NAG Library Routine Document
F11DTF
1 Purpose
F11DTF computes a block diagonal incomplete
factorization of a complex sparse non-Hermitian matrix, represented in coordinate storage format. The diagonal blocks may be composed of arbitrary rows and the corresponding columns, and may overlap. This factorization can be used to provide a block Jacobi or additive Schwarz preconditioner, for use in combination with
F11BSF or
F11DUF.
2 Specification
SUBROUTINE F11DTF ( |
N, NNZ, A, LA, IROW, ICOL, NB, ISTB, INDB, LINDB, LFILL, DTOL, PSTRAT, MILU, IPIVP, IPIVQ, ISTR, IDIAG, NNZC, NPIVM, IWORK, LIWORK, IFAIL) |
INTEGER |
N, NNZ, LA, IROW(LA), ICOL(LA), NB, ISTB(NB+1), INDB(LINDB), LINDB, LFILL(NB), IPIVP(LINDB), IPIVQ(LINDB), ISTR(LINDB+1), IDIAG(LINDB), NNZC, NPIVM(NB), IWORK(LIWORK), LIWORK, IFAIL |
REAL (KIND=nag_wp) |
DTOL(NB) |
COMPLEX (KIND=nag_wp) |
A(LA) |
CHARACTER(1) |
PSTRAT(NB), MILU(NB) |
|
3 Description
F11DTF computes an incomplete
factorization (see
Meijerink and Van der Vorst (1977) and
Meijerink and Van der Vorst (1981)) of the (possibly overlapping)
diagonal blocks
,
, of a complex sparse non-Hermitian
by
matrix
. The factorization is intended primarily for use as a block Jacobi or additive Schwarz preconditioner (see
Saad (1996)), with one of the iterative solvers
F11BSF and
F11DUF.
The
NB diagonal blocks need not consist of consecutive rows and columns of
, but may be composed of arbitrarily indexed rows, and the corresponding columns, as defined in the arguments
INDB and
ISTB. Any given row or column index may appear in more than one diagonal block, resulting in overlap. Each diagonal block
,
, is factorized as:
where
and
is lower triangular with unit diagonal elements,
is diagonal,
is upper triangular with unit diagonals,
and
are permutation matrices, and
is a remainder matrix.
The amount of fill-in occurring in the factorization of block can vary from zero to complete fill, and can be controlled by specifying either the maximum level of fill , or the drop tolerance .
The parameter defines the pivoting strategy to be used in block . The options currently available are no pivoting, user-defined pivoting, partial pivoting by columns for stability, and complete pivoting by rows for sparsity and by columns for stability. The factorization may optionally be modified to preserve the row-sums of the original block matrix.
The sparse matrix
is represented in coordinate storage (CS) format (see
Section 2.1.1 in the F11 Chapter Introduction). The array
A stores all the nonzero elements of the matrix
, while arrays
IROW and
ICOL store the corresponding row and column indices respectively. Multiple nonzero elements may not be specified for the same row and column index.
The preconditioning matrices
,
, are returned in terms of the CS representations of the matrices
4 References
Meijerink J and Van der Vorst H (1977) An iterative solution method for linear systems of which the coefficient matrix is a symmetric M-matrix Math. Comput. 31 148–162
Meijerink J and Van der Vorst H (1981) Guidelines for the usage of incomplete decompositions in solving sets of linear equations as they occur in practical problems J. Comput. Phys. 44 134–155
Saad Y (1996) Iterative Methods for Sparse Linear Systems PWS Publishing Company, Boston, MA
5 Parameters
- 1: N – INTEGERInput
On entry: , the order of the matrix .
Constraint:
.
- 2: NNZ – INTEGERInput
On entry: the number of nonzero elements in the matrix .
Constraint:
.
- 3: A(LA) – COMPLEX (KIND=nag_wp) arrayInput/Output
On entry: the nonzero elements in the matrix
, ordered by increasing row index, and by increasing column index within each row. Multiple entries for the same row and column indices are not permitted. The routine
F11ZNF may be used to order the elements in this way.
On exit: the first
NNZ entries of
A contain the nonzero elements of
and the next
NNZC entries contain the elements of the matrices
, for
stored consecutively. Within each block the matrix elements are ordered by increasing row index, and by increasing column index within each row.
- 4: LA – INTEGERInput
On entry: the dimension of the arrays
A,
IROW and
ICOL as declared in the (sub)program from which F11DTF is called. These arrays must be of sufficient size to store both
(
NNZ elements) and
(
NNZC elements).
Constraint:
.
- 5: IROW(LA) – INTEGER arrayInput/Output
- 6: ICOL(LA) – INTEGER arrayInput/Output
On entry: the row and column indices of the nonzero elements supplied in
A.
Constraints:
IROW and
ICOL must satisfy these constraints (which may be imposed by a call to
F11ZAF):
- and , for ;
- either or both and , for .
On exit: the row and column indices of the nonzero elements returned in
A.
- 7: NB – INTEGERInput
On entry: the number of diagonal blocks to factorize.
Constraint:
.
- 8: ISTB() – INTEGER arrayInput
-
On entry:
, for
, holds the index in arrays
INDB,
IPIVP,
IPIVQ and
IDIAG defining block
.
holds the sum of the number of rows in all blocks plus
.
Constraint:
, for .
- 9: INDB(LINDB) – INTEGER arrayInput
On entry:
INDB must hold the row indices appearing in each diagonal block, stored consecutively. Thus the elements
to
are the row indices in the
th block.
Constraint:
, for .
- 10: LINDB – INTEGERInput
On entry: the dimension of the arrays
INDB,
IPIVP,
IPIVQ and
IDIAG as declared in the (sub)program from which F11DTF is called.
Constraint:
.
- 11: LFILL(NB) – INTEGER arrayInput
On entry: if
its value is the maximum level of fill allowed in the decomposition of the block
(see
Section 8.2 in F11DNF). A negative value of
indicates that
will be used to control the fill in block
instead.
- 12: DTOL(NB) – REAL (KIND=nag_wp) arrayInput
On entry: if
then
is used as a drop tolerance in block
to control the fill-in (see
Section 8.2 in F11DNF); otherwise
is not referenced.
Constraint:
if , , for .
- 13: PSTRAT(NB) – CHARACTER(1) arrayInput
On entry:
, for
, specifies the pivoting strategy to be adopted in block
as follows:
- No pivoting is carried out.
- Pivoting is carried out according to the user-defined input values of IPIVP and IPIVQ.
- Partial pivoting by columns for stability is carried out.
- Complete pivoting by rows for sparsity, and by columns for stability, is carried out.
Suggested value:
, for .
Constraint:
, , or , for .
- 14: MILU(NB) – CHARACTER(1) arrayInput
-
On entry:
, for
, indicates whether or not the factorization in block
should be modified to preserve row-sums (see
Section 8.4 in F11DNF).
- The factorization is modified.
- The factorization is not modified.
Constraint:
or , for .
- 15: IPIVP(LINDB) – INTEGER arrayInput/Output
- 16: IPIVQ(LINDB) – INTEGER arrayInput/Output
On entry: if
, then
and
must specify the row and column indices of the element used as a pivot at elimination stage
of the factorization of block
. Otherwise
IPIVP and
IPIVQ need not be initialized.
Constraint:
if
, the elements
to
of
IPIVP and
IPIVQ must both hold valid permutations of the integers on
.
On exit: the row and column indices of the pivot elements, arranged consecutively for each block, as for
INDB. If
and
, then the element in row
and column
of
was used as the pivot at elimination stage
.
- 17: ISTR() – INTEGER arrayOutput
On exit:
, gives the starting address in the arrays
A,
IROW and
ICOL of row
of the matrix
, for
and
.
contains .
- 18: IDIAG(LINDB) – INTEGER arrayOutput
On exit:
, gives the address in the arrays
A,
IROW and
ICOL of the diagonal element in row
of the matrix
, for
and
.
- 19: NNZC – INTEGEROutput
On exit: the sum total number of nonzero elements in the matrices
, for .
- 20: NPIVM(NB) – INTEGER arrayOutput
On exit: if
it gives the number of pivots which were modified during the factorization to ensure that
exists.
If
no pivot modifications were required, but a local restart occurred (see
Section 8.3 in F11DNF). The quality of the preconditioner will generally depend on the returned values of
, for
.
If is large, for some , the preconditioner may not be satisfactory. In this case it may be advantageous to call F11DTF again with an increased value of , a reduced value of , or .
- 21: IWORK(LIWORK) – INTEGER arrayWorkspace
- 22: LIWORK – INTEGERInput
On entry: the dimension of the array
IWORK as declared in the (sub)program from which F11DTF is called.
Constraint:
.
- 23: IFAIL – INTEGERInput/Output
-
On entry:
IFAIL must be set to
,
. If you are unfamiliar with this parameter you should refer to
Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is
.
When the value is used it is essential to test the value of IFAIL on exit.
On exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
-
On entry, .
Constraint: for all .
On entry, for , and .
Constraint: for all .
On entry, and .
Constraint: for all .
On entry, .
Constraint: .
On entry, and .
Constraint: .
On entry, , and .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: or for all .
On entry, .
Constraint: .
On entry, and .
Constraint: .
On entry, .
Constraint: .
On entry, and .
Constraint: .
On entry, .
Constraint: , , or for all .
-
On entry, element
of
A was out of order.
On entry, and .
Constraint: for all .
On entry, and .
Constraint: for all .
On entry, location of was a duplicate.
-
On entry, the user-supplied value of
IPIVP for block
lies outside the range
.
On entry, the user-supplied value of
IPIVP for block
was repeated.
On entry, the user-supplied value of
IPIVQ for block
lies outside the range
.
On entry, the user-supplied value of
IPIVQ for block
was repeated.
-
The number of nonzero entries in the decomposition is too large.
The decomposition has been terminated before completion.
Either increase
LA, or reduce the fill by reducing
LFILL, or increasing
DTOL.
7 Accuracy
The accuracy of the factorization of each block will be determined by the size of the elements that are dropped and the size of any modifications made to the pivot elements. If these sizes are small then the computed factors will correspond to a matrix close to . The factorization can generally be made more accurate by increasing the level of fill , or by reducing the drop tolerance with .
If F11DTF is used in combination with
F11BSF or
F11DUF, the more accurate the factorization the fewer iterations will be required. However, the cost of the decomposition will also generally increase.
F11DTF calls
F11DNF
internally for each block
. The comments and advice provided in
Section 8 in F11DNF on timing, control of
fill, algorithmic details, and choice of parameters, are all
therefore relevant to F11DTF, if interpreted blockwise.
9 Example
This example program reads in a sparse matrix
and then defines a block partitioning of the row indices with a user-supplied overlap and computes an overlapping incomplete
factorization suitable for use as an additive Schwarz preconditioner. Such a factorization is used for this purpose in the example program of
F11DUF.
9.1 Program Text
Program Text (f11dtfe.f90)
9.2 Program Data
Program Data (f11dtfe.d)
9.3 Program Results
Program Results (f11dtfe.r)