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Research on the influence of attention and emotion of tea drinkers based on artificial neural network


  • Tea can help to regulate the mood of human. Based on the influence of tea on people's mood and attention, this study explored the tea concentration when the mood and attention of drinkers are in the best state, and established the best concentration model of tea. Using sampling experiment method to collect objective data, which are then combined with questionnaire survey method to collect subjective data, using the results to establish a neural network algorithm model to test the accuracy of the neural network algorithm model. Experiments show that the correlation coefficient of the output value of the BP neural network model constructed in this study is basically consistent with the actual prediction result. After obtaining data such as age, gender, frequency of tea drinking, and tea drinking concentration of tea drinkers, the constructed back propagation (BP) neural network model can accurately predict the mental state score of tea drinkers. The research will provide certain data support and theoretical basis for the follow-up development of the tea industry. Follow-up work needs to be performed in order to further adjust the scope and accuracy of the control model. Then, a more complete and accurate advanced BP neural network model can be established for different types of tea and other parameters.

    Citation: Biyun Hong, Yang Zhang. Research on the influence of attention and emotion of tea drinkers based on artificial neural network[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3423-3434. doi: 10.3934/mbe.2021171

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  • Tea can help to regulate the mood of human. Based on the influence of tea on people's mood and attention, this study explored the tea concentration when the mood and attention of drinkers are in the best state, and established the best concentration model of tea. Using sampling experiment method to collect objective data, which are then combined with questionnaire survey method to collect subjective data, using the results to establish a neural network algorithm model to test the accuracy of the neural network algorithm model. Experiments show that the correlation coefficient of the output value of the BP neural network model constructed in this study is basically consistent with the actual prediction result. After obtaining data such as age, gender, frequency of tea drinking, and tea drinking concentration of tea drinkers, the constructed back propagation (BP) neural network model can accurately predict the mental state score of tea drinkers. The research will provide certain data support and theoretical basis for the follow-up development of the tea industry. Follow-up work needs to be performed in order to further adjust the scope and accuracy of the control model. Then, a more complete and accurate advanced BP neural network model can be established for different types of tea and other parameters.



    Dedicated to Giuseppe Mingione on his 50th anniversary.

    Minimizers of the variable exponent energy |u|p(x)dx have been studied in hundreds of papers. In almost all cases, it is assumed that there exist constants c,C(1,) such that cp(x)C for all x. However, it is possible to use limiting procedures to study the borderline cases when p(x)=1 or p(x)= for some points [34,35]. In recent years, minimizers of non-autonomous functionals

    infuΩφ(x,|u|)dx

    have been studied when φ has generalized Orlicz growth with tentative applications to anisotropic materials [57] and image processing [31]. Again, the upper and lower growth rates are usually assumed to lie in (1,). In this article we consider the case when the upper growth rate is allowed to equal in some points and the lower growth rate is greater than n, the dimension. We prove the Harnack inequality for minimizers of such energies.

    Let us recall some information of the context by way of motivation. PDE with generalized Orlicz growth have been studied in many papers lately, both in the general setting and in particular special cases, such as the double phase case (e.g., [3,5,16,17,53]), perturbed variable exponent [52], Orlicz variable exponent [27], degenerate double phase [4], Orlicz double phase [6,10], variable exponent double phase [18,49,50], multiple-phase [7,22], and double variable exponent [56]. Our framework includes all these cases.

    In the generalized Orlicz case it is known that solutions with given boundary values exist [15,28,33], minimizers or solutions with given boundary values are locally bounded, satisfy Harnack's inequality and belong to C0,αloc [9,36,37,55] or C1,αloc [38,39], quasiminimizers satisfy a reverse Hölder inequality [32], minimizers for the obstacle problem are continuous [41] and the boundary Harnack inequality holds for harmonic functions [12]. Some articles deal with the non-doubling [13] or parabolic [54] case as well as with the Gauss image problem [44]. We refer to the surveys [11,48] and monographs [14,30,42] for an overview. Advances have also been made in the field of (p,q)-growth problems [19,20,21,45,46,47].

    In [8,9], the Harnack inequality was established in the doubling generalized Orlicz case for bounded or general solutions. In the current paper, we consider the effect of removing the assumption that the growth function is doubling thus allowing the upper growth rate to equal . The approach is based on ideas from [35,43] involving approximating the energy functional. This is more difficult compared to the variable exponent case, since the form of the approximating problem is unclear as is the connection between solutions and minimizers. Additionally, the challenge in taking limits without the doubling assumption is to track the dependence of various constants on the parameters and to ensure that no extraneous dependence is introduced in any step. Nevertheless, we improve even the result for the variable exponent case.

    Let us consider an example of our main result, Theorem 5.5. In the variable exponent case φ(x,t):=tp(x) we compare with our previous result [35,Theorem 6.4]. In the previous result, we assumed that 1p is Lipschitz continuous, but now we only need the more natural log-Hölder continuity.

    Furthermore, the previous result applied only to small balls in which the exponent was (locally) bounded. The next example shows that the new result applies even to some sets where the the exponent is unbounded.

    Example 1.1 (Variable exponent). Define p:B1(n,] on the unit ball B1 as p(x):=2nloge|x|. Hence p(0)= but p< a.e. Assume that fW1,p()(B1) with ϱp()(|f|)<. If uf+W1,p()0(B1) is a minimizer of the p()-energy, then the Harnack inequality

    supBr(u+r)CinfBr(u+r)

    holds for r14. The constant C depends only on n and ϱp()(|f|). Note that Br we have, in the notation of Theorem 5.5, p=2nloger and q=2nlog2er so that qp=1+log2+log1rlog2+log1r is bounded independent of r.

    In the double phase case we also obtain a corollary of Theorem 4.6 which improves earlier results in that the dependence of the constant is only on qp, not p and q. Note that the usual assumption of Hölder continuity of a is a special case of the inequality in the lemma, see [30,Proposition 7.2.2]. Also note that the "+r" in the Harnack inequality is not needed in this case, since the double phase functional satisfies (A1) in the range [0,K|B|] rather than [1,K|B|].

    Corollary 1.2 (Double phase). Let ΩRn be a bounded domain, n<p<q and H(x,t):=tp+a(x)tq. Assume that fW1,H(Ω) and

    a(x)a(y)+|xy|αwithqp1+αn

    for every x,yΩ. Then any minimizer u of the φ-energy with boundary value function f satisfies the Harnack inequality

    supBruCinfBru.

    The constant C depends only on n, qp and ϱH(|f|).

    We briefly introduce our definitions. More information on Lφ-spaces can be found in [30]. We assume that ΩRn is a bounded domain, n2. Almost increasing means that there exists a constant L1 such that f(s)Lf(t) for all s<t. If there exists a constant C such that f(x)Cg(x) for almost every x, then we write fg. If fgf, then we write fg.

    Definition 2.1. We say that φ:Ω×[0,)[0,] is a weak Φ-function, and write φΦw(Ω), if the following conditions hold for a.e. xΩ:

    yφ(y,f(y)) is measurable for every measurable function f:ΩR.

    tφ(x,t) is non-decreasing.

    φ(x,0)=limt0+φ(x,t)=0 and limtφ(x,t)=.

    tφ(x,t)t is L-almost increasing on (0,) with constant L independent of x.

    If φΦw(Ω) is additionally convex and left-continuous with respect to t for almost every x, then φ is a convex Φ-function, and we write φΦc(Ω). If φ does not depend on x, then we omit the set and write φΦw or φΦc.

    For φΦw(Ω) and ARn we denote φ+A(t):=esssupxAΩφ(x,t) and φA(t):=esssupxAΩφ(x,t).

    We next define the un-weightedness condition (A0), the almost continuity conditions (A1) and the growth conditions (aInc) and (aDec). Note that the constants Lp and Lq are independent of x even though p and q can be functions.

    Definition 2.2. Let s>0, p,q:Ω[0,) and let ω:Ω×[0,)[0,) be almost increasing with respect to the second variable. We say that φ:Ω×[0,)[0,) satisfies

    (A0) if there exists β(0,1] such that φ(x,β)1φ(x,1β) for a.e. xΩ;

    (A1- ω) if for every K1 there exists β(0,1] such that, for every ball B,

    φ+B(βt)φB(t)whenωB(t)[1,K|B|];

    (A1- s) if it satisfies (A1-ω) for ω(x,t):=ts;

    (A1) if it satisfies (A1-φ);

    (aInc)p() if tφ(x,t)tp(x) is Lp-almost increasing in (0,) for some Lp1 and a.e. xΩ;

    (aDec)q() if tφ(x,t)tq(x) is Lq-almost decreasing in (0,) for some Lq1 and a.e. xΩ.

    We say that (aInc) holds if (aInc)p holds for some constant p>1, and similarly for (aDec). If in the definition of (aInc)p() we have Lp=1, then we say that φ satisfies (Inc)p(), similarly for (Dec)q().

    Note that if φ satisfies (aInc)p with a constant Lp, then it satisfies (aInc)r for every r(0,p) with the same constant Lp. This is seen as follows, with s<t:

    φ(x,s)sr=sprφ(x,s)spsprLpφ(x,t)tp=Lp(st)prφ(x,t)trLpφ(x,t)tr.

    Condition (A1) with K=1 was studied in [30] under the name (A1). The condition (A1-ω) was introduced in [8] to combine (A1) and (A1-n) as well as other cases. It is the appropriate assumption if we have a priori information that the solution is in W1,ω or the corresponding Lebesgue or Hölder space. The most important cases are ω=φ and ω(x,t)=ts, that is (A1) and (A1-s).

    Definition 2.3. Let φΦw(Ω) and define the modular ϱφ for uL0(Ω), the set of measurable functions in Ω, by

    ϱφ(u):=Ωφ(x,|u(x)|)dx.

    The generalized Orlicz space, also called Musielak–Orlicz space, is defined as the set

    Lφ(Ω):={uL0(Ω):limλ0+ϱφ(λu)=0}

    equipped with the (Luxemburg) quasinorm

    uLφ(Ω):=inf{λ>0:ϱφ(uλ)1}.

    We abbreviate uLφ(Ω) by uφ if the set is clear from context.

    Definition 2.4. A function uLφ(Ω) belongs to the Orlicz–Sobolev space W1,φ(Ω) if its weak partial derivatives 1u,,nu exist and belong to Lφ(Ω). For uW1,φ(Ω), we define the quasinorm

    uW1,φ(Ω):=uLφ(Ω)+uLφ(Ω).

    We define Orlicz–Sobolev space with zero boundary values W1,φ0(Ω) as the closure of {uW1,φ(Ω):suppuΩ} in W1,φ(Ω).

    In the definition uLφ(Ω) is an abbreviation of |u|Lφ(Ω). Again, we abbreviate uW1,φ(Ω) by u1,φ if Ω is clear from context. W1,φ0(Ω) is a closed subspace of W1,φ(Ω), and hence reflexive when W1,φ(Ω) is reflexive. We write f+W1,φ0(Ω) to denote the set {f+v:vW1,φ0(Ω)}.

    Definition 2.5. We say that uW1,φloc(Ω) is a local minimizer if

    supphφ(x,|u|)dxsupphφ(x,|(u+h)|)dx

    for every hW1,φ(Ω) with supphΩ. We say that uW1,φ(Ω) is a minimizer of the φ-energy with boundary values fW1,φ(Ω), if ufW1,φ0(Ω), and

    Ωφ(x,|u|)dxΩφ(x,|v|)dx

    for every vf+W1,φ0(Ω).

    Let hW1,φ(Ω) have compact support in Ω, fW1,φ(Ω) and uf+W1,φ0(Ω) is a minimizer of the φ-energy. Then u+hf+W1,φ0(Ω) by the definition. By the φ-energy minimizing property,

    Ωφ(x,|u|)dxΩφ(x,|(u+h)|)dx;

    the integrals over the set Ωsupph cancel, and so u is a local minimizer. Hence every minimizer uW1,φ(Ω) of the φ-energy with boundary values f is a local minimizer.

    We denote by φ the conjugate Φ-function of φΦw(Ω), defined by

    φ(x,t):=sups0(stφ(x,s)).

    From this definition, we have Young's inequality stφ(x,s)+φ(x,t). Hölder's inequality holds in generalized Orlicz spaces for φΦw(Ω) with constant 2 [30,Lemma 3.2.13]:

    Ω|u||v|dx2uφvφ.

    We next generalize the relation φ(φ(t)t)φ(t) which is well-known in the convex case, to weak Φ-functions. The next results are written for φΦw but can be applied to φΦw(Ω) point-wise.

    Lemma 3.1. Let φΦw satisfy (aInc)1 with constant L. Then

    φ(φ(t)Lt)φ(t)L.

    Proof. When st we use sφ(t)Ltφ(s)sφ(t)Ltφ(t)L to obtain

    φ(φ(t)Lt)=sups0(sφ(t)Ltφ(s))max{φ(t)L,sups>t(sφ(t)Ltφ(s))}.

    On the other hand, by (aInc)1 we conclude that sφ(t)Ltφ(s) when s>t, so the second term is non-positive and the inequality is established.

    If φΦw is differentiable, then

    ddtφ(t)tp=φ(t)tpptp1φ(t)t2p=φ(t)tp+1[tφ(t)φ(t)p].

    Thus φ satisfies (Inc)p if and only if tφ(t)φ(t)p. Similarly, φ satisfies (Dec)q if and only if tφ(t)φ(t)q. It also follows that if φ satisfies (Inc)p and (Dec)q, then

    1qtφ(t)φ(t)1ptφ(t) (3.2)

    and so φ satisfies (aInc)p1 and (aDec)q1. We next show that the last claim holds even if only (aInc) or (aDec) is assumed of φ which is convex but not necessarily differentiable.

    For φΦc we denote the left and right derivative by φ and φ+, respectively. We define the left derivative to be zero at the origin, i.e., φ(0):=0. Assume that φ satisfies (aInc)p with p>1, and let t0>0 be such that φ(t0)<. Then

    φ+(0)=limt0+φ(t)tlimt0+Lptp1φ(t0)tp0=0.

    Since φ+ is right-continuous we also obtain that

    limt0+φ+(t)=φ+(0)=0.

    Lemma 3.3. Let φΦc satisfy (aInc)p and (aDec)q with constants Lp and Lq, respectively. Then

    1(Lqe1)qtφ+(t)φ(t)2ln(2Lp)ptφ(t)

    for every t0, and φ and φ+ satisfy (aInc)p1 and (aDec)q1, with constants depending only on qp, Lp and Lq.

    Proof. Since φ is convex we have

    φ(t)=t0φ+(τ)dτ=t0φ(τ)dτ,

    for a proof see e.g., [51,Proposition 1.6.1,p. 37]. Let r[0,1). Since the left derivative is increasing, we obtain

    φ(t)φ(rt)=trtφ(τ)dτ(trt)φ(t).

    Thus

    tφ(t)φ(t)φ(rt)1rφ(t)1Lprp1r

    where in the second inequality we used (aInc)p of φ. Choosing r:=(2Lp)1/p we get

    1Lprp1r=1/21(2Lp)1/p=p2p(1(2Lp)1/p).

    Writing h:=1p and x:=2Lp, we find that

    p(1(2Lp)1/p)=1xhhdxsds|s=0=lnx,

    where the inequality follows from convexity of sxs. Thus tφ(t)p2ln(2Lp)φ(t).

    Let R>1. Since φ+ is increasing, we obtain

    φ(Rt)φ(t)=Rttφ+(τ)dτ(Rtt)φ+(t).

    Thus

    tφ+(t)φ(Rt)φ(t)R1φ(t)LqRq1R1

    where (aDec)q of φ was used in the second inequality. With R:=1+1q we get

    tφ+(t)Lq(1+1q)q11/qφ(t)q(Lqe1)φ(t).

    We have established the inequality of the claim.

    We abbreviate cq:=1Lqe1 and cp:=2ln(2Lp). Since φ is convex, we have φφ+ and so

    cqqtφ(t)cqqtφ+(t)φ(t)cpptφ(t)cpptφ+(t)

    Thus we obtain by (aDec)q of φ for 0<s<t that

    φ+(t)tq1qcqφ(t)tqqLqcqφ(s)sqqpLqcpcqφ+(s)sq1

    and (aDec)q1 of φ+ follows. The proof for (aInc)p1 is similar as are the proofs for φ.

    Before Lemma 3.3 we noted that limt0+φ+(x,t)=0, and hence

    lim|y|0φ+(x,|y|)|y|yz=(lim|y|0+φ+(x,|y|))(lim|y|0+y|y|z)=0

    for zRn. In light of this, we define

    φ+(x,|u|)|u|uh:=0whenu=0.

    Theorem 3.4. Let φΦc(Ω) satisfy (aInc)p and (aDec)q with 1<pq. Denote φh:=φ+χ{uh0}+φχ{uh<0}. If uW1,φloc(Ω), then the following are equivalent:

    (i) u is a local minimizer;

    (ii) supphφh(x,|u|)|u|uhdx0 for every hW1,φ(Ω) with supphΩ.

    Proof. Let hW1,φ(Ω) with E:=supphΩ be arbitrary. Define g:Ω×[0,1][0,] by g(x,ε):=|(u(x)+εh(x))|; in the rest of the proof we omit the first variable and abbreviate g(x,ε) by g(ε).

    Note that g(ε)2=|u(x)|2+ε2|h(x)|2+2εu(x)h(x) and g0. Thus in [0,1] the function g has a local minimum at zero for xΩ with u(x)h(x)0 and a maximum otherwise. This determines whether we obtain the right- or left-derivative and so

    limε0+φ(x,g(ε))φ(x,g(0))ε=φh(x,g(0))g(0)=φh(x,|u|)|u|uh (3.5)

    for almost every xE.

    Let us then find a majorant for the expression on the left-hand side of (3.5). By convexity,

    |φ(x,g(ε))φ(x,g(0))ε|φ+(x,max{g(ε),g(0)})|g(ε)g(0)|ε

    for a.e. xE. Since ε[0,1] we have

    max{g(ε),g(0)}max{|u|+ε|h|,|u|}|u|+|h|.

    By the triangle inequality,

    |g(ε)g(0)ε|=||u+εh||u|ε||h||u|+|h|.

    Combining the estimates above, we find that

    |φ(x,g(ε))φ(x,g(0))ε|φ+(x,|u|+|h|)(|u|+|h|).

    By Lemma 3.3, φ+(x,t)tφ(x,t) for every t0, so that

    φ+(x,|u|+|h|)(u|+|h|)φ(x,|u|+|h|).

    By (aDec),

    φ(x,|u|+|h|)φ(x,2|u|)+φ(x,2|h|)Lq2q(φ(x,|u|)+φ(x,|h|))a.e.

    Combining the estimates, we find that

    |φ(x,g(ε))φ(x,g(0))ε|φ(x,|u|)+φ(x,|h|)a.e.

    The right hand side is integrable by [30,Lemma 3.1.3(b)], since |u|,|h|Lφ(Ω) and φ satisfies (aDec). Thus we have found a majorant. By dominated convergence and (3.5), we find that

    Eφh(x,|u|)|u|uhdx=limε0+Eφ(x,g(ε))φ(x,g(0))εdx. (3.6)

    Let us first show that (ⅰ) implies (ⅱ). By (ⅰ),

    Eφ(x,g(ε))φ(x,g(0))εdx0

    for ε(0,1], and hence (ⅱ) follows by (3.6).

    Let us then show that (ⅱ) implies (ⅰ). For θ[0,1] and s,t0 we have

    g(θt+(1θ)s)=|θu+θth+(1θ)u+(1θ)sh||θu+θth|+|(1θ)u+(1θ)sh|=θg(t)+(1θ)g(s),

    so g(ε) is convex. Since tφ(x,t) and g(ε) are convex for almost every xE, and tφ(x,t) is also increasing, the composed function tφ(x,g(t)) is convex for a.e. xE. Thus

    Eφ(x,g(1))φ(x,g(0))dxEφ(x,g(ε))φ(x,g(0))εdx.

    Since the above inequality holds for every ε(0,1), (3.6) implies that

    Eφ(x,g(1))φ(x,g(0))dxEφh(x,|u|)|u|uhdx0,

    which is (i).

    We conclude the section by improving the Caccioppoli inequality from [8]; in this paper we only need the special case =1 and s=q, but we include the general formulation for possible future use. We denote by η a cut-off function in BR, more precisely, ηC0(BR), χBσRηχBR and |η|2(1σ)R, where σ(0,1). Note that the auxiliary function ψ is independent of x in the next lemma. Later on we will choose ψ to be a regularized version of φ+B. Note also that the constant in the lemma is independent of q1.

    Lemma 3.7 (Caccioppoli inequality). Suppose φΦc(Ω) satisfies (aInc)p and (aDec)q with constants Lp and Lq, and let ψΦw be differentiable and satisfy (A0), (Inc)p1 and (Dec)q1, p1,q11. Let β(0,1] be the constant from (A0) of ψ. If u is a non-negative local minimizer and η is a cut-off function in BRΩ, then

    BRφ(x,|u|)ψ(u+RβR)ηsdxKBRψ(u+RβR)φ(x,Ku+RβR)ηsqdx

    for any >1p1 and sq, where K:=8sq(Lqe1)Lqln(2Lp)p(p11)(1σ)+Lp.

    Proof. Let us simplify the notation by writing ˜u:=u+R and v:=˜uβR. Since u=˜u, we see that ˜u is still a local minimizer. By (A0) of ψ and v1β, we have 0ψ(v)1.

    We would like to use Theorem 3.4 with h:=ψ(v)ηs˜u. Let us first check that h is a valid test function for a local minimizer, that is hW1,φ(BR) and has compact support in BRΩ. As ˜uLφ(BR) and |h|˜u, it is immediate that hLφ(BR). By a direct calculation,

    h=ψ(v)1ηs˜uψ(v)v+sψ(v)ηs1˜uη+ψ(v)ηs˜u.

    Note that ˜uv=v˜u. Since ψ is differentiable we may use (3.2) to get

    |ψ(v)1ηsψ(v)v˜u|ψ(v)1q1ψ(v)|˜u|q1|˜u|Lφ(BR).

    For the third term in h, we obtain |ψ(v)ηs˜u||˜u|Lφ(BR). The term with η is treated as h itself. Thus hW1,φ(BR). Since s>0 and ηC0(BR), h has compact support in BRΩ and so it is a valid test-function for a local minimizer.

    We next calculate

    ˜uh=ψ(v)1ηs[ψ(v)vψ(v)]|˜u|2+sψ(v)ηs1˜u˜uη.

    The inequality p1ψ(t)ψ(t)t from (3.2) implies that ψ(v)vψ(v)(p11)ψ(v)>0. Since ˜u is a local minimizer, we can use the implication (i)(ii) of Theorem 3.4 to conclude that

    [p11]BRφh(x,|˜u|)|˜u|ψ(v)ηsdxsBRφh(x,|˜u|)ψ(v)˜u|η|ηs1dx.

    Since φφhφ+, we obtain 1q(Lqe1))tφh(x,t)φ(x,t)2ln(2Lp)ptφh(x,t) from Lemma 2. Using also |η|˜u21σv, we have

    BRφ(x,|˜u|)ψ(v)ηsdx4sq(Lqe1)ln(2Lp)p(p11)(1σ)BRφ(x,|˜u|)|˜u|ηs1ψ(v)vdx,

    Note that the constant in front of the integral can be estimated from above by K2Lq.

    Next we estimate the integrand on the right hand side. By Young's inequality

    φ(x,|˜u|)|˜u|vφ(x,ε1qLpv)+φ(x,ε1qL1pφ(x,|˜u|)|˜u|),

    where 1q+1q=1. We choose ε:=LpKη(x)(0,1] and use (aInc)q' of φ [30,Proposition 2.4.9] (which holds with constant Lq) and Lemma 3.1 to obtain

    φ(x,ε1qL1pφ(x,|˜u|)|˜u|)Lqεφ(x,φ(x,|˜u|)Lp|˜u|)LqεLpφ(x,|u|)=LqKη(x)φ(x,|u|).

    In the other term we estimate ε1qLpK11qL1qpε1q=η1qK and use (aDec)q of φ:

    φ(x,ε1qLpv)Lqη1qφ(x,Kv).

    With these estimates we obtain that

    BRφ(x,|˜u|)ψ(v)ηsdx12BRφ(x,|˜u|)ψ(v)ηsdx+K2BRψ(v)φ(x,Kv)ηsqdx.

    The first term on the right-hand side can be absorbed in the left-hand side. This gives the claim.

    The next observation is key to applications with truly non-doubling growth.

    Remark 3.8. In the previous proof the assumption (aDec)q is only needed in the set η0 since we can improve the estimate on the right-hand side integral to |η|˜u21σvχ{η0} and only drop the characteristic function in the final step.

    The following definition is like [8,Definition 3.1], except φ+Br has replaced φBr. Furthermore, we are more precise with our estimates so as to avoid dependence on p and q.

    Definition 4.1. Let φΦw(Br) satisfy (aInc)p with p1 and constant Lp. We define ψBr:Br[0,] by setting

    ψBr(t):=t0τp1sups(0,τ]φ+Br(s)spdτfort0.

    It is easy to see that ψBrΦw. Using that φ is increasing for the lower bound and (aInc)p for the upper bound, we find that

    ln(2)φ+Br(t2)=tt/2τp1φ+Br(t/2)τpdτψBr(t)t0tp1Lpφ+Br(t)tpdτ=Lpφ+Br(t). (4.2)

    As in [8,Definition 3.1], we see that ψBr is convex and satisfies (Inc)p. If φ satisfies (A0), so does ψBr, since φ+BrψBr. If φ satisfies (aDec)q, then ψBr is strictly increasing and satisfies (aDec)q, and, as a convex function, also (Dec) [30,Lemma 2.2.6].

    We note in both the above reasoning and in the next theorem that constants have no direct dependence on p or q, only on Lp, Lq and qp.

    Theorem 4.3 (Bloch-type estimate). Let φΦc(Ω) satisfy (A0) and (A1). Let B2rΩ with r1 and φ|B2r satisfy (aInc)p and (aDec)q with p,q[n,). If u is a non-negative local minimizer, then

    Br|log(u+r)|ndxC,

    where C depends only on n, Lp, Lq, qp, the constants from (A0) and (A1), and ϱφ(|u|).

    Proof. Let us first note that φ satisfies (aInc)n with the constant Lp. Let β be the smaller of the constants from (A0) and (A1). Denote v:=u+2r2βr and γ:=2Kβ, where K is from Caccioppoli inequality (Lemma 3.7) with =1, s=q and σ=12. Since pn, we see that

    K16q2(Lqe1)Lqln(2Lp)p(p1)+Lp16(Lqe1)Lqln(2Lp)(qp)2nn1+Lp.

    When |u|>γv, we use (aInc)n to deduce that

    φB2r(γv)vnγnφ(x,γv)(γv)nγnLpφ(x,|u|)|u|n

    for a.e. xBr. Rearranging gives |u|nvnφ(x,|u|)φB2r(γv). Since v1β and γ1, we obtain by (A0) that φB2r(γv)1. If also φB2r(γv)1|B2r|, then φ+B2r(βγv)φB2r(γv) by (A1). Otherwise, (φB2r(γv))1|B2r|. In either case,

    |u|nvnφ(x,|u|)φB2r(γv)φ(x,|u|)(1φ+B2r(βγv)+|B2r|)

    for a.e. xBr. When |u|γv, we use the estimate |u|nvnγn instead. Since u+r12(u+2r)=βrv, we obtain that

    Br|log(u+r)|ndx=Br|u|n(u+r)ndx1(βr)nBr|u|nvndxBrφ(x,|u|)φ+B2r(βγv)+|B2r|φ(x,|u|)+1dx=Brφ(x,|u|)φ+B2r(βγv)dx+2nϱφ(|u|)+1.

    It remains to bound the integral on the right-hand side.

    Let ψB2r be as in Definition 4.1, let ηC0(B2r) be a cut-off function such that η=1 in Br and choose ψ(t):=ψB2r(βγt). Then

    Brφ(x,|u|)φ+B2r(βγv)dxB2rφ(x,|u|)φ+B2r(βγv)ηqdxLpB2rφ(x,|u|)ψ(v)ηqdx,

    where the second inequality follows from (4.2). We note that ψ satisfies (A0), (Inc)p and (Dec). Now we use the Caccioppoli inequality (Lemma 3.7) for φ and ψ with =1, s=q and σ=12 to get

    B2rφ(x,|u|)ψ(v)ηqdxKB2rφ(x,Kv)ψ(v)dxln(2)K;

    the last inequality holds by (4.2) and γ=2Kβ since

    φ(x,Kv)ψ(v)=φ(x,Kv)ψB2r(βγv)ln(2)φ(x,Kv)φ+B2r(12βγv)ln(2).

    We next show that the Bloch estimate implies a Harnack inequality for suitable monotone functions. We say that a continuous function u is monotone in the sense of Lebesgue, if it attains its extrema on the boundary of any compact set in its domain of definition. We say that φΦw(Ω) is positive if φ(x,t)>0 for every t>0 and a.e. xΩ. If φ satisfies (aDec)q() for q< a.e., then it is positive.

    Lemma 4.4. If φΦw(Ω) is positive, then every continuous local minimizer is monotone in the sense of Lebesgue.

    Proof. Let uW1,φloc(Ω)C(Ω) be a local minimizer and DΩ. Fix M>maxDu and note that (uM)+ is zero in some neighborhood of D since u is continuous. Thus h:=(uM)+χD belongs to W1,φ(Ω)C(Ω) and has compact support in Ω. Using that u is a local minimizer, we obtain that

    supphφ(x,|u|)dxsupphφ(x,|(uh)|)dx=0.

    Since φ(x,t)>0 for every t>0 and a.e. xΩ, it follows that u=0 a.e. in supph. Thus h=0 a.e. in Ω. Since h is continuous and equals 0 in ΩD, we conclude that h0.

    Hence uM in D. Letting MmaxDu, we find that umaxDu. The proof that minDuu in D is similar.

    For xΩ we write rx:=12dist(x,Ω). Let 1 {\leqslant} p < \infty . In [43,Definition 3.6] a function u:\Omega \to \mathbb{R} is called a Bloch function if

    \sup\limits_{x\in \Omega} r_x^p \mathit{{\rlap{-} \smallint }}_{B_{r_x}} |\nabla u|^p \, dx < \infty.

    Note that if u is an analytic function in the plane and p = 2 , then by the mean value property

    \sup\limits_{x\in \Omega} r_x \Big(\mathit{{\rlap{-} \smallint }}_{B_{r_x}} |u'|^2 \, dx \Big)^\frac12 \approx \sup\limits_{x\in \Omega} d(x,\partial \Omega)\,|u'(x)| \approx \sup\limits_{x\in \Omega} (1-|x|^2) |u'(x)|,

    which connects this with Bloch functions in complex analysis. In the next theorem we assume for \log u a Bloch-type condition. In the case p = n the next result was stated in [35,Lemma 6.3].

    Lemma 4.5. Let u:\Omega\to (0, \infty) be continuous and monotone in the sense of Lebesgue. If B_{4r}\Subset\Omega , and

    r^p \mathit{{\rlap{-} \smallint }}_{B_{2r}}|\nabla\log u|^p \, dx {\leqslant} A

    for p > n-1 , then

    \sup\limits_{B_{r}} u {\leqslant} C\inf\limits_{B_{r}}u

    for some C depending only on A , p and n .

    Proof. Denote v: = \log u . Since the logarithm is increasing, v is monotone in the sense of Lebesgue because u is.

    As u is continuous and positive in \overline{B_{3r}}\subset \Omega , it is bounded away from 0 . Thus v\in W^{1, p}(B_{2r}) is uniformly continuous in B_{3r} . Mollification gives a sequence (v_i)_{i = 0}^\infty of functions in C^\infty(B_{2r})\cap W^{1, p}(B_{2r}) , such that v is the limit of v_i in W^{1, p}(B_{2r}) and v_i\to v pointwise uniformly in B_{2r} , as i\to\infty [25,Theorem 4.1 (ii),p. 146]. By the Sobolev–Poincaré embedding W^{1, p}(\partial B_R)\to C^{0, 1- \frac{n-1}{p}}(\partial B_R) ,

    \Big(\mathop {{\rm{osc}}}\limits_{\partial B_R}v_i \Big)^p \lesssim R^{p-n+1} \int_{\partial B_R} |\nabla v_i|^p\,dS

    for every R\in(0, 2r) , where dS denotes the (n-1) -dimensional Hausdorff measure and the constant depends only on p and n (see, e.g., [26,Lemma 1], stated for the case n = p = 3 ). Integrating with respect to R gives

    \int_r^{2r} \Big(\mathop {{\rm{osc}}}\limits_{\partial B_R}v_i\Big)^p\,dR \lesssim \int_r^{2r} R^{p-n+1} \int_{\partial B_R} |\nabla v_i|^p\,dS\,dR \lesssim r^{p-n+1}\int_{B_{2r}} |\nabla v_i|^p\,dx.

    Since v_i \to v uniformly, we obtain that (\mathop {{\rm{osc}}}\limits_{\partial B_R}v)^p = \lim_{i\to\infty}(\mathop {{\rm{osc}}}\limits_{\partial B_R}v_i)^p for every R . Using this and v_i \to v in W^{1, p}(B_{2r}) , it follows by Fatou's Lemma that

    \begin{align*} \int_r^{2r}\Big(\mathop {{\rm{osc}}}\limits_{\partial B_R}v\Big)^p\,dR &{\leqslant} \liminf\limits_{i\to\infty}\int_r^{2r}\Big(\mathop {{\rm{osc}}}\limits_{\partial B_R}v_i\Big)^p\,dR\\ & {\leqslant} \liminf\limits_{i\to\infty} Cr^{p-n+1}\int_{B_{2r}} |\nabla v_i|^p\,dx = Cr^{p-n+1}\int_{B_{2r}} |\nabla v|^p\,dx. \end{align*}

    As v is continuous and monotone in the sense of Lebesgue, we have that \mathop {{\rm{osc}}}\limits_{B_r}v {\leqslant} \mathop {{\rm{osc}}}\limits_{B_R}v = \mathop {{\rm{osc}}}\limits_{\partial B_R}v for R\in(r, 2r) , and therefore

    r\Big(\mathop {{\rm{osc}}}\limits_{B_r}v\Big)^p {\leqslant} \int_r^{2r}\Big(\mathop {{\rm{osc}}}\limits_{\partial B_R}v\Big)^p\,dR {\leqslant} Cr^{p-n+1}\int_{B_{2r}} |\nabla v|^p\,dx {\leqslant} CrA.

    Since

    \mathop {{\rm{osc}}}\limits_{B_r}v = \sup\limits_{x,y\in B_r}|v(x)-v(y)| = \sup\limits_{x,y\in B_r}\bigg|\log\frac{u(x)}{u(y)}\bigg| = \log\frac{\sup\limits_{B_r}u}{\inf\limits_{B_r}u},

    it now follows that

    \sup\limits_{B_r}u {\leqslant} \exp\big((CA)^{1/p}\big)\inf\limits_{B_r}u.

    We conclude this section with the Harnack inequality for local minimizers. The novelty of the next theorem, apart from the technique, is that the constant depends only on \frac qp , not on p and q separately.

    Theorem 4.6 (Harnack inequality). Let {\varphi} \in \Phi_{\mathit{\rm{c}}}(\Omega) satisfy {\rm{(A0)}} and (A1). We assume that B_{2r}\subset \Omega with r{\leqslant} 1 and {\varphi}|_{B_{2r}} satisfies {{\rm{(aInc)}}_{{p}}} and {{\rm{(aDec)}}_q} with p, q\in (n, \infty) .

    Then any non-negative local minimizer u \in W^{1, {\varphi}}_{{{\rm{loc}}}}(\Omega) satisfies the Harnack inequality

    \sup\limits_{B_r}(u+r) {\leqslant} C\inf\limits_{B_r} (u+r)

    when B_{4r}\subset \Omega . The constant C depends only on n , \beta , L_p , L_q , \frac qp and {\varrho}_{\varphi}(|\nabla u|) .

    Proof. Let u \in W^{1, {\varphi}}_{{{\rm{loc}}}}(\Omega) be a non-negative local minimizer. By Theorem 4.3,

    \int_{B_r} |\nabla\log(u+r)|^n \,dx {\leqslant} C,

    where C depends only on n , L_p , L_q , \frac qp , the constants from {\rm{(A0)}} and (A1), and {\varrho}_{\varphi}(|\nabla u|) . Since p > n , {\varphi} satisfies {{\rm{(aInc)}}_{{p}}} and u\in W^{1, {\varphi}}_{{{\rm{loc}}}}(\Omega) , u is continuous and Lemma 4.4 yields that u+r is monotone in the sense of Lebesgue. Thus we can apply Lemma 4.5 to u+r , which gives the Harnack inequality.

    Let us study minimizers with given boundary values.

    Definition 5.1. Let p \in [1, \infty) , {\varphi}\in \Phi_{\rm{c}}(\Omega) and define, for \lambda{\geqslant} 1 ,

    {\varphi}_\lambda(x,t) : = \int_0^t \tfrac p\lambda \tau^{p-1} + \min\{{\varphi}_-'(x,\tau), p\lambda \tau^{p-1}\}\, d\tau = \tfrac 1\lambda t^p + \int_0^t \min\{{\varphi}_-'(x,\tau), p\lambda \tau^{p-1}\}\, d\tau.

    Note that since t \mapsto {\varphi}_\lambda(x, t) is convex for a.e. x \in \Omega , the left derivative {\varphi}_-' exists for a.e. x \in \Omega , and therefore the above definition makes sense.

    Lemma 5.2. If {\varphi}\in \Phi_{\mathit{\rm{c}}}(\Omega) , then {\varphi}_\lambda\in \Phi_{\mathit{\rm{c}}}(\Omega) satisfies {\varphi}_\lambda(\cdot, t)\approx t^p with constants depending on \lambda . Furthermore,

    \min\{{\varphi}(x, \tfrac t2), \lambda (\tfrac t2)^p\} + \tfrac 1\lambda t^p {\leqslant} {\varphi}_\lambda(x,t) {\leqslant} {\varphi}(x, t) + \tfrac 1\lambda t^p

    for \lambda{\geqslant} 1 , {\varphi}_\lambda(x, t) {\leqslant} {\varphi}_\Lambda(x, t)+\tfrac1\lambda t^p for any \Lambda{\geqslant} \lambda{\geqslant} 1 , and {\varphi}_\lambda\to {\varphi} as \lambda\to\infty .

    Proof. It follows from the definition that \tfrac p\lambda \tau^{p-1} {\leqslant} {\varphi}_\lambda' (x, \tau){\leqslant} p(\tfrac1\lambda +\lambda)\tau^{p-1} . Integrating over \tau\in [0, t] gives {\varphi}_\lambda(\cdot, t)\approx t^p . Let \Lambda{\geqslant} \lambda{\geqslant} 1 . Since the minimum in the integrand is increasing in \lambda , we see that

    {\varphi}_\lambda(x,t) {\leqslant} {\varphi}_\Lambda(x,t) + \big(\tfrac1\lambda-\tfrac1\Lambda\big)t^p {\leqslant} {\varphi}(x,t) + \tfrac1\lambda t^p,

    and thus \limsup_{\lambda\to\infty}{\varphi}_\lambda{\leqslant} {\varphi} . On the other hand, Fatou's Lemma gives

    \begin{align*} {\varphi}(x,t) & = \int_0^t \lim\limits_{\lambda\to\infty}(\tfrac p\lambda \tau^{p-1} + \min\{{\varphi}_-'(x,\tau), p\lambda \tau^{p-1}\})\, d\tau \\ &{\leqslant} \liminf\limits_{\lambda\to\infty} \int_0^t \tfrac p\lambda \tau^{p-1} + \min\{{\varphi}_-'(x,\tau), p\lambda \tau^{p-1}\}\, d\tau = \liminf\limits_{\lambda\to\infty} {\varphi}_\lambda(x,t). \end{align*}

    One of the terms in the minimum \min\{{\varphi}_-'(x, \tau), p\lambda \tau^{p-1}\} is achieved in at least a set of measure \frac t2 . Since both terms are increasing in \tau , this implies that

    {\varphi}_\lambda(x,t) {\geqslant} \min\bigg\{\int_0^{t/2}{\varphi}_-'(x,\tau)\, d\tau,\int_0^{t/2}p\lambda \tau^{p-1}\, d\tau\bigg\} + \tfrac 1\lambda t^p = \min\{{\varphi}(x, \tfrac t2), \lambda (\tfrac t2)^p\} +\tfrac 1\lambda t^p.

    Since {\varphi}_\lambda(x, t)\approx t^p , it follows by [30,Proposition 3.2.4] that W^{1, {\varphi}_\lambda}(\Omega) = W^{1, p}(\Omega) and the norms \|\cdot\|_{{\varphi}_\lambda} and \|\cdot\|_p are comparable. However, the embedding constant blows up as \lambda\to \infty unless {\varphi} also satisfies {\left( {{\rm{aDec}}} \right)_p}. This approximation approach is similar to that in [24]. Note in the next results that f is bounded by the Sobolev embedding in W^{1, p}(\Omega) .

    Lemma 5.3. Let q:\Omega \to (n, \infty) , and let {\varphi}\in \Phi_{\mathit{\rm{c}}}(\Omega) satisfy {\rm{(A0)}}, {{\rm{(aInc)}}_{{p}}} and {\left( {{\rm{aDec}}} \right)_{q(\cdot)}}, p > n . Assume that f\in W^{1, {\varphi}}(\Omega) with \varrho_{\varphi}(\nabla f) < \infty . Then there exists a sequence (u_{\lambda_k}) of Dirichlet {\varphi}_{\lambda_k} -energy minimizers with the boundary value function f and a minimizer of the {\varphi} -energy u_\infty\in f+W^{1, {\varphi}}_0(\Omega) such that u_{\lambda_k}\to u_\infty uniformly in \Omega as \lambda_k \to \infty .

    Proof. Note that we use W^{1, {\varphi}_\lambda}(\Omega) = W^{1, p}(\Omega) and W^{1, {\varphi}_\lambda}_0(\Omega) = W^{1, p}_0(\Omega) several times in this proof. Let \lambda{\geqslant} p . Note that f\in W^{1, p}(\Omega) since t^p\lesssim{\varphi}(x, t)+1 by {\rm{(A0)}} and (aInc)p. By [29,Theorem 6.2] there exists a minimizer u_\lambda\in f + W_0^{1, p}(\Omega) of

    \int_\Omega{\varphi}_\lambda(x,|\nabla u|)\,dx.

    Fix \lambda{\geqslant} 1 . By Lemma 5.2 and t^p\lesssim{\varphi}(x, t)+1 , we have t^p\lesssim \min\{{\varphi}(x, \frac t2), \lambda t^p\}+1 \lesssim {\varphi}_\lambda(x, t) +1 . Also by the same lemma, {\varphi}_\lambda\lesssim {\varphi} + \tfrac{1}{\lambda} t^p\lesssim {\varphi} +1 . Since f is a valid test-function and u_\lambda is a {\varphi}_\lambda -minimizer, we have

    \begin{split} \int_\Omega |\nabla u_\lambda|^p\,dx &\lesssim \int_\Omega{\varphi}_\lambda(x,|\nabla u_\lambda|)+1\,dx {\leqslant}\int_\Omega{\varphi}_\lambda(x,|\nabla f|)+1\,dx \lesssim \int_\Omega{\varphi}(x,|\nabla f|)+1\,dx < \infty, \end{split}

    and hence {\varrho}_p(\nabla u_\lambda) is uniformly bounded. Note that the implicit constants do not depend on \lambda .

    Since u_\lambda-f\in W^{1, p}_0(\Omega) , the Poincaré inequality implies that

    \|u_\lambda-f\|_p \lesssim \|\nabla (u_\lambda-f)\|_{p} \lesssim \|\nabla u_\lambda\|_p +\|\nabla f\|_p {\leqslant} c.

    Therefore, \|u_\lambda\|_p {\leqslant} \|u_\lambda-f\|_p + \|f\|_p {\leqslant} c and so \|u_\lambda\|_{1, p} is uniformly bounded. Since f + W_0^{1, p}(\Omega) is a closed subspace of W^{1, p}(\Omega) , it is a reflexive Banach space. Thus there exists a sequence (\lambda_k)_{k = 1}^\infty tending to infinity and a function u_\infty \in f + W_0^{1, p}(\Omega) such that u_{\lambda_k}\rightharpoonup u_\infty in W^{1, p}(\Omega) . Since p > n , the weak convergence u_{\lambda_k}-f\rightharpoonup u_\infty-f in W^{1, p}_0(\Omega) and compactness of the Sobolev embedding [1,Theorem 6.3 (Part IV),p. 168] imply that u_{\lambda_k}-f \to u_\infty-f in the supremum norm. Hence u_{\lambda_k} \to u uniformly in \Omega .

    We note that the modular {\varrho}_{{\varphi}_\lambda} satisfies the conditions of [23,Definition 2.1.1]. Hence, it is weakly lower semicontinuous by [23,Theorem 2.2.8], and we obtain that

    \begin{equation} \begin{split} \int_\Omega{\varphi}_\lambda(x,|\nabla u_\infty|)\,dx &{\leqslant} \liminf\limits_{k\to\infty} \int_\Omega{\varphi}_\lambda(x,|\nabla u_{\lambda_k}|)\,dx {\leqslant} \liminf\limits_{k\to\infty} \int_\Omega (1+\tfrac C{\lambda_k}){\varphi}_{\lambda_k}(x,|\nabla u_{\lambda_k}|) + \tfrac C{\lambda_k}\,dx\\ &{\leqslant} \liminf\limits_{k\to\infty} \int_\Omega{\varphi}_{\lambda_k}(x,|\nabla u_{\lambda_k}|) \,dx \lesssim \int_\Omega {\varphi}(x,|\nabla f|)+1\,dx \end{split} \end{equation} (5.4)

    for fixed \lambda{\geqslant} 1 , where in the second inequality we used Lemma 5.2 and the fact that t^p \lesssim {\varphi}_\lambda(x, t) +1 . It follows by monotone convergence that

    \int_\Omega{\varphi}(x,|\nabla u_\infty|)\,dx = \lim\limits_{\lambda\to\infty} \int_\Omega \min\{{\varphi}(x,|\nabla u_\infty|), \lambda |\nabla u_\infty|^p\}\,dx {\leqslant} \limsup\limits_{\lambda\to\infty}\int_\Omega {\varphi}_\lambda(x,|\nabla u_\infty|)\,dx,

    and hence |\nabla u_\infty| \in L^{\varphi}(\Omega) . Since p > n , \Omega is bounded and u_\infty -f \in W^{1, p}_0(\Omega) , we obtain by [58,Theorem 2.4.1,p. 56] that u_\infty - f\in L^\infty(\Omega) . Moreover, L^\infty(\Omega)\subset L^{\varphi}(\Omega) since \Omega is bounded and {\varphi} satisfies {\rm{(A0)}}. These and f\in L^{\varphi}(\Omega) yield that u_\infty \in L^{{\varphi}}(\Omega) . Hence we have u_\infty \in W^{1, {\varphi}}(\Omega) . Since u_\infty -f \in W^{1, p}_0(\Omega) and p > n , it follows that u_\infty-f can be continuously extended by 0 in \Omega^c [2,Theorem 9.1.3]. Then we conclude as in [40,Lemma 1.26] that u_\infty-f\in W^{1, {\varphi}}_0(\Omega) .

    We conclude by showing that u_\infty is a minimizer. Suppose to the contrary that there exists u\in f+W^{1, {\varphi}}_0(\Omega) with

    \int_\Omega{\varphi}(x,|\nabla u_\infty|)\,dx - \int_\Omega{\varphi}(x,|\nabla u|)\,dx = : \varepsilon > 0.

    By {\varphi}_\lambda\lesssim {\varphi}+1 , {\varphi}_\lambda\to{\varphi} and dominated convergence, there exists \lambda_0 such that

    \int_\Omega{\varphi}_\lambda(x,|\nabla u_\infty|)\,dx - \int_\Omega{\varphi}_\lambda(x,|\nabla u|)\,dx {\geqslant}\tfrac \varepsilon2

    for all \lambda{\geqslant} \lambda_0 . From the lower-semicontinuity estimate (5.4) we obtain k_0 such that

    \int_\Omega{\varphi}_\lambda(x,|\nabla u_\infty|)\,dx {\leqslant} \int_\Omega{\varphi}_{\lambda_k}(x,|\nabla u_{\lambda_k}|)\,dx + \tfrac \varepsilon4

    for all k{\geqslant} k_0 . By increasing k_0 if necessary, we may assume that \lambda_k{\geqslant} \lambda_0 when k{\geqslant} k_0 . For such k we choose \lambda = \lambda_k above and obtain that

    \int_\Omega{\varphi}_{\lambda_k}(x,|\nabla u|) + \tfrac \varepsilon2 {\leqslant} \int_\Omega{\varphi}_{\lambda_k}(x,|\nabla u_{\lambda_k}|)\,dx + \tfrac \varepsilon4.

    This contradicts u_{\lambda_k} being a {\varphi}_{\lambda_k} -minimizer, since u \in f + W^{1, {\varphi}}_0(\Omega) \subset f + W^{1, {\varphi}_{\lambda_k}}_0(\Omega) . Hence the counter-assumption was incorrect, and the minimization property of u_\infty is proved.

    We conclude this paper with the Harnack inequality for {\varphi} -harmonic functions. Here we use Remark 3.8 to handle the possibility that q could be unbounded and thus {\varphi} non-doubling, like in Example 1.1. This is possible since q^\circ is the supremum of q only in the annulus, not the whole ball.

    Theorem 5.5 (Harnack inequality). Let p, q : \Omega \to (n, \infty) , and {\varphi} \in \Phi_{\mathit{\rm{c}}}(\Omega) be strictly convex and satisfy {\rm{(A0)}}, (A1), {({\rm{aInc}})_{p(\cdot)}} and {\left( {{\rm{aDec}}} \right)_{q(\cdot)}} with \inf p > n . Assume that f\in W^{1, {\varphi}}(\Omega) with {\varrho}_{\varphi}(|\nabla f|) < \infty . Then there exists a unique minimizer u of the {\varphi} -energy with boundary values f . Let B_{4r}\subset \Omega , p^-: = \inf\limits_{B_{2r}} p and q^\circ: = \sup\limits_{B_{2r}\setminus B_r} q . If \frac{q^\circ}{p^-} < \infty , then the Harnack inequality

    \sup\limits_{B_r}(u+r) {\leqslant} C\inf\limits_{B_r} (u+r)

    holds for all non-negative minimizers with C depending only on n , \beta , L_p , L_q , \frac{q^\circ}{p^-} and {\varrho}_{\varphi}(|\nabla f|) .

    Proof. By Lemma 5.3, there exists a sequence (u_k)\subset f+W^{1, p}_0(\Omega) of minimizers of the {\varphi}_{\lambda_k} energy which converge uniformly to a minimizer u_\infty\in f+W^{1, {\varphi}}_0(\Omega) of the {\varphi} -energy. Since {\varphi} is strictly convex, the minimizer is unique and so u = u_\infty .

    From {\rm{(A0)}} and {\left( {{\rm{aInc}}} \right)_{{p^ - }}} we conclude that t^{p^-}\lesssim{\varphi}(x, t)+1 . It follows from Lemma 5.2 that {\varphi}_\lambda(\cdot, t)\simeq {\varphi}(\cdot, t)+\frac1\lambda t^{p^-} . Thus {\varphi}_\lambda satisfies {\rm{(A0)}} and {\rm{(A1)}} with the same constants as {\varphi} . Since u_k\to u in L^\infty(\Omega) and u is non-negative we can choose a sequence \varepsilon_k\to 0^+ such that u_k+ \varepsilon_k is non-negative. By Theorem 4.1 with Remark 3.8,

    \int_{B_r} |\nabla\log(u_k+ \varepsilon_k+r)|^n \,dx {\leqslant} C,

    where C depends only on n , L_p , L_q , \frac{q^\circ}{p^-} , \beta from {\rm{(A0)}} and (A1), and {\varrho}_{{\varphi}_{\lambda_k}}(|\nabla u_k|) . Since u_k is a minimizer, {\varrho}_{{\varphi}_{\lambda_k}}(|\nabla u_k|){\leqslant} {\varrho}_{{\varphi}_{\lambda_k}}(|\nabla f|)\lesssim {\varrho}_{\varphi}(|\nabla f|)+1 . Thus by Lemma 4.5, we have

    \sup\limits_{B_r}(u_k+ \varepsilon_k+r) {\leqslant} C\inf\limits_{B_r} (u_k+ \varepsilon_k+r),

    with C independent of k . Since u_k+ \varepsilon_k\to u_\infty uniformly, the claim follows.

    Peter Hästö was supported in part by the Jenny and Antti Wihuri Foundation.

    The authors declare no conflict of interest.



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