ndarray-crout-decomposition

LU decomposition using the crout algorithm



example

`` js
var ndarray = require('ndarray');
var zeros = require('zeros');
var show = require('ndarray-show');
var crout = require('ndarray-crout-decomposition');

var A = ndarray([ 4, 3, 6, 3 ], [ 2, 2 ]);
var L = zeros([ 2, 2 ]);
var U = zeros([ 2, 2 ]);

crout(A, L, U);
console.log('L=\n' + show(L));
console.log('U=\n' + show(U));
`

output:

`
L=
4.000 0.000
3.000 -1.500
U=
1.000 1.500
0.000 1.000
`

or to save space, you can use a single matrix to store the L and U values:

` js
var ndarray = require('ndarray');
var zeros = require('zeros');
var show = require('ndarray-show');
var crout = require('ndarray-crout-decomposition');

var A = ndarray([ 4, 3, 6, 3 ], [ 2, 2 ]);
var LU = zeros([ 2, 2 ]);

crout(A, LU);
console.log('LU=\n' + show(LU));
`

output:

`
LU=
4.000 1.500
3.000 -1.500
`

methods

` js
var crout = require('ndarray-crout-decomposition')
`

var ok = crout(A, L, U)

Decompose the matrix A into L and U, mutating L and U in-place.

A is not modified.

If you don't pass in a U matrix, L will be used to store both the L and
U values, omitting the diagonal of ones from U to make room.

If A was non-square or the algorithm could not find a solution, ok is
false. Otherwise ok is true.

install

With npm do:

`
npm install ndarray-crout-decomposition
``

license

MIT