For a bounded real function, inserting partition points increases its lower Darboux sum and decreases its upper Darboux sum. This follows interval by interval because infima increase on smaller sets while suprema decrease, and the new widths sum to the original width. A common partition refinement consequently proves $s(f,D_1)\le S(f,D_2)$ for arbitrary two partitions.

Cyclotomic units include roots of unity and suitable ratios $(1-{\zeta }^a)/(1-\zeta )$ in cyclotomic fields. Compatible roots $\zeta$ make these ratios norm-compatible along cyclotomic towers. Their closed norm-limit module is a concrete submodule of global units, and its local images generate the power series in the Iwasawa main conjecture.

The cut capacity of $(S,S^c)$, with $S^c=V\setminus S$ the complement of a set, isOnly outward directed edges contribute; incoming and internal edges contribute nothing.^[ref]

Here is the accounting behind the bound, using nonnegative original edge amounts $f_{uv}$ in a finite flow network. Sum the divergence of a flow over the vertices in $S$:An edge with both endpoints in $S$ appears once with a plus sign at its tail and once with a minus sign at its head, so those contributions cancel. An outward edge appears only with a plus sign; an incoming edge appears only with a minus sign. Since $s\in S$, $t\notin S$ and every other vertex satisfies flow conservation, the left side is the flow value. ThusThe inequality uses two facts separately: each forward flow is at most its flow network edge capacity, and each backward flow is nonnegative. This is the weak duality bound underlying the max-flow min-cut theorem. It is an accounting proof, requiring no choice of augmenting paths.

For $S=\{s,b\}$ in the running example, explicitly add the two divergences of a flow:The internal $s\to b$ edge cancels. The $a\to b$ edge is a genuine original directed edge entering $S$, so its flow is subtracted; this is different from a backward edge in a residual network. Its flow network edge capacity is neither added to nor subtracted from cut capacity, which remains $c_{sa}+c_{bt}=6$.

Equality in the bound holds precisely when every outward edge is saturated and every incoming edge carries zero flow, becauseand every term is nonnegative. For $S=\{s\}$ the two outward edges are saturated and there are no incoming edges, so the gap is zero. For $S=\{s,b\}$ all outward edges are still saturated, but the incoming flow of $1$ gives a gap of $1$. This explains exactly why the second cut of a flow network is not a minimum cut.

A minimum cut is a cut of a flow network of least cut capacity for the prescribed source $s$ and sink $t$. The max-flow min-cut theorem equates that least cut capacity with the greatest flow value.^[ref]

To certify a particular minimum cut, exhibit a feasible flow whose flow value equals its cut capacity. Every feasible flow is bounded above by every cut capacity, so this single equality proves both optimalities.^[ref]

In the running example, the four possible source sides $\{s\}$, $\{s,a\}$, $\{s,b\}$ and $\{s,a,b\}$ have cut capacities $5,5,6,5$, respectively. The feasible flow of flow value $5$ therefore certifies three different minimum cuts. A minimum cut need not be unique; the optimum cut capacity is the same for all of them. Merely saturating many edges does not give this certificate: flow conservation and the particular cut of a flow network must also be checked.

For $F_{\mu }(t)=\mu ((-\infty ,t])$, every difference quotient at $x$ is nonnegative and bounded by $4\mu (B(x,2|h|))/{\lambda }_1(B(x,2|h|))$. The spherical derivative of a singular measure vanishes, so the two-sided derivative is zero Lebesgue almost everywhere. A singular distribution function need not be constant: differentiation almost everywhere does not recover increments without absolute continuity of a function of the function.

For the nearest-neighbour graph on ${\mathbb{Z}}^d$, a box $B_n=[-n,n{]}^d\cap {\mathbb{Z}}^d$ has $(2n+1{)}^d$ graph vertices and outer graph vertex boundary of size $2d(2n+1{)}^{d-1}$. Thus $|{\partial }^{+}{B}_n|/|B_n|\to 0$. This boundary-to-volume property is the geometric mechanism that contradicts a positive volume density of trifurcation points in the uniqueness proof. It realizes the boundary-growth property of the translation amenable group on the lattice.

In one critical chiral RNS string sector, light-cone gauge in string theory leaves eight transverse directions. The Neveu–Schwarz sector massless states $b_{-1/2}^i|0;k⟩$ give eight spacetime bosonic polarizations. The Ramond zero-mode Clifford algebra gives sixteen ground-state polarizations, split into two eight-dimensional spinor chiralities. The supersymmetric GSO projection keeps one chirality, giving eight spacetime fermionic polarizations. Without that projection the massless counts do not agree. Counting a chiral sector is distinct from tensoring left and right sectors into closed-string states.

For convergence in distribution of random elements in a separable complete metric space, one can construct copies of the sequence and its limit on one probability space which converge almost surely. The copies preserve the individual laws; they need not preserve the original dependence between sequence elements. Suitable more general versions permit a separably supported limit in a metric space.

The Pareto distribution describes a positive random variable whose tail probability falls as a power law. In the shape–minimum convention used here, $X\sim \mathrm{Pareto}(\alpha ,\beta )$ has shape parameter $\alpha >0$ and minimum $\beta >0$, with probability support $[\beta ,\infty )$. Its probability density function isThe minimum $\beta$ has the same units as $X$; the shape parameter $\alpha$ is dimensionless. At fixed $\alpha$, changing $\beta$ gives a scale family, since $X/\beta \sim \mathrm{Pareto}(\alpha ,1)$. This convention is also used in uniform-Pareto conjugacy.^[ref]

The survival function and cumulative distribution function areFor $0<p<1$, solving $F(x)=p$ gives the quantile functionIts right-hand limit at $p=0$ is $\beta$; it tends to infinity as $p\uparrow 1$.^[ref] There is no probability mass at $\beta$: changing a probability density function's value at that single point changes no probabilities.

Choose $\alpha =2$ and $\beta =10$ as an original theoretical example. Then $\mathbb{P}(X>20)=1/4$, $\mathbb{P}(X>100)=1/100$, the median is $10\sqrt{2}\simeq 14.142$, and the 0.99 quantile is $Q(0.99)=100$. These chosen values and all figures below describe mathematical probability distributions, not actual wealth observations.

On this log-log plot, write $r=x/\beta$ to obtain $\log \overline{F}(\beta r)=-\alpha \log r$. The slope is $-\alpha$, whereas the probability density function has power exponent $\alpha +1$. Multiplying a threshold by ten divides its tail probability by $10^{\alpha }$. A larger $\alpha$ therefore concentrates the Pareto distribution nearer $\beta$; a smaller $\alpha$ makes extreme values more plausible. This exact scaling is useful when modelling large positive losses or sizes over several orders of magnitude.

The Pareto distribution is a heavy-tailed distribution: for every $t>0$, the exponential moment $\mathbb{E}[e^{tX}]$ diverges because $e^{tx}{x}^{-\alpha -1}$ eventually grows without bound. In contrast, the Laplace distribution has exponentially decreasing tails and some finite positive exponential moments. The Student's t-distribution also has power tails, but on both sides of the real line, with a smooth central region rather than a positive minimum.

For $0<A<B<\infty$, this density makes $\log X$ uniform between $\log A$ and $\log B$. It is a proper finite-interval version of the scale-invariant prior. The chosen logarithm base changes only the endpoint coordinates.

An invertible bounded linear operator has a bounded inverse when $\Vert u\Vert \le C\Vert Ku\Vert$ for all $u$.

For a matrix with nonnegative entries, row sums at most $R$ and column sums at most $C$ imply an ${\ell }^2$ operator norm at most $\sqrt{RC}$. Indeed the Cauchy-Schwarz inequality gives $|{\sum }_j{A}_{ij}{x}_j{|}^2\le ({\sum }_j{A}_{ij}){\sum }_j{A}_{ij}|x_j{|}^2$. Summing over $i$ bounds the result by $RC{\sum }_j|x_j{|}^2$. Integral kernels and positive weight functions give analogous forms. The symmetric case has equal row and column bounds.

A bounded linear operator $T:L^1(\Omega )\to L^1(\Omega )$ preserves positivity when it takes nonnegative functions to nonnegative functions. By linearity it also preserves the pointwise order: $u\le v$ implies $Tu\le Tv$. For example, integration against a nonnegative kernel preserves positivity, as does the identity operator. This is order positivity, which differs from a positive quadratic form on a Hilbert space.

The value ${\mathrm{cd}}_p(G)$ is the least $n$ such that all cohomology in degrees greater than $n$ vanishes for every discrete $p$-primary torsion module. Vanishing in degree $n+1$ for all such modules suffices by dimension shifting. Dimension does not increase on closed subgroups. For a field, use its absolute Galois group.

For a closed subgroup $P$ of a profinite group $G$ and a discrete $G$-module $M$, restriction gives $H^q(P,M)={\underrightarrow{\lim }}_{U\supseteq P,U\mathrm{open}}{H}^q(U,M)$. Thus a class restricting to zero on $P$ already restricts to zero on one such open subgroup. The property follows by extending continuous finite-image cochains and their identities to a sufficiently small open neighborhood of $P$.

A standardized maturity-and-quantity agreement whose quote changes generate margin payments as gains and losses are marked to market. A newly settled contract has zero value, while its quoted delivery price is generally nonzero. In the continuous-settlement idealization, futures pricing uses a pricing-measure martingale quote.

An agreement to exchange an underlying at a fixed delivery price on a specified maturity, without the futures convention of repeated settlement. Its fair delivery quote is ${\mathbb{E}}_Q[D_{tT}{S}_T\mid {\mathcal{F}}_t]/{\mathbb{E}}_Q[D_{tT}\mid {\mathcal{F}}_t]$. It may differ from a futures contract quote when rates are stochastic.

A barrier contract paying a specified fixed amount if the underlying touches a specified barrier before expiry. Payment at touch and payment at expiry are different conventions. For payment at touch, valuation uses the truncated discounted Brownian first passage after converting a geometric stock barrier to a log-price level; discounting solely at expiry would price a different contract.

Two sequences of probability laws are mutually contiguous when contiguity of probability measures holds in both directions. A positive mean-one limit of their likelihood ratio is a standard sufficient criterion.

Let $L_n$ be the probability density function of the absolutely continuous part of $Q_n$ relative to $P_n$. If $L_n$ converges in distribution under $P_n$ to $L>0$ almost surely with $\mathbb{E}L=1$, then $P_n,Q_n$ are mutually contiguous. Indeed, $\mathbb{E}{L}_n\le 1$ and convergence to a mean-one limit imply uniform integrability and vanishing singular mass, giving forward contiguity. For reverse contiguity, $P_n(A_n)\le P_n(L_n\le c)+Q_n(A_n)/c$, and one first takes $n\to \infty$ and then $c\downarrow 0$.