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On a bounded domain, classical solutions continuous on the closure have difference $w=u-v$ satisfying $D_i(A_{ij}{D}_{j}w)=0$, where $A={\int }_0^1{F}_{pp}(x,Dv+tDw)dt$. The coefficients are uniformly elliptic and locally smooth. Apply the weak maximum principle on inner domains whose closures lie in the original domain, then let their boundary distances tend to zero. The common continuous boundary values make their boundary differences tend uniformly to zero, proving uniqueness without assuming global bounds on derivatives of the linearized coefficients.

For scalar minimizers of $\int F(Du)$ with $F\in C^2({\mathbb{R}}^n)$ and globally bounded uniformly positive Hessian matrix, no higher differentiability of $F$ is needed for interior gradient Hölder continuity. The weak Euler-Lagrange equation is $\mathrm{div}DF(Du)=0$. Difference quotients solve uniformly elliptic equations with coefficient matrices obtained by averaging $D^{2}F$ along gradient segments. The Caccioppoli inequality and difference quotient characterization give $u\in H_{\mathrm{loc}}^2$. The Sobolev chain rule then gives $\mathrm{div}(D^{2}F(Du)D(D_{k}u))=0$. The De Giorgi-Nash-Moser theorem makes each derivative Hölder continuous, with exponent depending only on dimension and the ellipticity ratio. This scalar conclusion should not be assumed for vector-valued elliptic systems.

For $2\le p<\infty$, the Clarkson inequality isFor $1<p\le 2$ and $q=p/(p-1)$, the other Clarkson inequality isTogether these inequalities prove uniform convexity of $L^p$ for $1<p<\infty$.

Every nonempty closed convex subset of a uniformly convex Banach space has a unique nearest point to each vector. A minimizing sequence is Cauchy: otherwise uniform convexity would put some midpoint strictly closer than the infimum. Completeness and closedness give its limiting point. In ${\ell }^p$, $p\ge 2$, the Clarkson inequality makes this quantitative: the distance between two minimizing vectors tends to zero after subtracting the midpoint's lower distance bound. Nonemptiness is required.

A fixed-mass spherical shell has acceleration $\ddot{R}=-GM/R^2+\Lambda R/3$, in units $c=1$. At a finite maximum radius $R_m$, inward acceleration requires $\eta =\Lambda R_m^3/(3GM)<1$. Equality is the unstable static radius, approached only in the limiting separatrix case rather than a finite expansion-to-collapse turnaround. The effective potential reaches its maximum there, so an initially expanding bound inner-branch shell also needs energy below that maximum.

Couple the process with independent uniform edge labels, and expose $p_{-}=(\log n-\frac{1}{4}\log \log n)/n$. A spanning-tree union bound excludes components of orders $2$ through $n/\log n$, and a cut bound excludes orders up to $n/2$. The graph has one large component and at most $\log n$ isolated vertices, with at least one isolated vertex. Before $p_{+}=(\log n+\frac{1}{4}\log \log n)/n$, the probability of any new edge between those isolated vertices is $O((\log n{)}^2\log \log n/n)=o(1)$. All have joined the large component by $p_{+}$ by the isolated-vertex threshold in the Erdős-Rényi model. Throughout this window the only obstruction to connectivity is an isolated vertex, establishing the equality of hitting times.

For initial thickness $H(1+k^2{z}_0^2)$ and constant positive force, the material-map integral is dominated by a shrinking region around the minimum thickness as $\Delta \uparrow H$. With $k\ne 0$, the integral of $1/[H-\Delta +H{k}^2{z}_0^2]$ gives the displayed inverse-square-root divergence, whose coefficient is independent of the initial half-length. If $k=0$, every material point thins equally and $L=H{L}_0/(H-\Delta )$, with the stronger inverse-time divergence $L\sim 4\mu H{L}_0/[F(t^{\ast }-t)]$. These are ideal slender-sheet predictions up to their singular limit.

Let $0\le s\le k\le n$ and form the integer matrix with rows indexed by $S\subseteq [n]$, $|S|\le s$, columns indexed by $k$-subsets $T$, and entry $M_{S,T}=1_{S\subseteq T}$. Its matrix rank over any finite field ${\mathbb{F}}_p$ is at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. Over the rational numbers, the row of a $j$-subset $S$ is ${\left(\genfrac{}{}{0ex}{}{k-j}{s-j}\right)}^{-1}$ times the sum of the rows of all $s$-subsets containing $S$. Thus the rows of degree exactly $s$ span, giving the bound over the rational numbers. Every larger square minor therefore has integer determinant zero, and remains zero modulo $p$. The matrix rank cannot increase under this reduction. This argument avoids dividing by an integer that might vanish in the finite field, and gives the sharp uniform-layer step in the Frankl-Wilson theorem.

The displayed solenoidal vector field has $\nabla \times \mathbf{b}=k\mathbf{b}$, ${\nabla }^2\mathbf{b}=-k^2\mathbf{b}$ and ${\mathbf{e}}_z\times \mathbf{b}=-i\mathbf{b}$. Thus the non-gradient part of its linearized Lorentz force about $\mathbf{B}=Jr{\mathbf{e}}_{\varphi }$ vanishes. Choosing $p_1=-\mathbf{B}\cdot \mathbf{b}/{\mu }_0$ cancels the magnetic-pressure gradient, and $\mathbf{u}=0$, $\omega =0$ is a neutral perturbation. It is regular at the axis and realizes the endpoint $m=2$, $\alpha =0$. A boundary condition can exclude this growing-in-radius field, but without such a condition strict positivity at the endpoint cannot be asserted.

Nonnegative weights ${\lambda }_A$ on coordinate subsets form a fractional uniform cover when the displayed equality holds for each coordinate. Rational weights can be multiplied by a common denominator to become an integer uniform cover. The uniform covers theorem then gives $\log |S|\le {\sum }_A{\lambda }_A\log |S_A|$. Exact equality of coordinate coverage matters when projection measures may be less than one.

A subset of a normed space is norm bounded if it is bounded under every continuous linear functional. Apply the Uniform boundedness principle on the complete dual space to the evaluation family $f\mapsto f(x)$. Hahn-Banach theorem identifies the evaluation norm with $\Vert x\Vert$, even when the original normed space is not complete. As a consequence, two norms with exactly the same continuous dual as sets must be equivalent: apply this criterion to either norm's unit ball in the other norm.

Let $U_1,\dots ,U_n$ be independent uniform random variables on $[0,1]$, let ${\theta }_i>0$, and set $S={\sum }_i{\theta }_i\in (0,1)$ and $X_t={\sum }_i{\theta }_i{1}_{\{U_i\le t\}}$. ThenIndeed, the complementary crossing is that of level one by the uniform-arrival residual mass martingale $M_t=(S-X_t)/(1-t)$, with $M_0=S$. Its jumps are downward, so its first crossing of one is continuous. An arrival coincides with a possible crossing time with probability zero: the possible times belong to the finite deterministic set obtained from subsets of the weights. Stop at that crossing or at $r<1$. The stopped values are in $[0,1]$, and as $r\uparrow 1$ they converge to the crossing indicator because all arrivals occur before one almost surely. The optional stopping theorem and dominated convergence theorem give crossing probability $S$. For $S=1$, the strict displayed inequality already fails at time zero.

A labelled unicyclic component has a unique cycle of length $k\ge 3$, with a rooted forest attached to its vertices. Choose its vertices in $\left(\genfrac{}{}{0ex}{}{n}{k}\right)$ ways and its unoriented cycle in $(k-1)!/2$ ways. The labelled forest count with prescribed roots gives $k{n}^{n-k-1}$ attachments. Summing over $k$ and putting $j=n-k$ yields the displayed identity.

A basic sequence is suppression-unconditional with constant $C$ if every coordinate deletion satisfies the displayed inequality. When $C=1$, deletion never increases the norm. Uniformly bounded finite-coordinate projections imply unconditional convergence of the basis expansions; for real scalars, the sign-change constant is at most $2C$.

Let $T_C(X)$ consist of finite normalized sequences for which every scalar combination and sign change satisfy $\Vert {\sum }_i{\epsilon }_i{a}_i{x}_i\Vert \le C\Vert {\sum }_i{a}_i{x}_i\Vert$. These inequalities give a closed tree on a Polish space when $X$ is separable. An infinite branch is an unconditional basic sequence. If there is no such sequence, each fixed-constant tree is well-founded and has countable rank of a well-founded tree. Consequently, finite unconditional configurations with one fixed constant and arbitrarily high countable ranks force an infinite unconditional sequence.

An ultraviolet subdivergence arises when loop momenta internal to a proper subgraph tend to infinity while the momenta entering that subgraph are held fixed. It can occur even if simultaneous scaling of all momenta of the full Feynman diagram is convergent. Recursive local counterterms subtract these subgraph divergences before the remaining overall divergence is analyzed. A tadpole diagram inserted into an otherwise convergent six-point phi-fourth theory Feynman diagram is a simple massive example.

A topological space is both a compact space and a Hausdorff space exactly when every ultrafilter on its underlying set converges to exactly one point. Existence of all ultrafilter limits characterizes compactness. If a space is not Hausdorff, two distinct points have neighbourhood systems whose combined finite intersections are nonempty; the ultrafilter lemma extends these to an ultrafilter converging to both points. Conversely, disjoint neighbourhoods prevent such a common limit.

For an iid sample and symmetric square-integrable kernel $h$ of fixed order $m$, put $\eta =\mathbb{E}h(X_1,\dots ,X_m)$ and $h_1(x)=\mathbb{E}[h(x,X_2,\dots ,X_m)]-\eta$. If ${\zeta }_1=\mathrm{Var}(h_1(X_1))>0$, the displayed limit holds. The first Hoeffding projection is $(m/n){\sum }_i{h}_1(X_i)$; its orthogonal remainder is $O_{L^2}(n^{-1})$, giving the limit by the ordinary central limit theorem. Degenerate kernels require a different limiting theory.

Exchangeability of an independent identically distributed sample makes every permuted kernel have the same expectation. Averaging over permutations therefore leaves the target functional unchanged and gives a symmetric kernel. The U-statistic then averages this kernel over the unordered r-element sample subsets.

The symmetric kernel $h(x,y)=(x-y{)}^2/2$ has expectation equal to the population variance for independent identically distributed observations with finite second moment. Its U-statistic reduces to the displayed unbiased sample variance, using ${\sum }_{i<j}(X_i-X_j{)}^2=n{\sum }_i(X_i-\overline{X}{)}^2$.