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