This article is about distance-preserving functions. For other mathematical uses, see isometry (disambiguation). For non-mathematical uses, see Isometric.

In mathematics, an isometry (or congruence, or congruent transformation) is a distance-preserving transformation between metric spaces, usually assumed to be bijective.[1]

A composition of two opposite isometries is a direct isometry. A reflection in a line is an opposite isometry, like R 1 or R 2 on the image. Translation T is a direct isometry: a rigid motion.[2]


Given a metric space (loosely, a set and a scheme for assigning distances between elements of the set), an isometry is a transformation which maps elements to the same or another metric space such that the distance between the image elements in the new metric space is equal to the distance between the elements in the original metric space. In a two-dimensional or three-dimensional Euclidean space, two geometric figures are congruent if they are related by an isometry;[3] the isometry that relates them is either a rigid motion (translation or rotation), or a composition of a rigid motion and a reflection.

Isometries are often used in constructions where one space is embedded in another space. For instance, the completion of a metric space M involves an isometry from M into M', a quotient set of the space of Cauchy sequences on M. The original space M is thus isometrically isomorphic to a subspace of a complete metric space, and it is usually identified with this subspace. Other embedding constructions show that every metric space is isometrically isomorphic to a closed subset of some normed vector space and that every complete metric space is isometrically isomorphic to a closed subset of some Banach space.

An isometric surjective linear operator on a Hilbert space is called a unitary operator.

Formal definitions

Let X and Y be metric spaces with metrics dX and dY. A map ƒ : XY is called an isometry or distance preserving if for any a,bX one has


An isometry is automatically injective;[1] otherwise two distinct points, a and b, could be mapped to the same point, thereby contradicting the coincidence axiom of the metric d. This proof is similar to the proof that an order embedding between partially ordered sets is injective. Clearly, every isometry between metric spaces is a topological embedding (i.e. a homeomorphism).

A global isometry, isometric isomorphism or congruence mapping is a bijective isometry. Like any other bijection, a global isometry has a function inverse. The inverse of a global isometry is also a global isometry.

Two metric spaces X and Y are called isometric if there is a bijective isometry from X to Y. The set of bijective isometries from a metric space to itself forms a group with respect to function composition, called the isometry group.

There is also the weaker notion of path isometry or arcwise isometry:

A path isometry or arcwise isometry is a map which preserves the lengths of curves; such a map is not necessarily an isometry in the distance preserving sense, and it need not necessarily be bijective, or even injective. This term is often abridged to simply isometry, so one should take care to determine from context which type is intended.


Linear isometry

Given two normed vector spaces V and W, a linear isometry is a linear map f : VW that preserves the norms:

for all v in V. Linear isometries are distance-preserving maps in the above sense. They are global isometries if and only if they are surjective.

By the Mazur-Ulam theorem, any isometry of normed vector spaces over R is affine.

In an inner product space, the fact that any linear isometry is an orthogonal transformation can be shown by using polarization to prove <Ax, Ay> = <x, y> and then applying the Riesz representation theorem.


That is, an ε-isometry preserves distances to within ε and leaves no element of the codomain further than ε away from the image of an element of the domain. Note that ε-isometries are not assumed to be continuous.
Note that as mentioned in the introduction this is not necessarily a unitary element because one does not in general have that left inverse is a right inverse.

See also


  1. 1 2 Coxeter 1969, p. 29

    "We shall find it convenient to use the word transformation in the special sense of a one-to-one correspondence among all points in the plane (or in space), that is, a rule for associating pairs of points, with the understanding that each pair has a first member P and a second member P' and that every point occurs as the first member of just one pair and also as the second member of just one pair...

    In particular, an isometry (or "congruent transformation," or "congruence") is a transformation which preserves length..."

  2. Coxeter 1969, p. 46

    3.51 Any direct isometry is either a translation or a rotation. Any opposite isometry is either a reflection or a glide reflection.

  3. Coxeter 1969, p. 39

    3.11 Any two congruent triangles are related by a unique isometry.

  4. Beckman, F. S.; Quarles, D. A., Jr. (1953). "On isometries of Euclidean spaces" (PDF). Proceedings of the American Mathematical Society. 4: 810–815. doi:10.2307/2032415. MR 0058193.
    Let T be a transformation (possibly many-valued) of () into itself.
    Let be the distance between points p and q of , and let Tp, Tq be any images of p and q, respectively.
    If there is a length a > 0 such that whenever , then T is a Euclidean transformation of onto itself.
  5. Roweis, S. T.; Saul, L. K. (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–2326. doi:10.1126/science.290.5500.2323. PMID 11125150.
  6. Saul, Lawrence K.; Roweis, Sam T. (2003). "Think globally, fit locally: Unsupervised learning of nonlinear manifolds". Journal of Machine Learning Research. 4 (June): 119–155. Quadratic optimisation of (page 135) such that
  7. Zhang, Zhenyue; Zha, Hongyuan (2004). "Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment". SIAM Journal on Scientific Computing. 26 (1): 313–338. doi:10.1137/s1064827502419154.
  8. Zhang, Zhenyue; Wang, Jing (2006). "MLLE: Modified Locally Linear Embedding Using Multiple Weights". Advances in Neural Information Processing Systems. 19. It can retrieve the ideal embedding if MLLE is applied on data points sampled from an isometric manifold.


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