# 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]
// }
``````