Solves the system of linear equations A*X=B or A'*X=B. More...
#include "slu_sdefs.h"
Include dependency graph for sgssvx.c:
Functions | |
| void | sgssvx (superlu_options_t *options, SuperMatrix *A, int *perm_c, int *perm_r, int *etree, char *equed, float *R, float *C, SuperMatrix *L, SuperMatrix *U, void *work, int lwork, SuperMatrix *B, SuperMatrix *X, float *recip_pivot_growth, float *rcond, float *ferr, float *berr, GlobalLU_t *Glu, mem_usage_t *mem_usage, SuperLUStat_t *stat, int *info) |
Detailed Description
Copyright (c) 2003, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from U.S. Dept. of Energy)
All rights reserved.
The source code is distributed under BSD license, see the file License.txt at the top-level directory.
– SuperLU routine (version 3.0) – Univ. of California Berkeley, Xerox Palo Alto Research Center, and Lawrence Berkeley National Lab. October 15, 2003
Function Documentation
| void sgssvx | ( | superlu_options_t * | options, |
| SuperMatrix * | A, | ||
| int * | perm_c, | ||
| int * | perm_r, | ||
| int * | etree, | ||
| char * | equed, | ||
| float * | R, | ||
| float * | C, | ||
| SuperMatrix * | L, | ||
| SuperMatrix * | U, | ||
| void * | work, | ||
| int | lwork, | ||
| SuperMatrix * | B, | ||
| SuperMatrix * | X, | ||
| float * | recip_pivot_growth, | ||
| float * | rcond, | ||
| float * | ferr, | ||
| float * | berr, | ||
| GlobalLU_t * | Glu, | ||
| mem_usage_t * | mem_usage, | ||
| SuperLUStat_t * | stat, | ||
| int * | info | ||
| ) |
Purpose
SGSSVX solves the system of linear equations A*X=B or A'*X=B, using the LU factorization from sgstrf(). Error bounds on the solution and a condition estimate are also provided. It performs the following steps:
1. If A is stored column-wise (A->Stype = SLU_NC):
1.1. If options->Equil = YES, scaling factors are computed to
equilibrate the system:
options->Trans = NOTRANS:
diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B
options->Trans = TRANS:
(diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B
options->Trans = CONJ:
(diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B
Whether or not the system will be equilibrated depends on the
scaling of the matrix A, but if equilibration is used, A is
overwritten by diag(R)*A*diag(C) and B by diag(R)*B
(if options->Trans=NOTRANS) or diag(C)*B (if options->Trans
= TRANS or CONJ). 1.2. Permute columns of A, forming A*Pc, where Pc is a permutation
matrix that usually preserves sparsity.
For more details of this step, see sp_preorder.c. 1.3. If options->Fact != FACTORED, the LU decomposition is used to
factor the matrix A (after equilibration if options->Equil = YES)
as Pr*A*Pc = L*U, with Pr determined by partial pivoting.1.4. Compute the reciprocal pivot growth factor.
1.5. If some U(i,i) = 0, so that U is exactly singular, then the
routine returns with info = i. Otherwise, the factored form of
A is used to estimate the condition number of the matrix A. If
the reciprocal of the condition number is less than machine
precision, info = A->ncol+1 is returned as a warning, but the
routine still goes on to solve for X and computes error bounds
as described below. 1.6. The system of equations is solved for X using the factored form
of A. 1.7. If options->IterRefine != NOREFINE, iterative refinement is
applied to improve the computed solution matrix and calculate
error bounds and backward error estimates for it. 1.8. If equilibration was used, the matrix X is premultiplied by
diag(C) (if options->Trans = NOTRANS) or diag(R)
(if options->Trans = TRANS or CONJ) so that it solves the
original system before equilibration. 2. If A is stored row-wise (A->Stype = SLU_NR), apply the above algorithm
to the transpose of A: 2.1. If options->Equil = YES, scaling factors are computed to
equilibrate the system:
options->Trans = NOTRANS:
diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B
options->Trans = TRANS:
(diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B
options->Trans = CONJ:
(diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B
Whether or not the system will be equilibrated depends on the
scaling of the matrix A, but if equilibration is used, A' is
overwritten by diag(R)*A'*diag(C) and B by diag(R)*B
(if trans='N') or diag(C)*B (if trans = 'T' or 'C'). 2.2. Permute columns of transpose(A) (rows of A),
forming transpose(A)*Pc, where Pc is a permutation matrix that
usually preserves sparsity.
For more details of this step, see sp_preorder.c. 2.3. If options->Fact != FACTORED, the LU decomposition is used to
factor the transpose(A) (after equilibration if
options->Fact = YES) as Pr*transpose(A)*Pc = L*U with the
permutation Pr determined by partial pivoting.2.4. Compute the reciprocal pivot growth factor.
2.5. If some U(i,i) = 0, so that U is exactly singular, then the
routine returns with info = i. Otherwise, the factored form
of transpose(A) is used to estimate the condition number of the
matrix A. If the reciprocal of the condition number
is less than machine precision, info = A->nrow+1 is returned as
a warning, but the routine still goes on to solve for X and
computes error bounds as described below. 2.6. The system of equations is solved for X using the factored form
of transpose(A). 2.7. If options->IterRefine != NOREFINE, iterative refinement is
applied to improve the computed solution matrix and calculate
error bounds and backward error estimates for it. 2.8. If equilibration was used, the matrix X is premultiplied by
diag(C) (if options->Trans = NOTRANS) or diag(R)
(if options->Trans = TRANS or CONJ) so that it solves the
original system before equilibration.See supermatrix.h for the definition of 'SuperMatrix' structure.
Arguments
options (input) superlu_options_t*
The structure defines the input parameters to control
how the LU decomposition will be performed and how the
system will be solved.A (input/output) SuperMatrix*
Matrix A in A*X=B, of dimension (A->nrow, A->ncol). The number
of the linear equations is A->nrow. Currently, the type of A can be:
Stype = SLU_NC or SLU_NR, Dtype = SLU_D, Mtype = SLU_GE.
In the future, more general A may be handled. On entry, If options->Fact = FACTORED and equed is not 'N',
then A must have been equilibrated by the scaling factors in
R and/or C.
On exit, A is not modified if options->Equil = NO, or if
options->Equil = YES but equed = 'N' on exit.
Otherwise, if options->Equil = YES and equed is not 'N',
A is scaled as follows:
If A->Stype = SLU_NC:
equed = 'R': A := diag(R) * A
equed = 'C': A := A * diag(C)
equed = 'B': A := diag(R) * A * diag(C).
If A->Stype = SLU_NR:
equed = 'R': transpose(A) := diag(R) * transpose(A)
equed = 'C': transpose(A) := transpose(A) * diag(C)
equed = 'B': transpose(A) := diag(R) * transpose(A) * diag(C).perm_c (input/output) int*
If A->Stype = SLU_NC, Column permutation vector of size A->ncol,
which defines the permutation matrix Pc; perm_c[i] = j means
column i of A is in position j in A*Pc.
On exit, perm_c may be overwritten by the product of the input
perm_c and a permutation that postorders the elimination tree
of Pc'*A'*A*Pc; perm_c is not changed if the elimination tree
is already in postorder. If A->Stype = SLU_NR, column permutation vector of size A->nrow,
which describes permutation of columns of transpose(A)
(rows of A) as described above.perm_r (input/output) int*
If A->Stype = SLU_NC, row permutation vector of size A->nrow,
which defines the permutation matrix Pr, and is determined
by partial pivoting. perm_r[i] = j means row i of A is in
position j in Pr*A. If A->Stype = SLU_NR, permutation vector of size A->ncol, which
determines permutation of rows of transpose(A)
(columns of A) as described above. If options->Fact = SamePattern_SameRowPerm, the pivoting routine
will try to use the input perm_r, unless a certain threshold
criterion is violated. In that case, perm_r is overwritten by a
new permutation determined by partial pivoting or diagonal
threshold pivoting.
Otherwise, perm_r is output argument.etree (input/output) int*, dimension (A->ncol)
Elimination tree of Pc'*A'*A*Pc.
If options->Fact != FACTORED and options->Fact != DOFACT,
etree is an input argument, otherwise it is an output argument.
Note: etree is a vector of parent pointers for a forest whose
vertices are the integers 0 to A->ncol-1; etree[root]==A->ncol.equed (input/output) char*
Specifies the form of equilibration that was done.
= 'N': No equilibration.
= 'R': Row equilibration, i.e., A was premultiplied by diag(R).
= 'C': Column equilibration, i.e., A was postmultiplied by diag(C).
= 'B': Both row and column equilibration, i.e., A was replaced
by diag(R)*A*diag(C).
If options->Fact = FACTORED, equed is an input argument,
otherwise it is an output argument.R (input/output) float*, dimension (A->nrow)
The row scale factors for A or transpose(A).
If equed = 'R' or 'B', A (if A->Stype = SLU_NC) or transpose(A)
(if A->Stype = SLU_NR) is multiplied on the left by diag(R).
If equed = 'N' or 'C', R is not accessed.
If options->Fact = FACTORED, R is an input argument,
otherwise, R is output.
If options->zFact = FACTORED and equed = 'R' or 'B', each element
of R must be positive.C (input/output) float*, dimension (A->ncol)
The column scale factors for A or transpose(A).
If equed = 'C' or 'B', A (if A->Stype = SLU_NC) or transpose(A)
(if A->Stype = SLU_NR) is multiplied on the right by diag(C).
If equed = 'N' or 'R', C is not accessed.
If options->Fact = FACTORED, C is an input argument,
otherwise, C is output.
If options->Fact = FACTORED and equed = 'C' or 'B', each element
of C must be positive.L (output) SuperMatrix*
The factor L from the factorization
Pr*A*Pc=L*U (if A->Stype SLU_= NC) or
Pr*transpose(A)*Pc=L*U (if A->Stype = SLU_NR).
Uses compressed row subscripts storage for supernodes, i.e.,
L has types: Stype = SLU_SC, Dtype = SLU_S, Mtype = SLU_TRLU.U (output) SuperMatrix*
The factor U from the factorization
Pr*A*Pc=L*U (if A->Stype = SLU_NC) or
Pr*transpose(A)*Pc=L*U (if A->Stype = SLU_NR).
Uses column-wise storage scheme, i.e., U has types:
Stype = SLU_NC, Dtype = SLU_S, Mtype = SLU_TRU.work (workspace/output) void*, size (lwork) (in bytes)
User supplied workspace, should be large enough
to hold data structures for factors L and U.
On exit, if fact is not 'F', L and U point to this array.lwork (input) int
Specifies the size of work array in bytes.
= 0: allocate space internally by system malloc;
> 0: use user-supplied work array of length lwork in bytes,
returns error if space runs out.
= -1: the routine guesses the amount of space needed without
performing the factorization, and returns it in
mem_usage->total_needed; no other side effects.See argument 'mem_usage' for memory usage statistics.
B (input/output) SuperMatrix*
B has types: Stype = SLU_DN, Dtype = SLU_S, Mtype = SLU_GE.
On entry, the right hand side matrix.
If B->ncol = 0, only LU decomposition is performed, the triangular
solve is skipped.
On exit,
if equed = 'N', B is not modified; otherwise
if A->Stype = SLU_NC:
if options->Trans = NOTRANS and equed = 'R' or 'B',
B is overwritten by diag(R)*B;
if options->Trans = TRANS or CONJ and equed = 'C' of 'B',
B is overwritten by diag(C)*B;
if A->Stype = SLU_NR:
if options->Trans = NOTRANS and equed = 'C' or 'B',
B is overwritten by diag(C)*B;
if options->Trans = TRANS or CONJ and equed = 'R' of 'B',
B is overwritten by diag(R)*B.X (output) SuperMatrix*
X has types: Stype = SLU_DN, Dtype = SLU_S, Mtype = SLU_GE.
If info = 0 or info = A->ncol+1, X contains the solution matrix
to the original system of equations. Note that A and B are modified
on exit if equed is not 'N', and the solution to the equilibrated
system is inv(diag(C))*X if options->Trans = NOTRANS and
equed = 'C' or 'B', or inv(diag(R))*X if options->Trans = 'T' or 'C'
and equed = 'R' or 'B'.recip_pivot_growth (output) float*
The reciprocal pivot growth factor max_j( norm(A_j)/norm(U_j) ).
The infinity norm is used. If recip_pivot_growth is much less
than 1, the stability of the LU factorization could be poor.rcond (output) float*
The estimate of the reciprocal condition number of the matrix A
after equilibration (if done). If rcond is less than the machine
precision (in particular, if rcond = 0), the matrix is singular
to working precision. This condition is indicated by a return
code of info > 0.FERR (output) float*, dimension (B->ncol)
The estimated forward error bound for each solution vector
X(j) (the j-th column of the solution matrix X).
If XTRUE is the true solution corresponding to X(j), FERR(j)
is an estimated upper bound for the magnitude of the largest
element in (X(j) - XTRUE) divided by the magnitude of the
largest element in X(j). The estimate is as reliable as
the estimate for RCOND, and is almost always a slight
overestimate of the true error.
If options->IterRefine = NOREFINE, ferr = 1.0.BERR (output) float*, dimension (B->ncol)
The componentwise relative backward error of each solution
vector X(j) (i.e., the smallest relative change in
any element of A or B that makes X(j) an exact solution).
If options->IterRefine = NOREFINE, berr = 1.0.Glu (input/output) GlobalLU_t * If options->Fact == SamePattern_SameRowPerm, it is an input; The matrix A will be factorized assuming that a factorization of a matrix with the same sparsity pattern and similar numerical values was performed prior to this one. Therefore, this factorization will reuse both row and column scaling factors R and C, both row and column permutation vectors perm_r and perm_c, and the L & U data structures set up from the previous factorization. Otherwise, it is an output.
mem_usage (output) mem_usage_t*
Record the memory usage statistics, consisting of following fields:
- for_lu (float) The amount of space used in bytes for L data structures.
- total_needed (float) The amount of space needed in bytes to perform factorization.
- expansions (int) The number of memory expansions during the LU factorization.
stat (output) SuperLUStat_t*
Record the statistics on runtime and floating-point operation count.
See slu_util.h for the definition of 'SuperLUStat_t'.info (output) int*
= 0: successful exit
< 0: if info = -i, the i-th argument had an illegal value
> 0: if info = i, and i is
<= A->ncol: U(i,i) is exactly zero. The factorization has
been completed, but the factor U is exactly
singular, so the solution and error bounds
could not be computed.
= A->ncol+1: U is nonsingular, but RCOND is less than machine
precision, meaning that the matrix is singular to
working precision. Nevertheless, the solution and
error bounds are computed because there are a number
of situations where the computed solution can be more
accurate than the value of RCOND would suggest.
> A->ncol+1: number of bytes allocated when memory allocation
failure occurred, plus A->ncol.
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