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For a fixed bounded measurable uniformly elliptic operator in divergence form, solutions bounded by one have uniform local Caccioppoli inequalities and Hölder continuity estimates. A diagonal subsequence argument yields a local weak $W^{1,2}$ limit, while the Arzelà-Ascoli theorem gives uniform convergence on a fixed smaller closed Euclidean ball. The weak equation passes to the limit because the coefficients are fixed and bounded. A global supremum normalization does not force the interior limit to be nonzero.

A linear polyhedron in ${\mathbb{R}}^n$ is full-dimensional when its affine hull is all of ${\mathbb{R}}^n$. Equivalently, it contains an open Euclidean ball. An open Euclidean ball spans the ambient space. Conversely, a full-dimensional convex set contains $n+1$ affinely independent points; their convex hull is a simplex with nonempty interior, so the set contains an open Euclidean ball.

For an integer linear polyhedron $P=\{x\in {\mathbb{R}}^n:Ax\ge b\}$ with coefficients bounded by $U\ge 1$, put $D=[(n+1)U{]}^{n+1}$ and $0<\epsilon <1/D$. If $x_0\in P$, every $h$ with $\Vert h{\Vert }_2\le \epsilon /(nU)$ satisfies $A_{i}h\ge -\sqrt{n}U\Vert h{\Vert }_2\ge -\epsilon$. Thus $P_{\epsilon }$ contains a positive-radius Euclidean ball and is a full-dimensional linear polyhedron. If $P$ is empty, the normalized integer Farkas infeasibility gap gives $\lambda \ge 0$, $A^T\lambda =0$, $1^T\lambda =1$ and $b^T\lambda \ge 1/D$. A point of $P_{\epsilon }$ would give $0={\lambda }^{T}Ax\ge b^T\lambda -\epsilon >0$, a contradiction. Consequently this relaxation preserves emptiness and supplies the inner-volume bound needed by the ellipsoid method, even when $P$ has empty interior.

Let $v\le 0$ be $C^1$ on a connected domain and $C^2$ away from its zero set, with $\Delta v+v=0$ wherever $v<0$. If negative and zero points coexist, an interior Euclidean ball in $\{v<0\}$ touches its zero set. On that Euclidean ball $\Delta v=-v>0$, so the Hopf boundary point lemma forces a nonzero derivative at an interior maximum, contrary to $Dv=0$. Thus $v$ is identically zero or strictly negative. Mere Lipschitz continuity does not suffice: $-\max \{\sin x_1,0\}$ on $(-1,1{)}^n$ has both negative and zero regions.

For a connected finite-dimensional Riemannian manifold, completeness of the Riemannian distance, geodesic completeness, and compactness of every closed bounded subset are equivalent. These conditions imply that any two points are joined by a minimizing geodesic. Pairwise existence of minimizing geodesics alone does not imply completeness: an open Euclidean ball is incomplete, yet the line segment between any two of its points stays in the ball and minimizes length.

An epsilon-net in a metric space is a set within distance $\epsilon$ of every point. A maximal $\epsilon$-separated set is an epsilon-net: any uncovered point could be added. On the unit sphere of ${\mathbb{R}}^k$, disjoint Euclidean balls of radius $\epsilon /2$ around separated points lie in the ball of radius $1+\epsilon /2$. Comparing Lebesgue measures gives a net of cardinality at most $(1+2/\epsilon {)}^k$.

The bounded-forcing norm alone cannot control the Hölder seminorm of the Hessian matrix. On a unit Euclidean ball take $u_m=m^{-2}\sin (m{x}_1)$ and $f_m=\Delta u_m=-\sin (m{x}_1)$. The derivative supremum norm $|u_m{|}_2$ and $\Vert f_m{\Vert }_{\infty }$ stay bounded, but the Hessian matrix difference between zero and $\pi /(2m)e_1$ gives $[D^2{u}_m{]}_{\alpha }\ge (2m/\pi {)}^{\alpha }$ on $B_{1/2}$.

On two copies of the real line with their usual topologies, let $U=(1,2)$ and $V=(2,4)$. The coordinate projection maps giveThe first two open sets are unbounded strips; their set intersection is an open rectangle. The point $(1.4,3)$ passes both coordinate tests, whereas $(2,3)$ fails the first because the endpoint $2$ is excluded.

Every point of an open set in the Euclidean plane has such an open rectangle around it inside that open set. Conversely, every open rectangle contains a small Euclidean ball around each of its points. Thus the product topology here is the usual metric topology of the Euclidean plane. With finitely many factors all coordinates may be restricted; on an infinite Cartesian product a basic cylinder set must leave all but finitely many factors free.

An affine linear classifier in ${\mathbb{R}}^k$ has VC dimension $k+1$. The upper bound follows from the intersecting convex hull partition theorem: for any $k+2$ points, label the two parts oppositely. The two convex hulls cannot be strictly separated by an affine hyperplane, since they intersect. A weak boundary convention gives the same contradiction: an affine score is nonnegative on the positive convex hull and strictly negative on the negative convex hull. The lower bound comes from the $k+1$ affinely independent vertices of a simplex, because an affine score can interpolate arbitrary signs there.

Consequently, parts (i) and (ii) immediately giveThe one-sided orientation of the lifted separators permits the sharper, exact conclusionTo prove the remaining upper bound, take any $d+2$ distinct points and a nonzero affine dependence ${\lambda }_i$ as in part (c). Write $Q={\sum }_i{\lambda }_i\Vert x_i{\Vert }^2$. Reverse every coefficient if necessary so that $Q\ge 0$. Prescribe positive labels when ${\lambda }_i>0$ and negative labels when ${\lambda }_i<0$; zero coefficients may be labelled arbitrarily. If a Euclidean ball realized these labels, its score $g(x)=r^2-\Vert x-c{\Vert }^2$ would satisfyIndeed, every positive-coefficient term is nonnegative and every negative-coefficient term is strictly positive, with at least one of the latter. On the other hand, expanding the score and using the two affine dependence identities givesa contradiction. No $d+2$ points are shattered, completing the exact determination of the VC dimension of Euclidean balls. In the exceptional zero-dimensional Euclidean space, every positive-radius Euclidean ball is the whole singleton space, so the VC dimension is zero.

For $d\ge 1$, take the $d+1$ vertices $v_0,\dots ,v_d$ of a regular simplex in ${\mathbb{R}}^d$, centered at zero and scaled so that $\Vert v_j\Vert =1$. For example, realize them asin the $d$-dimensional vector subspace $\{z\in {\mathbb{R}}^{d+1}:{\sum }_j{z}_j=0\}$, identified isometrically with ${\mathbb{R}}^d$. These vertices are affinely independent.

Assign arbitrary desired signs $s_j\in \{-1,1\}$, positive for inclusion. Affine independence implies that there is an affine function $\ell (x)=a\cdot x+b$ with $\ell (v_j)=s_j$ for every $j$: the $d+1$ interpolation conditions form an invertible linear system. For a sufficiently small $\eta >0$, chooseAt every vertex,Thus the Euclidean ball contains exactly the positively labelled vertices, with none on its boundary. This construction also covers the all-positive and all-negative labellings. Therefore

Expanding the squared Euclidean norm givesIn lifted coordinates $(z,t)=\Phi (x)=(x,\Vert x{\Vert }^2)$, this is the closed half-spaceEvery labelling of the original points obtainable by a Euclidean ball is therefore obtainable on their lifted images by an affine linear classifier. The feature map is injective, since it preserves the first $d$ coordinates; distinct original points remain distinct. If the original points are shattered, every labelling of the lifted points is realized, as required.

Here “linear separator” means a separator with an intercept, namely an affine hyperplane. If a convention demands strict separation on the finite sample, increase the constant term of the positive score $r^2-\Vert c{\Vert }^2+2c\cdot z-t$ by a sufficiently small positive amount. Positive points then have strictly positive score, and the finitely many negative scores remain strictly negative. A homogeneous separator can alternatively be obtained after adjoining one more constant coordinate; this distinction matters for dimension counts.

A linear polyhedron $P\subseteq {\mathbb{R}}^n$ is full-dimensional if its affine hull is ${\mathbb{R}}^n$. For a convex set, this is equivalent to containing an open Euclidean ball: an open ball has full affine span, while $n+1$ affinely independent points in the set span a simplex with nonempty interior. In particular, a nonempty linear polyhedron need not be full-dimensional; a singleton or a hyperplane is an example.

Assume $U\ge 1$, increasing the coefficient bound to one if necessary. If $x_0\in P$ and $A_i$ denotes the $i$th row, then $\Vert A_i{\Vert }_2\le \sqrt{n}U$. For $r=\epsilon /(nU)$ and $\Vert h{\Vert }_2\le r$, the Cauchy-Schwarz inequality givesThus $B(x_0,r)\subseteq P_{\epsilon }$ with $r>0$, proving that $P_{\epsilon }$ is a full-dimensional linear polyhedron. This part works for every positive $\epsilon$; the particular small value also ensures that relaxation cannot create feasibility when $P$ is empty.

Here is that exact-feasibility point. Put $D=[(n+1)U{]}^{n+1}$. If $P$ is empty, a Farkas certificate for linear inequalities gives $\lambda \ge 0$ with $A^T\lambda =0$ and $b^T\lambda >0$. Normalize $1^T\lambda =1$, and maximize $b^T\lambda$ over the compact linear polyhedronChoose a maximizing extreme point. Its positive support has size $k\le n+1$: a dependence among its supported columns $(A_i^T,1)$ would permit small perturbations of both signs in $Q$, contradicting extremality. Select $k$ independent equations for those $k$ coordinates. Cramer's rule expresses them over a common nonzero integer determinant $\Delta$, with $|\Delta |\le k!U^k\le D$. Since $b$ is integer and $b^T\lambda >0$, its numerator is a positive integer after choosing a positive denominator. Hence $b^T\lambda \ge 1/D$. This is the normalized integer Farkas infeasibility gap. A point in $P_{\epsilon }$ would implya contradiction. Consequently $P=\varnothing$ if and only if $P_{\epsilon }=\varnothing$.

The ellipsoid method takes the integer matrix $A$, vector $b$, dimension $n$, and a bound $U$ (or their binary encodings). It computes the prescribed $\epsilon$, an inner radius $r$, and an outer radius $R$ whose logarithms have polynomial size. For an explicit outer bound, let $M=(nU{)}^n$. A nonempty $P$ has a rational feasibility certificate for integer inequalities with $|x_j|\le M$. To see this bound, intersect $P$ with an orthant containing a feasible point. This intersection is a nonempty linear polyhedron containing no whole nontrivial affine line, and it has an extreme point. Indeed, if its active rows do not yet span the ambient space, move along a nonzero direction annihilating them. The orthant prevents motion in both directions from being feasible forever, so in at least one direction a new independent row becomes active. Repeating at most $n$ times produces an extreme point. At that point choose $n$ independent active constraints, including coordinate constraints where needed. Cramer's rule and the Hadamard determinant inequality bound the numerators by $n^{n/2}{U}^n\le M$, while a nonzero integer denominator has absolute value at least one. Thus the point has norm at most $nM$. Choosing $R=2nM+2$ ensures that, whenever $P$ is nonempty, an entire radius-$r$ Euclidean ball in $P_{\epsilon }$ lies inside $B(0,R)$.

Start with the ellipsoid $E_0=B(0,R)$ and retain $K=P_{\epsilon }\cap B(0,R)$. At each iteration test the centre $c$ against every relaxed inequality. If it passes all tests, $P_{\epsilon }$ is nonempty and hence so is $P$. Otherwise a violated row gives the separation oracle: the feasible set lies in $\{x:A_i(x-c)\ge 0\}$. Cut the current ellipsoid by this half-space through its centre and replace it by the standard containing ellipsoid. Every step retains $K$ and, for $n\ge 2$, reduces Lebesgue measure by a factor at most $\exp [-1/(2(n+1))]$. In dimension one the corresponding step bisects an interval.

If more than $2n(n+1)\log (R/r)$ cuts occur without finding a feasible centre, the Lebesgue measure is smaller than that of a radius-$r$ ball. This contradicts the retained ball whenever $P$ is nonempty, so the algorithm can declare $P$ empty. Both $\log R$ and $\log (1/r)$ are polynomial in $n$ and $\log U$, and each inequality test uses $O(mn)$ arithmetic operations for $m$ rows. The bit implementation uses rational rounding with outward enlargement of the containing ellipsoids; the encoding bounds allow polynomial precision while keeping a constant fraction of the volume decrease. Detailed update and rounding formulae are not needed for this account. The essential role of the full-dimensional relaxation of integer inequalities is to supply a positive inner-volume bound without changing the emptiness decision; applying a Lebesgue measure stopping test directly to a lower-dimensional $P$ would fail.

A norm $p$ is nonnegative with $p(x)=0$ exactly when $x=0$, satisfies $p(ax)=|a|p(x)$, and obeys $p(x+y)\le p(x)+p(y)$. The Euclidean norm has positivity and absolute homogeneity immediately. For the triangle inequality, positivity of $\Vert x-ty{\Vert }_2^2$ for all real $t$ gives the Cauchy-Schwarz inequality $|x\cdot y|\le \Vert x{\Vert }_2\Vert y{\Vert }_2$ (with $y=0$ immediate). Hencewhich proves the triangle inequality after taking square roots.

For the prescribed set, use the Minkowski functionalSince $U$ is open and contains zero, some Euclidean ball of radius $r>0$ lies inside it. Since it is bounded, it lies inside a ball of radius $R$. These inclusions imply $\Vert x{\Vert }_2/R\le p_U(x)\le \Vert x{\Vert }_2/r$, so the functional is finite and positive for nonzero $x$; it is zero at zero. Symmetry gives $p_U(-x)=p_U(x)$, and substitution in the infimum gives $p_U(ax)=a{p}_U(x)$ for $a>0$, hence absolute homogeneity for all real scalars.

Convexity and $0\in U$ imply $sU\subseteq U$ for $0\le s\le 1$. Consequently, for $a>p_U(x)$ and $b>p_U(y)$, one has $x/a,y/b\in U$. Convexity then givesso $p_U(x+y)\le a+b$. Letting $a\downarrow p_U(x)$ and $b\downarrow p_U(y)$ proves the triangle inequality.

Finally, if $p_U(x)<1$, there is a $t<1$ with $x\in tU\subseteq U$. Conversely, if $x\in U$ and $x\ne 0$, openness permits $(1+\delta )x\in U$ for some $\delta >0$, so $p_U(x)\le 1/(1+\delta )<1$; zero is immediate. Thus $\boxed{U=\{x:p_U(x)<1\}}$, completing the norm from a bounded symmetric convex neighbourhood construction.

Every point has arbitrarily small path-connected open neighbourhoods. Indeed, in a coordinate chart homeomorphic to ${\mathbb{R}}^n$, take the inverse image of a sufficiently small open Euclidean ball. The ball is convex, hence path-connected, and its inverse image is path-connected by the homeomorphism. If the supplied neighbourhood is not itself open, first take an open neighbourhood within it and a ball contained in its chart image. This proves that $X$ is a locally path-connected space.

Fix $x_0\in X$ and let $A$ consist of the points joined to $x_0$ by a path. For $x\in A$, a path-connected open neighbourhood $U_x$ is contained in $A$: concatenate a path from $x_0$ to $x$ with one from $x$ to any point of $U_x$. Hence $A$ is open. For $x\notin A$, the same kind of neighbourhood cannot meet $A$, since a meeting point would provide a path to $x$. Hence $X\setminus A$ is also open. Since $X$ is a connected space and $A$ is nonempty, $A=X$. This proves $X$ is path-connected, including the general principle that connected locally path-connected spaces are path-connected. The empty-space case, if allowed, is vacuous.

For a $d$-dimensional normed vector space, with unit ball $K$ and John ellipsoid $\mathcal{E}$, its volume ratio isIt is at least $1$ and invariant under invertible linear coordinate changes.

For ${\ell }_1^n$, the unit ball has volume $2^n/n!$: its intersection with each orthant is the standard simplex of volume $1/n!$. Its John ellipsoid is $n^{-1/2}{B}_2^n$. To verify maximum volume, let $A{B}_2^n$ be any centered inscribed ellipsoid. For each sign vector $\epsilon \in \{-1,1{\}}^n$, containment gives $|A^T\epsilon |\le 1$. Averaging over independent Rademacher random variables yields $\mathrm{tr}(A{A}^T)\le 1$. The arithmetic-geometric mean inequality for its squared singular values gives $|\det A|\le n^{-n/2}$. The scalar matrix $n^{-1/2}I$ attains this bound because $\Vert x{\Vert }_1\le \sqrt{n}|x|$. Centered ellipsoids suffice: if $a+A{B}_2^n$ is contained in the symmetric convex unit ball, averaging it with $-a+A{B}_2^n$ gives the centered copy with the same volume.

Using the stated Euclidean ball volume and $n=2k$,since $(2k)!/k!={\prod }_{j=k+1}^{2k}j\ge k^k$. Therefore $\mathrm{vr}({\ell }_1^{2k})\le \sqrt{8/\pi }$ uniformly in $k$.

The subsequent assertion for an arbitrary $2k$-dimensional space is false as printed. Here is a counterexample independent of any asymptotic section theorem. Take $E={\ell }_{\infty }^{2k}$. Its John ellipsoid is $B_2^{2k}$: an inscribed $A{B}_2^{2k}$ has every row of $A$ of Euclidean length at most $1$, so the Hadamard inequality gives $|\det A|\le 1$, attained by $A=I$. Let $F$ be any $k$-dimensional subspace and $f_1,\dots ,f_k$ an orthonormal basis of it. The random unit vectorhas $|x|=1$. Since ${\sum }_j{f}_j(i{)}^2\le 1$ for each coordinate $i$, the moment-generating function bound for independent Rademacher random variables givesChoose $r=\sqrt{2\log (8k)/k}$; the latter probability is at most $1/2$, so some unit vector in $F$ has $\Vert x{\Vert }_{\infty }\le r$. The proposed section inequality would therefore forceThus even one half-dimensional section cannot have the printed universal constant.

The valid orthogonal splitting with bounded volume ratio is that $L$ may depend on $V=\mathrm{vr}(E)$, and $L=32V^2$ suffices. This supplies a dimension-independent constant for the preceding ${\ell }_1^{2k}$ case, or whenever $V$ is bounded uniformly. We prove the complete qualified assertion, including its epsilon-net ingredient.

In John position, $\Vert x{\Vert }_E\le |x|$. Polar integration of the radial function $\rho (u)=1/\Vert u{\Vert }_E$ giveswhere $\sigma$ is normalized spherical measure.

A maximal $\epsilon$-separated subset of $S^{k-1}$ is an epsilon-net. Balls of radius $\epsilon /2$ about its points have disjoint interiors and lie in the ball of radius $1+\epsilon /2$. Comparing volumes bounds its cardinality by $(1+2/\epsilon {)}^k$. Finite existence follows from this packing bound, and maximality proves the net property without assuming a separate covering theorem.

Choose two orthogonal $k$-dimensional subspaces $F_0,F_0^{\perp }$ and one such net on each unit sphere. Rotate both by a common uniformly distributed orthogonal matrix $U$. This distribution can be obtained from Question 1 applied to the compact orthogonal group with its Frobenius metric; left and right multiplication are transitive isometries. Consequently $Uv$ is uniformly distributed on $S^{2k-1}$ for every fixed unit vector $v$. The union bound shows that the probability that some rotated net point has norm less than $2\epsilon$ is at mostSet $\epsilon =(32V^2{)}^{-1}$. Because $V\ge 1$, the bracket is at most $1/4+1/256$, and twice its $k$th power is less than $1$ for every $k\ge 1$. Some rotation therefore makes every point of both nets have norm at least $2\epsilon$.

The triangle inequality and $\Vert x{\Vert }_E\le |x|$ imply that $\Vert \cdot {\Vert }_E$ is $1$-Lipschitz for the Euclidean metric. Every unit vector of $U{F}_0$ or $U{F}_0^{\perp }$ is within $\epsilon$ of a good net point and thus has norm at least $\epsilon$. By homogeneity, on both orthogonal subspaces,For ${\ell }_1^{2k}$ the first computation gives the absolute choice $L=256/\pi$. The missing bounded-volume ratio restriction is essential; the literal unrestricted assertion cannot be proved.

Choose a maximal subset $U\subset S^{n-1}$ whose distinct points have Euclidean distance greater than one. The volumetric bound for Euclidean metric nets makes the construction finite: the open Euclidean balls of radius $1/2$ about the points of $U$ are disjoint and all lie in $(3/2)B^n$, soMaximality means that $U$ is a metric net of radius one on the unit sphere. Thus every $\theta \in S^{n-1}$ has some $u\in U$ with $\Vert u-\theta \Vert \le 1$. Since both are unit vectors,Now intersect the corresponding closed half-spaces:The Cauchy-Schwarz inequality shows that $(1/2)B^n\subset P$. Conversely, write any nonzero $x\in P$ as $x=t\theta$ and choose $u$ as above. Then $t/2\le t(u\cdot \theta )=u\cdot x\le 1/2$, so $t\le 1$. Hence $P\subset B^n$, which also proves boundedness. This convex polytope has at most $|U|\le 3^n$ facets, since redundant inequalities can only reduce their number. It meets the required inclusions with $r=2$.

Assume an interior sphere condition at $y$, and assume the coefficients are bounded and the principal part defines a uniformly elliptic operator near its tangent Euclidean ball. Shrinking the Euclidean ball while keeping it tangent if necessary, arrange that $\overline{B_R(z)}\setminus \{y\}\subset \Omega$. Write $\nu =(y-z)/R$, the outward radial direction. The conclusion of the Hopf boundary point lemma in the regularity stated in the question isIf the one-sided normal derivative exists, this says ${\partial }_{\nu }u(y)>0$. For a $C^1$ boundary its outward normal agrees with the tangent Euclidean ball's radial normal. Continuity alone does not guarantee that the derivative exists, so its existence must be added when asserting a finite derivative.

On the annulus $R/2<r=|x-z|<R$, use the barrierThe operator has no zeroth-order term, and differentiation givesfor sufficiently large $k$: the quadratic-in-$k$ term is at least $k^2\lambda R^2$, while the remaining terms are bounded multiples of $k$.

On the inner sphere, compactness and strict inequality give a positive minimum of $u(y)-u(x)$. Choose $\epsilon >0$ small enough that $u-u(y)+\epsilon h\le 0$ there. The same inequality holds on the outer sphere since $h=0$. The weak maximum principle for elliptic operators yields $u-u(y)+\epsilon h\le 0$ throughout the annulus. Along the inward radius,This proves the lemma. The interior sphere condition supplies precisely the geometry needed for the barrier; no global boundary regularity is used.

Here a domain is connected. Suppose both a negative point and a zero point exist. The open set $D=\{v<0\}$ then has a boundary point $q$ inside $\Omega$. Choose $z\in D$ close enough to $q$ that the Euclidean ball of radius $R=\mathrm{dist}(z,Z)$ stays compactly inside $\Omega$; it is contained in $D$ and touches $Z$ at some $y$. Attainment of this distance follows by restricting to a compact neighbourhood of $q$.

On that Euclidean ball $v$ is twice differentiable andThe Hopf boundary point lemma gives a strictly positive outward radial derivative at $y$. But $y$ is an interior maximum of the $C^1$ function $v$ on $\Omega$, so $Dv(y)=0$. This contradiction proves the Hopf dichotomy with classical regularity away from the zero set:

For the requested weaker-regularity counterexample take $\Omega =(-1,1{)}^n$ andIt is a locally Lipschitz function and nonpositive. Its negative set is $x_1>0$, where $v=-\sin x_1$ is smooth and $\Delta v+v=0$. On $x_1\le 0$ it is zero. The normal derivative jumps from zero to minus one across $x_1=0$, so it is not $C^1$. All other hypotheses hold; imposing the equation also across that interface would be an additional condition absent from the question.

Use the paper's classical norm convention: $|u{|}_{2;U}$ is the derivative supremum norm through order two, not an $L^2$ norm. Write $H(u;U)=[D^{2}u{]}_{\alpha ;U}$ and $Q(u,f)=|u{|}_{2;B_1}+|f{|}_{0,\alpha ;B_1}$, where the latter is the full Hölder norm of the forcing.

Fix $0<\delta <1$. If the asserted estimate failed, rescaling the functions by their global Hessian matrix Hölder seminorm would produce $u_j,f_j$ withIn particular $Q(u_j,f_j)\to 0$. Choose $x_j,y_j\in B_{1/2}$ for whichThe supremum norm of $D^2{u}_j$ tends to zero, so $r_j=|y_j-x_j|\to 0$. Let $P_j$ be the quadratic Taylor polynomial of $u_j$ at $x_j$, and defineThe domains contain $B_{1/(2r_j)}$, and $U_j,D{U}_j,D^2{U}_j$ vanish at zero. MoreoverThe right side is bounded by $[f_j{]}_{\alpha }|z{|}^{\alpha }$ and tends to zero locally uniformly. The normalized Hessian matrix bound controls $|D^2{U}_j(z)|\le |z{|}^{\alpha }$; integration along line segments then bounds $D{U}_j,U_j$ on every compact Euclidean ball. Arzelà-Ascoli theorem and a diagonal subsequence argument give convergence in $C^2$ on compact subsets to an entire harmonic function $U$, with $[D^{2}U{]}_{\alpha ;{\mathbb{R}}^n}\le 1$ and $D^{2}U(0)=0$.

After a further subsequence $z_j=(y_j-x_j)/r_j\to z_{\ast }$ with $|z_{\ast }|=1$. The normalization ensures $|D^{2}U(z_{\ast })|\ge \delta /2$. But each second derivative of $U$ is harmonic and has finite global Hölder seminorm with exponent below one. The supplied Liouville theorem makes each derivative constant; it is the globally Hölder case of the polynomial-growth Liouville theorem for harmonic functions. Its value at zero makes it zero, a contradiction. This blow-up compactness proof of an interior Schauder estimate establishes

To absorb the term on the larger Euclidean ball, rescale this estimate to Euclidean balls contained in $B_s$. For $1/2\le r<s<1$, pairs separated by less than $(s-r)/2$ are controlled in a Euclidean ball centred at their first point; farther pairs are controlled directly by $\Vert D^{2}u{\Vert }_{\infty }$. ThusThis is the covering step in the Simon absorption lemma. Take $r_j=1-2^{-j-1}$ and fix $\delta <2^{-2-\alpha }$. Iterating makes the remainder ${\delta }^{j}H(u;B_{r_j})$ tend to zero, since the global seminorm is finite; the error terms form a convergent geometric series. Consequently

For the requested failure of the weaker estimate, take $u_m=m^{-2}\sin (m{x}_1)$ and $f_m=-\sin (m{x}_1)$. The derivative supremum norm $|u_m{|}_2$ and $\Vert f_m{\Vert }_{\infty }$ remain bounded, but comparison at $0$ and $\pi e_1/(2m)$, both in $B_{1/2}$ for large $m$, givesThis proves the necessity of Hölder forcing for Schauder estimates. Every member of the sequence is smooth; failure comes from the increasing frequency, not from any lack of individual regularity.

Adopt the sign convention that a weak supersolution for $Lu=D_i(a^{ij}{D}_{j}u)$ satisfies $Lu\le 0$, or $\int a^{ij}{D}_{j}u{D}_i\phi \ge 0$ for every nonnegative compactly supported test function. For a nonnegative supersolution, the Weak Harnack inequality gives some $q>0$ and $C_H\ge 1$, depending only on $n,\lambda ,\Lambda$, such thatHere $|E{|}^{-1}{\int }_E$ denotes the average over $E$; equivalently the right side gives a lower bound for the essential infimum. One may take a sufficiently small positive exponent, so no assumption $q\ge 1$ is needed. The inequality is unchanged under translations and dilations of the Euclidean balls.

Since a Sobolev space element is defined up to changes on a null set, the infimum is initially essential. After choosing a continuous representative it is the ordinary infimum. Without choosing such a representative, an arbitrary pointwise infimum cannot be bounded by an integral.