Minkowski distance

The Minkowski distance is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance.

Definition

The Minkowski distance of order p between two points

$X=(x_{1},x_{2},\ldots ,x_{n}){\text{ and }}Y=(y_{1},y_{2},\ldots ,y_{n})\in \mathbb {R} ^{n}$

is defined as:

$\left(\sum _{i=1}^{n}|x_{i}-y_{i}|^{p}\right)^{1/p}$

For $p\geq 1$, the Minkowski distance is a metric as a result of the Minkowski inequality. When $p<1$, the distance between (0,0) and (1,1) is $2^{1/p}>2$, but the point (0,1) is at a distance 1 from both of these points. Since this violates the triangle inequality, for $p<1$ it is not a metric.

Minkowski distance is typically used with p being 1 or 2. The latter is the Euclidean distance, while the former is sometimes known as the Manhattan distance. In the limiting case of p reaching infinity, we obtain the Chebyshev distance:

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

Similarly, for p reaching negative infinity, we have:

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

The Minkowski distance can also be viewed as a multiple of the power mean of the component-wise differences between P and Q.

The following figure shows unit circles with various values of p:

See also

• L^p space

External links

Simple IEEE 754 implementation in C++