Multiplicative Schwarz methods for discontinuous Galerkin approximations of elliptic problems

DOI:10.1051/m2an:2008012 www.esaim-m2an.org

MULTIPLICATIVE SCHWARZ METHODS FOR DISCONTINUOUS GALERKIN APPROXIMATIONS OF ELLIPTIC PROBLEMS

Paola F. Antonietti

and Blanca Ayuso

Abstract. In this paper we introduce and analyze some non-overlapping multiplicative Schwarz meth- ods for discontinuous Galerkin (DG) approximations of elliptic problems. The construction of the Schwarz preconditioners is presented in a unified framework for a wide class of DG methods. For symmetric DG approximations we provide optimal convergence bounds for the corresponding error propagation operator, and we show that the resulting methods can be accelerated by using suitable Krylov space solvers. A discussion on the issue of preconditioning non-symmetric DG approximations of elliptic problems is also included. Extensive numerical experiments to confirm the theoretical results and to assess the robustness and the efficiency of the proposed preconditioners are provided.

Mathematics Subject Classification. 65N30, 65N55.

Received December 29, 2006. Revised August 13, 2007.

Published online April 1st, 2008.

1. Introduction

Based on a totally discontinuous finite element space, the first discontinuous Galerkin (DG) method was proposed in the seventies for the numerical approximation of hyperbolic problems by Reed and Hill [32], and independently, in the context of elliptic and parabolic equations in [3,20]. For a while, the use of DG methods was partially abandoned mainly due to the much larger number of degrees of freedom they require compared with continuous Galerkin finite element methods. However, the numerous and advantageous properties of DG methods (e.g., tremendous flexibility in terms of mesh design and choice of shape functions, easy treatment of non-conforming meshes, straightforward design ofhp-adaptivity strategies and weak approximation of boundary conditions) have motivated in recent years a renewed interest in DG approximations. In particular, the develop- ment of DG methods for elliptic problems has undergone a rapid development (see, e.g., [2,4,5,7,17,18,33,36]).

In spite of the active development of these methods, its practical utility is still very much limited by the size of the resulting algebraic linear systems of equations. A direct factorization of such systems might not be a viable option, and the use of iterative methods, such as Krylov space methods (conjugate gradients(CG),generalized minimal residual (GMRES), etc.) can result in a very slow convergence. As a consequence, the development of efficient solvers has started very recently to receive substantial attention, particularly, in connection with domain decomposition (DD) methods [1,12,22,27] and multigrid techniques [11,13,19,24,26].

Keywords and phrases. Domain decomposition methods, Schwarz preconditioners, discontinuous Galerkin methods.

1 Dipartimento di Matematica, Universit`a di Pavia, Via Ferrata 1, 27100 Pavia, Italy. [email protected]

2 Istituto di Matematica Applicata e Tecnologie Informatiche - CNR, Via Ferrata 1, 27100 Pavia, Italy. [email protected]

Article published by EDP Sciences c EDP Sciences, SMAI 2008

We focus here on Schwarz DD methods, that provide a very natural way to construct preconditioners that can be accelerated by Krylov space methods. The key idea is that, instead of solving one huge problem on a domain, it could be convenient (or necessary) to solve many smaller problems on single subdomains. The selection of the local problems has to be done to ensure the fast convergence of the method. To provide global interaction between the subregions and to improve the convergence in case of many subdomains, it also necessary to introduce a global coarse solver (with very few degrees of freedom per subdomain). We remark that, while Schwarz methods are by now well developed and understood for classical discretization methods [31,35,37,38]

(see also [29,30] for practical applications), very few works are devoted to DG approximations [1,12,22,27], all from the last five years.

In this paper we propose and study some new multiplicative non-overlapping Schwarz preconditioners for the algebraic linear systems of equations arising from a wide class of DG approximations of elliptic problems.

Following with the research started in [1], we provide a unified framework for the construction and analysis of multiplicative Schwarz preconditioners, which really share the features of the classical Schwarz methods. As in [1], we focus on theh-version of DG approximations, but here we also allow for the use of non-matching grids.

In contrast to classical (conforming) discretization methods, some freedom in the definition of the local solvers arises due to the lack of continuity constraints across the element interfaces inherent to DG approximations.

While in [22,27] the local solvers are defined as the restriction of the global bilinear form to each subdomain, we define the local solvers as the corresponding DG approximation of the continuous problem, but set in the subdomain. These two approaches, which coincide in the conforming case, are no longer the same in the DG framework. From our approach, the resulting local solvers turn out to be approximate rather than exact, as those used in [22,27]. This implies that a local stability property, that provides a one-sided measure of the approximation properties of the local bilinear forms, has to be shown.

For symmetric DG approximations, our analysis follows the abstract convergence theory of multiplicative Schwarz methods [8]. To complete the analysis, the presence ofapproximatelocal solvers implies the need of a technical assumption on the size of the penalty parameter which does not seem to be required in practice, as our numerical experiments indicate. For all the DG schemes stabilized by penalizing the jumps of the discrete solution across neighboring elements, we show that the energy norm of the error propagation operator is strictly less than one, and we exploit this result to prove that our preconditioner can be indeed accelerated with the GMRES iterative solver. Since the multiplicative Schwarz method is non-symmetric even for symmetric DG approximations we also present the corresponding symmetrized preconditioner and provide a bound for the condition number of the resulting preconditioned matrices, which allows us to conclude that the symmetrized multiplicative method can indeed be accelerated with the CG iterative solver. To the best of our knowledge this is the first time that a multiplicative preconditioner for DG approximations is considered. Furthermore, due to the close relation between multiplicative Schwarz andmultigridmethods [38,41] (both are product type iterative methods), the construction and analysis presented here might throw some new light in the research of multigridpreconditioners for DG approximations, a field where few contributions [11,13,19,24,25] can be found so far.

Following with the research initiated in [1], we also discuss the issue of preconditioning the non-symmetric NIPG [33] and IIPG [18] approximations with multiplicative Schwarz methods. We examine the possible use of two different, but “related”, existing theories for analyzing the observed convergence of the proposed product iterative methods: the abstract theory of multiplicative Schwarz methods for non-symmetric problems originally carried out by Cai and Widlund in [15], and the Eisenstat et al. GMRES convergence theory [21]. In both cases we provide numerical negative answers. On the one hand, while the lack of symmetry of the NIPG and IIPG schemes might in principle suggest the extension/adaptation of the abstract theory of [15], we numerically demonstrate that such a theory cannot be applied for the analysis of our preconditioners. The underlying reason is related to the “size” of the skew-symmetric part of the Schwarz operators; namely it is not a low order compact perturbation of the symmetric part as the theory of [15] requires. On the other hand, we also demonstrate that the Eisenstatet al. GMRES convergence theory [21], generally advocated in the analysis of Schwarz methods, cannot be applied to explain the observed convergence of the proposed preconditioners.

The construction and analysis of iterative methods for the solution of the linear systems of equations arising from non-symmetric DG methods is nowadays attracting special attention. Very recently, in [9] the authors overcome the main difficulty of this issue by considering a newweakly over-penalizedversion of the original NIPG method (see [10] for the details on the DG scheme), and prove the convergence of a multigridmethod for this new DG discretization. By overpenalizing the method, the authors circumvent the lack of adjoint consistency, and succeed in proving the requiredL²-error estimates for carrying out the analysis of themultigridscheme. It is worth noticing that, for the method proposed in [10], the skew-symmetric part of the operator turns out to be a low order compact perturbation of the symmetric part. Therefore, the issue of analyzing iterative methods for the original NIPG and IIPG schemes remains an open problem. Also, the construction and analysis of Schwarz methods for this new non-symmetric DG scheme surely merit some further research.

We wish to note that, after we were submitting the paper, a new theory for non-stagnation of the GMRES method has came out [34]. The issue of exploring this less restrictive GMRES convergence theory, deserves without doubt further investigation and will be the subject of a future research.

Extensive numerical experiments are presented to confirm the convergence results, which also show the good performance and scalability (that is the independence of the convergence rate on the number of subdomains) of the proposed preconditioners. Also, we numerically compare the proposed preconditioners with the additive version studied in [1], and we show that, although the multiplicative preconditioner is less parallelizable, it is far faster than the additive one. We also address numerically the effect of the selection of the subdomain partition on the performance of our preconditioners. For that purpose we have considered two elliptic problems with a discontinuous diffusion matrix, and an anisotropic diffusion matrix. In both cases, the performance of the proposed preconditioners does not deteriorate. Furthermore, in the anisotropic case, we show that a clever ordering favours and speed up substantially the convergence of the product iterative method.

The paper is organized as follows. In Section2we set up our notation and briefly recall the unified framework for DG approximations of elliptic problems given in [4]. In Section 3 we provide a unified framework for the construction of the Schwarz preconditioners. The convergence analysis for symmetric DG approximations is given in Section 4; while in Section 5 we discuss the issue of preconditioning non-symmetric DG methods.

Numerical experiments on conforming meshes and on meshes with hanging-nodes are provided in Section 6.

Finally, in Section 7we draw some conclusions.

2. Discontinuous Galerkin discretization

In this section, we introduce the model problem we will consider, set up some notation, and, following [4], briefly review the discontinuous finite element approximation of second order elliptic problems and the theoret- ical tools we shall require.

We consider the following model problem

−∆u=f in Ω, u= 0 on∂Ω, (2.1)

where Ω⊂R^d,d= 2,3, is a smooth convex domain or a convex polygon or polyhedron andf is a given function in L²(Ω).

Remark 2.1. We point out that, in spite of considering a simplified model problem, the results presented here also apply to more general second order elliptic operators in divergence form, with possibly discontinuous coefficients (see Sect.6.3). Furthermore, with slightly minor changes, other kinds of boundary conditions could also be considered,e.g., non-homogeneous Dirichlet and/or Neumann boundary conditions¹.

1In the case of Neumann boundary conditions prescribed on the whole boundary∂Ω some compatibility conditions on the source term need to be assumed to ensure the well-posedness of the problem.

2.1. Notation

Throughout the paper,C and care used to denote generic positive constants that may not be the same at different occurrences but that are always mesh-independent. The notationx≈ymeans that there exist positive constantsc andCsuch thatc x≤y≤C x.

Meshes and traces.Let Th be a shape-regular (not necessarily matching) partition of Ω into disjoint open elementsT such that Ω =∪T∈ThT where eachT ∈ This an affine image of a fixed master elementT, and where T is either the open unit d-simplex or the open unit hypercube inR^d, d= 2,3. Denoting byh_T the diameter of the elementT ∈ Th, we define the mesh sizeh= max_T∈Th{hT}.

An interior face of Th (if d = 2, “face” means “edge”) is the (non-empty) interior of ∂T⁺∩∂T⁻, where T⁺ andT⁻ are two adjacent elements ofTh, not necessarily matching. Similarly, aboundary faceof Th is the (non-empty) interior of ∂T∩∂Ω, where T is a boundary element ofTh. We denote by E^I and E^B the sets of all interior and boundary faces of Th, respectively, and set E =E^I∪E^B. We will use the convention that

Eϕds=

e∈E

eϕds.

We introduce thelocal mesh sizefunctionh∈L^∞(E) defined as follows: h(x) = min{h_T⁺, h_T−}, forxin the interior of∂T⁺∩∂T⁻, andh(x) =h_T, forxin the interior of∂T∩∂Ω. We shall refer toThas the “fine” mesh and we shall always proceed under the assumption that the local mesh size has bounded variation,i.e., there exists a constantC >0 such that

C⁻¹h_T− ≤h_T+≤Ch_T−, (2.2)

for all T^± ∈ Th such that the interior ofT⁺∩T⁻ in non empty. Roughly speaking, we avoid the mesh to be indefinitely refined in only one part of the domain. We also assume there existsC >0 such that for allT ∈ Th

and for alle∈E,h_T ≤C h_e, whereh_e is the diameter ofe∈E.

Finite element spaces.For a given partition Th of Ω and an approximation order _h ≥ 1, we define the discontinuous finite element spacesV_h andΣ_h as

V_h={v∈L²(Ω) :v|T ∈ M^h(T) ∀T ∈ Th}, Σ_h={σ∈[L²(Ω)]^d :v|T ∈[M^h(T)]^d ∀T ∈ Th}, whereM^h(T) is either the spaceP^h(T) of polynomials of degree at most_h onT, forT a triangle or a tetra- hedron, orQ^h(T) forT a parallelogram or a parallelepiped, and whereQ^h(T) is the mapping toT ofQ^h(T) (i.e., polynomials of degree at most_h in each variable onT).

Trace operators.Let e ∈ E^I be an interior face shared by two elements T⁺ and T⁻ with outward normal unit vectorsn^±, respectively. Denoting byv^± andτ^± the traces of piecewise smooth scalar and vector-valued functions v and τ, respectively, taken from the interior of ∂T^±, we define the following jump and weighted average operators

[[v]] =v⁺n⁺+v⁻n⁻, [[τ]] =τ⁺·n⁺+τ⁻·n⁻,

{{v}}_δ =δv⁺+ (1−δ)v⁻, {{τ}}_δ =δτ⁺+ (1−δ)τ⁻, δ∈[0,1].

For δ = 1/2, we drop the subindex and simply write {{·}}. On a boundary face e ∈ E^B, we set {{v}}_δ = v, [[v]] =vn,{{τ}}_δ=τ and [[τ]] =τ·n.

Finally, we define the local lifting operators (see [4,14])r_e: [L²(e)]^d−→Σ_h andl_e:L²(e)−→Σ_h by

Ωr_e(φ)·τdx=−

e

φ· {{τ}}ds,

Ωl_e(q)·τdx=−

e

q[[τ]] ds ∀τ ∈Σ_h. (2.3)

Table 1. Numerical fluxes on interior faces, theoretical requirement on α_∗ = min_e∈Eα_e for stability and symmetry of the corresponding bilinear form.

Method u(u) σ(σ, u) Stability condition Symmetry

SIPG [3] {{u}} {{∇hu}} −α_eh⁻¹[[u]] α_∗>α Yes BRMPS [6] {{u}} {{∇hu}}+α_e{{re([[u]])}} α_∗>α Yes SIPG(δ) [36] {{u}}_(1−δ) {{∇hu}}_δ−α_eh⁻¹[[u]] α_∗>α Yes NIPG [33] {{u}}+ [[u]]·nT {{∇hu}} −α_eh⁻¹[[u]] α_∗>0 No IIPG [18] {{u}}+ 1/2 [[u]]·nT {{∇hu}} −α_eh⁻¹[[u]] α_∗>α No BMMPR [14] {{u}} {{σ}}+α_e{{re([[u]])}} α_∗>α Yes LDG [17] {{u}} −β·[[u]] {{σ}}+β·[[σ]]−α_eh⁻¹[[u]] α_∗>0 Yes

2.2. Discontinuous Galerkin approximations

By introducing the auxiliary flux variableσ=∇u, rewriting problem (2.1) as a first order system of equations, and following [4], theprimaldiscontinuous Galerkin (DG) formulation reads as follows: findu_h∈V_hsuch that,

A(u_h, v_h) =

Ω

f v_hdx ∀v_h∈V_h, (2.4)

where

A(uh, v_h) =

Ω∇hu_h· ∇hv_hdx+

E[[u−u_h]]· {{∇hv_h}}ds+

E^I{{u−u_h}}[[∇hv_h]] ds

−

E{{σ}} ·[[v_h]] ds−

E^I[[σ]]{{vh}}ds. (2.5) Here ∇h denotes the elementwise application of the operator∇, and u(u_h) and σ =σ(σ h, u_h) are the scalar and vectornumerical fluxes, respectively. Their definition as suitable linear combinations of averages and jumps ofu_handσhdetermines the different DG methods (see [4] and Tab.1above). The stability of the DG methods is achieved by penalizing the jumps ofu_h over each facee∈E. As a consequence, the resulting bilinear forms contain a stabilization termS(·,·) that is eitherS^h(·,·) orS^r(·,·) defined as

S^h(u_h, v_h) =

e∈E

α_e

e

h⁻¹[[u_h]]·[[v_h]] ds, S^r(u_h, v_h) =

e∈E

α_e

Ωr_e([[u_h]])·r_e([[v_h]]) ds ∀u_h, v_h∈V_h, (2.6) where α_e>0 is a parameter independent of the mesh size. We define α_∗ = min_e∈Eα_e and α^∗ = max_e∈Eα_e, and assume thatα^∗≥α_∗≥1 andα^∗≈α_∗.

In Table1 we collect the definitions of the numerical fluxes on internal faces for the DG methods considered in this paper. On boundary faces, the definition modifies according to [4], Section 3.4 (in particular, for the LDG method, β = (0,0)^T on E^B). To ensure stability, some of the DG methods require α_∗ = min_e∈Eα_e sufficiently large,i.e.,α_∗>α(see the summary in Tab.1).

2.3. Theoretical tools

In this section we recall the basic tools we shall require in the analysis of our multiplicative Schwarz methods.

From now on, since no confusion might arise, we drop the subindexhin the discrete functions belonging toV_h andΣ_h.

We refer to [16] for a local inverse inequality that holds true for piecewise polynomials of a given order, and to [3] for a trace inequality that holds true for (regular enough) piecewise functions. We also recall that the inverse and trace inequality constants only depend on the shape regularity of the partitionTh and, for the inverse inequality, on the polynomial approximation degree.

For the analysis of our two-level multiplicative Schwarz methods for symmetric DG approximations we consider the norm induced by the bilinear formA(·,·),i.e.

v²_A=A(v, v) ∀v∈V_h. (2.7) Observe that, for all the symmetric DG methods, provided the penalty parameter is taken so as to ensure the coercivity of A(·,·) (see Tab. 1), A(·,·) does indeed define an inner product and ·_A is a norm. Moreover, continuity and stability w.r.t. the norm·_Aare straightforwardly derived for all symmetric DG approximation, by using the definition ofA(·,·) and the standard Cauchy-Schwarz inequality

(i) Continuity: |A(u, v)| ≤ uAvAfor allu, v∈V_h; (ii) Coercivity: A(v, v) =v²_A>0 for allv∈V_h,v= 0.

For the non-symmetric NIPG and IIPG methods, sinceA(·,·) is not longer symmetric, it does not define an inner product. Therefore, we shall consider instead the inner product defined by the symmetric part ofA(·,·) (and its induced norm),i.e.,

a(u, v) =A(u, v) +A(v, u)

2 , v²_a=a(v, v) ∀u, v∈V_h. (2.8) We wish to stress that, althoughva=v_A for allv ∈V_h (since a(v, v) =A(v, v)), when dealing with non- symmetric DG approximations, we shall rather use the notation·ato emphasize that only the symmetric part a(·,·) ofA(·,·) defines an inner product. The issue of preconditioning non-symmetric DG methods is discussed in Section5.

3. Multiplicative Schwarz methods

In this section, we present our two-level algorithms for the family of the DG methods including both symmetric and non-symmetric schemes. We start by setting some notation and introducing the assumptions on the partitions. Then we describe the two-level algorithms in an abstract general form and from the algebraic point of view.

3.1. Non-overlapping partitions, local and coarse solvers

Let {TNs, N_s > 0} be a family of partition of the domain Ω into N_s non-overlapping subdomains, i.e., TNs={Ωi, i= 1, . . . , N_s}with Ω =_Ns

i=1Ω_i, and let{TH, H >0}and{Th, h >0}be the families of coarse and fine partitions (possibly with hanging nodes) with global mesh sizesH and h, respectively. We assume that they are nested,i.e.,

TNs ⊆ TH⊆ Th. (3.1)

For each subdomain Ω_i ofTNs,i= 1, . . . , N_s, we denote byEi the set of all faces ofE (recall thatE is the set of faces of the fine partitionTh) belonging to Ω_i; we also set

E_i^I ={e∈Ei: e∩∂Ω_i=∅}, E_i^B={e∈Ei :e∩∂Ω_i∩∂Ω=∅}, Γ_i={e∈Ei :e⊂∂Ω_i\∂Ω}, and observe thatEi=E_i^I∪E_i^B∪Γ_i.

Fori= 1, . . . , N_s, we define thelocal spaces V_hⁱ=

v∈L²(Ω_i) :v|T ∈ M^h(T) ∀T∈ Th, T ⊂Ω_i , Σⁱ_h= [V_hⁱ]^d,

whereM^his defined as before. TheprolongationoperatorsR^T_i :V_hⁱ−→V_hare defined as the classical inclusion operators from V_hⁱ to V_h, and therestriction operatorsR_i, are defined as the transpose ofR^T_i with respect to the L²-inner product. For vector-valued functions R^T_i and R_i are defined componentwise. Notice also that, Σ_h=R^T₁Σ¹_h⊕. . .⊕R^T₁Σ^N_h^s andV_h=R^T₁V_h¹⊕. . .⊕R^T_NsV_h^Ns.

Fori= 1, . . . , N_s, we define thelocal solversby considering the DG approximation of the model problem (2.1) but restricted to Ω_i, that is

−∆u_i=f|_Ωi on Ω_i, u_i= 0 on∂Ω_i.

Hence, in view of (2.5), the local bilinear formsAi:V_hⁱ×V_hⁱ−→Rare defined by:

Ai(u_i, v_i) =

Ωi

∇hu_i· ∇hv_idx+

Ei

[[u_i−u_i]]· {{∇hv_i}}ds+

E_i^I{{u_i−u_i}}[[∇hv_i]] ds

−

Ei

{{σi}} ·[[v_i]] ds−

E_i^I[[σi]]{{vi}}ds, (3.2) hereu_iandσi are thelocalnumerical fluxes. Their definition is given in terms of the corresponding definition of the global numerical fluxesuandσ; those determining the global DG method. That is, on internal facese∈E_i^I, the definition of u_i and σi coincides with that of u and σ on interior faces (see Tab. 1), and, on boundary facese∈E_i^B∪Γ_i, they are defined asuandσ on boundary faces. Notice that, eache∈Γ_iis in fact a boundary face for the local partition, but an interior face for the global partition. See [1] for further details on the relation between the localand global numerical fluxes.

From the definition of the local solvers, it turns out that our local solvers areapproximate, in the sense that A(R^T_i u_i, R_i^Tu_i) = Ai(u_i, u_i). This is in contrast with the methods proposed in [12,22,27] where exact local solvers were employed. As a consequence, in our convergence analysis alocal stability property (see Lem. 4.3 and Cor.4.4 below) will be required.

The last step is the construction of thecoarse solver. For a given approximation order_H, 0≤_H≤_h, the coarse spaces are defined as

V_h⁰≡V_H=

v∈L²(Ω) :v|D∈ M^H(D) ∀D∈ TH , Σ⁰_h≡Σ_H = [V_h⁰]^d.

In view of (3.1), it follows thatV_h⁰≡V_H⊆V_handΣ⁰_h≡Σ_H⊆Σ_h. The prolongation operatorR₀^T :V_h⁰−→V_h is defined as the natural injection operator fromV_h⁰ toV_h, and, as before,R₀ is defined as the transpose ofR^T₀ with respect to theL²-inner product. For vector-valued functions R^T₀ andR₀ are defined componentwise. We define the coarse solverA0:V_h⁰×V_h⁰−→Ras the restriction ofA(·,·) toV_h⁰×V_h⁰,i.e.,

A0(u₀, v₀) =A(R₀^Tu₀, R^T₀v₀) ∀u₀, v₀∈V_h⁰. (3.3) We refer to [1] for further details.

3.2. Algebraic formulation and algorithmic aspects

In this section we describe our two-level multiplicative Schwarz algorithms in the variational framework given in [28] and from the algebraic point of view.

Fori= 0, . . . , N_s, we define the following projection-like operators

P_i=R^T_iP_i:V_h−→R^T_i V_hⁱ⊂V_h, (3.4)

where the operatorsP_i:V_h−→V_hⁱ are defined by

Ai(P_iu, v_i) =A(u, R^T_i v_i) ∀v_i∈V_hⁱ. (3.5) The coercivity of the local and coarse bilinear formsAi(·,·) guarantees that the operatorsP_i (and thereforeP_i) are well defined. The multiplicative Schwarz operator we propose is defined by

P_mu=I−(I−P_Ns)(I−P_Ns−1). . .(I−P₀), (3.6) where I : V_h −→V_h is the identity operator. Following [8,38], the multiplicative Schwarz method consists in replacing the discrete problemAu=f by the equation P_muu=g, with an appropriate right hand sideg. We note that, even for the symmetric DG approximations, the corresponding operatorsP_mu are non symmetric, and therefore a suitable iterative solver as thegeneralized minimal residual(GMRES) method has to be used for solving the resulting linear systems of equations. This is in contrast with the situation encountered for the additive Schwarz operator proposed in [1] and defined as

P_ad=

Ns

i=0

P_i, (3.7)

which turns out to be symmetric for all symmetric DG methods and non-symmetric for non-symmetric DG ap- proximations. Nevertheless, following [38], asymmetrized version of the multiplicative Schwarz operator (3.6) can be defined as follows:

P_mu^sym=I−(I−P₀^∗). . .(I−P_N^∗_s−1)(I−P_N^∗_s)(I−P_Ns)(I−P_Ns−1). . .(I−P₀), (3.8) where, fori= 0, . . . , N_s,P_i^∗is the adjoint operator ofP_i with respect to the inner productA(·,·) for symmetric DG methods, and with respect toa(·,·) for the non-symmetric NIPG and IIPG approximations. It is clear that for the symmetrized multiplicative Schwarz method, a linear solver designed for symmetric linear systems as theconjugate gradient(CG) method can be used as an acceleration method.

Now we present our multiplicative Schwarz method from the algebraic point of view. Denoting byA∈R^n×n, with n = dim (V_h), the matrix representation of the bilinear form (2.5), the algebraic formulation of (2.4) is given byAu=f, where f∈Rⁿ and u∈Rⁿ are the coefficient vectors on the right hand side of (2.4) and of the unknownu, respectively. Fori = 0, . . . , N_s, we set n_i = dim (V_hⁱ). For each local space, let A_i ∈ R^ni×ni be the matrix representation of the local bilinear forms defined in (3.2) and let A₀ ∈ R^n0×n0 be the matrix representation of the coarse bilinear form defined in (3.3). Fori= 0, . . . , N_s, letR^T_i ∈R^n×ni andR_i∈R^ni×n be the matrix representation of the prolongation and restriction operatorsR^T_i andR_i, respectively. Then, the matrix representation of the projection-like operators defined in (3.4) is given by

P_i=R^T_iA⁻¹_i R_iA∈R^n×n, i= 0, . . . , N_s. (3.9) The multiplicative Schwarz operator (3.6) can be written as the product of a suitable preconditioner, namely B_mu, and A, the former involving only the prolongation operators R^T_i, the restriction operatorsR_i and the local operatorsA⁻¹_i . More precisely,

P_mu=I−(I−P_Ns)(I−P_Ns−1). . .(I−P₀) =B_muA, (3.10) whereI∈R^n×n is the identity matrix. Analogously, for the additive Schwarz operator (3.7), we have

P_ad=

Ns

i=0

P_i=B_adA.

We wish to stress that in our computations the preconditionerB_mu has not been built explicitly. In fact, the iterative solver used for the solution of the resulting linear system usually requires a routine that compute the action of the preconditioner on a generic vector. The following algorithm describes how to compute the action of the preconditionerB_muon a generic vectorx∈Rⁿ.

Algorithm 3.1. Action of the multiplicative Schwarz preconditionerBmu on a generic vector x∈Rⁿ. functionz=Bmux

z=R^T₀A⁻¹₀ R₀x;

for i= 1, . . . , N_s

z=z+R^T_iA⁻¹_i Ri(x−Az);

end

A similar algorithm can be written for the symmetrized multiplicative Schwarz method (3.8), by taking into account that the matrix representation of the operator P_i^∗ is given by P^∗_i = A⁻¹_s P^T_i A_s, where A_s is the matrix representation of the symmetric part of A(·,·), i.e., A_s = (A+A^T)/2. Clearly, whenever the underling DG scheme is symmetric, A_s =A and the identity (3.9) givesP^∗_i = A⁻¹(R^T_i A⁻¹_i R_iA)^TA =P_i. We point out that, while the application of the additive preconditioner (3.7) does not involve any product with the original matrix A, for the multiplicative preconditioner (3.6) (and also for its symmetrized version (3.8)) a product with A is required for the application of each local component (see Algorithm 3.1). Notice that, while the additive Schwarz preconditioner can be viewed as a block Jacobi preconditioner, the multiplicative one corresponds to a block Gauss-Seidel preconditioner. That is, the local solvers are performed sequentially, rather than in parallel, and consequently the multiplicative Schwarz preconditioner is not as parallelizable as the additive one. However, as we will show in the numerical experiments (see Sect.6), the multiplicative Schwarz method is far faster than the additive one. Finally, we note that due to the different iterative solver required in each case (GMRES for the multiplicative preconditioner and CG for the additive preconditioner) each iteration in the multiplicative method is slightly more expensive than in the additive one.

4. Convergence analysis for symmetric DG approximations

In this section, following the abstract framework introduced in [8,38] (see also [37]), we present the analysis of the multiplicative Schwarz methods for symmetric DG approximations stabilized with the term S^h(·,·).

4.1. Preliminary results

We start by revising some key results proved in [1], for both symmetric and non-symmetric DG approxima- tions, in the case of conforming grids. Their extension to non-matching grids is straightforward and we omit the details for the sake of brevity. We also include some preliminary results that we will need in our analysis.

Proposition 4.1 (stable decomposition [1]). Let A(·,·) be the bilinear form of a stable and consistent DG method. For anyu∈V_h, let u=_Ns

i=0R^T_iu_i,u_i ∈V_hⁱ,i= 0, . . . , N_s, whereu₀∈V_h⁰=V_H is defined by u₀|D= 1

meas(D)

D

udx ∀D∈ TH,

andu₁, . . . , u_Ns are determined (uniquely) by u−R^T₀u₀=R^T₁u₁+. . .+R^T_Nsu_Ns. Then,

Ns

i=0

Ai(u_i, u_i)≤α^∗C₀²A(u, u),

whereα^∗= max_e∈Eα_e, and

C₀²=O

⎛

⎝max

D∈TH

diam(D) min_T∈Th

T⊂Ddiam(T)

⎞

⎠·

Remark 4.2. Whenever the fine and coarse partitions are globally quasi uniform,i.e.,H_D≈H for allD∈ TH, andh_T ≈hfor allT ∈ Th, the stable decomposition constantC₀² given in Proposition4.1can be rewritten in a more simple way asC₀²=O(H/h).

For symmetric DG approximations, Proposition 4.1 guarantees that the following (strictly positive) lower bound on the minimum eigenvalue of the additive Schwarz operatorP_ad (3.7) can be derived

A(Padu, u)≥ 1

α^∗C₀²A(u, u) ∀u∈V_h. (4.1) Next, result ensures that a local stability property which provides a one-sided measure of the approximation properties of the local bilinear forms holds true.

Lemma 4.3 (local stability [1]). Let A(·,·) be the bilinear form of a stable and consistent DG method. Then, there existsC_ω>0 such that, for all u_i∈V_hⁱ

A(R^T_i u_i, R^T_i u_i)≤ωAi(u_i, u_i), ω= 1 +C_ω, i= 1, . . . , N_s, (4.2) with C_ω defined as

C_ω=C

C_re+ 1

√α_∗

, (4.3)

where C only depends on the inverse and trace inequality constants and, for the LDG method, on an upper bound of the functionβwhich enters into the definition of the numerical fluxes. Moreover,C_re = 0only for the DG methods in which the stability term involves the local lifting operators r_e(·)defined in (2.3).

As a consequence of Lemma4.3, we have the following result.

Corollary 4.4. Let A(·,·) be the bilinear form of a stable and consistent DG method stabilized by means of S^h(·,·). Then, there existsα >0 such that ifα_∗= min_e∈Eα_e> α,

A(R^T_i u_i, R_i^Tu_i)≤ωAi(u_i, u_i) ∀u_i∈V_hⁱ, i= 1, . . . , N_s, with ω∈(1,2).

Proof. From (4.2) we get

A(R^T_i u_i, R_i^Tu_i)≤(1 +C_ω)Ai(u_i, u_i) ∀u_i ∈V_hⁱ,

withC_ωdefined as in (4.3). By hypothesis, since we are considering DG methods stabilized by means ofS^h(·,·), C_re = 0 andC_ω=C/√

α_∗. Letα≥C²; then, ifα_∗> α,C_ω<1, and therefore A(R_i^Tu_i, R^T_iu_i)≤(1 +C_ω)Ai(u_i, u_i) =ωAi(u_i, u_i),

with 1< ω <2.

Remark 4.5. As in the classical Schwarz theory [8], our convergence analysis relies upon the hypothesis that the local stability constantω∈(0,2) (see Lem.4.8below). This implies that we have to restrict ourselves to all the symmetric DG approximations stabilized by means ofS^h(·,·) for which, by adding a technical hypothesis on the size of the penalty parameter, we can guaranteeω∈(1,2). We point out that such a restriction is only technical and it is not required in practice as we will show by numerical experiments in Section6. Furthermore, for the DG methods stabilized by means of S^r(·,·), the hypothesis ω ∈ (1,2) can never be satisfied even by increasing the size of the penalty parameter.

Remark 4.6. We wish to stress that, the restriction on the size parameter given in Corollary4.4is moderate.

In fact, as stated in Lemma 4.3, the constantC can be easily seen to depend only on the trace and inverse inequality constants, and, for the LDG method, on an upper bound of the function β which enters into the definition of the numerical fluxes.

For symmetric DG approximations, the following strengthened Cauchy-Schwarz inequalities hold. For 1 ≤ i, j≤ N_s, there exist constants 0≤ ε_ij≤ 1, such that

|A(R^T_iv_i, R^T_jv_j)| ≤ ε_ijA(R^T_iv_i, R^T_iv_i)^1/2A(R^T_jv_j, R^T_jv_j)^1/2 ∀v_i∈V_hⁱ ∀v_j∈V_h^j. (4.4) Let ρ(E) be the spectral radius of E ={ε_ij}1≤i,j≤Ns, it can be shown thatρ(E)≤ 1 +N_c, where N_c is the maximum number of adjacent subdomains that a given subdomain can have. By using the standard Cauchy- Schwarz inequality together with (4.4) and Lemma4.3, it easily follows

A _N

s

i=1

P_iv, v

≤ A _N

s

i=1

P_iv,

Ns

i=1

P_iv 1/2

A(v, v)^1/2≤ ω ρ(E)A(v, v) ∀v∈V_h. (4.5)

Next, we prove a result for the projection-like operatorsP_i defined in (3.4). We first notice that, for all the symmetric DG approximations, the operators P_i are self-adjoint with respect to A(·,·); in fact, by using the symmetry ofA(·,·) and the definition (3.4) ofP_i, we get

A(P_iu, v) =A(v, R^T_iP_iu) =Ai(P_iv,P_iu) =A(u, R^T_i P_iv) =A(u, P_iv), i= 0, . . . , N_s. (4.6) Lemma 4.7. Let A(·,·) be the bilinear form of a symmetric DG method. For i = 0, . . . , N_s, let P_i be the projection-like operators defined in (3.4). Then, for all u, v∈V_h,

A(Piu, v)≤ A(Piu, u)^1/2A(Piv, v)^1/2, i= 0, . . . , N_s (4.7) A(Piu, P_jv)≤ω ε_ijA(Piu, u)^1/2A(Pjv, v)^1/2, i, j= 1, . . . , N_s (4.8) where ω is the local stability constant given in Lemma 4.3 and, for i, j = 1, . . . , N_s, ε_ij are the entries of the correlation matrix E and, therefore,ε_ij= 1 if∂Ω_i∩∂Ω_j =∅ andε_ij = 0otherwise.

Proof. The self-adjointness (4.6) ofP_i, the definitions (3.4) and (3.5) of P_i and P_i, respectively, together with the standard Cauchy-Schwarz inequality, lead to

A(Piu, v) =Ai(P_iu,P_iv)≤ Ai(P_iu,P_iu)^1/2Ai(P_iv,P_iv)^1/2=A(u, Piu)^1/2A(v, Piv)^1/2.

By using again (4.6) we reach (4.7). Similarly, the definition (3.5) ofP_itogether with the strengthened Cauchy- Schwarz inequalities (4.4), Lemma 4.3, the definition (3.5) and the identity (4.6) yield

A(Piu, P_jv) =A(R^T_iP_iu, R^T_jP_jv)≤ε_ijA(R^T_iP_iu, R^T_iP_iu)^1/2A(R^T_jP_jv, R^T_jP_jv)^1/2

≤ω ε_ijAi(P_iu,P_iu)^1/2Aj(P_jv,P_jv)^1/2=ω ε_ijA(u, Piu)^1/2A(v, Pjv)^1/2.

By using the identity (4.6) we reach (4.8), and the proof is complete.