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For a measurable set $E\subseteq U$, definewhere $1_E$ is its indicator function. This is finite because $U$ has finite measure and hence $1_E\in L^p(U)$. If the sets $E_k$ are pairwise disjoint and $E={\bigcup }_{k\ge 1}{E}_k$, thenThe continuity of the positive linear functional $\Lambda$ therefore givesso $\nu$ is a measure by countable additivity. Moreover, a set of zero Lebesgue measure has zero indicator in $L^p$, so $\nu$ is absolutely continuous with respect to Lebesgue measure.

The Radon-Nikodym theorem now supplies a measurable function $\omega \ge 0$ such thatConsequently, first for nonnegative simple functions and then, by approximation by nonnegative simple functions, for every nonnegative $f\in L^p(U)$,Applying the stated characterization of the Lp norm to $\omega$ shows that $\omega \in L^q(U)$ and $\Vert \omega {\Vert }_q\le \Vert \Lambda \Vert$. The Holder inequality makes $f\mapsto {\int }_{U}f\omega$ continuous on $L^p(U)$, while bounded simple functions form a dense subset; henceThis is the positive functional representation on Lp.

Let $T(x)=x+\alpha (\mathrm{mod}1)$ with irrational $\alpha$. This irrational rotation of the circle preserves Lebesgue measure and is ergodic. Apply the Birkhoff ergodic theorem to the indicator function $1_A$. Its limitis invariant, hence almost everywhere constant by ergodicity. Its integral must equal that of $1_A$, so the constant is $\mu (A)$. Thereforefor Lebesgue measure-almost every $x\in (0,1]$.

The function $\varphi (x)={\max }_{v\in C}⟨x,v⟩$ is the support function ${\sigma }_C$. For a nonempty compact convex set,so its convex conjugate is the indicator function ${\iota }_C$. Applying the Moreau decomposition,Multiplication of an indicator function by a positive scalar does not change it, and its proximal operator is the Euclidean projection onto a convex set. Therefore

Suppose first that the system is ergodic and $K_{F}g=g$. For each real $t$, the level setis invariant modulo a null set, so $\omega (E_t)\in \{0,1\}$. The distribution function of $\mathrm{Re}g$ can therefore jump only once, which makes $\mathrm{Re}g$ constant almost everywhere. The same argument applies to $\mathrm{Im}g$.

Conversely, if $E$ is invariant, then $K_F{1}_E=1_E$. If every invariant $L^2$ function is constant, the indicator function $1_E$ is almost everywhere zero or one, and hence $\omega (E)=0$ or $1$. This proves the invariant-function characterization of ergodicity.

Apply the preceding Fourier argument to $f=1_A$, retaining the frequencies needed to make the oscillation of $1_A\ast 1_A\ast 1_A$ strictly smaller than its mean ${\alpha }^3$. The standard optimized cutoff gives a set $\Gamma \subseteq \hat{G}$ withsuch that the nonnegative function $F=1_A\ast 1_A\ast 1_A$ cannot fall from a maximal value to zero under any shift in $B(\Gamma ,\alpha )$. If $x$ maximizes $F$, thenThe support of a convolution of indicator functions is the corresponding sumset, so

The pushforward measure of $\mu$ under $g$ is $g_{\ast }\mu$, defined for $B\in \mathcal{B}$ byThe Lebesgue decomposition theorem says that if $\nu$ and $\mu$ are sigma-finite measures on the same measurable space, then uniquely

Let $f:[0,\infty )\to \mathbb{R}\cup +\infty$ be convex with $f(1)=0$. If $\lambda$ dominates the probability distributions $P,Q$, with densities $p,q$, their f-divergence isusing the lower-semicontinuous perspective convention where $q=0$. This definition is independent of the dominating measure.

The data processing inequality for f-divergences statesTo prove it, take $\lambda =P+Q$ and let $\mathcal{G}=g^{-1}(\mathcal{B})$. If $p=dP/d\lambda$ and $q=dQ/d\lambda$, then the pullbacks of the densities of $g_{\ast }P$ and $g_{\ast }Q$ with respect to $g_{\ast }\lambda$ are respectively ${\mathbb{E}}_{\lambda }[p\mid \mathcal{G}]$ and ${\mathbb{E}}_{\lambda }[q\mid \mathcal{G}]$. The perspective $F(a,b)=bf(a/b)$ of a convex function is jointly convex. Conditional Jensen inequality therefore givesIntegration proves the claim.

The Squared Hellinger distance isIf $P,Q$ have densities $p,q$ with respect to a sigma-finite measure $\mu$, this becomes

Fix any probability distribution $Q$ and set $p_j=P_j(A_j)$, $q_j=Q(A_j)$, and $t_j=H^2(P_j,Q)$. Applying the data processing inequality to the indicator function of $A_j$ gives the Bernoulli Hellinger boundThe hinted inequality impliesThe function $t\mapsto \sqrt{t(1-t/4)}$ is concave on $[0,2]$. Since the $A_j$ form a set partition, ${\sum }_j{q}_j=1$, and Jensen inequality givesTaking the infimum over $Q$ proves the stated inequality.

The perimeter of a measurable set relative to an open domain is the total variation seminorm of its indicator function there. For a smooth region it equals the Hausdorff measure of the boundary lying inside the domain. Global perimeter uses the entire space as the domain; an internal interface shared by two regions appears in both relative perimeters and must be counted only once in a segmentation energy.

To prevent a Turing functional ${\Phi }_e^B$ from computing the indicator function of an enumerated set $A$, reserve a permanently unique fresh witness $x$. On observing a convergent computation with oracle use $u$, enumerate $x$ into $A$ if the answer is zero; otherwise keep it out. Restrain subsequent lower-priority enumerations into $B$ below $u$. Higher-priority actions may initialize this strategy and retire its witness, but witnesses are never reused. In a finite-injury priority construction, mathematical induction on priority shows that the final computation, if observed, remains correct as a computation from the final oracle and disagrees with $A$ at $x$. If no computation is ever observed after the final initialization, the final oracle computation diverges, since any convergent computation uses only finitely many eventually stable oracle bits.

The difference between a weighted integral and a discrete approximation is a quadrature error. For a Hölder continuous function on a partition of an interval with mesh $\Delta$, a left endpoint approximation has error of order ${\Delta }^{\alpha }$. For a normalized window indicator function, only the two endpoint cells contribute and the error is bounded by a constant times $\Delta /h$.

The total variation seminorm of the indicator function within an open set counts only its interior interface. The perimeter of a zero extension may add a boundary contribution.

For real coefficients ${\rho }_1=1$ supported on permitted divisors up to $L$, put ${\lambda }_d={\sum }_{[u,v]=d}{\rho }_u{\rho }_v$. Then ${\sum }_{d\mid n}{\lambda }_d=({\sum }_{d\mid n}{\rho }_d{)}^2$ is an upper bound for the indicator function of numbers free of the forbidden prime factors. Its support has $d\le L^2$. If the coefficients vanish on non-squarefree integers and satisfy $|{\rho }_d|\le 1$, then $|{\lambda }_d|\le 3^{\omega (d)}$: each prime factor of $d$ occurs in $u$, in $v$, or in both.

The BV lattice inequality $|D\min (v,w)|+|D\max (v,w)|\le |Dv|+|Dw|$ applied to two indicator functions gives the perimeter inequality. For smooth approximations, minima and maxima partition the two gradients; sequential lower semicontinuity passes the estimate to BV. It prevents a crossing of minimizers with strictly ordered set forcing.

The support function of a nonempty set $C$ is ${\sigma }_C(x)={\sup }_{v\in C}⟨x,v⟩$. It is convex and positively homogeneous, and for a closed convex set its convex conjugate is the indicator function of $C$.

For $1\le p<\infty$ and $f\in L^p({\mathbb{R}}^d)$, translations converge to $f$ in the Lp space norm. First prove this for continuous compactly supported functions, using uniform continuity and a common bounded support. Their density in Lp space, together with the norm-preserving translation map and the triangle inequality, proves the assertion for arbitrary $f$. It implies convergence of averages over shrinking intervals, a basic approximate identity argument. The corresponding assertion fails for general $L^{\infty }$ functions, such as an indicator function with a jump.

The triangle count of a finite simple graph is the number of unordered triples of vertices inducing a triangle in a graph. In a tripartite graph with parts $X,Y,Z$ and adjacency indicator functions $g_{XY},g_{YZ},g_{XZ}$, it equals $|X||Y||Z|{\mathbb{E}}_{x,y,z}{g}_{XY}(x,y)g_{YZ}(y,z)g_{XZ}(x,z)$, using uniform expectations on nonempty parts.

Let a tripartite graph have nonempty parts $X,Y,Z$, pair edge density of a bipartite graph values $\alpha ,\beta ,\gamma$ on $XY,YZ,XZ$, and $\Vert G(X,Y)-\alpha {\Vert }_{\square }\le c$. If every $z\in Z$ has exactly $\beta |Y|$ neighbors in $Y$, its normalized triangle count $\tau$ obeysThe constant-degree assumption makes the contribution of the constant $\alpha$ exactly $\alpha \beta \gamma$. For each fixed $z$, apply the bilinear correlation bound for the box norm to the indicator functions of its two vertex neighbourhoods. Their squared $L^2$ norms are $\beta$ and the relative $X$-degree of $z$. Average over $z$ and use the Cauchy-Schwarz inequality to bound the mean square root of that degree by $\sqrt{\gamma }$.

In a one-period complete market with deterministic initial holdings, two integrable positive pricing variables assigning the same prices to every traded asset are equal almost surely. Replicate indicator functions to obtain equality of their integrals on every event, then apply this to the event where one density exceeds the other.