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Research article

The new Topp-Leone exponentied exponential model for modeling financial data

  • We proposed in this article a new three-parameter distribution, which is referred as the Topp-Leone exponentiated exponential model is proposed. It is used in modeling claim and risk data applied in actuarial and insurance studies. The probability density function of the suggested distribution can be unimodel and positively skewed. Different distributional and mathematical properties of the TL-EE model were provided. Furthermore, we established a maximum likelihood estimation method for estimating the unknown parameters involved in the model, and some actuarial measures were calculated. Also, the potential of these actuarial statistics were provided via numerical simulation experiments. Finally, two real datasets of insurance losses were analyzed to prove the performance and superiority of the suggested model among all its competitors distributions.

    Citation: Hassan Alsuhabi. The new Topp-Leone exponentied exponential model for modeling financial data[J]. Mathematical Modelling and Control, 2024, 4(1): 44-63. doi: 10.3934/mmc.2024005

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  • We proposed in this article a new three-parameter distribution, which is referred as the Topp-Leone exponentiated exponential model is proposed. It is used in modeling claim and risk data applied in actuarial and insurance studies. The probability density function of the suggested distribution can be unimodel and positively skewed. Different distributional and mathematical properties of the TL-EE model were provided. Furthermore, we established a maximum likelihood estimation method for estimating the unknown parameters involved in the model, and some actuarial measures were calculated. Also, the potential of these actuarial statistics were provided via numerical simulation experiments. Finally, two real datasets of insurance losses were analyzed to prove the performance and superiority of the suggested model among all its competitors distributions.



    In its original formulation (see [54]) the Signorini problem in linear elastostatics consists in finding the equilibrium configuration of an elastic body Ω resting on a frictionless rigid support EΩ in its natural configuration and subject to body forces and surface forces acting on ΩE; precisely, if u:ΩR3 denotes the displacement field of the body, C represents the classical linear elasticity tensor and E denotes the linear strain tensor, we assume that

    Q(x,E):=12ETC(x)E

    is the corresponding strain energy density (see [27]) and that the body is subject to a load system of forces f:ΩR3 and g:ΩER3 such that

    L(u):=Ωfu dx+ΩEgu dH2 (1.1)

    is the load potential, where H2 is the two-dimensional Hausdorff measure. Assuming that H2(E)>0, the variational formulation of the Signorini problem consists in finding a minimizer of the functional

    E(u):=ΩQ(x,E(u))dxL(u) (1.2)

    among all u in the Sobolev space H1(Ω;R3) such that un0 H2-a.e. on E, where n is the inward unit vector normal to Ω. A classical result (see [22]) states that a solution of (1.2) exists if the following condition is verified: Every infinitesimal rigid displacement v fulfills L(v)0 if vn0 H2-a.e. on E and L(v)=0 if and only if vn0 H2-a.e. on E. Moreover if E is planar, that is EΩ{x3=0}, and if L(e3)<0, fC0,α(¯Ω;R3) and gL2(ΩE;R3) then a minimizer of (1.2) exists if and only if the above condition holds (see [22, Theorem XXXII] and [10]): In particular if Ω is the cylinder

    Ω:={x:(x1ax3)2+x22<R2, 0<x3<H},
    E:={x:x21+x22<R2, x3=0},

    with a0,R>0,H>0, and f=e3, g=0, then a minimum is attained if and only if aH<2R that is 0ϑ:=arctana<arctan2R/H where ϑ is the inclination of the cylinder with respect to the x3-axis.

    More recent formulations of constrained problems in the calculus of variations use the notion of capacity (see Section 2 for details) leading to consider more general geometries since any set of null capacity has null H2 measure (see [57, Theorem 4]) but there exist sets of null H2 measure and strictly positive capacity (see [1, Theorem 5.4.1]). Indeed, a proper generalization of the latter case is to assume that the set E{x30} has positive capacity and accordingly modify the obstacle condition by requiring x3+u3(x)0 on E up to a set of null capacity (shortly, q.e. on E): The existence of minimizers for this general setting was proved by [12, Theorem 4.5]. Although the original obstacle formulation given in [54] may look different from the generalized notion exploited in this work, it can be shown (see Remark 2.3) that if the set EΩ is regular in an appropriate sense (see Remark 2.3) then the two frameworks coincide.

    Like in the analysis of many problems in linear elastostatics, it is quite natural to ask whether there exists a sequence of functionals in finite elasticity whose minimizing sequences converge to a minimizer of (1.2), possibly under suitable compatibility conditions on the functional L: Such an approach provides a variational justification of the linearized theory and could help finding other reasonable models rigorously deduced.

    In this paper we show sharp conditions on L entailing that a wide class of energy functionals in finite elasticity fulfill this variational property in the context of obstacle problems; in addition we also show examples of loads leading to the failure of this convergence. In this perspective, denoting by y:ΩR3 the deformation field and by h>0 an adimensional parameter, we introduce a family of energy functionals defined by

    Fh(y):=h2ΩW(x,y(x))dxh1L(yx) (1.3)

    where L is defined as in (1.1) and W:Ω×R3×3[0,+] is the strain energy density. For every xΩ, the function W(x,) is assumed to be frame indifferent and attaining its minimum value 0 at rigid deformations only. We also assume that W is C2-regular in a neighborhood of rigid deformations and satisfies a natural coercivity condition, see (2.22).

    According to a standard approach in the deduction of linearized theories in continuum mechanics, if yh is a minimizing sequence of Fh (see (2.40)) in a class Ah of deformations satisfying a suitable obstacle constraint, we aim to investigate whether F(yh) converges, as h goes to 0, to the minimum of E (with C=D2W(x,I)) among displacements fulfilling u3(x)+x30 q.e. on E. Since here the aim is the deduction of the Signorini problem in linear elasticity, it is natural to assume that the unilateral constraint in nonlinear approximating problems takes the form x3+h1(yh,3x3)0 on E that is

    yh,3(1h)x3  on E. (1.4)

    We define the functionals Gh coupling the energies Fh with the unilateral constraint due to rigid obstacle:

    Gh(y)={Fh(y),  if  y3(1h)x3 on E, +,  else, (1.5)

    where E, the portion of the elastic body sensitive to the obstacle, has an horizontal projection with non negligible capacity. Moreover we have to assume that

    L(yx)0 (1.6)

    for every deformation y=y(x) fulfilling (1.4) and such that

    ΩW(x,y)dx=0. (1.7)

    On the contrary if y satisfied (1.4) and (1.7) but L(yx)>0 then

    Gh(y)=h1L(yx) as h0+.

    Under our assumptions on W, Eq (1.7) holds true if and only if y is rigid deformation, i.e., y(x)=Rx+c for some RSO(3) and cR3, while (1.4) is fulfilled by these y if and only if (Rx)3+c3(1h)x3 on E, a condition which is satisfied for every h>0 if and only if

    c3((RI)x)3on E. (1.8)

    Thus, due to (1.6) we have to assume this geometrical compatibility between load and obstacle

    L((RI)x+c)0,RSO(3), cR3 verifying (1.8). (1.9)

    Nevertheless though (1.9) entails the compatibility assumptions of Theorem 4.5 in [12] and though compatibility assumptions of Theorem 4.5 in [12] entail the existence of minimizers of the Signorini functional in linear elasticity, these compatibility assumptions alone do not warrant the equiboundedness from below of approximating functionals Gh (see Example 3.6 below). In the main result of this paper (Theorem 2.4) we show that if L satisfies the necessary condition (1.9) together with L(e3)<0 and

    L((Rxx)1e1+(Rxx)2e2)0,RSO(3), (1.10)

    then, under some capacitary assumptions on E (see (2.13)), we have

    limh0(infGh)=minG, (1.11)

    where

    G(u):={ΩQ(x,E(u))dxmaxSL,EL(Ru),  if  x3+u30 on E,  +,  else,  (1.12)
    Q(x,F):=12FTD2W(x,I)F,FR3×3,xΩ,
    SL,E={RSO(3): L((RI)x)minxEess((Rx)3x3)L(e3)=0}.

    Along this paper Eess denotes the essential part of E with respect to the capacity (see (2.9)), a closed canonical subset of ¯E such that EEess has null capacity.

    Under the hypotheses detailed previously we will show (see Lemma 3.8) that either SL,E{I} or SL,E={RSO(3):Re3=e3}. If SL,E{I} then clearly GE, hence in this case the minimum of Signorini problem in linearized elasticity is the limit of the infGh but, quite surprisingly, the second alternative is much more subtle and indeed we are able to exhibit examples such that

    minG<minE, (1.13)

    namely a gap between limh0(infGh) and minE may appear (see Section 5). However the coincidence of minimizers of G and E may hold true even if SL,E is not reduced to the identity matrix: In particular, if Ω is contained in the upper half-space, E is either ¯Ω or Ω, the load

    L(v):=Ωfv3dx+Ωgv3dH2

    satisfies condition (1.9) and L(e3)<0, then L(v)=L(Rv) for every RSL,E hence minG=minE, say the energy of minimizing sequences for Gh converges to the minimum energy of E. On the other hand, it is always possible to rotate the external forces in such a way that GR and ER (the functionals obtained replacing the load functional L with LR defined by LR(v):=L(Rv)) have the same minimum as shown in Theorem 5.5.

    For several contributions facing issues strictly connected with the context of the present paper we refer to [3,4,5,6,8,9,11,13,14,15,16,17,18,19,21,23,25,26,27,28,29,32,33,34,35,36,37,38,39,40,41,42,43,46,47,48,49,50,51,52,53,55,56].

    We set a+:=max{a,0}, a:=max{a,0} for every aR; notations x=(x1,x2,x3) and y=(y1,y2,y3) represent generic points in R3; ej, j=1,2,3 denote the unitary basis vectors of R3, R3×3 is the set of 3×3 real matrices, endowed with the Euclidean norm |F|=Tr(FTF). R3×3sym (resp. R3×3skew) denotes the subset of symmetric (resp. skew-symmetric) matrices. For every FR3×3 we define symF:=12(F+FT), SO(3) will denote the special orthogonal group and for every RSO(3) there exist ϑ[0,2π] and aR3, |a|=1 such that the following Euler-Rodrigues representation formula holds

    Rx=x+sinϑ(ax)+(1cosϑ)(a(ax)),xR3. (2.1)

    For every compact set KRN we define the capacity of K by setting (see [1, Definition 2.2.1])

    capK=inf{w2H1(RN):wC0(RN),w1onK}. (2.2)

    If GRN is open we define (see [1, Definition 2.2.2])

    capG:=sup{capK:K compact, KG} (2.3)

    and, since (see [1, Proposition 2.2.3])

    capK=inf{capG:G open, KG},K compact, (2.4)

    we may extend the above definitions to an arbitrary set by setting (see [1, Definition 2.2.4])

    capE:=inf{capG:G open, EG},ERN. (2.5)

    A straightforward consequence of (2.3) and (2.5) is that

    capE1capE2, E1E2RN. (2.6)

    On the other hand, for every ERN the Bessel capacity is defined as (see [1, Definition 2.3.3])

    CapE:=inf{f2L2(RN):f0, a.e. onRN,(fg1)(x)1 xE}, (2.7)

    where g1L1(R3) is the first-order Bessel kernel in RN defined as the inverse Fourier transform of (1+|ξ|2)1/2, say

    g1(x):=(2π)NR3(1+|ξ|2)1/2eixξdξ=12π0t(N+1)/2eπ|x|2/tet/(4π)dt.

    Notice that since f0 a.e. we have that fg1 is everywhere defined if we allow it to take the value + (see [1, Definition 2.3.1]) and that fg1 is l.s.c. by Proposition 2.3.2 of [1]. Thus inequality (fg1)(x)1 for every xE appearing in formula (2.7) has a precise meaning.

    In addition it is possible to show that there exist two constants α,β>0 such that

    αCapEcapEβCapE,ERN, (2.8)

    (see [1, Definition 2.2.6 and Proposition 2.3.13]).

    A property is said to hold quasi-everywhere (q.e. for short) if it holds true outside a set of zero capacity. It is convenient to introduce (see [12]) a canonical representative of the set E, called the essential part of E and denoted by Eess, which nevertheless coincides with E itself whenever it is a smooth closed manifold or the closure of an open subset of RN.

    For every set ER3 we define the essential part Eess of E (with respect to the capacity) by

    Eess := {C: C is closed and cap(EC)=0}. (2.9)

    It has been shown in [12] that

    Eess  is a closed subset of ¯E, (2.10)
    cap(EEess)=0, (2.11)
    capE=0  if and only if Eess=. (2.12)

    In the following coA, affA, riA, rA and projA denote respectively, the closed convex hull of the set AR3 (say, the intersection of all convex sets containing A), the affine hull of the set A (say, the smallest affine space containing A), the relative interior of A (say, the interior part of A with respect to the affine hull of A), the relative boundary of A (say, the boundary of A with respect to the affine hull of A: riA=¯AriA) and the projection of A onto the horizontal plane {x3=0}.

    Throughout the paper we will assume that

    cap(proj(coEess))>0. (2.13)

    Notice that capE>0 does not imply (2.13) whereas the converse is true: Indeed, if capE=0 then by (2.12) we get Eess= so proj(coEess)= and cap(proj(coEess))=0, a contradiction to (2.13).

    In the following Ω will denote the reference configuration of an elastic body and it is always assumed to be a nonempty, bounded, connected, Lipschitz open set in R3. We need to show that any function in the Sobolev space H1(Ω;R3) actually has a precise representative defined quasi-everywhere on the whole ¯Ω with respect to the capacity. Indeed, if uuH1(Ω;R3) and vH1(R3;R3) is a Sobolev extension of uu, it is well known (see [1, Proposition 6.1.3]) that the limit

    v(x) := limr0 1|Br(x)|Br(x)v(ξ)dξ (2.14)

    exists for q.e. xR3. The function v is called the precise representative of v and is a quasicontinuous function in R3, that is to say, for every ε>0 there exists an open set VR3 such that capV<ε and v is continuous in R3V. We claim that if v1, v2 are two distinct Sobolev extensions of uu then

    v1(x) = v2(x),q.e. x¯Ω. (2.15)

    The claim is trivial for q.e. xΩ, thus we are left to show (2.15) for q.e. xΩ.

    Ler R>0 such that ¯ΩBR(0) and let ΩR:=BR(0)¯Ω. Since Ω has Lipschitz boundary it is well known (see [2]) that

    limr0|Br(x)ΩR||Br(x)|=12 (2.16)

    and for H2 a.e. xΩ,

    uu(x)=limr02|Br(x)|Br(x)ΩRv1(ξ)dξ=limr0 2|Br(x)|Br(x)ΩRv2(ξ)dξ,H2a.e.xΩ, (2.17)

    where we have denoted again with uu the trace of uu on Ω. Hence

    1|Br(x)|Br(x)ΩR(v1(ξ)v2(ξ))dξ=0,H2a.e. xΩ,

    so, by taking account ΩΩR and by recalling that Ω is Lipschitz, we may apply Theorem 2.1 of [20] to v1v2H1(ΩR;R3) and we get

    1|Br(x)|Br(x)ΩR(v1(ξ)v2(ξ))dξ=0, q.e. xΩ.

    Since v1=v2=uu a.e. in Br(x)ΩR the claim follows easily by (2.14). Therefore if uuH1(Ω;R3) we may define its precise representative for quasi-every x on ¯Ω by

    uu(x)=limr0 1|Br(x)|Br(x)v(ξ)dξ,q.e. x¯Ω, (2.18)

    where v is any Sobolev extension of uu.

    The function uu is pointwise quasi-everywhere defined by (2.18) and is quasicontinuous on ¯Ω i.e., for every ε>0 there exists a relatively open set V¯Ω such that capV<ε and uu is continuous in ¯ΩV.

    Let L3 and B3 denote respectively the σ-algebras of Lebesgue measurable and Borel measurable subsets of R3 and let W:Ω×R3×3[0,+] be L3×B9-measurable satisfying the following assumptions, see also [3,45]:

    W(x,RF)=W(x,F),  RSO(3),   FR3×3,  for a.e. xΩ, (2.19)
    minFW(x,F)=W(x,I)=0,for a.e. xΩ (2.20)

    and as far as it concerns the regularity of W, we assume that there exist an open neighborhood U of SO(3) in R3×3, an increasing function ω:R+R satisfying limt0+ω(t)=0 and a constant K>0 such that for a.e. xΩ

    W(x,)C2(U),|D2W(x,I)|Kand|D2W(x,F)D2W(x,G)|ω(|FG|),F,GU. (2.21)

    Moreover we assume that there exists C>0 such that for a.e. xΩ

    W(x,F)C(d(F,SO(3)))2,FR3×3, (2.22)

    where d(,SO(3)) denotes the Euclidean distance function from the set of rotations.

    The frame indifference assumption (2.19) implies that there exists a function V such that

    W(x,F)=V(x,12(FTFI)),for a.e. xΩ, FR3×3. (2.23)

    By (2.19)–(2.21), for a.e. xΩ, we have W(x,R)=DW(x,R)=0 for any RSO(3). By (2.23), for a.e. xΩ, given BR3×3 and h>0, we have

    W(x,I+hB)=V(x,hsymB+12h2BTB)

    and (2.20), (2.21) together imply

    limh0h2W(x,I+hB)=12symBD2V(x,0) symB=12BTD2W(x,I)B,BR3×3.

    Hence, by (2.22) and polar decomposition [27], we obtain, for a.e. xΩ and every BR3×3, eventually as h0+ (since det(I+hB)>0 for small h)

    12BTD2W(x,I)B=limh0h2W(x,I+hB)lim suph0Ch2d2(I+hB,SO(3))=lim suph0Ch2|(I+hB)T(I+hB)I|2=C|symB|2.

    Moreover, as noticed also in [44], by expressing the remainder of Taylor's expansion in terms of the x-independent modulus of continuity ω of D2W(x,) on the set U from (2.21), we have

    |W(x,I+hB)h22symBD2W(x,I) symB|h2ω(h|B|)|B|2 (2.24)

    for any small enough h (such that hBU). Similarly, V(x,) is C2 in a neighborhood of the origin in R3×3, with an x-independent modulus of continuity η:R+R, which is increasing and such that limt0+η(t)=0, and we have

    |V(x,hB)h22symBD2V(x,0) symB|h2η(h|B|)|B|2 (2.25)

    for any small enough h.

    We mention a general class of energy densities W (the so called Yeoh materials) fulfilling the assumptions above (2.19)–(2.22) and for which the main result of the present paper (see Theorem 2.4 below) applies.

    Example 2.1. For simplicity, we consider the homogeneous case and assume that a standard isochoric-volumetric decomposition of elastic energy density by setting

    W(F):={Wiso(F(detF)1/3)+Wvol(F),if detF>0,+,if detF0, (2.26)

    where Wiso is an energy density of Yeoh type which is defined by choosing

    Wiso(F):=3k=1ck(|F|23)k (2.27)

    with coefficients ck>0 and Wvol(F)=g(detF) for some convex gC2(R+) such that

    {g(t)0 for all t>0,g(t)=0 if and only if t=1,g(1)>0,limt0+g(t)=+,there exists  C>0 and r2 such that  g(t)Ctr,for t>0 sufficiently large. (2.28)

    It is easy to check that with this choice the energy density satisfies all assumptions from (2.19) to (2.21) while inequality (2.22) has been proven in [43].

    It is worth noticing that when material constants are suitably chosen then also Ogden-type energies may fulfil assumptions (2.19)–(2.22) and we refer to [43] for all details.

    We introduce a body force field fL6/5(Ω,R3) and a surface force field gL4/3(Ω,R3). From now on, f and g will always be understood to satisfy these summability assumptions. The load functional is the following linear functional

    L(v):=Ωfvdx+ΩgvdH2(x),vH1(Ω,R3). (2.29)

    We note that since Ω is a bounded Lipschitz domain, the Sobolev embedding H1(Ω,R3)L6(Ω,R3) and the Sobolev trace embedding H1(Ω,R3)L4(Ω,R3) imply that L is a bounded functional over H1(Ω,R3) and throughout the paper we denote its norm with L.

    For every RSO(3) we set

    CR:={c:c3minxEess((Rx)3x3)} (2.30)

    and, as we have observed in the Introduction, we must assume the following geometrical compatibility between load and obstacle

    L((RI)x+c)0,RSO(3), cCR (2.31)

    together with

    L((Rxx)αeα)0,RSO(3), (2.32)

    the summation convention over repeated index α=1,2 being understood all along this paper. It can be shown that condition (2.31) is equivalent to (see Remark 3.4 below)

    L(e3)0=L(e1)=L(e2),Φ(R,E,L)0,RSO(3), (2.33)

    where we have set

    Φ(R,E,L):=L((RI)x)L(e3)minxEess{((Rx)3x3)}

    and from now on we will use (2.33) in place of (2.31). On the other hand Remark 4.5 below will show that also condition (2.32) is in fact unavoidable.

    If E¯Ω{x:x30}, the classical Signorini problem in linear elasticity can be described as the minimization of the functional E:H1(Ω,R3)R{+} defined by

    E(u):={ΩQ(x,E(u))dxL(u),if uA, +,otherwise in H1(Ω,R3), (2.34)

    where E(u):=symu, Q(x,F)=12FTCF with C=D2W(x,I) and A denotes the set of admissible displacements, defined by

    A := {uuH1(Ω;R3): u3(x)+x30 q.e xE}. (2.35)

    The meaning of such constraint is that, if the portion E of the elastic body is contained in {x30} in the reference configuration, then the deformed configuration of E, namely {y(x):=x+u(x),xE}, is constrained to remain in {y30}.

    For every yH1(Ω,R3) we introduce the set

    M(y):=argmin{Ω|yR|2dx: RSO(3)}. (2.36)

    Thus, due to the rigidity inequality of [24], there exists a constant C=C(Ω)>0 such that for every yH1(Ω,R3) and every RM(y)

    Ω(d(y,SO(3)))2dxCΩ|yR|2dx , (2.37)

    where d(F,SO(3)):=min{|FR|:RSO(3)}.

    We introduce the set of admissible deformations Ah as

     Ah:={yH1(Ω,R3): y3(x)x3hx3  q.e. xE} (2.38)

    and the rescaled finite elasticity functionals Gh:H1(Ω,R3)R{+} by setting

    Gh(y)={h2ΩW(x,y)dxh1L(yx), if yAh,+, otherwise. (2.39)

    It is readily seen that, for every RSO(3) and for every cR3 such that

    c3minEess((Rx)3x3)

    the map y(x):=Rx+c belongs to Ah for every h>0. In the sequel we use the short notations Gj:=Ghj and Aj:=Ahj whenever {hj}jN is a sequence of strictly positive real numbers such that hj0+ as j+.

    We say that (yj)jNH1(Ω,R3) is a minimizing sequence of the sequence of functionals Gj if

    limj+(Gj(yj)infH1(Ω,R3)Gj)=0. (2.40)

    The main focus of the paper is to investigate whether minimizers of (2.34) can be approximated by minimizing sequences of the sequence of functionals Gj, as defined by (1.5) and (2.40).

    To this end we introduce the functionals I, ˜G, G:H1(Ω,R3)R{+} defined by

    I(uu):=minbR2ΩQ(x,E(u)+12bα(eαe3+e3eα))dx, (2.41)
    ˜G(u):={I(uu)maxRSL,EL(Ru),if uA, +,otherwise in H1(Ω,R3), (2.42)

    and

    G(u):={ΩQ(x,E(u)dxmaxRSL,EL(Ru),if uA, +,otherwise in H1(Ω,R3), (2.43)

    where

    SL,E={RSO(3):Φ(R,E,L)=0}. (2.44)

    Remark 2.2. It is worth noticing that GE, since ISL,E and it is straightforward checking that I, ˜G, G are all continuous with respect to the strong convergence in H1(Ω;R3).

    Before stating the main result in Theorem 2.4, we show the next Remark with some insight on technicalities implied by precise obstacle formulation in the Sobolev space H1(Ω).

    Remark 2.3. If wH1(Ω) then wH1(Ω) too. Moreover, both (w) and (w) are quasicontinuous in ¯Ω and (w)=(w)=w a.e. in Ω. Then, by [30], (w)=(w) q.e. in ¯Ω. Therefore the condition (w)=0 q.e. in Eess is equivalent to w0 q.e. in Eess.

    In particular we claim that

    (w)=0  q.e. in ¯Ω (2.45)

    is equivalent to

    w0  a.e. in Ω  and  w0  H2a.e. on Ω. (2.46)

    Indeed if w0 a.e. in Ω then (w)=0 a.e. in Ω and hence (w)=0 q.e. in Ω.

    If w0  H2a.e. on Ω and v is a Sobolev extension of w then

    limr01|Br(x)Ω|Br(x)Ωv(ξ)dξ=limr01|Br(x)Ω|Br(x)Ωv(ξ)dξ=0, H2a.e.xΩ.

    and by taking (2.16) into account we get

    limr02|Br(x)|Br(x)Ωv(ξ)dξ=limr02|Br(x)|Br(x)Ωv(ξ)dξ=0,H2a.e.xΩ.

    By recalling that Ω is a Lipschitz set, it is easily checked that Ω is Ahlfors 2-regular, that is there are constants c1,c2>0 such that

    c1r2H2(ΩBr(x))c2r2 (2.47)

    for every 0<r<diam(Ω) and for every xΩ. Therefore if we choose R>0 such that ¯ΩBR(0) we may apply Proposition 6.1.3. of [1] and Theorem 2.1 of [20] both in H1(Ω) and in H1(BR(0)¯Ω) and we get

    limr02|Br(x)|Br(x)Ωv(ξ)dξ=limr02|Br(x)|Br(x)Ωv(ξ)dξ=0, q.e. xΩ,

    that is (2.46) implies (2.45).

    Conversely if (2.45) holds then (w)=0 a.e. in Ω and H2 a.e. on Ω. Therefore w0 a.e. in Ω and by recalling again that the negative part of the trace of w and the trace of its negative part coincide H2 a.e. on Ω we get w0 H2 a.e. on Ω thus proving (2.46) and the claim.

    Similarly, again by Theorem 2.1 of [20], if EessΩ is Ahlfors 2-regular then the condition w0  q.e. on E is equivalent to w0  H2 a.e. on E, so the classical framework of [12,31,54] is equivalent to ours in this case as it was claimed in the Introduction.

    The convergence result is stated in the next theorem, referring to (2.36) and (2.40).

    Theorem 2.4. Assume (2.13), (2.19)–(2.22), (2.32)–(2.33) and L(e3)<0. Let hj0+ as j+ and let (¯yj)jNH1(Ω,R3) be a minimizing sequence of Gj. If RjM(¯yj) for every jN, then there are ¯cjR3 such that the sequence

    ¯uj(x):=hj1RTJ{(¯yj¯cjRjx)αeα+(¯yj,3x3)e3} (2.48)

    is weakly compact in H1(Ω,R3). Therefore up to subsequences, ¯uuj¯u in H1(Ω,R3) and also

    Gj(yj)˜G(¯u)=minH1(Ω,R3)˜G=minH1(Ω,R3)G,as j+. (2.49)

    Remark 2.5. Since ˜GG then equality min˜G=minG is equivalent to argminGargmin˜G with possible strict inclusion (see Remark 5.6), thus in general ¯u may not belong to argminG.

    Remark 2.6. Conditions (2.32) and (2.33) are compatible. Indeed set

    Ω:={x:x21+x22<1, 0<x3<1},E:={x:x21+x22<1, x3=0}, (2.50)

    and f=0, g=e31E. It is readily seen that L(e3)<0=L(e1)=L(e2) and

    L((Rxx)αeα)=0,  Φ(R,E,L)=π1R2330,RSO(3),

    thus both (2.32) and (2.33) are fulfilled.

    On the other hand condition (2.32) does not entail (2.33). Indeed if Ω and E are as in (2.50), f=e3 g=0 then is not since if ¯R=e1e1e2e2e3e3 a direct calculation yields

     Φ(¯R,E,L)=2Ωx3dx+|Ω|minEess(2x3)=π>0. (2.51)

    Eventually (2.33) does not imply (2.32), see Remark 4.5.

    Example 2.7. Here we show an example where the all the assumptions in Theorem 2.4 concerning the geometry of the material body Ω and its portion E sensitive to the constraint and their compatibility with the loads are fulfilled. Set

    Ω:={xR3:x21+x22<1,0<x3<1},E:=¯Ω, (2.52)
    f:=pe3,g0,p<0. (2.53)

    Then L(u)=Ωfudx, condition (2.33) is satisfied and SL,E={RSO(3):Re3=e3}.

    Indeed it is readily seen that L(e3)=p|Ω|<0=L(e1)=L(e2); moreover if RSO(3) and we denote its entries as Rij i,j=1,2,3 then, taking into account p<0, we get

    Φ(R,E,L)=π(R11+R222)pπmin¯Ω{R31x1+R32x2+(R331)x3}=πp2(R331)+pπR231+R232+pπ(1R33)+=πp2(1R33)+pπ1R2330 (2.54)

    and Φ(R,E,L)=0 if and only if R33=1 that is Re3=e3 as claimed.

    Both conditions (2.13) and (2.32) are trivially fulfilled.

    We emphasize that the above claims still hold true if the assumption on E in (2.52) is weakened by allowing any EΩ such that E fulfills coEess=¯Ω.

    This section makes explicit the properties of admissible loads by exploiting the conditions stated by (2.32) and (2.33).

    Lemma 3.1. Assume that (2.32) holds. Then

    L((ax)αeα)=0  and  L((a(ax))αeα)0   aR3. (3.1)

    Proof. By the Euler-Rodrigues formula (2.32) entails

    L(sinϑϑ(ax)αeα+1cosϑϑ(a(ax))αeα)0 (3.2)

    for every ϑ(0,2π) and by letting ϑ0+ we get L((ax)αeα)0 for every aR3 hence L((ax)αeα)=0 for every aR3. The second inequality in (3.1) follows by the previous one.

    Remark 3.2. It is worth noticing that, by inserting a=e1 or a=e2, the condition (3.1) entails L(x3e2)=0 and L(x3e1)=0 respectively.

    Lemma 3.3. Assume (2.13) and (2.31). Then

    (1) L(e1)=L(e2)=0 and L(e3)0;

    (2) L(e3x)=0;

    (3) L(e3(e3x))0;

    (4) there exists xLricoEess such that  L(a(xxL))=0  aR3.

    Proof. By choosing R=I in (2.31) we get L(c)0 for every cCI={cR3:c30}. Since c1e1+c2e2CI for every c1,c2R, we get L(e1)=L(e2)=0. Moreover c3e3CI for c30 entails L(e3)0. Thus (1) is proved and (2.31) entails

    Φ(R,E,L):=L((RI)x)L(e3)minxEess{((Rx)3x3)}0,RSO(3), (3.3)

    that is by the Euler-Rodrigues formula

    φa(ϑ):=L(sinϑ(ax)+(1cosϑ)a(ax))minxEess(sinϑ(ax)3+(1cosϑ)a(ax)3)L(e3)  0,aR3,|a|=1, ϑ[0,2π]. (3.4)

    If a=e3 then Re3=e3 and (3.4) reads

    φ(ϑ):=L(sinϑ(e3x)+(1cosϑ)e3(e3x))0,ϑ[0,2π]. (3.5)

    By φ(0)=φ(2π)=0 and φ(ϑ)0 we get 0φ(0)=L(e3x)=φ(2π)0. Thus (2) is proved. By (2) and (3.5) we get L(e3(e3x))0, say (3). In order to show (4), first we notice that L(e1)=L(e2)=0 entail for every ξR3

    L(aξ)=L(3j=1ajejξ)=3j=1ajL(ejξ)=a1L(ξ3e2+ξ2e3)+a2L(ξ3e1ξ1e3)+a3L(ξ2e1+ξ1e2)=a(ξ2e1ξ1e2)L(e3)=(aξ)3L(e3), (3.6)

    moreover, L(e3x)=0 entails

    L(ax)=a1L(e1x)+a2L(e2x),aR3. (3.7)

    Let us assume first that L(e3)<0. In this case we can set

    ˜x1 = L(e2x)L(e3),˜x2 = L(e1x)L(e3), (3.8)

    hence, by (3.6)–(3.8),

    L(a˜x)=(a˜x)3L(e3)=(a1˜x2a2˜x1)L(e3)=a1L(e1x)+a2L(e2x)=L(ax)

    say

    L(ax)=L(a˜x),aR3, ˜x{(˜x1,˜x2,z):zR}. (3.9)

    Since φa(0)=φa(2π)=0 and φa(ϑ)0 for every aR3,|a|=1 and for every ϑ[0,2π], (3.4) entails

    0lim supϑ0+φa(ϑ)ϑ=L(ax)minxEess(ax)3L(e3),|a|=1, (3.10)
    0lim infϑ2πφa(ϑ)ϑ2π=L(ax)maxxEess(ax)3L(e3),|a|=1. (3.11)

    Hence

    maxxEess(ax)3 L(e3)L(ax)minxEess(ax)3 L(e3),

    so, by (3.6), (3.8) and (3.9),

    maxxEess(ax)3 L(e3)(a˜x)3L(e3)minxEess(ax)3 L(e3). (3.12)

    By taking account of L(e3)<0, we find

    minxEess(ax)3(a˜x)3maxxEess(ax)3,aR3: |a|=1,

    hence, by linearity and by homogeneity,

    minxcoEess(ax)3(a˜x)3maxxcoEess(ax)3,aR3. (3.13)

    By subtracting (a˜x)3 on each term of inequality (3.13) we get

    minycoEess˜x(ay)30maxycoEess˜x(ay)3

    for every aR3 and for every ˜x{(˜x1,˜x2,z):zR}.

    We claim that at least one of the above inequalities is strict for every aR3 such that a21+a220. Indeed, if by contradiction there was ¯a=(¯a1,¯a2,¯a3) with ¯a21+¯a220 such that

    minxcoEess˜x(¯ax)3=maxxcoEess˜x(¯ax)3=0,˜x{(˜x1,˜x2,z),zR},

    then

    (coEess˜x)  {x: ¯a1x2¯a2x1=0}.

    Since the plane {¯a1x2¯a2x1=0} is orthogonal to {x3=0} we obtain

    cap(proj{xR3:¯a1x2¯a2x1=0})=0,

    hence

    0=cap(proj(coEess˜x))=cap(proj(coEess))

    which contradicts (2.13).

    Without loss of generality we can proceed by assuming that the first inequality is strict, say

    minxcoEess˜x(ax)3<0

    for every aR3 such that a21+a220 and for every ˜x{(˜x1,˜x2,z),zR}. Hence, by setting T:=proj(coEess˜x), we get

    minxT(ax)3<0 (3.14)

    for every aR3 such that a21+a220. For every (a1,a2)R2 such that a21+a220 we set now

    Γ(a1,a2):={(x1,x2)R2:a1x2a2x1min(x1,x2)T(a1x2a2x1)}

    then {Γ(a1,a2): a21+a22=1} is the set of half-planes supporting T. Since T is closed and convex, we get

    T =a21+a22=1Γ(a1,a2).

    By (3.14), we get

    dist((0,0),Γ(a1,a2))=|minxT(a1x2a2x1)|>0.

    Hence we deduce the existence of (˜a1,˜a2): ˜a21+˜a22=1 such that

    min˜a21+˜a22=1dist((0,0),Γ(a1,a2))=|minxT(˜a1x2˜a2x1)|>0,

    so (0,0)riT that is (˜x1,˜x2,0)riproj(coEess).

    We are left to show that there exists ˜x3 such that xL:=(˜x1,˜x2,˜x3)ricoEess. To this aim it is readily seen that by taking account of cap(proj(coEess))>0, we get aff(proj(coEess)))={x3=0} so there exists ¯r>0 such that

    {(x1,x2):|x1˜x1|2+|x2˜x2|2<¯r2}proj(coEess)).

    Let now

    J:={z:(x1,x2,z)coEess}

    and assume that (x1,x2,z)(coEess)(ricoEess) for every zJ. Then

    Br(x1,x2,z)coEess,Br(x1,x2,z)((affcoEess)(coEess))

    for every zJ and for every r>0, therefore by recalling that affprojcoEess={x3=0}

    projBr(x1,x2,z)projcoEess,
    projBr(x1,x2,z)({x3=0}projcoEess)

    for every r>0. This is a contradiction since

    projBr(x1,x2,z){(x1,x2):|x1˜x1|2+|x2˜x2|2<¯r2}proj(coEess)

    for some r>0 thus (4) is proved in this case since by construction L(a(xxL)=0 for every aR3. Eventually, if L(e3)=0, (2.33) reduces to

    L((RI)x)0,RSO(3),

    say sinϑL(ax)+(1cosϑ)L(a(ax))0 for all aR3, thus, by repeating the analysis made on (3.5), we get

    L(ax)=0,aR3,

    and, since riprojcoEess due to (2.13), by exploiting identity (3.6) with ξ=˜x we obtain, for whatever choice of ˜xriprojcoEess

    L(a(x˜x))=L(a˜x)=(a˜x)3L(e3)=0,aR3,

    that is (4) is proven also in this case.

    Remark 3.4. Conditions (2.31) and (2.33) are equivalent as claimed in Subsection 2.2.

    Indeed, as it has been pointed out in the proof of Lemma 3.3, condition (2.31) implies that L(e3)0, L(e1)=L(e2)=0 and

    Φ(R,E,L):=L((RI)x)L(e3)minxEess((Rx)3x3)0,RSO(3). (3.15)

    Conversely if the latter condition holds and L(e3)0,  L(e1)=L(e2)=0, then

    L((RI)x+c)=L((RI)x+c3e3)0

    for every cR3 such that c3minxEess((Rx)3x3)}.

    Remark 3.5. We emphasize that conditions (1) and (4) in Lemma 3.3 together with (2.13) and L(e3)<0 coincide with conditions (4.9)–(4.11) of Theorem 4.5 of [12], which provides the solution to Signorini problem in linear elasticity.

    The whole set of conditions (1)–(4) appearing in the claim of Lemma 3.3 together with condition (2.13) on the set E is not equivalent to admissibility of the loads as expressed by (2.33): This phenomenon is made explicit by subsequent Example 3.6.

    Example 3.6. Let Ω={x:x21+x22<1,0<x3<H}, E=Eess={(x1,x2,0):x21+x221} and L(v)=Ωpv3dx with p<0, say f=pe3, g=0.

    Then E fulfills (2.13), since capE>0 and proj(coEess)=Eess¯Ω{x3=0}; moreover all claims (1)–(4) of Lemma 3.3 hold true: Indeed

    L(e1)=L(e2)=0,L(e3)=p|Ω|<0,
    L(e3x)=Ω(e3)(e3x)dx=0,
    L(e3(e3x)=Ω(e3)(e3(e3x))dx=0,

    eventually, by choosing xL=(0,0,0)ri (coEess)=E and by taking the symmetry of Ω into account, we get

    L(a(xxL))=L(ax) = Ω(e3)(ax)dx = aΩ(x2e1+x1e2)dx=0.

    Nevertheless, condition (2.33) is violated, since we can consider the π radians rotation around axis e1 which keeps E above the horizontal plane (obstacle boundary) but capsizes the body below the horizontal plane, namely ˜RSO(3) defined by ˜Rx=x1e1x2e2x3e3. Thus

    L((˜RI)x)L(e3)minEess((˜Rx)3x3)=2pΩx3dxp|Ω|minEess(2x3)=pπH2>0.

    The assumptions in Lemma 3.3 and in Theorem 2.4 cannot be weakened by assuming only capE>0 in place of (2.13) as it is shown in the next example, thus proving that Theorem 2.4 is a sharp result with respect to the sets E subject to the constraint that are admissible.

    Example 3.7. Choose f=e3, g=0 and

    Ω={x:x21+x22<1, x2>0, 0<x3<1},
    E=Eess={(x1,x2,x3)¯Ω: x2=0}.

    It is readily seen that condition (2.32) is fulfilled since L((Rxx)αeα)=0 moreover, since L(e3)<0=L(e1)=L(e2),

    Φ(R,E,L)=23R32+π2(1R33)+minEess{R31x1+R32x2+(R331)x3}|Ω|=π|R31|π(R32)23R32+π2(R331)=π|R31|π2|R32|+(π223)R32+π2(R331)0

    for every RSO(3), then also condition (2.33) is satisfied. Nevertheless it can be easily checked that capE>0 but cap(proj(coEess))=0, thus E does not fulfil (2.13).

    If there was ¯xricoEess such that L(a(x¯x))=0 for every aR3 then we get

    L(a¯x)=L(ax)=Ωe3(ax)dx=Ω(a1x2a2x1)dx=23a1;

    then, since L(e1)=L(e2)=0, we could apply (3.6) and find

    23a1=L(a¯x)=(a¯x)3L(e3)=π(a1¯x2a2¯x1),aR3,

    hence ¯x1=0, ¯x2=23π, thus

    ¯xricoEess={(x1,x2,x3)¯Ω:x2=0},

    a contradiction. Thus claim (4) of Lemma 3.3 cannot hold true in this case.

    Moreover if we set wk(x):=k(x2e3x3e2), then E(wk)=0, wk,3+x3x30 on E whence wkA and it is readily seen that

    L(Rv)=L(v), RSL,E, (3.16)

    hence

    G(wk)=L(wk)=Ωe3wkdx=kΩx2dx=23k,as k+

    that is infAG= so the convergence of the energies claimed in Theorem 2.4 fails to be true in this case thus showing sharpness of condition (2.13).

    Lemma 3.8. Assume that (2.13), (2.33) hold and that L(e3)<0. Then

    eitherSL,E={I}orSL,E={RSO(3):Re3=e3}. (3.17)

    Proof. First, we prove the inclusion

    SL,E{RSO(3):Re3=e3}.

    Indeed if R belongs to SL,E and a is a rotation axis of R with |a|=1, then

    φa(ϑ):=Φ(R,E,L)=L(sinϑ(ax)+(1cosϑ)(a(ax)))minEess{sinϑ(ax)3+(1cosϑ)(a(ax))3}L(e3)=0 (3.18)

    for every ϑ[0,2π]. By arguing now as in the proof of (4) of Lemma 3.3, we get

    0limϑ0+φa(ϑ)ϑ=L(ax)minxEess(ax)3L(e3) limϑ2πφa(ϑ)ϑ2π=L(ax)maxxEess(ax)3L(e3) 0 (3.19)

    and, since L(e3)<0, we get

    minxEess(ax)3=maxxEess(ax)3 (3.20)

    that is the function

    x(ax)3=a1x2a2x1

    is constant on Eess hence it is constant in co(Eess) thus capprojcoEess>0 entails a1=a2=0 that is Re3=e3 as claimed.

    We notice that ISL,E and, the other hand, if \mathcal S_{\mathcal L, E}\not\equiv\{\, \mathbf I\, \} then there is

    \widetilde {\mathbf R}\in \mathcal S_{\mathcal L,E}\subset \{\,\mathbf R\in SO(3): \mathbf R\mathbf e_3 = \mathbf e_3\,\}

    such that \widetilde {\mathbf R}\not = \mathbf I and

    \begin{equation*} \widetilde {\mathbf R}{\mathbf{x}}\, = \,{\mathbf{x}}\,+\,\sin\widetilde\vartheta\, (\mathbf e_3\wedge {\mathbf{x}})\,+\,\big(1-\cos\widetilde\vartheta\big)\, \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) \end{equation*}

    for every {\mathbf{x}}\in{{\mathbb R}}^3 and for some suitable \widetilde\vartheta\in(0, 2\pi) . By taking (2) of Lemma 3.3 into account, we get

    \begin{eqnarray*} 0 & = & \Phi(\widetilde{\mathbf R},E,\mathcal L) \ = \ \mathcal L\Big( (\widetilde{\mathbf R} - \mathbf I)\,{{\bf x}}\Big)-\,\mathcal L(\mathbf e_3) \,\min\limits_{{\mathbf{x}}\in E_{ess}}\big(\,(\widetilde{\mathbf R}-\mathbf I){\mathbf{x}})\big)_3\, \\ & = & \mathcal L\Big( (\widetilde{\mathbf R} - \mathbf I)\,{{\bf x}}\Big) \ = \ \sin\widetilde\vartheta\, \mathcal L (\mathbf e_3\wedge {\mathbf{x}})\,+\, \big(1-\cos\widetilde\vartheta\big)\, \mathcal L \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) \, \, \\ & = & \big(1-\cos\widetilde\vartheta\big)\, \mathcal L \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) , \end{eqnarray*}

    thus \ \mathcal L \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) = 0 .

    Therefore for any other \mathbf R\in SO(3), \ \mathbf R\neq\mathbf I such that \mathbf R\mathbf e_3 = \mathbf e_3 there is \vartheta\in(0, 2\pi) such that

    \begin{equation*} {\mathbf R}{\mathbf{x}}\, = \,{\mathbf{x}}\,+\,\sin\vartheta\, (\mathbf e_3\wedge {\mathbf{x}})\,+\,\big(1-\cos\vartheta\big)\, \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big), \qquad\forall\,{\mathbf{x}}\in{{\mathbb R}}^3 , \end{equation*}

    thus, by taking again (2) of Lemma 3.3 into account, we get

    \begin{eqnarray*} \mathcal L\Big( ({\mathbf R} - \mathbf I)\,{{\bf x}}\Big)\!\!\!\!&-&\!\!\!\!\,\mathcal L(\mathbf e_3) \,\min\limits_{{\mathbf{x}}\in E_{ess}}\big(\,({\mathbf R}-\mathbf I){\mathbf{x}})\big)_3\, \\ & = & \mathcal L\Big( ({\mathbf R} - \mathbf I)\,{{\bf x}}\Big) \ = \ \sin\vartheta\, \mathcal L (\mathbf e_3\wedge {\mathbf{x}})\,+\, \big(1-\cos\vartheta\big)\, \mathcal L \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) \, \, \\ & = & \big(1-\cos\vartheta\big)\, \mathcal L \big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) = 0 \end{eqnarray*}

    that is \mathbf R belongs to \mathcal S_{\mathcal L, E} thus concluding the proof of the lemma.

    Remark 3.9. It is possible to show that both alternatives in Lemma 3.8 can actually occur. Indeed in Example 2.7 we have exhibited an example in which \mathcal S_{\mathcal L, E} = \{\mathbf R\in SO(3): \mathbf R\mathbf e_3 = \mathbf e_3\} and we show here that also the other alternative may occur. Indeed set

    \begin{equation} \Omega: = \{{{\bf x}}\!\in\!\mathbb R^3\!: x_1^2+x_2^2\! < \!1,\,0\! < \!x_3\! < \!1\}, \quad E\!: = \! \overline \Omega,\quad \end{equation} (3.21)
    \begin{equation} \mathbf f: = -{\mathbf e}_3,\quad \mathbf g = {\mathbf 1}_{\partial_{l} \Omega}\mathbf n, \end{equation} (3.22)

    where \partial_{l} \Omega is the lateral boundary of \Omega and \mathbf n the unit outward vector normal to \partial_{l} \Omega . If \mathbf R\in SO(3) and we denote its entries as R_{ij}\ i, j = 1, 2, 3, then

    \mathcal L\left ((\mathbf R{{\bf x}}-{{\bf x}}\right)_\alpha\mathbf e_\alpha) = \sum\limits_{i = 1,2}(R_{ii}-1)\int_{\partial_{l} \Omega}x_i^2\,d\mathcal H^2\le 0

    that is condition (2.32) is satisfied. Moreover since

    \mathcal L(\mathbf e_3) = -| \Omega| < 0 = \mathcal L(\mathbf e_1) = \mathcal L(\mathbf e_2)

    and

    \begin{equation} \begin{array}{ll} \Phi(\mathbf R, E, \mathcal L)&=\mathcal L\left (\sum_{j=1}^3(\mathbf R{{\bf x}}-{{\bf x}})_j \mathbf e_j\right)+\pi\,(R_{11}+R_{22}-2) + \pi\,\min\limits_{\overline \Omega}\big\{R_{31}x_1+R_{32}x_2+(R_{33}-1)x_3\big\} \\ &\\ & = -\frac{\pi}{2}(R_{33}-1)-\pi\sqrt{R_{31}^2+R_{32}^2}-\pi (1-R_{33})^+ \sum\limits_{i=1,2}(R_{ii}-1)\int_{\partial_{l} \Omega}x_i^2\,d\mathcal H^2\\\ &\\ & = -\frac{\pi}{2}(1-R_{33})-\pi\sqrt{1-R_{33}^2}+ \sum\limits_{i=1,2}(R_{ii}-1)\int_{\partial_{l} \Omega}x_i^2\,d\mathcal H^2 \le 0 \end{array} \end{equation} (3.23)

    and equality holds if and only if R_{11} = R_{22} = R_{33} = 1 then condition (2.33) is satisfied and \mathcal S_{\mathcal L, E}\equiv \{\mathbf I\} as claimed.

    Lemma 3.10. Assume (2.13), (2.33) and \mathcal L(\mathbf e_3) < 0 . Let \mathbf R_j\in SO(3) be a sequence of rotations such that \, \mathbf R_j\mathbf e_3\neq\mathbf e_3 , \, \mathbf R_j\mathbf e_3\to \mathbf e_3 as j\to +\infty . Then

    \begin{equation} \limsup\limits_{j\to +\infty} \ \frac { \Phi (\mathbf R_j ,E, \mathcal L )} {|\mathbf R_j\mathbf e_3-\mathbf e_3 |\,|\mathcal L(\mathbf e_3)|} \ < \ 0 . \end{equation} (3.24)

    Proof. \Phi (\mathbf R_j, E, \mathcal L)\le 0 , by (2.33). Hence the \limsup in (3.24) cannot be strictly positive.

    We assume by contradiction

    \begin{equation} \limsup\limits_{j\to+\infty} \ \frac { \Phi (\mathbf R_j ,E, \mathcal L )} {|\mathbf R_j\mathbf e_3-\mathbf e_3 |\,|\mathcal L(\mathbf e_3)|} \ = \ 0 . \end{equation} (3.25)

    By Euler-Rodrigues formula there are sequences \mathbf a_j\in {{\mathbb R}}^3 and \vartheta_j\in [0, 2\pi] , such that |\mathbf a_j| = 1 and

    \begin{equation} \mathbf R_j{\mathbf{x}}\, = \, {\mathbf{x}}+ (\sin \vartheta_j)(\mathbf a_j\wedge {\mathbf{x}}) + (1-\cos \vartheta_j)\big((\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})\big), \qquad \forall {\mathbf{x}}\in{{\mathbb R}}^3, \end{equation} (3.26)

    thus a direct computation yields

    \begin{equation} |\mathbf R_j\mathbf e_3-\mathbf e_3 | \, = \, \sqrt {a_{1,j}^{\,2}+a_{2,j}^{\,2}}\,\sqrt{2\,(1-\cos \vartheta_j)}. \end{equation} (3.27)

    By taking account of \mathbf R_j\mathbf e_3\!\neq\!\mathbf e_3 and \mathbf R_j\mathbf e_3\to \mathbf e_3 as j\!\to\! +\infty , we get \mathbf a_j\neq \mathbf e_3 , \vartheta_j\!\in\! (0, 2\pi) and therefore, up to subsequences, we may assume: that \mathbf a_j\to \mathbf a, \ \vartheta_j\to \vartheta\in [0, 2\pi], that either \vartheta\in \{0, 2\pi\} or a_3 = 1 and that \mu_j\, {a_{i, j}}\to\, \alpha_i, \ i = 1, 2 with \alpha_1^2+\alpha_2^2 = 1 , where we have set

    \mu_j: = (a_{1,j}^{\,2}+a_{2,j}^{\,2})^{-\frac{1}{2}}.

    For every {\mathbf{x}}\in\Omega and {\mathbf{v}}\in \mathbb R^2, |{\mathbf{v}}| = 1, set

    \begin{equation} \begin{array}{ll} \mathbf p_{{\mathbf{v}}}({\mathbf{x}})& = \big(\, (x_1-\widetilde x_1)\mathbf e_1 +(x_2-\widetilde x_2)\mathbf e_2 +(x_3-\widetilde x_3)\mathbf e_3 \,\big) \wedge (-v_1\mathbf e_2+v_2\mathbf e_1) \\ &\\ & = (v_1(x_1-\widetilde x_1)+v_2(x_2-\widetilde x_2))\,\mathbf e_3\,+\, (x_3-\widetilde x_3) (v_1\mathbf e_1 +v_2\mathbf e_2), \\ \end{array} \end{equation} (3.28)

    where (\widetilde x_1, \widetilde x_2, \widetilde x_3) = {\mathbf{x}}_{\mathcal L}\in {\mathop{{{\rm{ri}}}}\nolimits}{\mathop{{{\rm{co}}}}\nolimits} E_{ess} is chosen as in the proof of (4) in Lemma 3.3. Hence, by taking account of (1) and (4) of Lemma 3.3, we have

    \begin{equation*} 0 = \mathcal L\big(\mathbf p_{{\mathbf{v}}}\big)\, = \, \mathcal L\big( (v_1 x_1 +v_2 x_2)\,\mathbf e_3 \big) - (v_1 \widetilde x_1 +v_2 \widetilde x_2) \, \mathcal L(\mathbf e_3) + \mathcal L\big(\, x_3(v_1 \mathbf e_1 +v_2 \mathbf e_2)\big) \end{equation*}

    that is

    \begin{equation} (v_1 \widetilde x_1 +v_2 \widetilde x_2) \,\mathcal L(\mathbf e_3)\, = \, \mathcal L\big( (v_1 x_1 +v_2 x_2)\mathbf e_3\big) + \mathcal L\big(\, x_3(v_1 \mathbf e_1 +v_2 \mathbf e_2)\big). \end{equation} (3.29)

    By (2) of Lemma 3.3 we know \mathcal L(\mathbf e_3\wedge {\mathbf{x}}) = 0 , then (3.8) entails

    \begin{equation} \mu_j\, \mathcal L(\mathbf a_j\wedge {\mathbf{x}}) = a_{1,j}\mu_j \,\mathcal L(\mathbf e_1\wedge {\mathbf{x}}) + a_{2,j}\mu_j \, \mathcal L(\mathbf e_2\wedge {\mathbf{x}}) \to (\alpha_1\widetilde x_2-\alpha_2\widetilde x_1) \,\mathcal L(\mathbf e_3) \end{equation} (3.30)

    and by (3) of Lemma 3.3 we have

    0\ge\mathcal L\big(\mathbf e_3\wedge (\mathbf e_3\wedge {\mathbf{x}})\big) = \mathcal L(x_3\mathbf e_3 -{\mathbf{x}}) = -\mathcal L(x_1\mathbf e_1 +x_2\mathbf e_2).

    By taking (3.29) into account and by recalling that either \vartheta\in \{0, 2\pi\} or a_3 = 1 , we get

    \begin{eqnarray*} &&\mu_j\,\mathcal L\big(\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})\big)\sin\frac{\vartheta_j}{2} = \mu_j\,\mathcal L\big((\mathbf a_j\cdot{\mathbf{x}})\,\mathbf a_j- {\mathbf{x}}\big)\sin\frac{\vartheta_j}{2} \\ && = \mu_j \sin\frac{\vartheta_j}{2}\Big(a_{1,j}\,\mathcal L\big((\mathbf a_j\cdot{\mathbf{x}})\,\mathbf e_1\big) + a_{2,j}\,\mathcal L\big((\mathbf a_j\cdot{\mathbf{x}})\,\mathbf e_2\big) + \mathcal L\big((a_{3,j}^{\,2}-1)\,x_3\,\mathbf e_3\big)\!-\! \mathcal L(x_1\mathbf e_1+x_2\mathbf e_2)\Big) \\ &&\le \ \mathcal L\big(\alpha_1x_3\mathbf e_1+\alpha_2x_3\mathbf e_2+ (\alpha_1x_1+\alpha_2x_2)\,\mathbf e_3 \big)\sin\frac{\vartheta}{2} + o(1) \\ && = (\alpha_1\widetilde x_1+\alpha_2\widetilde x_2)\,\sin\frac{\vartheta}{2}\,\mathcal L(\mathbf e_3) + o(1) , \end{eqnarray*}

    that is

    \begin{equation} \limsup\limits_{j\to + \infty}\ \mu_j\,\mathcal L\big(\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})\big)\sin\frac{\vartheta_j}{2} \ \le\ (\alpha_1\widetilde x_1+\alpha_2\widetilde x_2)\,\sin\frac{\vartheta}{2}\mathcal L(\mathbf e_3)\ . \end{equation} (3.31)

    Let now \eta\in C([0, 2\pi]) such that \eta(\vartheta) = \left (2(1-\cos\vartheta)\right)^{-\frac{1}{2}}\sin (\vartheta) for every \vartheta\in (0, 2\pi) .

    By recalling that either a_3 = 1 or \vartheta \in \{0, 2\pi\} we get

    \begin{equation} \mu_j(\mathbf a_j\wedge {\mathbf{x}})_3 \longrightarrow \alpha_1x_2-\alpha_2x_1 , \quad \mu_j(\mathbf a_j \wedge (\mathbf a_j\wedge {\mathbf{x}}))_3\,\sin\frac{\vartheta_j}{2} \longrightarrow (\alpha_1x_1+\alpha_2x_2)\sin\frac{\vartheta}{2}, \end{equation} (3.32)

    so, by taking (3.30)–(3.32) into account we obtain

    \begin{equation*} \begin{array}{ll} 0& = \limsup\limits_{j\to +\infty} \ \frac { \Phi (\mathbf R_j ,E, \mathcal L )} {|\mathbf R_j\mathbf e_3-\mathbf e_3 |\,|\mathcal L(\mathbf e_3)|}\\ &\\ & = \limsup\limits_{j\to +\infty} \frac {\mu_j} {|\mathcal L(\mathbf e_3)|} \, \Big\{ \eta(\vartheta_j)\mathcal L(\mathbf a_j \wedge \mathbf x) + \mathcal L\left (\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})\right )\sin\frac{\vartheta_j}{2}+\\ &\\ & -\min\limits_{{\mathbf{x}}\in E_{ess}}\left\{\eta(\vartheta_j) (\mathbf a_j\wedge{{\bf x}})_3+\sin\frac{\vartheta_j}{2}(\mathbf a_j\wedge (\mathbf a_h\wedge{\mathbf{x}}))_3\right\}\mathcal L(\mathbf e_3)\Big\} \\ &\\ & \le\min\limits_{{\mathbf{x}}\in E_{ess}}\Big\{ \eta(\vartheta)(\alpha_1x_2-\alpha_2x_1)+ (\alpha_1x_1+\alpha_2x_2)\sin \frac{\vartheta}{2}\Big\}+\\ &\\ & -\eta(\vartheta)(\alpha_1\widetilde x_2-\alpha_2\widetilde x_1)- (\alpha_1\widetilde x_1+\alpha_2 \widetilde x_2)\sin \frac{\vartheta}{2} \\ &\\ & = \min\limits_{{\mathbf{x}}\in co(E_{ess})} \Big\{ \eta(\vartheta)(\alpha_1(x_2-\widetilde x_2) -\alpha_2(x_1-\widetilde x_1))+ \big( \alpha_1(x_1-\widetilde x_1) + \alpha_2(x_2-\widetilde x_2) \big)\sin\frac{\vartheta}{2} \Big\} \le \ 0, \\ \end{array} \end{equation*}

    since (\widetilde x_1, \widetilde x_2, \widetilde x_3)\in {\mathop{{{\rm{ri}}}}\nolimits}{\mathop{{{\rm{co}}}}\nolimits} E_{ess} . Therefore the function

    g(x_1,x_2): = \eta(\vartheta)(\alpha_1(x_2-\widetilde x_2) -\alpha_2(x_1-\widetilde x_1))+ \big( \alpha_1(x_1-\widetilde x_1) + \alpha_2(x_2-\widetilde x_2) \big)\sin\frac{\vartheta}{2}

    attains its minimum on {\mathop{{{\rm{proj}}}}\nolimits}({\mathop{{{\rm{co}}}}\nolimits} E_{ess}) at (\widetilde x_1, \widetilde x_2)\in {\mathop{{{\rm{ri}}}}\nolimits} ({\mathop{{{\rm{proj}}}}\nolimits}({\mathop{{{\rm{co}}}}\nolimits} E_{ess})) hence, by taking into account of {\mathop{{{\rm{aff}}}}\nolimits} ({\mathop{{{\rm{proj}}}}\nolimits}({\mathop{{{\rm{co}}}}\nolimits} E_{ess})) = \{x_3 = 0\}, we get

    0 = |\nabla g(\widetilde x_1,\widetilde x_2)|^2 = \left(\eta(\vartheta)\alpha_1+\alpha_2\sin\frac{\vartheta}{2}\right)^2+\left(\alpha_1\sin\frac{\vartheta}{2}-\alpha_2\eta(\vartheta)\right)^2 = 2\left(\eta^2(\vartheta)+\sin^2\frac{\vartheta}{2}\right) > 0,

    a contradiction. Thus

    \begin{equation*} \limsup\limits_{j\to +\infty} \ \frac {\Phi (\mathbf R_j ,E, \mathcal L )} {|\mathbf R_j\mathbf e_3-\mathbf e_3|\,|\mathcal L(\mathbf e_3)|}\ < 0 \end{equation*}

    and the proof is achieved.

    Remark 3.11. If {\mathop{{{\rm{cap}}}}\nolimits} ({\mathop{{{\rm{proj}}}}\nolimits}({\mathop{{{\rm{co}}}}\nolimits} E_{ess})) = 0 then the claim of Lemma 3.10 may be false even if {\mathop{{{\rm{cap}}}}\nolimits} E > 0 . For instance, set for every j\in \mathbb N\setminus \{0\}

    \begin{equation*} \mathbf R_j: = \mathbf e_1\otimes\mathbf e_1+(1-j^{-1})(\mathbf e_2\otimes\mathbf e_2+\mathbf e_3\otimes\mathbf e_3)+\sqrt{2\,j^{-1}-j^{-2}}\,(\mathbf e_3\otimes\mathbf e_2-\mathbf e_2\otimes\mathbf e_3), \end{equation*}

    let

    \begin{equation*} \Omega: = \{{\mathbf{x}}: x_1^2+x_2^2 < 1,\ 0 < x_3 < 1\}, \end{equation*}
    \begin{equation*} E: = \overline \Omega\cap\{x_2 = 0,\ 0 < x_3 < \frac{1}{2}\}, \end{equation*}

    and \mathbf f: = -\mathbf e_3, \ \mathbf g = \mathbf 0. It is straightforward checking that \mathbf R_j\mathbf e_3\neq \mathbf e_3, \mathbf R_j \to \mathbf I , moreover since \mathcal L(\mathbf e_3) < 0 = \mathcal L(\mathbf e_1) = \mathcal L(\mathbf e_2),

    \begin{equation} \mathcal L((\mathbf R-\mathbf I){{\bf x}})-\min\limits_{{{\bf x}}\in E_{ess}}((\mathbf R{{\bf x}})_3-x_3)\}\,\mathcal L(\mathbf e_3) = \,-\,\pi \,|R_{31}|\,\le\, 0, \qquad \forall\,\mathbf R\in SO(3) \end{equation} (3.33)

    and

    \begin{equation} \mathcal L((\mathbf R-\mathbf I){{\bf x}})_\alpha\mathbf e_\alpha) = 0, \qquad \forall\,\mathbf R\in SO(3), \end{equation} (3.34)

    conditions (2.32) and (2.33) are satisfied. Nevertheless

    \begin{equation} \mathcal L((\mathbf R_j-\mathbf I){\mathbf{x}})-\min\limits_{E_{ess}}((\mathbf R_j-\mathbf I){\mathbf{x}})_3) \mathcal L(\mathbf e_3) = 0 \end{equation} (3.35)

    and the claim of Lemma 3.10 cannot be true in this case.

    This section contains the proof of our main result. We start by showing that sequences of deformations with equibounded energy correspond (up to suitably tuned rotations and translations of the horizontal components) to displacements that are equibounded in H^1 .

    Lemma 4.1. (compactness) Assume that E , \mathcal L and \mathcal W fulfil (2.13), (2.19)–(2.22), (2.33) and \mathcal L(\mathbf e_3) < 0 . If \, 0 < h_j\to 0^+ as j\to +\infty then for every {\mathbf{y}}_j\in H^1(\Omega; \mathbb R^3) with \mathcal G_j({\mathbf{y}}_j)\le M < +\infty there are \mathbf R_j\in SO(3), \ \mathbf c_j\in \mathcal C_{\mathbf R_j} such that

    \begin{equation} \mathbf R_j\to \mathbf R\in \mathcal S_{\mathcal L, E} \end{equation} (4.1)

    and the sequence

    {h_j}^{-1}\big(\,{\mathbf{y}}_j\,-\,\mathbf R_j{\mathbf{x}}\,-\mathbf c_j\,\big)_{\alpha}\mathbf e_{\alpha}+{h_j}^{-1}(y_{j,3}-x_3)\mathbf e_3

    is bounded in H^1(\Omega; \mathbb R^3) .

    Proof. Referring to (2.36), we can choose {\mathbf R}_j\in \mathcal M({\mathbf{y}}_j) in such a way that, up to subsequence and without relabelling, {\mathbf R}_j\to\mathbf R . Then we define \mathbf c_j = (c_{j, 1}, c_{j, 2}, c_{j, 3}) by

    \begin{equation} c_{j,\alpha} = \, |\Omega|^{-1}\!\!\int_\Omega ({\mathbf{y}}_j ({\mathbf{x}})-\mathbf R_j {\mathbf{x}})_\alpha\,d{\mathbf{x}}, \qquad \alpha = 1,2, \end{equation} (4.2)
    \begin{equation} c_{j,3} = -\min\limits_{{\mathbf{x}}\in E_{ess}} ((\mathbf R_j-\mathbf I){\mathbf{x}})_3. \end{equation} (4.3)

    By the rigidity inequality ([24]) there exists a constant C = C(\Omega) > 0 such that

    \begin{equation} \begin{array}{ll} M& \ge\ \mathcal G_j({\mathbf{y}}_j) \,\ge\, C\, h_j^{-2}\!\! \int_\Omega |\nabla {\mathbf{y}}_j- \mathbf R_j|^2 \,d{\mathbf{x}}- h_j^{-1} \mathcal L({\mathbf{y}}_j-{\mathbf{x}})\ \\ & = \, C\, h_j^{-2} \!\!\int_\Omega |\nabla {\mathbf{y}}_j- \mathbf R_j|^2 \,d{\mathbf{x}}- h_j^{-1} \mathcal L({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j) - h_j^{-1} \mathcal L(\mathbf R_j{\mathbf{x}}-{\mathbf{x}}+\mathbf c_j). \end{array} \end{equation} (4.4)

    Thus, by (2.33), \mathcal L(\mathbf e_1)\! = \!\mathcal L(\mathbf e_2)\! = \!0 and the definition of c_{j, 3} , we get

    \begin{equation} M \,\ge\, C\, h_j^{-2} \int_\Omega |\nabla {\mathbf{y}}_j- \mathbf R_j|^2 \,d{\mathbf{x}}- h_j^{-1} \mathcal L({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)\, \end{equation} (4.5)

    and Poincaré inequality entails, for every \varepsilon\! > \!0 ,

    \begin{equation} \begin{array}{ll} h_j^{-1} \! \sum\limits_{\alpha = 1}^{2} \mathcal L\big(\,({\mathbf{y}}_j-{\mathbf R}_j{\mathbf{x}}-{\mathbf c}_j)_\alpha \,{\mathbf e}_{\alpha} \big) \, &\le\, h_j^{-1} C_P \|\mathcal L\|_{*} \Big ( \sum\limits_{\alpha = 1}^{2}\! \int_\Omega |\,(\nabla{{\mathbf{y}}_j}-{\mathbf R}_j)_\alpha\, |^2\,d{\mathbf{x}} \Big )^{1/2}\!\! \\ & \le\, \frac {C_P \|\mathcal L\|^2_{*}} {2\varepsilon} \,+\, \frac {\varepsilon\,h_j^{-2}\,C_P} {2}\, \sum\limits_{\alpha = 1}^{2}\int_\Omega |(\nabla{\mathbf{y}}_j-{\mathbf R}_j)_{\alpha}|^2\,d{\mathbf{x}}. \\ \end{array} \end{equation} (4.6)

    Estimates (4.5) and (4.6) together with Young inequality provide

    \begin{eqnarray} \quad M \!\!\!\!&\ge&\!\!\!\! h_j^{-2}\! \left(\!C-\frac {\varepsilon\, C_P}{2}\!\right) \!\! \int_\Omega \!|\nabla {\mathbf{y}}_j\!-\! \mathbf R_j|^2 d{\mathbf{x}} -\frac {C_P \,\|\mathcal L\|^2_{*}}{2\,\varepsilon} - h_j^{-1} \!\mathcal L\big(({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3 \,\mathbf e_3 \big)\! \\ \!\!\!\!&\ge&\!\!\!\! h_j^{-2}\! \left(\!C-\frac {\varepsilon\, C_P}{2}\right) \!\int_\Omega \!|\nabla {\mathbf{y}}_j\!-\! \mathbf R_j|^2 \,d{\mathbf{x}} -\frac {C_P \|\mathcal L\|^2_{*}} {2\,\varepsilon} \\ && \ \ - h_j^{-1} \,\|\mathcal L\|_{*} \, \big (\, \| ({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3 \|_{L^2(\Omega)} + \| \nabla ({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}})_3 \|_{L^2(\Omega)} \,\big) \\ \!\!\!\!&\ge&\!\!\!\! h_j^{-2}\! \left(\!C-\frac {\varepsilon\,C_P}{2}-\frac {\varepsilon} 2\right) \!\int_\Omega \!|\nabla {\mathbf{y}}_j\!-\! \mathbf R_j|^2 \,d{\mathbf{x}} -\left(\frac {C_P } {2\,\varepsilon} + \frac 1 {2\,\varepsilon}\right) \|\mathcal L\|^2_{*} \\ && \ \ - h_j^{-1} \,\|\mathcal L\|^2_{*} \, \big (\, \| ({\mathbf{y}}_j-\mathbf R_h{\mathbf{x}}-\mathbf c_j)_3 \|_{L^2(\Omega)} . \end{eqnarray} (4.7)

    By choosing \varepsilon = C/ (C_P+1) , we get

    \begin{eqnarray} && h_j^{-2}\ \frac {C}{2} \int_\Omega \!|\nabla {\mathbf{y}}_j\!-\! \mathbf R_j|^2 \,d{\mathbf{x}} \ \\ && \le\, M\, +\, \frac {(C_P+1)^2}{2C} \|\mathcal L\|^2_{*} \,+\, h_j^{-1} \,\|\mathcal L\|^2_{*} \, \| ({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3 \|_{L^2(\Omega)} . \end{eqnarray} (4.8)

    Thus, if we show that \ h_j^{-1} \| ({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3 \|_{L^2(\Omega)}\, is uniformly bounded then, due to estimate (4.8), \|h_j^{-1}(\nabla{\mathbf{y}}_j-\mathbf R_j)\|_{L^2(\Omega)} is equibounded too. So \, h_j^{-1} \, ({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)\, is uniformly bounded in H^{1}(\Omega; {{\mathbb R}}^3) and we set

    \begin{equation} M_1: = \sup\limits_{j}\|\mathcal L\|_*\|{\mathbf{y}}_j-\mathbf R_j{{\bf x}}-\mathbf c_j\|_{H^1( \Omega;\mathbb R^3)} > 0. \end{equation} (4.9)

    To this aim we assume by contradiction that, up to subsequences,

    \begin{equation} t_j\ : = \ h_j^{-1} \, \| \,({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3\, \|_{L^2(\Omega)}\ \to\ +\infty\ \end{equation} (4.10)

    and set w_j \ : = t_j^{-1} h_j^{-1} \, ({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3. Then

    \begin{equation} \|w_j\|_{L^2(\Omega)} = 1,\qquad |\nabla {\mathbf{y}}_j\!-\! \mathbf R_j|^2\ = \ \sum\limits_{\alpha = 1}^2 \,|\,\nabla ({\mathbf{y}}_j\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2 \ +\ h_j^{\,2} t_j^{\,2}\,|\nabla w_j|^2 , \end{equation} (4.11)
    \begin{eqnarray} && C t_j^{\,2}\int_\Omega|\nabla w_j|^2\,d{\mathbf{x}} \,-\, t_j\,\mathcal L(w_j\,\mathbf e_3)\, \\ \le\, &&\!\! M-C\,h_j^{-2}\, \sum\limits_{\alpha = 1}^2 \,\int_\Omega|\, \nabla ({\mathbf{y}}_j\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2\,d{\mathbf{x}}\,+ \,h_j^{-1}\, \sum\limits_{\alpha = 1}^2 \, \mathcal L \big( \,({\mathbf{y}}_j\!-\! \mathbf R_j{\mathbf{x}}-\mathbf c_j)_\alpha\,\mathbf e_\alpha\,\big)\, \\ \le\, &&\!\! M-C\,h_j^{-2}\, \sum\limits_{\alpha = 1}^2 \,\int_\Omega|\, \nabla ({\mathbf{y}}_h\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2\,d{\mathbf{x}}\,+ \, \frac {C_P\,\|\mathcal L\|^2_{*} }{2\,\varepsilon} \, \\ +&&\!\! \frac {h_j^{-2}\, \varepsilon\,C_P} {2} \sum\limits_{\alpha = 1}^2\int_\Omega |\nabla ({\mathbf{y}}_j\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2\,d{\mathbf{x}}, \end{eqnarray} (4.12)

    and by choosing \varepsilon = 2C/C_P in (4.12) we get

    \begin{equation} C\, t_j^{\,2}\int_\Omega|\nabla w_j|^2\,d{\mathbf{x}} \,-\, t_j\,\mathcal L(w_j\,\mathbf e_3)\ \le\ \frac{C_P^2\,\|\mathcal L\|^2_{*}}{4\,C}+M \end{equation} (4.13)

    while, by choosing \varepsilon = C/C_P , (4.12) yields

    \begin{eqnarray} && \frac 1 2 \,C\,h_j^{-2}\, \sum\limits_{\alpha = 1}^2 \,\int_\Omega|\, \nabla ({\mathbf{y}}_h\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2\,d{\mathbf{x}}\,+\,C\, t_j^{\,2}\,|\nabla w_j|^2\,-t_j\, \mathcal L(w_h\,\mathbf e_3)\ \\ && \qquad \le \ \frac {C_P^2}{2C}\,\|\mathcal L\|^2_{*}+M. \end{eqnarray} (4.14)

    Thus

    \begin{equation} \frac 1 2 \,C\,\frac {h_j^{-2}}{t_j^{\,2}}\, \sum\limits_{\alpha = 1}^2 \,\int_\Omega|\, \nabla ({\mathbf{y}}_j\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2\,d{\mathbf{x}}\ \le\ \frac 1 {t_j}\, \mathcal L(w_j\,\mathbf e_3) \,+\, \frac 1 {{t_j}^{\,2}}\,\frac {C_P^2}{2C}\,\|\mathcal L\|^2_{*}+\frac{M}{t_j^2}. \end{equation} (4.15)

    Normalization \|w_j\|_{L^2} = 1 entails, for every \varepsilon > 0 ,

    \begin{eqnarray} \mathcal L \big( w_j\,\mathbf e_3\big) &\le& \|\mathcal L\|_{*} \big( \,\|w_j\|_{L^2}+ \| \nabla w_j \|_{L^2}\,\big) \, = \, \|\mathcal L\|_{*} \big( \,1+ \| \nabla w_j \|_{L^2}\,\big)\, \\ &\le& \|\mathcal L\|_{*}\,\,+\, \frac {\|\mathcal L\|^2_{*}}{2\,\varepsilon}\,+\, \frac{\varepsilon}2 \, \| \nabla w_j \|_{L^2}^2, \end{eqnarray} (4.16)

    and choosing \varepsilon = C\, t_j^2 therein we get, by (4.13),

    \begin{equation} \frac {C}2t_j^2\,\int_\Omega|\nabla w_j|^2\,\,d{\mathbf{x}}\ \le \ t_j\,\|\mathcal L\|_{*} + \frac {\|\mathcal L\|^2_{*}} {2C\,t_j}+M, \end{equation} (4.17)

    thus \int_\Omega |\nabla w_j|^2\, \, d{\mathbf{x}}\to 0 so by (4.11) w_j\to w in H^1(\Omega; {{\mathbb R}}^3) with \nabla w = 0 a.e. in \Omega that is w is a constant function since \Omega is a connected open set.

    Combining estimates (4.15)–(4.17), we get

    \begin{eqnarray} && \frac 1 2 C\,\frac {h_j^{-2}}{t_j^{\,2}}\, \sum\limits_{\alpha = 1}^2 \,\int_\Omega|\, \nabla ({\mathbf{y}}_j\!-\! \mathbf R_j{\mathbf{x}})_\alpha\,|^2\,d{\mathbf{x}} \ \\ && \qquad \le \ \frac 1 {t_j} \left( \|\mathcal L\|^2_{*}\,+\, \frac {\|\mathcal L\|^2_{*}}{2}\,+\, \frac {1}{2}\, \| \nabla w_j \|_{L^p}^2 \right) \,+\, \frac 1 {{t_j}^{\,2}}\,\frac {C_P^2}{2\,C_R\,C}\,\|\mathcal L\|^2_{*}+\frac{M}{t_j^2}, \end{eqnarray} (4.18)

    hence

    \begin{equation} \frac{1}{h_j\,t_j}\,\nabla \big( {\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}\big)_\alpha\ \to \ 0, \qquad \hbox{in }L^2(\Omega), \qquad \hbox{if }\alpha = 1,2 \end{equation} (4.19)

    and

    \begin{equation} h_j^{-1}t_j^{-1}\,\left({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j\right)\ \to\ w\,\mathbf e_3, \qquad \hbox{q.e. } {\mathbf{x}}\in E . \end{equation} (4.20)

    Moreover, by (4.13), we get

    \begin{equation*} \mathcal L ( w_j\,\mathbf e_3) \ \ge \ -\,\frac {C_P^{\,2}}{4\,C\, t_j}\, \|\mathcal L\|^2_{*}. \end{equation*}

    Hence, due to \mathcal L (w_j\, \mathbf e_3)\to \mathcal L (w\, \mathbf e_3) = w\, \mathcal L(\mathbf e_3) , we have w\, \mathcal L (\mathbf e_3)\!\ge\! 0, thus, by taking into account of \mathcal L (\mathbf e_3) < 0, we get w\le 0 and eventually, by \|w_j\|_{L^2} = 1 , we obtain w\ < \ 0 . Then, by (2.33), (4.4) and (4.9), we get

    \begin{equation} 0\ \le -\Phi(\mathbf R_j,E,\mathcal L) = \ \mathcal L \left(\,{\mathbf{x}}\,-\,\mathbf R_j{\mathbf{x}}\,-\,\mathbf c_j\,\right) \le \ (M+M_1)\,h_j . \end{equation} (4.21)

    Hence, due to \mathbf R_j\to \mathbf R, we have \Phi(\mathbf R, E, \mathcal L) = 0 thus \mathbf R\in \mathcal S_{\mathcal L, E} and (4.1) is proven.

    We notice that either \mathbf R_j\mathbf e_3\not = \mathbf e_3 for j large enough or \mathbf R_j\mathbf e_3 = \mathbf e_3 for infinitely many j . In the first case, by taking account of \mathcal L(\mathbf e_3) < 0, Lemma 3.10 entails

    \begin{equation} \limsup\limits_{j\to +\infty} \ \frac { \Phi(\mathbf R_j,E,\mathcal L)}{|\mathbf R_j\mathbf e_3-\mathbf e_3 |\,|\mathcal L(\mathbf e_3)|} \ < \ 0 . \end{equation} (4.22)

    By (4.21) we get \Phi(\mathbf R_j, E, \mathcal L)\ge - (M+M_1) h_j hence

    \begin{equation} \gamma: = \liminf\limits _{j\to +\infty}\frac {h_j}{|\mathbf R_j\mathbf e_3-\mathbf e_3 |} > 0 \end{equation} (4.23)

    and for large enough j (4.23) yields

    \begin{equation} |\mathbf R_j\mathbf e_3-\mathbf e_3 |\,\le\, \, \frac{2h_j}{\gamma}. \end{equation} (4.24)

    Therefore, for every {\mathbf{x}}\in\Omega

    \begin{equation} \frac {|(\mathbf R_j{\mathbf{x}})_3-{\mathbf{x}}_3|} {\ h_j\,t_j} \le \frac {|{{\bf x}}||\mathbf R_j^T\mathbf e_3-\mathbf e_3|} {\ h_j\,t_j}\mathop{\longrightarrow}^{j\to +\infty}\ 0, \end{equation} (4.25)

    hence, by taking into account of c_{j, 3}\! = \!-\min\, \left\{\, (\mathbf R_j{\mathbf{x}})_3-{\mathbf{x}}_3\, :\, {\mathbf{x}}\in E_{ess}\, \right\} , we get for q.e. {\mathbf{x}}\!\in\! E

    \begin{equation} \begin{array}{rl} \frac {{\mathbf{y}}_{j,3}^*-{\mathbf{x}}_3} {\ h_j\,t_j} = & \frac {{\mathbf{y}}_{j,3}^*-(\mathbf R_j{\mathbf{x}})_3-\mathbf c_{j,3}} {\ h_j\,t_j} \,+\, \frac {(\mathbf R_j{\mathbf{x}})_3-{\mathbf{x}}_3+\mathbf c_{j,3}} {\ h_j\,t_j} \\ = & \frac {{\mathbf{y}}_{h_j,3}^*-(\mathbf R_j{\mathbf{x}})_3-\mathbf c_{j,3}} {\ h_j\,t_j} \,+\, o(1) \ \mathop{\longrightarrow}^{j\to +\infty}\ w \ < \ 0,\\ \end{array} \end{equation} (4.26)

    a contradiction since {\mathbf{y}}_{j, 3}^*\ge (1-h_j)x_3 for q.e. {\mathbf{x}}\in E , that is (h_j\, t_j)^{-1}\big({\mathbf{y}}_{j, 3}^*-{\mathbf{x}}_3\big)\ge -\, x_3/t_j\longrightarrow 0 as j\to +\infty for q.e. {\mathbf{x}}\in E_{ess} . Therefore in this case the sequence t_j is bounded so h_j^{-1}\left({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j\right) is equibounded in H^1(\Omega; {{\mathbb R}}^3) and in particular h_j^{-1}\left({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j\right)_{\alpha}\mathbf e_{\alpha} is equibounded in H^1(\Omega; {{\mathbb R}}^3) .

    In the second case we may assume that \mathbf R_j\mathbf e_3 = \mathbf e_3 for every j so c_{j, 3} = 0 for every j . By arguing as in the previous case we may assume that

    \begin{equation*} \label{contradiction} t_j\ : = \ h_j^{-1} \, \| \,({\mathbf{y}}_j-\mathbf R_j{\mathbf{x}}-\mathbf c_j)_3\, \|_{L^2(\Omega)} = h_j^{-1}\| y_{j,3}- x_3\|_{L^2(\Omega)}\ \to\ +\infty \end{equation*}

    and by setting w_j \ : = t_j^{-1}h_j^{-1} \, (y_{j, 3}- x_3) we get w_j\to w < 0 as before which is again a contradiction, so t_j is a bounded sequence. Eventually we are left to show that, in the first case, h_j^{-1}(y_{j, 3}-x_3) is equibounded in H^1(\Omega; {{\mathbb R}}^3) . To this aim let C > 0 such that

    \left\|h_j^{-1}\left({\mathbf{y}}_{j,3}-(\mathbf R_j{\mathbf{x}})_3-\mathbf c_{j,3}\right)\right\|_{H^1( \Omega;\mathbb R^3)}\le C

    for every j\in \mathbb N and assume that for every n\in \mathbb N there exists j_n such that

    \begin{equation} \left\|h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-{\mathbf{x}}_3)\right\|_{H^1( \Omega;\mathbb R^3)}\ge n. \end{equation} (4.27)

    Then for every n > C we have \mathbf R_{j_n}\mathbf e_3\not = \mathbf e_3 otherwise c_{j_n, 3} = 0 and

    n\le \left\|h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-{\mathbf{x}}_3)\right\|_{H^1( \Omega;\mathbb R^3)} = \left\|h_{j_n}^{-1}\left({\mathbf{y}}_{j_n,3}-(\mathbf R_{j_n}{\mathbf{x}})_3-\mathbf c_{j_n,3}\right)\right\|_{H^1( \Omega;\mathbb R^3)} \le C,

    a contradiction. By taking account of (4.23) and (4.25) there exists \widetilde C > 0 such that

    |\mathbf R_{j_n}\mathbf e_3- \mathbf e_3|\le \widetilde C h_{j_n}

    for every n > C , hence

    \begin{equation} \begin{array}{ll} & \left |h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-{\mathbf{x}}_3)\right | \\ & \le \left |h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-(\mathbf R_{j_n}{\mathbf{x}})_3-\mathbf c_{j_n,3})\right |+h_{j_n}^{-1} |(\mathbf R_{j_n}{\mathbf{x}})_3-x_3| \\ & \le \left |h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-(\mathbf R_{j_n}{\mathbf{x}})_3-\mathbf c_{j_n,3})\right |+h_{j_n}^{-1}|\mathbf R_{j_n}\mathbf e_3- \mathbf e_3||{\mathbf{x}}| \\ & \le \left |h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-(\mathbf R_{j_n}{\mathbf{x}})_3-\mathbf c_{j_n,3})\right |+\widetilde C\sup\limits_{ \Omega}|{\mathbf{x}}|\\ \end{array} \end{equation} (4.28)

    thus showing that

    \left\|h_{j_n}^{-1}({\mathbf{y}}_{j_n,3}-{\mathbf{x}}_3)\right\|_{H^1( \Omega;\mathbb R^3)}\le C+\widetilde C\sup\limits_{ \Omega}|{\mathbf{x}}|| \Omega|

    which contradicts (4.27) and proves that h_j^{-1}(y_{j, 3}-x_3) is equibounded in H^1(\Omega; {{\mathbb R}}^3) .

    Remark 4.2. If {\mathop{{{\rm{cap}}}}\nolimits} ({\mathop{{{\rm{proj}}}}\nolimits}({\mathop{{{\rm{co}}}}\nolimits} E_{ess}))\! = \!0 , the claim of Lemma 4.1 may fail even if {\mathop{{{\rm{cap}}}}\nolimits} E > 0. Indeed, choose \Omega, E, \mathbf f, \mathbf g, \mathbf R_j as in Remark 3.11 and set h_j = j^{-1}. Thus both (2.32) and (2.33) are satisfied but (2.13) is not. It is readily seen that {\mathbf{y}}_j({\mathbf{x}}): = \mathbf R_j{\mathbf{x}} belongs to \mathcal A_j since y_{j, 3} = (1-j^{-1})x_3 on E and that \mathcal G_j({\mathbf{y}}_j) = \pi|\, \Omega|/2 for every j , but

    \begin{equation*} j\,(y_{j,3}-x_3) = j\,(x_2\sqrt{2j^{-1}-j^{-2}}-x_3 j^{-1}) \end{equation*}

    is not equibounded in H^1(\Omega; {{\mathbb R}}^3) as j\!\to\!+\infty : thus claim of Lemma 4.1 fails in this case.

    Lemma 4.3. Assume that E , \mathcal L and \mathcal W fulfil conditions (2.13), (2.19)–(2.22), (2.33) and \mathcal L(\mathbf e_3) < 0. Choose {\mathbf{y}}_j, \ \mathbf R_j as in Lemma 4.1 and set

    \begin{equation} {\mathbf{z}}_j({{\bf x}}): = h_j^{-1}\{(\mathbf R_j{\mathbf{x}})_3-x_3) - \min\limits_{{\mathbf{x}}\in E_{ess}} \big( (\mathbf R_j{\mathbf{x}})_3-x_3 \big)\}\mathbf e_3. \end{equation} (4.29)

    Then there exist b_1, \ b_2, \ b_3\in \mathbb R such that by setting

    \begin{equation} {\mathbf{z}}({{\bf x}}): = (b_{1}x_1+b_{2}x_2+b_{3})\mathbf e_3 \end{equation} (4.30)

    we have, up to subsequences, {\mathbf{z}}_j {\rightharpoonup} {\mathbf{z}} in w^*-W^{1, \infty}(\Omega; \mathbb R^3) .

    Proof. We may assume that \mathbf R_j\mathbf e_3\neq \mathbf e_3 for infinitely many j otherwise {\mathbf{z}}_j\equiv 0 for j large enough and thesis is obvious. Therefore by Euler-Rodrigues formula, there are sequences \mathbf a_j\in {{\mathbb R}}^3 and \vartheta_j\in (0, 2\pi) , s.t. |\mathbf a_j| = 1, \ \mathbf a_j\neq \mathbf e_3 and

    \begin{equation} \mathbf R_j{\mathbf{x}}\, = \, {\mathbf{x}}+ (\sin \vartheta_j)(\mathbf a_j\wedge {\mathbf{x}}) + (1-\cos \vartheta_j)\big((\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})\big), \qquad \forall {\mathbf{x}}\in{{\mathbb R}}^3. \end{equation} (4.31)

    By recalling (3.27) and (4.1) we have, up to subsequences, \mathbf R_j\mathbf e_3\to \mathbf e_3 . Then, up to subsequences, we may assume: that \mathbf a_j\to \mathbf a, \ \vartheta_j\to \vartheta\in [0, 2\pi], that either \vartheta\in \{0, 2\pi\} or a_3 = 1 and that \mu_j\, {a_{i, j}}\to\, \alpha_i, \ i = 1, 2 with \alpha_1^2+\alpha_2^2 = 1 , where we set \mu_j: = (a_{1, j}^{\, 2}+a_{2, j}^{\, 2})^{-\frac{1}{2}} . By recallling (4.24) we may assume that, up to subsequences,

    h_j^{-1} \min\limits_{{\mathbf{x}}\in E_{ess}} \big( (\mathbf R_j{\mathbf{x}})_3-x_3 \big)\to \beta

    for some \beta\in \mathbb R . Moreover by exploiting (3.27), (3.26), we get

    \begin{equation} \begin{array}{ll} & h_j^{-1}((\mathbf R_j{\mathbf{x}})_3-{\mathbf{x}}_3)\\ &\\ & = \frac{\mu_j}{\sqrt{2\,(1-\cos \vartheta_j)}}\big(\sin \vartheta_j(\mathbf a_j\wedge {\mathbf{x}})_3 + (1-\cos \vartheta_j)\big((\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})_3\big)\big)\frac{|\mathbf R_j\mathbf e_3-\mathbf e_3 |}{h_j}\\ &\\ & = \left (\mu_j\eta(\vartheta_j)(\mathbf a_j\wedge {\mathbf{x}})_3+\mu_j\big((\mathbf a_j\wedge (\mathbf a_j\wedge {\mathbf{x}})\big)_3\sin\frac{\vartheta_j}{2}\right)\frac{|\mathbf R_j\mathbf e_3-\mathbf e_3 |}{h_j} \end{array} \end{equation} (4.32)

    where \eta\in C([0, 2\pi]) is such that \eta(\vartheta) = \left (2(1-\cos\vartheta)\right)^{-\frac{1}{2}}\sin\vartheta for every \vartheta\in (0, 2\pi) .

    By arguing as in (3.32) and by taking (4.24) into account we get, up to subsequences,

    \begin{equation} h_j^{-1}((\mathbf R_j{\mathbf{x}})_3-x_3) \to \lambda\left (\eta(\vartheta)(\alpha_1x_2-\alpha_2x_1)+(\alpha_1x_1+\alpha_2x_2)\sin\frac{\vartheta}{2}\right ),\quad \forall\, {{\bf x}}\in \Omega \end{equation} (4.33)

    for some \lambda\ge 0 . On the other hand \nabla{\mathbf{z}}_j = h_j^{-1}(\mathbf R_j\mathbf e_3-\mathbf e_3) and (4.24) entail \|\nabla{\mathbf{z}}_j\|_{\infty}\le C for some C > 0 so {\mathbf{z}}_j{\rightharpoonup} {\mathbf{z}} in w^*-W^{1, \infty}(\Omega:\mathbb R^3) whenever b_1 = \lambda(-\alpha_{2}\eta(\vartheta)+\alpha_1\sin\frac{\vartheta}{2}), \ b_2 = \lambda(\alpha_{1}\eta(\vartheta)+\alpha_2\sin\frac{\vartheta}{2}), \ b_3 = -\beta.

    Lemma 4.4. (Lower bound) Assume that E , \mathcal L , \mathcal W fulfil the conditions (2.13)–(2.22), (2.32), (2.33) and \mathcal L(\mathbf e_3) < 0 . If h_j\to 0^+ as j\to +\infty then, for every sequence of deformations {\mathbf{y}}_j\in H^1(\Omega; \mathbb R^3) such that \mathcal G_j({\mathbf{y}}_j)\le M < +\infty and for every \mathbf R_j\in \mathcal M({\mathbf{y}}_j) there exist \mathbf c_j\in \mathcal C_{\mathbf R_j} such that by setting

    \begin{equation} {\mathbf{u}}_j({{\bf x}})\,: = \,{h_j}^{-1}\mathbf R_j^T\!\left\{ \big(\mathbf y_j-\mathbf c_j-\mathbf R_j{{\bf x}}\big)_\alpha\,\mathbf e_\alpha\,+\,(y_{j,3}-x_3)\mathbf e_3\,\right\}, \end{equation} (4.34)

    there is {\mathbf{u}}\in\mathcal A such that up to subsequences {\mathbf{u}}_j{\rightharpoonup} {\mathbf{u}} weakly in H^1(\Omega; {{\mathbb R}}^3) and

    \begin{equation} \liminf\limits_{j\to +\infty}\mathcal G_j({\mathbf{y}}_j)\ge \widetilde{\mathcal G}({\mathbf{u}}). \end{equation} (4.35)

    Proof. Due to Lemma 4.1, the sequence defined in (4.34) is equibounded in H^1(\Omega; \mathbb R^3) hence there exists {\mathbf{u}}\in H^1(\Omega; \mathbb R^3) such that up to subsequences {\mathbf{u}}_j{\rightharpoonup}{\mathbf{u}} in H^1(\Omega; \mathbb R^3) . By recalling Lemma A1 of [12] we get, again up to subsequences, {\mathbf{u}}_j^*({\mathbf{x}})\to {\mathbf{uu}}^*({\mathbf{x}}) for q.e. {\mathbf{x}}\in E hence by taking account of

    u_{j,3}^* = {h_j}^{-1}( y_{j,3}^*-x_3)\ge {h_j}^{-1}(x_3-h_jx_3-x_3) = -x_3

    for q.e. {\mathbf{x}}\in E we get u_{3}^*\ge -x_3 for q.e. {\mathbf{x}}\in E that is {\mathbf{uu}}\in \mathcal A .

    By taking account of \mathcal G_j({\mathbf{y}}_j)\le M and by arguing as in Lemma 4.1 the sequence h_j^{-1}({\mathbf{y}}_j-\mathbf R_j-\mathbf c_j) is bounded in H^1(\Omega; \mathbb R^3) hence (4.4) entails

    \begin{equation} 0\ \le \ \mathcal L \left({\mathbf{x}}-\mathbf R_j{\mathbf{x}}-\mathbf c_j\right)\ \le \ (M+M_1)\,h_j . \end{equation} (4.36)

    Therefore, by recalling (4.2), (4.3) and that, up to subsequences, \mathbf R_j\to \mathbf R we get \mathbf R\in \mathcal S_{\mathcal L, E} . By defining {\mathbf{z}}_j as in Lemma 4.3 and by setting

    \mathbf D_{j}: = \mathbb E({\mathbf{u}}_{j})+\frac{1}{2}h_{j}\nabla{\mathbf{u}}_{j}^{T}\nabla{\mathbf{u}}_{j},\ \mathbf F_j: = \mathbb E(\mathbf R_j^T{\mathbf{z}}_{j})+\frac{1}{2}h_{j}\nabla(R_j^T{\mathbf{z}}_{j})^{T}\nabla(R_j^T{\mathbf{z}}_{j})

    a straightforward calculation shows that

    \begin{equation} \nabla{\mathbf{y}}_j^T\nabla{\mathbf{y}}_j-\mathbf I = 2h_j(\mathbf D_{j}+\mathbf F_j). \end{equation} (4.37)

    If now

    \begin{equation*} \label{Bj} B_j: = \{x\in \Omega: \sqrt h_j|\nabla{\mathbf{u}}_j|\le 1\}, \end{equation*}

    we immediately notice that, by Tchebycheff inequality, | \Omega\setminus B_j|\to 0 as j\to +\infty and that for large enough j

    \begin{equation} h_j|\mathbf D_j|\le \sqrt{h_j}\left(\sqrt{h_j}|\nabla{\mathbf{v}}_j|+\tfrac12h_j^{3/2}|\nabla {\mathbf{v}}_j^T||\nabla {\mathbf{v}}_j|\right)\le 2\sqrt{h_j}, \qquad \hbox{on }B_j. \end{equation} (4.38)

    Moreover by Lemma 4.3 there exists C > 0 such that

    \begin{equation} h_j |\mathbf F_j|\le C h_j, \quad\text{in}\ \Omega \end{equation} (4.39)

    hence by defining {\mathbf{z}} as in (4.30) and by taking account of \mathbf R_j\to\mathbf R\in \mathcal S_{\mathcal L, E} , we get

    \begin{equation} \mathbf F_j{\rightharpoonup} \mathbb E({\mathbf{z}}),\quad w^*-L^{\infty}( \Omega;\mathbb R^{3\times 3}). \end{equation} (4.40)

    By taking account of \mathcal L(\mathbf e_\alpha) = 0 for \alpha = 1, 2 , (2.32) entails

    \begin{equation} \begin{array}{ll} &\mathcal L({\mathbf{y}}_j-{{\bf x}}) = \mathcal L((y_{j,3}-x_3)\mathbf e_3)+\mathcal L(({\mathbf{y}}_j-\mathbf R_j{{\bf x}}-\mathbf c_j)_\alpha\mathbf e_\alpha) \\ &\\ &+\mathcal L((\mathbf R_j{{\bf x}}-{{\bf x}})_\alpha\mathbf e_\alpha)\le h_j\mathcal L(\mathbf R_j{\mathbf{u}}_j) \end{array} \end{equation} (4.41)

    thus, since \eta is increasing, by (2.21)–(2.23), (2.25), (4.37)–(4.40) we get for large j

    \begin{equation} \begin{aligned} \mathcal G_j({\mathbf{y}}_j)&\ge\frac1{h_j^2}\int_{B_j}\mathcal V({{\bf x}},h_j \mathbf D_j+h_j \mathbf F_j)\,d{{\bf x}}-\mathcal L(\mathbf R_j{\mathbf{u}}_j) \\ & \ge \int_{B_j}\mathcal Q({{\bf x}},\mathbf D_j+\mathbf F_j)\,d{{\bf x}}-\int_{B_j} \eta(h_j\mathbf D_j+h_j\mathbf F_j)|\mathbf D_j+\mathbf F_j|^2\,d{{\bf x}}-\mathcal L(\mathbf R_j{\mathbf{u}}_j) \\&\ge \int_ \Omega \mathcal Q({{\bf x}},{\mathbf 1}_{B_j}(\mathbf D_j+\mathbf F_j))\,d{{\bf x}}- \eta(3\sqrt h_j)\int_ \Omega|{\mathbf 1}_{B_j}(\mathbf D_j+\mathbf F_j)|^2\,d{{\bf x}}-\mathcal L(\mathbf R_j{\mathbf{u}}_j). \end{aligned} \end{equation} (4.42)

    Since h_{j}\nabla{\mathbf{u}}_{j}^T\nabla{\mathbf{u}}_j\to 0 a.e. in \Omega and |{\mathbf 1}_{B_j}h_{j}\nabla{\mathbf{u}}_{j}^T\nabla{\mathbf{u}}_j| \le 1, by taking account of | \Omega\setminus B_j|\to 0 as j\to +\infty we get {\mathbf 1}_{B_j}h_{j}\nabla{\mathbf{u}}_{j}^T\nabla{\mathbf{u}}_j{\rightharpoonup} 0 weakly in L^2(\Omega, \mathbb R^{3\times3}) . By taking account of {\mathbf 1}_{B_j}\nabla{\mathbf{u}}_j{\rightharpoonup} \nabla{\mathbf{u}} weakly in L^2(\Omega, \mathbb R^{3\times3}) and (4.40), we then obtain

    \begin{equation} {\mathbf 1}_{B_j}(\mathbf D_j+\mathbf F_j){\rightharpoonup} \mathbb E({\mathbf{u}})+\mathbb E({\mathbf{z}}) = \mathbb E({\mathbf{u}})+\frac12 b_\alpha(\mathbf e_\alpha\otimes\mathbf e_3+\mathbf e_3\otimes\mathbf e_\alpha)\ \ (\alpha = 1,2) \end{equation} (4.43)

    weakly in L^2(\Omega, \mathbb R^{3\times 3}) . Since \mathbf R_j\to\mathbf R\in \mathcal S_{\mathcal L, E} , then

    \begin{equation*} \lim\limits_{j\to +\infty}\mathcal L(- \mathbf R_j {\mathbf{u}}_j) = -\mathcal L( \mathbf R {\mathbf{u}}) \end{equation*}

    and by (2.21) and (4.42), the weak L^2(\Omega, \mathbb R^{3\times3}) lower semicontinuity of the map \mathbf B\mapsto\int_\Omega \mathcal Q({{\bf x}}, \mathbf B)d{{\bf x}} entails

    \begin{equation*} \label{pi} \liminf\limits_{j\to +\infty}\mathcal G_j({\mathbf{y}}_j)\ge \int_ \Omega \mathcal Q({{\bf x}},\mathbb E({\mathbf{u}})+\mathbb E({\mathbf{z}}))\,d{{\bf x}}-\mathcal L(\mathbf R{\mathbf{u}})\ge \widetilde{\mathcal G}({\mathbf{u}}) \end{equation*}

    which, by recalling (4.30), ends the proof.

    Remark 4.5. If condition (2.32) is not satisfied then the thesis of Lemma 4.4 may fail. Indeed let \mathbf f: = -\mathbf e_3+6(x_3-\frac{1}{2})\mathbf e_1, \ \mathbf g = \mathbf 0 and

    \begin{equation*} E = \Omega: = \{{\mathbf{x}}: x_1^2+x_2^2 < 1,\ 0 < x_3 < 1\}. \end{equation*}

    It is straightforward checking that \mathcal L(\mathbf e_3) < 0 = \mathcal L(\mathbf e_1) = \mathcal L(\mathbf e_2) and

    \begin{equation} \begin{array}{ll} & \mathcal L((\mathbf R-\mathbf I){{\bf x}})-\min\limits_{{{\bf x}}\in E_{ess}}((\mathbf R{{\bf x}})_3-x_3)\}\,\mathcal L(\mathbf e_3) \\ &\\ & = \frac{\pi}{2}R_{13}-\frac{\pi}{2}(1-R_{33})-\pi\sqrt{1-R_{33}^2} \\ &\\ & \le\frac{\pi}{2}\sqrt{1-R_{33}^2} -\frac{\pi}{2}(1-R_{33})-\pi\sqrt{1-R_{33}^2} \\ &\\ & = -\frac{\pi}{2}\sqrt{1-R_{33}}\left\{\sqrt{1-R_{33}}+\sqrt{1+R_{33}}\right\}\le -\frac{\pi\sqrt 2}{2}\sqrt{1-R_{33}}\le 0 \end{array} \end{equation} (4.44)

    for every \mathbf R\in SO(3) . On the other hand if R_{13} > 0 we have

    \mathcal L((\mathbf R{{\bf x}}-{{\bf x}})_\alpha\mathbf e_\alpha) = \frac{\pi}{2}R_{13} > 0,

    so (2.33) is satisfied while (2.32) is not. Choose now h_j: = j^{-1},

    \begin{equation*} \mathbf R_j: = \mathbf e_2\otimes\mathbf e_2+(1-j^{-2})(\mathbf e_1\otimes\mathbf e_1+\mathbf e_3\otimes\mathbf e_3)+j^{-1}\sqrt{2 -j^{-2}}\,(-\mathbf e_3\otimes\mathbf e_1+\mathbf e_1\otimes\mathbf e_3) \end{equation*}

    and set {\mathbf{y}}_j: = \mathbf R_j{{\bf x}}+j^{-1}\sqrt{2 -j^{-2}}\mathbf e_3 . It is readily seen that

    \begin{equation} \begin{array}{ll} y_{j,3}& = -j^{-1}\sqrt{2 -j^{-2}}x_1+(1-j^{-2})x_3+j^{-1}\sqrt{2 -j^{-2}} \\ &\\ & \ge (1-j^{-2})x_3\ge x_3-j^{-1}x_3\\ \end{array} \end{equation} (4.45)

    hence {\mathbf{y}}_j\in \mathcal A_j and by taking (4.2) into account we get ({\mathbf{y}}_j-\mathbf R_j{{\bf x}}- \mathbf c_j)_{\alpha} \equiv 0, \ \alpha = 1, 2 . Therefore bearing in mind that \mathbf R_j^T\to \mathbf I we have

    {\mathbf{u}}_j = j\mathbf R_j^T((y_{j,3}-x_3)\mathbf e_3) = \mathbf R_j^T\left\{(\sqrt{2 -j^{-2}}(1-x_1)+j^{-1}x_3)\mathbf e_3\right\}\to {\mathbf{u}}: = \sqrt 2(1-x_1)\mathbf e_3

    and by Lemma 3.8 we get \mathbf R{\mathbf{u}} = {\mathbf{u}} for every \mathbf R\in \mathcal S_{\mathcal L, E}, hence

    \widetilde{\mathcal G}({\mathbf{u}})\ge -\max\limits_{\mathbf R\in \mathcal S_{\mathcal L, E}}\mathcal L(\mathbf R{\mathbf{u}}) = -\mathcal L({\mathbf{u}}) = \pi\sqrt 2.

    On the other hand by taking account of

    y_{j,1}-x_1 = -j^{-2}x_1+j^{-1}\sqrt{2-j^{-2}}x_3,\ y_{j,2}-x_2 = 0

    and

    y_{j,3}-x_3 = j^{-1}\sqrt{2 -j^{-2}}(1-x_1)+j^{-2}x_3

    it is straightforward checking that

    \mathcal G_j({\mathbf{y}}_j) = -j\mathcal L({\mathbf{y}}_j-{{\bf x}}) = \pi\sqrt{2-j^{-2}}+\pi j^{-1}-\frac{\pi}{2}\sqrt{2-j^{-2}}\to \frac{\pi\sqrt 2}{2} < \widetilde{\mathcal G}({\mathbf{u}})

    thus proving that the claim of Lemma 4.4 fails in this case.

    Lemma 4.6. (Upper bound) Assume (2.13), (2.19)–(2.22), (2.32), (2.33), \mathcal L(\mathbf e_3)\! < \!0 and let 0 < h_j\to 0^+ as j\to +\infty . For every {\mathbf{u}}\in C^1(\overline\Omega, \mathbb R^3) there exists \widetilde{{\mathbf{y}}}_j\in C^1(\overline\Omega, \mathbb R^3) such that

    \limsup\limits_{j\to +\infty} {\mathcal G}_j(\widetilde{\mathbf y}_j) \le \widetilde{\mathcal G}({\mathbf{u}}).

    Proof. We assume without loss of generality that {\mathbf{u}}\in \mathcal A and let

    \mathbf b^*\in {\mathop{{{\rm{argmin}}}}\nolimits} \left\{\int_ \Omega \mathcal Q({{\bf x}}, \mathbb E({\mathbf{u}})+\frac{1}{2}b_{\alpha}(\mathbf e_{\alpha}\otimes\mathbf e_3+\mathbf e_{3}\otimes\mathbf e_\alpha))\,d{{\bf x}}: \mathbf b\in \mathbb R^2\right\},
    \begin{equation} \widetilde{\mathbf{u}}({{\bf x}}): = {\mathbf{u}}({{\bf x}})+x_3(b^*_1\mathbf e_1+b^*_2\mathbf e_2). \end{equation} (4.46)

    It is readily seen that \widetilde{\mathbf{u}}\in \mathcal A , that \mathbb E(\widetilde{\mathbf{u}}) = \mathbb E({\mathbf{u}})+\frac{1}{2}b_{\alpha}^*(\mathbf e_{\alpha}\otimes\mathbf e_3+\mathbf e_{3}\otimes\mathbf e_\alpha) hence

    \begin{equation} \mathcal I({\mathbf{u}}) = \int_ \Omega \mathcal Q({{\bf x}}, \mathbb E(\widetilde{\mathbf{u}}))\,d{{\bf x}}. \end{equation} (4.47)

    Moreover, by (3.1) of Lemma 3.1 and Remark 3.2 we obtain

    \begin{equation} \mathcal L(\mathbf R \widetilde{\mathbf{u}}) = \mathcal L(\mathbf R{\mathbf{u}})+\mathcal L(x_3(b^*_1\mathbf R\mathbf e_1+b^*_2\mathbf R\mathbf e_2)) = \mathcal L(\mathbf R{\mathbf{u}}), \qquad \forall\, \mathbf R\in \mathcal S_{\mathcal L, E}. \end{equation} (4.48)

    Therefore by choosing

    \widetilde{\mathbf R}\in {\mathop{{{\rm{argmin}}}}\nolimits}\left\{ -\mathcal L(\mathbf R\widetilde{\mathbf{u}}): \mathbf R\in \mathcal S_{\mathcal L, E}\right\}

    we get

    \widetilde{\mathcal G}({\mathbf{u}}) = \int_ \Omega \mathcal Q({{\bf x}},\mathbb E(\widetilde{\mathbf{u}}))\,d{{\bf x}}-\mathcal L(\widetilde{\mathbf R}\widetilde{\mathbf{u}}).

    By setting \widetilde{\mathbf y}_j: = \widetilde{\mathbf R}({{\bf x}}+h_j\widetilde{\mathbf{u}}) , taking account \mathcal S_{\mathcal L, E}\subset \{\mathbf R: \mathbf R\mathbf e_3 = \mathbf e_3\} and Lemma 3.8, we get \mathcal L(\widetilde{\mathbf R}{{\bf x}}-{{\bf x}}) = 0 and for q.e. {\mathbf{x}}\in E

    \widetilde y_{j,3}^* = x_3+h_j\widetilde u_{3}\ge (1-h_j)x_3.

    Therefore \widetilde{{\mathbf{y}}}_j\in \mathcal A_j and by (2.24) we get

    \limsup\limits_{j\to+\infty} \mathcal |\mathcal G_{h_j}(\mathbf y_j)-\widetilde{\mathcal G}({\mathbf{u}})| \le \limsup\limits_{j\to+\infty}\int_{\Omega} \left|\frac{1}{h_j^2}\mathcal W({{\bf x}},\mathbf I+h_j\nabla\widetilde{\mathbf{u}})- \mathcal Q({{\bf x}},\mathbb E(\widetilde{\mathbf{uu}}))\right |\,d{{\bf x}} = 0

    which proves the lemma.

    We are now in a position to prove our main theorem.

    Proof of Theorem 2.4. If (\overline{\mathbf y}_j)_{j\in\mathbb N}\subset H^1(\Omega, \mathbb R^3) is a minimizing sequence for \mathcal G_j then \mathcal G_j(\overline{\mathbf y}_j)\le \mathcal G_j(\mathbf x) = 0, moreover if \mathbf R_j belong \mathcal A(\overline{\mathbf y}_j) and \overline{\mathbf c}_j is defined by (4.2) and (4.3), then Lemma 4.1 entails that the sequence

    \begin{equation*} {\overline{\mathbf{u}}}_j({{\bf x}}): = {h_j}^{-1}\mathbf R_j^T\left\{\big({\overline{\mathbf{y}}}_j\,-\,\mathbf R_j{\mathbf{x}}\,-{\overline{\mathbf c}}_j\,\big)_{\alpha}\mathbf e_{\alpha}+({\overline y}_{j,3}-x_3)\mathbf e_3\right\} \end{equation*}

    is bounded in H^1(\Omega; \mathbb R^3) . Therefore up to subsequences {\overline{\mathbf{u}}}_j\to \overline{\mathbf{u}} weakly in H^1(\Omega; {{\mathbb R}}^3), so, by Lemma 4.4, we have \overline{\mathbf{u}}\in \mathcal A and

    \begin{equation*} \label{liminf} \liminf\limits_{j\to +\infty}{\mathcal G}_j({\overline{\mathbf y}}_j)\ge \widetilde{\mathcal G}(\overline{\mathbf{u}}). \end{equation*}

    On the other hand, by Lemma 4.6, for every {\mathbf{u}}\in C^1(\overline\Omega, \mathbb R^3)\cap\mathcal A there exists a sequence \mathbf y_j\in C^1(\overline\Omega, \mathbb R^3) such that

    \limsup\limits_{j\to +\infty} {\mathcal G}_j(\mathbf y_j) \le \widetilde{\mathcal G}({\mathbf{u}}).

    Since

    \begin{equation} {\mathcal G}_j(\overline{\mathbf{y}}_j)+o(1) = \inf\limits_{H^1( \Omega,\mathbb R^3)}\mathcal G_{j}\le {\mathcal G}_j(\mathbf y_j),\qquad \mbox{as } j\to+\infty,\end{equation} (4.49)

    by passing to the limit as j\to +\infty , we get

    \begin{equation} \widetilde{\mathcal G}(\overline{\mathbf{u}})\le\widetilde {\mathcal G}({\mathbf{u}}), \qquad \forall {\mathbf{u}}\in C^1(\overline\Omega,\mathbb R^3)\cap\mathcal A. \end{equation} (4.50)

    Now fix a generic {\mathbf{u}}\!\in\! \mathcal A and denote again by {\mathbf{u}} a Sobolev extension of {\mathbf{u}} to the whole \mathbb R^3 . We claim that there exists {\mathbf{u}}_j\in C^1(\overline\Omega, \mathbb R^3)\cap\mathcal A such that {\mathbf{u}}_j\to {\mathbf{u}} in H^1(\Omega; \mathbb R^3) : Indeed, since \widetilde u_3({\mathbf{x}})+x_3\ge 0 for q.e. {\mathbf{x}}\in E , by Lemma A.4 it is enough to choose u_{3, j}: = \eta_j-x_3 where \eta_j\in C^1(\mathbb R^3), \ \eta_j\ge 0\ \text{q.e. in}\ E, \ \eta_j \to u_3+x_3 in H^1(\mathbb R^3) (here u_3+x_3 denotes also an extension to the whole H^1(\mathbb R^3) ) and u_{\alpha, j}: = u_\alpha*\rho_j, \ \alpha = 1, 2 where \rho_j is a sequence of smooth mollifiers. By (4.50) we have

    \widetilde{\mathcal G}(\overline{\mathbf{u}})\le \widetilde{\mathcal G}({\mathbf{u}}_j)

    whence by Remark 2.2,

    \widetilde{\mathcal G}(\overline{\mathbf{u}})\le \lim\limits_{j\to +\infty}\widetilde{\mathcal G}({\mathbf{u}}_j) = \widetilde{ \mathcal G}({\mathbf{u}}), \qquad \forall\, {\mathbf{u}}\in \mathcal A,

    that is \overline{\mathbf{u}}\in{\mathop{{{\rm{argmin}}}}\nolimits} \widetilde{\mathcal G} .

    We show that {\mathcal G}_j(\mathbf y_j)\to \widetilde{\mathcal G}(\overline{\mathbf{u}}) : By Lemma A.4 in the Appendix, for every {\varepsilon} > 0 there is \overline{\mathbf{u}}_{\varepsilon}\in C^1(\overline \Omega; \mathbb R^3))\cap\mathcal A such that

    \begin{equation*} \widetilde{\mathcal G}(\overline{\mathbf{u}}_{\varepsilon}) < \widetilde{\mathcal G}(\overline{\mathbf{u}})+{\varepsilon} \end{equation*}

    and by Lemma 4.6 there exists \mathbf y_{j, {\varepsilon}}\in C^1(\overline \Omega; \mathbb R^3) such that by taking account of (4.49) we have

    \limsup\limits_{j\to +\infty} {\mathcal G}_j(\overline{\mathbf y}_j) \le \limsup\limits_{j\to +\infty} {\mathcal G}_j(\mathbf y_{j,{\varepsilon}}) \le \widetilde{\mathcal G}({\mathbf{u}}_{\varepsilon}) < \widetilde{\mathcal G}(\overline{\mathbf{u}})+{\varepsilon}

    for every {\varepsilon} > 0 . Since by Lemma 4.4,

    \liminf\limits_{j\to +\infty}{\mathcal G}_j(\overline{\mathbf y}_j)\ge \widetilde{\mathcal G}(\overline{\mathbf{u}}),

    we get {\mathcal G}_j(\mathbf y_j)\to \widetilde{\mathcal G}(\overline{\mathbf{u}}) as claimed.

    We are only left to show that \min\widetilde{\mathcal G} = \min\mathcal G. To this aim we show first that for every {\mathbf{u}}\in \mathcal A there exists {\mathbf{u}}_*\in \mathcal A such that \mathcal G({\mathbf{u}}_*) = \widetilde{\mathcal G}({\mathbf{u}}) . Indeed if \widetilde{\mathbf{u}} is defined as in (4.46) then by (4.47) and (4.48) we get

    \begin{equation} \widetilde{\mathcal G}({\mathbf{u}}) = \mathcal I({\mathbf{u}})-\max\limits_{\mathbf R\in \mathcal S_{\mathcal L, E}}\mathcal L(\mathbf R{\mathbf{u}}) = \int_ \Omega \mathcal Q({{\bf x}},\mathbb E(\widetilde{\mathbf{u}})\,d{{\bf x}}-\max\limits_{\mathbf R\in \mathcal S_{\mathcal L, E}}\mathcal L(\mathbf R\widetilde{\mathbf{u}}) = \mathcal G(\widetilde{\mathbf{u}}) \end{equation} (4.51)

    as claimed. By recalling that \widetilde{\mathcal G}(\overline{\mathbf{u}}) = \min \widetilde{\mathcal G}\le \inf \mathcal G let us assume that inequality is strict. Then by (4.51) there exists {\overline{\mathbf{u}}}_*\in \mathcal A such that \mathcal G({\overline{\mathbf{u}}}_*) = \widetilde{\mathcal G}(\overline{\mathbf{u}}) < \inf \mathcal G , a contradiction. Thus again by (4.51) \mathcal G({\overline{\mathbf{u}}}_*) = \widetilde{\mathcal G}(\overline{\mathbf{u}}) = \min \mathcal G .

    In this section we will exhibit a choice of energy density \mathcal W , open set \Omega , dead loads \mathbf f, \mathbf g and set E\!\subset\! \overline \Omega fulfilling all the assumptions of Theorem 2.4 but such that the minimum of the limit functional \mathcal G is strictly less than the minimum of the Signorini functional (see [45] for a counterexample exhibiting an analogous gap for unconstrained pure traction problem).

    We shall consider the energy density already defined in (2.26) by setting

    \begin{equation} \mathcal{W}(\mathbf{F}): = \left\{\begin{array}{ll} \mathcal{W}_{ \rm{iso}}\left(\frac{\mathbf{F}}{(\det\mathbf{F})^{1/3}}\right)+\mathcal{W}_{ \rm{vol}}(\mathbf{F}),\qquad&\mbox{if } \det\mathbf F > 0, \\ +\infty,\qquad&\mbox{if } \det\mathbf F\le 0 , \end{array}\right. \end{equation} (5.1)

    where {\mathcal W}_{ \rm{iso}} is the energy density of Yeoh type defined in (2.27) with 2c_1 = \mu > 0 and \mathcal W_{ \rm{vol}}(\mathbf F) = g(\det\mathbf F) where g:\mathbb R_+\to\mathbb R is the convex C^2 function (satisfying (2.28) with r = 2 ) defined by

    \begin{equation*} g(t) = \frac{\mu}{6}(t^2-1-2\log t). \end{equation*}

    By recalling Example 2.1 it is readily seen that \mathcal W satisfies (2.14)–(2.17) and by taking into account of

    \det(\mathbf I+ h\mathbf B) = 1+h\, {{\rm{Tr}}} \mathbf B+(h^2/2)\big( ( {{\rm{Tr}}} \mathbf B)^2- {{\rm{Tr}}} \,\mathbf B^2\big) + h^3\det\mathbf B

    and {{\rm{Tr}}} (\mathbf B^T \mathbf B) \! = \! |\mathbf B|^2 for every \mathbf B\in \mathbb R^{3\times3} , we obtain as h\to 0

    \begin{equation*} \label{taylor} \frac{|\mathbf I+ h \mathbf B|^2}{\det (\mathbf I+h \mathbf B)^{2/3}}-3\, = \, h^2 \big(\,2\,|\mathbf B|^2-\tfrac{2}{3}| {{\rm{Tr}}} \mathbf B|^2\,\big)+o(h^2) \end{equation*}

    for every \mathbf B\in \mathbb R^{3\times3}_{\mathrm{sym}} . Moreover by recalling (2.27) (with 2c_1 = \mu )

    \begin{equation*} \mathcal W_{ \rm{vol}}(\mathbf I+ h \mathbf B) = g(\det(\mathbf I+ h\mathbf B)) = \frac{h^2}{2}| {{\rm{Tr}}} \mathbf B|^2+ o(h^2) = \frac{\mu}{3}\,| {{\rm{Tr}}} \mathbf B|^2\,h^2\,+ \,o(h^2) , \end{equation*}
    \begin{equation*} \mathcal W_{ \rm{iso}}(\mathbf I+ h \mathbf B)\, = \, \frac{\mu}{2}\,h^2\left( \,2\,|\mathbf B|^2-\frac 2 3 ( {{\rm{Tr}}} \mathbf B)^2 \,\right) \,+ \,o(h^2), \end{equation*}

    so

    \begin{equation} \frac{1}{2}\mathbf B\,D^2\mathcal W(\mathbf I)\,\mathbf B\, = \,\mu\,|\mathbf B|^2. \end{equation} (5.2)

    Let

    \begin{equation} \Omega: = \{{{\bf x}}\in\mathbb R^3: x_1^2+x_2^2 < 1,\,0 < x_3 < 1\}, \quad E: = \{{{\bf x}}\in\mathbb R^3: x_1^2+x_2^2 < 1,\ x_3 = 0\}\\ \end{equation} (5.3)

    and \varphi\in C^2(\overline E) such that

    \begin{equation} \begin{array}{ll} & \Delta\varphi\not\equiv 0\,,\quad \varphi(x_1,x_2) = \phi(r)\,,\quad r: = \sqrt{x_1^2+x_2^2}\,, \quad \phi(1)\! = \!\phi'(1)\! = \! \int_0^1 r^2\phi'(r)\,dr = 0\\ \end{array} \end{equation} (5.4)

    (for instance \phi(r): = 1-6r^2+9r^4-4r^6 fulfills (5.4)). It is readily seen that condition (2.13) is fulfilled and that E_{ess} = \overline E . We define

    \begin{equation} \begin{array}{ll} \mathbf R_*&: = -\mathbf e_1\otimes \mathbf e_2+ \mathbf e_2\otimes \mathbf e_1+\mathbf e_3\otimes \mathbf e_3,\\ &\\ \mathcal L({\mathbf{u}})\!&: = \!\int_\Omega u_{\alpha}\,\nabla_{\alpha}\varphi \,d{{\bf x}}- \int_E u_3(x_1,x_2,0)\,dx_1\,dx_2. \\ \end{array} \end{equation} (5.5)

    Condition (2.32) is satisfied since

    \mathcal L((\mathbf R{{\bf x}}-{{\bf x}})_{\alpha}\mathbf e_\alpha) = \pi\,(R_{11}+R_{22}-2)\int_0^1r^2\phi'(r)\,dr = 0,\qquad \forall\, \mathbf R\in SO(3).

    Moreover \mathcal L(\mathbf e_1) = \mathcal L(\mathbf e_2) = 0, \ \mathcal L(\mathbf e_3) < 0 and

    \begin{equation} \begin{array}{ll} \Phi(\mathbf R, E, \mathcal L)& = \pi\,(R_{11}+R_{22}-2)\int_0^1r^2\phi'(r)\,dr+ \,\pi\,\min\limits_{E_{ess}}\big\{R_{31}x_1+R_{32}x_2+(R_{33}-1)x_3\big\}\\ &\\ & = -\pi\sqrt{R_{31}^2+R_{32}^2}\le 0.\\ \end{array} \end{equation} (5.6)

    so (2.33) is fulfilled too. By taking account of \mathbf R_*\mathbf e_3 = \mathbf e_3 , we get

    \Phi(\mathbf R_*,E, \mathcal L) = -2\pi\int_0^1r^2\phi'(r)\,dr = 0

    whence \mathbf R_*\in \mathcal S_{\mathcal L, E} and Lemma 3.8 entails \mathcal S_{\mathcal L, E} = \{\mathbf R: \mathbf R\mathbf e_3 = \mathbf e_3\} . Since E fulfills (2.13), \mathcal W satisfies (2.14)–(2.17) and \mathcal L satisfies (2.27) together with \mathcal L(\mathbf e_3) < 0 then, by taking into account of (5.2), Theorem 2.4 entails

    \begin{equation} \inf\mathcal G_j\to \min\limits_{{\mathbf{u}}\in\mathcal A} \mathcal G = \min\left\{\mu\int_ \Omega|\mathbb E({\mathbf{u}})|^2\,d{\mathbf{x}}-\max\limits_{\mathbf R\in \mathcal S_{\mathcal L, E}}\mathcal L(\mathbf R{\mathbf{u}}) : {\mathbf{u}}\in\mathcal A\right\}. \end{equation} (5.7)

    We set

    \begin{equation} \mathcal E({\mathbf{uu}}): = \mu\int_ \Omega|\mathbb E({\mathbf{u}})|^2\,d{\mathbf{x}}-\mathcal L({\mathbf{u}}) \end{equation} (5.8)

    and, for every \mathbf R\in \mathcal S_{\mathcal L, E},

    \begin{equation} \mathcal E_{\mathbf R}({\mathbf{uu}}): = \mu\int_ \Omega|\mathbb E({\mathbf{u}})|^2\,d{\mathbf{x}}-\mathcal L(\mathbf R{\mathbf{u}}). \end{equation} (5.9)

    We aim to show

    \begin{equation} \min\{\mathcal E_{\mathbf R_*}({\mathbf{uu}}): {\mathbf{u}}\in \mathcal A\} < \min\{\mathcal E({\mathbf{uu}}): {\mathbf{u}}\in \mathcal A\} \end{equation} (5.10)

    so that, once (5.10) is proved, we deduce

    \begin{equation} \min\limits_{{\mathbf{u}}\in\mathcal A} \mathcal G < \min\limits_{{\mathbf{u}}\in\mathcal A} \mathcal E. \end{equation} (5.11)

    In order to show inequality (5.10) we need some properties of minimizers of \mathcal E which have been essentially proven in [45]. In the following \overline{\mathbb E}(\cdot) will denote the upper-left 2\times2 submatrix of \mathbb E(\cdot) and \overline{\mathbf R}\in SO(2) the upper-left 2\times2 submatrix of any \mathbf R\in \mathcal S_{\mathcal L, E} .

    Lemma 5.1. Let {\mathbf{u}}\in \mathcal A and let

    \begin{equation} {\mathbf{v}}({{\bf x}})\!: = v_\alpha(x_1,x_2)\mathbf e_\alpha+v_3(x_3)\mathbf e_3,\ \alpha\! = \!1,2 \end{equation} (5.12)

    where

    v_\alpha(x_1,x_2): = \int_0^1\!u_\alpha({\mathbf{x}}) \,dx_3,\ \alpha\! = \!1,2,\quad v_3(x_3): = \pi^{-1}\!\!\!\int_{E} \!u_3({\mathbf{x}}) dx_1dx_2.

    Then {\mathbf{v}}\in \mathcal A and

    \begin{equation} {\mathcal E}_{\mathbf R}({\mathbf{u}})\ge \mathcal J_{\overline{\mathbf R}}(\overline{\mathbf{v}}),\qquad \forall\, \mathbf R\in. \mathcal S_{\mathcal L, E}, \end{equation} (5.13)

    where \overline{\mathbf{v}}: = v_\alpha\mathbf e_\alpha , and

    \begin{equation} {\mathcal J}_{\overline{\mathbf R}}(\overline{\mathbf{v}}): = \mu\int_E|\overline{\mathbb E}(\overline{\mathbf{v}})|^2\,dx_1dx_2-\int_E\overline{\mathbf R}^{T}\nabla\varphi\cdot \overline{\mathbf{v}}\ \,dx_1dx_2. \end{equation} (5.14)

    In particular if \mathbf R = \mathbf I , then (5.13) reduces to \mathcal E({\mathbf{u}})\ge \mathcal J(\overline{\mathbf{v}}) having set \mathcal J: = \mathcal J_{\overline{\mathbf I}} .

    Proof. Since u_{3}^*\ge 0 q.e. on E = \partial \Omega\cap \{x_3 = 0\} then by Remark 2.3 we get u_3\ge 0\ \ \mathcal H^2 - q.e. in E that is v_3(0)\ge 0 hence, again by Remark 2.3, {\mathbf{v}}\in \mathcal A . Moreover, by using the notation u_{3, 3}: = \partial_3u_3 , Jensen inequality entails

    \begin{equation} \begin{array}{ll} {\mathcal E}_{\mathbf R}({\mathbf{u}}) \!\!&\ge\ \mu \int_E\left|\int_0^1\overline{\mathbb E}({\mathbf{u}})\,dx_3\right|^2\,dx_1\,dx_2 \,+\,\mu\,\pi\int_0^1\left|\frac1\pi\int_E u_{3,3}\,dx_1\,dx_2\right|^2\,dx_3\\ &\\ & \ \ \ \ -\int_E {\overline R}_{\beta\alpha}\nabla_\beta \varphi\left(\int_0^1u_\alpha\,dz\right)\,dx_1\,dx_2\,+\int_E u_3(x_1,x_2,0)\,dx_1\,dx_2\\ &\\ &\ge \ \ \mathcal J_{\overline{\mathbf R}}(\overline v)+\mu\,\pi\int_0^1|\dot v_3|{\,^2}\,dx_3+\pi v_3(0)\ge \mathcal J_{\overline{\mathbf R}}(\overline v), \end{array} \end{equation} (5.15)

    thus proving the lemma.

    We need now the following characterization of minimizers of \mathcal J which has been given in [45].

    Lemma 5.2. There exists \overline{\Phi}\in H^2(E) such that

    \begin{equation} \min\limits_{{\mathbf{u}}\in H^1(E)} \mathcal J({\mathbf{u}}) = \mathcal J(\nabla\overline{\Phi})\ge \min\limits_{\Phi\in H^2(E)}\int_E(2\,\mu\,\Phi_{,12}^2+\mu\,\Phi_{,11}^2+\mu\,\Phi_{,22}^2+\Phi\Delta\varphi)\,dx_1dx_2, \end{equation} (5.16)

    where we have used the notation \Phi_{, \alpha\beta}: = \partial^2_{\alpha\beta}\Phi .

    A straightforward application of Lemma 5.1 (with \mathbf R = \mathbf I ) and Lemma 5.2 yields the following precise calculation of the energy level of {\mathbf{u}}\in {\mathop{{{\rm{argmin}}}}\nolimits}_{\mathcal A}\mathcal E .

    Lemma 5.3. There holds

    \begin{equation} \min\limits_{{\mathbf{u}}\in \mathcal A}\mathcal E({\mathbf{u}}) = \min\limits_{\Phi\in H^2(E)}\int_E(2\mu\Phi_{,12}^2+\mu\Phi_{,11}^2+ \mu\Phi_{,22}^2+\Phi\Delta\varphi)\,dx_1dx_2. \end{equation} (5.17)

    Proof. It is readily seen that any displacement of the kind (\nabla\Phi(x_1, x_2), v_3(x_3))\in \mathcal A if and only if \Phi\in H^2(E), \ v_3\in H^1(0, 1) and v_3(0)\ge 0 . Therefore, by Lemmas 5.1 and 5.2, we get

    \begin{equation*} \begin{array}{ll} \min\limits_{{\mathbf{u}}\in \mathcal A}\mathcal E({\mathbf{u}})&\ge \min\limits_{\Phi\in H^2(E)}\int_E(2\mu\Phi_{,12}^2+\mu\Phi_{,11}^2+ \mu\Phi_{,22}^2+\Phi\Delta\varphi)\,dx_1dx_2 \\ &\\ & +\inf\left\{\mu\,\pi\!\int_0^1 \! |\dot v_3|^2\,dx_3+\pi \,v_3(0)\,: v_3\in H^1(0,1),\ v_3(0)\ge 0\right\}\\ &\\ & = \min\limits_{\Phi\in H^2(E)}\int_E(2\mu\Phi_{,12}^2+\mu\Phi_{,11}^2+ \mu\Phi_{,22}^2+\Phi\Delta\varphi)\,dx_1dx_2. \end{array} \end{equation*}

    The opposite inequality follows by choosing {\mathbf{v}}: = (\nabla\Phi, 0) with \Phi\in H^2(B) and by taking into account of

    \min\limits_{{\mathbf{u}}\in \mathcal A}\mathcal E({\mathbf{u}})\le \mathcal E({\mathbf{v}})

    for every choice of \Phi\in H^2(E) . Let now \Phi\in H^2(E) . Then {\mathbf{v}}: = \Phi_{, 2}\mathbf e_1 -\Phi_{, 1}\mathbf e_2\in \mathcal A and a direct computation shows that

    \begin{equation} \min\limits_{\mathcal A}{\mathcal E}_{{\mathbf R}_*}\le \min\limits_{\Phi\in H^2(E)} \int_{E}(2\mu{\Phi_{,12}}^2+\frac{\mu}{2}(\Phi_{,22}-\Phi_{,11})^2+ \Phi \Delta\varphi)\,dx_1dx_2. \end{equation} (5.18)

    Therefore inequality (5.10) is an immediate consequence of the next proposition.

    Proposition 5.4. There holds

    \begin{equation} \begin{array}{ll} & \min\limits_{\Phi\in H^2(E)} \int_{E}(2\mu{\Phi_{,12}}^2+\frac{\mu}{2}(\Phi_{,22}-\Phi_{,11})^2+ \Phi \Delta\varphi) \,dx_1dx_2 \\ & < \min\limits_{\Phi\in H^2(E)}\int_E(2\mu\Phi_{,12}^2+\mu\Phi_{,11}^2+ \mu\Phi_{,22}^2+\Phi\Delta\varphi)\,dx_1dx_2.\end{array} \end{equation} (5.19)

    Proof. The proof is the same of formula (5.14) of [45].

    The previous explicit example shows that a gap phenomenon may actually develop. Nevertheless one can prove that whenever \mathbf f, \ \mathbf g satisfy (2.33) then they can be suitable rotated in order to avoid the gap. In order to state such result, we introduce suitable notation: Set

    \begin{equation} \mathcal L_{\mathbf R}({\mathbf{v}}): = \mathcal L(\mathbf R{\mathbf{v}}) = \int_ \Omega\mathbf R^T\mathbf f\cdot{\mathbf{v}}\,d{{\bf x}}+\int_{\partial \Omega}\mathbf R^T\mathbf g\cdot{\mathbf{v}}\,d\mathcal H^2, \qquad \forall\, \mathbf R\in SO(3) , \end{equation} (5.20)

    say \mathcal L_{\mathbf R} is the load functional associated to the external forces \mathbf R^T\mathbf f, \mathbf R^T\mathbf g and let \mathcal E_{\mathbf R} be the functional defined by replacing \mathcal L with \mathcal L_{\mathbf R} in the definition of \mathcal E .

    Theorem 5.5. Assume (2.13), (2.33), \mathcal L(\mathbf e_3) < 0 and \mathbf R\in \mathcal S_{\mathcal L, E} . Then the functional \mathcal L_{\mathbf R} fulfills (2.33) and \mathcal S_{\mathcal L, E}\equiv\mathcal S_{\mathcal L_{\mathbf R}, E} . Moreover, if {\mathbf{u}} minimizes \mathcal G over H^1 \Omega, {{\mathbb R}}^3), \mathbf R\in\mathcal S_{\mathcal L, E} attains the maximum in definition (1.12) of \mathcal G({\mathbf{u}}) then {\mathbf{u}}\in {\mathop{{{\rm{argmin}}}}\nolimits} {\mathcal E}_{\mathbf R} and

    \begin{equation} \min\limits_{H^1( \Omega,{{\mathbb R}}^3)}\mathcal G\ \, = \,\min\limits_{H^1( \Omega,{{\mathbb R}}^3)}\mathcal E_{\mathbf R}. \end{equation} (5.21)

    Proof. By Lemma 3.8 we have either \mathcal S_{\mathcal L, E}\equiv \{\mathbf I\} or \mathcal S_{\mathcal L, E} = \{ \mathbf R: \mathbf R\mathbf e_3 = \mathbf e_3\} : In the first case there is nothing to prove, in the second one by (2.33) we get

    \begin{equation} 0 = \Phi(\mathbf R, E, \mathcal L) = \mathcal L((\mathbf R-\mathbf I){\mathbf{x}}). \end{equation} (5.22)

    Therefore for any other \mathbf S\in SO(3) by taking account of \mathbf R\mathbf e_3 = \mathbf e_3 and (5.22) we have

    \begin{equation} \Phi(\mathbf S, E, \mathcal L_{\mathbf R}) = \Phi({\mathbf R}\mathbf S, E, \mathcal L)\le 0 \end{equation} (5.23)

    that is \mathcal L_{\mathbf R} satisfies (2.33). By Remark 3.5 conditions (4.9)–(4.11) of Theorem 4.5 in [12] are fulfilled hence \mathcal E_{\mathbf R} achieves a finite minimum. Moreover since \mathbf R\mathbf e_3 = \mathbf e_3 implies \mathbf R^2\mathbf e_3 = \mathbf e_3 , (5.23) together with Lemma 3.8 entails

    \begin{equation} \Phi(\mathbf R, E, \mathcal L_{\mathbf R}) = \Phi({\mathbf R}^2, E, \mathcal L) = 0, \end{equation} (5.24)

    whence \mathbf R\in \mathcal S_{\mathcal L_{\mathbf R}, E} whenever \mathbf R\in \mathcal S_{\mathcal L, E} . Then since \mathcal L_{\mathbf R}(\mathbf e_3) = \mathcal L(\mathbf R\mathbf e_3) = \mathcal L(\mathbf e_3) < 0 we get, again by Lemma 3.8, \mathcal S_{\mathcal L_{\mathbf R}, E}\not\equiv \{\mathbf I\} hence \mathcal S_{\mathcal L_{\mathbf R}, E} = \{ \mathbf R: \mathbf R\mathbf e_3 = \mathbf e_3\} = \mathcal S_{\mathcal L, E} as claimed.

    We conclude by checking that if {\mathbf{u}} minimizes \mathcal G then it is also a minimizer of \mathcal E_{\mathbf R} over H^1(\Omega, {{\mathbb R}}^3) . If {\mathbf{u}}\in H^1(\Omega, \mathbb R^3) minimizes \mathcal G and \mathbf R attains the maximum then

    \begin{aligned}\min\limits_{H^1( \Omega,\mathbb R^3)}\mathcal G& = \mathcal G({\mathbf{u}}) = \int_ \Omega\mathcal Q({{\bf x}},\mathbb E({\mathbf{u}}))\,d{{\bf x}}-\mathcal L({\mathbf R} {\mathbf{u}}) = \mathcal E_{\mathbf R}({\mathbf{u}}). \end{aligned}

    Thus since \mathcal G\le \mathcal E_{\mathbf R} then (5.21) is proven.

    Remark 5.6. By choosing \mathcal W as in (5.1), \Omega, E as in (5.3) we provide an example where the inclusion {\mathop{{{\rm{argmin}}}}\nolimits} \mathcal G \subset {\mathop{{{\rm{argmin}}}}\nolimits} \widetilde{\mathcal G} is strict. Indeed Lemma 5.1 shows that for every \mathbf R\in \mathcal S_{\mathcal L, E} there exists \mathbf w\in{\mathop{{{\rm{argmin}}}}\nolimits}\mathcal E_{\mathbf R} such that \mathbf w({{\bf x}}) = w_{\alpha}(x_1, x_2)\mathbf e_{\alpha} . By Theorem 5.5 there exists {\mathbf{u}}\in {\mathop{{{\rm{argmin}}}}\nolimits} \mathcal G\subset {\mathop{{{\rm{argmin}}}}\nolimits} \widetilde{\mathcal G} such that {\mathbf{u}}({{\bf x}}) = u_{\alpha}(x_1, x_2)\mathbf e_{\alpha} . Then we can set {\mathbf{u}}_*({{\bf x}}): = {\mathbf{u}}({{\bf x}})+x_3\mathbf e_1 . By taking account of Lemma 3.1 and Remark 3.2, we get \mathcal L(\mathbf R{\mathbf{u}}) = \mathcal L(\mathbf R{\mathbf{u}}_*) for every \mathbf R\in \mathcal S_{\mathcal L, E} hence \widetilde {\mathcal G}({\mathbf{u}}) = \widetilde {\mathcal G}({\mathbf{u}}_*) and {\mathbf{u}}_*\in {\mathop{{{\rm{argmin}}}}\nolimits} \widetilde{\mathcal G} . Moreover, again by taking account of Lemma 3.1 and Remark 3.2, we have

    \mathcal G({\mathbf{u}}_*)- \mathcal G({\mathbf{u}}) = \mu\int_ \Omega |\mathbb E({\mathbf{u}}_*)|^2\,d{{\bf x}}-\mu\int_ \Omega |\mathbb E({\mathbf{u}})|^2\,d{{\bf x}} = \frac{\mu}{2}| \Omega| > 0,

    thus {\mathbf{u}}_*\not\in {\mathop{{{\rm{argmin}}}}\nolimits} \mathcal G and the inclusion is strict in this case.

    We have showed a rigorous variational linearization for a classical obstacle problem in nonlinear elasticity, namely an elastic body subject to pure traction load, supported on a unilateral rigid plane. Under suitable geometric admissibility conditions on the loads we obtain coincidence of minima with the classical Signorini problem in linear elasticity. On the other hand, we have shown the existence of loads violating such admissibility condition and entailing a gap between the minimum of the two problems.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    Francesco Maddalena and Franco Tomarelli are members of the Unione Matematica Italiana and the Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM). The authors are partially funded by INdAM–GNAMPA 2023 Project Codice CUP–E53C22001930001, "Modelli variazionali ed evolutivi per problemi di adesione e di contatto", Italian M.U.R. PRIN grant number 2022J4FYNJ "Variational Methods for Stationary and Evolution Problems with Singularities and Interfaces", INdAM–GNAMPA 2023 Project Codice CUP E53C22001930001, "Modelli variazionali ed evolutivi per problemi di adesione e di contatto".

    Research Project of National Relevance "Evolution problems involving interacting scales" granted by the Italian Ministry of Education, University and Research (MIUR Prin 2022, project code 2022M9BKBC, Grant No. CUP D53D23005880006).

    All authors declare no conflicts of interest in this paper.

    For reader's convenience and aiming to the precise formulation of unilateral constraint, in this section we encompass some results about capacity theory which are essential to achieve the results of present paper and somehow present in the literature, though they are spread in several different contexts and not easy to find as stated in this form: In particular Proposition A.1 and Eq (A.2) can be proven as like as Propositions 5.8.3 and 5.8.4 in [7] although the results seem slightly different.

    Proposition A.1. Let G an open bounded subset of {{\mathbb R}}^N . Then

    \begin{equation} {\mathop{{{\rm{cap}}}}\nolimits} G = \inf \left\{\|w\|_{H^1({{\mathbb R}}^N)}^2: w\!\in\! C^{\infty}_0(\mathbb R^N),\, w\ge 1\,{{on}}\ G\right\}. \end{equation} (A.1)

    The above property can be generalized to every bounded subset of {{\mathbb R}}^N by the following:

    Proposition A.2. Let E a bounded subset of {{\mathbb R}}^N . Then

    \begin{equation} \begin{array}{ll} {\mathop{{{\rm{cap}}}}\nolimits} E\!\!& = \,\inf \left\{\|w\|_{H^1({{\mathbb R}}^N)}^2: w\!\in\! C^{\infty}_0(\mathbb R^N),\, w\ge 1\,\;{{on \;a\; neighborhood\; of}}\, E\right\} \\ &\\ & = \, \inf \left\{\|w\|_{H^1({{\mathbb R}}^N)}^2: w\!\in\! C^{\infty}_0(\mathbb R^N; [0,1]),\, w\equiv 1\,{{on\; a \;neighborhood \;of}}\, E\right\}. \end{array} \end{equation} (A.2)

    We state and prove some results which play a crucial role in the proof of our main theorem.

    In the sequel \Omega will denote an open bounded subset of {{\mathbb R}}^N with Lipschitz boundary and E will denote a subset of \overline \Omega such that {\mathop{{{\rm{cap}}}}\nolimits} E > 0 .

    Lemma A.3. Let u\in H^1(\Omega), \ u\ge 0 a.e. in \Omega such that u^*({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in D , where D is a closed subset of \overline \Omega . Then there is an extension v\in H^1({{\mathbb R}}^N) of u such that {\mathop{{{\rm{spt}}}}\nolimits} v is compact, v\ge 0\ {{a.e.\; in}}\ {{\mathbb R}}^N and

    \begin{equation*} \lim\limits_{r\to 0^+}\frac{1}{\left |B_r({\mathbf{x}})\right |}\int_{B_r({\mathbf{x}})}v({\boldsymbol{\xi}})\,d{\boldsymbol{\xi}} = 0 \end{equation*}

    for q.e. {\mathbf{x}}\in D .

    Proof. We recall that u^* is defined as

    \begin{equation} u^*({\mathbf{x}}) = \lim\limits_{r\to 0^+}\frac{1}{\left |B_r({\mathbf{x}})\right |}\int_{B_r({\mathbf{x}})}w({\boldsymbol{\xi}})\,d{\boldsymbol{\xi}} \end{equation} (A.3)

    for q.e. {\mathbf{x}}\in \overline \Omega , where w is any Sobolev extension of u . Therefore the claim follows easily by choosing a cut off function \varphi\in C^{\infty}_0({{\mathbb R}}^N), \ \varphi\equiv 1 on \Omega and by setting v: = w^+\varphi which is a Sobolev extension of u with compact support since v = u a.e. in \Omega , and {\mathop{{{\rm{spt}}}}\nolimits} v\subset {\mathop{{{\rm{spt}}}}\nolimits} \varphi .

    Lemma A.4. Let u\in H^1(\mathbb R^N) with compact support such that u\ge 0\ \mathit{\text{a.e. in}}\ {{\mathbb R}}^N and u^*({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E . Then there exists a sequence u_j\in C^1(\mathbb R^N)\cap H^1(\mathbb R^N), \ u_j\ge 0\ \mathit{\text{in}}\ {{\mathbb R}}^N such that u_j({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E and u_j\to u in H^1(\mathbb R^3) .

    Proof. For every j\in \mathbb N, \ j\ge 1 let \overline u_j: = \min\{u, j^{\frac{1}{4}}\}\in H^1(\mathbb R^N) . By Theorem 3.11.6 and Remark 3.11.7 of [57], there exists v_j\in C^1(\mathbb R^N)\cap H^1(\mathbb R^N) with {\mathop{{{\rm{spt}}}}\nolimits} v_j\subset \{{{\bf x}}: d({{\bf x}}, {\mathop{{{\rm{spt}}}}\nolimits} {\overline u}_j) \le j^{-1}\} such that if F_j: = \{{\mathbf{x}}: v_j({\mathbf{x}})\neq {\overline u}_j({\mathbf{x}})\} then

    \begin{equation} {\mathop{{{\rm{Cap}}}}\nolimits} F_j < \frac{1}{j},\quad \|v_j-{\overline u}_j\|_{H^1(\mathbb R^N)} < \frac{1}{j}, \end{equation} (A.4)

    so (2.8) entails

    \begin{equation} {\mathop{{{\rm{cap}}}}\nolimits} F_j < \beta j^{-1}. \end{equation} (A.5)

    By recalling that {\mathop{{{\rm{spt}}}}\nolimits} {\overline u}_j is compact we get that F_j is bounded so, by taking account of (A.2), there exists w_j\in C^{\infty}_0(\mathbb R^N; [0, 1]), \ w_j\equiv 1 in a neighbourhood U_j of F_j such that

    \begin{equation} \| w_j\|_{H^1({{\mathbb R}}^N)}^2 < \beta j^{-1}. \end{equation} (A.6)

    We define u_j: = (1-w_j)v_j : it is readily seen that u_j\in C^1(\mathbb R^N)\cap H^1(\mathbb R^N), \ u_j\ge 0\ \text{in}\ {{\mathbb R}}^N , that u_j({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E\cap U_j and that u_j\equiv \overline u_j(1-w_j) outside U_j , hence, by recalling that {\overline u_j}^*({\mathbf{x}}) = u^*({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E , we get u_j({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E\setminus U_j that is u_j({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E . We claim that u_j\to u in H^1(\mathbb R^N) : to this aim, by noticing that u_j - u = v_j- \overline u_j+\overline u_j-u-v_jw_j and that \overline u_j\to u in H^1(\mathbb R^N) , thanks to (A.4) we have only to show that v_jw_j\to 0 in H^1(\mathbb R^N) . We first notice that by setting

    B_j: = \{{\mathbf{x}}: |w_j({\mathbf{x}})|\ge j^{-\frac{1}{4}}\},

    then by (A.6) and Tchebichev inequality we get |B_j|\le \beta j^{-\frac{1}{2}} , therefore

    \begin{equation} \begin{array}{ll} \int_{\mathbb R^N} |w_j|^2|v_j|^2\,d{\mathbf{x}} & = \int_{B_j}|w_j|^2|v_j|^2\,d{\mathbf{x}} +\int_{\mathbb R^3\setminus B_j} |w_j|^2|v_j|^2\,d{\mathbf{x}}\\ & \le \int_{B_j}|v_j|^2\,d{\mathbf{x}}+j^{-\frac{1}{2}} \int_{\mathbb R^N\setminus B_j}|v_j|^2\,d{\mathbf{x}} \\ & \le 2\int_{B_j}|v_j-\overline u_j|^2\,d{\mathbf{x}}+2\int_{B_j}|\overline u_j|^2\,d{\mathbf{x}} \\ & +2j^{-\frac{1}{2}} \int_{\mathbb R^N}|v_j-\overline u_j|^2\,d{\mathbf{x}}+2j^{-\frac{1}{2}} \int_{\mathbb R^N}|\overline u_j|^2\,d{\mathbf{x}}\to 0,\\ \end{array} \end{equation} (A.7)

    since

    \begin{equation*} \|v_j-\overline u_j\|_{H^1(\mathbb R^N)} < \frac{1}{j},\quad \overline u_j\to u\ \text{in}\ H^1(\mathbb R^N),\quad |B_j|\to 0. \end{equation*}

    Analogously by recalling (A.6), that w_j\equiv 1 on U_j , that v_j\equiv \overline u_j outside U_j and \|\overline u_j\|_{\infty}^2\le \sqrt j , we get

    \begin{equation} \begin{array}{ll} \int_{\mathbb R^N} |\nabla(w_jv_j)|^2\,d{\mathbf{x}}&\le 2\int_{\mathbb R^N} |w_j|^2|\nabla v_j|^2\,d{\mathbf{x}}+2\int_{\mathbb R^N\setminus U_j} |v_j|^2|\nabla w_j|^2\,d{\mathbf{x}} \\ & \le 2\int_{\mathbb R^N} |w_j|^2|\nabla v_j|^2\,d{\mathbf{x}}+2\|{\overline u}_j\|_{\infty}^2\int_{\mathbb R^N\setminus U_j} |\nabla w_j|^2\,d{\mathbf{x}} \\ \\ & \le 2\int_{\mathbb R^N\setminus B_j} |w_j|^2|\nabla v_j|^2\,d{\mathbf{x}} +2\int_{B_j} |w_j|^2|\nabla v_j|^2\,d{\mathbf{x}}+2j^{-\frac{1}{2}}\to 0,\\ \end{array} \end{equation} (A.8)

    as in (A.7) thus proving the lemma.

    Lemma A.5. Let u\in H^1(\Omega) such that u^*({\mathbf{x}})\ge 0 for q.e. {\mathbf{x}}\in E . Then there exists a sequence u_j\in C^1(\overline \Omega) such that u_j({\mathbf{x}})\ge 0 for q.e. {\mathbf{x}}\in E and u_j\to u in H^1(\Omega) .

    Proof. We recall that by Remark 2.3 u^*({\mathbf{x}})\ge 0 for q.e. {\mathbf{x}}\in E if and only if (u^-)^*({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E_{ess} and that E_{ess} is a closed subset of \overline \Omega . By Lemma A.3 there exists a Sobolev extension v of u^- such that {\mathop{{{\rm{spt}}}}\nolimits} v is compact, v\ge 0\ \text{a.e. in}\ {{\mathbb R}}^3 and

    \begin{equation*} \lim\limits_{r\to 0^+}\frac{1}{\left |B_r({\mathbf{x}})\right |}\int_{B_r({\mathbf{x}})}v({\boldsymbol{\xi}})\,d{\boldsymbol{\xi}} = 0 \end{equation*}

    for q.e. {\mathbf{x}}\in E_{ess} , so by Lemma A.4, there exists a sequence v_j\in C^1(\mathbb R^N)\cap H^1(\mathbb R^N), \ v_j\ge 0\ \text{in}\ {{\mathbb R}}^N such that v_j({\mathbf{x}}) = 0 for q.e. {\mathbf{x}}\in E_{ess} and v_j\to v in H^1(\mathbb R^N) . Let now w be a Sobolev extension of u^+ . We may assume without loss of generality that w\ge 0 a.e. in {{\mathbb R}}^N and if \rho_j is a sequence of smooth mollifiers then w_j: = w*\rho_j\ge 0 and w_j\to w in H^1(\mathbb R^N) . Therefore by setting u_j: = w_j-v_j , we have u_j\in C^1(\overline \Omega) , u_j({\mathbf{x}})\ge 0 for q.e. {\mathbf{x}}\in E and u_j\to u in H^1(\Omega) thus proving the lemma.

    Remark A.6. If E is a non empty subset of \overline \Omega and u\in H^1(\Omega) we say that u\ge 0 on E in the sense of H^1(\Omega) if there exists a sequence u_j\in C^1(\overline \Omega) such that u_j\ge 0 on E and u_j\to u in H^1(\Omega) (according to [31, Definition 5.1]). We claim that (u^-)^* = 0 q.e. in E (or equivalently u^*\ge 0 q.e. in E ) if and only if u\ge 0 on E in the sense of H^1(\Omega) : Indeed if (u^-)_* = 0 q.e. in E then Lemma A.5 provides a sequence u_j\in C^1(\overline \Omega) such that u_j\to u in H^1(\Omega), \ u_j\ge 0 on E , while the converse follows easily by recalling that if u_j\to u in H^1(\Omega) then, up to subsequences, u_j({{\bf x}})\to u^{*}({{\bf x}}) for q.e. {\mathbf{x}}\in E . $



    [1] M. A. Meraou, M. Z. Raqab, Statistical properties and different estimation procedures of poisson-Lindley distribution, J. Stat. Theory Appl., 20 (2021), 33–45. https://doi.org/10.2991/jsta.d.210105.001 doi: 10.2991/jsta.d.210105.001
    [2] F. Hashemi, M. Naderi, A. Jamalizadeh, Normal mean-variance Lindley Birnbaum-Saunders distribution, Stat. Interface, 12 (2019), 585–597. https://doi.org/10.4310/SII.2019.V12.N4.A8 doi: 10.4310/SII.2019.V12.N4.A8
    [3] R. J. Abdelghani, M. A. Meraou, M. Z. Raqab, Bivariate compound distribution based on Poisson maxima of gamma variates and related applications, Int. J. Appl. Math., 34 (2021), 957. https://doi.org/10.12732/ijam.v34i5.6 doi: 10.12732/ijam.v34i5.6
    [4] M. Naderi, F. Hashemi, A. Bekker, A. Jamalizadeh, Modeling right-skewed fnancial data streams: a likelihood inference based on the generalized Birnbaum-Saunders mixture model, Appl. Math. Comput., 376 (2021), 125109. https://doi.org/10.1016/j.amc.2020.125109 doi: 10.1016/j.amc.2020.125109
    [5] S. Nadarajah, The skew logistic distribution, AStA Adv. Stat. Anal., 93 (2009), 187–203. https://doi.org/10.1007/s10182-009-0105-6 doi: 10.1007/s10182-009-0105-6
    [6] S. Chakraborty, P. J. Hazarika, M. M. Ali, A new skew logistic distribution and its properties, Pak. J. Stat., 28 (2012), 513–524.
    [7] Z. Ahmad, E. Mahmoudi, M. Alizadeh, Modelling insurance losses using a new beta power transformed family of distributions, Commun. Stat., 51 (2022), 4470–4491. https://doi.org/10.1080/03610918.2020.1743859 doi: 10.1080/03610918.2020.1743859
    [8] A. W. Marshall, I. A. Olkin, New method for adding a parameter to a family of distributions with application to the exponential and Weibull families, Biometrika, 84 (1997), 641–652. https://doi.org/10.1093/biomet/84.3.641 doi: 10.1093/biomet/84.3.641
    [9] A. Mahdavi, D. Kundu, A new method for generating distributions with an application to exponential distribution, Commun. Stat., 46 (2017), 6543–6557. https://doi.org/10.1080/03610926.2015.1130839 doi: 10.1080/03610926.2015.1130839
    [10] Z. Ahmad, M. Ilyas, G. G. Hamedani, The extended alpha power transformed family of distributions: properties and applications, J. Data Sci., 17 (2019), 726–741. https://doi.org/10.6339/JDS.201910_17(4).0006 doi: 10.6339/JDS.201910_17(4).0006
    [11] M. Alizadeh, M. H. Tahir, M. C. Gauss, M. Mansoor, M. Zubair, G. G. Hamedani, The Kumaraswamy Marshal-Olkin family of distributions, J. Egypt. Math. Soc., 23 (2015), 546–557. https://doi.org/10.1016/j.joems.2014.12.002 doi: 10.1016/j.joems.2014.12.002
    [12] A. Fayomi, E. M. Almetwally, M. E. Qura, Exploring new horizons: advancing data analysis in kidney patient infection rates and UEFA champions league scores using bivariate Kavya-Manoharan transformation family of distributions, Mathematics, 11 (2023), 2986. https://doi.org/10.3390/math11132986 doi: 10.3390/math11132986
    [13] A. Ahmad, N. Alsadat, M. N. Atchade, S. Q. ul Ain, A. M. Gemeay, M. A, Meraou, et al., New hyperbolic sine-generator with an example of Rayleigh distribution: simulation and data analysis in industry, Alex. Eng. J., 73 (2023), 415–426. https://doi.org/10.1016/j.aej.2023.04.048 doi: 10.1016/j.aej.2023.04.048
    [14] I. Elbatal, S. M. Alghamdi, F. Jamal, S. Khan, E. M. Almetwally, M. Elgarhy, Kavya-Manoharan Weibull-g family of distributions: statistical inference under progressive type-Ⅱ censoring scheme, Adv. Appl. Stat., 87 (2023), 191–223. https://doi.org/10.17654/0972361723034 doi: 10.17654/0972361723034
    [15] A. A. M. Teamah, A. A. Elbanna, A. M. Gemeay, Heavy-tailed log-logistic distribution: properties, risk measures and applications, Stat. Optim. Inf. Comput., 9 (2021), 910–941. https://doi.org/10.19139/soic-2310-5070-1220 doi: 10.19139/soic-2310-5070-1220
    [16] J. Zhao, Z. Ahmad, E. Mahmoudi, E. H. Hafez, M. M. M. El-Din, A new class of heavy-tailed distributions: modeling and simulating actuarial measures, Complexity, 2021 (2021), 5580228. https://doi.org/10.1155/2021/5580228 doi: 10.1155/2021/5580228
    [17] P. M. Chiroque-Solano, F. A. da S. Moura, A heavy-tailed and overdispersed collective risk model, arXiv, 2021. https://doi.org/10.48550/arXiv.2101.09022
    [18] A. M. Basheer, Alpha power inverse weibull distribution with reliability application, J. Taibah Univ. Sci., 13 (2019), 423–432. https://doi.org/10.1080/16583655.2019.1588488 doi: 10.1080/16583655.2019.1588488
    [19] E. Yildirim, E. S. Ilıkkan, A. M. Gemeay, N. Makumi, M. E. Bakr, O. S. Balogun, Power unit Burr-XII distribution: statistical inference with applications, AIP Adv., 13 (2023), 105107. https://doi.org/10.1063/5.0171077 doi: 10.1063/5.0171077
    [20] M. Shakil, M. Munir, N. Kausar, M. Ahsanullah, A. Khadim, M. Sirajo, et al., Some inferences on three parameters Birnbaum-Saunders distribution: statistical properties, characterizations and applications, Comput. J. Math. Stat. Sci., 2 (2023), 197–222. https://doi.org/10.21608/cjmss.2023.224583.1011 doi: 10.21608/cjmss.2023.224583.1011
    [21] L. P. Sapkota, V. Kumar, A. M. Gemeay, M. E. Bakr, O. S. Balogun, A. H. Muse, New Lomax-G family of distributions: statistical properties and applications, AIP Adv., 13 (2023), 095128. https://doi.org/10.1063/5.0171949 doi: 10.1063/5.0171949
    [22] A. Z. Afify, A. M. Gemeay, N. A. Ibrahim, The heavy-tailed exponential distribution: risk measures, estimation, and application to actuarial data, Mathematics, 8 (2020), 1276. https://doi.org/10.3390/math8081276 doi: 10.3390/math8081276
    [23] A. A. M. Teamah, M. A. Elbanna, A. M. Gemeay, Heavy-tailed log-logistic distribution: properties, risk measures and applications, Stat. Optim. Inf. Comput., 9 (2021), 910–941. https://doi.org/10.19139/soic-2310-5070-1220 doi: 10.19139/soic-2310-5070-1220
    [24] A. M. Gemeay, K. Karakaya, M. E. Bakr, O. S. Balogun, M. N. Atchadé, E. Hussam, Power Lambert uniform distribution: statistical properties, actuarial measures, regression analysis, and applications, AIP Adv., 13 (2023), 095318. https://doi.org/10.1063/5.0170964 doi: 10.1063/5.0170964
    [25] M. Kamal, M. N. Atchadé, Y. M. Sokadjo, Nayabuddin, E. Hussam, A. M. Gemeay, et al., Statistical study for Covid-19 spread during the armed crisis faced by Ukrainians, Alex. Eng. J., 78 (2023), 419–425. https://doi.org/10.1016/j.aej.2023.07.040 doi: 10.1016/j.aej.2023.07.040
    [26] S. D. Tomarchio, A. Punzo, Dichotomous unimodal compound models: application to the distribution of insurance losses, J. Appl. Stat., 47 (2020), 2328–2353. https://doi.org/10.1080/02664763.2020.1789076 doi: 10.1080/02664763.2020.1789076
    [27] A. Al-Shomrani, O. Arif, A. Shawky, S. Hanif, M. Q. Shahbaz, Topp-Leone family of distributions: some properties and application, Pak. J. Stat. Oper. Res., 12 (2016), 443–451. https://doi.org/10.18187/pjsor.v12i3.1458 doi: 10.18187/pjsor.v12i3.1458
    [28] D. O. Tuoyo, F. C. Opone, N. Ekhosuehi, Topp-Leone Weibull distribution: its properties and applications, Earthline J. Math. Sci., 7 (2021), 381–401. https://doi.org/10.34198/ejms.7221.381401 doi: 10.34198/ejms.7221.381401
    [29] G. M. Ibrahim, A. S. Hassan, E. M. Almetwally, H. M. Almongy, Parameter estimation of alpha power inverted Topp-Leone distribution with applications, Intell. Autom. Soft Comput., 29 (2021), 353–371. https://doi.org/10.32604/iasc.2021.017586 doi: 10.32604/iasc.2021.017586
    [30] L. P. Sapkota, Topp-Leone Fréchet distribution with theory and application, Janapriya J. Interdiscip. Stud., (2021), 65–80.
    [31] A. S. Alyami, E. Ibrahim, N. Alotaibi, E. M. Almetwally, M. H. Okasha, M. Elgarhy, Topp-Leone modified Weibull model: theory and applications to medical and engineering data, Appl. Sci., 12 (2022), 10431. https://doi.org/10.3390/app122010431 doi: 10.3390/app122010431
    [32] A. A. Ogunde, O. E. Adenijia, Type Ⅱ Topp-Leone Bur Ⅻ distribution: properties and applications to failure time data, Sci. Afr., 16 (2022), e01200. https://doi.org/10.1016/j.sciaf.2022.e01200 doi: 10.1016/j.sciaf.2022.e01200
    [33] A. E. Teamah, A. A. Elbanna, A. M. Gemeay, Right truncated fréchet-weibull distribution: statistical properties and application, Delta J. Sci., 41 (2019), 20–29. https://doi.org/10.21608/djs.2020.139880 doi: 10.21608/djs.2020.139880
    [34] M. M. Ristić, N. Balakrishnan, The gamma-exponentiated exponential distribution, J. Stat. Comput. Simul., 82 (2012), 1191–1206. https://doi.org/10.1080/00949655.2011.574633 doi: 10.1080/00949655.2011.574633
    [35] M. Nagy, E. M. Almetwally, A. M. Gemeay, H. S. Mohammed, T. M. Jawa, N. Sayed-Ahmed, et al., The new novel discrete distribution with application on COVID-19 mortality numbers in Kingdom of Saudi Arabia and Latvia, Complexity, 2021 (2021), 7192833. https://doi.org/10.1155/2021/7192833 doi: 10.1155/2021/7192833
    [36] T. A. de Andrade, M. Bourguignon, G. M. Cordeiro, The exponentiated generalized extended exponential distribution, J. Data Sci., 14 (2016), 393–413. https://doi.org/10.6339/JDS.201607_14(3).0001 doi: 10.6339/JDS.201607_14(3).0001
    [37] M. A. Almuqrin, A. M. Gemeay, M. A. El-Raouf, M. Kilai, R. Aldallal, E. Hussam, A flexible extension of reduced kies distribution: properties, inference, and applications in biology, Complexity, 2022 (2022), 6078567. https://doi.org/10.1155/2022/6078567 doi: 10.1155/2022/6078567
    [38] M. M. Salama, E. S. A. El-Sherpieny, A. E. A. Abd-Elaziz, The length-biased weighted exponentiated inverted exponential distribution: properties and estimation, Comput. J. Math. Stat. Sci., 2 (2023), 181–196.
    [39] M. M. Raqab, M. Ahsanullah, Estimation of the location and scale parameters of generalized exponential distribution based on order statistics, J. Stat. Comput. Simul., 69 (2021), 109–123. https://doi.org/10.1080/00949650108812085 doi: 10.1080/00949650108812085
    [40] G. Mustafa, M. Ijaz, F. Jamal, Order statistics of inverse pareto distribution, Comput. J. Math. Stat. Sci., 1 (2022), 51–62. https://doi.org/10.21608/cjmss.2022.272724 doi: 10.21608/cjmss.2022.272724
    [41] S. Nadarajah, The exponentiated exponential distribution: a survey, AStA Adv. Stat. Anal., 95 (2011), 219–251. https://doi.org/10.1007/s10182-011-0154-5 doi: 10.1007/s10182-011-0154-5
    [42] S. E. Abu-Youssef, B. I. Mohammed, M. G. Sief, An extended exponentiated exponential distribution and its properties, Int. J. Comput. Appl., 121 (2015), 1–6. https://doi.org/10.5120/21533-4518 doi: 10.5120/21533-4518
    [43] E. S. A. El-Sherpieny, E. M. Almetwally, A. H. Muse, E. Hussam, Data analysis for COVID-19 deaths using a novel statistical model: simulation and fuzzy application, Plos One, 18 (2023), e0283618. https://doi.org/10.1371/journal.pone.0283618 doi: 10.1371/journal.pone.0283618
    [44] E. Hussam, M. A. Sabry, M. M. A. El-Raouf, E. M. Almetwally, Fuzzy vs. traditional reliability model for inverse Weibull distribution, Axioms, 12 (2023), 582. https://doi.org/10.3390/axioms12060582 doi: 10.3390/axioms12060582
    [45] C. Chesneau, V. Kumar, M. Khetan, M. Arshad, On a modified weighted exponential distribution with applications, Math Comput. Appl., 27 (2022), 17. https://doi.org/10.3390/mca27010017 doi: 10.3390/mca27010017
    [46] R. D. Gupta, D. Kundu, Generalized exponential distribution: existing results and some recent developments, J. Stat. Plan. Infer., 137 (2007), 3537–3547. https://doi.org/10.1016/j.jspi.2007.03.030 doi: 10.1016/j.jspi.2007.03.030
    [47] M. A. Meraou, N. M. Al-Kandari, M. Z. Raqab, D. Kundu, Analysis of skewed data by using compound poisson exponential distribution with applications to insurance claims, J. Stat. Comput. Simul., 92 (2022), 928–956. https://doi.org/10.1080/00949655.2021.1981324 doi: 10.1080/00949655.2021.1981324
    [48] M. A. Meraou, N. M. Al-Kandari, M. Z. Raqab, Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data, J. Stat. Theory Pract., 16 (2022), 56. https://doi.org/10.1007/s42519-022-00282-8 doi: 10.1007/s42519-022-00282-8
    [49] N. M. Alfaer, A. M. Gemeay, H. M. Aljohani, A. Z. Afify, The extended log-logistic distribution: inference and actuarial applications, Mathematics, 9 (2021), 1386. https://doi.org/10.3390/math9121386 doi: 10.3390/math9121386
    [50] C. Dutang, A. Charpentier, CASdatasets: insurance datasets, 2019. Available from: http://dutangc.free.fr/pub/RRepos/web/CASdatasets-index.html.
    [51] V. Brazauskas, A. Kleefeld, Modeling severity and measuring tail risk of Norwegian fire claims, N. Amer. Actuar. J., 20 (2016), 1–16. https://doi.org/10.1080/10920277.2015.1062784 doi: 10.1080/10920277.2015.1062784
    [52] A. Bander, S. Hanaa, The Poisson-Lomax distribution, Rev. Colomb. Estad., 37 (2014), 223–243. https://doi.org/10.15446/rce.v37n1.44369 doi: 10.15446/rce.v37n1.44369
    [53] V. G. Cancho, F. Louzada-Neto, G. D. Barriga, Poisson-exponential lifetime distribution, Comput. Stat. Data Anal., 55 (2011), 677–686. https://doi.org/10.1016/j.csda.2010.05.033 doi: 10.1016/j.csda.2010.05.033
    [54] M. E. Ghitany, D. K. Al-Mutairi, N. Balakrishnan, L. J. Al-Enezi, Power lindley distribution and associated inference, Comput. Stat. Data Anal., 64 (2013), 20–33. https://doi.org/10.1016/j.csda.2013.02.026 doi: 10.1016/j.csda.2013.02.026
    [55] K. Adamidis, T. Dimitrakopoulou, S. Loukas, On an extension of the exponential-geometric distribution, Stat. Probab. Lett., 73 (2005), 259–269. https://doi.org/10.1016/j.spl.2005.03.013 doi: 10.1016/j.spl.2005.03.013
    [56] S. Sen, S. K. Ghosh, H. Al-Mofleh, The mirra distribution for modeling time-to-event data sets, Springer, 2021, 59–73. https://doi.org/10.1007/978-981-16-13685
    [57] N. Alsadat, A. S. Hassan, A. M. Gemeay, C. Chesneau, M. Elgarhy, Different estimation methods for the generalized unit half-logistic geometric distribution: using ranked set sampling, AIP Adv., 13 (2023), 085230. https://doi.org/10.1063/5.0169140 doi: 10.1063/5.0169140
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