/*! \file
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.
*/
/*! @file zgsisx.c
* \brief Computes an approximate solutions of linear equations A*X=B or A'*X=B
*
*
* -- SuperLU routine (version 4.2) --
* Lawrence Berkeley National Laboratory.
* November, 2010
* August, 2011
*
*/
#include "slu_zdefs.h"
/*! \brief
*
*
* Purpose
* =======
*
* ZGSISX computes an approximate solutions of linear equations
* A*X=B or A'*X=B, using the ILU factorization from zgsitrf().
* An estimation of the condition number is provided.
* The routine performs the following steps:
*
* 1. If A is stored column-wise (A->Stype = SLU_NC):
*
* 1.1. If options->Equil = YES or options->RowPerm = LargeDiag, 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 fills a small number on the diagonal entry, that is
* U(i,i) = ||A(:,i)||_oo * options->ILU_FillTol ** (1 - i / n),
* and info will be increased by 1. The factored form of A is used
* to estimate the condition number of the preconditioner. 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.
*
* 1.6. The system of equations is solved for X using the factored form
* of A.
*
* 1.7. options->IterRefine is not used
*
* 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.
*
* 1.9. options for ILU only
* 1) If options->RowPerm = LargeDiag, MC64 is used to scale and
* permute the matrix to an I-matrix, that is Pr*Dr*A*Dc has
* entries of modulus 1 on the diagonal and off-diagonal entries
* of modulus at most 1. If MC64 fails, dgsequ() is used to
* equilibrate the system.
* ( Default: LargeDiag )
* 2) options->ILU_DropTol = tau is the threshold for dropping.
* For L, it is used directly (for the whole row in a supernode);
* For U, ||A(:,i)||_oo * tau is used as the threshold
* for the i-th column.
* If a secondary dropping rule is required, tau will
* also be used to compute the second threshold.
* ( Default: 1e-4 )
* 3) options->ILU_FillFactor = gamma, used as the initial guess
* of memory growth.
* If a secondary dropping rule is required, it will also
* be used as an upper bound of the memory.
* ( Default: 10 )
* 4) options->ILU_DropRule specifies the dropping rule.
* Option Meaning
* ====== ===========
* DROP_BASIC: Basic dropping rule, supernodal based ILUTP(tau).
* DROP_PROWS: Supernodal based ILUTP(p,tau), p = gamma*nnz(A)/n.
* DROP_COLUMN: Variant of ILUTP(p,tau), for j-th column,
* p = gamma * nnz(A(:,j)).
* DROP_AREA: Variation of ILUTP, for j-th column, use
* nnz(F(:,1:j)) / nnz(A(:,1:j)) to control memory.
* DROP_DYNAMIC: Modify the threshold tau during factorizaion:
* If nnz(L(:,1:j)) / nnz(A(:,1:j)) > gamma
* tau_L(j) := MIN(tau_0, tau_L(j-1) * 2);
* Otherwise
* tau_L(j) := MAX(tau_0, tau_L(j-1) / 2);
* tau_U(j) uses the similar rule.
* NOTE: the thresholds used by L and U are separate.
* DROP_INTERP: Compute the second dropping threshold by
* interpolation instead of sorting (default).
* In this case, the actual fill ratio is not
* guaranteed smaller than gamma.
* DROP_PROWS, DROP_COLUMN and DROP_AREA are mutually exclusive.
* ( Default: DROP_BASIC | DROP_AREA )
* 5) options->ILU_Norm is the criterion of measuring the magnitude
* of a row in a supernode of L. ( Default is INF_NORM )
* options->ILU_Norm RowSize(x[1:n])
* ================= ===============
* ONE_NORM ||x||_1 / n
* TWO_NORM ||x||_2 / sqrt(n)
* INF_NORM max{|x[i]|}
* 6) options->ILU_MILU specifies the type of MILU's variation.
* = SILU: do not perform Modified ILU;
* = SMILU_1 (not recommended):
* U(i,i) := U(i,i) + sum(dropped entries);
* = SMILU_2:
* U(i,i) := U(i,i) + SGN(U(i,i)) * sum(dropped entries);
* = SMILU_3:
* U(i,i) := U(i,i) + SGN(U(i,i)) * sum(|dropped entries|);
* NOTE: Even SMILU_1 does not preserve the column sum because of
* late dropping.
* ( Default: SILU )
* 7) options->ILU_FillTol is used as the perturbation when
* encountering zero pivots. If some U(i,i) = 0, so that U is
* exactly singular, then
* U(i,i) := ||A(:,i)|| * options->ILU_FillTol ** (1 - i / n).
* ( Default: 1e-2 )
*
* 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 or options->RowPerm = LargeDiag, 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 fills a small number on the diagonal entry, that is
* U(i,i) = ||A(:,i)||_oo * options->ILU_FillTol ** (1 - i / n).
* And info will be increased by 1. The factored form of A is used
* to estimate the condition number of the preconditioner. 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.
*
* 2.6. The system of equations is solved for X using the factored form
* of transpose(A).
*
* 2.7. If options->IterRefine is not used.
*
* 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_Z, 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, or
* if options->RowPerm = NO.
*
* 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).
*
* If options->RowPerm = LargeDiag, MC64 is used to scale and permute
* the matrix to an I-matrix, that is A is modified as follows:
* P*Dr*A*Dc has entries of modulus 1 on the diagonal and
* off-diagonal entries of modulus at most 1. P is a permutation
* obtained from MC64.
* If MC64 fails, zgsequ() is used to equilibrate the system,
* and A is scaled as above, but no permutation is involved.
* On exit, A is restored to the orginal row numbering, so
* Dr*A*Dc is returned.
*
* 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 MC64 first then followed 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) double*, 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->Fact = FACTORED and equed = 'R' or 'B', each element
* of R must be positive.
*
* C (input/output) double*, 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_Z, 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_Z, 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_Z, 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_Z, 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) double*
* 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) double*
* 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.
*
* 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\U 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: number of zero pivots. They are replaced by small
* entries due to options->ILU_FillTol.
* = 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.
*
*/
void
zgsisx(superlu_options_t *options, SuperMatrix *A, int *perm_c, int *perm_r,
int *etree, char *equed, double *R, double *C,
SuperMatrix *L, SuperMatrix *U, void *work, int lwork,
SuperMatrix *B, SuperMatrix *X,
double *recip_pivot_growth, double *rcond,
GlobalLU_t *Glu, mem_usage_t *mem_usage, SuperLUStat_t *stat, int *info)
{
DNformat *Bstore, *Xstore;
doublecomplex *Bmat, *Xmat;
int ldb, ldx, nrhs, n;
SuperMatrix *AA;/* A in SLU_NC format used by the factorization routine.*/
SuperMatrix AC; /* Matrix postmultiplied by Pc */
int colequ, equil, nofact, notran, rowequ, permc_spec, mc64;
trans_t trant;
char norm[1];
int i, j, info1;
double amax, anorm, bignum, smlnum, colcnd, rowcnd, rcmax, rcmin;
int relax, panel_size;
double diag_pivot_thresh;
double t0; /* temporary time */
double *utime;
int *perm = NULL; /* permutation returned from MC64 */
/* External functions */
extern double zlangs(char *, SuperMatrix *);
Bstore = B->Store;
Xstore = X->Store;
Bmat = Bstore->nzval;
Xmat = Xstore->nzval;
ldb = Bstore->lda;
ldx = Xstore->lda;
nrhs = B->ncol;
n = B->nrow;
*info = 0;
nofact = (options->Fact != FACTORED);
equil = (options->Equil == YES);
notran = (options->Trans == NOTRANS);
mc64 = (options->RowPerm == LargeDiag);
if ( nofact ) {
*(unsigned char *)equed = 'N';
rowequ = FALSE;
colequ = FALSE;
} else {
rowequ = strncmp(equed, "R", 1)==0 || strncmp(equed, "B", 1)==0;
colequ = strncmp(equed, "C", 1)==0 || strncmp(equed, "B", 1)==0;
smlnum = dmach("Safe minimum"); /* lamch_("Safe minimum"); */
bignum = 1. / smlnum;
}
/* Test the input parameters */
if (options->Fact != DOFACT && options->Fact != SamePattern &&
options->Fact != SamePattern_SameRowPerm &&
options->Fact != FACTORED &&
options->Trans != NOTRANS && options->Trans != TRANS &&
options->Trans != CONJ &&
options->Equil != NO && options->Equil != YES)
*info = -1;
else if ( A->nrow != A->ncol || A->nrow < 0 ||
(A->Stype != SLU_NC && A->Stype != SLU_NR) ||
A->Dtype != SLU_Z || A->Mtype != SLU_GE )
*info = -2;
else if ( options->Fact == FACTORED &&
!(rowequ || colequ || strncmp(equed, "N", 1)==0) )
*info = -6;
else {
if (rowequ) {
rcmin = bignum;
rcmax = 0.;
for (j = 0; j < A->nrow; ++j) {
rcmin = SUPERLU_MIN(rcmin, R[j]);
rcmax = SUPERLU_MAX(rcmax, R[j]);
}
if (rcmin <= 0.) *info = -7;
else if ( A->nrow > 0)
rowcnd = SUPERLU_MAX(rcmin,smlnum) / SUPERLU_MIN(rcmax,bignum);
else rowcnd = 1.;
}
if (colequ && *info == 0) {
rcmin = bignum;
rcmax = 0.;
for (j = 0; j < A->nrow; ++j) {
rcmin = SUPERLU_MIN(rcmin, C[j]);
rcmax = SUPERLU_MAX(rcmax, C[j]);
}
if (rcmin <= 0.) *info = -8;
else if (A->nrow > 0)
colcnd = SUPERLU_MAX(rcmin,smlnum) / SUPERLU_MIN(rcmax,bignum);
else colcnd = 1.;
}
if (*info == 0) {
if ( lwork < -1 ) *info = -12;
else if ( B->ncol < 0 || Bstore->lda < SUPERLU_MAX(0, A->nrow) ||
B->Stype != SLU_DN || B->Dtype != SLU_Z ||
B->Mtype != SLU_GE )
*info = -13;
else if ( X->ncol < 0 || Xstore->lda < SUPERLU_MAX(0, A->nrow) ||
(B->ncol != 0 && B->ncol != X->ncol) ||
X->Stype != SLU_DN ||
X->Dtype != SLU_Z || X->Mtype != SLU_GE )
*info = -14;
}
}
if (*info != 0) {
i = -(*info);
input_error("zgsisx", &i);
return;
}
/* Initialization for factor parameters */
panel_size = sp_ienv(1);
relax = sp_ienv(2);
diag_pivot_thresh = options->DiagPivotThresh;
utime = stat->utime;
/* Convert A to SLU_NC format when necessary. */
if ( A->Stype == SLU_NR ) {
NRformat *Astore = A->Store;
AA = (SuperMatrix *) SUPERLU_MALLOC( sizeof(SuperMatrix) );
zCreate_CompCol_Matrix(AA, A->ncol, A->nrow, Astore->nnz,
Astore->nzval, Astore->colind, Astore->rowptr,
SLU_NC, A->Dtype, A->Mtype);
if ( notran ) { /* Reverse the transpose argument. */
trant = TRANS;
notran = 0;
} else {
trant = NOTRANS;
notran = 1;
}
} else { /* A->Stype == SLU_NC */
trant = options->Trans;
AA = A;
}
if ( nofact ) {
register int i, j;
NCformat *Astore = AA->Store;
int nnz = Astore->nnz;
int *colptr = Astore->colptr;
int *rowind = Astore->rowind;
doublecomplex *nzval = (doublecomplex *)Astore->nzval;
if ( mc64 ) {
t0 = SuperLU_timer_();
if ((perm = intMalloc(n)) == NULL)
ABORT("SUPERLU_MALLOC fails for perm[]");
info1 = zldperm(5, n, nnz, colptr, rowind, nzval, perm, R, C);
if (info1 != 0) { /* MC64 fails, call zgsequ() later */
mc64 = 0;
SUPERLU_FREE(perm);
perm = NULL;
} else {
if ( equil ) {
rowequ = colequ = 1;
for (i = 0; i < n; i++) {
R[i] = exp(R[i]);
C[i] = exp(C[i]);
}
/* scale the matrix */
for (j = 0; j < n; j++) {
for (i = colptr[j]; i < colptr[j + 1]; i++) {
zd_mult(&nzval[i], &nzval[i], R[rowind[i]] * C[j]);
}
}
*equed = 'B';
}
/* permute the matrix */
for (j = 0; j < n; j++) {
for (i = colptr[j]; i < colptr[j + 1]; i++) {
/*nzval[i] *= R[rowind[i]] * C[j];*/
rowind[i] = perm[rowind[i]];
}
}
}
utime[EQUIL] = SuperLU_timer_() - t0;
}
if ( !mc64 & equil ) { /* Only perform equilibration, no row perm */
t0 = SuperLU_timer_();
/* Compute row and column scalings to equilibrate the matrix A. */
zgsequ(AA, R, C, &rowcnd, &colcnd, &amax, &info1);
if ( info1 == 0 ) {
/* Equilibrate matrix A. */
zlaqgs(AA, R, C, rowcnd, colcnd, amax, equed);
rowequ = strncmp(equed, "R", 1)==0 || strncmp(equed, "B", 1)==0;
colequ = strncmp(equed, "C", 1)==0 || strncmp(equed, "B", 1)==0;
}
utime[EQUIL] = SuperLU_timer_() - t0;
}
}
if ( nofact ) {
t0 = SuperLU_timer_();
/*
* Gnet column permutation vector perm_c[], according to permc_spec:
* permc_spec = NATURAL: natural ordering
* permc_spec = MMD_AT_PLUS_A: minimum degree on structure of A'+A
* permc_spec = MMD_ATA: minimum degree on structure of A'*A
* permc_spec = COLAMD: approximate minimum degree column ordering
* permc_spec = MY_PERMC: the ordering already supplied in perm_c[]
*/
permc_spec = options->ColPerm;
if ( permc_spec != MY_PERMC && options->Fact == DOFACT )
get_perm_c(permc_spec, AA, perm_c);
utime[COLPERM] = SuperLU_timer_() - t0;
t0 = SuperLU_timer_();
sp_preorder(options, AA, perm_c, etree, &AC);
utime[ETREE] = SuperLU_timer_() - t0;
/* Compute the LU factorization of A*Pc. */
t0 = SuperLU_timer_();
zgsitrf(options, &AC, relax, panel_size, etree, work, lwork,
perm_c, perm_r, L, U, Glu, stat, info);
utime[FACT] = SuperLU_timer_() - t0;
if ( lwork == -1 ) {
mem_usage->total_needed = *info - A->ncol;
return;
}
if ( mc64 ) { /* Fold MC64's perm[] into perm_r[]. */
NCformat *Astore = AA->Store;
int nnz = Astore->nnz, *rowind = Astore->rowind;
int *perm_tmp, *iperm;
if ((perm_tmp = intMalloc(2*n)) == NULL)
ABORT("SUPERLU_MALLOC fails for perm_tmp[]");
iperm = perm_tmp + n;
for (i = 0; i < n; ++i) perm_tmp[i] = perm_r[perm[i]];
for (i = 0; i < n; ++i) {
perm_r[i] = perm_tmp[i];
iperm[perm[i]] = i;
}
/* Restore A's original row indices. */
for (i = 0; i < nnz; ++i) rowind[i] = iperm[rowind[i]];
SUPERLU_FREE(perm); /* MC64 permutation */
SUPERLU_FREE(perm_tmp);
}
}
if ( options->PivotGrowth ) {
if ( *info > 0 ) return;
/* Compute the reciprocal pivot growth factor *recip_pivot_growth. */
*recip_pivot_growth = zPivotGrowth(A->ncol, AA, perm_c, L, U);
}
if ( options->ConditionNumber ) {
/* Estimate the reciprocal of the condition number of A. */
t0 = SuperLU_timer_();
if ( notran ) {
*(unsigned char *)norm = '1';
} else {
*(unsigned char *)norm = 'I';
}
anorm = zlangs(norm, AA);
zgscon(norm, L, U, anorm, rcond, stat, &info1);
utime[RCOND] = SuperLU_timer_() - t0;
}
if ( nrhs > 0 ) { /* Solve the system */
doublecomplex *rhs_work;
/* Scale and permute the right-hand side if equilibration
and permutation from MC64 were performed. */
if ( notran ) {
if ( rowequ ) {
for (j = 0; j < nrhs; ++j)
for (i = 0; i < n; ++i)
zd_mult(&Bmat[i+j*ldb], &Bmat[i+j*ldb], R[i]);
}
} else if ( colequ ) {
for (j = 0; j < nrhs; ++j)
for (i = 0; i < n; ++i) {
zd_mult(&Bmat[i+j*ldb], &Bmat[i+j*ldb], C[i]);
}
}
/* Compute the solution matrix X. */
for (j = 0; j < nrhs; j++) /* Save a copy of the right hand sides */
for (i = 0; i < B->nrow; i++)
Xmat[i + j*ldx] = Bmat[i + j*ldb];
t0 = SuperLU_timer_();
zgstrs (trant, L, U, perm_c, perm_r, X, stat, &info1);
utime[SOLVE] = SuperLU_timer_() - t0;
/* Transform the solution matrix X to a solution of the original
system. */
if ( notran ) {
if ( colequ ) {
for (j = 0; j < nrhs; ++j)
for (i = 0; i < n; ++i) {
zd_mult(&Xmat[i+j*ldx], &Xmat[i+j*ldx], C[i]);
}
}
} else { /* transposed system */
if ( rowequ ) {
for (j = 0; j < nrhs; ++j)
for (i = 0; i < A->nrow; ++i) {
zd_mult(&Xmat[i+j*ldx], &Xmat[i+j*ldx], R[i]);
}
}
}
} /* end if nrhs > 0 */
if ( options->ConditionNumber ) {
/* The matrix is singular to working precision. */
/* if ( *rcond < dlamch_("E") && *info == 0) *info = A->ncol + 1; */
if ( *rcond < dmach("E") && *info == 0) *info = A->ncol + 1;
}
if ( nofact ) {
ilu_zQuerySpace(L, U, mem_usage);
Destroy_CompCol_Permuted(&AC);
}
if ( A->Stype == SLU_NR ) {
Destroy_SuperMatrix_Store(AA);
SUPERLU_FREE(AA);
}
}