In mathematical analysis, **Lipschitz continuity**, named after 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 (or * modulus of uniform continuity *). For instance, every function that has bounded first derivatives 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 differentiable**⊂**Lipschitz continuous**⊂**α-Hölder continuous**

where 0 < α ≤ 1. We also have

**Lipschitz continuous**⊂**absolutely continuous**.

Given two metric spaces (*X*, *d*_{X}) and (*Y*, *d*_{Y}), where *d*_{X} denotes the metric on the set *X* and *d*_{Y} is the metric on set *Y*, a function *f* : *X* → *Y* is called **Lipschitz continuous** if there exists a real constant *K* ≥ 0 such that, for all *x*_{1} and *x*_{2} in *X*,

^{ [3] }

Any such *K* is referred to as **a Lipschitz constant** for the function *f*. The smallest constant is sometimes called **the (best) Lipschitz constant**; however, in most cases, the latter notion is less relevant. 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* : *R* → *R* is called Lipschitz continuous if there exists a positive real constant K such that, for all real *x*_{1} and *x*_{2},

In this case, *Y* is the set of real numbers **R** with the standard metric *d*_{Y}(*y _{1}*,

In general, the inequality is (trivially) satisfied if *x*_{1} = *x*_{2}. 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 *x*_{1} ≠ *x*_{2},

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

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.

If there exists a *K*≥ 1 with

then *f* is called **bilipschitz** (also written **bi-Lipschitz**). 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.

- Lipschitz continuous functions
- The function defined for all real numbers is Lipschitz continuous with the Lipschitz constant
*K*= 1, because it is everywhere differentiable and the absolute value of the derivative is bounded above by 1. See the first property listed below under "Properties". - Likewise, the sine function is Lipschitz continuous because its derivative, the cosine function, is bounded above by 1 in absolute value.
- The function
*f*(*x*) = |*x*| defined on the reals is Lipschitz continuous with the Lipschitz constant equal to 1, by the reverse triangle inequality. This is an example of a Lipschitz continuous function that is not differentiable. More generally, a norm on a vector space is Lipschitz continuous with respect to the associated metric, with the Lipschitz constant equal to 1.

- The function defined for all real numbers is Lipschitz continuous with the Lipschitz constant
- Lipschitz continuous functions that are not everywhere differentiable
- The function

- Lipschitz continuous functions that are everywhere differentiable but not continuously differentiable
- The function , whose derivative exists but has an essential discontinuity at .

- Continuous functions that are not (globally) Lipschitz continuous
- The function
*f*(*x*) = √*x*defined on [0, 1] is*not*Lipschitz continuous. This function becomes infinitely steep as*x*approaches 0 since its derivative becomes infinite. However, it is uniformly continuous,^{ [4] }and both Hölder continuous of class*C*^{0, α}for α ≤ 1/2 and also absolutely continuous on [0, 1] (both of which imply the former).

- The function
- Differentiable functions that are not (locally) Lipschitz continuous
- The function
*f*defined by*f*(0) = 0 and*f*(*x*) =*x*^{3/2}sin(1/*x*) for 0<*x*≤1 gives an example of a function that is differentiable on a compact set while not locally Lipschitz because its derivative function is not bounded. See also the first property below.

- The function
- Analytic functions that are not (globally) Lipschitz continuous
- The exponential function becomes arbitrarily steep as
*x*→ ∞, and therefore is*not*globally Lipschitz continuous, despite being an analytic function. - The function
*f*(*x*) =*x*^{2}with domain all real numbers is*not*Lipschitz continuous. This function becomes arbitrarily steep as*x*approaches infinity. It is however locally Lipschitz continuous.

- The exponential function becomes arbitrarily steep as

- An everywhere differentiable function
*g*:**R**→**R**is Lipschitz continuous (with*K*= sup |*g*′(*x*)|) if and only if it has bounded first derivative; one direction follows from the mean value theorem. In particular, any continuously differentiable function is locally Lipschitz, as continuous functions are locally bounded so its gradient is locally bounded as well. - A Lipschitz function
*g*:**R**→**R**is absolutely continuous and therefore is differentiable almost everywhere, that is, differentiable at every point outside a set of Lebesgue measure zero. Its derivative is essentially bounded in magnitude by the Lipschitz constant, and for*a*<*b*, the difference*g*(*b*) −*g*(*a*) is equal to the integral of the derivative*g*′ on the interval [*a*,*b*].- Conversely, if
*f*:*I*→**R**is absolutely continuous and thus differentiable almost everywhere, and satisfies |*f′*(*x*)| ≤*K*for almost all*x*in*I*, then*f*is Lipschitz continuous with Lipschitz constant at most*K*. - More generally, Rademacher's theorem extends the differentiability result to Lipschitz mappings between Euclidean spaces: a Lipschitz map
*f*:*U*→**R**^{m}, where*U*is an open set in**R**^{n}, is almost everywhere differentiable. Moreover, if*K*is the best Lipschitz constant of*f*, then whenever the total derivative*Df*exists.

- Conversely, if
- For a differentiable Lipschitz map
*f*:*U*→**R**^{m}the inequality holds for the best Lipschitz constant of f, and it turns out to be an equality if the domain U is convex.^{[ further explanation needed ]} - Suppose that {
*f*} is a sequence of Lipschitz continuous mappings between two metric spaces, and that all_{n}*f*have Lipschitz constant bounded by some_{n}*K*. If*f*converges to a mapping_{n}*f*uniformly, then*f*is also Lipschitz, with Lipschitz constant bounded by the same*K*. In particular, this implies that the set of real-valued functions on a compact metric space with a particular bound for the Lipschitz constant is a closed and convex subset of the Banach space of continuous functions. This result does not hold for sequences in which the functions may have*unbounded*Lipschitz constants, however. In fact, the space of all Lipschitz functions on a compact metric space is a subalgebra of the Banach space of continuous functions, and thus dense in it, an elementary consequence of the Stone–Weierstrass theorem (or as a consequence of Weierstrass approximation theorem, because every polynomial is locally Lipschitz continuous). - Every Lipschitz continuous map is uniformly continuous, and hence
*a fortiori*continuous. More generally, a set of functions with bounded Lipschitz constant forms an equicontinuous set. The Arzelà–Ascoli theorem implies that if {*f*} is a uniformly bounded sequence of functions with bounded Lipschitz constant, then it has a convergent subsequence. By the result of the previous paragraph, the limit function is also Lipschitz, with the same bound for the Lipschitz constant. In particular the set of all real-valued Lipschitz functions on a compact metric space_{n}*X*having Lipschitz constant ≤*K*is a locally compact convex subset of the Banach space*C*(*X*). - For a family of Lipschitz continuous functions
*f*_{α}with common constant, the function (and ) is Lipschitz continuous as well, with the same Lipschitz constant, provided it assumes a finite value at least at a point. - If
*U*is a subset of the metric space*M*and*f*:*U*→**R**is a Lipschitz continuous function, there always exist Lipschitz continuous maps*M*→**R**which extend*f*and have the same Lipschitz constant as*f*(see also Kirszbraun theorem). An extension is provided by

- where
*k*is a Lipschitz constant for*f*on*U*.

Let *U* and *V* be two open sets in **R**^{n}. A function *T* : *U* → *V* is called **bi-Lipschitz** if it is a Lipschitz homeomorphism onto its image, and its inverse is also Lipschitz.

Using bi-Lipschitz mappings, it is possible to define a Lipschitz structure on a topological manifold, since there is a pseudogroup structure on bi-Lipschitz homeomorphisms. This structure is intermediate between that of a piecewise-linear manifold and a smooth manifold. In fact a PL structure gives rise to a unique Lipschitz structure;^{ [5] } it can in that sense 'nearly' be smoothed.

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^{ [6] } if

for some *C* and for all *x*_{1} and *x*_{2}.

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

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*) = *e*^{−x}, with *C* = 0.

In mathematics, a **continuous function** is a function that does not have any abrupt changes in value, known as discontinuities. More precisely, a function is continuous if arbitrarily small changes in its output can be assured by restricting to sufficiently small changes in its input. If not continuous, a function is said to be *discontinuous*. Up until the 19th century, mathematicians largely relied on intuitive notions of continuity, during which attempts such as the epsilon–delta definition were made to formalize it.

In mathematics, a **contraction mapping**, or **contraction** or **contractor**, on a metric space (*M*, *d*) is a function *f* from *M* to itself, with the property that there is some nonnegative real number such that for all *x* and *y* in *M*,

In mathematics, **real analysis** is the branch of mathematical analysis that studies the behavior of real numbers, sequences and series of real numbers, and real functions. Some particular properties of real-valued sequences and functions that real analysis studies include convergence, limits, continuity, smoothness, differentiability and integrability.

In mathematics, a function *f* is **uniformly continuous** if, roughly speaking, it is possible to guarantee that *f*(*x*) and *f*(*y*) be as close to each other as we please by requiring only that *x* and *y* are sufficiently close to each other; unlike ordinary continuity, where the maximum distance between *f*(*x*) and *f*(*y*) may depend on *x* and *y* themselves.

**Distributions**, also known as **Schwartz distributions** or **generalized functions**, are objects that generalize the classical notion of functions in mathematical analysis. Distributions make it possible to differentiate functions whose derivatives do not exist in the classical sense. In particular, any locally integrable function has a distributional derivative. Distributions are widely used in the theory of partial differential equations, where it may be easier to establish the existence of distributional solutions than classical solutions, or appropriate classical solutions may not exist. Distributions are also important in physics and engineering where many problems naturally lead to differential equations whose solutions or initial conditions are distributions, such as the Dirac delta function.

In functional analysis and related areas of mathematics, **Fréchet spaces**, named after Maurice Fréchet, are special topological vector spaces. They are generalizations of Banach spaces. All Banach and Hilbert spaces are Fréchet spaces. Spaces of infinitely differentiable functions are typical examples of Fréchet spaces, many of which are typically *not* Banach spaces.

In calculus, **absolute continuity** is a smoothness property of functions that is stronger than continuity and uniform continuity. The notion of absolute continuity allows one to obtain generalizations of the relationship between the two central operations of calculus—differentiation and integration. This relationship is commonly characterized in the framework of Riemann integration, but with absolute continuity it may be formulated in terms of Lebesgue integration. For real-valued functions on the real line, two interrelated notions appear: *absolute continuity of functions* and *absolute continuity of measures.* These two notions are generalized in different directions. The usual derivative of a function is related to the *Radon–Nikodym derivative*, or *density*, of a measure.

In mathematics, specifically differential calculus, the **inverse function theorem** gives a sufficient condition for a function to be invertible in a neighborhood of a point in its domain: namely, that its *derivative is continuous and non-zero at the point*. The theorem also gives a formula for the derivative of the inverse function. In multivariable calculus, this theorem can be generalized to any continuously differentiable, vector-valued function whose Jacobian determinant is nonzero at a point in its domain, giving a formula for the Jacobian matrix of the inverse. There are also versions of the inverse function theorem for complex holomorphic functions, for differentiable maps between manifolds, for differentiable functions between Banach spaces, and so forth.

In functional analysis, a **bounded linear operator** is a linear transformation *L* : *X* → *Y* between topological vector spaces (TVSs) X and Y that maps bounded subsets of X to bounded subsets of Y. If X and Y are normed vector spaces, then L is bounded if and only if there exists some *M* ≥ 0 such that for all x in X,

In mathematical analysis, a family of functions is **equicontinuous** if all the functions are continuous and they have equal variation over a given neighbourhood, in a precise sense described herein. In particular, the concept applies to countable families, and thus *sequences* of functions.

The **Arzelà–Ascoli theorem** is a fundamental result of mathematical analysis giving necessary and sufficient conditions to decide whether every sequence of a given family of real-valued continuous functions defined on a closed and bounded interval has a uniformly convergent subsequence. The main condition is the equicontinuity of the family of functions. The theorem is the basis of many proofs in mathematics, including that of the Peano existence theorem in the theory of ordinary differential equations, Montel's theorem in complex analysis, and the Peter–Weyl theorem in harmonic analysis and various results concerning compactness of integral operators.

In mathematics, a **Sobolev space** is a vector space of functions equipped with a norm that is a combination of *L ^{p}*-norms of the function together with its derivatives up to a given order. The derivatives are understood in a suitable weak sense to make the space complete, i.e. a Banach space. Intuitively, a Sobolev space is a space of functions possessing sufficiently many derivatives for some application domain, such as partial differential equations, and equipped with a norm that measures both the size and regularity of a function.

In mathematical analysis, a **modulus of continuity** is a function ω : [0, ∞] → [0, ∞] used to measure quantitatively the uniform continuity of functions. So, a function *f* : *I* → **R** admits ω as a modulus of continuity if and only if

In mathematical analysis, the **smoothness** of a function is a property measured by the number of continuous derivatives it has over some domain. At the very minimum, a function could be considered "smooth" if it is differentiable everywhere. At the other end, it might also possess derivatives of all orders in its domain, in which case it is said to be **infinitely differentiable** and referred to as a **C-infinity function**.

In geometric topology, **Busemann functions** are used to study the large-scale geometry of geodesics in Hadamard spaces and in particular Hadamard manifolds. They are named after Herbert Busemann, who introduced them; he gave an extensive treatment of the topic in his 1955 book "The geometry of geodesics".

In mathematics, a real or complex-valued function *f* on *d*-dimensional Euclidean space satisfies a **Hölder condition**, or is **Hölder continuous**, when there are nonnegative real constants *C*, α>0, such that

In mathematics, there is in mathematical analysis a class of **Sobolev inequalities**, relating norms including those of Sobolev spaces. These are used to prove the **Sobolev embedding theorem**, giving inclusions between certain Sobolev spaces, and the Rellich–Kondrachov theorem showing that under slightly stronger conditions some Sobolev spaces are compactly embedded in others. They are named after Sergei Lvovich Sobolev.

In mathematics, the **Lebesgue differentiation theorem** is a theorem of real analysis, which states that for almost every point, the value of an integrable function is the limit of infinitesimal averages taken about the point. The theorem is named for Henri Lebesgue.

In mathematics, **Gårding's inequality** is a result that gives a lower bound for the bilinear form induced by a real linear elliptic partial differential operator. The inequality is named after Lars Gårding.

- ↑ Sohrab, H. H. (2003).
*Basic Real Analysis*. Vol. 231. Birkhäuser. p. 142. ISBN 0-8176-4211-0. - ↑ Thomson, Brian S.; Bruckner, Judith B.; Bruckner, Andrew M. (2001).
*Elementary Real Analysis*. Prentice-Hall. p. 623. - ↑ Searcóid, Mícheál Ó (2006), "Lipschitz Functions",
*Metric Spaces*, Springer undergraduate mathematics series, Berlin, New York: Springer-Verlag, ISBN 978-1-84628-369-7 - ↑ Robbin, Joel W.,
*Continuity and Uniform Continuity*(PDF) - ↑ "Topology of manifolds",
*Encyclopedia of Mathematics*, EMS Press, 2001 [1994] - ↑ 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. doi:10.1137/S0363012995293694.

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