# Function slu #

Calculate the Sparse Matrix LU decomposition with full pivoting. Sparse Matrix `A`

is decomposed in two matrices (`L`

, `U`

) and two permutation vectors (`pinv`

, `q`

) where

`P * A * Q = L * U`

## Syntax #

```
math.slu(A, order, threshold)
```

### Parameters #

Parameter | Type | Description |
---|---|---|

`A` |
SparseMatrix | A two dimensional sparse matrix for which to get the LU decomposition. |

`order` |
Number | The Symbolic Ordering and Analysis order: 0 - Natural ordering, no permutation vector q is returned 1 - Matrix must be square, symbolic ordering and analisis is performed on M = A + A’ 2 - Symbolic ordering and analisis is performed on M = A’ * A. Dense columns from A’ are dropped, A recreated from A’. This is appropriatefor LU factorization of unsymmetric matrices. 3 - Symbolic ordering and analisis is performed on M = A’ * A. This is best used for LU factorization is matrix M has no dense rows. A dense row is a row with more than 10*sqr(columns) entries. |

`threshold` |
Number | Partial pivoting threshold (1 for partial pivoting) |

### Returns #

Type | Description |
---|---|

Object | The lower triangular matrix, the upper triangular matrix and the permutation vectors. |

### Throws #

Type | Description —- | ———–

## Examples #

```
const A = math.sparse([[4,3], [6, 3]])
math.slu(A, 1, 0.001)
// returns:
// {
// L: [[1, 0], [1.5, 1]]
// U: [[4, 3], [0, -1.5]]
// p: [0, 1]
// q: [0, 1]
// }
```