In mathematics, Chebyshev distance (or Tchebychev distance), Maximum metric, or L∞ metric is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension. It is named after Pafnuty Chebyshev.
It is also known as chessboard distance, since in the game of chess the minimum number of moves needed by a king to go from one square on a chessboard to another equals the Chebyshev distance between the centers of the squares, if the squares have side length one, as represented in 2-D spatial coordinates with axes aligned to the edges of the board. For example, the Chebyshev distance between f6 and e2 equals 4.
The Chebyshev distance between two vectors or points p and q, with standard coordinates and , respectively, is
This equals the limit of the Lp metrics:
hence it is also known as the L∞ metric.
On a chess board, 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 Lp metrics are equal – they are just the absolute value of the difference.
The two dimensional Manhattan distance also has circles 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 the planar Manhattan distance.
However, this 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.
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.
See also 
- Cyrus. D. Cantrell (2000). Modern Mathematical Methods for Physicists and Engineers. Cambridge University Press. ISBN 0-521-59827-3.
- James M. Abello, Panos M. Pardalos, and Mauricio G. C. Resende (editors) (2002). Handbook of Massive Data Sets. Springer. ISBN 1-4020-0489-3.
- David M. J. Tax, Robert Duin, and Dick De Ridder (2004). Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. John Wiley and Sons. ISBN 0-470-09013-8.
- André Langevin and Diane Riopel (2005). Logistics Systems. Springer. ISBN 0-387-24971-0.