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Singular value decomposition

returns
the singular
values of matrix `s`

= svd(`A`

)`A`

in descending order.

`[`

produces an economy-size
decomposition of `U`

,`S`

,`V`

]
= svd(`A`

,'econ')`m`

-by-`n`

matrix `A`

:

`m > n`

— Only the first`n`

columns of`U`

are computed, and`S`

is`n`

-by-`n`

.`m = n`

—`svd(A,'econ')`

is equivalent to`svd(A)`

.`m < n`

— Only the first`m`

columns of`V`

are computed, and`S`

is`m`

-by-`m`

.

The economy-size decomposition removes extra rows or columns
of zeros from the diagonal matrix of singular values, `S`

,
along with the columns in either `U`

or `V`

that
multiply those zeros in the expression `A = U*S*V'`

.
Removing these zeros and columns can improve execution time and reduce
storage requirements without compromising the accuracy of the decomposition.