# lsqminnorm

Minimum norm least-squares solution to linear equation

## Description

`X = lsqminnorm(`

returns an array `A`

,`B`

)`X`

that solves the linear equation *AX =
B* and minimizes the value of `norm(A*X-B)`

. If
several solutions exist to this problem, then `lsqminnorm`

returns the solution that minimizes `norm(X)`

. If
`B`

has multiple columns, then the previous statements are true
for each column of `X`

and `B`

,
respectively.

## Examples

## Input Arguments

## Tips

The minimum-norm solution computed by

`lsqminnorm`

is of particular interest when several solutions exist. The equation*Ax = b*has many solutions whenever`A`

is underdetermined (fewer rows than columns) or of low rank.`lsqminnorm(A,B,tol)`

is typically more efficient than`pinv(A,tol)*B`

for computing minimum norm least-squares solutions to linear systems.`lsqminnorm`

uses the complete orthogonal decomposition (COD) to find a low-rank approximation of`A`

, while`pinv`

uses the singular value decomposition (SVD). Therefore, the results of`pinv`

and`lsqminnorm`

do not match exactly.For sparse matrices,

`lsqminnorm`

uses a different algorithm than for dense matrices, and therefore can produce different results.

## Extended Capabilities

## Version History

**Introduced in R2017b**