Inmathematics, theEuclidean distance between twopoints inEuclidean space is thelength of theline segment between them. It can be calculated from theCartesian coordinates of the points using thePythagorean theorem, and therefore is occasionally called thePythagorean distance.

These names come from the ancientGreek mathematiciansEuclid andPythagoras. In the Greekdeductivegeometry exemplified by Euclid'sElements, distances were not represented as numbers but line segments of the same length, which were considered "equal". The notion of distance is inherent in thecompass tool used to draw acircle, whose points all have the same distance from a commoncenter point. The connection from the Pythagorean theorem to distance calculation was not made until the 18th century.

The distance between two objects that are not points is usually defined to be the smallest distance among pairs of points from the two objects. Formulas are known for computing distances between different types of objects, such as thedistance from a point to a line. In advanced mathematics, the concept of distance has been generalized to abstractmetric spaces, and other distances than Euclidean have been studied. In some applications instatistics andoptimization, the square of the Euclidean distance is used instead of the distance itself.

The distance between any two points on thereal line is theabsolute value of the numerical difference of their coordinates, theirabsolute difference. Thus if${\displaystyle p}$  and${\displaystyle q}$  are two points on the real line, then the distance between them is given by:^[1]

$${\displaystyle d(p,q)=|p-q|.}$$ 

A more complicated formula, giving the same value, but generalizing more readily to higher dimensions, is:^[1]

$${\displaystyle d(p,q)=\sqrt{(p-q{)}^2}.}$$ 

In this formula,squaring and then taking thesquare root leaves any positive number unchanged, but replaces any negative number by its absolute value.^[1]

In theEuclidean plane, let point${\displaystyle p}$  haveCartesian coordinates${\displaystyle (p_1,p_2)}$  and let point${\displaystyle q}$  have coordinates${\displaystyle (q_1,q_2)}$ . Then the distance between${\displaystyle p}$  and${\displaystyle q}$  is given by:^[2]

$${\displaystyle d(p,q)=\sqrt{(p_1-q_1{)}^2+(p_2-q_2{)}^2}.}$$ 

This can be seen by applying thePythagorean theorem to aright triangle with horizontal and vertical sides, having the line segment from${\displaystyle p}$  to${\displaystyle q}$  as itshypotenuse. The two squared formulas inside the square root give the areas of squares on the horizontal and vertical sides, and the outer square root converts the area of the square on the hypotenuse into the length of the hypotenuse.^[3] In terms of thePythagorean addition operation${\displaystyle \oplus }$ , available in manysoftware libraries ashypot, the same formula can be expressed as:^[4]

$${\displaystyle d(p,q)=(p_1-q_1)\oplus (p_2-q_2)=\mathsf{h}\mathsf{y}\mathsf{p}\mathsf{o}\mathsf{t}(p_1-q_1,p_2-q_2).}$$ 

It is also possible to compute the distance for points given bypolar coordinates. If the polar coordinates of${\displaystyle p}$  are${\displaystyle (r,\theta )}$  and the polar coordinates of${\displaystyle q}$  are${\displaystyle (s,\psi )}$ , then their distance is^[2] given by thelaw of cosines:

$${\displaystyle d(p,q)=\sqrt{r^2+s^2-2rs\cos (\theta -\psi )}.}$$ 

When${\displaystyle p}$  and${\displaystyle q}$  are expressed ascomplex numbers in thecomplex plane, the same formula for one-dimensional points expressed as real numbers can be used, although here the absolute value sign indicates thecomplex norm:^[5]

$${\displaystyle d(p,q)=|p-q|.}$$ 

In three dimensions, for points given by their Cartesian coordinates, the distance is

$${\displaystyle d(p,q)=\sqrt{(p_1-q_1{)}^2+(p_2-q_2{)}^2+(p_3-q_3{)}^2}.}$$ 

In general, for points given by Cartesian coordinates in${\displaystyle n}$ -dimensional Euclidean space, the distance is^[6]

$${\displaystyle d(p,q)=\sqrt{(p_1-q_1{)}^2+(p_2-q_2{)}^2+\cdots +(p_n-q_n{)}^2}.}$$ 

The Euclidean distance may also be expressed more compactly in terms of theEuclidean norm of theEuclidean vector difference:

$${\displaystyle d(p,q)=\Vert p-q\Vert .}$$ 

For pairs of objects that are not both points, the distance can most simply be defined as the smallest distance between any two points from the two objects, although more complicated generalizations from points to sets such asHausdorff distance are also commonly used.^[7] Formulas for computing distances between different types of objects include:

• Thedistance from a point to a line, in the Euclidean plane^[8]
• Thedistance from a point to a plane in three-dimensional Euclidean space^[8]
• Thedistance between two lines in three-dimensional Euclidean space^[9]

The distance from a point to acurve can be used to define itsparallel curve, another curve all of whose points have the same distance to the given curve.^[10]

The Euclidean distance is the prototypical example of the distance in ametric space,^[11] and obeys all the defining properties of a metric space:^[12]

• It issymmetric, meaning that for all points${\displaystyle p}$  and${\displaystyle q}$ ,${\displaystyle d(p,q)=d(q,p)}$ . That is (unlike road distance with one-way streets) the distance between two points does not depend on which of the two points is the start and which is the destination.^[12]
• It ispositive, meaning that the distance between every two distinct points is apositive number, while the distance from any point to itself is zero.^[12]
• It obeys thetriangle inequality: for every three points${\displaystyle p}$ ,${\displaystyle q}$ , and${\displaystyle r}$ ,${\displaystyle d(p,q)+d(q,r)\ge d(p,r)}$ . Intuitively, traveling from${\displaystyle p}$  to${\displaystyle r}$  via${\displaystyle q}$  cannot be any shorter than traveling directly from${\displaystyle p}$  to${\displaystyle r}$ .^[12]

Another property,Ptolemy's inequality, concerns the Euclidean distances among four points${\displaystyle p}$ ,${\displaystyle q}$ ,${\displaystyle r}$ , and${\displaystyle s}$ . It states that

$${\displaystyle d(p,q)\cdot d(r,s)+d(q,r)\cdot d(p,s)\ge d(p,r)\cdot d(q,s).}$$ 

For points in the plane, this can be rephrased as stating that for everyquadrilateral, the products of opposite sides of the quadrilateral sum to at least as large a number as the product of its diagonals. However, Ptolemy's inequality applies more generally to points in Euclidean spaces of any dimension, no matter how they are arranged.^[13] For points in metric spaces that are not Euclidean spaces, this inequality may not be true. Euclideandistance geometry studies properties of Euclidean distance such as Ptolemy's inequality, and their application in testing whether given sets of distances come from points in a Euclidean space.^[14]

According to theBeckman–Quarles theorem, any transformation of the Euclidean plane or of a higher-dimensional Euclidean space that preserves unit distances must be anisometry, preserving all distances.^[15]

In many applications, and in particular when comparing distances, it may be more convenient to omit the final square root in the calculation of Euclidean distances, as the square root does not change the order (${\displaystyle d_1^2>d_2^2}$  if and only if${\displaystyle d_1>d_2}$ ). The value resulting from this omission is thesquare of the Euclidean distance, and is called thesquared Euclidean distance.^[16] For instance, theEuclidean minimum spanning tree can be determined using only the ordering between distances, and not their numeric values. Comparing squared distances produces the same result but avoids an unnecessary square-root calculation and sidesteps issues of numerical precision.^[17] As an equation, the squared distance can be expressed as asum of squares:

$${\displaystyle d^2(p,q)=(p_1-q_1{)}^2+(p_2-q_2{)}^2+\cdots +(p_n-q_n{)}^2.}$$ 

Beyond its application to distance comparison, squared Euclidean distance is of central importance instatistics, where it is used in the method ofleast squares, a standard method of fitting statistical estimates to data by minimizing the average of the squared distances between observed and estimated values,^[18] and as the simplest form ofdivergence to compareprobability distributions.^[19] The addition of squared distances to each other, as is done in least squares fitting, corresponds to an operation on (unsquared) distances calledPythagorean addition.^[20] Incluster analysis, squared distances can be used to strengthen the effect of longer distances.^[16]

Squared Euclidean distance does not form a metric space, as it does not satisfy the triangle inequality.^[21] However it is a smooth, strictlyconvex function of the two points, unlike the distance, which is non-smooth (near pairs of equal points) and convex but not strictly convex. The squared distance is thus preferred inoptimization theory, since it allowsconvex analysis to be used. Since squaring is amonotonic function of non-negative values, minimizing squared distance is equivalent to minimizing the Euclidean distance, so the optimization problem is equivalent in terms of either, but easier to solve using squared distance.^[22]

The collection of all squared distances between pairs of points from a finite set may be stored in aEuclidean distance matrix, and is used in this form in distance geometry.^[23]

In more advanced areas of mathematics, when viewing Euclidean space as avector space, its distance is associated with anorm called theEuclidean norm, defined as the distance of each vector from theorigin. One of the important properties of this norm, relative to other norms, is that it remains unchanged under arbitrary rotations of space around the origin.^[24] ByDvoretzky's theorem, every finite-dimensionalnormed vector space has a high-dimensional subspace on which the norm is approximately Euclidean; the Euclidean norm is theonly norm with this property.^[25] It can be extended to infinite-dimensional vector spaces as theL² norm orL² distance.^[26] The Euclidean distance gives Euclidean space the structure of atopological space, theEuclidean topology, with theopen balls (subsets of points at less than a given distance from a given point) as itsneighborhoods.^[27]

Other common distances inreal coordinate spaces andfunction spaces:^[28]

• Chebyshev distance (L^∞ distance), which measures distance as the maximum of the distances in each coordinate.
• Taxicab distance (L¹ distance), also called Manhattan distance, which measures distance as the sum of the distances in each coordinate.
• Minkowski distance (L^p distance), a generalization that unifies Euclidean distance, taxicab distance, and Chebyshev distance.

For points on surfaces in three dimensions, the Euclidean distance should be distinguished from thegeodesic distance, the length of a shortest curve that belongs to the surface. In particular, for measuring great-circle distances on the Earth or other spherical or near-spherical surfaces, distances that have been used include thehaversine distance giving great-circle distances between two points on a sphere from their longitudes and latitudes, andVincenty's formulae also known as "Vincent distance" for distance on a spheroid.^[29]

Euclidean distance is the distance inEuclidean space. Both concepts are named after ancient Greek mathematicianEuclid, whoseElements became a standard textbook in geometry for many centuries.^[30] Concepts oflength anddistance are widespread across cultures, can be dated to the earliest surviving "protoliterate" bureaucratic documents fromSumer in the fourth millennium BC (far before Euclid),^[31] and have been hypothesized to develop in children earlier than the related concepts of speed and time.^[32] But the notion of a distance, as a number defined from two points, does not actually appear in Euclid'sElements. Instead, Euclid approaches this concept implicitly, through thecongruence of line segments, through the comparison of lengths of line segments, and through the concept ofproportionality.^[33]

ThePythagorean theorem is also ancient, but it could only take its central role in the measurement of distances after the invention ofCartesian coordinates byRené Descartes in 1637. The distance formula itself was first published in 1731 byAlexis Clairaut.^[34] Because of this formula, Euclidean distance is also sometimes called Pythagorean distance.^[35] Although accurate measurements of long distances on the Earth's surface, which are not Euclidean, had again been studied in many cultures since ancient times (seehistory of geodesy), the idea that Euclidean distance might not be the only way of measuring distances between points in mathematical spaces came even later, with the 19th-century formulation ofnon-Euclidean geometry.^[36] The definition of the Euclidean norm and Euclidean distance for geometries of more than three dimensions also first appeared in the 19th century, in the work ofAugustin-Louis Cauchy.^[37]