A path of light propagation in the geometrical optics approximation. In a smoothly varying propagation speed, Fermat principle makes its travel time stationary.

Starting from $0<a_1\le b_1$, replace the lower endpoint by the geometric mean and the upper endpoint by the arithmetic mean. The arithmetic-geometric mean inequality gives $a_n\le a_{n+1}\le b_{n+1}\le b_n$. The bounded monotone sequence theorem gives limits, and the arithmetic recurrence forces them to coincide. The limiting value is the arithmetic-geometric mean of the initial pair.

The essential two-person bargaining domain consists of a compact convex set of feasible utility vectors $F$, a disagreement point $d\in F$, and at least one feasible vector strictly exceeding $d$ coordinatewise. The Nash bargaining solution selects a jointly feasible improvement using a rule characterized by Pareto efficiency, bargaining symmetry, positive affine invariance in bargaining and bargaining independence of irrelevant alternatives.

A player maximizes the worst payoff it can receive against the other player's actions. Allowing the opponent to mix does not lower the worst payoff beyond a pure action, since the payoff is linear in that mixture. A security level payoff is an individual guarantee, not generally a Nash equilibrium payoff.

With one initial ancestor and independent offspring counts of finite mean and variance, conditioning on the current generation gives the displayed recurrences. For $\mu \ne 1$ the variance is ${\sigma }^2{\mu }^{n-1}({\mu }^n-1)/(\mu -1)$ for positive generation index. At $\mu =1$ it is $n{\sigma }^2$. At zero offspring mean the distribution is concentrated at zero and later generations vanish. These formulas are for the Galton-Watson process, not arbitrary dependent reproduction.

Conditioning on the initial individual's children gives independent copies of the population $n$ generations later. Their PGFs multiply, and averaging the product over the offspring number yields $G(G_n(s))$. Conditioning on the last generation gives $G_n(G(s))$ as well; both agree because $G_n$ is the $n$fold iterate of $G$.

The offspring probabilities $P(K=j)=2^{-j-1}$ for $j\ge 0$ have expected value one. For one initial individual, the branching-process generating-function iteration givesFor $n\ge 1$ the PGF disk is $|s|<1+1/n$; $G_0(s)=s$ separately. Conditional on survival, the population is positive geometric with success probability $1/(n+1)$, leading to the exponential limit of surviving geometric branching.

The total progeny counts all individuals, including the initial ancestor, in a Galton-Watson process. It is infinite on survival. If the offspring mean is $m<1$, the expected generation size in a Galton-Watson process and the monotone convergence theorem give $\mathbb{E}T=1/(1-m)$.

A Galton-Watson process whose offspring law is a binomial distribution $\mathrm{Bin}(n,p)$ has mean $np$ and the displayed offspring probability generating function. Its branching extinction probability is the smallest root of $q=f(q)$ in $[0,1]$. It dominates a breadth-first exploration of a binomial random graph by allowing each explored vertex up to $n$ independent possible children, including previously exposed or already discovered vertices as phantom children.

A Galton-Watson process with a Poisson distribution of offspring of mean $\mu$ has this probability generating function. For $\mu =1+\epsilon >1$ its branching survival probability $r$ satisfies $-\log (1-r)=(1+\epsilon )r$, and $2\epsilon /(1+2\epsilon )\le r\le 2\epsilon$. Indeed $\epsilon ={\sum }_{j\ge 1}{r}^j/(j+1)$ lies between $r/2$ and $r/[2(1-r)]$. These exact inequalities belong to the Poisson branching process, not to every finite binomial branching process.

Let $q\in (0,1]$ be the branching extinction probability of a Galton-Watson process with offspring probability generating function $f$. Conditional on extinction, the probability of $j$ children is multiplied by $q^j/q$: every child's descendants must also become extinct. Hence the new probability generating function is $f_q$ and its mean is $f^{\prime }(q)$. If $f^{\prime }(q)<1$, the total progeny of a branching process has conditional expected value $1/(1-f^{\prime }(q))$, and unconditionally $\mathbb{E}[T;T<\infty ]=q/(1-f^{\prime }(q))$. The Markov inequality gives a finite-total-progeny tail bound without discarding the survival mass.

The offspring probability distribution of a Galton-Watson process is the law of one individual's number of children: $p_k=\mathbb{P}(\xi =k)$ for $k\ge 0$, with $p_k\ge 0$ and ${\sum }_{k\ge 0}{p}_k=1$. Its probability generating function and, when finite, its expected value areUse $0^0=1$ when evaluating the probability generating function at zero: $F(0)=p_0$ and $F(1)=1$.

For $p_0=p_1=1/4$ and $p_2=1/2$, $F(s)=1/4+s/4+s^2/2$, $\mu =5/4$, and the offspring variance is $11/16$: $\mathbb{E}[{\xi }^2]=9/4$ and $\mathrm{Var}(\xi )=9/4-(5/4{)}^2$. The generation probability mass functions pictured under Galton-Watson process come from repeatedly composing this probability generating function. They describe the whole generation, whereas $(p_k)$ describes one individual's reproduction. ^[ref]

The whole offspring distribution matters for extinction. Another law, $p_0=3/8$ and $p_2=5/8$, has the same offspring mean $5/4$ but branching extinction probability $3/5$, since $3/8+5s^2/8=s$ has roots of a polynomial $3/5$ and $1$. Consequently the two laws have the same expected generation size in a Galton-Watson process at each generation but different survival chances. A binomial branching process and a Poisson branching process likewise specify different offspring distributions; a common mean does not make their probability generating functions identical.

A compact Galois module is a compact profinite abelian group equipped with a continuous Galois group action. An abelian pro-p group is naturally a compact ${\mathbb{Z}}_p$-module. A continuous action of the Galois group of a Zp-extension extends to its Iwasawa algebra of a Zp-extension. This compact topology differs from the discrete topology usually used in Galois cohomology.

For a perfect fluid in general relativity of constant equation of state $P=w\rho$, conservation gives $\rho ={\rho }_0{a}^{-3(1+w)}$. In units $8\pi G=1$, the Friedmann equation becomes ${\dot{a}}^2/2+V(a)=0$. Allowed scales have $V\le 0$, and the Friedmann acceleration equation is $\ddot{a}=-V^{\prime }(a)$. The curvature term shifts the potential vertically; positive cosmological constant bends it downward and negative cosmological constant bends it upward.

With positive curvature, zero cosmological constant and radiation density ${\rho }_0{a}^{-4}$ in units $8\pi G=1$, $A=\sqrt{{\rho }_0/3}$. The scale factor is positive for $0<\tau <\pi$, reaches its maximum at $\pi /2$, and has a Big Crunch at conformal time $\pi$ or proper time $2A$. This complements the closed matter-dominated Friedmann solution.

For a nonempty bounded subset $E$ of Euclidean space, let $N_{\delta }(E)$ be the least number of radius-$\delta$ balls covering $E$. Its lower and upper box-counting dimensions are the lower and upper limits of $\log N_{\delta }(E)/\log (1/\delta )$ as $\delta \downarrow 0$. If these limits agree, their common value is its Minkowski dimension. A cover by $N_{\delta }(E)$ balls implies $|E_{\delta }|{\lesssim }_n{\delta }^n{N}_{\delta }(E)$ for the open $\delta$-neighborhood. Conversely, a maximal separated subset gives the reverse comparison up to fixed changes of scale. This connects small-scale covering counts with neighborhood Lebesgue measure.

The angular-frequency Fourier transform of a finite measure, or a complex measure of finite total variation, is defined by the displayed integral. It is bounded by total variation and continuous by dominated convergence. An integrable density recovers the ordinary Fourier transform of a function. A measure supported on a curved surface instead leads to a Fourier extension operator.

Multiplication by a unit-modulus linear phase shifts the Fourier transform. Substituting $e^{ia\cdot x}$ into the defining integral gives the displayed identity. For a surface measure this permits independent translations of cap transforms without changing their input norms or angular supports.

For a piecewise smooth decaying function, a jump in its $j$th derivative at $a$ contributes the displayed algebraic term to its high-frequency Fourier transform. Distributionally, $D^{j+1}f$ contains $[f^{(j)}{]}_a\delta (x-a)$, and integration by parts or the derivative-transform identity supplies the factor $(ik{)}^{-j-1}$. Adequate integrability of the subsequent derivatives controls the remainder. Smooth localized polynomial terms give no algebraic cusp contribution.

Every null subset of the circle lies in the divergence set of the Fourier series of some continuous complex-valued function. One can arrange unbounded Fourier partial sums there. The compact-set Fourier amplification lemma, compact batching of a small open set and frequency-separated Fourier block series construct the function by a summable series of small blocks. The result covers nonclosed and dense null sets; it does not require the divergence set to equal the originally specified set.