Global continuity and higher integrability of a minimizer of an obstacle problem under generalized Orlicz growth conditions
Arttu Karppinen

TL;DR
This paper proves boundary continuity and higher gradient integrability for minimizers of obstacle problems under generalized Orlicz growth, extending known results and introducing new findings for Orlicz and double phase growth conditions.
Contribution
It establishes boundary continuity and higher integrability of minimizers under generalized Orlicz growth, generalizing previous results and providing new insights for Orlicz and double phase growth.
Findings
Proves boundary continuity of minimizers.
Establishes higher integrability of gradients.
Extends results to Orlicz and double phase growth cases.
Abstract
We prove continuity up to the boundary of the minimizer of an obstacle problem and higher integrability of its gradient under generalized Orlicz growth. The result recovers similar results obtained in the special cases of polynomial growth, variable exponent growth and produces new results for Orlicz and double phase growth.
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Global continuity and higher integrability of a minimizer of an obstacle problem under generalized Orlicz growth conditions
Arttu Karppinen
Abstract.
We prove continuity up to the boundary of the minimizer of an obstacle problem and higher integrability of its gradient under generalized Orlicz growth. The result recovers similar results obtained in the special cases of polynomial growth, variable exponent growth and produces new results for Orlicz and double phase growth.
Key words and phrases:
Dirichlet energy integral, minimizer, obstacle problem, generalized Orlicz space, Musielak–Orlicz spaces, nonstandard growth, continuity, higher integrability.
2010 Mathematics Subject Classification:
49N60 (35J60, 35B65, 46E35)
1. Introduction
We study the obstacle problem related to the Dirichlet energy integral over a bounded domain with boundary values in the Sobolev sense
[TABLE]
where the infimum is taken over functions such that, given functions , we have almost everywhere and . In this paper we assume that satisfies generalized Orlicz growth conditions (see Section 2). This class of growth conditions generalize several interesting special cases such as the standard polynomial growth , Orlicz growth , see for example [4], variable exponent growth , see for example [8, 27] and double phase case , see for example [1]. Additionally, the problem is motivated by the study of partial differential equations, see for example [11].
In this paper we prove two main results of which the first concerns the boundary continuity of a minimizer of the obstacle problem. For definitions and assumptions, see Sections 2 and 4. To best of our knowledge, the result is new even in the special cases of Orlicz and double phase growth.
Theorem 1.1**.**
Let be strictly convex and satisfy (A0), (A1), (A1-), (aInc) and (aDec). Let and be such that and let be the continuous minimizer of the -obstacle problem from Theorem 5.8. If satisfies the capacity fatness condition (2.8), then
[TABLE]
A similar result in the generalized Orlicz setting without the obstacle has been proven in [13].
The proof for interior continuity follows outlines given in the book of Björns’ [3]. The proof of the main theorem and few intermediate results are analogous to [22], since scaling of the minimizer does not preserve minimality in the generalized Orlicz case. We also the study relationship of the measure density condition and -fatness: the former implies the latter when (Lemma 6.4). For further information about capacities in this context, see for example [2, 13] in and [26] in metric measure spaces.
The second main result is global higher integrability of the gradient:
Theorem 1.2** (Global higher integrability of the gradient).**
Suppose that satisfies conditions (A0), (A1), (aInc) and (aDec). Additionally suppose that the measure density condition (2.7) is fulfilled at every point with a constant , and let be the minimizer of the -obstacle problem, where and for some and . Then there exist and a constant such that and
[TABLE]
This result continues the recently published article [17], where the authors proved local higher integrability of the gradient of the quasiminimizer. Now the result is improved to a global result and the problem is generalized with an obstacle . These results are steps towards higher regularity results of the minimizer such as Hölder continuity for every exponent and Hölder continuity of the gradient. For example in [21] local higher integrability of the gradient is used several times in the proof. Again, to best of our knowledge, produces new results in special cases of Orlicz and double phase growth. For variable exponent analogue, see [9].
The strategy of the proof is to combine two Caccioppoli inequalities with the previously proven Sobolev–Poincaré inequality to lay ground for Gehring’s lemma. The first Caccioppoli inequality handles the interior case with the obstacle and the second inequality handles balls nearly overlapping with the boundary of . To achieve global results in general we assume that the measure density condition (2.7) is fulfilled at every boundary point.
2. Properties of generalized -functions
By we denote a bounded domain, i.e. a bounded, open and connected set. When and are open sets and is compact, by we mean that . The measure of a set is denoted by . By or we denote a generic constant whose value may change between appearances. A function is almost increasing if there exists a constant such that for all (more precisely, -almost increasing). Almost decreasing is defined analogously. A function is called convex if for every . Strict convexity assumes that the previous inequality is strict.
Definition 2.1**.**
We say that is a weak -function, and write , if
- •
For every the function is measurable and for every the function is increasing.
- •
and for every .
- •
The function is -almost increasing for and every .
- •
The function is left-continuous for and every .
If, additionally, is convex, we denote and say that is a convex -function.
By we mean the left inverse of , defined as
[TABLE]
Let us write and ; and abbreviate . Throughout the paper we need one or multiple of the following assumptions.
- (A0)
There exists such that .
- (A1)
There exists such that, for every ball ,
[TABLE]
- (A1-)
There exists such that, for every ball ,
[TABLE]
We also introduce the following assumptions, which are of different nature. They are related to the and conditions from Orlicz space theory.
- (aInc)p
There exists such that is -almost increasing in .
- (aDec)q
There exists such that is -almost decreasing in .
We write (aInc) if there exists such that (aInc)p holds, similarly for (aDec). For brevity, we may write for example that a constant , in which case depends on the dimension and some or all of the parameters listed in the previous assumptions related to .
Despite the technical formulation of the assumptions, each of them has an intuitive interpretation. (A0) declares the space to be unweighed, (A1) is a continuity assumption with respect to the space variable, while (A1-) takes account of the dimension also. These are generalizations of the -Hölder continuity of the variable exponent spaces and the assumption of the double phase case. Lastly, (aInc)p and (aDec)q state that globally grows faster than and slower than .
We say that is doubling if there exists a constant such that for every and every . If is doubling with constant , then by iteration
[TABLE]
for every and every , where . For the proof see for example [3, Lemma 3.3, p. 66]. Note that doubling also yields that
[TABLE]
Since (aDec) is equivalent to doubling [18, Lemma 2.6], inequality (2.3) holds for satisfying (aDec). In the proofs we often use the phrase like ”using (aDec)” and mean doubling or its consequence (2.3).
Generalized Orlicz and Orlicz–Sobolev spaces have been studied with our assumptions for example in [13, 14, 15, 16, 17, 18]. We recall some definitions. We denote by the set of measurable functions in and the integral average of a function over a set is denoted by . Additionally, we denote the positive and negative part of a function as and .
Definition 2.4**.**
Let and define the modular for by
[TABLE]
The generalized Orlicz space, also called Musielak–Orlicz space, is defined as the set
[TABLE]
equipped with the (Luxemburg) norm
[TABLE]
If the set is clear from the context we abbreviate by . A function belongs to local generalized Orlicz space if for every compact set .
A function belongs to the generalized Orlicz–Sobolev space if its weak partial derivatives exist and belong to . The norm of Orlicz–Sobolev space is defined as , where is the weak gradient of . Additionally we define as the closure of the space with respect to the norm of Orlicz–Sobolev space.
The definition of is reasonable, as is dense in if satisfies (A0), (A1) and (aDec) and is bounded [15, Theorem 6.4.6] (boundedness of frees us of the assumption (A2)).
The modular and the norm have the following useful property, called the unit ball property [15, Lemma 3.2.5]. However, in our case we need only the following implication which follows from the definition of the norm
[TABLE]
Next we recall the definition of relative Sobolev capacity of a set as a another way to measure the size of a set. Basic properties of this capacity have been studied in [2].
Definition 2.6**.**
Let and . Then relative Sobolev capacity of is defined as
[TABLE]
where the infimum is taken over the set of all functions with in an open set containing .
In order to attain global results, some regularity of the boundary has to be assumed. In this paper we use the measure density and capacity fatness conditions
[TABLE]
[TABLE]
where is a ball centred at a point and for some and . The measure density condition is often sufficiently general as for example all domains with Lipschitz boundary satisfy it and therefore it is commonly used in regularity theory. However the capacity fatness condition was used in [13] so we get the more general result with ease in the case of boundary continuity.
Even though we consider minimizing problem in we assume that is defined in the whole since later we need to consider the complement of due to previous boundary conditions.
3. Auxiliary results
Let us first collect some general lemmas, which are not related to the obstacle problem directly. First, we state the following lemma [13, Lemma 2.11], to which we refer to throughout the paper.
Lemma 3.1**.**
Let be bounded. Let satisfy (A0), (A1) and (aDec). If is non-negative and , then .
The next lemma is intuitively clear, and follows easily from the previous lemma.
Lemma 3.2**.**
Let satisfy (A0), (A1) and (aDec). Let and . If almost everywhere in , then .
Proof.
If we subtract from all the terms in the inequality and notice that if and only if , we can assume that almost everywhere in . Now since is non-negative and , Lemma 3.1 implies that . ∎
Lastly, we prove a lemma regarding sequences of maxima and minima which is important when we are comparing functions pointwise or handling just the positive part of a function. The restriction to subsequences is not severe since later on we need the existence of a sequence rather than convergence of a specific sequence.
Lemma 3.3**.**
Let satisfy (A0), and (aDec). If converge to and respectively in , then there are subsequences such that and in .
Proof.
Because, for example, , it suffices to show that if converges to in , then converges to , where is the pointwise converging subsequence. This subsequence exists because assumption (A0) and (aInc)1 imply that [16, Lemma 4.4]. Since , is increasing and norm convergence is equivalent to modular convergence when satisfies (aDec) [15, Corollary 3.3.4], we get
[TABLE]
as .
As for the gradients, using (2.3)
[TABLE]
as the first integral converges by dominated convergence [16, Theorem 4.1] ((aDec) takes care of extra assumption that for the dominating function and if in some subset of , then so is ) and the second integral convergences by assumption. ∎
The proof of the following Jensen type inequality can be found for example in [17, 19, 20]. Here we have chosen and simplified the assumptions on as we do not need the sharp result. Note that if satisfies (aDec), then the constant can be transferred to the right-hand side as a constant .
Lemma 3.4**.**
Let satisfy assumptions (A0) and (A1). There exists such that
[TABLE]
for every ball and with .
The proof of next proposition can be found in [15, Proposition 6.3.13] and is the local version of the Sobolev–Poincaré inequality. One of the main ingredients in proving Theorem 1.1 is to use this inequality also with balls that overlap the complement of . As with the Jensen’s inequality, the constant can be transferred to the right-hand side as with (aDec).
Proposition 3.5** (Sobolev–Poincaré inequality).**
Let satisfy assumptions (A0) and (A1) and let . Then there exists a constant such that
[TABLE]
for every with .
The following is a classical iteration lemma. For the proof, see for example [18, Lemma 4.2].
Lemma 3.6**.**
Let be a bounded non-negative function in the interval and let be an increasing function which is doubling. Assume that there exists such that
[TABLE]
for all . Then
[TABLE]
where the implicit constant depends only on the doubling constant and .
The following form of Gehring’s lemma can be found from [10, Theorem 6.6 and Corollary 6.1].
Lemma 3.7** (Gehring’s lemma).**
Let be non-negative. Assume that for some and that there exists such that
[TABLE]
for every ball . Then there exists such that
[TABLE]
4. Properties of local minimizers and local superminimizers
In this paper we do not only cover (local) minimizers but also the minimizer of the so called obstacle problem. Since minimizers of obstacle problems and local superminimizers are closely related, we collect basic results regarding local superminimizers also.
Definition 4.1**.**
Let be a function, called obstacle, and let be a function, which assigns the boundary values. We define admissible functions for the obstacle problem as a set
[TABLE]
Additionally, we say that a function is a minimizer of the -obstacle problem if
[TABLE]
for all .
If is a minimizer of the -obstacle solution, we call it a minimizer in .
Definition 4.2**.**
Let . A function is a local minimizer of the -energy in if
[TABLE]
for all with , where is the smallest closed set such that is non-zero almost everywhere in that set.
If the inequality is assumed only for all nonnegative or nonpositive , then is called a local superminimizer or local subminimizer, respectively.
The next lemma shows that we can often assume the test function to be pointwise bounded.
Lemma 4.3**.**
Let satisfy (aDec). If satisfies
[TABLE]
for all bounded with , then is a local minimizer of the -energy in .
Proof.
Since satisfies (aDec), bounded Sobolev functions are dense in [15, Lemma 6.4.2]. From the proof we see that if , then truncations of at level , , converge to in . Additionally, . Therefore, let be as in Definition 4.2 and be its truncations. Then, as is assumed to be a local minimizer when tested with bounded Sobolev functions with compact support and convergence in modular and norm are equivalent as satisfies (aDec), we get
[TABLE]
Next, as is a truncation, we split the integration domain accordingly
[TABLE]
by Lebesgue’s monotone converge theorem for increasing and decreasing sequences and the fact that every integral is finite. Thus combining two previous displays, we see that is a local minimizer of the -energy in . ∎
Next we give a suitably general condition for non-emptiness of and flexibility for the boundary function . We then show that being a minimizer of the obstacle problem is a local property with suitable boundary values. For the rest of the paper we implicitly assume that is non-empty.
Proposition 4.4**.**
Let satisfy (A0), (A1) and (aDec) and let . Then if and only if .
Proof.
Suppose first that . Then by Lemma 3.1 we see that
[TABLE]
Now the conclusion follows from Lemma 3.2.
Suppose then that and define . Now
[TABLE]
Therefore . ∎
As matters essentially only in the boundary, it can be modified inside . This is useful, as for technical reasons we would like to be above the obstacle in .
Lemma 4.5**.**
Let satisfy (A0) and (A1) and suppose that . Then , where almoset everywhere in .
Proof.
Define . First, we notice that . Second, from Lemma 3.1 we deduce
[TABLE]
Now, as satisfies (A0) and (A1), Lemma 3.2 implies that and it is clear that . Thus we can use instead of as the function assigning boundary values. ∎
Lemma 4.6**.**
Let . Then a function is a minimizer of the -obstacle problem if and only if is a minimizer of the -obstacle problem for every open .
Proof.
Let us first suppose that is a minimizer of the -obstacle problem. Let . Since , there exist functions such that . By a zero extension we see that for every , which implies that has a zero extension to , denoted by . Now we can define
[TABLE]
which belongs to as and it has the correct boundary values in the Sobolev sense. Now, because is a minimizer of the -obstacle problem, we get
[TABLE]
After subtracting from both sides we see that is also a minimizer of the -obstacle problem.
The other direction follows immediately by choosing . ∎
We recall that a solution of the -obstacle problem is a local superminimizer [3, Proposition 7.16] and an opposite relation also holds.
Proposition 4.7**.**
Let . Then a function is a local superminimizer in if and only if is a minimizer of -obstacle problem for every open .
Proof.
Suppose first that is a local superminimizer in . Since we have and therefore . Now, let be arbitrary and denote . Clearly almost everywhere in and thus is nonnegative. Now, we use the local superminimality of in the set and the fact that almost everywhere in the set to get
[TABLE]
So is a minimizer of a -obstacle problem.
Now suppose that is a minimizer of a -obstacle problem for every . Let be nonnegative such that and let be an open set such that . Therefore is an admissible test function for local superminimizers. As is a minimizer of the -obstacle problem and , we have
[TABLE]
and therefore is a local superminimizer in . ∎
Remark 4.8*.*
From the previous proof we get also the following result: If a local superminimizer in belongs to , it is a minimizer of the -obstacle problem.
Next we prove a comparison principle for the obstacle problem. Strong assumptions are needed to guarantee uniqueness of the minimizer. Note that comparison principle also implies uniqueness of the minimizer of the -obstacle problem.
Proposition 4.9** (Comparison principle).**
Let be strictly convex and satisfy (A0), (A1) and (aDec). Let , and let and be solutions to the and -obstacle problems, respectively. If almost everywhere in and , then almost everywhere in .
Proof.
Let and . Note that by Lemma 3.1 since both and the constant function [math] belong to and satisfies (A0), (A1) and (aDec) . Now
[TABLE]
Therefore, as and belong to and satisfies the assumptions in Lemma 3.2, we get that . This in turn implies that
[TABLE]
Because almost everywhere in , we see that .
Now let and . As before, we get
[TABLE]
By assumptions and Lemma 3.1, the functions , and belong to , so from Lemma 3.2 we deduce that . Again,
[TABLE]
Finally, since almost everywhere in , we see that .
Let . Since is a minimizer of the -obstacle problem, we find
[TABLE]
Now it follows that
[TABLE]
and therefore
[TABLE]
Now since is a minimizer of the -obstacle problem, so is . But because is strictly convex and satisfies (A0), the minimizer of the obstacle problem has to be unique [16, Theorem 7.5]. Therefore almost everywhere in and thus almost everywhere in . ∎
The following result is not needed in the rest of the paper, but as it follows quickly from the Comparison principle, we present it for the interested reader.
Proposition 4.10**.**
Let satisfy (A0), (A1), (aDec) and be strictly convex. Let be a minimizer of the -obstacle problem and be a local superminimizer. Then almost everywhere in .
Proof.
Since , we have that and by Proposition 4.7 is the minimizer of the -obstacle problem. Since and have the same boundary values in Sobolev sense and we have from the comparison principle (Proposition 4.9) that almost everywhere in . ∎
5. Continuity in the interior
Later in Section 6 we prove boundary continuity results relating to the solution of the obstacle problem. The proofs rely heavily to similar results inside a domain and the main strategy is to prove irrelevance of the obstacle in most of the points in . At first in this section we collect the relevant results from [13] and formulate them for the obstacle problem and for balls instead of cubes. The original reason for cubes has been to employ Krylov–Safanov covering theorem.
The first lemma corresponds to [13, Lemma 3.2], where instead of minimizer of the -obstacle problem there is a local quasisubminimizer. All we need to note is that in the proof instead of being negative, we have that if . We also define for any with radius . If for some , we have and the estimate is trivial.
Lemma 5.1** (Caccioppoli inequality).**
Let satisfy (aDec). Let be a minimizer of the -obstacle problem. Then for all in we have
[TABLE]
where the constant depends only on the (aDec) constants of .
Now since satisfies the previous Caccioppoli inequality, we have the following boundedness result [13, Proposition 3.3].
Proposition 5.3**.**
Let satisfy (A0), (A1), (aInc)p and (aDec)q. Suppose that satisfies the Caccioppoli inequality (5.2). Then there exists such that
[TABLE]
for every in , where , when such that . Here is such that and , is a constant that depends on and , and the constant depends only on the parameters in assumptions and the dimension n.
By assuming (A1-) and boundedness of the minimizer instead of assuming (A1) we have the following result [13, Corollary 3.6].
Proposition 5.4**.**
Let satisfy (A0), (A1-n) and (aDec) and suppose that is locally bounded and satisfies the Caccioppoli inequality (5.2). Then
[TABLE]
when with such that and almost everywhere in and . The constant depends only on the parameters in assumptions (A0), (A1-n) and (aDec), , , and . Especially the constant is independent of .
Next we use the fact that is also a local superminimizer (Proposition 4.7) to get an infimum estimate from below [13, Theorem 4.3]. Since we are aiming for the weak Harnack inequality we need to assume also nonnegativity of the minimizer .
Proposition 5.5** (The weak Harnack inequality).**
Let satisfy (A0), (A1-n), (aInc) and (aDec). Let be locally bounded nonnegative local (quasi)superminimizer or a locally bounded minimizer of an obstacle problem in . Then there exists an exponent such that
[TABLE]
for every with and . The constant depends only on the parameters in the assumptions and .
The final result we borrow from non-obstacle case is [13, Theorem 4.4]. It follows directly to our case since a minimizer of the -obstacle problem is also a local superminimizer (Proposition 4.7).
Proposition 5.6**.**
Let satisfy (A0), (A1-n), (aInc) and (aDec). Let be a locally bounded minimizer of the -obstacle problem which is bounded from below and set
[TABLE]
Then is lower semicontinuous and almost everywhere.
The next scheme is to use lower semicontinuous representatives to prove continuity of . The first lemma shows that can be defined pointwise everywhere.
Lemma 5.7**.**
Let satisfy (A0), (A1-), (aInc) and (aDec). Assume that is a locally bounded local superminimizer in . Then
[TABLE]
for all .
Proof.
Fix and denote when is small enough to guarantee that . As is assumed to be locally bounded we may assume that . Note that is a constant when and are fixed. Therefore the function is a local superminimizer in the set when and are fixed. The weak Harnack inequality (Proposition 5.5) implies
[TABLE]
Note that by Hölder’s inequality we can choose in Proposition 5.5. Since is bounded, the right-hand side converges to [math] as . Therefore we get
[TABLE]
Combining this with the fact that is locally bounded (Proposition 5.4) we find that
[TABLE]
In conclusion
[TABLE]
Since is the lower semicontinuous representative, the previous limit implies
[TABLE]
for all . ∎
Finally we can prove the continuity of the minimizer of a -obstacle problem in . This proof is a modification of [3, Theorem 8.29]. By lower semicontinuously regularized we mean that , that is .
Theorem 5.8**.**
Assume that is continuous and . Let satisfy (A0), (A1), (A1-), (aInc) and (aDec). Let be a minimizer of the -obstacle problem. Then the lower semicontinuously regularized representative of a minimizer is continuous.
Moreover, if is convex, then is a local minimizer (and therefore locally Hölder continuous) in the open set with boundary values .
Proof.
Let us denote the lower semicontinuous representative of still by . To show that is continuous, we need to prove that
[TABLE]
for all . By local boundedness (Proposition 5.3) and lower semicontinuity this implies that is real valued and continuous.
Let and be positive. By continuity of we can pick a radius such that and . Also, the ball can be chosen to satisfy
[TABLE]
as is finite by the Proposition 5.4 and it is lower semicontinuous. Now lower semicontinuity of and continuity of imply that
[TABLE]
Now from Proposition 5.4 and (5.9) we have for , and ,
[TABLE]
From Lemma 5.7 we have
[TABLE]
Therefore
[TABLE]
Thus the claim follows by letting .
Next we prove the second claim. We see that is open by the continuity of and . Since satisfies (aDec), by Lemma 4.3 it is enough to test the local minimizer with bounded and compactly supported Sobolev functions. Therefore, let be bounded and compactly supported. Since and are continuous and in , there exists such that in the compact set . By boundedness of , we can choose such that
[TABLE]
in . Therefore . Now, since is a minimizer of the -obstacle problem (Lemma 4.6) and is convex, we see that
[TABLE]
Next we subtract the first term on the right-hand side and divide by to obtain
[TABLE]
Now, since almost everywhere in the set , we get
[TABLE]
Subtracting the last term on the right-hand side from both sides, we get
[TABLE]
By Lemma 4.3, is a local minimizer in and from [18, Corollary 1.5] we obtain local Hölder continuity of in . ∎
6. Continuity up to the boundary
In order to prove the first main theorem, we need to define regular boundary points of a set . In [13, Theorem 1.1] it was proven that a point is regular if the so called -fatness condition is satisfied at and if is regular enough. In Proposition 6.4 we prove that the measure density condition (2.7) implies -fatness when .
Definition 6.1**.**
Let denote the minimizer with boundary values . If , then
[TABLE]
Let . A point is called regular if
[TABLE]
for all .
The next theorem is the main result of [13].
Theorem 6.2**.**
Let be bounded and . Let be strictly convex and satisfy (A0), (A1), (A1-n), (aInc) and (aDec). If is locally -fat at , then is a regular boundary point.
Most often capacity of balls is somewhat straightforward to compute. This is the case also with -capacity, if we assume (aDec), as we have the estimate [13, Lemma 2.8]
[TABLE]
It is also noteworthy to mention that upper and lower bounds are comparable when (A1-) is in force.
Next we extend the relation between measure density condition and capacity fatness to generalized Orlicz case. Note that the assumption corresponds to the classical -fatness situation, where it is commonly assumed that since otherwise singleton sets have positive capacity.
In the following proof, we need Poincaré inequality for the function . This can be proven in the almost same way as in [15, Proposition 6.2.10] with assumptions (A0) and (A1). The necessary modification is to take an equivalent convex -function and use [15, Lemma 4.3.2] instead of the Key estimate [15, Theorem 4.3.3]. This has the advantage of not introducing the additive term as in the general Poincaré inequality for generalized Orlicz functions. By the assumption (aDec) we can place the constant of equivalence in front of .
Lemma 6.4**.**
Let satisfy (A0), (A1), (A1-) and (aDec)q. If and the measure density condition is satisfied at , then the complement of is locally -fat at .
Proof.
Denote .
Now with (aDec) and Poincaré inequality [15, Corollary 7.4.1] we estimate
[TABLE]
Taking infimum over functions we get
[TABLE]
As (A1-) implies that and are comparable, from (6.5) and the measure density condition (2.7) we deduce
[TABLE]
where the last inequality follows from (6.3). Thus the capacity fatness condition is satisfied at . ∎
Finally we are ready to prove the continuity of a minimizer up to the boundary.
Proof of Theorem 1.1.
By Lemma 4.5 we can assume that . Let us first show that
[TABLE]
Let us denote . If , then (6.6) holds trivially. Let us then suppose that is not the empty set. If , then there would exist an open set containing and (6.6) would follow again trivially. Therefore let . First we need to show that .
Since satisfies (A0), (A1), (aDec) and is bounded, is dense in [15, Theorem 6.4.6]. Let us denote and notice by continuity of and that it has compact support in for every . From [18, Lemma 3.4] we have that compactly supported Sobolev–Orlicz functions belong to , especially for every . By monotone convergence [16, Theorem 4.1], converges to in and therefore by Lemma 3.3 has a subsequence converging to in . Since is closed, we see that .
Since by assumption, in , by Theorem 5.8 is a local minimizer in with . Since , the capacity fatness condition with respect to is satisfied at :
[TABLE]
where the first inequality follows from monotonicity of capacity [13, (C2) on p. 6]. Now it follows from Theorem 6.2 that is a regular boundary point, that is
[TABLE]
Since in we get (6.6).
It remains to show that
[TABLE]
Let be the unique minimizer with . By the comparison principle (Proposition 4.9) we have that in . Therefore by regularity of we get
[TABLE]
Together (6.6) and (6.7) yield the result. ∎
7. Higher integrability of the gradient
We start by proving two Caccioppoli inequalities: one inside the domain and one near the boundary. The proofs are quite standard and similar usage of test functions can be found from example in [5]. Of the assumptions in the following Caccioppoli inequality (A0), (A1) and (aInc) are only to use Sobolev–Poincaré inequality for , which combines terms involving and for simpler result. Compared to the Caccioppoli inequality previously presented in Lemma 5.1, now we do not limit ourselves to the positive part of the minimizer and the obstacle appears as an energy rather than a bound for the constant . The second Caccioppoli inequality on the other hand leverages the boundary function rather than the obstacle.
Lemma 7.1** (Interior Caccioppoli inequality).**
Let satisfy (A0), (A1), (aInc) and (aDec), and let be a minimizer of the -obstacle problem where . Then we have
[TABLE]
in the ball with and , and a constant .
Proof.
Choose . Let be a cut-off function such that in , , in and . Let be the following test function
[TABLE]
First, it needs to be shown that is an admissible test function for a suitable obstacle problem. Indeed, since , because , and
[TABLE]
almost everywhere in because almost everywhere in .
A direct calculation yields
[TABLE]
Since is a minimizer of the obstacle problem , we deduce that is a minimizer of for which is an admissible test function. Therefore it follows from Lemma 4.6 that
[TABLE]
Using (aDec) and the definition of we get
[TABLE]
Since in , we see that in . Also, by decreasing the set on the left-hand side of the inequality and increasing the set on the right-hand side, we get
[TABLE]
Now we use the hole-filling trick by adding to both sides of the previous inequality and get of them in the left-hand side while having just constant on the right-hand side. Now after dividing the inequality by we get a constant as the first constant on the right-hand side
[TABLE]
Identifying this inequality with the one in iteration Lemma 3.6, we see after changing to averages that
[TABLE]
As before, we can use (aDec) to obtain
[TABLE]
Finally using (aDec) and, as satisfies (A0), (A1) and (aInc)p, Sobolev–Poincaré inequality (Proposition 3.5) with we can estimate the term containing
[TABLE]
Therefore we get as an interior Caccioppoli inequality
[TABLE]
Lastly, we use (aDec) to convert from radius to diamater. ∎
Lemma 7.3** (Caccioppoli inequality over the boundary).**
Let satisfy (aDec) and let be a minimizer of the -obstacle problem where . Assume that there exists a compact set such that in or that satisfies also (A0) and (A1). Then we have
[TABLE]
in the ball with , and , where and constant the depends only on and .
Proof.
If satisfies (A0) and (A1), Lemma 4.5 allows us to assume that and therefore we can take the compact set as . As for the Caccioppoli inequality, we choose and to be a cut-off function such that in , , in and . This time we use as a test function. Here we note that , since in and the radius is small enough. Using similar approach as in proof of interior Caccioppoli inequality, we get
[TABLE]
Again by decreasing and increasing integration domains and noting that in , we continue
[TABLE]
Repeating the hole-filling trick as in the previous Caccioppoli inequality, we get
[TABLE]
and thus repeating the iteration, Lemma 3.6, we end up with
[TABLE]
Now we divide by the measure of balls
[TABLE]
Finally we use (aDec) to change from to diameter and get the desired Caccioppoli inequality. ∎
Next we prove the global higher integrability result.
Proof of Theorem 1.2.
Let be a ball with and a radius satisfying
[TABLE]
where satisfies the assumptions in the Sobolev–Poincaré inequality (Proposition 3.5) and is the constant of the same inequality. If , from Caccioppoli inequality (Lemma 7.1) we have
[TABLE]
For the first term on the right-hand side we can use Sobolev–Poincaré inequality (Proposition 3.5) and introduce a constant from (7.6) such that
[TABLE]
Now if , then we use the Caccioppoli inequality over the boundary (Lemma 7.3)
[TABLE]
The idea is to use Sobolev–Poincaré inequality also to the term involving , but this needs some preparation, as there is no integral average on the right-hand side. First we notice that since , it has a zero extension belonging to as in the proof of Lemma 4.6. This allows us to extend the domain of integration form to . Second, we note that using (aDec) we can increase the radii of balls
[TABLE]
Next we choose a ball , where . It is easily seen that . Also by appealing to measure density condition (2.7), we see that there exists a constant such that
[TABLE]
For brevity, let us denote and . Let us also recall that
[TABLE]
when has positive measure [23, Lemma 2.3].
Now by (7.9) the set has positive measure and therefore it is meaningful to state that . With this we can write
[TABLE]
After an application of (aDec) we get
[TABLE]
The first term on the right-hand side can be estimated with Sobolev–Poincaré inequality (Proposition 3.5) since (7.6) is in force. Let us then use (7.10) to estimate the last term
[TABLE]
Now by using (7.9) and (aDec) we get
[TABLE]
From (7.6) we especially have that . Thus by Sobolev–Poincaré inequality (Proposition 3.5) with and (7.6) we have that
[TABLE]
By the unit-ball property (2.5), we see that , so the assumptions of the Jensen type estimate (Lemma 3.4) are satisfied. Now using it to pull the integral out from the and noticing that outer integral average is redundant, we continue
[TABLE]
Now the last integral is in a form to which the Sobolev–Poincaré inequality is applicable and we see that (after the backwards substitution )
[TABLE]
where the equality follows, as outside of . Now finishing with triangle inequality, (aDec) and Hölder’s inequality we conclude that
[TABLE]
Combining the Caccioppoli inequalities (Lemmas 7.1 and 7.3), (7.7) and (7.13) we have
[TABLE]
Now let
[TABLE]
Writing (7.14) with functions and we get
[TABLE]
where has higher integrability as . Now we can use Gehring’s lemma, Lemma 3.7, which yields a number and a constant such that
[TABLE]
The theorem follows after a covering argument since is bounded and is compact. ∎
Acknowledgements
I would like to thank Petteri Harjulehto and Peter Hästö for their insightful comments on the manuscript. This research was partially supported by Turku University Foundation.
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