In this paper, we discuss a higher-order convergent numerical method for a two-parameter singularly perturbed differential equation with a discontinuous convection coefficient and a discontinuous source term. The presence of perturbation parameters generates boundary layers, and the discontinuous terms produce interior layers on both sides of the discontinuity. In order to obtain a higher-order convergent solution, a hybrid monotone finite difference scheme is constructed on a piecewise uniform Shishkin mesh, which is adapted inside the boundary and interior layers. On this mesh (including the point of discontinuity), the present method is almost second-order parameter-uniform convergent. The current scheme is compared with the standard upwind scheme, which is used at the point of discontinuity. The numerical experiments based on the proposed scheme show higher-order (almost second-order) accuracy compared to the standard upwind scheme, which provides almost first-order parameter-uniform convergence.
Citation: M. Chandru, T. Prabha, V. Shanthi, H. Ramos. An almost second order uniformly convergent method for a two-parameter singularly perturbed problem with a discontinuous convection coefficient and source term[J]. AIMS Mathematics, 2024, 9(9): 24998-25027. doi: 10.3934/math.20241219
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In this paper, we discuss a higher-order convergent numerical method for a two-parameter singularly perturbed differential equation with a discontinuous convection coefficient and a discontinuous source term. The presence of perturbation parameters generates boundary layers, and the discontinuous terms produce interior layers on both sides of the discontinuity. In order to obtain a higher-order convergent solution, a hybrid monotone finite difference scheme is constructed on a piecewise uniform Shishkin mesh, which is adapted inside the boundary and interior layers. On this mesh (including the point of discontinuity), the present method is almost second-order parameter-uniform convergent. The current scheme is compared with the standard upwind scheme, which is used at the point of discontinuity. The numerical experiments based on the proposed scheme show higher-order (almost second-order) accuracy compared to the standard upwind scheme, which provides almost first-order parameter-uniform convergence.
Dedicated to Professor Neil S. Trudinger on occasion of his 80th birthday.
One classical problem in convex geometry is the Minkowski problem, which is to find convex hypersurfaces in Rn+1 whose Gaussian curvature is prescribed as a function defined on Sn in terms of the inverse Gauss map. It has been settled by the works of Minkowski [23], Alexandrov [1], Fenchel and Jessen [28], Nirenberg [25], Pogorelov [26], Cheng and Yau [3], etc.. In smooth catagory, the Minkowski problem is equivalent to solve following Monge-Ampère equation
det(∇2u+ugSn)=fonSn, |
where u is the support function of the convex hypersurface, ∇2u+ugSn the spherical Hessian matrix of the function u. If we take an orthonormal frame on Sn, the spherical Hessian of u is Wu(x):=uij(x)+u(x)δij, whose eigenvalues are actually the principal radii of the hypersurface.
The general problem of finding a convex hypersurface, whose k-th symmetric function of the principal radii is the prescribed function on its outer normals for 1≤k<n, is often called the Christoffel-Minkowski problem. It corresponds to finding convex solutions of the nonlinear Hessian equation
σk(Wu)=fonSn. |
This problem was settled by Guan et al [14,15]. In [16], Guan and Zhang considered a mixed Hessian equation as follows
σk(Wu(x))+α(x)σk−1(Wu(x))=k−2∑l=0αl(x)σl(Wu(x)),x∈Sn, | (1.1) |
where α(x),αl(x)(0≤l≤k−1) are some functions on Sn. By imposing some group-invariant conditions on those coefficient's functions as in [11], the authors proved the existence of solutions.
Let M be a hypersurface of Euclidean space Rn+1 and M=graphu in a neighbourhood of some point at which we calculate. Let A be the second fundamental form of M, λ(A)=(λ1,⋯,λn)∈Rn the eigenvalues of A with respect to the induced metric of M⊂Rn+1, i.e., the principle curvatures of M, and σk(λ) the k-th elementary symmetric function, σ0(λ)=1. It is natural to study the prescribing curvature problems on this aspect. In 1980s, Caffarelli, Nirenberg and Spruck studied the prescribing Weingarten curvature problem. The problem is equivalent to solve the following equation
σk(λ)(X)=f(X),X∈M. |
When k=n, the problem is just the Minkowski problem; when k=1, it is the prescribing mean curvature problem, c.f. [30,33]. The prescribing Weingarten curvature problem has been studied by many authors, we refer to [2,9,11,12,13,29,37] and references therein for related works. Recently, Zhou [36] generalised above mixed prescribed Weingarten curvature equation. He obtained interior gradient estimates for
σk(A)+α(x)σk−1(A)=k−2∑l=0αl(x)σl(A),x∈Br(0)⊂Rn | (1.2) |
where σk(A):=σk(λ(A)), and the coefficients satisfy αk−2>0 and αl≥0 for 0≤l≤k−3.
Mixed Hessian type of equations arise naturally from many important geometric problems. One example is the so-called Fu-Yau equation arising from the study of the Hull-Strominger system in theoretical physics, which is an equation that can be written as the linear combination of the first and the second elementary symmetric functions
σ1(i∂ˉ∂(eu+α′e−u))+α′σ2(i∂ˉ∂u)=ϕ | (1.3) |
on n-dimensional compact Kähler manifolds. There are a lot of works related to this equation recently, see [6,7,27] for example. Another important example is the special Lagrangian equations introduced by Harvey and Lawson [18], which can be written as the alternative combinations of elementary symmetric functions
sinθ([n2]∑k=0(−1)kσ2k(D2u))+cosθ([n−12]∑k=0(−1)kσ2k+1(D2u))=0. |
This equation is equivalent to
F(D2u):=arctanλ1+⋯+arctanλn=θ |
where λi's are the eigenvalues of D2u. It is called supercritical if θ∈((n−2)π2,nπ2) and hypercritical if θ∈((n−1)π2,nπ2). The Lagrangian phase operator F is concave for the hypercritical case and has convex level sets for the supercritical case, while in general F fails to be concave. For subcritical case, i.e., 0≤θ<(n−2)π2, solutions of the special Lagrangian equation can fail to have interior estimates [24,35]. Jacob-Yau [20] initiated to study the deformed Hermitian Yang-Mills (dHYM) equation on a compact Kähler manifold (M,ω):
Re(χu+√−1ω)n=cotθ0Im(χu+√−1ω)n, |
where χ is a closed real (1,1)-form, χu=χ+√−1∂ˉ∂u, and θ0 is the angles of the complex number ∫M(χ+√−1ω)n, u is the unknown real smooth function on M. Jacob-Yau showed that dHYM equation has an equivalent form of special Lagrangian equation. Collins-Jacob-Yau [5] solved the dHYM equation by continuity method and Fu-Zhang [8] gave an alternative approach by dHYM flow, both of which considered in the supercritical case. For more results concerning about dHYM equation and special Lagrangian equation, one can consult Han-Jin [17], Chu-Lee [4] and the references therein. Note that for n=3 and hypercritical θ∈(π,3π2), the special Lagrangian equation (1.3) is
σ3(D2u)+tanθσ2(D2u)=σ1(D2u)+tanθσ0(D2u) |
which is included in (1.1).
In this paper we derive interior curvature bounds for admissible solutions of a class of curvature equations subject to affine Dirichlet data. Let Ω be a bounded domain in Rn, and let u∈C4(Ω)∩C0,1(ˉΩ) be an admissible solution of
{σk(λ)+g(x,u)σk−1(λ)=k−2∑l=0αl(x,u)σl(λ)inΩ,u=ϕon∂Ω, | (1.4) |
where g(x,u) and αl(x,u)>0, l=0,1,⋯,k−2, are given smooth functions on ˉΩ×R and ϕ is affine, λ=(λ1,⋯,λn) is the vector of the principal curvatures of graph u. u is the admissible solution in the sense that λ∈Γk for points on the graph of u, with
Γk={λ∈Rn|σ1(λ)>0,⋯,σk(λ)>0}. |
For simplicity we denote F=Gk−∑k−2l=0αlGl and Gl=σl(λ)/σk−1(λ) for l=0,1,⋯,k−2,k. The ellipticity and concavity properties of the operator F have been proved in [16]. Our main result is as follows.
Theorem 1.1. Assume that for every l (0≤l≤k−2), αl,g∈C1,1(ˉΩ×R), αl>0, and g>0 or g<0. ϕ is affine in (1.4). For any fixed β>0, if u∈C4(Ω)∩C0,1(ˉΩ) is an admissible solution of (1.4), then there exists a constant C, depending only on n,k,β,||u||C1(ˉΩ),αl,g and their first and second derivatives, such that the second fundamental form A of graph u satisfies
|A|≤C(ϕ−u)β. |
Remark 1.1. Comparing with [16], here we require g>0 or g<0 additionally. Also our curvature estimates still hold if αl≡0 for some 0≤l≤k−2. More over, if αl≡0 for all l=0,1,⋯,k−2, Eq (1.4) becomes the Hessian quotient equation and the results can be followed from [29].
To see that this is an interior curvature estimate, we need to verify that ϕ−u>0 on Ω. We apply the strong maximum principle for the minimal graph equation. Since ϕ is affine, it satisfies the following minimal graph equation
Qu:=(1+|Du|2)△u−uiujuij=nH(1+|Du|2)32=0onΩ. |
Since u is k-admissible solution, and n≥k≥2, graph of u is mean-convex and Qu>Qϕ=0. By the comparison principle for quasilinear equations (Theorem 10.1 in [10]), we then have ϕ>u on Ω.
The main application of the curvature bound of Theorem 1.1 is to extend various existence results for the Dirichlet problem for curvature equations of mixed Hessian type.
Theorem 1.2. Let Ω be a bounded domain in Rn, let αl,g∈C1,1(ˉΩ×R) satisfying inf|g|>0, ∂ug(x,u)≤0, αl>0 and ∂uαl(x,u)≥0. Suppose there is an admissible function u_∈C2(Ω)∩C0,1(ˉΩ) satisfying
F[u_]≥−g(x,u_)inΩ,u_=0on∂Ω. | (1.5) |
Then the problem
F[u]=−g(x,u)inΩ,u=0on∂Ω. | (1.6) |
has a unique admissible solution u∈C3,α(Ω)∩C0,1(ˉΩ) for all α∈(0,1).
Remark 1.2. ∂ug≤0, ∂uαl(x,u)≥0 and the existence of sub-solutions are required in the C0 estimate. The C1 interior estimate is a slightly modification of the result in Theorem 5.1.1 [36] since the coefficients g, αl of (1.2) are independent of u. We use conditions ∂ug≤0 and ∂uαl(x,u)≥0 again to eliminate extra terms in the C1 estimate.
As a further application of the a priori curvature estimate we also consider a Plateau-type problem for locally convex Weingarten hypersurfaces. Let Σ be a finite collection of disjoint, smooth, closed, codimension 2 submanifolds of Rn+1. Suppose Σ bounds a locally uniformly convex hypersurface M0 with
f(n)(λ0):=σnσn−1(λ0)−n−2∑l=0αlσlσn−1(λ0)≥c, |
where λ0=(λ01,⋯,λ0n) are the principal curvatures of M0 and αl's are positive constants, c≠0 is a constant. Is there a locally convex hypersurface M with boundary Σ and f(n)(λ)=c, where λ=(λ1,⋯,λn) are the principal curvatures of M?
Theorem 1.3. Let Σ, f(n)(λ) be as above. If Σ bounds a locally uniformly convex hypersurface M0 with f(n)(λ0)≥c at each point of M0. Then Σ bounds a smooth, locally convex hypersurface M with f(n)(λ)=c at each point of M.
We compute using a local orthonormal frame field ˆe1,⋯,ˆen defined on M= graph u in a neighbourhood of the point at which we are computing. The standard basis of Rn+1 is denoted by e1,⋯,en+1. Covariant differentiation on M in the direction ˆei is denoted by ∇i. The components of the second fundamental form A of M in the basis ˆe1,⋯,ˆen are denoted by (hij). Thus
hij=⟨Dˆeiˆej,ν⟩, |
where D and ⟨⋅,⋅⟩ denote the usual connection and inner product on Rn+1, and ν denotes the upward unit normal
ν=(−Du,1)√1+|Du|2. |
The differential equation in (1.4) can then be expressed as
F(A,X)=−g(X). | (2.1) |
As usual we denote first and second partial derivatives of F with respect to hij by Fij and Fij,rs. We assume summation from 1 to n over repeated Latin indices unless otherwise indicated. Following two lemmas are similar to the ones in [29] with minor changes, so we omit the proof.
Lemma 2.1. The second fundamental form hab satisfies
Fij∇i∇jhab=−Fij,rs∇ahij∇bhrs+Fijhijhaphpb−Fijhiphpjhab−∇a∇bg+k−2∑l=0(∇aαl∇bGl+∇bαl∇aGl)+k−2∑l=0∇a∇bαl⋅Gl. |
Lemma 2.2. For any α=1,⋯,n+1, we have
Fij∇i∇jνα+Fijhiphpjνα=⟨∇g,eα⟩−k−2∑l=0⟨∇αl,eα⟩Gl. |
Lemma 2.3. There is a constant C>0, depending only on n,k,infαl,|g|C0, so that for any l=0,1,⋯,k−2,
|Gl|≤C. |
Proof. Proof by contradiction. If the result is not true, then for any integer i, there is an admissible solution u(i), a point x(i)∈Ω and an index 0≤l(i)≤k−2, so that
σl(i)σk−1(λ[u(i)])>iatx(i). |
By passing to a subsequence, we may assume l(i)→l∞ and x(i)→x∞∈ˉΩ as i→+∞. Therefore
limi→+∞σl∞σk−1(λ[u(i)])(x(i))=+∞, |
or we may simply write σl∞σk−1→+∞ if no ambiguilty arises. Since αl∞>0, and g is bounded, by (1.4) we have σkσk−1→+∞. For i large enough, σk>0. By Newton-MacLaurin inequalities, we have
σl∞σk−1=σl∞σl∞+1⋯σk−2σk−1≤C(σk−1σk)k−1−l∞→0. |
We therefore get a contradiction.
Proof of Theorem 1.1. Here the argument comes from [29]. Let η=ϕ−u. η>0 in Ω. For a function Φ to be chosen and a constant β>0 fixed, we consider the function
˜W(X,ξ)=ηβ(expΦ(νn+1))hξξ |
for all X∈M and all unit vector ξ∈TXM. Then ˜W attains its maximum at an interior point X0∈M, in a direction ξ0∈TX0M which we may take to be ˆe1. We may assume that (hij) is diagonal at X0 with eigenvalues λ1≥λ2≥⋯≥λn. Without loss of generality we may assume that the ˆe1,⋯,ˆen has been chosen so that ∇iˆej=0 at X0 for all i,j=1,⋯,n. Let τ=ˆe1. Then W(X)=˜W(X,τ) is defined near X0 and has an interior maximum at X0. Let Z:=habτaτb. By the special choice of frame and the fact that hij is diagonal at X0 in this frame, we can see that
∇iZ=∇ih11and∇i∇jZ=∇i∇jh11atX0 |
Therefore the scalar function Z satisfies the same equation as the component h11 of the tensor hij. Thus at X0, we have
∇iWW=β∇iηη+Φ′∇iνn+1+∇ih11h11=0 | (2.2) |
and
∇i∇jWW−∇iW∇jWW2=β(∇i∇jηη−∇iη∇jηη2)+Φ″∇iνn+1∇jνn+1+Φ′∇i∇jνn+1+∇i∇jh11h11−∇ih11∇jh11h211 | (2.3) |
is nonpositive in the sense of matrices at X0. By Lemmas 2.1 and 2.2, we have, at X0,
0≥βFij(∇i∇jηη−∇iη∇jηη2)+Φ″Fij∇iνn+1∇jνn+1−(Φ′νn+1+1)Fijhiphpj+Fijhijh11−∇1∇1gh11+Φ′⟨∇g,en+1⟩−1h11Fij,rs∇1hij∇1hrs−Fij∇ih11∇jh11h211−k−2∑l=0Φ′⟨∇αl,en+1⟩σlσk−1+k−2∑l=01h11(2∇1αl⋅∇1σlσk−1+∇1∇1αl⋅σlσk−1). | (2.4) |
Using Gauss's formula
∇i∇jXα=hijνα, |
we have
∇1∇1g(X)=n+1∑α=1∂g∂Xα∇1∇1Xα+n+1∑α,β=1∂2g∂Xα∂Xβ∇1Xα∇1Xβ=n+1∑α=1∂g∂Xαναh11+n+1∑α,β=1∂2g∂Xα∂Xβ∇1Xα∇1Xβ. |
Consequently,
|∇1∇1gh11|≤C. |
For the same reason, we have for all l=0,⋯,k−2,
|∇1∇1αlh11|≤C. |
Taking Lemma 2.3 into count, we estimate the two terms in the last line of (2.4) as
−k−2∑l=0Φ′⟨∇αl,en+1⟩σlσk−1+k−2∑l=01h11∇1∇1αl⋅σlσk−1≥−C|Φ′|−C. |
Recall that F=Gk−∑αlGl and it is well-known that the operator (σk−1σl)1k−1−l is concave for 0≤l≤k−2. It follows that
(1Gl)1k−1−lisaconcaveoperatorfor∀l=0,1,⋯,k−2. |
For any symmetric matrix (Bij)∈Rn×n, we have
{(1Gl)1k−1−l}ij,rsBijBrs≤0. |
Direct computation shows that
Gij,rslBijBrs≥1Gl⋅k−lk−1−l⋅(GijlBij)2. |
Note that Gk is also a concave operator.
−1h11Fij,rs∇1hij∇1hrs+k−2∑l=02h11∇1αl⋅∇1σlσk−1=−1h11Gij,rsk∇1hij∇1hrs+k−2∑l=0αlh11Gij,rsl∇1hij∇1hrs+k−2∑l=02h11∇1αl⋅∇1σlσk−1≥1h11k−2∑l=0G−1lαlCl(∇1Gl+∇1αlClαlGl)2−1h11k−2∑l=0(∇1αl)2ClαlGl≥−Ch11 |
where Cl=k−lk−1−l. By the homogeneity of Gl's, we see that
Fijhij=Gk+k−2∑l=0αl(k−1−l)Gl≥Gk+k−2∑l=0αlσlσk−1≥inf|g|>0. |
Using Lemma 2.3 again, we have
Fijhij≤C. |
Next we assume that ϕ has been extended to be constant in the en+1 direction.
∇i∇jη=n∑α,β=1∂2ϕ∂Xα∂Xβ∇iXα∇jXβ+n∑α=1∂ϕ∂Xα∇i∇jXα−∇i∇jXn+1=n∑α=1∂ϕ∂Xαναhij−hijνn+1. |
Consequently,
Fij∇i∇jη=(n∑α=1∂ϕ∂Xανα−νn+1)Fijhij. |
Using above estimates in (2.4), we have, at X0,
0≥−Cβη−βFij∇iη∇jηη2+Φ″Fij∇iνn+1∇jνn+1−Fij∇ih11∇jh11h211−(Φ′νn+1+1)Fijhiphpj+inf|g|h11−C(1+|Φ′|). | (2.5) |
Next, using (2.2), we have
Fij∇ih11∇jh11h211=Fij(β∇iηη+Φ′∇iνn+1)(β∇jηη+Φ′∇jνn+1)≤(1+γ−1)β2Fij∇iη∇jηη2+(1+γ)(Φ′)2Fij∇iνn+1∇jνn+1 |
for any γ>0. Therefore at X0 we have, since |∇η|≤C,
0≥−Cβη−C[β+(1+γ−1)β2]∑ni=1Fiiη2+[Φ″−(1+γ)(Φ′)2]Fij∇iνn+1∇jνn+1−[Φ′νn+1+1]Fijhiphpj+inf|g|h11−C(1+|Φ′|). | (2.6) |
We choose a positive constant a, so that
a≤12νn+1=12√1+|Du|2 |
which depends only on supΩ|Du|. Therefore
1νn+1−a≤1a≤C. |
We now choose
Φ(t)=−log(t−a). |
Then
Φ′(t)=−1t−a,Φ″(t)=1(t−a)2, |
and
−(Φ′t+1)=at−a,Φ″−(1+γ)(Φ′)2=−γ(t−a)2. |
By direct computation, we have ∇iνn+1=−hip⟨ˆep,en+1⟩, and therefore
Fij∇iνn+1∇jνn+1=Fijhiphjq⟨ˆep,en+1⟩⟨ˆeq,en+1⟩≤Fijhiphpj. |
Next we choose 0<γ≤a22, then we have
−(Φ′t+1)+[Φ″−(1+γ)(Φ′)2]=at−a−γ(t−a)2≥12a2(t−a)2>0. |
Thus we have
0≥−Cβη−C(β,a)η−2(n∑i=1Fii)+inf|g|h11−C(a). | (2.7) |
In the following we show that ∑ni=1Fii≤C. By the definition of operator F and Lemma 2.3, we have
n∑i=1Fii=n∑i=1(σkσk−1)ii−n∑i=1k−2∑l=0αl(σlσk−1)ii=n∑i=1σk−1(λ|i)σk−1−σkσ2k−1n∑i=1σk−2(λ|i)+α0σ2k−1n∑i=1σk−2(λ|i)+k−2∑l=1αl∑iσlσk−2(λ|i)−∑iσk−1σl−1(λ|i)σ2k−1=n−k+1−(n−k+2)σkσk−2σ2k−1+(n−k+2)α0σk−2σ2k−1+k−2∑l=1αl(n−k+2)σlσk−2−(n−l+1)σk−1σl−1σ2k−1≤n−k+1+(n−k+2)|σkσk−1Gk−2|+(n−k+2)|α0|C0|Gk−2G0|+(n−k+2)|Gk−2|k−2∑l=1|αl|C0|Gl|. |
From Eq (1.4) we have |Gk|≤C, therefore ∑iFii≤C. At X0, we get an upper bound
λ1≤C(β,a)η2. |
Consequently, W(X0) satisfies an upper bound. Since W(X)≤W(X0), we get the required upper bound for the maximum principle curvature. Since λ∈Γk and n≥k≥2, u is at least mean-convex and
n∑i=1λi>0. |
Therefore λn≥−(n−1)λ1 and
|A|=√n∑i=1λ2i≤C(n)λ1≤C(ϕ−u)β. |
In this section we prove Theorem 1.2. By comparison principle, we have 0≥u≥u_. For any Ω′⋐Ω, infΩ′u_≤u≤c(Ω′)<0. First we show the gradient bound of admissible solutions of (1.6). We need following lemmas to prove the gradient estimate.
Lemma 3.1. Suppose A={aij}n×n satisfies λ(A)∈Γk−1, a11<0 and {aij}2≤i,j≤n is diagonal, then
n∑i=2∂F∂a1ia1i≤0. | (3.1) |
Proof. Let
B=(a110⋯00a22…0⋮⋮⋱⋮00⋯ann),C=(0a12⋯a1na210⋯0⋮⋮⋱⋮an10⋯0). |
A(t):=B+tC, f(t):=F(A(t)). Suppose a1i=ai1 for all 2≤i≤n. Directly we have
σk(A(t))=σk(B)−t2n∑i=2a21iσk−2(B|1i), |
where (B|ij) is the submatrix of B formed by deleting i-th, j-th rows and columns. Easily we see that for t∈[−1,1], λ(A(t))∈Γk−1 and f is concave on [−1,1]. f(−1)=f(1)=F(A). So f′(1)≤0. While
f′(1)=2n∑i=2∂F∂a1ia1i. |
Remark 3.1. By the concavity of σkσk−1, we can prove following inequality with λ(B)∈Γk−1
σk−2(B|1i)σk−1(B)−σk−3(B|1i)σk(B)≥0∀2≤i≤n. | (3.2) |
We let f(t)=σkσk−1(A(t)).
f′(1)=−2(∑ni=2a1iσk−2(B|1i))σk−1(A)−σk(A)(∑ni=2a21iσk−3(B|1i))σ2k−1(A)≤0. |
Equivalently,
σk−1(B)(n∑i=2a21iσk−2(B|1i))−σk(B)(n∑i=2a21iσk−3(B|1i))≥0. | (3.3) |
We can choose a1i>0 small enough and a1j=0 for j≠i and 2≤j≤n, so that λ(A)∈Γk−1. Then (3.3) implies (3.2).
Lemma 3.2. Let αk−2>0 and αl≥0 for 0≤l≤k−3. Suppose symmetric matrix A={aij}n×n satisfying
λ(A)∈Γk−1,a11<0,and{aij}2≤i,j≤nisdiagonal. |
Then
∂F∂a11≥C0(n∑i=1∂F∂aii) | (3.4) |
where C0 depends on n,k,|u|C0,|g|C0,infαk−2.
Proof. Note that
∂∂a11(σlσk−1(A))=σl−1(A|1)σk−1(A)−σl(A)σk−2(A|1)σ2k−1(A)=n∑i=2a21iσ2k−1(A)[σl−2(A|1i)σk−2(A|1)−σl−1(A|1)σk−3(A|1i)]+σ−2k−1(A)[σl−1(A|1)σk−1(A|1)−σl(A|1)σk−2(A|1)]. |
For 0≤l≤k−2,
∂∂a11(σlσk−1(A))≤−Cn,lσl(A|1)σk−2(A|1)σ2k−1(A). |
As for l=k,
∂∂a11(σkσk−1(A))≥Cn,kσ2k−1(A|1)σ2k−1(A)≥Cn,k. |
Therefore
∂F∂a11≥Cn,k+Cn,kinfαk−2σ2k−2(A|1)σ2k−1(A). |
Next we compute ∑ni=1∂F∂aii as
n∑i=1∂F∂aii=n−k+1−(n−k+2)σkσk−2σ2k−1(A)+(n−k+2)α0σk−2σ2k−1(A)+k−2∑l=1αl(n−k+2)σl(A)σk−2(A)−(n−l+1)σk−1(A)σl−1(A)σ2k−1(A)≤n−k+1−(n−k+2)σk−2(A)σk−1(A)(σkσk−1(A)−k−2∑l=0αlσlσk−1(A))≤Cn,k+Cn,k|g|C0σk−2σk−1(A)≤C(n,k,|g|C0)+C(n,k,|g|C0)(σk−2σk−1(A))2≤C(n,k,|g|C0)+C(n,k,|g|C0)σ2k−2(A|1)σ2k−1(A)≤C(n,k,|g|C0,infαk−2)∂F∂a11. |
Lemma 3.3. For any Ω′⋐Ω, there is a constant C depending only on Ω′,n,k,αl,g and their first derivatives, such that if u is an admissible solution of (1.6), then
|Du|≤C |
on Ω′.
Proof. Since we require that ∂ug≤0 and ∂uαl≥0, we only need to modify the equation (5.42) in [36] (i.e., (A.6)), where extra terms ∑k−2l=0(αl)ulogu1σl(A)σk−1(A)−gulogu1 should be included. These terms are all good terms and Zhou's proof will also hold in our case. For reader's convenience, we sketch the proof in the appendix below.
Now we give the proof of Theorem 1.2.
Proof of Thorem 1.2. The theorem can be proved by solving uniformlly elliptic approximating problems.
Fϵ[uϵ]=−gϵ(x,uϵ)inΩ,uϵ=0on∂Ω, |
for ϵ>0 small, and u_ is an admissible subsolution for each of the approximating problems. By the comparison principle and Theorem 1.1, the interior gradient estimates in [36](modified), we have uniform C2 interior estimates for uϵ. Then Evans-Krylov's theory, together with Schauder theory, imply uniform estimates for ||uϵ||C3,α(Ω′) for any Ω′⋐Ω. Theorem 2 then follows by extracting a suitable subsequence as ϵ→0.
In this section we prove Theorem 1.3. The notion of locally convex hypersurface we use is the same as that in [29].
Definition 4.1. A compact, connected, locally convex hypersurface M (possibly with boundary) in Rn+1 is an immersion of an n-dimensional, compact, oriented and connected manifold N (possibly with boundary) in Rn+1, that is, a mapping T:N→M⊂Rn+1, such that for any p∈N there is a neighbourhood ωp⊂N such that
● T is a homeomorphism from ωp to T(ωp);
● T(ωp) is a convex graph;
● the convexity of T(ωp) agrees with the orientation.
Since M is immersed, a point x∈M may be the image of several points in N. Since M and N are compact, T−1(x) consists of only finitely many points. Let r>0 and x∈M. For small enough r, T−1(M∩Bn+1r(x)) consists of several disjoint open sets U1,⋯,Us of N such that T|Ui is a homeomorphism of Ui onto T(Ui) for each i=1,⋯,s. By an r-neighbourhood ωr(x) of x in M we mean any one of the sets T(Ui). We say that ωr(x) is convex if ωr(x) lies on the boundary of its convex hull.
We shall use following lemma (see [32] Theorem A) to prove Theorem 1.3.
Lemma 4.1. Let M0⊂BR(0) be a locally convex hypersurface with C2-boundary ∂M0. Suppose that on ∂M0, the principal curvatures λ01,⋯,λ0n of M0 satisfy
C−10≤λ0i≤C0,i=1,2,⋯,n, |
for some C0>0. Then there exist positive constants r and α, depending only on n,C0,R and ∂M0, such that for any point p∈M0, each r-neighbourhood ωr(p) of p is convex, and there is a closed cone Cp,α with vertex p and angle α such that ωr(p)∩Cp,α={p}.
Note that for any point p∈M0, if one chooses the axial direction of the cone Cp,α as the xn+1-axis, then each δ-neighbourhood of p can be represented as a graph,
xn+1=u(x),|x|≤δ, |
for any δ<rsin(α/2). The cone condition also implies
|Du(x)|≤C,|x|<δ, |
where C>0 only depends on α. Lemma 4.1 holds not just for M0, but also for a family of locally convex hypersurfaces, with uniform r and α.
For 2≤k≤n, denote
f(k)(λ)=σkσk−1(λ)−n−2∑l=0αlσlσk−1(λ). |
αl's are positive constants. With the aid of Lemma 4.1, we use the Perron method to obtain a viscosity solution of the Plateau problem for the curvature function f(n), using the following lemma.
Lemma 4.2. Let Ω be a bounded domain in Rn with Lipschitz boundary. Let ϕ∈C0,1(ˉΩ) be a k-convex viscosity subsolution of
f(k)(λ)=σkσk−1(λ)−k−2∑l=0αlσlσk−1(λ)=cinΩ, | (4.1) |
where αl>0 and c≠0 are all constants. Then there is a viscosity solution u of (4.1) such that u=ϕ on ∂Ω.
Proof. The proof uses the well-known Perron method. Let Ψ denote the set of k-convex subsolutions v of (4.1) with v=ϕ on ∂Ω. Then Ψ is not empty and the required solution u is given by
u(x)=sup{v(x):v∈Ψ}. |
It is a standard argument. The key ingredient that needs to be mentioned is the solvability of the Dirichlet problem
f(k)(λ)=cinBr,u=u0on∂Br, | (4.2) |
in small enough balls Br⊂Rn, if u0 is any Lipschitz viscosity subsolution of (4.2). This is a consequence of [31] Theorem 6.2 with slight modification.
Using Lemma 4.2 and the argument of [32], we conclude that there is a locally convex hypersurface M with boundary Σ which satisfies the equation f(n)(λ)=c in the viscosity sense; that is, for any point p∈M, if M is locally represented as the graph of a convex function u (by Lemma 4.1), then u is a viscosity solution of f(n)(λ)=c.
Following we discuss the regularity of M. The interior regularity follows in the same way as [29].
Boundary regularity
The boundary regularity of M is a local property. The boundary estimates we need are contained in [19,21]. However, they can not be applied directly to M. Since we are working in a neighbourhood of a boundary point p0∈M, which we may take to be the origin, we may assume that for a smooth bounded domain Ω⊂Rn with 0∈∂Ω and small enough ρ>0 we have
M∩(Bρ×R)=graphu,M0∩(Bρ×R)=graphu0, |
where u∈C∞(Ωρ)∩C0,1(ˉΩρ), and u0∈C∞(ˉΩρ) are k-convex solutions of
f(k)[u]=cinΩρ,f(k)[u0]≥cinΩρ, |
with
u≥u0inΩρ,u=u0on∂Ω∩Bρ. |
We may choose the coordinate system in Rn in such a way that Ω is uniformly convex, and moreover, so that for some ϵ0>0 we have
σk−1(κ′)σk−2(κ′)≥ϵ0>0 | (4.3) |
on ∂Ω∩Bρ, where κ′=(κ′1,⋯,κ′n−1) denotes the vector of principal curvatures of ∂Ω. We recall that the principal curvatures of graph(u) are the eigenvalues of the matrix
(I−Du⊗Du1+|Du|2)(D2u√1+|Du|2). |
We denote σk(p,r) as the k-th elementary symmetric function of the eigenvalues of the matrix
(I−p⊗p1+|p|2)r,p=(p1,⋯,pn),r=(rij)n×n. |
Let f(k)(p,r)=σkσk−1(p,r)−∑k−2l=0αl(1+|p|2)k−l2σlσk−1(p,r). λ(r) is the vector formed by eigenvalues of r. For any p∈Rn and symmetric matrices r,s with λ(r),λ(s)∈Γk, we have
∑i,j∂f(k)∂rij(p,r)sij≥f(k)(p,s)+k−2∑l=0(k−l)αl(1+|p|2)k−l2σlσk−1(p,r). | (4.4) |
For later purposes we note the simple estimate, if r≥0,
11+|p|2σk(0,r)≤σk(p,r)≤σk(0,r), |
and the development
σk(p,r)=1+|˜p|21+|p|2rnnσk−1(˜p,˜r)+O((|rst|k)(s,t)≠(n,n)), |
where p=(p1,⋯,pn)∈Rn, r=(rij)n×n, ˜p=(p1,⋯,pn−1)∈Rn−1, ˜r=(rij)i,j=1,⋯,n−1.
We suppose that ∂Ω is the graph of ω:Bn−1ρ(0)⊂Rn→R and u(˜x,ω(˜x))=φ(˜x). Furthermore, ω(0)=0, Dω(0)=0, Dφ(0)=0 and ω is a strictly convex function of ˜x. The curvature equation is equivalent to
f(k)(Du,D2u)=c√1+|Du|2 | (4.5) |
defined in some domain Ω⊂Rn. We have following boundary estimates for second derivatives of u.
Lemma 4.3. Let u∈C3(ˉΩ) be a k-convex solution of (4.5). We assume (4.3) with ϵ>0. Then the estimate
|D2u(0)|≤C(n,k,αl,c,ϵ,||ω||C3,||φ||C4,||u||C1,λmin(D2ω(0))) | (4.6) |
holds true where λmin denotes the smallest eigenvalue.
Remark 4.1. On ∂Ω, we have for i,j=1,⋯,n−1,
ui+unωi=φi,uij+uinωj+unjωi+unnωiωj+unωij=φij. |
Therefore |uij(0)|=|φij(0)−un(0)ωij(0)|≤C. It remains to show that |uin(0)|≤C and |unn(0)|≤C. We follow [19,21] to obtain mixed second derivative boundary estimates and double normal second derivative boundary estimate.
Proof. Let
Ωd,κ={x(˜x,xn)∈Ω||˜x|<d,ω(˜x)<xn<˜ω(˜x)+κ2d2} |
where 0<d<ρ, ˜ω(˜x):=ω(˜x)−κ2|˜x|2, and κ>0 is chosen small enough such that ˜ω is still strictly convex. We decompose ∂Ωd,κ=∂1Ωd,κ∪∂2Ωd,κ∪∂3Ωd,κ with
∂1Ωd,κ={x∈∂Ωd,κ|xn=ω(˜x)},∂2Ωd,κ={x∈∂Ωd,κ|xn=ω(˜x)+κ2d2},∂3Ωd,κ={x∈∂Ωd,κ||˜x|=d}. |
Our lower barrier function v will be of the form
v(x)=θ(˜x)+h(ρ(x)) | (4.7) |
where θ(˜x) is an arbitrary C2-function, h(ρ)=exp{Bρ}−exp{κBd2} and ρ(x)=κd2+˜ω(˜x)−xn. Denote Fij=∂f(k)(Du,D2u)∂uij.
Mixed second derivative boundary estimates
By (4.4) and Lemma 2.3, we have
Fijvij≥f(k)(Du,D2v)+C |
where C depends only on n,k,αl's, c,||Du||C0. We choose an orthonormal frame {bi}ni=1 with bn=−Dρ|Dρ| and denote v(s)=∂v∂bs. Directly, we have
v(s)=θ(s)+h′ρ(s),(1≤s≤n−1);v(n)=θ(n)−h′√1+|D˜ω|2;v(st)=θ(st)+h′˜ω(st),(s,t)≠(n,n);v(nn)=θ(nn)+h′˜ω(nn)+h″(1+|D˜ω|2). |
We may choose d small so that |Du| is also small. Note that |D˜ω| is small since we can choose d,κ small. By choosing large enough B, we caculate
f(k)(Du,D2v)=σkσk−1(Du,D2v)−k−2∑l=0αl(1+|Du|2)k−l2σlσk−1(Du,D2v)≥(1−ϵ)σkσk−1(0,D2v)−2k−2∑l=0αlσlσk−1(0,D2v)≥(1−ϵ)2h′σk−1σk−2(0,˜ω(st))−2k−2∑l=1αl(h′)l−k+1σl−1σk−2(0,˜ω(st))−o(B−1) |
where in the last line, 1≤s,t≤n−1. Finally, we see that for large enough B and small enough d and κ the estimate
(1−δ)h′≤|Dv|≤(1+δ)h′ |
is valid for small δ. Therefore
Fijvij≥(1−ϵ)σk−1σk−2(0,˜ω(st))|Dv|+C. | (4.8) |
Let τ be a C2-smooth vector field which is tangential along ∂Ω. Following [19,21] we then introduce the function
w=1−exp(−a˜w)−b|x|2 |
where ˜w=uτ−12∑n−1i=1u2s and a,b are positive constants. Since on ∂1Ωd,κ, u=φ, and
w|∂1Ωd,κ≥aφτ−c|˜x|2,w(0)=0,w|∂2Ωd,κ∪∂3Ωd,κ≥−M |
for suitable constants c,M depending on a,b,||u||C1 and ||φ||C1. By differentiation of Eq (4.5), we obtain
Fijuijp+Fiuip=cˉvp |
where Fi:=∂f(k)∂ui and ˉv:=√1+|Du|2.
Fij˜wij=Fijuijpτp+Fij(upjτpi+upiτpj)+Fijτijpup−n−1∑s=1Fij(uisujs+usijus)=c(ˉvpτp−n−1∑s=1ˉvsus)−Fiuipτp+n−1∑s=1Fiuisus+Fij(upjτpi+upiτpj)+Fijτijpup−n−1∑s=1Fijuisujs. | (4.9) |
By the definition of ˜w, we have
c(ˉvpτp−n−1∑s=1ˉvsus)=cˉv(⟨D˜w,Du⟩−Hess(τ)(Du,Du)). | (4.10) |
Then we compute Fi. Denote bij=δij−uiujˉv2 and cij=bipupj. f(k) can be rewritten as
f(k)=f(k)(cij,ˉv)=σkσk−1(cij)−k−2∑l=0αlˉvk−lσlσk−1(cij). |
Directly we have
Fi=∂f(k)∂ui=∂f(k)∂cpq∂cpq∂ui+∂f(k)∂ˉv∂ˉv∂ui=−1ˉv2fiq(k)uqlul−1ˉv2fpq(k)uiqup+2ˉv3fpq(k)upululqui−k−2∑l=0αl(k−l)ˉvk−l−2σlσk−1(cij)ui |
where fpq(k):=∂f(k)∂cpq. Therefore
−Fiuipτp+n−1∑s=1Fiuisus=(−1ˉv2fiq(k)uqlul−1ˉv2fpq(k)uiqup+2ˉv3fpq(k)upululqui−k−2∑l=0αl(k−l)ˉvk−l−2σlσk−1(cij)ui)(−˜wi+upτpi). | (4.11) |
In order to derive the right hand side of (4.11), we use the same coordinate system as [21], which corresponds to the projection of principal curvature directions of the graph of u onto Rn⊃Ω. Fixing a point y∈Ω, we choose a basis of eigenvectors ˆe1,⋯,ˆen of the matrix (cij) at y, corresponding to the eigenvalues λ1,⋯,λn and orthonormal with respect to the inner product given by the matrix I+Du⊗Du. Using a subscript α to denote differentiation with respect to ˆeα, α=1,⋯,n, so that
uα=ˆeiαui=⟨Du,ˆeα⟩,uαα=λα=ˆeiαˆejαuij. |
Then we obtain
1ˉv2fiq(k)uqlul(˜wi−upτpi)=1ˉv2∂f(k)∂λαλαuα(˜ωα−Hess(τ)(Du,ˆeα))≤δ∂f(k)∂λαλ2α+C(δ)∂f(k)∂λα˜w2α+C(δ)n∑α=1∂f(k)∂λα. |
The second term of (4.11) can be estimated in the same way as above. As for the third term of (4.11), we calculate as
|fpq(k)upululq|=|fpq(k)(upq−cpq)|≤C|Du|2. |
Thus
−Fiuipτp+n−1∑s=1Fiuisus≤2δ∂f(k)∂λαλ2α+C(δ)∂f(k)∂λα˜w2α+C(δ)n∑α=1∂f(k)∂λα+C|˜wiui−τijuiuj|. | (4.12) |
Let (ηαi) denote the inverse matrix to (ˆeiα), we write
usα=ˆeiαuis=λαηαs. |
Furthermore,
n−1∑s=1∂f(k)∂λαu2sα=∂f(k)∂λαλ2αn−1∑s=1(ηαs)2. |
Now we reason similarly to [21]. If for all α=1,⋯,n, we have
n−1∑s=1(ηαs)2≥ϵ>0 | (4.13) |
where ϵ is a small postive number. Then we clearly have
n−1∑s=1∂f(k)∂λαu2sα≥ϵ∂f(k)∂λαλ2α. | (4.14) |
On the other hand, if (4.13) is not true, then
n−1∑s=1(ηγs)2<ϵ |
for some γ, which implies
n−1∑s=1(ηαs)2≥δ0>0 |
for all α≠γ. Hence
n−1∑s=1∂f(k)∂λαu2sα≥δ0∑α≠γ∂f(k)∂λαλ2α. | (4.15) |
Then we use Theorem 3, 4 in [22] to deduce that
∑α≠γ(σkσk−1),αλ2α≥1C(n,k)(σkσk−1),αλ2α,∑α≠γ(−σlσk−1),αλ2α≥1C(n,k,l)(−σlσk−1),αλ2α,∑α≠γ(−1σk−1),αλ2α≥1C(n,k,0)(−1σk−1),αλ2α−1C(n,k,0)σ1σk−1 |
where subscript ',α' denotes differentiation with respect to λα. Therefore,
n−1∑s=1∂f(k)∂λαu2sα≥δ′∂f(k)∂λαλ2α−C. | (4.16) |
Combing (4.9), (4.10), (4.12), (4.16), we have
Fij˜wij≤C|⟨D˜w,Du⟩|+CFij˜wi˜wj+Cn∑i=1Fii | (4.17) |
where we have chosen δ<<δ′, so that ∂f(k)∂λαλ2α can be discarded. Note that in (4.17), we also have used the fact that ∑ni=1Fii≥C0>0. By choosing a,b large, we conclude that
Fijwij≤C|⟨Dw,Du⟩|. | (4.18) |
From (4.8), (4.18), by comparison principle, we have at 0,
uτn(0)=1awn(0)≥1avn(0). |
Since τ is an arbitrary tangential direction at 0∈∂Ω, if we replace τ by −τ, we get an upper bound for uτn(0).
Double normal second derivative boundary estimate
We turn to estimate |unn(0)|. The idea is to estimate unn in a first step at some optimally chosen point y and in a second step conclude from this the estimate in the given point. We introduce a smooth moving orthonormal frame {b1,⋯,bn} with bn=(−ω˜x,1)/√1+|ω˜x|2 being the upward normal to ∂Ω. Here ω˜x is the gradient of ω(˜x). Let
G=σk−1σk−2(u(˜x),u(˜x˜x))−k−2∑l=1αl√1+|Du|2k−lσl−1σk−2(u(˜x),u(˜x˜x))−c√1+|Du|2 |
on ∂Ω, where u(˜x)=(∂u∂b1,⋯,∂u∂bn−1) and u(˜x˜x)=(∂2u∂bi∂bj)1≤i,j≤n−1. For simplicity, we denote ˜p=u(˜x), ˜r=u(˜x˜x), ˉv=√1+|Du|2. First we observe that
f(k)(p,r)<limrnn→+∞f(k)(p,r)=σk−1σk−2(˜p,˜r)−k−2∑l=1αlˉvk−lσl−1σk−2(˜p,˜r) |
from what we see that G>0. Hence the function
˜G=G(x)+4|˜x|2ˉρ2ˉG |
with ˉG=max{G(x)|x∈∂Ω,|˜x|<ρ} and 0<ˉρ<ρ attains its minimum over ∂Ω∩Bρ(0) at some point y∈∂Ω∩Bˉρ/2(0). If |unn(y)|<C, then G(y)>C−1>0.
G(0)=˜G(0)≥˜G(y)>G(y)>C−1>0. |
Therefore G(0) is strictly positive and we have
|unn(0)|<+∞. |
To check that |unn(y)|<+∞, we proceed in essentially the same way as in mixed second derivative estimates. The point y plays the role of the origin and the function ˜w is defined as
˜w(x)=−(un(x)−un(y))−K|Du(x)−Du(y)|2 |
where K is a sufficiently big constant. In order to apply the comparison principle, we need to obtain that
w(x)≥˜θ(˜x)−C|˜x−˜y|2(x∈∂Ω∩Bρ(0)) |
where ˜θ is some C2-smooth function. We reason similarly to Lemma 2.5 in [19]. The choice of the moving frame gives
u(s)=φ(s),u(st)=φ(st)−unω(st)(s,t=1,⋯,n−1). |
By the concavity of σk−1σk−2(˜p,˜r), −σlσk−2(˜p,˜r)(l=0,⋯,k−3) in ˜r and the convexity of √1+|˜p|2 in ˜p, we compute
0≤˜G(x)−˜G(y)≤g(y,x)(un(y)−un(x))+h(y,x) | (4.19) |
with
g(y,x)=(σk−1σk−2)st(˜p(x),˜r(y))ω(st)(x)−k−2∑l=1αlˉvk−l(x)(σl−1σk−2)st(˜p(x),˜r(y))ω(st)(x)+(k−2∑l=1αl(k−l)ˉvk−l−2(y)σl−1σk−2(˜p(y),˜r(y))+cˉv−1(y))(un(y)−ui(y)ωi(x)) |
and
h(y,x)=σk−1σk−2(φ˜x(x),˜r(y))−σk−1σk−2(φ˜x(y),˜r(y))+(σk−1σk−2)st(φ˜x(x),˜r(y))Ψst(y,x)−k−2∑l=1αlˉvk−l(σl−1σk−2)st(φ˜x(x),˜r(y))Ψst(y,x)+k−2∑l=1αlˉvk−l(σl−1σk−2(φ˜x(y),˜r(y))−σl−1σk−2(φ˜x(x),˜r(y)))+[cˉv−1−k−2∑l=1αl(k−l)ˉvk−l−2σl−1σk−2(˜p(y),˜r(y))]⋅A+4ˉGˉρ2(|˜x|2−|˜y|2) |
where Ψst(y,x)=φ(st)(x)−φ(st)(y)−un(y)(ω(st)(x)−ω(st)(y)), A=[φi(y)−φi(x)−un(y)(ωi(y)−ωi(x))]ui(y). We may take ˜θ(˜x)=−hg(y,x) if we can show that g(y,x)>0. This is true since |Du| is small and −(σl−1σk−1)st is semi-positive definite, together with condition (4.3). This completes the proof of the boundary regularity.
The authors were supported by NSFC, grant nos. 12031017 and 11971424.
The authors declare no conflict of interest.
In this appendix, we sketch the proof of Lemma 3.3 for reader's convenience. For the original proof, see [36].
Without loss of generality, we assume Ω=Br(0). Let ρ=r2−|x|2, M=oscBru, ˜g(u)=1M(M+u−infBru), ϕ(x,ξ)=ρ(x)˜g(u)log(uξ(x)). This auxiliary function ϕ comes from [34]. Suppose ϕ attains its maximum at (x0,e1). Furthermore, by rotating e2,⋯,en, we can assume that {uij(x0)}2≤i,j≤n is diagonal. Thus φ(x)=logρ(x)+log˜g(u(x))+loglogu1 also attains a local maximum at x0∈Br(0). At x0, we have
0=φi=ρiρ+˜gi˜g+u1iu1logu1, | (A.1) |
0≥φij=ρijρ−ρiρjρ2+˜gij˜g−˜gi˜gj˜g2+u1iju1logu1−(1+1logu1)u1iu1ju21logu1. | (A.2) |
Only in this proof we denote that Fij:=∂F∂uij. Fij is positive definite. Taking trace with φij and using (A.1), we have
0≥Fijφij=Fij(ρijρ+2ρiρ˜gj˜g+˜gij˜g)+Fij(u1iju1logu1−(1+2logu1)u1iu1ju21logu1):=A+B. | (A.3) |
It is well-known that the principal curvatures of graph u are the eigenvalues of matrix A=(aij)n×n:
aij=1W(uij−uiululjW(W+1)−ujululiW(W+1)+uiujupuqupqW2(W+1)2) |
where W=√1+|Du|2. Next we compute Fij at x0.
∂aij∂uij={1W3i=j=1,1W2i=1,j≥2ori≥2,j=1,1Wi≥2,j≥2. |
For two different sets {p,q}≠{i,j}, ∂apq∂uij=0. Therefore
Fij=∂F∂aij∂aij∂uij={1W3∂F∂a11i=j=1,1W2∂F∂aiji=1,j≥2ori≥2,j=1,1W∂F∂aiji≥2,j≥2. |
Direct computation shows that
A=−2ρ(n∑i=1∂F∂uii)+1M˜g(n∑i,j=1∂F∂uij⋅uij)+2u1Mρ˜gn∑i=1∂F∂u1i⋅ρi, |
n∑i=1∂F∂uii=∂F∂a111W3+n∑i=2∂F∂aii1W≤1Wn∑i=1∂F∂aii, |
n∑i,j=1∂F∂uij⋅uij=n∑i,j=1∂F∂aij⋅aij=σkσk−1(A)−k−2∑l=0αl(l−k+1)σlσk−1(A). |
By (A.1), suppose that u1≫1, then we have u11<0 and
2u1Mρ˜gn∑i=1∂F∂u1i⋅ρi=2u1Mρ˜g(∂F∂u11ρ1+n∑i=2∂F∂u1iρi)=2u1Mρ˜g(∂F∂a11ρ1W3−n∑i=2∂F∂a1iu1iρW2u1logu1)≥−4ru1MW3ρ˜g∂F∂a11−2M˜glogu1n∑i=2∂F∂a1ia1i≥−4ru1MW3ρ˜gn∑i=1∂F∂aii |
where we have used (3.1). Therefore
A≥(−2Wρ−Cu1W3)(n∑i=1∂F∂aii)+1M˜g(−g+k−2∑l=0αl(k−l)σlσk−1(A)). | (A.4) |
In the following we turn to estimate B. By the definition of aij, we have at x0,
∂a11∂x1=1W3u111−3u1W5u211−2u1W3(W+1)n∑k=2u2k1, |
for i≥2,
∂a1i∂x1=1W2u1i1−2u1W4u11u1i−u1W2(W+1)u1iuii−u1W3(W+1)u11u1i, |
∂aii∂x1=1Wuii1−u1W3u11uii−2u1W2(W+1)u21i, |
for i≥2,j≥2,i≠j,
∂aij∂x1=1Wuij1−2ui1uj1u1W2(W+1). |
Taking derivatives with respect to x1 on both sides of (1.4), we have
n∑i,j=1∂F∂aij∂aij∂x1−k−2∑l=0(αl),1σlσk−2=−g,1. |
For the first term of B, we calculate as
n∑i,j=1∂F∂uijuij1u1logu1=1u1logu1(∂F∂a11u111W3+2∑i≥2∂F∂a1iu1i1W2+∑i,j≥2∂F∂aijuij1W), |
Fijuij1=∂F∂a11(∂a11∂x1+3u1W5u211+2u1W3(W+1)∑k≥2u2k1)+2∑i≥2∂F∂a1i(∂a1i∂x1+2u1W4u11u1i+u1u1iuiiW2(W+1)+u1u11u1iW3(W+1))+∑i≠j≥2∂F∂aij(∂aij∂x1+2u1ui1uj1W2(W+1))+n∑i=2∂F∂aii(∂aii∂x1+u1u11uiiW3+2u1u21iW2(W+1))=−g,1+k−2∑l=0(αl),1σlσk−1(A)+u1u11W2(−g+k−2∑l=0(k−l)αlσlσk−1(A))+∂F∂a11(2u1W5u211+2u1W3(W+1)∑k≥2u2k1)+2∑i≥2∂F∂a1i(u1W4u11u1i+u1u1iuiiW2(W+1)+u1u11u1iW3(W+1))+2∑i≠j≥2∂F∂aiju1ui1uj1W2(W+1)+2n∑i=2∂F∂aiiu1u21iW2(W+1). |
For the second term of \mathcal{B} , we calculate
\sum\limits_{i,j = 1}^n\frac{\partial F}{\partial u_{ij}}u_{1i}u_{1j} = \frac{\partial F}{\partial a_{11}}\frac{u_{11}^2}{W^3}+2\sum\limits_{i = 2}^n\frac{\partial F}{\partial a_{1i}}\frac{u_{11}u_{1i}}{W^2}+\sum\limits_{2\leq i,j\leq n}\frac{\partial F}{\partial a_{ij}}\frac{u_{1i}u_{1j}}{W}. |
Therefore
\begin{align*} \mathcal{B} = &\frac{1}{u_1\log u_1}\Big(-g_{,1}+\sum\limits_{l = 0}^{k-2}(\alpha_l)_{,1}\frac{\sigma_{l}}{\sigma_{k-1}}(A)\Big)\\ &+\frac{u_{11}}{W^2\log u_1}\Big(-g+\sum\limits_{l = 0}^{k-2}(k-l)\alpha_l\frac{\sigma_l}{\sigma_{k-1}}(A)\Big)\\ &+\Big(\frac{2}{W^5\log u_1}-(1+\frac{2}{\log u_1})\frac{1}{u_1^2\log u_1W^3}\Big)\\ &\frac{\partial F}{\partial a_{11}}u_{11}^2+\frac{2}{W^3(W+1)\log u_1}\sum\limits_{k\geq 2} \frac{\partial F}{\partial a_{11}}u_{k1}^2\\ &+\Big(\frac{2}{W^4\log u_1}+\frac{2}{W^3(W+1)\log u_1}\\ &-(1+\frac{2}{\log u_1})\frac{2}{W^2u_1^2\log u_1}\Big)\sum\limits_{i = 2}^n\frac{\partial F}{\partial a_{1i}}u_{11}u_{1i}\\ &+\frac{2}{W^2(W+1)\log u_1}\sum\limits_{i = 2}^n\frac{\partial F}{\partial a_{1i}}u_{1i}u_{ii}\\ &+\Big(\frac{2}{W^2(W+1)\log u_1}-\frac{1+2/\log u_1}{Wu_1^2\log u_1}\Big)\times\\ &\sum\limits_{2\leq i,j\leq n}\frac{\partial F}{\partial a_{ij}}u_{1i}u_{1j}. \end{align*} |
Since \{\frac{\partial F}{\partial a_{ij}}\}_{1\leq i, j\leq n} is positive definite, so is \{\frac{\partial F}{\partial a_{ij}}\}_{2\leq i, j\leq n} . W = \sqrt{1+u_1^2}\approx u_1 . Therefore
\begin{align} \mathcal{B}\geq&\frac{1}{u_1\log u_1}\Big(-g_{,1}+\sum\limits_{l = 0}^{k-2}(\alpha_l)_{,1}\frac{\sigma_{l}}{\sigma_{k-1}}(A)\Big)\\&+\frac{u_{11}}{W^2\log u_1}\Big(-g+\sum\limits_{l = 0}^{k-2}(k-l)\alpha_l\frac{\sigma_l}{\sigma_{k-1}}(A)\Big)\\ &+\frac{1-\delta}{W^5\log u_1}\frac{\partial F}{\partial a_{11}}u_{11}^2+\frac{2}{W^2(W+1)\log u_1}\big(\sum\limits_{i = 2}^n\frac{\partial F}{\partial a_{1i}}u_{1i}u_{ii}\big) \end{align} | (A.5) |
where \delta > 0 is a small constant, depending only on u_1 . By (A.3), (A.4), (A.5), we have
\begin{align} 0\geq& (-\frac{2}{W\rho}-\frac{Cu_1}{W^3})(\sum\limits_{i = 1}^n\frac{\partial F}{\partial a_{ii}})+(\frac{1}{M\tilde g}+\frac{u_{11}}{W^2\log u_1})(-g+\sum\limits_{l = 0}^n\alpha_l(k-l)\frac{\sigma_l}{\sigma_{k-1}})\\ &+\frac{1}{u_1\log u_1}(-g_{,1}+\sum\limits_{l = 0}^{k-2}(\alpha_l)_{,1}\frac{\sigma_l}{\sigma_{k-1}})+\frac{1-\delta}{W^5\log u_1}\frac{\partial F}{\partial a_{11}}u_{11}^2\\ &+\frac{2}{W^2(W+1)\log u_1}\big(\sum\limits_{i = 2}^n\frac{\partial F}{\partial a_{1i}}u_{1i}u_{ii}\big). \end{align} | (A.6) |
Since we require that g_u\leq 0 and (\alpha_l)_u\geq 0 ,
-g_{,1}+\sum\limits_{l = 0}^{k-2}(\alpha_l)_{,1}\frac{\sigma_l}{\sigma_{k-1}}\geq-\frac{\partial g}{\partial x_1}+\sum\limits_{l = 0}^{k-2}\frac{\partial \alpha_l}{\partial x_1}\frac{\sigma_l}{\sigma_{k-1}}. |
We claim that
\begin{equation} \sum\limits_{i = 2}^n\frac{\partial F}{\partial a_{1i}}u_{1i}u_{ii}\geq -C\frac{u_1^2\log^2 u_1}{W}\frac{|D\rho|^2}{\rho^2}\frac{\partial F}{\partial a_{11}}. \end{equation} | (A.7) |
We deter the proof of (A.7). By (A.1), we see that the leading term in (A.6) is \frac{1-\delta}{W^5\log u_1}\frac{\partial F}{\partial a_{11}}u_{11}^2\approx\frac{\log u_1}{W}\frac{\partial F}{\partial a_{11}} > 0 . Other terms have order at most O(W^{-1}) , therefore
\log u_1\leq C. |
The interior gradient estimate is proved after we check (A.7). Let \Upsilon = \{2\leq j\leq n|a_{jj}\geq 0\} . Note that a_{11} < 0 and \lambda(A)\in\Gamma_k .
\begin{align*} \sum\limits_{i = 2}^{n} \frac{\partial F}{\partial a_{1 i}} u_{1 i} u_{i i} = &-\sum\limits_{i = 2}^{n}\left[\frac{\sigma_{k-2}(A|1i) \sigma_{k-1}(A)-\sigma_{k-3}(A|1 i) \sigma_{k}(A)}{\sigma_{k-1}^{2}(A)}\right.\\ &\left.+\sum\limits_{l = 1}^{k-2} \alpha_{l}\frac{\sigma_{k-3}(A|1 i) \sigma_{l}(A)-\sigma_{l-2}(A|1 i) \sigma_{k-1}(A)}{\sigma_{k-1}^{2}(A)}\right] a_{i 1} u_{1 i} u_{i i} \\ \geq &-\sum\limits_{i \in \Upsilon}^{n}\left[\frac{\sigma_{k-2}(A|1 i) \sigma_{k-1}(A)-\sigma_{k-3}(A|1 i) \sigma_{k}(A)}{\sigma_{k-1}^{2}(A)}\right.\\ &\left.+\sum\limits_{l = 1}^{k-2} \alpha_{l}\frac{\sigma_{k-3}(A|1 i) \sigma_{l}(A)-\sigma_{l-2}(A|1 i) \sigma_{k-1}(A)}{\sigma_{k-1}^{2}(A)}\right] a_{i i} \frac{u_{1 i}^{2}}{W} \\ \geq &-\sum\limits_{i \in \Upsilon}\left[\frac{a_{i i} \sigma_{k-2}(A|1 i) \sigma_{k-1}(A)}{\sigma_{k-1}^{2}(A)}+\sum\limits_{l = 1}^{k-2} \alpha_{l}\frac{a_{i i} \sigma_{k-3}(A|1 i) \sigma_{l}(A)}{\sigma_{k-1}^{2}(A)}\right] \frac{u_{1 i}^{2}}{W} \\ \geq &-\sum\limits_{i \in \Upsilon}\left[\frac{C_{n,k}\sigma_{k-1}(A|1) \sigma_{k-1}(A)}{\sigma_{k-1}^{2}(A)}+\sum\limits_{l = 1}^{k-2} \alpha_{l}\frac{C_{n,k}\sigma_{k-2}(A|1) \sigma_{l}(A)}{\sigma_{k-1}^{2}(A)}\right] \frac{u_{1 i}^{2}}{W} \\ \geq &-\left[\frac{C_{n,k}\sigma_{k-1}(A|1) \sigma_{k-1}(A)}{\sigma_{k-1}^{2}(A)}+\sum\limits_{l = 1}^{k-2} \alpha_{l}\frac{C_{n,k}\sigma_{k-2}(A|1) \sigma_{l}(A)}{\sigma_{k-1}^{2}(A)}\right] \sum\limits_{i = 2}^{n} \frac{u_{1 i}^{2}}{W} \\ \geq &-\left[C_{n,k}\frac{\sigma_{k-1}(A|1) \sigma_{k-1}(A)-\sigma_{k-2}(A|1) \sigma_{k}(A)}{\sigma_{k-1}^{2}(A)}\right.\\ &+\left.\sum\limits_{l = 0}^{k-2}\alpha_lC_{n,k}\frac{\sigma_{k-2}(A|1)\sigma_l(A)-\sigma_{l-1}(A)\sigma_{k-1}(A)}{\sigma_{k-1}^2(A)}\right]\sum\limits_{i = 2}^n\frac{u_{1i}^2}{W}\\ \geq &-C(n,k)\sum\limits_{i = 2}^n\frac{u_{1i}^2}{W}\frac{\partial F}{\partial a_{11}}\\ \geq &-C(n,k)\frac{u_1^2\log^2u_1}{W}\frac{|D\rho|^2}{\rho^2}\frac{\partial F}{\partial a_{11}}. \end{align*} |
Thus (A.7) holds and the gradient estimate is proved.
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