This paper develops uniform preconditioners for elliptic operators of various orders, enabling efficient solutions without constructing dual meshes, by leveraging operator preconditioning techniques.
Contribution
It introduces a novel framework for uniform preconditioning of elliptic operators of order between 0 and 2, avoiding dual mesh construction and maintaining linear complexity.
Findings
01
Preconditioners are effective across a range of elliptic operator orders.
02
The approach avoids the need for dual mesh construction.
03
Preconditioner application costs are linear in problem size.
Abstract
Using the framework of operator or Cald\'{e}ron preconditioning, uniform preconditioners are constructed for elliptic operators of order 2s∈[0,2] discretized with continuous finite (or boundary) elements. The cost of the preconditioner is the cost of the application an elliptic opposite order operator discretized with discontinuous or continuous finite elements on the same mesh, plus minor cost of linear complexity. Herewith the construction of a so-called dual mesh is avoided.
Tables4
Table 1. Table 1. Spectral condition numbers of the preconditioned hypersingular system, using uniform refinements, discretized by continuous piecewise linears 𝒮 𝒯 0 , 1 superscript subscript 𝒮 𝒯 0 1 \mathscr{S}_{\mathcal{T}}^{0,1} , with α = 0.05 𝛼 0.05 \alpha=0.05 .
The preconditioners 𝑮 𝒯 − 1 , 0 superscript subscript 𝑮 𝒯 1 0 \bm{G}_{\mathcal{T}}^{-1,0} and 𝑮 𝒯 0 , 1 superscript subscript 𝑮 𝒯 0 1 \bm{G}_{\mathcal{T}}^{0,1} are constructed using the single layer operator discretized on 𝒰 𝒯 = 𝒮 𝒯 − 1 , 0 subscript 𝒰 𝒯 superscript subscript 𝒮 𝒯 1 0 \mathscr{U}_{\mathcal{T}}=\mathscr{S}_{\mathcal{T}}^{-1,0} (Sect. 5.2.1 ) and 𝒰 𝒯 = 𝒮 𝒯 0 , 1 subscript 𝒰 𝒯 superscript subscript 𝒮 𝒯 0 1 \mathscr{U}_{\mathcal{T}}=\mathscr{S}_{\mathcal{T}}^{0,1} (Sec 5.3.1 ), respectively, where have used β 1 = 0.65 subscript 𝛽 1 0.65 \beta_{1}=0.65 in the first case and β 1 = 0.34 subscript 𝛽 1 0.34 \beta_{1}=0.34 in the second case.
dofs
Table 2. Table 2. In the same setting as Table 1 , but using compressed hierarchical matrices.
dofs
Table 3. Table 3. Spectral condition numbers of the preconditioned hypersingular system, using uniform refinements, discretized by continuous piecewise cubics 𝒮 𝒯 0 , 3 superscript subscript 𝒮 𝒯 0 3 \mathscr{S}_{\mathcal{T}}^{0,3} , with α = 0.05 𝛼 0.05 \alpha=0.05 .
The higher order preconditioners 𝑮 𝒯 − 1 , 0 superscript subscript 𝑮 𝒯 1 0 \bm{G}_{\mathcal{T}}^{-1,0} and 𝑮 𝒯 0 , 1 superscript subscript 𝑮 𝒯 0 1 \bm{G}_{\mathcal{T}}^{0,1} are constructed as described in Sect. 6.1.1 , by using the
preconditioners from Table 1 with
constants β 1 = 0.65 , β 2 = 0.065 formulae-sequence subscript 𝛽 1 0.65 subscript 𝛽 2 0.065 \beta_{1}=0.65,\beta_{2}=0.065 in the first case and β 1 = 0.34 , β 2 = 0.065 formulae-sequence subscript 𝛽 1 0.34 subscript 𝛽 2 0.065 \beta_{1}=0.34,\beta_{2}=0.065 in the second case.
dofs
Table 4. Table 4. Spectral condition numbers of the preconditioned hypersingular system discretized by 𝒮 𝒯 0 , 1 superscript subscript 𝒮 𝒯 0 1 \mathscr{S}_{\mathcal{T}}^{0,1} using local refinements at each of the eight cube corners.
Both preconditioners 𝑮 𝒯 − 1 , 0 superscript subscript 𝑮 𝒯 1 0 \bm{G}_{\mathcal{T}}^{-1,0} and 𝑮 𝒯 0 , 1 superscript subscript 𝑮 𝒯 0 1 \bm{G}_{\mathcal{T}}^{0,1} are constructed with same parameters as in Table 1 , and are compared against
diagonal preconditioning. The second column is defined by h 𝒯 , m i n : = min T ∈ 𝒯 h T h_{{\mathcal{T}},min}\mathrel{\mathop{\ordinarycolon}}=\min_{T\in{\mathcal{T}}}h_{T} .
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Full text
Uniform preconditioners for problems of positive order
Rob Stevenson, Raymond van Venetië
Korteweg-de Vries Institute for Mathematics,
University of Amsterdam,
P.O. Box 94248,
1090 GE Amsterdam, The Netherlands
Using the framework of operator or Caldéron preconditioning, uniform preconditioners are constructed for elliptic operators of order 2s∈[0,2] discretized with continuous finite (or boundary) elements.
The cost of the preconditioner is the cost of the application an elliptic opposite order operator discretized with discontinuous or continuous finite elements on the same mesh, plus minor cost of linear complexity. Herewith the construction of a so-called dual mesh is avoided.
Key words and phrases:
Operator preconditioning, uniform preconditioners, finite- and boundary elements
2010 Mathematics Subject Classification:
65F08, 65N38, 65N30, 45Exx.
The first author has been supported by NSF Grant DMS 172029.
The second author has been supported by the Netherlands Organization for Scientific Research
(NWO) under contract. no. 613.001.652
1. Introduction
This paper deals with the construction of uniform preconditioners for operators of positive order,
using the framework of ‘operator preconditioning’ ([Hip06]).
It will build on our experiences with this approach for problems of negative order reported in [Sv18].
For some d-dimensional domain (or manifold) Ω, a measurable, closed, possibly empty γ⊂∂Ω, and an s∈[0,1],
we consider the Sobolev space
[TABLE]
For VT⊂V a closed, e.g. finite dimensional subspace, and AT:VT→VT′ some boundedly invertible linear operator,
we are interested in constructing a preconditionerGT:VT′→VT.
More specifically, thinking of a a family of spaces VT and operators AT:VT→VT′, our aim is
to construct preconditioners GT such that GTAT:VT→VT is uniformly boundedly invertible.
It is well-known that such preconditioners of multi-level type are available. The advantage of operator preconditioning is, however, that it does not require a hierarchy of trial spaces.
In order to apply the operator preconditioning framework, one needs to construct families of closed subspaces WT⊂W:=V′,
uniformly boundedly invertible BT:WT→WT′, and uniformly boundedly invertible DT:VT→WT′.
Then the resulting preconditioners GT are of the form
[TABLE]
The canonical setting is that for A:V→V′, i.e., an operator of order 2s, and an opposite order operator B:W→W′,
both boundedly invertible and coercive, it holds that
(ATu)(v):=(Au)(v) (u,v∈VT),
(BTu)(v):=(Bu)(v) (u,v∈WT), and (DTu)(v):=⟨u,v⟩L2(Ω) (u∈VT,v∈WT).
A typical example for s=1/2 is that A is the Hypersingular Integral operator, and B is the Weakly Singular Integral operator.
A careful selection of WT has to be made to ensure that DT:VT→WT′ is uniformly boundedly invertible.
A suitable family of (VT,WT) pairs has been introduced in [Ste02, BC07].
Here T is a triangular partition of a two-dimensional domain or manifold, VT is the
space of continuous piecewise linears w.r.t. T, and WT
is a subspace of the space of piecewise constants w.r.t. a barycentric refinement of T,
constructed by subdividing each triangle into 6 subtriangles by connecting its vertices and midpoints with its barycenter.
It has been shown in [Ste02, HUT16] that the preconditioner arising from these pairs (VT,WT) is a uniform preconditioner
for families of partitions that satisfy a certain mildly-grading condition.
A problem with the constructions from [Ste02, BC07] appears when one considers the matrix representation GT in the standard bases, i.e.
GT=DT−1BTDT−⊤.
Indeed, this matrix DT is not diagonal, and its inverse is densely populated so that it has to be
approximated.
Moreover, in order to get a uniform preconditioner, the accuracy with which DT−1 has to be approximated increases with a decreasing mesh-size.
As a result, an application of DT−1 cannot be expected to execute in linear time.
Another (practical) issue with these constructions is the need for the construction of the non-standard barycentrical refinement of T.
This refinement increases the number of elements by a factor 6, and therefore also increases the cost of evaluating BT:WT→WT′.
1.1. Contributions
With VT being the space of continuous piecewise linears, the construction of WT presented in this paper improves on the existing approach from [Ste02, BC07] concerning the following aspects:
•
The matrix representation DT of DT will be diagonal, allowing one to (exactly) evaluate DT−1 in linear time;
•
The operator GT will be a uniformly well-conditioned preconditioner for families of uniformly shape regular partitions, without requiring
a mildly-grading assumption on the partitions;
•
By using a stable decomposition of WT into a standard finite element space UT w.r.t. T (either
being the space of piecewise constants or UT=VT) and
some bubble space, our BT will be the sum of the corresponding Galerkin discretization operator of the opposite order operator B, and an operator whose representation is a diagonal, with which the undesired barycentrical refinement is avoided;
•
The construction of WT applies in any space dimension, and extends to non piecewise planar
manifolds.
We will extend the preconditioners to higher order finite element spaces by applying a subspace correction framework.
1.2. Outline
Sect. 2 recalls some notation that will be used throughout the article.
In Sect. 3 the general theory of operator preconditioning is summarized.
In Sect. 4, the framework is specialized to operators of positive order discretized with continuous piecewise linears.
Sect. 5 give two constructions of BT∈Lisc(WT,WT′) that avoid any refinement of the partition T that underlies the trial space VT.
In Sect. 6 the preconditioners are generalized to higher order finite element spaces, and to spaces defined on manifolds.
Finally, in Sect. 7 we report some numerical results obtained with the new preconditioners.
2. Notations
Notations 2.1*.*
In this work, by λ≲μ we will mean that λ can be bounded by a multiple of μ, independently of parameters which λ and μ may depend on, with the sole exception of the space dimension d, or in the manifold case, on the parametrization of the manifold that is used to define the finite element spaces on it. Obviously, λ≳μ is defined as μ≲λ, and λ≂μ as λ≲μ and λ≳μ.
Notations 2.2*.*
For normed linear spaces Y and Z, in this paper for convenience over R, L(Y,Z) will denote the space of bounded linear mappings Y→Z endowed with the operator norm ∥⋅∥L(Y,Z). The subset of invertible operators in L(Y,Z) with inverses in L(Z,Y)
will be denoted as Lis(Y,Z).
The condition number of a C∈Lis(Y,Z) is defined as κY,Z(C):=∥C∥L(Y,Z)∥C−1∥L(Z,Y).
For Y a reflexive Banach space and C∈L(Y,Y′) being coercive, i.e.,
[TABLE]
both C and ℜ(C):=21(C+C′) are in Lis(Y,Y′) with
[TABLE]
The set of coercive C∈Lis(Y,Y′) is denoted as Lisc(Y,Y′).
If C∈Lisc(Y,Y′), then C−1∈Lisc(Y′,Y) and ∥ℜ(C−1)−1∥L(Y,Y′)≤∥C∥L(Y,Y′)2∥ℜ(C)−1∥L(Y′,Y).
Given a family of operators Ci∈Lis(Yi,Zi) (Lisc(Yi,Zi)), we will write Ci∈Lis(Yi,Zi) (Lisc(Yi,Zi)) uniformly in i, or simply ‘uniform’, when
[TABLE]
or
[TABLE]
Notations 2.3*.*
Given a finite collection Υ={υ} in a linear space, we set the synthesis operator
[TABLE]
Equipping R#Υ with the Euclidean scalar product ⟨⋅,⋅⟩, and identifying (R#Υ)′ with R#Υ using the corresponding Riesz map, we infer that the adjoint of FΥ, known as the analysis operator, satisfies
[TABLE]
A collection Υ is a basis for its span when FΥ∈Lis(R#Υ,spanΥ) (and so FΥ′∈Lis((spanΥ)′,R#Υ).)
Two countable collections Υ=(υi)i and Υ~=(υ~i)i in a Hilbert space will be called biorthogonal when ⟨Υ,Υ~⟩=[⟨υj,υ~i⟩]ij is an invertible diagonal matrix, and biorthonormal when it is the identity matrix.
3. Operator preconditioning
We shortly recap the idea of opposite order preconditioning,
which is based on the following result, see [Hip06, Sect. 2].
Proposition 3.1**.**
Let V,W be reflexive Banach spaces.
If B∈Lis(W,W′) and D∈Lis(V,W′), then
[TABLE]
and
[TABLE]
If additionally B∈Lisc(W,W′), then G∈Lisc(V′,V), and
[TABLE]
Let be given families of finite dimensional spaces VT for T∈T, and operators AT∈Lis(VT,VT′) uniformly in T∈T.
Then in light of Proposition 3.1 we will seek preconditioners for AT of the form
[TABLE]
where BT∈Lis(WT,WT′) and DT∈Lis(VT,WT′) (both uniformly in T∈T), and
[TABLE]
A typical situation is that for some reflexive Banach space V and A∈Lisc(V,V′), it holds that
VT⊂V (thus equipped with ∥⋅∥V) and (ATu)(v):=(Au)(v) (u,v∈VT), so that indeed
AT∈Lisc(VT,VT′) uniformly in T∈T.
Then for a suitable reflexive Banach space W, an operator B∈Lisc(W,W′), and a subspace WT⊂W (thus equipped with ∥⋅∥W), one
can take (BTw)(z):=(Bw)(z) (w,z∈WT), giving BT∈Lisc(WT,WT′) uniformly.
A possible construction of DT∈Lis(VT,WT′) uniformly is discussed in the next proposition.
For some D∈Lis(V,W′), let DT∈L(VT,WT′) be defined by
(DTv)(w):=(Dv)(w). Then
[TABLE]
In our applications, the choices for W and D will be obvious, and the key ingredient for the construction of a uniform preconditioner GT will be the selection of WT that allows for a uniformly bounded Fortin projector PT.
3.1. Implementation
Let ΦT=(ϕi)i and ΨT=(ψi)i be bases for VT and WT, respectively.
Then in coordinates the preconditioned system reads as
[TABLE]
where
[TABLE]
By identifying a map in L(R#ΦT,R#ΦT) with a #ΦT×#ΦT matrix by equipping R#ΦT with the canonical basis (ei)i one has,
[TABLE]
and similarly,
[TABLE]
Preferably DT is such that its inverse can be applied in linear complexity, as is the case when DT is diagonal.
A goal of this paper is to construct such a diagonal DT.
Remark 3.3*.*
Using σ(⋅) and ρ(⋅) to denote the spectrum and spectral radius of an operator,
clearly σ(GTAT)=σ(GTAT). So for the spectral condition number we have
[TABLE]
which thus holds true independently of the choice of the basis ΦT for VT.
Furthermore, in view of an application of Conjugate Gradients, if AT and BT are coercive and self-adjoint, then AT and GT are positive definite and symmetric.
Equipping RdimVT with ∣∣∣⋅∣∣∣:=∥(GT)−21⋅∥ or ∣∣∣⋅∣∣∣:=∥(AT)21⋅∥, in that case we have
[TABLE]
4. Application to operators discretized with continuous piecewise linears
For a bounded polytopal domain Ω⊂Rd, a measurable, closed, possibly empty γ⊂∂Ω, and an s∈[0,1], we take
[TABLE]
which forms the Gelfand triple V↪L2(Ω)≃L2(Ω)′↪W. We define the operator D∈Lis(V,W′) as the unique extension
to V×W of the duality pairing
[TABLE]
which satisfies ∥D∥L(V,W′)=∥D−1∥L(W′,V)=1.
Let (T)T∈T be a family of conforming partitions of Ω into closed uniformly shape regular d-simplices.
Thanks to the conformity and the uniform shape regularity, for d>1 we know that neighbouring T,T′∈T, i.e. T∩T′=∅, have uniformly comparable sizes.
For d=1, we impose this uniform ‘K-mesh property’ explicitly.
For some T∈T, denote NT0 as the subset of vertices that are not on γ, where we assume that γ is the (possibly empty) union of (d−1)-faces of T∈T.
For T∈T, write NT for the set of its vertices, set NT0:=NT0∩NT, hT:=∣T∣1/d, and the piecewise constant function hT by hT∣T=hT (T∈T).
For any vertex ν∈NT0, define the patch ωT,ν:=⋃{T∈T:ν∈T}T and the local mesh size hT,ν:=∣ωT,ν∣1/d.
We omit notational dependence on T if it is clear from the context, and simply write ων and hν.
Let the discretization space VT be the space of continuous piecewise linears, zero on γ,
[TABLE]
equipped with the nodal bases
[TABLE]
defined by ϕν(ν′):=δνν′ (ν,ν′∈NT0).
For future reference, define the space of discontinuous piecewise constants by
[TABLE]
equipped with the basis
[TABLE]
where 1K is defined by, for any K⊆Ω,
1K:=1 on K, and 1K:=0 elsewhere.
4.1. The subspace WT
We will construct the preconditioning space WT as
[TABLE]
for a collection ΨT⊂L2(Ω) that is biorthogonal to ΦT,
and for which the biorthogonal projector PT∈L(W,W) onto WT is uniformly bounded.
We require the collection ΨT:={ψν∈W:ν∈NT0} to satisfy
[TABLE]
Existence of such collections will be shown later in Sect. 5.
4.2. Bounded Fortin projector
From the assumptions (4.2) it follows that the biorthogonal Fortin projector PT:H0,γ1(Ω)′→L2(Ω) onto WT with ran(I−PT)=VT⊥L2(Ω) exists, and is given by
[TABLE]
Uniform boundedness of ∥PT∥L(W,W) follows from uniform boundedness
of its adjoint PT′, which can be shown similarly as in [Sv18, Thm. 3.2]111Note that the roles of V and W are interchanged compared to [Sv18].:
Theorem 4.1**.**
It holds that supT∈T∥PT∥L(W,W)=supT∈T∥PT′∥L(V,V)<∞.
Proof.
Let T∈T. Define ωT(0):=T for T∈T, and for i=1,…, denote ωT(i):=⋃{T′∈T:T′∩ωT(i−1)=∅}T′. The adjoint PT′:L2(Ω)→H0,γ1(Ω) onto VT is given by
[TABLE]
Properties of the nodal basis functions, ∥ϕν∥L2(Ω)2≂hνd and ∥ϕν∥H1(Ω)2≲hνd−2, in combination with (4.2), can be used to show that, for T∈T and k∈{0,1},
[TABLE]
from which we may directly conclude that
[TABLE]
For proving boundedness in H0,γ1(Ω),
we consider the Scott-Zhang ([SZ90]) interpolator ΠT:H0,γ1(Ω)→VT.
From (4.3) and properties of the ΠT [SZ90, (3.8) and (4.3)], we deduce that
[TABLE]
and consequently
[TABLE]
An application of the Riesz-Thorin interpolation theorem yields the result.
∎
The basis ΨT has the crucial benefit that the matrix representation of DT, i.e.
[TABLE]
is diagonal, and thus easily invertible, cf. Sect. 3.1.
Combining the theorem with Proposition 3.2 gives the following corollary (without requiring additional
assumptions on the family of partitions T).
Corollary 4.2**.**
Suppose we have BT∈Lisc(WT,WT′) uniformly.
With DT:VT→WT defined by (DTv)(w):=⟨v,w⟩L2(Ω), we find
that GT=DT−1BT(DT′)−1∈Lisc(VT′,VT) is a uniform preconditioner of AT∈Lisc(VT,VT′).
Given some B∈Lisc(W,W′), a possible choice for BT∈Lisc(WT,WT′) uniformly in T∈T,
is (BTu)(v):=(Bu)(v) (u,v∈WT). For d∈{2,3} and W′=V=H21(Ω), a suitable B is given by the Weakly Singular Integral operator, whereas for
W′=V=H0021(Ω):=[L2(Ω),H01(Ω)]21,2, the
recently in [HJHUT18] introduced Modified Weakly Singular Integral operator can be applied. Similar comments apply to screens.
5. Construction of BT∈Lisc(WT,WT′)
We expect it to be impossible to construct a basis ΨT in the
(standard) spaces ST−1,0 or ST0,1 that is local and biorthogonal to ΦT as required in assumption (4.2).
One remedy is to construct ΨT in a (finite element) space w.r.t. a refined partition T∗≻T.
However, this implies that some opposite order operator B∈Lisc(W,W′) has to be discretized on a space w.r.t. the refined partition T∗.
This increases the cost of the preconditioner, and moreover, increases implementational complexity as one has to actually construct this refined partition.
To circumvent (explicit) dependence on the refined partition T∗, we shall apply the idea described in [Sv18, Sec. 3.3].
That is, we will construct an operator BT∈Lisc(WT,WT′) by decomposing the space WT into a
a standard finite element space UT, either ST−1,0 (in Sect. 5.2) or ST0,1 (in Sect. 5.3), and some bubble space BT.
On UT we will apply the Galerkin discretization operator of the opposite order operator B,
whereas on the bubble space BT a diagonal scaling will suffice.
In the first subsection we present this construction of BT for some abstract WT. In the subsequent subsections, we will present two viable options for WT, leading
to two different preconditioners.
5.1. Stable decomposition
The role of the space ‘Y’ is the next abstract proposition is going to be played by WT.
Proposition 5.1**.**
Let Z be an inner product space, Q∈L(Z,Z) a projector, and with U:=ranQ, let B:=ran(Id−Q), BU∈Lisc(U,U′), and BB∈Lisc(B,B′).
Then for any subspace Y⊂Z,
[TABLE]
is bounded and coercive — B∈Lisc(Y,Y′) — with
[TABLE]
where ∥Q∥:=∥Q∥L(Z,Z).
Proof.
Let y,y~∈Y. Write u=Qy, b=(Id−Q)y, and similarly u~=Qy~, b~=(Id−Q)y~.
We have
[TABLE]
and
[TABLE]
With γ:=sup0=(u,b)∈U×B∥u∥Z∥b∥Z∣⟨u,b⟩Z∣, for 0=(u,b)∈U×B we have ∥u∥Z2+∥b∥Z2∥u+b∥Z2∈[1−γ,1+γ].
Using that 1−γ21=∥Q∥ (see e.g. [Szy06, (5.5), (5.7), (6.2)]), the proof is easily completed.
∎
Remark 5.2*.*
For a quantitatively weaker result as Proposition 5.1 to hold it is actually sufficient when Q is only defined on Y,
and neither is it needed that it is a projector.
Under these relaxed conditions, obvious estimates show bounds as in Proposition 5.1 with the factors ∥Q∥2+∥Q∥4−∥Q∥2 and 1+1−∥Q∥−2 reading as ∥Q∣Y∥2+(1+∥Q∣Y∥)2 and 2, respectively. Both original factors are equal to 1 when Q is an orthogonal projector.
We are going to apply this abstract proposition with ‘Y’=WT,
‘U’=UT being a standard finite element space,
‘B’=BT being a suitably constructed ‘bubble space’,
and ‘Z’=ZT:=UT+BT, all equipped with the norm on W.
The resulting ‘B’ will be the BT∈Lisc(WT,WT′) we are seeking.
In order to apply above proposition, what is left is the construction of a (uniformly) bounded projector defined on ZT.
Furthermore, to allow for a simple preconditioner on BT we would like to find a setting in which on this bubble space the W-norm is equivalent to a weighted L2-norm.
Both issues will be dealt with in the next two lemmas. The operator QT∣ZT in the first lemma will play the role of ‘Q’ in Proposition 5.1.
Lemma 5.3**.**
Let QT∈L(L2(Ω),H0,γ1(Ω)′) be a projector, UT⊆ranQT and BT⊆ran(Id−QT) be subspaces of L2(Ω), and with ZT:=UT+BT, let
supT∈T∥QT∣ZT∥L((ZT,∥⋅∥L2(Ω)),L2(Ω))≲1, (boundedness in L2(Ω))
3. (3)
∥hT⋅∥L2(Ω)≲∥⋅∥H0,γ1(Ω)′* on ZT. (inverse inequality)*
Then QT∣ZT:ZT→ZT is a projector, ranQT∣ZT=UT, ran(Id−QT∣ZT)=BT, and
(i)
supT∈T∥QT∣ZT∥L((ZT,∥⋅∥W),W)<∞,
2. (ii)
∥⋅∥W≂∥hTs⋅∥L2(Ω)* on BT.*
Proof.
The first three statements are easily verified.
From (1) it follows that for u∈H0,γ1(Ω)′:
[TABLE]
Together with the inverse inequality on ZT, this gives (uniform) boundedness of ∥(Id−QT)∣ZT∥L((ZT,∥⋅∥H0,γ1(Ω)′),H0,γ1(Ω)′)
and thus of
∥QT∣ZT∥L((ZT,∥⋅∥H0,γ1(Ω)′),H0,γ1(Ω)′). The first result then follows from (2) and an interpolation argument.
By the inverse inequality on BT and the previously derived inequality, we have for bT∈BT⊆ran(Id−QT) that
[TABLE]
Another interpolation argument yields the second result.
∎
Lemma 5.4**.**
Suppose that ∥⋅∥W≂∥hTs⋅∥L2(Ω) holds on BT,
and that ΘT is a uniformly ∥hTs⋅∥L2(Ω)-stable basis for BT, i.e.
[TABLE]
then, for any β1>0, an operator BTB∈Lisc(BT,BT′) is given by
[TABLE]
Remark 5.5*.*
It is not possible to construct BT∈Lis(WT,WT′) directly as a diagonal scaling operator.
Indeed, this would require ∥wT∥W≲∥hTswT∥L2(Ω) for wT∈WT.
Suppose this to be true, then by L2(Ω)-boundedness of the biorthogonal projector PT,
we would find for vT∈VT that
[TABLE]
which is known not to be true for smooth functions in VT.
Concluding: If, given a family of subspaces WT⊂L2(Ω), one can find a family of projectors QT∈L(L2(Ω),H0,γ1(Ω)′),
subspaces UT⊆ranQT (of finite element type) and BT⊆ran(Id−QT) such that
[TABLE]
(with these spaces equipped with ∥⋅∥W-norm) and the conditions of Lemma 5.3 are satisfied, then given BTU∈Lisc(UT,UT′) and BTB∈Lisc(BT,BT′), the operator BTW defined by
[TABLE]
is in Lisc(WT,WT′).
Moreover, assuming a uniformly ∥hTs⋅∥L2(Ω)-stable basis for BT, the operator BTB can be of simple diagonal scaling type, where a natural definition for BTU is by (BTu)(u~):=(Bu)(u~) (u,u~∈UT) for some opposite order operator B∈Lisc(W,W′).
Finally, since QT enters the implementation, we search this projector to be of local type.
5.2. A space WT decomposable into the piecewise constants and bubbles
In this subsection, we construct WT=spanΨT such that both ΨT is biorthogonal to ΦT
(Assumption (4.2)),
and WT allows an appropriate decomposition into the space of piecewise constants UT:=ST−1,0 and a bubble space BT.
Fix T∈T and let T∗≻T be a uniform red-refinement, i.e. every simplex
T∈T is subdivided into 2d subsimplices.222Red-refinement is not uniquely defined for d≥3, but the refined simplices at the corners of the ‘parent simplex’
are uniquely determined which suffices for our goal.
We define ΨT={ψT,ν:ν∈NT0}⊂ST∗−1,0 by taking a
weighted difference of ‘patch indicator’ functions:
[TABLE]
Lemma 5.6**.**
The collection ΨT satisfies Assumption (4.2) with suppψT,ν=ωT,ν and
[TABLE]
Proof.
Clearly suppψT,ν=ωT,ν, so we are left to show the biorthogonality condition.
Fix some vertex ν∈NT0. For a simplex Tν∈T with ν∈Tν, we have
[TABLE]
Let T∗,ν∈T∗ be the (unique) simplex with ν∈T∗,ν⊂Tν. From the refinement equation satisfied by the nodal hats, and ∣T∗,ν∣=2−d∣Tν∣, it follows that
By Lemma 5.6 it has been established that the Fortin interpolator is uniformly bounded, and that DT is represented by a diagonal matrix.
The next proposition verifies the conditions imposed in Sect. 5.1 for the construction of BT.
Proposition 5.7**.**
Let UT:=ST−1,0, WT:=spanΨT as constructed above, QT be the L2(Ω)-orthogonal projector onto UT,
ΘT:=(Id−QT)ΨT,
and
BT:=spanΘT.
Then WT⊂ZT:=UT+BT((5.2)), the conditions of Lemma 5.3 are satisfied, in particular QTψν=1ων, and ΘT is a uniformly ∥hTs⋅∥L2(Ω)-stable basis for BT as required for Lemma 5.4.
Proof.
The first statement follows from WT⊂L2(Ω).
The first two conditions of Lemma 5.3 are obviously valid.
Concerning the third condition, the inverse inequality ∥hT⋅∥L2(Ω)≲∥⋅∥H0,γ1(Ω)′ holds, for general d, on ST∗−1,0, see e.g. [Sv18, Lem. 3.4], and thus in particular on ZT.
The property QTψν=1ων is easily checked.
We are left to show that the collection of bubbles {θν:=(Id−QT)ψν:ν∈NT0} is ∥hTs⋅∥L2(Ω)-stable.
Pick some T∈T, then the normalized ‘bubble element matrix’ satisfies
[TABLE]
For d≥2, this constant (symmetric) (d+1)×(d+1) matrix is strictly diagonally dominant, and therefore positive definite. We conclude this proposition by
[TABLE]
Remark 5.8*.*
For d=1, the bubbles arising from ΨT as given in (5.4) do not form a ∥hTs⋅∥L2(Ω)-stable collection.
Instead, with T∗∗≻T being the two times uniform red-refinement, one can consider ψT,ν=3161ωT∗∗,ν−311ωT,ν for which the statements of Lemma 5.6 and
Proposition 5.7 are again valid.
5.2.1. Implementation
The matrix representation of preconditioner FΦT−1GT(FΦT′)−1 is given by
[TABLE]
With ΨT as constructed in (5.4), we find that
DT=FΨT′DTFΦT is given by
[TABLE]
Given some BTU∈Lisc(UT,UT′)
(recall that UT=ST−1,0),
then by taking BT as described in (5.3), we have
[TABLE]
where, using that FΘT−1(Id−QT)FΨT=Id by ΘT=(I−QT)ΨT,
[TABLE]
Recall the canonical basis ΣT for UT from (4.1).
Using QTψν=1ων shows that
[TABLE]
From (5.6), we infer that ∥hTsθν∥L2(Ω)2≂∣ων∣1+d2s.
By making a harmless modification to the definition of BTB in (5.1) based on this equivalency, we obtain that
[TABLE]
The matrix BTU depends on the operator BTU∈Lisc(UT,UT′) that is selected.
The canonical choice is the Galerkin discretization operator on UT of a B∈Lisc(W,W′).
The cost of the application of GT is the cost of the application of BTU plus cost that scales linearly in #T.
5.3. A space WT decomposable into the continuous piecewise linears and bubbles
We follow the same program as in the previous subsection Sect. 5.2 but now with UT:=ST0,1, being the space of continuous piecewise linears.
Other than in Sect. 5.2,
we cannot apply Proposition 5.1 for QT being the orthogonal projector onto UT,
since with the current choice of this space it will not be a local projector.
As an alternative, we take QT to be some biorthogonal projector. The question whether it enjoys an approximation property is answered in the following lemma.
Lemma 5.9**.**
For ν∈NT, so including vertices on γ, let ϕν∈L2(Ω) be such that
[TABLE]
for some constant R>0, and
[TABLE]
Denote UT:=span{ϕν:ν∈NT0}, so without vertices on γ.
The biorthogonal projector QT:u↦∑ν∈NT0⟨ϕν,ϕν⟩L2(Ω)⟨u,ϕν⟩L2(Ω)ϕν, for which ranQT=ST0,1 and
ran(Id−QT)=UT⊥L2(Ω), satisfies the approximation property
[TABLE]
and ∥QT∥L(L2(Ω),L2(Ω))≲1.
Proof.
We use the same strategy as in [Sv18]. That is, we define a Scott-Zhang type quasi-interpolator ΠT:H1(Ω)→L2(Ω), cf. [SZ90].
For every ν∈NT, select a (d−1)-face eν of some T∈T with ν∈eν and eν⊂γ if ν∈γ. We define ΠT by
[TABLE]
Since gT,ν(1)=1, using the properties from (5.7) one can show, cf. proof of [Sv18, Thm. 3.2] for details, that
[TABLE]
By construction, gT,ν(u)=0 for ν on γ and u∈H0,γ1(Ω),
and therefore ranΠT∣H0,γ1(Ω)⊂UT.
Finally, combined with L2(Ω)-boundedness and locality of QT′, and the fact that QT′ reproduces UT, we find that
[TABLE]
The last statement can be proven similarly as in the proof of Theorem 4.1.
∎
As before, let T∗≻T denote a uniform red-refinement of T, and
for any T∈T and ν∈NT, let T∗,ν∈T∗ denote the simplex with ν∈T∗,ν⊂T.
For ν∈NT, so including boundary vertices, define
and so determine a valid biorthogonal projector QT via Lemma 5.9.
For T∗∗≻T∗ a uniform red-refinement of T∗, we define ΘT:={θT,ν:ν∈NT0} by
[TABLE]
Since red-refinement subdivides each simplex into d subsimplices, one infers that
[TABLE]
so that in particular BT⊂kerQT.
Defining ΨT:={ψT,ν:ν∈NT0} by
[TABLE]
calculations as in the proof of Lemma 5.6 show the following result.
Lemma 5.10**.**
The collection ΨT satisfies Assumption (4.2) with suppψT,ν=ωT,ν and
[TABLE]
So the Fortin interpolator is uniformly bounded, and DT is represented by a diagonal matrix. Next we verify
the conditions imposed in Sect. 5.1 for the construction of BT.
Proposition 5.11**.**
Let UT, QT, BT, and WT:=spanΨT be defined as above.
Then WT⊂ZT:=UT+BT((5.2)), the conditions of Lemma 5.3 are satisfied,
in particular ΦT=QTΨT and so ΘT=(Id−QT)ΨT,
and lastly, ΘT is an ∥hTs⋅∥L2(Ω)-orthogonal basis for BT as required for Lemma 5.4.
Proof.
The first statement is obviously true. We have already verified the first two conditions of Lemma 5.3. The third
condition follows from this inverse inequality on ST∗∗−1,1 (see e.g. [Sv18, (5.14)]), and
ΦT=QTΨT is a consequence of (5.8).
The last statement follows from ∣suppθν∩suppθν′∣=0 when ν=ν′.
∎
5.3.1. Implementation
Suppose that we have some operator BTU∈Lisc(UT,UT′) uniformly (here UT=ST0,1).
The matrix representation of the preconditioner GT, with
BT from (5.3) and
the bases from Proposition 5.11, becomes
[TABLE]
with these matrices given by
[TABLE]
where we used that FΦT−1QTFΨT=Id and FΘT−1(Id−QT)FΨT=Id, and
where, based on ∥hTsθν∥L2(Ω)2≂∣ων∣1+d2s,
we made an harmless modification to the operator BTB from Lemma 5.4.
6. Extensions
6.1. Higher order
Add the superscript 1 to the spaces defined so far,
e.g. write VT1 for ST0,1 with its nodal basis ΦT1, and similarly use GT1 for the associated preconditioner from either Sect. 5.2 or Sect. 5.3.
We will now consider a (family of) higher order continuous piecewise polynomials, i.e. for some ℓ∈{2,3,…} let
[TABLE]
Because we have an inverse inequality on VT, we can construct a uniform preconditioner GT∈Lis(VT′,VT) using an additive subspace correction method.
That is, we consider
the overlapping decomposition VT=VT1+VT2, where
these spaces are given by
[TABLE]
Proposition 6.1**.**
For k∈{1,2}, let GTk∈Lisc((VTk)′,VTk), then for ITk:VTk→VT the trivial embedding, we find that
GT:=∑k=12ITkGTk(ITk)′∈Lisc(VT′,VT),
with
[TABLE]
Proof.
We have the (standard) inverse inequality ∥u∥V≲∥hT−su∥L2(Ω) for u∈VT.
Let u∈VT, then for any (u1,u2)∈VT1×VT2 with u1+u2=u we find
[TABLE]
Denote ΠT1:H0,γ1(Ω)→VT1 for the Scott-Zhang interpolator ([SZ90]).
For u∈VT, take u1=ΠT1u∈VT1 and u2=u−ΠT1u∈VT2, then from approximation properties of the interpolator we infer
[TABLE]
Since apparently for u∈VT,
[TABLE]
the result follows from theory of subspace correction methods, e.g. [Osw94].
∎
On the space VT1 we can apply the operator GT1 constructed earlier, whereas on VT2 a simple scaling operator suffices.
Denote NT0,ℓ for the set of canonical Lagrange evaluation points of ST0,ℓ, and let ΦTℓ={ϕνℓ:ν∈NT0,ℓ} be the corresponding nodal basis.
For some constant β2>0, define an operator RT:VT2→(VT2)′ by
[TABLE]
Proposition 6.2**.**
The operator GT2:=RT−1 satisfies GT2∈Lisc((VT2)′,VT2) uniformly.
Proof.
It is not hard to see that the result follows if ΦTℓ is a (uniformly) ∥hT−s⋅∥L2(Ω)-stable basis. Writing NT0,ℓ:=NT0,ℓ∩T, this stability can be deduced from
[TABLE]
6.1.1. Implementation
Equipping VT and VT2 by ΦTℓ, and VT1 by ΦT1, the matrix representation of GT:=∑k=12ITkGTk(ITk)′∈Lisc(VT′,VT) is given by
Let Γ be a compact
d-dimensional Lipschitz, piecewise smooth manifold in Rd′ for some d′≥d with or without boundary ∂Γ.
For some closed measurable γ⊂∂Γ and s∈[0,1], let
[TABLE]
We assume that Γ is given as the closure of the disjoint union of ∪i=1pχi(Ωi), with, for 1≤i≤p,
χi:Rd→Rd′ being some smooth regular parametrization, and Ωi⊂Rd an open polytope.
W.l.o.g. assuming that for i=j, Ωi∩Ωj=∅, we define
[TABLE]
Let T be a family of conforming partitions T of Γ into ‘panels’ such that, for 1≤i≤p, χ−1(T)∩Ωi is a uniformly shape regular conforming partition of Ωi into d-simplices (that for d=1 satisfies a uniform K-mesh property).
We assume that γ is a (possibly empty) union of ‘faces’ of T∈T (i.e., sets of type χi(e), where e is a (d−1)-dimensional face of χi−1(T)).
The usual lowest order boundary element spaces are defined by
[TABLE]
with their canonical bases denoted as ΣT={1T:T∈T}
and ΦT={ϕν:ν∈NT0}, respectively, with NT0 the vertices of T not on γ.
The construction of the preconditioners in the domain case relied on the explicit construction of a collection ΨT biorthogonal to ΦT, and on the explicit computation of a (bi)orthogonal projection of WT:=spanΨT onto either ST−1,0 or ST0,1, where orthogonality was interpreted w.r.t. the L2(Ω)-scalar product.
Both the construction of ΨT and the computation of the (bi)orthogonal projection could be reduced to computations on the individual elements in the partition, which yielded explicit expressions.
When attempting to transfer everything to the manifold case, a problem is the appearance of a generally non-constant weight x∈∣∂χ(x)∣ in L2(Γ)-scalar product
[TABLE]
To deal with this, following [Sv18], on L2(Γ) we define an additional ‘mesh-dependent’ scalar product
[TABLE]
which is constructed by replacing on each χ−1(T), the Jacobian ∣∂χ∣ by its average ∣χ−1(T)∣∣T∣ over χ−1(T), and interpret (bi)orthogonality with respect to this scalar product.
Now all steps in the construction of the preconditioners carry over, and yield preconditioners for the manifold case whose implementations are exactly as described in Sect. 5.2.1 and Sect. 5.3.1,
where the patch volumes ∣ωT,ν∣ now should be read as the volumes of the patches on Γ.
To prove that the constructed preconditioners are indeed uniform preconditioners requires additional work due to the use of the mesh-dependent scalar product. We refer to [Sv18] for details.
The key ingredient is that not only the norm associated to ⟨⋅,⋅⟩L2(Γ) is uniformly equivalent to ∥⋅∥L2(Γ), but also that ⟨⋅,⋅⟩L2(Γ) and ⟨⋅,⋅⟩T are close in the sense that
[TABLE]
7. Numerical Results
Let Γ=∂[0,1]3⊂R3 be the boundary of the unit cube, V:=H1/2(Γ), W:=H−1/2(Γ),
and VT=ST0,ℓ⊂V the trial space of continuous piecewise polynomials of degree ℓ w.r.t. a partition T.
We shall evaluate preconditioning of essentially a discretized Hypersingular Integral operator.
The Hypersingular Integral operator A~∈L(V,V′) is only semi-coercive, since it has a non-trivial kernel equal to span{1}.
Solving A~u=f for f with f(1)=0 is, however, equivalent to solving Au=f with A given by (Au)(v)=(A~u)(v)+α⟨u,1⟩L2(Γ)⟨v,1⟩L2(Γ) for some α>0.
This operator A is in Lisc(V,V′), and we shall consider preconditioning discretizations AT∈Lisc(VT,VT′) of A.
By comparing different values numerically, we found α=0.05 to give good results in our examples.
As opposite order operator B we take the Weakly Singular integral operator, which on compact 2-dimensional manifolds is known to be in Lisc(W,W′).
We will compare preconditioners GT based on the discretizations BTU∈Lisc(UT,UT′) of B, for UT=ST−1,0 or UT=ST0,1 equipped with the canonical bases ΣT={1T:T∈T} and ΦT={ϕν:ν∈NT}, respectively,
cf. Sect. 5.2.1 or Sect. 5.3.1.
For ℓ=1 (the lowest order case) and VT being equipped with ΦT, the matrix representation of the preconditioner GT reads either as (Sect. 5.2.1)
[TABLE]
where BTU=(BΣT)(ΣT), DT=diag{∣ων∣:ν∈NT},
(pT)Tν={10if T⊂ων,otherwise, and β1>0 is some constant, or
as (Sect. 5.3.1)
[TABLE]
where BTU=(BΦT)(ΦT), DT=diag{∣d+1ων∣:ν∈NT}, and β1>0 is some constant.
For ℓ>1 denote the above GT by either GT1,−1,0 or GT1,0,1, then, with VT=ST0,ℓ being equipped with the standard nodal basis {ϕνℓ:ν∈NTℓ}, the matrix representation of the preconditioner GT∈Lisc((ST0,ℓ)′,ST0,ℓ) from Sect. 6.1.1 is
[TABLE]
where either ∗=−1,0 or ∗=0,1, and
(qT)ν′ν=ϕν′ℓ(ν) (ν′∈NTℓ,ν∈NT1).
The (full) matrix representations of the discretized singular integral operators AT and BTU are calculated using the BEM++ software package [SBA*+*15].
Condition numbers are determined using Lanczos iteration with respect to ∣∣∣⋅∣∣∣:=∥AT21⋅∥.
7.1. Uniform refinements
Consider a conforming triangulation T1 of Γ consisting of 2 triangles per side, so 12 triangles with 8 vertices in total.
We let T be the sequence {Tk}k≥1 of uniform newest vertex bisections, where Tk≻Tk−1 is found by bisecting each triangle from Tk−1.
With VT=ST0,1, Table 1 compares the condition numbers for the preconditioned system given by Sect. 5.2.1 (UT=ST−1,0)
and by Sect. 5.3.1 (UT=ST0,1).
We see that the condition numbers remain nicely bounded, and that both choices give similar condition numbers.
Instead of using the ‘full matrices’, we can consider compressed hierarchical matrices to approximate the stiffness matrices AT and BTU for finer partitions.
Table 2 gives the condition numbers, again for uniform refinements, but now using hierarchical matrices based on adaptive cross approximation [Hac99, Beb00].
We see that even for large systems, our preconditioner gives very satisfactory results.
Finally, consider the (higher order) trial space VT=ST0,3. Table 3 gives condition numbers for the preconditioned system, using the method described in
Sect. 6.1.1.
7.2. Local refinements
Here we take T to be the sequence {Tk}k≥1 of locally refined triangulations, where Tk≻Tk−1 is constructed using
conforming newest vertex bisection to refine all triangles in Tk−1 that touch a corner of the cube.
Table 4 gives condition numbers of the preconditioned hypersingular system discretized by continuous piecewise linears, i.e. VT=ST0,1.
The condition numbers remain bounded under local refinements, confirming uniformity of the preconditioner w.r.t. T.
8. Conclusion
Using the framework of operator preconditioning, we have constructed uniform preconditioners for elliptic operators of orders 2s∈[0,2] discretized by continuous finite (or boundary) elements.
The evaluation of the preconditioners requires the application of an opposite order operator plus minor cost of linear complexity. Compared to earlier proposals, both the construction of a so-called dual-mesh and the inversion of a non-diagonal matrix are avoided, and our results are valid without constraints on the mesh-grading.
For lowest order finite elements the computed condition numbers of the preconditioned system are below 2.5.
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