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A function $f$ on a convex set is strictly convex whenwhenever $x\ne y$ and $0<t<1$.

A differentiable function is $\alpha$-strongly convex when

The pointwise maximum of finitely many convex functions is convex because

A finite convex function on an open convex subset of a finite-dimensional real vector space is locally Lipschitz, and therefore continuous. In one dimension this follows by bounding its secant slopes between two slightly wider endpoints.

If a convex subset of ${\mathbb{R}}^n$ is invariant under cyclic coordinate permutation, averaging the $n$ cyclic images of $x$ shows thatalso belongs to the set.

Binomial distributions with trial count tending to infinity and success probability tending to zero converge to a Poisson law when their product converges.

Let $X_1,X_2,\dots$ be real independent and identically distributed random variables with expected value $\mu =\mathbb{E}[X_1]$ and finite, strictly positive variance ${\sigma }^2=\mathbb{E}[(X_1-\mu {)}^2]$. Write $S_n={\sum }_{j=1}^n{X}_j$ and ${\overline{X}}_n=S_n/n$ for their sum and sample mean. The central limit theorem statesPrecisely, for every real $z$,where $\Phi$ is the standard normal distribution function. The conclusion is convergence in distribution to the standard normal distribution. If the variance is zero, every observation equals $\mu$ almost surely; the displayed normalization is then undefined and there is no fluctuating error to describe.

The striking feature is that the observations need not have a normal distribution. They may be binary, bounded, or strongly skewed. Their detailed probability distribution disappears from this limit except through the expected value and variance. The subtraction removes predictable growth: $\mathbb{E}[S_n]=n\mu$. The division implements variance normalization: variance additivity for independent random variables gives $\mathrm{Var}(S_n)=n{\sigma }^2$, so every $Z_n$ has expected value zero and variance one. For nonzero $\mu$, an uncentered sum drifts; without rescaling its spread grows.

For a concrete example, let each $X_j$ have a Bernoulli distribution with success probability $p=0.2$. Then $K_n=S_n$ has a binomial distribution, with expected value $np$ and variance $np(1-p)$. Its exact probability mass function and its normalized count areHere $\left(\genfrac{}{}{0ex}{}{n}{k}\right)$ is a binomial coefficient. Individual trials are conspicuously skewed, yet their centered, rescaled counts approach the same standard normal distribution as sums of fair trials.

At $n=100$, $K_n$ has expected value $20$ and standard deviation $4$. The exact binomial distribution gives $\mathbb{P}(16\le K_n\le 24)=0.740141\dots$. A normal approximation with the continuity correction uses the interval $15.5$ to $24.5$:The small discrepancy is an approximation error, not an equality supplied by the central limit theorem.

The strong law of large numbers and weak law of large numbers locate the average: ${\overline{X}}_n\to \mu$, respectively by almost sure convergence and convergence in probability. The central limit theorem magnifies that shrinking error and describes its shape. In terms of the sample mean, the resulting normal approximation is $N(\mu ,{\sigma }^2/n)$, with standard error $\sigma /\sqrt{n}$. This explains why four times as many independent observations halve the standard error. When $\sigma$ is known, ${\overline{X}}_n\pm 1.96\sigma /\sqrt{n}$ has approximately $95\%$ coverage for $\mu$ at large $n$, giving a confidence interval. This coverage refers to repeated samples, not a guarantee for an individual interval.

A useful application is the Monte Carlo method: estimate a success probability by the proportion of successful simulated trials. Nicholas Metropolis and S. Ulam's original paper, "The Monte Carlo Method" (1949), illustrates this with games of solitaire and estimation of a region's volume by randomly selected points, and discusses why estimates require probabilistic error control. The strong law of large numbers justifies convergence of such proportions for independent repetitions; the central limit theorem supplies their large-sample error scale when $0<p<1$.^[ref]

There are three useful limits to this picture:

A historical original-paper reference is J. W. Lindeberg's "Eine neue Herleitung des Exponentialgesetzes in der Wahrscheinlichkeitsrechnung" (1922), published in Mathematische Zeitschrift 15, pp. 211–225. The Lindeberg condition and Lindeberg-Feller central limit theorem below describe a version for sums whose summands need not share a probability distribution.^[ref]

A brief proof outline uses the characteristic function of $Y=(X_1-\mu )/\sigma$. Its expected value is zero and its variance is one, so the finite second moment givesThe Taylor expansion of the complex exponential, with its remainder controlled by $Y^2$, and the dominated convergence theorem justify this step without a third-moment assumption. Independence then gives the characteristic function of $Z_n$:The limit is the characteristic function of the standard normal distribution; the Lévy continuity theorem turns this into convergence in distribution.

A sequence $(Z_n)$ satisfies a large deviation principle with speed $n$ and rate function $I$ when probabilities of measurable sets $A$ decay exponentially between the bounds

A family of integrable random variables is uniformly integrable whenAlmost-sure or probabilistic convergence together with uniform integrability upgrades to $L^1$ convergence.

If $X_n\to X$ in probability and ${\sup }_n\Vert X_n{\Vert }_r<\infty$, then $X_n\to X$ in $L^p$ for every $1\le p<r$. The $L^r$ bound gives uniform integrability of the $p$th powers, while convergence in probability controls their bounded part.

Random variables $X_n$ converge in probability to $X$ whenfor every $\epsilon >0$.

Random vectors $X_n$ converge weakly to $X$ exactly when every linear projection $u\cdot X_n$ converges weakly to $u\cdot X$. The reverse implication follows because these one-dimensional limits give pointwise convergence of the multivariate characteristic functions.

A sequence of random variables converges almost surely when the set of outcomes on which pointwise convergence fails has probability zero.

Random variables $X_n$ converge in distribution, or converge weakly, to $X$ whenfor every bounded continuous function $f$. For real random variables this is equivalent to convergence of the distribution functions at every continuity point of the limiting distribution function.

The SWAP gate maps $|x⟩|y⟩$ to $|y⟩|x⟩$.

For the Newton map $g(x)=x-f(x)/f^{\prime }(x)$ near a simple root $r$,Bounds $|f^{\prime }|\ge \delta$ and $|f^{\prime \prime }|\le M$ make $|g^{\prime }|<1$ on a sufficiently small closed interval around $r$. The contraction mapping theorem then gives the unique local fixed point.

If a self-map $T$ of a nonempty complete metric space has an iterate $T^n$ that is a contraction mapping, the contraction mapping theorem gives a unique fixed point $a$ of $T^n$. The maps commute, so $T^n(Ta)=T(T^{n}a)=Ta$, and uniqueness forces $Ta=a$. Conversely every fixed point of $T$ is fixed by $T^n$. Thus $a$ is the unique fixed point of $T$.

Suppose $T_{\lambda }$ are contractions of one complete metric space with a common constant $k<1$, and $T_{\lambda }x$ depends continuously on $\lambda$ for each fixed $x$. Their fixed points $x_{\lambda }$ depend continuously on $\lambda$, because for a fixed parameter ${\lambda }_0$,

A Hankel contour runs along both banks of a branch cut and circles its branch point, converting the jump of a complex power into a sine factor.

Integrating along a straight segment bounds a complex integral by segment length times the supremum of the integrand.