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.


The Minkowski distance of order p between two points

is defined as:

For , the Minkowski distance is a metric as a result of the Minkowski inequality. When , the distance between (0,0) and (1,1) is , but the point (0,1) is at a distance 1 from both of these points. Since this violates the triangle inequality, for 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:

Similarly, for p reaching negative infinity, we have:

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:

Unit circles using different Minkowski distance metrics.

See also

External links

Simple IEEE 754 implementation in C++

This article is issued from Wikipedia - version of the 11/1/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.