1.7 KiB
1.7 KiB
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]
// }