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Every four-dimensional Einstein metric with $R_{ab}=\Lambda g_{ab}$ has constant Ricci scalar $R=4\Lambda$. The vacuum metric f(R) gravity tensor reduces to $(\Lambda f^{\prime }(4\Lambda )-f(4\Lambda )/2)g_{ab}$, giving the displayed necessary and sufficient condition for a fixed cosmological constant. Both function and derivative may vanish, so division by $f^{\prime }$ is unnecessary. Requiring this for all real $\Lambda$ and a smooth $f$ instead yields $f(R)=C{R}^2$.

Every real continuous function of supremum norm at most one on the Cantor set is a uniform limit of convex combinations of continuous sign functions. Approximate on a finite Cantor cylinder partition by values $a_j\in [-1,1]$. The sign vector $s$ has weight ${\prod }_j(1+s_j{a}_j)/2$, whose coordinate means are $a_j$. This proves a closed convex hull identity without weak compactness.

For a Galton-Watson process started from one ancestor, this is the probability that every generation is nonempty. It is the complement of the branching extinction probability. The binomial branching survival correction distinguishes finite-binomial reproduction from the Poisson branching process near mean one.

An income stream with per-period variance $b^2$ and correlation $\rho$ with a Gaussian traded price increment produces a hedge holding $-\rho b/\sigma$ in addition to speculative demand. The residual income variance is $b^2(1-{\rho }^2)$. With independent period pairs, the same conditional quadratic minimization applies at each date. A deterministic income fee changes the optimized value but not the hedge holding.

For Ornstein-Uhlenbeck process volatility, matching powers of $\sigma$ reduces the transform PDE to a scalar Riccati equation for $R$, a linear equation for $Q$ and an integral for $P$. All coefficients start at zero. The representation exists up to the Riccati moment-explosion horizon; it need not remain finite on every horizon for arbitrary exponential powers.

Euler's number is the value $\exp (1)={\sum }_{n=0}^{\infty }1/n!$ of the exponential function, approximately $2.71828$. It is the base of the natural logarithm. Its factorial-series tail gives an elementary proof of the irrationality of e.

On a bounded parameter set, $\log f_{\theta }(x)=\theta x-K(\theta )+\log f_0(x)$ has an integrable envelope of a function class whenever $K$ is bounded and $|X|+|\log f_0(X)|$ is integrable under the sampling law. A compact subset of the interior natural-parameter domain bounds the cumulant function of an exponential family and provides moments under model sampling; log-base-density integrability is an additional condition.

A terminal portfolio wealth floor is a prescribed lower bound, possibly a random variable, on terminal portfolio portfolio wealth. In a complete market, its state-price density cost cannot exceed initial portfolio wealth. Nonnegative admissibility replaces a possibly negative floor by its positive part.

If a negative differentiable concave utility function satisfies $\mathbb{E}U(\theta X)>-\infty$ on an open interval, outer secants at four points bound the sample derivative on a smaller compact interval. Negativity makes the endpoint values absolutely integrable. The dominated convergence theorem then gives $d\mathbb{E}U(\theta X)/d\theta =\mathbb{E}[X{U}^{\prime }(\theta X)]$ and continuous derivative when $U^{\prime }$ is continuous. This is a justification of differentiation, rather than an assumption of integrability of marginal utility.

A holding-dependent cost proportional to the absolute position size. In a one-period gain objective it changes the sample payoff to $\theta X-\epsilon |\theta |$, giving gain parameter $X-\epsilon$ for positive holdings and $X+\epsilon$ for negative holdings. The objective is concave but can have a kink at zero.

For a positive claim and $r>0$, the tail integral formula for moments gives the displayed expression. In particular the first moment is ${\int }_0^M\overline{F}(x)dx$ and the raw second moment is $2{\int }_0^{M}x\overline{F}(x)dx$. If the claim law has a density, capping adds an atom of a measure at $M$ with mass $\mathbb{P}(X\ge M)$.

For positive claim sizes with finite second moment in a compound Poisson distribution aggregate of parameter $\lambda$, the two excess of loss reinsurance payouts are $\min (X,M)$ and $(X-M{)}_{+}$. The derivative of their total aggregate variance is $2\lambda (M\overline{F}(M)-\mathbb{E}[(X-M{)}_{+}])$. Thus the displayed condition characterizes stationarity. When the tail probability is positive it says that the retention equals the mean residual life. Global minimality requires an additional sign or comparison argument.

One exact parity query determines whether the first two bits agree. If they agree, a second query reads one of them; otherwise it reads the third bit. Those values respectively determine the three-bit majority function. The matching lower bound follows from its degree-three multilinear polynomial.

Suppose a function has least period $r\mid N$ and distinct values on distinct period cosets. Two independent Fourier samples have the form $c_j=s_{j}N/r$ with uniform $s_j\in {\mathbb{Z}}_r$. The displayed candidate is $r/\gcd (r,s_1,s_2)$, so exact recovery occurs when no prime divisor of $r$ divides both residues. The Chinese remainder theorem gives probability ${\prod }_{\ell \mid r}(1-{\ell }^{-2})\ge 1/\zeta (2)=6/{\pi }^2$, using the Euler product. Unlike a single-sample coprimality probability, this bound is uniformly greater than one half. A candidate-period test makes successful recovery heralded when such a test is available.

For every infinite regular cardinal $\kappa$, there is a strictly eventually increasing sequence of ${\kappa }^{+}$ functions $\kappa \to \kappa$. At stage $0<\zeta <{\kappa }^{+}$ enumerate predecessors by $e:\kappa \to \zeta$ and put $f_{\zeta }(\delta )={\sup }_{\eta <\delta }(f_{e(\eta )}(\delta )+1)$. Regularity bounds each value below $\kappa$, while any predecessor's index is eventually included in the supremum.

For $s\ge 0$ and $\epsilon >0$, integrate over $0\le K\le s$ to obtain the displayed identity. Tonelli theorem then expresses the moment of a nonnegative random variable as the same integral of expected call payoffs, even when the moment is infinite. This is a static representation across strikes rather than a dynamic replicating strategy in a restricted finite-asset market.

For $\sigma >1$, logarithmic expansion of the Euler product reduces positivity to $3+4\cos \theta +\cos 2\theta =2(1+\cos \theta {)}^2$. At primes dividing the modulus the zeta contribution is positive by itself. If the last factor is bounded at the line-one point, a zero of the middle factor would outweigh the zeta pole and force this product to zero. This proves the relevant nonvanishing of Dirichlet L-functions on the line one.

A U-statistic averages a symmetric function of $m$ distinct sample observations over all unordered $m$-tuples. For independent and identically distributed observations it is an unbiased estimator of $\mathbb{E}H(X_1,\dots ,X_m)$. Omitting repeated indices can remove diagonal contributions in quadratic plug-in estimators.

An edge density exceeding the Turan theorem threshold for $K_{r+1}$ by a fixed positive amount forces a balanced $K_{r+1}(t)$ subgraph with logarithmic part size. Combine clique supersaturation by sampling with the dense clique family blow-up lemma. Dense binomial random graphs show that the logarithmic order is optimal for a uniform density guarantee.

For $X_j$ the number of tree components of order $j$ in a binomial random graph, choose their vertices, choose one of $j^{j-2}$ labelled trees by the Cayley formula, require its $j-1$ edges, and exclude both the remaining internal edges and all crossing edges. For $j=1$ interpret $j^{j-2}=1$, recovering the isolated vertex count. If $p=\lambda /n$ for fixed positive $\lambda$ and $j=O(\log n)$, the Stirling formula gives $\mathbb{E}{X}_j\sim n(\lambda e^{1-\lambda }{)}^j/(\lambda \sqrt{2\pi }{j}^{5/2})$. Distinct overlapping vertex sets cannot both be graph components; for disjoint sets their joint occurrence gains the factor $(1-p{)}^{-j^2}$ relative to the product, because their between-set edges must be absent only once.