

Here, by generalizing the work of [14], we analyze for general problems the convergence of the preconditioned GMRES method in its weighted version [12]. We identify a list of assumptions and estimates that are sufficient to obtain an upper bound on the norm of the preconditioned matrix, and a lower bound on the distance of its field of values from the origin. This analysis applies to a class of one-level overlapping domain decomposition preconditioners, with Robin-type or more general absorbing transmission conditions on the interfaces between subdomains. This type of preconditioners with the basic Robin-type transmission conditions was first introduced in ([20], 2007) for the Helmholtz equation and called OBDD-H (Overlapping Balancing Domain Decomposition for Helmholtz). It was later studied in ([18], 2015) for general symmetric positive definite problems and viewed as a symmetric variant of the ORAS preconditioner ([25], 2007), hence called SORAS (Symmetrized Optimized Restricted Additive Schwarz). Note that in [20] several one-level and two-level versions, with a coarse space based on plane waves, were tested numerically, and only later the one-level OBDD-H version was rigorously analyzed in [14], for the Helmholtz equation. In [18] a two-level version, with a spectral coarse space, was rigorously analyzed for general SPD problems.

We apply our general framework to the case of convection-diffusion equations for the analysis of one-level overlapping domain decomposition preconditioners with Robin-type transmission conditions. For these equations, the two-level overlapping case with Dirichlet transmission conditions was analyzed in [6, 7], where a coarse space is built from a coarse mesh whose elements are sufficiently small. As for the non overlapping case, it was studied with Robin or more general transmission conditions in e.g. [21, 22], see also [19] for some numerical results. In a different spirit, the Neumann–Neumann algorithm [5] was generalized to convection-diffusion equations in [1], and a coarse space not based on a coarse mesh was proposed in [2] although without convergence analysis.

The paper is structured as follows. In section 2 we first describe in detail the considered class of domain decomposition preconditioners and introduce notation for the global and local inner products and norms. In section 3 we state and prove the main theorem, which provides a general and practical tool for the rigorous convergence analysis of the preconditioner. This framework is applied in section 4 to the case of the heterogeneous reaction-convection-diffusion equation. After specifying the global and local bilinear forms, inner products and norms and the discretization, we prove estimates for the assumptions of the theorem for this equation, without making any a priori assumption on the regime of the physical coefficients nor of the numerical parameters. Finally, we discuss for a particular regime the resulting lower bound on the field of values.

## 2. Setting

Let denote the (potentially complex-valued) matrix arising from the discretization of the problem to be solved, posed in an open domain . The matrix is not necessarily positive definite nor self-adjoint. This means that here we do not necessarily require , where ; note that “self-adjoint matrix” is a synonym for “Hermitian matrix”. In particular, if is real-valued this means that here it does not need to be symmetric.

The definition of the preconditioner is based on a set of overlapping open subdomains , such that and each is a union of elements of the mesh of . Then we consider the set of the unknowns on the whole domain, so , and its decomposition into the non disjoint subsets corresponding to the different overlapping subdomains , with . Then one builds the following matrices (see e.g. [9, §1.3]):

• the restriction matrices from to the subdomain : they are Boolean matrices whose entry equals if the -th unknown in is the -th one in and vanishes otherwise;

• the extension by zero matrices from the subdomain to , which are Boolean matrices given by ;

• the partition of unity matrices , which are diagonal matrices with real non negative entries such that . They can be seen as matrices that properly weight the unknowns belonging to the overlap between subdomains;

• the local matrices , of size , arising from the discretization of subproblems posed in , with for instance Robin-type or absorbing111Absorbing boundary conditions are approximations of transparent boundary conditions. Basic absorbing boundary conditions are Robin-type boundary conditions, which consist in a weighted combination of Neumann-type and Dirichlet-type boundary conditions. Their precise definition depends on the specific problem. For instance, for Maxwell equations impedance boundary conditions are Robin-type absorbing boundary conditions. transmission conditions on the interfaces .

Finally, the one-level SORAS preconditioner is defined as

 M−1\coloneqqN∑j=1RTjDjB−1jDjRj.

Note that here the preconditioner is not self-adjoint when is not self-adjoint, even if we maintain the SORAS name, where S stands for ‘Symmetrized’. In fact, this denomination was introduced in [18] for SPD problems, since in that case the SORAS preconditioner is a symmetric variant of the ORAS preconditioner .

The weighted GMRES method [12]

differs from the standard one in the norm used for the residual minimization, which is not the standard Hermitian norm but a more general weighted norm. For vectors of degrees of freedom

, using the notation to indicate the Hermitian inner product, given a self-adjoint positive definite matrix , we consider the weighted norm

 ∥V∥Ω\coloneqq(V,V)1/2FΩ,where (V,W)FΩ\coloneqq(FΩV,W)=W∗FΩV.

Locally, on the subdomain , we consider a weighted norm represented by a self-adjoint positive definite matrix : for vectors of degrees of freedom local to , we define

 ∥Vj∥Ωj\coloneqq(Vj,Vj)1/2FΩj,where (Vj,Wj)FΩj\coloneqq(FΩjVj,Wj)=(Wj)∗FΩjVj.

Typically is a Neumann-type matrix on , that is, coming from an inner product at the continuous level with no boundary integral.

## 3. General theory

In order to apply Elman-type estimates for the convergence of weighted GMRES [12], such as [13, Theorem 5.1] or its improvement [4, Theorem 5.3], we need to prove an upper bound on the weighted norm of the preconditioned matrix, and a lower bound on the distance of its weighted field of values from the origin. Recall that the field of values (or numerical range) of a matrix with respect to the inner product induced by a matrix is the set defined as

 WF(C)=\set(V,CV)FV∈Cn,∥V∥F=1.

(Note that the convergence estimate for GMRES based on the field of values can be used only when this latter does not contain .)

The following theorem, which generalizes the theory for the Helmholtz equation developed in [14], identifies assumptions that are sufficient to obtain the two bounds. In particular, the proof was inspired by the one of [14, Theorem 3.11] and by the analysis in subsection [14, §3.2].

We will need the notation for the commutator .

###### Theorem 3.1.

For , assume that for all global vectors of degrees of freedom and local vectors of degrees of freedom in

 (3.1) (DjRjAV,Wj)=(DjBjRjV,Wj).

Suppose that there exists such that for all local vectors of degrees of freedom in , , we have

 (3.2) ∥∥∥N∑j=1RTjWj∥∥∥2Ω≤Λ0N∑j=1∥Wj∥2Ωj,

and such that for all global vectors of degrees of freedom

 (3.3) N∑j=1∥RjV∥2Ωj≤Λ1∥V∥2Ω.

For , suppose also that there exist such that for all local vectors of degrees of freedom in

 (3.4) ∥DjWj∥Ωj≤CD,j∥Wj∥Ωj, (3.5) |([Dj,Bj]Vj,Wj)|≤CDB,j∥Vj∥Ωj∥Wj∥Ωj,

and that satisfies the following inf-sup condition: there exists such that for all local vectors of degrees of freedom

 (3.6) ∥Uj∥Ωj≤Cstab,jmaxWj∈Cnj∖{0}⎛⎝|(BjUj,Wj)|∥Wj∥Ωj⎞⎠.

Then, we obtain the following upper bound on the norm of the preconditioned matrix:

 (3.7) maxV∈Cn∥M−1AV∥Ω∥V∥Ω≤√Λ0Λ1maxj=1,…,N{CD,j(Cstab,jCDB,j+CD,j)}.

If in addition, for , for all global vectors of degrees of freedom and local vectors of degrees of freedom in

 (3.8) (DjRjFΩV,Wj)=(DjFΩjRjV,Wj),

and there exists such that for all local vectors of degrees of freedom in

 (3.9) |([Dj,FΩj]Vj,Wj)|≤CDF,j∥Vj∥Ωj∥Wj∥Ωj,

then we obtain the following lower bound on the distance of the field of values of the preconditioned matrix from the origin:

 (3.10) minV∈Cn|(FΩV,M−1AV)|∥V∥2Ω≥1Λ0−Λ1maxj=1,…,N{CD,jCstab,jCDB,j}−Λ1maxj=1,…,N{CDF,j(Cstab,jCDB,j+CD,j)}.
###### Remark 3.2.

We will comment on assumptions (3.1), (3.2), (3.3), (3.8) in subsection 3.1. Note that in finite dimension, the constants in assumptions (3.4), (3.5), (3.6), (3.9) are finite, and in the statement of the theorem we actually mean that we are able to estimate these constants.

###### Proof.

To obtain both bounds an important quantity is

 ∥(B−1jDjRjA−DjRj)V∥Ωj.

For its estimate, for any vector of degrees of freedom local to , write

 (Bj(B−1jDjRjA−DjRj)V,Wj)=(DjRjAV,Wj)−(BjDjRjV,Wj)(DjBjRjV,Wj)−(BjDjRjV,Wj)=([Dj,Bj]RjV,Wj),

where assumption (3.1) was used. Thus we have found that is the solution to a local problem with a right-hand side involving the commutator between the partition of unity and the local matrix. So by the stability bound (3.6), we have:

Moreover by assumption (3.5)

 |([Dj,Bj]RjV,Wj)|≤CDB,j∥RjV∥Ωj∥Wj∥Ωj∀Wj.

Therefore

 (3.11) ∥(B−1jDjRjA−DjRj)V∥Ωj≤Cstab,jCDB,j∥RjV∥Ωj.

Together with (3.11), a direct consequence of (3.11) itself and assumption (3.4) will be also used repeatedly:

 (3.12) ∥B−1jDjRjAV∥Ωj≤(Cstab,jCDB,j+CD,j)∥RjV∥Ωj.

Now, it is easy to obtain the upper bound (3.7): for we have

 ∥∥∥N∑j=1RTjDjB−1jDjRjAV∥∥∥2ΩΛ0N∑j=1∥DjB−1jDjRjAV∥2ΩjΛ0N∑j=1C2D,j∥B−1jDjRjAV∥2ΩjΛ0N∑j=1C2D,j(Cstab,jCDB,j+CD,j)2∥RjV∥2ΩjΛ0Λ1maxj=1,…,N{C2D,j(Cstab,jCDB,j+CD,j)2}∥V∥2Ω,

where we have indicated above each inequality sign which equation was used.

The derivation of the lower bound (3.10) is more involved. First of all write

 (FΩV,N∑j=1RTjDjB−1jDjRjAV)=N∑j=1(FΩV,RTjDjB−1jDjRjAV)=N∑j=1(DjRjFΩV,B−1jDjRjAV)N∑j=1(DjFΩjRjV,B−1jDjRjAV),

where, beside applying assumption (3.8), we have used the fact that the partition of unity matrices are real-valued and diagonal, hence symmetric, and the restriction matrices satisfy . Now, we make appear the commutator between the partition of unity and the local inner product matrix, and also the quantity :

 (DjFΩjRjV,B−1jDjRjAV)=(FΩjDjRjV,B−1jDjRjAV)+([Dj,FΩj]RjV,B−1jDjRjAV)=(FΩjDjRjV,DjRjV)+(FΩjDjRjV,(B−1jDjRjA−DjRj)V)+([Dj,FΩj]RjV,B−1jDjRjAV).

Therefore

 (3.13) |(FΩV,M−1AV)|≥N∑j=1∥DjRjV∥2Ωj−N∑j=1|(FΩjDjRjV,(B−1jDjRjA−DjRj)V)|−N∑j=1|([Dj,FΩj]RjV,B−1jDjRjAV)|.

For the first term in (3.13) we use the partition of unity property and assumption (3.2) with :

 ∥V∥2Ω=∥∥∥N∑j=1RTj(DjRjV)∥∥∥2ΩΛ0N∑j=1∥DjRjV∥2Ωj,

so

 N∑j=1∥DjRjV∥2Ωj≥1Λ0∥V∥2Ω.

For the second term in (3.13), we use first the Cauchy-Schwarz inequality:

 N∑j=1|(FΩjDjRjV,(B−1jDjRjA−DjRj)V)|≤N∑j=1∥DjRjV∥Ωj∥(B−1jDjRjA−DjRj)V∥ΩjN∑j=1CD,jCstab,jCDB,j∥RjV∥2ΩjΛ1maxj=1,…,N{CD,jCstab,jCDB,j}∥V∥2Ω.

Finally for the third term in (3.13) we write

 N∑j=1|([Dj,FΩj]RjV,B−1jDjRjAV)|N∑j=1CDF,j∥RjV∥Ωj∥B−1jDjRjAV∥ΩjN∑j=1CDF,j(Cstab,jCDB,j+CD,j)∥RjV∥2ΩjΛ1maxj=1,…,N{CDF,j(Cstab,jCDB,j+CD,j)}∥V∥2Ω.

In conclusion, inserting these estimations in (3.13) we obtain the lower bound (3.10).

Note that the lower bound on the field of values (3.10) is interesting only if the positive term dominates the negative ones. The result could be improved by designing a suitable coarse space to add a second level to the standard SORAS preconditioner. For general problems this constitutes a real challenge currently; for symmetric positive definite problems, we refer to [18] for the definition of a coarse space and a two-level SORAS preconditioner leading to a robust lower bound on the spectrum.

### 3.1. Comments on the assumptions of Theorem 3.1

Assumptions (3.1) and (3.8) may appear unconventional at first glance, but they are satisfied for quite natural choices of the local sesquilinear form and continuous norm on the subdomains. More precisely, if the -th entry of the diagonal of is not zero, assumption (3.1) requires that the -th rows of and are equal; likewise assumption (3.8) requires that the -th rows of and are equal. First of all, note that typically the entries corresponding to of the partition of unity are zero. Moreover, arises from the discretization of a local sesquilinear form that usually is like the global sesquilinear form yielding but with the integrals on instead of and with an additional boundary integral on . In this case assumption (3.1) is satisfied. Likewise, assumption (3.8) is satisfied if the local continuous norm yielding is obtained from the global continuous norm yielding just by replacing with in the integration domain. As an illustration, see the bilinear forms , and the continuous norms , defined in §4 for the reaction-convection-diffusion equation and the proof of Lemma 4.7.

Assumptions (3.2) and (3.3) are classical inequalities in the domain decomposition framework. Inequality (3.2) is dubbed in [14] ‘a kind of converse to the stable splitting result’, and it can be viewed as a continuity property of the reconstruction operator . In [14, Lemma 3.6] the inequality is proved at the continuous level for the Helmholtz energy norm (see [14, eq. (1.15)]) with

 (3.14) Λ0=maxj=1,…,N#Λ(j),where Λ(j)\coloneqq\setiΩj∩Ωi≠∅,

in other words, is the maximum number of neighboring subdomains. Note that the proof in [14, Lemma 3.6] (essentially consisting in the one in [13, eq. (4.8)]) is more generally valid, for instance whenever the local continuous norm can be obtained from the global continuous norm just by replacing with in the integration domain, as before.

When the local and the global continuous norms are related as above again, it is immediate to prove inequality (3.3) with

 (3.15) Λ1=max\setm∃j1≠⋯≠jm such that meas(Ωj1∩⋯∩Ωjm)≠0,

that is is the maximal multiplicity of the subdomain intersection (this constant is like the one defined in [9, Lemma 7.13] and is slightly more precise than that was used in [14, eq. (2.10)]). Therefore and are geometric constants, related to the decomposition into overlapping subdomains.

## 4. The reaction-convection-diffusion equation

As an illustration of the general theory, we apply Theorem 3.1 to the case of the heterogeneous reaction-convection-diffusion equation; recall that the convergence theory for the (homogeneous) Helmholtz equation was developed in [14]. Let be an open bounded polyhedral domain. We study the heterogeneous reaction-convection-diffusion problem in conservative form, with Robin-type and Dirichlet boundary conditions:

 (4.1) ⎧⎪⎨⎪⎩c0u+div(au)−div(ν∇u)=fin Ω,ν∂u∂n−12a⋅nu+αu=gon ΓR,u=0on ΓD,

where , is the outward-pointing unit normal vector to , , a.e. in , , , and there exist , such that

 ν−≤ν(x)≤ν+a.e. in Ω,

, , , a.e. in . In this case all quantities are real-valued. Note that the appropriate Robin-type boundary condition here is not simply ; we will comment below about a possible choice of , see (4.2). Now, set . In order to find the variational formulation, multiply the equation by a test function and integrate over :

For the first divergence term use the identity , while for the second integrate by parts:

 ∫Ω12div(au)v=∫Ω−12ua⋅∇v+∫∂Ω12a⋅nuv,

and, also by integration by parts,

 ∫Ω−div(ν∇u)v=∫Ων∇u⋅∇v−∫∂Ων∂u∂nv.

Therefore, imposing the boundary conditions, the variational formulation is: find such that

 a(u,v)=F(v),for all v∈H10,D(Ω),

where is a non symmetric bilinear form defined as

 a(u,v)\coloneqq∫Ω((c0+12diva)uv+12a⋅∇uv−12ua⋅∇v+ν∇u⋅∇v)+∫ΓRαuv,

and

 F(v)\coloneqq∫Ωfv+∫ΓRgv.

With the notation

 ~c\coloneqqc0+12diva,

we write

 a(u,v)=∫Ω(~cuv+12a⋅∇uv−12ua⋅∇v+ν∇u⋅∇v)+∫ΓRαuv.

Suppose that there exist , such that

 ~c−≤~c(x)≤~c+a.e. in Ω,

where the positiveness of is a classical assumption in reaction-convection-diffusion equation literature, and define the weighted scalar product and norm

 (u,v)1,c\coloneqq∫Ω(~cuv+ν∇u⋅∇v),∥u∥1,c\coloneqq(u,u)1/21,c.

On each subdomain we consider the local problem with bilinear form

 aj(u,v)\coloneqq∫Ωj(~cuv+12a⋅∇uv−12ua⋅∇v+ν∇u⋅∇v)+∫∂Ωj∖ΓDαuv,

where we impose absorbing transmission conditions on the subdomain interface : for instance, we can choose a zeroth-order Taylor approximation of transparent conditions given by

 (4.2) α=√(a⋅n)2+4c0ν/2

(see e.g. [19] and the references therein). We define the local weighted scalar product and norm

 (u,v)1,c,Ωj\coloneqq∫Ωj(~cuv+ν∇u⋅∇v),∥u∥1,c,Ωj\coloneqq(u,u)1/21,c,Ωj,

which would correspond to Neumann-type boundary conditions on . Set

 ~c+,j \coloneqq∥~c∥L∞(Ωj), ~c−,j \coloneqq∥~c−1∥−1L∞(Ωj), so ~c−,j≤~c(x)≤~c+,ja.e. in Ωj, ν+,j \coloneqq∥ν∥L∞(Ωj), ν−,j \coloneqq∥ν−1∥−1L∞(Ωj), so ν−,j≤ν(x)≤ν+,ja.e. % in Ωj.
###### Remark 4.1.

For , if or are supported in and thus vanish on , then

 a(u,v)=aj(u,v),and(u,v)1,c=(u,v)1,c,Ωj.

For the finite element discretization, let be a family of conforming simplicial meshes of that are -uniformly shape regular as the mesh diameter tends to zero. We consider finite elements of order

 Vh=\setvh∈C0(¯¯¯¯Ω),vhτ∈Pr−1(τ)∀τ∈Th,vh|ΓD=0⊂H10,D(Ω).

Consider nodal basis functions (for example Lagrange basis functions), in duality with the degrees of freedom associated with nodes , that is

. Thus we can define the standard nodal Lagrange interpolation operator

. Assume that satisfies the standard interpolation error estimate (see e.g. [8, §3.1]): for , provided

 (4.3) ∥(I−Πh)v∥L2(τ)+h|(I−Πh)v|H1(τ)≤CΠhr|v|Hr(τ).

Assume that the subdomains are polyhedra with characteristic length scale , which means

###### Definition 4.2 (Characteristic length scale).

A domain has characteristic length scale if its diameter , its surface area , and its volume , where means uniformly bounded from below and above.

For each , denote by the space of functions in restricted to . So, , , , are defined as the matrices arising, respectively, from the finite element discretization of , on , and , on : for with vectors of degrees of freedom , and for with vectors of degrees of freedom

 (4.4) a(vh,wh) =(AV,W), aj(vjh,wjh) =(BjVj,Wj), (4.5) (vh,wh)1,c =(FΩV,W), (vjh,wjh)1,c,Ωj =(FΩjVj,Wj).

Consider partition of unity functions , , such that in , and , so in particular they are zero on . Assume that

 (4.6)

where is the size of the overlap between subdomains, and is required to be independent of the simplex and of the derivative multi-index . The diagonal matrices are constructed by interpolation of the functions , so the vector of degrees of freedom of is .

Next we need to introduce a technical ingredient, namely so-called multiplicative trace inequalities. Such estimates can be found e.g. in [17].

###### Lemma 4.3 (Multiplicative trace inequality, [17, last eq. on page 41]).

For any bounded Lipschitz open subset there exists such that, for all , we have .

Although the constant above does a priori depend on the shape of , it does not depend on its diameter (it is invariant under homothety). In the sequel we shall assume that there exists a fixed constant such that we have . This holds for example if the subdomains are assumed to be uniformly star-shaped i.e. there exists a fixed constant such that, for each there exists satisfying

 (4.7) ∀x∈∂Ωj,[x,xΩj]⊂¯¯¯¯Ωjand nj(x)⋅(x−xΩj)≥μ|x−xΩj|
###### Assumption 4.4.

The multiplicative trace estimates of Lemma 4.3 hold uniformly for all subdomains.

This assumption allows to derive uniform upper bounds for the continuity modulus of the bilinear forms and .

###### Lemma 4.5 (Continuity of the bilinear forms a and aj).

Assume that has characteristic length scale in the sense of Definition 4.2. Then for all

 a(u,v)≤Ccont∥u∥1,c∥v∥1,c,

where

 Ccont=~c+~c−ν+ν−+12∥a∥L∞(Ω)√ν−~c−+∥α∥L∞(Ω)Ctr√