package | JAMA For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R so that A = Q*R. The QR decompostion always exists, even if the matrix does not have full rank, so the constructor will never fail. The primary use of the QR decomposition is in the least squares solution of nonsquare systems of simultaneous linear equations. This will fail if isFullRank() returns false.
author | Paul Meagher
license | PHP v3.0
version | 1.1

### Methods

## QR Decomposition computed by Householder reflections.

    __construct(\matrix $A) : \Structure

### Parameters

#### $A

`\matrix`

Rectangular matrix

### Returns

`\Structure`to access R and the Householder vectors and compute Q.

## Return the Householder vectors

    getH() : \Matrix

### Returns

`\Matrix`Lower trapezoidal matrix whose columns define the reflections

## Generate and return the (economy-sized) orthogonal factor

    getQ() : \Matrix

### Returns

`\Matrix`orthogonal factor

## Return the upper triangular factor

    getR() : \Matrix

### Returns

`\Matrix`upper triangular factor

## Is the matrix full rank?

    isFullRank() : boolean

### Returns

`boolean`true if R, and hence A, has full rank, else false.

## Least squares solution of A*X = B

    solve(\Matrix $B) : \Matrix

### Parameters

#### $B

`\Matrix`

A Matrix with as many rows as A and any number of columns.

### Returns

`\Matrix`Matrix that minimizes the two norm of Q*R*X-B.

### Properties

##

    $QR : array

##

    $Rdiag : array

##

    $m : integer

##

    $n : integer

### Constants

## MatrixRankException

    MatrixRankException