Lipschitz continuity ($d_Y(f(x),f(y))\le L{d}_X(x,y)$)

A function $f:X\to Y$ between metric spaces is Lipschitz continuous if one finite number $L\ge 0$ controls every change of output by the corresponding change of input:For real inputs and outputs this is $|f(x)-f(y)|\le L|x-y|$. The same bound must work for every pair in the specified domain of a function. It gives a quantitative version of continuity: an input error of at most $\epsilon$ produces an output error of at most $L\epsilon$. The least possible bound is the Lipschitz constant. Lebl presents the real and metric-space definitions and their connection with uniform continuity. ^[ref]

Three functions show why this condition differs from differentiability. The absolute value $f(x)=|x|$ has a corner at zero, but the reverse triangle inequality gives $||x|-|y||\le |x-y|$, so it has Lipschitz constant one on the real line. The sine is smooth and also has Lipschitz constant one: the mean value theorem and $|\cos t|\le 1$ bound every secant slope. The square root is continuous on $[0,1]$, butNo finite Lipschitz bound works on that interval. At $h=1,1/4,1/16,1/64$, the ratios are $1,2,4,8$. Smaller input changes near zero therefore require larger amplification factors.

For a real function, Lipschitz continuity means that the absolute slopes of all secant lines are bounded. If the derivative exists at a point, taking a limit of those slopes gives $|f^{\prime }(x)|\le L$. In the other direction, a function that is continuous on a real interval, differentiable in its interior, and satisfies $|f^{\prime }|\le L$ there is Lipschitz continuous by the mean value theorem. The interval hypothesis matters: the theorem uses the segment connecting the two inputs.

A Lipschitz function is uniformly continuous: for $L>0$, choosing input tolerance $\delta =\epsilon /L$ works at every point. If $L=0$, the function is constant. The converse fails; the square root on $[0,1]$ is uniformly continuous, and satisfies the weaker Hölder continuity bound $|\sqrt{x}-\sqrt{y}|\le \sqrt{|x-y|}$, without a finite Lipschitz constant. These distinctions are developed in Lebl's worked examples. ^[ref]

The usefulness is controlled sensitivity. Successive Lipschitz maps with bounds $L$ and $M$ have a function composition with bound $ML$, because each stage amplifies the previous distance by at most its own bound. For an ordinary differential equation, Lipschitz continuity in the unknown state is the condition that makes nearby trial solutions controllably close in the Picard-Lindelöf theorem. For incomplete data, the McShane extension theorem fills in unobserved values while preserving the same bound. This one inequality connects error estimates, uniqueness, and interpolation.

A bi-Lipschitz map between metric spaces satisfies the displayed bounds for all input pairs, with constants $0<m\le M<\infty$. The lower bound makes it injective; its inverse on its image is Lipschitz continuous with bound $1/m$, while the map itself has Lipschitz bound $M$. Thus it controls both stretching and compression. It is a homeomorphism onto its image, preserves Hausdorff dimension, and has bi-Lipschitz distortion at most $M/m$. Uniformly multiplying every distance by the same positive factor has distortion one, as explained in metric embedding.

The bounded Lipschitz norm combines the Lipschitz constant and supremum norm of a bounded real-valued Lipschitz function on a metric space. Both summands satisfy the triangle inequality; the supremum term makes the sum definite even on constant functions. Its unit ball requires the sum of the two bounds to be at most one, a stronger requirement than bounding each separately by one.

On a separable metric space, a sequence uniformly bounded in bounded Lipschitz norm has a pointwise convergent subsequence. Enumerate a countable dense subset and use the Bolzano-Weierstrass theorem and diagonal subsequence argument to obtain convergence there. Uniform control of the Lipschitz constant then makes the subsequence Cauchy at every point. The pointwise closure of the bounded Lipschitz unit ball preserves the original norm bound after rescaling.

For every fixed $x\ne y$ and $z$, the sum $|f_i(x)-f_i(y)|/d(x,y)+|f_i(z)|$ is at most one. Pointwise convergence preserves this inequality. Taking the independent suprema over the pair $(x,y)$ and the point $z$ proves the assertion. Taking each bound separately would only give the weaker estimate two.

A one-sided Lipschitz condition controls the growth of distances between solutions of an ordinary differential equation. Differentiating the squared difference and applying the Gronwall inequality gives $\Vert y(t)-z(t)\Vert \le e^{\nu t}\Vert y(0)-z(0)\Vert$. The case $\nu \le 0$ is a dissipative vector field. Ordinary Lipschitz continuity implies such a bound, but the converse fails: $f(y)=-y^3$ has one-sided constant zero on the real line without being globally Lipschitz continuous.

A Lipschitz domain is an open connected subset of Euclidean space whose boundary is locally the graph of a Lipschitz continuous function after rotating and translating coordinates. Locally the domain lies on one side of that graph. This permits corners but excludes boundary cusps that cannot be represented with a finite Lipschitz constant. Bounded Lipschitz domains are a standard setting for Sobolev spaces and the Poincaré inequality for total variation.

Let $A$ be a nonempty subset of a metric space $X$, and let $f:A\to \mathbb{R}$ satisfy a Lipschitz bound $L$. Thenare real-valued Lipschitz functions on all of $X$, agree with $f$ on $A$, and obey the same bound $L$. Every other extension $H$ satisfying that bound lies between them, $G\le H\le F$. Despite the infimum in its formula, $F$ is the largest such extension: every given sample supplies an upper constraint $H(x)\le f(a)+Ld(x,a)$.

Take three real samples $f(0)=0$, $f(1)=1/2$, $f(2)=0$, and allow the bound $L=1$. Their least Lipschitz constant is only $1/2$, so this is deliberately a looser permitted bound. The two extensions areAt $x=1/2$, the allowable interval is $0=G(1/2)\le H(1/2)\le F(1/2)=1/2$. The interpolant obtained by linear interpolation is a piecewise linear function and has value $1/4$ there. Extending it constantly outside $[0,2]$ obeys the same bound on the entire real line. At $x=5/2$, the allowable interval is $[-1/2,1/2]$. Thus the data and the bound control possible values without selecting a unique curve.

The construction is useful when data are known only on a subset: it extends them without increasing an allowed Lipschitz bound. A short justification explains why the envelopes work. Fix $a_0\in A$. The triangle inequality and the bound on $f$ implyso the infimum is finite below; the term $a=a_0$ makes it finite above. At a sample $x=a_0$, all upper terms are at least $f(a_0)$, while that term gives equality. Finally, $d(x,a)\le d(y,a)+d(x,y)$ gives $F(x)\le F(y)+Ld(x,y)$ after taking an infimum; exchanging $x,y$ proves the bound. The lower envelope follows by applying the same argument to $-f$.

E. J. McShane's paper Extension of range of functions appeared in the Bulletin of the American Mathematical Society, volume 40, in 1934, pp. 837–842. ^{[ref] [ref]}

The Lipschitz constant is the least nonnegative number that works in the defining Lipschitz bound, equivalently the displayed supremum of distance ratios. A constant function has constant zero; for a domain with at most one point we also set it to zero. An infinite supremum means there is no finite Lipschitz bound. The least constant need not be attained by one pair of inputs.

For $f(x)=3x-2$ on the real line, every distinct pair has ratio $3$, so the Lipschitz constant is $3$. For $f(x)=x^2$ on $[-R,R]$, with $R>0$,Pairs $x=R$, $y=R-\epsilon$ have ratio $2R-\epsilon$, approaching $2R$. Thus the Lipschitz constant is $2R$, although no distinct pair realizes that exact value. Reporting the domain is essential: the same function has no finite global Lipschitz constant on $\mathbb{R}$.

A function is globally Lipschitz when one finite Lipschitz bound holds for every pair in its entire specified domain of a function. The sine and the absolute value have this property on the real line. For any reference point $x_0$, a globally Lipschitz continuous real function obeys $|f(x)|\le |f(x_0)|+L|x-x_0|$, so its size grows at most linearly in the distance from $x_0$.

The function $f(x)=x^2$ is a locally Lipschitz function on the real line, but is not globally Lipschitz there: pairs $x=n+1$, $y=n$ have ratio $2n+1$, which is unbounded. Restricting the domain to $[-R,R]$ does give a global bound $2R$. Global and local describe where one bound must work, rather than two intrinsic labels independent of the domain.

A Lipschitz bound is any valid inequality of the displayed form with a finite constant $L$. It need not use the least Lipschitz constant. For $f(x)=3x-2$, both $L=3$ and $L=5$ are valid, but $L=2$ fails. The tighter bound gives a sharper error estimate: input error at most $0.01$ guarantees output error at most $0.03$ using $L=3$, compared with the valid but weaker $0.05$ using $L=5$. The bound constrains errors; it does not state that every pair achieves that amplification.

A function between metric spaces is locally Lipschitz if every point has a neighbourhood on which one finite Lipschitz bound works for every pair. The neighbourhood and bound may depend on the point. A continuously differentiable real function on an open interval has this property: continuity of its derivative bounds that derivative on a sufficiently small closed interval, and the mean value theorem bounds every secant slope there.

The function $x^2$ is locally Lipschitz at every real point but has no single bound on the real line. The square root is locally Lipschitz on $(0,\infty )$, and has the bound $1/(2\sqrt{a})$ on $[a,b]$ for $0<a<b$. Adding zero to its domain changes the conclusion at that point: the ratios in the Lipschitz continuity example become unbounded, so it is not locally Lipschitz at zero. This distinction is what permits the delayed, nonunique ODE solutions illustrated under Picard-Lindelöf theorem.