# rank

Rank of matrix

## Description

## Examples

## Input Arguments

## More About

## Algorithms

`rank`

uses a method based on the singular value decomposition, or
SVD. The SVD algorithm is more time consuming than some alternatives, but it is also the
most reliable.

The rank of a matrix `A`

is computed as the number of singular values
that are larger than a tolerance. By default, the tolerance is
`max(size(A))*eps(norm(A))`

. However, you can specify a different
tolerance with the command `rank(A,tol)`

.

## Extended Capabilities

## Version History

**Introduced before R2006a**