Lipschitz continuity explained

In mathematical analysis, Lipschitz continuity, named after German mathematician Rudolf Lipschitz, is a strong form of uniform continuity for functions. Intuitively, a Lipschitz continuous function is limited in how fast it can change: there exists a real number such that, for every pair of points on the graph of this function, the absolute value of the slope of the line connecting them is not greater than this real number; the smallest such bound is called the Lipschitz constant of the function (and is related to the modulus of uniform continuity). For instance, every function that is defined on an interval and has a bounded first derivative is Lipschitz continuous.[1]

In the theory of differential equations, Lipschitz continuity is the central condition of the Picard–Lindelöf theorem which guarantees the existence and uniqueness of the solution to an initial value problem. A special type of Lipschitz continuity, called contraction, is used in the Banach fixed-point theorem.[2]

We have the following chain of strict inclusions for functions over a closed and bounded non-trivial interval of the real line:

Continuously differentiableLipschitz continuous

\alpha

-Hölder continuous,

where

0<\alpha\leq1

. We also have

Lipschitz continuousabsolutely continuousuniformly continuous.

Definitions

Given two metric spaces (X, dX) and (Y, dY), where dX denotes the metric on the set X and dY is the metric on set Y, a function f : XY is called Lipschitz continuous if there exists a real constant K ≥ 0 such that, for all x1 and x2 in X,

dY(f(x1),f(x2))\leKdX(x1,x2).

Any such K is referred to as a Lipschitz constant for the function f and f may also be referred to as K-Lipschitz. The smallest constant is sometimes called the (best) Lipschitz constant[3] of f or the dilation or dilatation[4] [5] [6] of f. If K = 1 the function is called a short map, and if 0 ≤ K < 1 and f maps a metric space to itself, the function is called a contraction.

In particular, a real-valued function f : RR is called Lipschitz continuous if there exists a positive real constant K such that, for all real x1 and x2,

|f(x1)-f(x2)|\leK|x1-x2|.

In this case, Y is the set of real numbers R with the standard metric dY(y1, y2) = |y1y2|, and X is a subset of R.

In general, the inequality is (trivially) satisfied if x1 = x2. Otherwise, one can equivalently define a function to be Lipschitz continuous if and only if there exists a constant K ≥ 0 such that, for all x1x2,

dY(f(x1),f(x2))
dX(x1,x2)

\leK.

For real-valued functions of several real variables, this holds if and only if the absolute value of the slopes of all secant lines are bounded by K. The set of lines of slope K passing through a point on the graph of the function forms a circular cone, and a function is Lipschitz if and only if the graph of the function everywhere lies completely outside of this cone (see figure).

A function is called locally Lipschitz continuous if for every x in X there exists a neighborhood U of x such that f restricted to U is Lipschitz continuous. Equivalently, if X is a locally compact metric space, then f is locally Lipschitz if and only if it is Lipschitz continuous on every compact subset of X. In spaces that are not locally compact, this is a necessary but not a sufficient condition.

More generally, a function f defined on X is said to be Hölder continuous or to satisfy a Hölder condition of order α > 0 on X if there exists a constant M ≥ 0 such that

dY(f(x),f(y))\leqMdX(x,y)\alpha

for all x and y in X. Sometimes a Hölder condition of order α is also called a uniform Lipschitz condition of order α > 0.

For a real number K ≥ 1, if

1
K

dX(x1,x2)\ledY(f(x1),f(x2))\leKdX(x1,x2)forallx1,x2\inX,

then f is called K-bilipschitz (also written K-bi-Lipschitz). We say f is bilipschitz or bi-Lipschitz to mean there exists such a K. A bilipschitz mapping is injective, and is in fact a homeomorphism onto its image. A bilipschitz function is the same thing as an injective Lipschitz function whose inverse function is also Lipschitz.

Examples

Lipschitz continuous functions that are everywhere differentiable:
  • Lipschitz continuous functions that are not everywhere differentiable:
  • Lipschitz continuous functions that are everywhere differentiable but not continuously differentiable:
  • Continuous functions that are not (globally) Lipschitz continuous:
  • Differentiable functions that are not (locally) Lipschitz continuous:
  • Analytic functions that are not (globally) Lipschitz continuous:
  • Properties

    \|Df(x)\|\leK

    whenever the total derivative Df exists.

    f:U\to\Rm

    the inequality
    \|Df\|
    W1,infty(U)

    \leK

    holds for the best Lipschitz constant

    K

    of

    f

    . If the domain

    U

    is convex then in fact
    \|Df\|
    W1,infty(U)

    =K

    .

    \sup\alphaf\alpha

    (and

    inf\alphaf\alpha

    ) is Lipschitz continuous as well, with the same Lipschitz constant, provided it assumes a finite value at least at a point.

    \tildef(x):=infu\in\{f(u)+kd(x,u)\},

    where k is a Lipschitz constant for f on U.

    Lipschitz manifolds

    A Lipschitz structure on a topological manifold is defined using an atlas of charts whose transition maps are bilipschitz; this is possible because bilipschitz maps form a pseudogroup. Such a structure allows one to define locally Lipschitz maps between such manifolds, similarly to how one defines smooth maps between smooth manifolds: if and are Lipschitz manifolds, then a function

    f:M\toN

    is locally Lipschitz if and only if for every pair of coordinate charts

    \phi:U\toM

    and

    \psi:V\toN

    , where and are open sets in the corresponding Euclidean spaces, the composition\psi^ \circ f \circ \phi:U \cap (f \circ \phi)^(\psi(V)) \to Vis locally Lipschitz. This definition does not rely on defining a metric on or .[7]

    This structure is intermediate between that of a piecewise-linear manifold and a topological manifold: a PL structure gives rise to a unique Lipschitz structure. While Lipschitz manifolds are closely related to topological manifolds, Rademacher's theorem allows one to do analysis, yielding various applications.[7]

    One-sided Lipschitz

    Let F(x) be an upper semi-continuous function of x, and that F(x) is a closed, convex set for all x. Then F is one-sided Lipschitz[8] if

    (x1-x

    T(F(x
    1)-F(x

    2))\leqC\Vertx1-x

    2
    2\Vert
    for some C and for all x1 and x2.

    It is possible that the function F could have a very large Lipschitz constant but a moderately sized, or even negative, one-sided Lipschitz constant. For example, the function

    \begin{cases} F:R2\toR,\ F(x,y)=-50(y-\cos(x)) \end{cases}

    has Lipschitz constant K = 50 and a one-sided Lipschitz constant C = 0. An example which is one-sided Lipschitz but not Lipschitz continuous is F(x) = ex, with C = 0.

    See also

    f:Rn\toRd,

    where

    d=\lceil15(ln|X|)/\varepsilon2\rceil.

    Notes and References

    1. Book: Sohrab, H. H. . Basic Real Analysis . 2003 . 231 . Birkhäuser . 142 . 0-8176-4211-0 .
    2. Book: Brian S. . Thomson . Judith B. . Bruckner . Andrew M. . Bruckner . Elementary Real Analysis . Prentice-Hall . 2001 . 623 . 978-0-13-019075-8 .
    3. Book: Benyamini . Yoav . Lindenstrauss . Joram . Geometric Nonlinear Functional Analysis . 2000 . American Mathematical Society . 0-8218-0835-4 . 11.
    4. Book: Burago . Dmitri . Burago . Yuri . Ivanov . Sergei . A Course in Metric Geometry . 2001 . American Mathematical Society . 0-8218-2129-6.
    5. Mahroo . Omar A . Shalchi . Zaid . Hammond . Christopher J . 'Dilatation' and 'dilation': trends in use on both sides of the Atlantic . British Journal of Ophthalmology . 2014 . 98 . 6 . 845–846 . 10.1136/bjophthalmol-2014-304986 . 24568871 .
    6. Book: Gromov . Mikhael . Mikhael Gromov (mathematician) . Rossi . Hugo . Prospects in Mathematics: Invited Talks on the Occasion of the 250th Anniversary of Princeton University, March 17-21, 1996, Princeton University . Quantitative Homotopy Theory . 1999 . American Mathematical Society . 0-8218-0975-X . 46.
    7. Jonathan . Rosenberg . Jonathan Rosenberg (mathematician) . Miniconferences on harmonic analysis and operator algebras (Canberra, 1987) . Applications of analysis on Lipschitz manifolds . 1988 . . Canberra . 269–283 .
    8. Donchev . Tzanko . Farkhi . Elza . 1998 . Stability and Euler Approximation of One-sided Lipschitz Differential Inclusions . SIAM Journal on Control and Optimization . 36 . 2 . 780–796 . 10.1137/S0363012995293694 .