Multilevel decompositions and norms for negative order Sobolev spaces
Abstract.
We consider multilevel decompositions of piecewise constants on simplicial meshes that are stable in for . Proofs are given in the case of uniformly and locally refined meshes. Our findings can be applied to define local multilevel diagonal preconditioners that lead to bounded condition numbers (independent of the mesh-sizes and levels) and have optimal computational complexity. Furthermore, we discuss multilevel norms based on local (quasi-)projection operators that allow the efficient evaluation of negative order Sobolev norms. Numerical examples and a discussion on several extensions and applications conclude this article.
Key words and phrases:
additive Schwarz, multilevel norms, subspace decomposition, preconditioner2010 Mathematics Subject Classification:
65F08, 65F35, 65N30, 65N381. Introduction
This work deals with the analysis of multilevel decompositions and multilevel norms of piecewise constant functions for the Sobolev spaces resp. with .
Stability results for subspace decompositions are needed in the analysis of, e.g., additive and multiplicative Schwarz preconditioners, see [31, 43] for an overview. An important use case is the definition of additive Schwarz preconditioners for weakly-singular integral equations [44, 26, 25]. The two dimensional case (1 dimensional boundary) follows from corresponding results in Sobolev spaces of opposite (and therefore positive) order whereas the higher dimensional case needs different techniques. Another application is given in fictitious domain methods [5]. In our recent work [19] we have defined a multilevel diagonal preconditioner for the weakly-singular integral operator which is optimal on locally refined meshes and closed boundaries for three-dimensional problems. The proofs are based on the abstract framework from [33]. Although verified numerically, optimality for open boundaries is not shown in [19]. Moreover, it is not clear if the techniques given in [19] extend to the general case with . In the recent work [3], additive multigrid methods are analyzed for problems involving the fractional Laplacian leading to level dependent condition number estimates.
A different approach is the framework of operator preconditioning, see [27] for an overview. One advantage is that the history of meshes is usually not needed. One drawback is the use of dual meshes and discretized operators of opposite order which often is computationally expensive. The latter issues have been tackled in a series of recent articles [40, 41, 42].
Multilevel norms for negative order Sobolev spaces and piecewise constant functions have been analyzed in [32] but do not lead to level independent estimates for . Multilevel norms for piecewise affine and globally continuous functions are found in, e.g., [6, 8, 32], see also the recent article [17]. Multigrid methods are analyzed in, e.g., [46, 29, 14, 12]. Other works that use a matrix-based approach to treat the evaluation of fractional Sobolev norms include [9, 2] but rely on the evaluation of fractional powers of non-trivial matrices or the use of wavelet bases. Wavelet techniques are used for boundary element methods, see, e.g., [35, 24, 15]. More details on the theory of (pre-)wavelet methods are found in, e.g., [37, 38, 10].
1.1. Some known results on multilevel norms
We recall some results on multilevel norms from [32] adopted to the notation used in the present work. Let denote a sequence of uniformly refined simplicial meshes with mesh sizes . For the range one gets from [32, Eq.(3)] that
(1) |
where denotes the orthogonal projection on the space of piecewise constants . The multilevel norm can be efficiently evaluated if .
However, the latter equivalence does not include one of the arguably most important cases, . It is shown ([32, Theorem 2]) that
and the factor can not be improved in general, thus, yielding suboptimal results.
1.2. Novel contributions
Rather than using duality arguments to transfer results for positive order Sobolev spaces to negative order spaces (see e.g. [32, Section 2]), we exploit the deep connection between interpolation and approximation spaces where we view as an intermediate space between and . At first glance this seems to further complicate the problem due to the necessity of handling the norm. However, some ideas resp. results from our recent work [20] and the work at hand show how to define local decompositions in the norm by using Haar-type functions that can be written as the divergence of Raviart–Thomas functions. Furthermore, we introduce and analyze locally defined projection operators in onto the space of piecewise constant functions. We note that for problems of positive order the Sobolev space is an intermediate space between and for some . Together with stability of the canonical basis of piecewise affine and globally continuous functions in this yields optimality of the BPX preconditioner, see, e.g., [7, 6] and references therein for a detailed analysis.
Let us summarize two of our main results that can be found in Section 3 and are valid for uniform as well as adaptive meshes: Let denote a sequence of meshes with mesh-size functions and facets . For , we define spaces where is supported on at most two elements of (which share the facet ) and if (a precise definition is found in Section 2.3) and use subsets that satisfy .
- •
-
•
Multilevel norms (Theorem 19): There exist locally defined operators such that
In particular, the constants are independent of the levels or the mesh-sizes. We note that the decomposition into one-dimensional subspaces implies that the associated preconditioner is of a multilevel diagonal scaling type.
1.3. Outline
In Section 2 we introduce notation and some basic results. In particular, Section 2.4 and Section 2.5 deal with the definition and analysis of local projection operators in negative order Sobolev spaces. In Section 3 we present our main results and their proofs are given in Section 4. Numerical experiments are presented in Section 5. The final Section 6 concludes this article with a discussion on applications and extensions including the case of higher-order polynomial spaces.
1.4. Notation
Throughout this work we write resp. if there exists a constant such that resp. . If both directions hold we write . In the main results dependencies on constants will be specified.
2. Preliminaries
2.1. Sobolev spaces
For a bounded Lipschitz domain () let
with norm . Here, denotes the or norm which is induced by the scalar product . Let denote the closed subspace of with vanishing traces and recall that defines an equivalent norm on . The dual space is equipped with the dual norm
where the duality is understood with respect to the extended inner product. Analogously, with norm
Throughout this work we consider a connected Lipschitz domain with boundary and skip indices in the notation of norms, e.g., we write instead of , instead of .
For we define the intermediate spaces resp. by real interpolation (-method, see, e.g. [11, Section 4]), i.e.,
We recall that the dual spaces resp. can be written as interpolation spaces as well, i.e.,
for all , see, e.g., [30, Chapter 1, Theorem 6.2]. An extensive overview on interpolation between Hilbert spaces and, in particular, Sobolev spaces is found in [11].
The space of fields with divergence in is denoted by . Also note that is a bounded operator.
2.2. Meshes and refinement
Let denote a regular mesh of consisting of open simplices, i.e., . With we define the mesh-size function by . The set of all facets of an element is denoted by . For our studies we also use the sets
The set of vertices of an element is denoted with , and
In this work we consider sequences of meshes where we assume that is generated from by refining certain (or all) elements. A common type of mesh refinement is, e.g., newest vertex bisection, see, e.g., [39] and references therein. The generation of an element denotes the number of iterated refinements (bisections) to obtain from a father element . We assume, given a suitable initial mesh , that the sequence generated by the mesh refinement strategy satisfies:
-
(A1)
Shape regularity: There exists a constant such that
-
(A2)
There exists and such that
-
(A3)
There exists a constant such that for all and all with unique father element ,
These assumptions are satisfied for, e.g., the newest vertex bisection, see [39] and [22].
We say that is a sequence of uniform meshes if (besides the assumptions from above)
-
•
and for all and .
Moreover, we assume that for a sequence of meshes there exists a sequence of uniform meshes with satisfying (A1)–(A3) with the same constants.
From the assumptions given in this section we can interpret the mesh-size functions of uniform meshes as constants.
Lemma 1.
Let be a regular mesh. Element patches are given by
The corresponding domains are denoted with and . Higher-order patches are denoted with an additional superscript, e.g., .
2.3. Discrete spaces and projections
For we denote with the space of polynomials of degree and set
Furthermore,
The space is equipped with the common basis where for all . Here, denotes the Kronecker- symbol.
We make use of the orthogonal projection and the orthogonal projection resp. the orthogonal projection . It is well-known that these operators satisfy the approximation properties
for all , which follow from duality and Poincaré inequalities.
Recall the inverse estimates
(2) |
for all and , see, e.g., [23, Theorem 3.6], as well as the local variant
which follows from a scaling argument. For a uniform mesh (recall that is constant) the interpolation inequality [30, Chapter 1, Proposition 2.8] and the inverse estimate (2) show
for all .
For the decompositions we use Haar-type functions which can be written as the divergence of Raviart–Thomas functions. Let denote the space of Raviart–Thomas functions of order . For each there exist unique elements with . For each there exists a unique element with and we set . Let denote the characteristic function of an element and let be the canonical local basis of with , vanishing normal trace on and
Here, denotes the surface measure of the facet and we set for . From [20, Lemma 1] we recall some scaling properties, where .
Lemma 2.
The equivalences
hold and the involved constants only depend on the shape regularity of and the dimension .
Let . Lemma 2 together with interpolation and inverse estimates from above show that
In the recent work [16] a local projection operator onto the Raviart–Thomas space has been defined that does not rely on regularity assumptions (the canonical Raviart–Thomas projection requires that with some so that the normal trace is well-defined in ).
Lemma 3 ([16, Theorem 3.2]).
Let with a connected domain be given and set
There exists an operator which satisfies
(3) | ||||||
(4) |
Moreover,
(5) |
for all and . The involved constant only depends on the space dimension , and the shape regularity of .
Finally, for a sequence we use the indices instead of in the notation of the corresponding operators, patches etc., e.g., instead of and instead of . Furthermore, we define operators with negative indices to be trivial, e.g., for .
2.4. A local projection operator in
In this section we define a local projection operator which plays a crucial role in the stability analysis that follows. We give a brief overview of the basic idea: First, we consider a Fortin operator based on the quasi-interpolation operator [36]. Then, we study its adjoint operator and, finally, we use the canonical projection (on piecewise constants) to define .
We consider the following variant: For each let and set
where is the unique element with for all . Some comments are in order:
Remark 4.
It is common to define the quasi-interpolation operator with containing exactly one element. The case has been used in various works, cf. [29, 42, 47]. We note that the authors of [47] define the operator with coefficients
where denotes the orthogonal projection on . This definition is identical to the operator above with which can be seen from the identity
Explicit representations of the coefficients are obtained by straightforward computations and symmetry arguments (see [42, Remark 3.3] or [29] for the case ). Extending the functions by one can show that (denoting with the domain associated to )
The constants , only depend on the space dimension but are independent of and .
For the remainder of this work we use in the definition of . The next lemma collects some well-known results on (see [47, Lemma 3.2–3.6]):
Lemma 5.
The operator satisfies:
-
(a)
Projection: .
-
(b)
Local boundedness: resp.
for all resp. and . -
(c)
Local approximation: for all .
The involved constants only depend on and shape regularity of .
Let denote the space of bubble functions with basis functions for and set . The operator defined in the next result is a Fortin-type operator (below this is called orthogonality property).
Lemma 6.
The operator defined by
has the following properties:
-
(a)
Quasi-projection: .
-
(b)
Orthogonality: for all and .
-
(c)
Local approximation property:
-
(d)
Locally bounded:
The involved constants only depend on and shape regularity of .
Proof.
Proof of (a). Using the projection property, i.e., for , we infer that
Proof of (b). This follows from the construction of the operator, i.e.,
With we denote the adjoint of . Writing we see
where
Note that by Remark 4 we have that on and otherwise. From this explicit representation it is thus straightforward to see that for all . We will use this fact in order to apply (local) inverse estimates, e.g. . Moreover, note that is bounded in so that is well-defined.
The next result follows more or less immediately from the properties described in Lemma 6.
Lemma 7.
The operator satisfies the following properties:
-
(a)
Quasi-projection: for all .
-
(b)
Approximation: for all .
-
(c)
Local boundedness for functions:
-
(d)
Global boundedness: for all .
The involved constants only depend on and shape regularity of .
Proof.
Proof of (a). The quasi-projection property can be seen from the orthogonality property yielding the identity
Proof of (b). Let and . Using the approximation property of we get that
Dividing by and taking the supremum over all shows the assertion.
Proof of (c). Local boundedness in can be derived from the local definition of the operator.
To see local boundedness in we extend any by on . Then, using duality, continuity of and we conclude that
Proof of (d). This follows directly from duality and global boundedness of in . ∎
Recall that the range of is a subspace of . In order to obtain a projection onto piecewise constants we apply the orthogonal projection.
Theorem 8.
The operator has the following properties:
-
(a)
Projection: .
-
(b)
Approximation: for all .
-
(c)
Local boundedness for functions:
-
(d)
Global boundedness: for all .
The involved constants only depend on and shape regularity of .
Proof.
Proof of (b). For the approximation property we note that
(6) |
The first term is estimated with the approximation property of and the local boundedness of (Lemma 7), i.e.,
The second term in (6) is estimated using the approximation property of , see Lemma 7.
Proof of (c). Note that is locally bounded. Together with the local boundedness of (Lemma 7) we get that
For the local bound in we stress that which can be seen from for yielding
The assertion then follows from the inverse inequality (2) and local boundedness of (Lemma 7).
Proof of (d). Boundedness in is not directly clear due to . However, using the approximation property of we get that and by applying the inverse inequality (2) we further conclude that
That is, is bounded in when restricted to . Using the latter estimate with and the boundedness of (Lemma 7) implies that
which concludes the proof. ∎
2.5. A local projection operator in
We follow the same ideas as in Section 2.4 but only point out the differences in the definition. The results below follow the same argumentations as in Section 2.4 and are therefore omitted. For each let and set
Throughout, we consider , see Remark 4. We define .
Before we state the results let us note that the local boundedness property Lemma 7(c) does not hold for the norms due to different scaling properties. Nevertheless, we use an auxiliary norm defined with as
Note that if then , thus, . In particular, we stress that local scaling arguments prove the inverse estimate
Lemma 9.
The operator satisfies the following properties:
-
(a)
Quasi-projection: for all .
-
(b)
Approximation: for all .
-
(c)
Local boundedness for functions:
-
(d)
Global boundedness: for all .
The involved constants only depend on and shape regularity of .
Theorem 10.
The operator has the following properties:
-
(a)
Projection: .
-
(b)
Approximation: for all .
-
(c)
Local boundedness for functions:
-
(d)
Global boundedness: for all .
The involved constants only depend on and shape regularity of .
2.6. Equivalent norms in interpolation spaces
In this section we collect some results on the relation between interpolation and approximation spaces. Recall the definition of a sequence of uniform meshes from Section 2.2.
Lemma 11.
Proof.
Fix some with . The scalar product is denoted with . Then, the reiteration theorem ([11, Theorem 2.2]), interpolation estimates in Hilbert spaces and inverse inequalities show that
where the matrix is defined by for and for . From Lemma 1 we get that , thus, we conclude that . The second assertion is proved following the same lines of argumentation. ∎
The next result presented will be a key ingredient in the stability analysis of multilevel decompositions. It shows the deep connection between approximation spaces and interpolation spaces. Such results are known and in fact the reason for the optimality of the BPX preconditioner. We refer to [7] for an overview and a short discussion as well as further references. A proof is included for the convenience of the reader where we follow the same argumentation as in [7, Theorem 1]. We note that the main argumentation is the use of the inverse inequality and the approximation property. One also uses the discrete version of the and method, see e.g. [34]. Moreover, note that for a sequence of uniform meshes is dense in and .
Theorem 12.
Proof.
Let . We consider the discrete and version of interpolation, see, e.g. [45, Section 1.7], where resp. denote the resp. functional. By the equivalence of interpolation norms ([4, Theorem 3.3.1]) and the equivalence of discrete interpolation norms ([45, Section 1.7]) we conclude that
where the infimum is taken over all possible decompositions. We note that the equivalence results in [45, Section 1.7] are given for but hold true for following the same proofs.
Note that . The inverse estimate, the equivalence (Lemma 1) and boundedness of the projection operators imply that
Therefore,
To see the other direction we use the approximation property and to get that
Then,
together with finishes the proof of the first equivalence.
The second equivalence follows the same argumentation with obvious modifications. ∎
As a consequence of the latter result we obtain a multilevel norm on resp. :
Corollary 13.
2.7. Additive Schwarz norms
Let denote a Hilbert space with norm and let denote a finite-dimensional subspace. Let , with and set
We say is a decomposition of if
To the decomposition we associate the additive Schwarz norm given by
A key ingredient in most works on additive Schwarz methods is to establish norm equivalence of the form
(9) |
This equivalence implies that the associated additive Schwarz preconditioner yields preconditioned systems with condition numbers depending only on . This is well-known in the context of additive Schwarz preconditioners and we refer the interested reader to [31, 43].
Note that (9) is equivalent to the following two estimates:
-
•
The lower bound in (9) is equivalent to: For every there exist , such that and
(10) -
•
The upper bound in (9) is equivalent to: For all , ,
(11)
In both estimates the constants , are independent of , .
The lower bound in (9) is called stability of the decomposition, see, e.g. [43, Assumption 2.2]. The upper bound in (9) is often proved by showing strengthened Cauchy–Schwarz inequalities, see, e.g. [43, Assumption 2.3]. Another closely related method is the multiplicative Schwarz method, see, e.g. [43, Section 2.2]. Convergence results of the latter method usually involve the same analytical tools, namely, the stability of subspace decompositions and strengthened Cauchy–Schwarz inequalities, see, e.g. [43, Theorem 2.9].
2.8. Stable splittings in and
In this section we recall some results of our work [20] on stable one-level decompositions which will be key ingredients in the proof of our main theorems for uniform meshes.
For a mesh we consider the decomposition of , i.e.,
where . We use the notation and recall the result [20, Theorem 3]:
Theorem 14.
For ,
and the involved constants depend only on , and shape regularity of .
A similar result is valid for the space with the decomposition of , i.e.,
where . Let us note that the latter splitting already implies the unique decomposition for , where denotes the projection to . Note that . Moreover, we stress that
We use the notation and recall [20, Theorem 4]:
Theorem 15.
For ,
and the involved constants depend only on , and shape regularity of .
Remark 16.
We note that the proof of the lower bound in Theorem 14 resp. Theorem 15 depends on regularity results of the Poisson equation (Dirichlet problem resp. Neumann problem) in . Regularity enters when estimating the norm of the Raviart–Thomas projection operator. One can get rid of the dependence on regularity results by switching to a different projection operator that is stable up to oscillation terms, e.g., the operator from [16, Theorem 3.2], see also Lemma 3.
3. Main results
Throughout this section let denote a sequence of meshes. For define
Note that if is a sequence of uniform meshes, then resp., .
3.1. Multilevel decomposition for
We consider the collection
and use the notation . Then, our first main result reads as follows:
Theorem 17.
That is a decomposition of follows from the proof of the lower bound given in Section 4.1 (uniform meshes) resp. Section 4.5 (adaptive meshes). The upper bound is shown in Section 4.2 (uniform meshes) resp. Section 4.6 (adaptive meshes). The proofs in the case of adaptive meshes rely on several localization arguments which are not needed in the case of uniform meshes and are therefore presented in a separate section.
3.2. Multilevel decomposition for
We consider the collection
where and set . Our second main result is
Theorem 18.
3.3. Multilevel norms for and
In this section we present our last two main results dealing with multilevel norms.
Theorem 19.
A similar result is valid for the space :
Theorem 20.
Remark 21.
Some of our results may be extended to since can be defined by interpolating and with some , see [11] for an overview on interpolation scales. In particular, the results from Section 2.6 can be extended to as well as our main results from Section 3 for uniform meshes. Major changes only involve a more general approximation property resp. inverse estimate. For a simpler presentation we consider only .
4. Proof of main results
4.1. Proof of lower bound in Theorem 17 (uniform meshes)
Let be given. For we define
We have that . Also recall that for the sequence of uniform meshes. By Theorem 14 and (10) there exists a stable splitting of into local contributions , i.e.,
Together with the inverse estimate we get that
Corollary 13 states that the right-hand side is equivalent to which finishes the proof. ∎
4.2. Proof of upper bound in Theorem 17 (uniform meshes)
4.3. Proof of lower bound in Theorem 18 (uniform meshes)
Let be given. For we define
Recall that for uniform meshes. We have that . According to Theorem 15 and (10) we can split each into
We stress that and define . Moreover, note that . Putting all estimates together and using an inverse inequality we conclude that
Applying Corollary 13 finishes the proof. ∎
4.4. Proof of upper bound in Theorem 18 (uniform meshes)
4.5. Proof of lower bound in Theorem 17 (adaptive meshes)
Let and consider the splitting
(16) |
Throughout we make the convention that operators with negative indices are trivial, i.e., , , for all .
The next result provides a decomposition of and a stability result:
Lemma 22.
Proof.
Step 1. According to Theorem 14 there exist functions , such that
Using the inverse estimate and for all we get that
Let . We split into two contributions and ,
(17) |
The first term can be localized on each whereas the second term can be localized using the partition of unity provided by the nodal functions .
Step 2. First, we provide a decomposition for . To that end let and consider the local Neumann problem
(18a) | ||||||
(18b) |
This problem admits a unique solution since
From the weak formulation and a Poincaré inequality ( for ) we deduce the stability estimate
(19) |
In a further step we use the operator from Lemma 3 and define
Here, . Note that by definition of the operator the normal trace of is zero on and thus . Furthermore,
and the commutativity property gives
Setting we conclude with the aforementioned properties that there exist coefficients such that
and
Moreover, Lemma 3 and (19) also imply that
The scaling properties (Lemma 2), the stability of the Raviart–Thomas basis ([20, Proposition 2]) show that
Step 3. We define local problems for the second contribution in (17): To that end we use the partition of unity and consider for the problem
(20a) | ||||||
(20b) | ||||||
(20c) |
For an interior node we have by Lemma 6 that and therefore
Thus, there exists a unique solution for all . For the surface measure is positive (at least one boundary facet is contained in that set). Thus, there exists a unique solution if . The weak formulation of (20) and Poincaré–Friedrichs’ inequalities lead to
Consequently, the weak formulation of (20), the latter estimate, local boundedness of and show that
for all . As before we use the operator from Lemma 3 and define
Note that by definition of the operator the normal trace of is zero on all facets with and thus . Furthermore,
Set , and
Define . The same arguments as in Step 2 then show that and
Note that in general . To tackle this issue we stress that there exist such that
To see this note that if then there exists , and . Furthermore, given we have that . This means that the number of coefficients that contribute to is uniformly bounded. Thus, we have shown that there exist with
Step 4. Combining the results from Step 1–3 above we conclude that there exist for all and such that
and
which finishes the proof. ∎
For the remainder of the proof we group the contributions that have the same scale (i.e., the supports are comparable). This allows us to establish a connection to a sequence of uniform refined meshes and, consequently, we can apply Theorem 12. The technique of relating uniform and adaptive meshes is quite common and used in, e.g., [12, 14, 13, 29, 18]. To that end we need some further notation: The collection of elements in the sequence is denoted by
Let denote a sequence of uniform refined meshes with . All quantities related to will be denoted with , , , etc. We set
The next result follows from our assumptions on the mesh refinement given in Section 2.2.
Lemma 23.
We recall the following summation property, see, e.g. [1, Theorem 4.1].
Lemma 24.
For two non-empty disjoint with and ,
To complete the proof of the lower bound in Theorem 17 it remains to show that
Let . Recall the definitions of , and from Lemma 23. Note that contains the father element of . Furthermore, is a local quasi-projection which satisfies that for . Combining these arguments together with an inverse estimate, local boundedness of (Lemma 7), Lemma 23 and Lemma 24, proves that
Therefore, the latter estimate and Lemma 23 (d) yield
A standard coloring argument and Lemma 24 show that
Then, with the norm equivalence from Theorem 12 we conclude that
It remains to prove that
This estimate follows with similar arguments as given above since is contained in a patch of fixed order around and resp. restricted to piecewise constants are local projections. Thus, for the decomposition from Lemma 22 we have proven that
where in the last estimate we used . This together with (10) finishes the proof of the lower bound in Theorem 17 for the case of adaptive meshes. ∎
4.6. Proof of upper bound in Theorem 17 (adaptive meshes)
Let , , be given and set
Recall that by (11) the proof of the upper bound is equivalent to show that
The basic idea is to reorder the sum into contributions of the same scale, similar as in Section 4.5. Let denote the sequence of uniform meshes with . The next result follows from properties of the mesh refinement given in Section 2.2.
Lemma 25.
We rewrite as
Note that there exists with for all . Since we use Lemma 11 to see that
Then, observe that
Using that is a bounded operator we infer that
From Lemma 25 we obtain that for all with and as well as that the number of functions with that do not vanish on is uniformly bounded by a constant leading to
Recall the scaling . Combining all estimates above we conclude that
which finishes the proof. ∎
4.7. Proof of Theorem 18 (adaptive meshes)
We only give a sketch of the proof since most arguments are the same as in Section 4.5 and Section 4.6.
For the proof of the upper bound let , , , be given and
Therefore, . The same arguments as in Section 4.6 show that
Putting all estimates together this proves the upper bound in Theorem 18.
To see the lower bound let be given and set , . Then,
Following the arguments from Lemma 22 we deduce that there exist with
and that
(21) |
The major differences are that instead of , we use , and instead of the local problem (20) we consider
i.e., pure Neumann boundary conditions. Moreover, we replace the definition of in Lemma 3 with
It remains to show that
Again, following the arguments in Section 4.5 we obtain the estimate
(22) |
At this point it is important to note that to get the latter bound we use the following result instead of Lemma 24 combined with the local boundedness of with respect to (see Lemma 9).
Lemma 26.
Let denote pairwise disjoint simply connected Lipschitz domains with positive measure and . Then,
where the involved constant only depends on .
Proof.
Let such that . Extend on by and note that and . Set and observe that
We have that which concludes the proof. ∎
4.8. Proof of Theorem 19 and Theorem 20
We only give the sketch of the proof of Theorem 19 as most arguments have already been given in Section 4.5 and Section 4.6. Moreover, the proof of Theorem 20 follows a similar argumentation and is thus omitted. Note that in Section 4.5 we have shown that
Also note that which proves .
To see the other direction we consider
(23) |
Clearly, and . As in Section 4.5 and Section 4.6 we use the sequence of uniform meshes with and note that there exists a function with such that and .With the partition of unity, , we consider
and note that . Thus, we can apply Lemma 11 which yields that
(24) |
We use the same observations as in Step 3 of the proof of Lemma 22: For an interior node we have and if then implies that since at least one facet of an element from is a boundary facet.
5. Numerical experiments
In this section we present some numerical examples to support the results from Theorem 17 and 19. All experiments have been realized using Matlab version 2017b on a Linux machine with an Intel i5-2520M processor and GB RAM.
Let denote the convex hull. We consider the initial mesh
where denote the corners of the domain and . We consider two sequences of meshes, namely, uniform meshes and locally refined meshes . The uniform mesh is created by dividing each element of into four son elements with equal area by applying (iteratively) the newest-vertex bisection rule. The adaptive mesh is created by marking all elements in the mesh which share the vertex for refinement and then applying the routine given in [21, Listing 5.2] (which also employs newest-vertex bisection).
In Section 5.1 we present results for the preconditioner induced by the decomposition from Theorem 17 and in Section 5.2 we show results corresponding to Theorem 19. In both cases we use a discrete norm which is defined by following the ideas from [2]: First, let denote the Riesz matrix with respect to the canonical basis of . Second, let denote the Riesz matrix of the discrete inner product from [20, Section 7.1], given by
Here, the entries of the above matrices are given by
for , and denotes the nodal basis of . Moreover, is an equivalent mesh-size function and can be freely chosen. For the following experiments we choose . Then, by [2, Proposition 3.2] the matrix
defines a discrete inner product which is equivalent to , i.e.,
Here, is a diagonal matrix with positive entries and is invertible with , see [2, Section 3.1] for a detailed description. We note that the latter equivalence result requires the existence of a projection operator onto which is bounded in and , see [2, Lemma 2.3]. In our situation such an operator is given by (Theorem 8).
5.1. Multilevel preconditioner in




We start with a description of the matrix representation of the preconditioner associated to the multilevel decomposition from Theorem 17. We use similar notations and definitions as in [19, Section 3.1]. Let denote the matrix representation of the embedding and let denote the representation of the Haar-type functions , i.e.,
Note that is sparse since is supported on at most two elements. Furthermore, let denote the diagonal matrix with entries
Recall that is the discrete norm induced by the matrix . The matrix representation of the multilevel preconditioner then reads
It follows from Theorem 17 and the additive Schwarz theory, see Section 2.7, that the spectral condition number of the preconditioned matrix is uniformly bounded, i.e,
In the experiments we also consider the diagonal preconditioner . Figure 1 and Figure 2 show the results for different values of for uniform and adaptive meshes, respectively. Note that the case is not covered in Theorem 17, cf. Remark 21. Although the condition numbers of for , are higher than in the case of the diagonally preconditioned matrices it seems that they reach an asymptotic uniform bound supporting the result from Theorem 17. We stress that in the case of the diagonal preconditioner the condition numbers are of order , see [1] for details on diagonally preconditioned systems for problems in fractional order Sobolev spaces. For and we observe that our proposed preconditioner outperforms the simple diagonal scaling and yields uniformly bounded condition numbers on uniform as well as locally refined meshes.
5.2. Multilevel norm in


Define the matrices by
for all . Note that corresponds to the multilevel norm from (1) and to the one from Theorem 19. (The only difference is the use of the equivalent mesh-size function .) The main idea of the numerical experiments in this section is to compute the optimal constants , for that satisfy
Figure 3 shows the results for adaptive meshes and . Note that we expect that deteriorates, see [32, Theorem 2] and Section 1.1 for the case of uniform meshes and . This can be seen in our results also for the adaptive meshes under consideration. Contrary, Theorem 19 predicts uniformly bounded , which is also observed in Figure 3.
6. Concluding remarks
First, all given theorems in Section 3 are valid if we replace the Lipschitz domain with a regular manifold . The proofs are almost identical with some minor modifications. The most notable are the use of Raviart–Thomas surface elements, the definition of the corresponding operator from Lemma 3, and the use of local Laplace–Beltrami problems in (18) resp. (20). In our work [19] we considered the case of a closed manifold and proved Theorem 18 for using different techniques (we constructed extension operators into spaces associated to the volume similar as in [28]). Numerical examples in [19, Section 4] provide the numerical evidence of the optimality of the preconditioners associated to the multilevel decompositions for the case . We stress that in [19] we did not prove optimality for open manifolds but only claimed it [19, Remark 4]. Thus, Theorem 18 provides the mathematical proof of this claim which is supported by numerical experiments [19, Section 4.4].
Second, concerning implementation of the preconditioners corresponding to Theorem 17 resp. Theorem 18 it is well-known that multilevel decompositions based on one-dimensional subspaces lead to (local) multilevel diagonal scaling preconditioners which are utmost simple to implement. Moreover, the preconditioners can be evaluated in operations and the storage requirements are units. We refer to [19, Section 3] for a short discussion.
Third, concerning implementation of the multilevel norms from Theorem 19 resp. Theorem 20 we note that the local definition of the involved operators imply that is supported only in a neighborhood of and therefore the multilevel norms can be evaluated in operations. Contrary to [2] our proposed multilevel norms do not rely on the evaluation of powers of a matrix.
Fourth, the decompositions resp. in Theorem 17 resp. Theorem 18 can be replaced by
Note that the additional space necessitates the inversion of the Riesz matrix corresponding to the inner product resp. or a discrete one on the coarsest level when implementing the preconditioners. However, tighter equivalence constants are expected.
Fifth, decompositions resp. multilevel norms for polynomial discretization spaces of higher order can be handled using the following observations: For some fixed consider
where is orthogonal to . Let denote a basis of with being a reference element and . Using affine transformations this basis defines a basis of for all . Then,
for with and . The case is trivial, the case can be seen from boundedness of restricted to polynomials and inverse estimates see, e.g. [20, Lemma 9]. The general case is derived from the latter two. The latter equivalence holds replacing with , thus, we conclude:
Corollary 27.
Corollary 28.
Finally, besides the already mentioned applications in preconditioning, the presented multilevel norms can be used in minimization problems involving negative order Sobolev spaces, which will be reported in future works.
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