Loading [MathJax]/jax/element/mml/optable/MathOperators.js
Research article Special Issues

Realizations of kinetic differential equations

  • The induced kinetic differential equations of a reaction network endowed with mass action type kinetics is a system of polynomial differential equations. The problem studied here is: Given a system of polynomial differential equations, is it possible to find a network which induces these equations; in other words: is it possible to find a kinetic realization of this system of differential equations? If yes, can we find a network with some chemically relevant properties (implying also important dynamic consequences), such as reversibility, weak reversibility, zero deficiency, detailed balancing, complex balancing, mass conservation, etc.? The constructive answers presented to a series of questions of the above type are useful when fitting differential equations to datasets, or when trying to find out the dynamic behavior of the solutions of differential equations. It turns out that some of these results can be applied when trying to solve seemingly unrelated mathematical problems, like the existence of positive solutions to algebraic equations.

    Citation: Gheorghe Craciun, Matthew D. Johnston, Gábor Szederkényi, Elisa Tonello, János Tóth, Polly Y. Yu. Realizations of kinetic differential equations[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 862-892. doi: 10.3934/mbe.2020046

    Related Papers:

    [1] David Cruz-Uribe, Michael Penrod, Scott Rodney . Poincaré inequalities and Neumann problems for the variable exponent setting. Mathematics in Engineering, 2022, 4(5): 1-22. doi: 10.3934/mine.2022036
    [2] Giovanni Scilla, Bianca Stroffolini . Partial regularity for steady double phase fluids. Mathematics in Engineering, 2023, 5(5): 1-47. doi: 10.3934/mine.2023088
    [3] Chiara Gavioli, Pavel Krejčí . Deformable porous media with degenerate hysteresis in gravity field. Mathematics in Engineering, 2025, 7(1): 35-60. doi: 10.3934/mine.2025003
    [4] Catharine W. K. Lo, José Francisco Rodrigues . On the obstacle problem in fractional generalised Orlicz spaces. Mathematics in Engineering, 2024, 6(5): 676-704. doi: 10.3934/mine.2024026
    [5] Ugo Gianazza, Sandro Salsa . On the Harnack inequality for non-divergence parabolic equations. Mathematics in Engineering, 2021, 3(3): 1-11. doi: 10.3934/mine.2021020
    [6] Lucio Boccardo, Giuseppa Rita Cirmi . Regularizing effect in some Mingione’s double phase problems with very singular data. Mathematics in Engineering, 2023, 5(3): 1-15. doi: 10.3934/mine.2023069
    [7] Fernando Farroni, Giovanni Scilla, Francesco Solombrino . On some non-local approximation of nonisotropic Griffith-type functionals. Mathematics in Engineering, 2022, 4(4): 1-22. doi: 10.3934/mine.2022031
    [8] Dario Bambusi, Beatrice Langella . A C Nekhoroshev theorem. Mathematics in Engineering, 2021, 3(2): 1-17. doi: 10.3934/mine.2021019
    [9] Claudia Lederman, Noemi Wolanski . Lipschitz continuity of minimizers in a problem with nonstandard growth. Mathematics in Engineering, 2021, 3(1): 1-39. doi: 10.3934/mine.2021009
    [10] Daniela De Silva, Ovidiu Savin . On the boundary Harnack principle in Hölder domains. Mathematics in Engineering, 2022, 4(1): 1-12. doi: 10.3934/mine.2022004
  • The induced kinetic differential equations of a reaction network endowed with mass action type kinetics is a system of polynomial differential equations. The problem studied here is: Given a system of polynomial differential equations, is it possible to find a network which induces these equations; in other words: is it possible to find a kinetic realization of this system of differential equations? If yes, can we find a network with some chemically relevant properties (implying also important dynamic consequences), such as reversibility, weak reversibility, zero deficiency, detailed balancing, complex balancing, mass conservation, etc.? The constructive answers presented to a series of questions of the above type are useful when fitting differential equations to datasets, or when trying to find out the dynamic behavior of the solutions of differential equations. It turns out that some of these results can be applied when trying to solve seemingly unrelated mathematical problems, like the existence of positive solutions to algebraic equations.


    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)

    for every x, y\in \Omega . Then any minimizer u of the {\varphi} -energy with boundary value function f satisfies the Harnack inequality

    \sup\limits_{B_r}u {\leqslant} C\inf\limits_{B_r} u.

    The constant C depends only on n , \frac qp and {\varrho}_H(|\nabla f|) .

    We briefly introduce our definitions. More information on L^{\varphi} -spaces can be found in [30]. We assume that \Omega \subset \mathbb{R}^n is a bounded domain, n{\geqslant} 2 . Almost increasing means that there exists a constant L{\geqslant} 1 such that f(s) {\leqslant} L f(t) for all s < t . If there exists a constant C such that f(x) {\leqslant} C g(x) for almost every x , then we write f \lesssim g . If f\lesssim g\lesssim f , then we write f \approx g .

    Definition 2.1. We say that {\varphi}: \Omega\times [0, \infty) \to [0, \infty] is a weak \Phi -function, and write {\varphi} \in \Phi_{\rm{w}}(\Omega) , if the following conditions hold for a.e. x \in \Omega :

    y \mapsto {\varphi}(y, f(y)) is measurable for every measurable function f:\Omega\to \mathbb{R} .

    t \mapsto {\varphi}(x, t) is non-decreasing.

    {\varphi}(x, 0) = \lim\limits_{t \to 0^+} {\varphi}(x, t) = 0 and \lim\limits_{t \to \infty}{\varphi}(x, t) = \infty .

    t \mapsto \frac{{\varphi}(x, t)}t is L -almost increasing on (0, \infty) with constant L independent of x .

    If {\varphi}\in \Phi_{\rm{w}}(\Omega) is additionally convex and left-continuous with respect to t for almost every x , then {\varphi} is a convex \Phi -function, and we write {\varphi} \in \Phi_{\rm{c}}(\Omega) . If {\varphi} does not depend on x , then we omit the set and write {\varphi} \in \Phi_{\rm{w}} or {\varphi} \in \Phi_{\rm{c}} .

    For {\varphi}\in \Phi_{\rm{w}}(\Omega) and A\subset \mathbb{R}^n we denote {\varphi}^+_{A}(t) : = \mathop {{\rm{ess}}\;{\rm{sup}}}\limits_{x \in A\cap \Omega} {\varphi}(x, t) and {\varphi}^-_{A}(t) : = \mathop {{\rm{ess}}\;{\rm{sup}}}\limits_{x \in A\cap \Omega} {\varphi}(x, t) .

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

    Definition 2.2. Let s > 0 , p, q:\Omega\to[0, \infty) and let \omega:\Omega\times [0, \infty)\to [0, \infty) be almost increasing with respect to the second variable. We say that {\varphi}:\Omega\times [0, \infty)\to [0, \infty) satisfies

    {\rm{(A0)}} if there exists \beta \in(0, 1] such that {\varphi}(x, \beta){\leqslant} 1 {\leqslant} {\varphi}(x, \frac1{\beta}) for a.e. x \in \Omega ;

    (A1- \omega) if for every K {\geqslant} 1 there exists \beta \in (0, 1] such that, for every ball B ,

    {\varphi}_B^+(\beta t) {\leqslant} {\varphi}_B^-(t) \quad{\rm{when}}\quad \omega_B^-(t) \in \bigg[1, \frac{K}{|B|}\bigg];

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

    (A1) if it satisfies (A1-φ);

    (aInc)_ {p(\cdot)} if t \mapsto \frac{{\varphi}(x, t)}{t^{p(x)}} is L_p -almost increasing in (0, \infty) for some L_p{\geqslant} 1 and a.e. x\in\Omega ;

    (aDec)_ {q(\cdot)} if t \mapsto \frac{{\varphi}(x, t)}{t^{q(x)}} is L_q -almost decreasing in (0, \infty) for some L_q{\geqslant} 1 and a.e. x\in\Omega .

    We say that (aInc) holds if (aInc)p holds for some constant p > 1 , and similarly for (aDec). If in the definition of {({\rm{aInc}})_{p(\cdot)}} we have L_p = 1 , then we say that {\varphi} satisfies {({\rm{Inc}})_{p(\cdot)}}, similarly for {\left({{\rm{Dec}}} \right)_{q(\cdot)}}.

    Note that if {\varphi} satisfies (aInc)p with a constant L_p , then it satisfies (aInc)r for every r\in (0, p) with the same constant L_p . This is seen as follows, with s < t :

    \frac{{\varphi}(x,s)}{s^r} = s^{p-r}\frac{{\varphi}(x,s)}{s^{p}} {\leqslant} s^{p-r}L_p\frac{{\varphi}(x,t)}{t^{p}} = L_p\Big{(}\frac{s}{t}\Big{)}^{p-r}\frac{{\varphi}(x,t)}{t^r} {\leqslant} L_p\frac{{\varphi}(x,t)}{t^r}.

    Condition {\rm{(A1)}} with K = 1 was studied in [30] under the name (A1 ' ). The condition (A1-ω) was introduced in [8] to combine {\rm{(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 W^{1, \omega} or the corresponding Lebesgue or Hölder space. The most important cases are \omega = {\varphi} and \omega(x, t) = t^s , that is {\rm{(A1)}} and (A1-s).

    Definition 2.3. Let {\varphi} \in \Phi_{\rm{w}}(\Omega) and define the modular \varrho_{\varphi} for u\in L^0(\Omega) , the set of measurable functions in \Omega , by

    \begin{align*} \varrho_{\varphi}(u) &: = \int_\Omega {\varphi}(x, |u(x)|)\,dx. \end{align*}

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

    \begin{align*} L^{\varphi}(\Omega) &: = \big\{u \in L^0(\Omega) : \lim\limits_{\lambda \to 0^+} \varrho_{\varphi}(\lambda u) = 0\big\} \end{align*}

    equipped with the (Luxemburg) quasinorm

    \begin{align*} \|u\|_{L^{\varphi}(\Omega)} &: = \inf \Big\{ \lambda > 0 : \varrho_{\varphi}\Big( \frac{u}{\lambda} \Big) {\leqslant} 1\Big\}. \end{align*}

    We abbreviate \|u\|_{L^{\varphi}(\Omega)} by \|u\|_{{\varphi}} if the set is clear from context.

    Definition 2.4. A function u \in L^{\varphi}(\Omega) belongs to the Orlicz–Sobolev space W^{1, {\varphi}}(\Omega) if its weak partial derivatives \partial_1 u, \ldots, \partial_n u exist and belong to L^{{\varphi}}(\Omega) . For u \in W^{1, {\varphi}}(\Omega) , we define the quasinorm

    \| u \|_{W^{1,{\varphi}}(\Omega)} : = \| u \|_{L^{\varphi}(\Omega)} + \| \nabla u \|_{L^{\varphi}(\Omega)}.

    We define Orlicz–Sobolev space with zero boundary values W^{1, {\varphi}}_0(\Omega) as the closure of \{u \in W^{1, {\varphi}}(\Omega) : {\rm{supp}}\; u \subset \Omega\} in W^{1, {\varphi}}(\Omega) .

    In the definition \| \nabla u \|_{L^{\varphi}(\Omega)} is an abbreviation of \big\| | \nabla u | \big\|_{L^{\varphi}(\Omega)} . Again, we abbreviate \| u \|_{W^{1, {\varphi}}(\Omega)} by \| u \|_{1, {\varphi}} if \Omega is clear from context. W^{1, {\varphi}}_0(\Omega) is a closed subspace of W^{1, {\varphi}}(\Omega) , and hence reflexive when W^{1, {\varphi}}(\Omega) is reflexive. We write f + W^{1, {\varphi}}_0(\Omega) to denote the set \{f + v: v \in W^{1, {\varphi}}_0(\Omega)\} .

    Definition 2.5. We say that u \in W^{1, {\varphi}}_{{{\rm{loc}}}}(\Omega) is a local minimizer if

    \int_{{\rm{supp}}\; h} {\varphi}(x,|\nabla u|) \,dx {\leqslant} \int_{{\rm{supp}}\; h} {\varphi}(x,|\nabla(u + h)|) \,dx

    for every h \in W^{1, {\varphi}}(\Omega) with {\rm{supp}}\; h \Subset \Omega . We say that u \in W^{1, {\varphi}}(\Omega) is a minimizer of the {\varphi} -energy with boundary values f \in W^{1, {\varphi}}(\Omega) , if u-f \in W^{1, {\varphi}}_0(\Omega) , and

    \int_{\Omega} {\varphi}(x, |\nabla u|) \, dx {\leqslant} \int_{\Omega} {\varphi}(x, |\nabla v|) \, dx

    for every v \in f+ W^{1, {\varphi}}_0(\Omega) .

    Let h \in W^{1, {\varphi}}(\Omega) have compact support in \Omega , f\in W^{1, {\varphi}}(\Omega) and u \in f+W^{1, {\varphi}}_0(\Omega) is a minimizer of the {\varphi} -energy. Then u+h \in f + W^{1, {\varphi}}_0(\Omega) by the definition. By the {\varphi} -energy minimizing property,

    \int_{\Omega} {\varphi}(x, |\nabla u|) \, dx {\leqslant} \int_{\Omega} {\varphi}(x, |\nabla (u+h)|) \, dx;

    the integrals over the set \Omega\setminus {\rm{supp}}\; h cancel, and so u is a local minimizer. Hence every minimizer u \in W^{1, {\varphi}}(\Omega) of the {\varphi} -energy with boundary values f is a local minimizer.

    We denote by {\varphi}^* the conjugate \Phi -function of {\varphi}\in \Phi_{\rm{w}}(\Omega) , defined by

    {\varphi}^*(x,t): = \sup\limits_{s{\geqslant} 0} (st-{\varphi}(x,s)).

    From this definition, we have Young's inequality st {\leqslant} {\varphi}(x, s) + {\varphi}^*(x, t) . Hölder's inequality holds in generalized Orlicz spaces for {\varphi}\in \Phi_{\rm{w}}(\Omega) with constant 2 [30,Lemma 3.2.13]:

    \int_\Omega |u|\, |v|\, dx {\leqslant} 2 \|u\|_{\varphi} \|v\|_{{\varphi}^*}.

    We next generalize the relation {\varphi}^*(\frac{{\varphi}(t)}{t}) {\leqslant} {\varphi}(t) which is well-known in the convex case, to weak \Phi -functions. The next results are written for {\varphi}\in \Phi_{\rm{w}} but can be applied to {\varphi}\in \Phi_{\rm{w}}(\Omega) point-wise.

    Lemma 3.1. Let {\varphi}\in \Phi_{\mathit{\rm{w}}} satisfy (aInc)1 with constant L . Then

    {\varphi}^*\Big( \frac{{\varphi}(t)}{Lt}\Big) {\leqslant} \frac{{\varphi}(t)}{L}.

    Proof. When s{\leqslant} t we use s\frac{{\varphi}(t)}{Lt} - {\varphi}(s) {\leqslant} s\frac{{\varphi}(t)}{Lt} {\leqslant} \frac{{\varphi}(t)}{L} to obtain

    {\varphi}^*\Big( \frac{{\varphi}(t)}{Lt}\Big) = \sup\limits_{s{\geqslant} 0} \Big(s\frac{{\varphi}(t)}{Lt} - {\varphi}(s) \Big) {\leqslant} \max \bigg\{\frac{{\varphi}(t)}{L}, \sup\limits_{s > t} \Big(s\frac{{\varphi}(t)}{Lt} - {\varphi}(s) \Big)\bigg\}.

    On the other hand, by (aInc)1 we conclude that s\frac{{\varphi}(t)}{Lt} {\leqslant} {\varphi}(s) when s > t , so the second term is non-positive and the inequality is established.

    If {\varphi}\in \Phi_{\rm{w}} is differentiable, then

    \begin{equation*} \frac d{dt}\frac{{\varphi}(t)}{t^p} = \frac{{\varphi}'(t) t^p- p t^{p-1}{\varphi}(t)}{t^{2p}} = \frac{{\varphi}(t)}{t^{p+1}}\bigg[ \frac{t{\varphi}'(t)}{{\varphi}(t)} - p \bigg]. \end{equation*}

    Thus {\varphi} satisfies {\left({{\rm{Inc}}} \right)_p} if and only if \frac{t{\varphi}'(t)}{{\varphi}(t)} {\geqslant} p . Similarly, {\varphi} satisfies {\left({{\rm{Dec}}} \right)_q} if and only if \frac{t{\varphi}'(t)}{{\varphi}(t)} {\leqslant} q . It also follows that if {\varphi} satisfies {\left({{\rm{Inc}}} \right)_p} and {\left({{\rm{Dec}}} \right)_q}, then

    \begin{equation} \tfrac{1}{q} t{\varphi}'(t) {\leqslant} {\varphi}(t){\leqslant} \tfrac{1}{p} t{\varphi}'(t) \end{equation} (3.2)

    and so {\varphi}' satisfies {{\rm{(aInc)}}_{p - 1}} and {\left({{\rm{aDec}}} \right)_{q - 1}}. We next show that the last claim holds even if only (aInc) or (aDec) is assumed of {\varphi} which is convex but not necessarily differentiable.

    For {\varphi} \in \Phi_{\rm{c}} we denote the left and right derivative by {\varphi}_-' and {\varphi}_+' , respectively. We define the left derivative to be zero at the origin, i.e., {\varphi}_-'(0): = 0 . Assume that {\varphi} satisfies {{\rm{(aInc)}}_{{p}}} with p > 1 , and let t_0 > 0 be such that {\varphi}(t_0) < \infty . Then

    {\varphi}'_+(0) = \lim\limits_{t\to 0^+} \frac{{\varphi}(t)}{t} {\leqslant} \lim\limits_{t\to 0^+} L_p t^{p-1} \frac{{\varphi}(t_0)}{t_0^p} = 0.

    Since {\varphi}_+' is right-continuous we also obtain that

    \lim\limits_{t\to 0^+}{\varphi}_+'(t) = {\varphi}_+'(0) = 0.

    Lemma 3.3. Let {\varphi} \in \Phi_{\mathit{\rm{c}}} satisfy {{\rm{(aInc)}}_{{p}}} and {{\rm{(aDec)}}_q} with constants L_p and L_q , respectively. Then

    \frac{1}{(L_qe-1) q} t{\varphi}'_+(t) {\leqslant} {\varphi}(t) {\leqslant} \frac{2\ln(2L_p)}p t{\varphi}'_-(t)

    for every t{\geqslant} 0 , and {\varphi}'_- and {\varphi}'_+ satisfy {{\rm{(aInc)}}_{p - 1}} and {\left({{\rm{aDec}}} \right)_{q - 1}}, with constants depending only on \frac{q}{p} , L_p and L_q .

    Proof. Since {\varphi} is convex we have

    {\varphi}(t) = \int_0^t {\varphi}'_+ (\tau) \, d \tau = \int_0^t {\varphi}'_- (\tau) \, d \tau,

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

    {\varphi}(t)-{\varphi}(rt) = \int_{rt}^t {\varphi}'_- (\tau) \, d \tau {\leqslant} (t- rt) {\varphi}'_- (t).

    Thus

    t{\varphi}'_- (t) {\geqslant} \frac{{\varphi}(t)-{\varphi}(rt)}{1-r} {\geqslant} {\varphi}(t)\frac{1-L_pr^p}{1-r}

    where in the second inequality we used {{\rm{(aInc)}}_{{p}}} of {\varphi} . Choosing r : = (2L_p)^{-1/p} we get

    \frac{1-L_pr^p}{1-r} = \frac{1/2}{1-(2L_p)^{-1/p}} = \frac{p}{2p(1-(2L_p)^{-1/p})}.

    Writing h : = \frac{1}{p} and x: = 2L_p , we find that

    p\left(1-(2L_p)^{-1/p}\right) = \frac{1-x^{-h}}{h} {\leqslant} \left.\frac{dx^s}{ds}\right|_{s = 0} = \ln x,

    where the inequality follows from convexity of s \mapsto x^s . Thus t{\varphi}'_- (t) {\geqslant} \frac{p}{2\ln (2L_p)}{\varphi}(t) .

    Let R > 1 . Since {\varphi}_+' is increasing, we obtain

    {\varphi}(Rt)-{\varphi}(t) = \int_{t}^{Rt} {\varphi}'_+(\tau) \, d \tau {\geqslant} (Rt -t) {\varphi}'_+ (t).

    Thus

    t{\varphi}'_+ (t) {\leqslant} \frac{{\varphi}(Rt)-{\varphi}(t)}{R-1} {\leqslant} {\varphi}(t)\frac{L_qR^q-1}{R-1}

    where {{\rm{(aDec)}}_q} of {\varphi} was used in the second inequality. With R : = 1+\frac{1}{q} we get

    t{\varphi}'_+(t) {\leqslant} \frac{L_q(1+\frac{1}{q})^q-1}{1/q} {\varphi}(t) {\leqslant} q(L_qe-1){\varphi}(t).

    We have established the inequality of the claim.

    We abbreviate c_q: = \frac1{L_qe-1} and c_p: = 2\ln(2L_p) . Since {\varphi} is convex, we have {\varphi}_-' {\leqslant} {\varphi}_+' and so

    \frac{c_q} q t{\varphi}'_-(t) {\leqslant} \frac{c_q} q t{\varphi}'_+(t) {\leqslant} {\varphi}(t) {\leqslant} \frac{c_p}p t{\varphi}'_-(t) {\leqslant} \frac{c_p}p t{\varphi}'_+(t)

    Thus we obtain by {{\rm{(aDec)}}_q} of {\varphi} for 0 < s < t that

    \frac{{\varphi}'_+(t)}{t^{q-1}} {\leqslant} \frac{q}{c_q} \frac{{\varphi}(t)}{t^{q}} {\leqslant} \frac{q L_q}{c_q} \frac{{\varphi}(s)}{s^{q}} {\leqslant} \frac qp\frac{L_q c_p}{c_q} \frac{{\varphi}'_+(s)}{s^{q-1}}

    and {\left({{\rm{aDec}}} \right)_{q - 1}} of {\varphi}'_+ follows. The proof for {{\rm{(aInc)}}_{p - 1}} is similar as are the proofs for {\varphi}'_- .

    Before Lemma 3.3 we noted that \lim_{t\to 0^+} {\varphi}_+'(x, t) = 0 , and hence

    \lim\limits_{|y|\to 0} \frac{{\varphi}_+'(x,|y|)}{|y|} y\cdot z = \Big(\lim\limits_{|y|\to 0^+} {\varphi}_+'(x,|y|)\Big) \Big(\lim\limits_{|y|\to 0^+} \frac{y}{|y|}\cdot z\Big) = 0

    for z\in \mathbb{R}^n . In light of this, we define

    \begin{equation*} \frac{{\varphi}_+'(x,|\nabla u|)}{|\nabla u|}\nabla u \cdot \nabla h : = 0 \qquad{\rm{when }}\;\nabla u = 0. \end{equation*}

    Theorem 3.4. Let {\varphi} \in \Phi_{\mathit{\rm{c}}}(\Omega) satisfy {{\rm{(aInc)}}_{{p}}} and {{\rm{(aDec)}}_q} with 1 < p {\leqslant} q . Denote {\varphi}'_h : = {\varphi}_+'\, \chi_{\{\nabla u \cdot \nabla h {\geqslant} 0\}} + {\varphi}_-'\, \chi_{\{\nabla u \cdot \nabla h < 0\}} . If u \in W_{{{{\rm{loc}}}}}^{1, {\varphi}}(\Omega) , then the following are equivalent:

    (i) u is a local minimizer;

    (ii) \int_{{\rm{supp}}\; h} \frac{{\varphi}'_h(x, |\nabla u|)}{|\nabla u|} \nabla u \cdot \nabla h \, dx {\geqslant} 0 for every h \in W^{1, {\varphi}}(\Omega) with {\rm{supp}}\; h \Subset \Omega .

    Proof. Let h \in W^{1, {\varphi}}(\Omega) with E : = {\rm{supp}}\; h \Subset \Omega be arbitrary. Define g: \Omega \times [0, 1] \to [0, \infty] by g(x, \varepsilon) : = |\nabla (u(x) + \varepsilon h(x))| ; in the rest of the proof we omit the first variable and abbreviate g(x, \varepsilon) by g(\varepsilon) .

    Note that g(\varepsilon)^2 = |\nabla u(x)|^2+ \varepsilon^2|h(x)|^2+2 \varepsilon \nabla u(x)\cdot \nabla h(x) and g{\geqslant} 0 . Thus in [0, 1] the function g has a local minimum at zero for x\in\Omega with \nabla u(x)\cdot \nabla h(x){\geqslant} 0 and a maximum otherwise. This determines whether we obtain the right- or left-derivative and so

    \begin{equation} \lim\limits_{ \varepsilon\to 0^+} \frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon} = {\varphi}'_h(x, g(0))g'(0) = \frac{{\varphi}'_h(x,|\nabla u|)}{|\nabla u|} \nabla u \cdot \nabla h \end{equation} (3.5)

    for almost every x \in E .

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

    \Big|\frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon}\Big| {\leqslant} {\varphi}_+'(x,\max \{ g( \varepsilon),g(0)\})\frac{|g( \varepsilon)-g(0)|}{ \varepsilon}

    for a.e. x\in E . Since \varepsilon \in [0, 1] we have

    \max \{ g( \varepsilon),g(0)\} {\leqslant} \max\{|\nabla u| + \varepsilon |\nabla h| , |\nabla u|\} {\leqslant} |\nabla u| + |\nabla h|.

    By the triangle inequality,

    \Big|\frac{g( \varepsilon) - g(0)}{ \varepsilon}\Big| = \Big|\frac{|\nabla u + \varepsilon\nabla h|-|\nabla u|}{ \varepsilon}\Big| {\leqslant} |\nabla h|{\leqslant} |\nabla u| + |\nabla h|.

    Combining the estimates above, we find that

    \Big|\frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon}\Big| {\leqslant} {\varphi}_+'(x, |\nabla u| + |\nabla h|)(|\nabla u| + |\nabla h|).

    By Lemma 3.3, {\varphi}_+'(x, t)t\lesssim{\varphi}(x, t) for every t {\geqslant} 0 , so that

    {\varphi}_+'(x, |\nabla u| +|\nabla h|)(\nabla u| + |\nabla h|) \lesssim {\varphi}(x, |\nabla u| + |\nabla h|).

    By (aDec),

    {\varphi}(x,|\nabla u| + |\nabla h|) {\leqslant} {\varphi}(x,2|\nabla u|) + {\varphi}(x,2|\nabla h|) {\leqslant} L_q2^q({\varphi}(x,|\nabla u|) + {\varphi}(x,|\nabla h|))\quad\rm{a.e.}

    Combining the estimates, we find that

    \Big|\frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon}\Big| \lesssim {\varphi}(x,|\nabla u|) + {\varphi}(x,|\nabla h|) \quad\rm{a.e.}

    The right hand side is integrable by [30,Lemma 3.1.3(b)], since |\nabla u|, |\nabla h| \in L^{\varphi}(\Omega) and {\varphi} satisfies (aDec). Thus we have found a majorant. By dominated convergence and (3.5), we find that

    \begin{equation} \int_E \frac{{\varphi}'_h(x,|\nabla u|)}{|\nabla u|} \nabla u \cdot \nabla h \,dx = \lim\limits_{ \varepsilon\to 0^+} \int_E \frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon} \,dx. \end{equation} (3.6)

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

    \begin{equation*} \int_E \frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon} \,dx {\geqslant} 0 \end{equation*}

    for \varepsilon \in (0, 1] , and hence (ⅱ) follows by (3.6).

    Let us then show that (ⅱ) implies (ⅰ). For \theta \in [0, 1] and s, t {\geqslant} 0 we have

    \begin{split} g(\theta t + (1-\theta)s) & = |\theta \nabla u + \theta t \nabla h + (1-\theta) \nabla u + (1-\theta) s \nabla h |\\ &{\leqslant} |\theta \nabla u + \theta t \nabla h |+ |(1-\theta) \nabla u + (1-\theta) s \nabla h | = \theta g(t) + (1-\theta) g(s), \end{split}

    so g(\varepsilon) is convex. Since t \mapsto {\varphi}(x, t) and g(\varepsilon) are convex for almost every x \in E , and t \mapsto {\varphi}(x, t) is also increasing, the composed function t \mapsto {\varphi}(x, g(t)) is convex for a.e. x \in E . Thus

    \int_E {\varphi}(x,g(1)) - {\varphi}(x,g(0)) \,dx {\geqslant} \int_E \frac{{\varphi}(x,g( \varepsilon)) - {\varphi}(x,g(0))}{ \varepsilon} \,dx.

    Since the above inequality holds for every \varepsilon \in (0, 1) , (3.6) implies that

    \int_E {\varphi}(x,g(1)) - {\varphi}(x,g(0)) \,dx {\geqslant} \int_E \frac{{\varphi}'_h(x,|\nabla u|)}{|\nabla u|} \nabla u \cdot \nabla h \,dx {\geqslant} 0,

    which is (i).

    We conclude the section by improving the Caccioppoli inequality from [8]; in this paper we only need the special case \ell = 1 and s = q , but we include the general formulation for possible future use. We denote by \eta a cut-off function in B_R , more precisely, \eta \in C_0^{\infty}(B_R) , \chi_{B_{\sigma R}} {\leqslant} \eta {\leqslant} \chi_{B_R} and |\nabla \eta| {\leqslant} \frac{2}{(1-\sigma)R} , where \sigma \in (0, 1) . Note that the auxiliary function \psi is independent of x in the next lemma. Later on we will choose \psi to be a regularized version of {\varphi}_B^+ . Note also that the constant in the lemma is independent of q_1 .

    Lemma 3.7 (Caccioppoli inequality). Suppose {\varphi} \in \Phi_{\mathit{\rm{c}}}(\Omega) satisfies {{\rm{(aInc)}}_{{p}}} and {{\rm{(aDec)}}_q} with constants L_p and L_q , and let \psi\in \Phi_{\mathit{\rm{w}}} be differentiable and satisfy {\rm{(A0)}}, {\left( {{\rm{Inc}}} \right)_{p1}} and {\left( {{\rm{Dec}}} \right)_{q1}}, p_1, q_1{\geqslant} 1 . Let \beta\in (0, 1] be the constant from {\rm{(A0)}} of \psi . If u is a non-negative local minimizer and \eta is a cut-off function in B_R\subset \Omega , then

    \begin{align*} \int_{B_R} {\varphi}(x, |\nabla u|) \psi( \tfrac{u+R}{\beta R} )^{-\ell} \eta^s \, dx {\leqslant} K \int_{B_R} \psi (\tfrac{u+R}{\beta R} )^{-\ell} {\varphi}\big(x,K\, \tfrac{u+R}{\beta R} \big) \eta^{s-q} \, dx \end{align*}

    for any \ell > \frac1{p_1} and s{\geqslant} q , where K : = \frac{8s q (L_qe-1)L_q\ln(2L_p)}{p (p_1\ell- 1)(1-\sigma)} + L_p .

    Proof. Let us simplify the notation by writing \tilde u : = u + R and v : = \frac{\tilde u}{\beta R} . Since \nabla u = \nabla \tilde u , we see that \tilde u is still a local minimizer. By {\rm{(A0)}} of \psi and v{\geqslant} \frac1\beta , we have 0{\leqslant} \psi(v)^{-\ell} {\leqslant} 1 .

    We would like to use Theorem 3.4 with h : = \psi(v)^{-\ell} \eta^{s} \tilde u . Let us first check that h is a valid test function for a local minimizer, that is h \in W^{1, {\varphi}}(B_R) and has compact support in B_R \subset \Omega . As \tilde u \in L^{{\varphi}}(B_R) and |h|{\leqslant} \tilde u , it is immediate that h \in L^{{\varphi}}(B_R) . By a direct calculation,

    \begin{align*} \begin{split} \nabla h & = -\ell \psi(v)^{-\ell-1} \eta^{s} \tilde u \psi'(v) \nabla v + s \psi(v)^{-\ell} \eta^{s-1} \tilde u \nabla \eta + \psi(v)^{-\ell} \eta^s \nabla \tilde u. \end{split} \end{align*}

    Note that \tilde u \nabla v = v \nabla \tilde u . Since \psi is differentiable we may use (3.2) to get

    \begin{align*} \big|\ell \psi(v)^{-\ell-1} \eta^{s} \psi'(v) v \nabla \tilde u \big| {\leqslant} \ell \psi(v)^{-\ell-1} q_1 \psi(v) | \nabla \tilde u| {\leqslant} q_1\ell |\nabla \tilde u| \in L^{{\varphi}}(B_R). \end{align*}

    For the third term in \nabla h , we obtain |\psi(v)^{-\ell} \eta^{s} \nabla \tilde u | {\leqslant} |\nabla \tilde u| \in L^{{\varphi}}(B_R) . The term with \nabla \eta is treated as h itself. Thus h \in W^{1, {\varphi}}(B_R) . Since s > 0 and \eta\in C^\infty_0(B_R) , h has compact support in B_R \subset \Omega and so it is a valid test-function for a local minimizer.

    We next calculate

    \begin{align*} \nabla \tilde u \cdot \nabla h & = -\psi(v)^{-\ell-1} \eta^{s} [\ell \psi'(v) v - \psi(v)] |\nabla \tilde u|^2 + s\psi(v)^{-\ell} \eta^{s-1} \tilde u \, \nabla\tilde u \cdot \nabla \eta . \end{align*}

    The inequality p_1 \psi(t) {\leqslant} \psi'(t) t from (3.2) implies that \ell \psi'(v) v - \psi(v) {\geqslant} (p_1\ell- 1)\psi(v) > 0 . Since \tilde u is a local minimizer, we can use the implication (i) \Rightarrow (ii) of Theorem 3.4 to conclude that

    \begin{align*} [p_1\ell- 1] \int_{B_R} {\varphi}'_h(x, |\nabla \tilde u|)|\nabla \tilde u| \psi(v)^{-\ell} \eta^{s} \, dx {\leqslant} s \int_{B_R} {\varphi}'_h(x, |\nabla \tilde u|) \psi(v)^{-\ell}\tilde u \,|\nabla \eta| \, \eta^{s-1} \, dx. \end{align*}

    Since {\varphi}'_-{\leqslant}{\varphi}'_h{\leqslant}{\varphi}'_+ , we obtain \frac{1}{q(L_qe-1))} t{\varphi}'_h(x, t) {\leqslant} {\varphi}(x, t) {\leqslant} \frac{2 \ln(2L_p)}p t{\varphi}'_h(x, t) from Lemma 2. Using also |\nabla \eta|\, \tilde u{\leqslant} \frac 2{1-\sigma}v , we have

    \begin{align*} & \int_{B_R} {\varphi}(x, |\nabla \tilde u|) \psi(v)^{-\ell} \eta^{s}\, dx {\leqslant} \frac{4 s q (L_qe-1)\ln(2L_p)}{p (p_1\ell- 1)(1-\sigma)} \int_{B_R} \frac{{\varphi}(x, |\nabla \tilde u|)}{|\nabla \tilde u|}\, \eta^{s-1}\psi(v)^{-\ell}v \, dx, \end{align*}

    Note that the constant in front of the integral can be estimated from above by \frac K{2L_q} .

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

    \begin{equation*} \frac{{\varphi}(x, |\nabla \tilde u|)}{|\nabla \tilde u|} v {\leqslant} {\varphi}\big(x, \varepsilon^{-\frac1{q'}}L_p v\big) + {\varphi}^*\big(x, \varepsilon^{\frac1{q'}} L_p^{-1}\tfrac{{\varphi}(x, |\nabla \tilde u|)}{|\nabla \tilde u|}\big), \end{equation*}

    where \frac1q + \frac1{q'} = 1 . We choose \varepsilon: = \frac{L_p}{K}\eta(x)\in (0, 1] and use (aInc)q' of {\varphi}^* [30,Proposition 2.4.9] (which holds with constant L_q ) and Lemma 3.1 to obtain

    \begin{split} {\varphi}^*\big(x, \varepsilon^{\frac1{q'}} L_p^{-1}\tfrac{{\varphi}(x, |\nabla \tilde u|)}{|\nabla \tilde u|}\big) {\leqslant} L_q \varepsilon {\varphi}^*\big(x, \tfrac{{\varphi}(x, |\nabla \tilde u|)}{L_p |\nabla \tilde u|}\big) &{\leqslant} \tfrac{L_q \varepsilon}{L_p}{\varphi}(x,|\nabla u|) = \tfrac{L_q}{K}\eta(x) {\varphi}(x,|\nabla u|). \end{split}

    In the other term we estimate \varepsilon^{-\frac1{q'}}L_p {\leqslant} K^{1-\frac1{q'}} L_p^{\frac1{q'}} \varepsilon^{-\frac1{q'}} = \eta^{-\frac1{q'}}K and use {{\rm{(aDec)}}_q} of {\varphi} :

    {\varphi}\big(x, \varepsilon^{-\frac1{q'}}L_p v\big) {\leqslant} L_q \eta^{1-q} {\varphi}\big(x, K v\big).

    With these estimates we obtain that

    \begin{align*} &\int_{B_R} \!{\varphi}(x, |\nabla \tilde u|) \psi(v)^{-\ell} \eta^{s} \, dx {\leqslant} \frac{1}{2}\! \int_{B_R} \!{\varphi}(x, |\nabla \tilde u|) \psi(v)^{-\ell} \eta^{s} \, dx + \frac K2 \! \int_{B_R}\! \psi(v)^{-\ell} {\varphi}(x, Kv) \eta^{s-q} \, dx. \end{align*}

    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 {{\rm{(aDec)}}_q} is only needed in the set \nabla \eta\ne 0 since we can improve the estimate on the right-hand side integral to |\nabla \eta|\, \tilde u{\leqslant} \frac 2{1-\sigma}v \chi_{\{\nabla \eta\ne 0\}} and only drop the characteristic function in the final step.

    The following definition is like [8,Definition 3.1], except {\varphi}_{B_r}^+ has replaced {\varphi}_{B_r}^- . Furthermore, we are more precise with our estimates so as to avoid dependence on p and q .

    Definition 4.1. Let {\varphi} \in \Phi_{\rm{w}}(B_r) satisfy {{\rm{(aInc)}}_{{p}}} with p{\geqslant} 1 and constant L_p . We define \psi_{B_r}: B_r \to [0, \infty] by setting

    \psi_{B_r}(t) : = \int_0^t \tau^{p-1} \sup\limits_{s \in (0,\tau]} \frac{{\varphi}_{B_r}^+(s)}{s^p} \,d\tau \quad\rm{for }t {\geqslant} 0.

    It is easy to see that \psi_{B_r}\in \Phi_{\rm{w}} . Using that {\varphi} is increasing for the lower bound and {{\rm{(aInc)}}_{{p}}} for the upper bound, we find that

    \begin{equation} \ln(2) {\varphi}_{B_r}^+\Big{(}\frac{t}{2}\Big{)} = \int_{t/2}^t \tau^{p-1} \frac{{\varphi}_{B_r}^+(t/2)}{\tau^p} \,d\tau {\leqslant} \psi_{B_r}(t) {\leqslant} \int_0^t t^{p-1} L_p \frac{{\varphi}_{B_r}^+(t)}{t^p} \,d\tau = L_p{\varphi}_{B_r}^+(t). \end{equation} (4.2)

    As in [8,Definition 3.1], we see that \psi_{B_r} is convex and satisfies {\left({{\rm{Inc}}} \right)_p}. If {\varphi} satisfies {\rm{(A0)}}, so does \psi_{B_r} , since {\varphi}^+_{B_r}\simeq\psi_{B_r} . If {\varphi} satisfies {{\rm{(aDec)}}_q}, then \psi_{B_r} is strictly increasing and satisfies {{\rm{(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 L_p , L_q and \frac qp .

    Theorem 4.3 (Bloch-type estimate). Let {\varphi} \in \Phi_{\mathit{\rm{c}}}(\Omega) satisfy {\rm{(A0)}} and (A1). Let B_{2r}\subset \Omega with r{\leqslant} 1 and {\varphi}|_{B_{2r}} satisfy {{\rm{(aInc)}}_{{p}}} and {{\rm{(aDec)}}_q} with p, q\in[n, \infty) . If u is a non-negative local minimizer, then

    \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|) .

    Proof. Let us first note that {\varphi} satisfies {\left( {{\rm{aInc}}} \right)_n} with the constant L_p . Let \beta be the smaller of the constants from {\rm{(A0)}} and (A1). Denote v : = \frac{u+2r}{2\beta r} and \gamma: = \frac{2K}\beta , where K is from Caccioppoli inequality (Lemma 3.7) with \ell = 1 , s = q and \sigma = \frac{1}{2} . Since p{\geqslant} n , we see that

    K {\leqslant} \frac{16 q^2 (L_qe-1)L_q\ln(2L_p)}{p(p-1)} + L_p {\leqslant} 16 (L_qe-1)L_q\ln(2L_p) \Big(\frac{q}{p}\Big)^2 \frac{n}{n-1} + L_p.

    When |\nabla u| > \gamma v , we use {\left( {{\rm{aInc}}} \right)_n} to deduce that

    \frac{{\varphi}_{B_{2r}}^-(\gamma v)}{v^n} {\leqslant} \gamma^n\frac{{\varphi}(x, \gamma v)}{(\gamma v)^n} {\leqslant} \gamma^n L_p\frac{{\varphi}(x,|\nabla u|)}{|\nabla u|^n}

    for a.e. x \in B_r . Rearranging gives \frac{|\nabla u|^n}{v^n}\lesssim \frac{{\varphi}(x, |\nabla u|)}{{\varphi}_{B_{2r}}^-(\gamma v)} . Since v {\geqslant} \frac1\beta and \gamma {\geqslant} 1 , we obtain by {\rm{(A0)}} that {\varphi}_{B_{2r}}^-(\gamma v){\geqslant} 1 . If also {\varphi}_{B_{2r}}^-(\gamma v) {\leqslant} \frac{1}{|B_{2r}|} , then {\varphi}_{B_{2r}}^+(\beta \gamma v) {\leqslant} {\varphi}_{B_{2r}}^-(\gamma v) by (A1). Otherwise, ({\varphi}_{B_{2r}}^-(\gamma v))^{-1} {\leqslant} |B_{2r}| . In either case,

    \frac{|\nabla u|^n}{v^n} \lesssim \frac{{\varphi}(x,|\nabla u|)}{{\varphi}_{B_{2r}}^-(\gamma v)} \lesssim {\varphi}(x,|\nabla u|) \Big(\frac{1}{{\varphi}_{B_{2r}}^+(\beta\gamma v)} + |B_{2r}|\Big)

    for a.e. x\in B_r . When |\nabla u| {\leqslant} \gamma v , we use the estimate \frac{|\nabla u|^n}{v^n}{\leqslant} \gamma^n instead. Since u+r {\geqslant} \frac{1}{2}(u+2r) = \beta r v , we obtain that

    \begin{split} & \int_{B_r} |\nabla\log(u+r)|^n \,dx =\\ & \int_{B_r} \frac{|\nabla u|^{n}}{(u+r)^n} \,dx {\leqslant} \frac{1}{(\beta r)^n}\int_{B_r} \frac{|\nabla u|^{n}}{v^n} \,dx \\ &\lesssim \mathit{{\rlap{-} \smallint }}_{B_r} \frac{{\varphi}(x,|\nabla u|)}{{\varphi}_{B_{2r}}^+(\beta\gamma v)} + |B_{2r}|\, {\varphi}(x,|\nabla u|) +1 \,dx\\ & = \mathit{{\rlap{-} \smallint }}_{B_r} \frac{{\varphi}(x,|\nabla u|)}{{\varphi}_{B_{2r}}^+(\beta\gamma v)} \,dx +2^n{\varrho}_{\varphi}(|\nabla u|)+1. \end{split}

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

    Let \psi_{B_{2r}} be as in Definition 4.1, let \eta \in C_0^\infty(B_{2r}) be a cut-off function such that \eta = 1 in B_r and choose \psi(t): = \psi_{B_{2r}}(\beta\gamma t) . Then

    \mathit{{\rlap{-} \smallint }}_{B_r} \frac{{\varphi}(x,|\nabla u|)}{{\varphi}_{B_{2r}}^+(\beta\gamma v)} \,dx \lesssim \mathit{{\rlap{-} \smallint }}_{B_{2r}} \frac{{\varphi}(x,|\nabla u|)}{{\varphi}_{B_{2r}}^+(\beta\gamma v)} \eta^q \,dx {\leqslant} L_p\mathit{{\rlap{-} \smallint }}_{B_{2r}} \frac{{\varphi}(x,|\nabla u|)}{\psi(v)} \eta^q \,dx,

    where the second inequality follows from (4.2). We note that \psi satisfies \left( {{\rm{A}}0} \right), {\left({{\rm{Inc}}} \right)_p} and \left( {{\rm{Dec}}} \right). Now we use the Caccioppoli inequality (Lemma 3.7) for {\varphi} and \psi with \ell = 1 , s = q and \sigma = \frac{1}{2} to get

    \mathit{{\rlap{-} \smallint }}_{B_{2r}} \frac{{\varphi}(x,|\nabla u|)}{\psi(v)} \eta^q\,dx {\leqslant} K \mathit{{\rlap{-} \smallint }}_{B_{2r}} \frac{{\varphi}(x,K v)}{\psi(v)} \,dx {\leqslant} \ln(2) K;

    the last inequality holds by (4.2) and \gamma = \frac{2K}\beta since

    \frac{{\varphi}(x,K v)}{\psi(v)} = \frac{{\varphi}(x,K v)}{\psi_{B_{2r}}(\beta\gamma v)} {\leqslant} \ln(2) \frac{{\varphi}(x,K v)}{{\varphi}_{B_{2r}}^+(\frac12\beta\gamma v)}{\leqslant} \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 {\varphi}\in \Phi_{\rm{w}}(\Omega) is positive if {\varphi}(x, t) > 0 for every t > 0 and a.e. x\in\Omega . If {\varphi} satisfies {\left( {{\rm{aDec}}} \right)_{q(\cdot)}} for q < \infty a.e., then it is positive.

    Lemma 4.4. If {\varphi}\in \Phi_{\mathit{\rm{w}}}(\Omega) is positive, then every continuous local minimizer is monotone in the sense of Lebesgue.

    Proof. Let u\in W_{{{{\rm{loc}}}}}^{1, {\varphi}}(\Omega)\cap C(\Omega) be a local minimizer and D\Subset\Omega . Fix M > \max_{\partial D} u and note that (u-M)_+ is zero in some neighborhood of \partial D since u is continuous. Thus h: = (u-M)_+ \chi_D belongs to W^{1, {\varphi}}(\Omega)\cap C(\Omega) and has compact support in \Omega . Using that u is a local minimizer, we obtain that

    \int_{{\rm{supp}}\; h}{\varphi}(x,|\nabla u|)\,dx {\leqslant} \int_{{\rm{supp}}\; h}{\varphi}(x,|\nabla (u-h)|)\,dx = 0.

    Since {\varphi}(x, t) > 0 for every t > 0 and a.e. x\in\Omega , it follows that \nabla u = 0 a.e. in {\rm{supp}}\; h . Thus \nabla h = 0 a.e. in \Omega . Since h is continuous and equals 0 in \Omega\setminus D , we conclude that h\equiv 0 .

    Hence u{\leqslant} M in D . Letting M\to \max_{\partial D} u , we find that u{\leqslant} \max_{\partial D} u . The proof that \min_{\partial D} u{\leqslant} u in D is similar.

    For x \in \Omega we write r_x : = \frac12 {\rm{dist}}(x, \partial \Omega) . 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.



    [1] V. Hárs and J. Tóth, On the inverse problem of reaction kinetics, In M. Farkas, editor, Colloquia Mathematica Societatis János Bolyai, volume 30, pages 363-379. Qualitative Theory of Differential Equations, 1979.
    [2] C. P. P. Arceo, E. C. Jose, A. Marin-Sanguino, et al., Chemical reaction network approaches to biochemical systems theory, Math. Biosci., 269 (2015), 135-152.
    [3] F. Horn and R. Jackson, General mass action kinetics, Arch. Ratl. Mech. Anal., 47 (1972), 81-116.
    [4] M. Feinberg, Complex balancing in general kinetic systems, Arch. Ratl. Mech. Anal., 49 (1972), 187-194.
    [5] F. Horn, Necessary and sufficient conditions for complex balancing in chemical kinetics, Arch. Ratl. Mech. Anal., 49 (1972), 172-186.
    [6] A. I. Volpert and S. I. Hudyaev, Analyses in Classes of Discontinuous Functions and Equations of Mathematical Physics, Martinus Nijhoff Publishers, Dordrecht, 1985. Russian original: 1975.
    [7] D. F. Anderson, A proof of the global attractor conjecture in the single linkage class case, SIAM J. Appl. Math., 71 (2011), 1487-1508.
    [8] C. Pantea, On the persistence and global stability of mass-action systems, SIAM J. Math. Anal., 44 (2012), 1636-1673.
    [9] M. Gopalkrshnan, E. Miller and A. Shiu, A geometric approach to the global attractor conjecture, SIAM J. Appl. Dyn. Syst., 13 (2014), 758-797.
    [10] G. Craciun, F. Nazarov and C. Pantea, Persistence and permanence of mass-action and power-law dynamical systems, SIAM J. Appl. Math., 73 (2013), 305-329.
    [11] G. Craciun, Toric differential inclusions and a proof of the global attractor conjecture, arXiv:1501.02860, 2016.
    [12] R. Aris, Prolegomena to the rational analysis of systems of chemical reactions, Archive Ration. Mech. An., 19 (1965), 81-99.
    [13] R. Aris, Mathematical aspects of chemical reaction, IEEC Fundamentals, 61 (1969), 17-29.
    [14] M. Dukarić, H. Errami, R. Jerala, et al., On three genetic repressilator topologies, React. Kinet. Mech. Cat., 126 (2019), 3-30.
    [15] D. Lichtblau, Symbolic analysis of multiple steady states in a MAPK chemical reaction network, J. Symb. Comp., 2018. submitted.
    [16] B. Boros, On the existence of positive steady states for weakly reversible mass-action systems, SIAM J. Math. Anal., 51 (2019), 435-449.
    [17] M. Feinberg, Foundations of Chemical Reaction Network Theory, Springer International Publishing, New York, 2019.
    [18] G. Craciun and P. Y. Yu, Mathematical analysis of chemical reaction systems, Isr. J. Chem., 50, 2018.
    [19] J. Tóth, A. L. Nagy and D. Papp, Reaction Kinetics: Exercises, Programs and Theorems, Mathematica for Deterministic and Stochastic Kinetics, Springer-Verlag, New York, 2018.
    [20] G. Lente, Deterministic kinetics in chemistry and systems biology: the dynamics of complex reaction networks, Springer, 2015.
    [21] M. Feinberg and F. J. M. Horn, Chemical mechanism structure and the coincidence of the stoichiometric and kinetic subspaces, Arch. Ratl. Mech. Anal., 66 (1977), 83-97.
    [22] G. Craciun and C. Pantea, Identifiability of chemical reaction networks, J. Math. Chem., 44 (2008), 244-259.
    [23] G. Craciun, J. Jin and P. Y. Yu, An efficient characterization of complex-balanced, detailedbalanced, and weakly reversible systems, SIAM J. Appl. Math., 2019. To appear.
    [24] P. Érdi and J. Tóth, Mathematical Models of Chemical Reactions. Theory and Applications of Deterministic and Stochastic models, Princeton University Press, Princeton, New Jersey, 1989.
    [25] G. Szederkényi, Comment on "identifiability of chemical reaction networks" by G. Craciun and C. Pantea, J. Math. Chem., 45 (2009), 1172-1174.
    [26] G. Szederkényi, Computing sparse and dense realizations of reaction kinetic systems, J. Math. Chem., 47 (2010), 551-568.
    [27] G. Szederkényi, K. M. Hangos and T. Péni, Maximal and minimal realizations of reaction kinetic systems: computation and properties, MATCH Commun. Math. Comput. Chem., 65 (2011), 309-332.
    [28] B. Ács, G. Szederkényi, Z. A. Tuza, et al., Computing linearly conjugate weakly reversible kinetic structures using optimization and graph theory, MATCH Commun. Math. Comput. Chem., 74 (2015), 489-512.
    [29] G. Ács, G. Szlobodnyik and G. Szederkényi, A computational approach to the structural analysis of uncertain kinetic systems, Comput. Physics Commun., 228 (2018), 83-95.
    [30] G. Szederkényi, A. Magyar and K. M. Hangos, Analysis and control of polynomial dynamic models with biological applications, Academic Press, London, San Diego, Cambridge, MA, Oxford, 2018.
    [31] J. Tóth, A formális reakciókinetika globális determinisztikus és sztochasztikus modelljéröl (On the global deterministic and stochastic models of formal reaction kinetics with applications), MTA SZTAKI Tanulmányok, 129 (1981), 1-166. In Hungarian.
    [32] G. Lipták, G. Szederkényi and K. M. Hangos, Computing zero deficiency realizations of kinetic systems, Syst. Control Lett., 81 (2015), 24-30.
    [33] G. Szederkényi and K. M. Hangos, Finding complex balanced and detailed balanced realizations of chemical reaction networks, J. Math. Chem., 49 (2011), 1163-1179.
    [34] M. Feinberg, Necessary and sufficient conditions for detailed balancing in mass action systems of arbitrary complexity, Chem. Eng. Sci., 44 (1989), 1819-1827.
    [35] V. N. Orlov and L. I. Rozonoer, The macrodynamics of open systems and the variational principle of the local potential II. Applications, J. Franklin Ins., 318 (1984), 315-347.
    [36] B. Joshi and A. Shiu, A survey of methods for deciding whether a reaction network is multistationary, Math. Model. Nat. Pheno., 10 (2015), 47-67.
    [37] G. Szederkényi, K. M. Hangos and Z. Tuza, Finding weakly reversible realizations of chemical reaction networks using optimization, MATCH Commun. Math. Comput. Chem., 67 (2012), 193-212.
    [38] M. D. Johnston, D. Siegel and G. Szederkényi, Computing weakly reversible linearly conjugate chemical reaction networks with minimal deficiency, Math. Biosci., 241 (2013), 88-98.
    [39] S. Schuster and R. Schuster, Detecting strictly detailed balanced subnetworks in open chemical reaction networks, J. Math. Chem., 6 (1991), 17-40.
    [40] M. D. Johnston, D. Siegel and G. Szederkényi, Dynamical equivalence and linear conjugacy of chemical reaction networks: new results and methods, MATCH Commun. Math. Comput. Chem., 68 (2012), 443-468.
    [41] M. D. Johnston, D. Siegel and G. Szederkényi, A linear programming approach to weak reversibility and linear conjugacy of chemical reaction networks, J. Math. Chem., 50 (2012), 274-288.
    [42] J. Rudan, G. Szederkényi, K. Hangos, et al., Polynomial time algorithms to determine weakly reversible realizations of chemical reaction networks, J. Math. Chem., 52 (2014), 1386-1404.
    [43] D. Csercsik, G. Szederkényi and K. M. Hangos, Parametric uniqueness of deficiency zero reaction networks, J. Math. Chem., 50 (2012), 1-8.
    [44] G. Craciun, J. Jin and P. Y. Yu, Uniqueness of kinetic realizations for weakly reversible deficiency zero networks, In preparation.
    [45] B. Boros and J. Hofbauer, Permanence of weakly reversible mass-action systems with a single linkage class, arXiv:1903.03071, 2019.
    [46] L. Cardelli, M. Tribastone and M. Tschaikowski, From electric circuits to chemical networks, arXiv:1812.03308, 2018.
    [47] D. Csercsik, G. Szederkényi and K. M. Hangos, Parametric uniqueness of deficiency zero reaction networks. J. Math. Chem., 50 (2012), 1-8.
    [48] J. Rudan, G. Szederkényi and K. M. Hangos, Efficient computation of alternative structures for large kinetic systems using linear programming, MATCH Commun. Math. Comput. Chem., 71 (2014), 71-92.
    [49] J. Rudan, G. Szederkényi, K. M. Hangos, et al., Polynomial time algorithms to determine weakly reversible realizations of chemical reaction networks, J. Math. Chem., (2014), 1-19.
    [50] G. Szederkényi, K. M. Hangos and D. Csercsik, Computing realizations of reaction kinetic networks with given properties, In A. N. Gorban and D. Roose, editors, Coping with Complexity: Model Reduction and Data Analysis, volume 75, pages 253-267. Springer, 2010.
    [51] J. Rudan, G. Szederkényi and K. M. Hangos, Computing dynamically equivalent realizations of biochemical reaction networks with mass conservation, In ICNAAM 2013: 11th International Conference of Numerical Analysis and Applied Mathematics, 21-27 September, Rhodes, Greece, AIP Conference Proceedings, volume 1558, pages 2356-2359, 2013. ISBN: 978-0-7354-1184-5.
  • This article has been cited by:

    1. Peter Hästö, Jihoon Ok, Regularity theory for non-autonomous problems with a priori assumptions, 2023, 62, 0944-2669, 10.1007/s00526-023-02587-3
    2. Michela Eleuteri, Petteri Harjulehto, Peter Hästö, Bounded variation spaces with generalized Orlicz growth related to image denoising, 2025, 310, 0025-5874, 10.1007/s00209-025-03731-9
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5631) PDF downloads(604) Cited by(14)

Figures and Tables

Figures(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog