Chebyshev distance

The Chebyshev distance between two vectors or points x and y, with standard coordinates ${\displaystyle x_{i}}$ and ${\displaystyle y_{i}}$, respectively, is

${\displaystyle D_{\rm {Chebyshev}}(x,y):=\max _{i}(|x_{i}-y_{i}|).\ }$

This equals the limit of the L_p metrics:

${\displaystyle \lim _{p\to \infty }{\bigg (}\sum _{i=1}^{n}\left|x_{i}-y_{i}\right|^{p}{\bigg )}^{1/p},}$

hence it is also known as the L_∞ metric.

Mathematically, the Chebyshev distance is a metric induced by the supremum norm or uniform norm. It is an example of an injective metric.

In two dimensions, i.e. plane geometry, if the points p and q have Cartesian coordinates ${\displaystyle (x_{1},y_{1})}$ and ${\displaystyle (x_{2},y_{2})}$, their Chebyshev distance is

${\displaystyle D_{\rm {Chebyshev}}=\max \left(\left|x_{2}-x_{1}\right|,\left|y_{2}-y_{1}\right|\right).}$

Under this metric, a circle of radius r, which is the set of points with Chebyshev distance r from a center point, is a square whose sides have the length 2r and are parallel to the coordinate axes.

On a chessboard, where one is using a discrete Chebyshev distance, rather than a continuous one, the circle of radius r is a square of side lengths 2r, measuring from the centers of squares, and thus each side contains 2r+1 squares; for example, the circle of radius 1 on a chess board is a 3×3 square.

In one dimension, all L_p metrics are equal – they are just the absolute value of the difference.

The two dimensional Manhattan distance has "circles" i.e. level sets in the form of squares, with sides of length √2r, oriented at an angle of π/4 (45°) to the coordinate axes, so the planar Chebyshev distance can be viewed as equivalent by rotation and scaling to (i.e. a linear transformation of) the planar Manhattan distance.

However, this geometric equivalence between L₁ and L_∞ metrics does not generalize to higher dimensions. A sphere formed using the Chebyshev distance as a metric is a cube with each face perpendicular to one of the coordinate axes, but a sphere formed using Manhattan distance is an octahedron: these are dual polyhedra, but among cubes, only the square (and 1-dimensional line segment) are self-dual polytopes. Nevertheless, it is true that in all finite-dimensional spaces the L₁ and L_∞ metrics are mathematically dual to each other.

On a grid (such as a chessboard), the points at a Chebyshev distance of 1 of a point are the Moore neighborhood of that point.

The Chebyshev distance is the limiting case of the order-${\displaystyle p}$ Minkowski distance, when ${\displaystyle p}$ reaches infinity.

The Chebyshev distance is sometimes used in warehouse logistics,[4] as it effectively measures the time an overhead crane takes to move an object (as the crane can move on the x and y axes at the same time but at the same speed along each axis).

It is also widely used in electronic Computer-Aided Manufacturing (CAM) applications, in particular, in optimization algorithms for these. Many tools, such as plotting or drilling machines, photoplotter, etc. operating in the plane, are usually controlled by two motors in x and y directions, similar to the overhead cranes.[5]

• King's graph
• Uniform norm
• Taxicab geometry