Quantcast

Documentation Center

  • Trial Software
  • Product Updates

ilu

Sparse incomplete LU factorization

Syntax

ilu(A,setup)
[L,U] = ilu(A,setup)
[L,U,P] = ilu(A,setup)

Description

ilu produces a unit lower triangular matrix, an upper triangular matrix, and a permutation matrix.

ilu(A,setup) computes the incomplete LU factorization of A. setup is an input structure with up to five setup options. The fields must be named exactly as shown in the table below. You can include any number of these fields in the structure and define them in any order. Any additional fields are ignored.

Field Name

Description

type

Type of factorization. Values for type include:

  • 'nofill'(default)—Performs ILU factorization with 0 level of fill in, known as ILU(0). With type set to 'nofill', only the milu setup option is used; all other fields are ignored.

  • 'crout'—Performs the Crout version of ILU factorization, known as ILUC. With type set to 'crout', only the droptol and milu setup options are used; all other fields are ignored.

  • 'ilutp' —Performs ILU factorization with threshold and pivoting.

If type is not specified, the ILU factorization with 0 level of fill in is performed. Pivoting is only performed with type set to 'ilutp'.

droptol

Drop tolerance of the incomplete LU factorization. droptol is a non-negative scalar. The default value is 0, which produces the complete LU factorization.

The nonzero entries of U satisfy

  abs(U(i,j)) >= droptol*norm(A(:,j)),

with the exception of the diagonal entries, which are retained regardless of satisfying the criterion. The entries of L are tested against the local drop tolerance before being scaled by the pivot, so for nonzeros in L

abs(L(i,j)) >= droptol*norm(A(:,j))/U(j,j).

milu

Modified incomplete LU factorization. Values for milu include:

  • 'row'—Produces the row-sum modified incomplete LU factorization. Entries from the newly-formed column of the factors are subtracted from the diagonal of the upper triangular factor, U, preserving column sums. That is, A*e = L*U*e, where e is the vector of ones.

  • 'col'—Produces the column-sum modified incomplete LU factorization. Entries from the newly-formed column of the factors are subtracted from the diagonal of the upper triangular factor, U, preserving column sums. That is, e'*A = e'*L*U.

  • 'off' (default)—No modified incomplete LU factorization is produced.

udiag

If udiag is 1, any zeros on the diagonal of the upper triangular factor are replaced by the local drop tolerance. The default is 0.

thresh

Pivot threshold between 0 (forces diagonal pivoting) and 1, the default, which always chooses the maximum magnitude entry in the column to be the pivot.

ilu(A,setup) returns L+U-speye(size(A)), where L is a unit lower triangular matrix and U is an upper triangular matrix.

[L,U] = ilu(A,setup) returns a unit lower triangular matrix in L and an upper triangular matrix in U.

[L,U,P] = ilu(A,setup) returns a unit lower triangular matrix in L, an upper triangular matrix in U, and a permutation matrix in P.

Limitations

ilu works on sparse square matrices only.

Examples

Start with a sparse matrix and compute the LU factorization.

A = gallery('neumann', 1600) + speye(1600);
setup.type = 'crout';
setup.milu = 'row';
setup.droptol = 0.1;
[L,U] = ilu(A,setup);
e = ones(size(A,2),1);
norm(A*e-L*U*e)

ans =

  1.4251e-014

This shows that A and L*U, where L and U are given by the modified Crout ILU, have the same row-sum.

Start with a sparse matrix and compute the LU factorization.

A = gallery('neumann', 1600) + speye(1600);
setup.type = 'nofill';
nnz(A)
ans =

        7840

nnz(lu(A))
ans =

      126478

nnz(ilu(A,setup))
ans =

        7840 

This shows that A has 7840 nonzeros, the complete LU factorization has 126478 nonzeros, and the incomplete LU factorization, with 0 level of fill-in, has 7840 nonzeros, the same amount as A.

More About

expand all

Tips

These incomplete factorizations may be useful as preconditioners for a system of linear equations being solved by iterative methods such as BICG (BiConjugate Gradients), GMRES (Generalized Minimum Residual Method).

References

[1] Saad, Yousef, Iterative Methods for Sparse Linear Systems, PWS Publishing Company, 1996, Chapter 10 - Preconditioning Techniques.

See Also

| |

Was this topic helpful?