/*! \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 zgssvx.c
 * \brief Solves the system of linear equations A*X=B or A'*X=B
 *
 * <pre>
 * -- SuperLU routine (version 7.0.0) --
 * Univ. of California Berkeley, Xerox Palo Alto Research Center,
 * and Lawrence Berkeley National Lab.
 * October 15, 2003
 * August 2024
 * </pre>
 */
#include "slu_zdefs.h"

/*! \brief
 *
 * <pre>
 * Purpose
 * =======
 *
 * ZGSSVX solves the system of linear equations A*X=B or A'*X=B, using
 * the LU factorization from zgstrf(). 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) 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->zFact = 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_t
 *         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.
 *
 * FERR    (output) double*, 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) double*, 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\\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: 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.
 * </pre>
 */

void zgssvx(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_t lwork, SuperMatrix* B, SuperMatrix* X, double* recip_pivot_growth, double* rcond, double* ferr, double* berr, GlobalLU_t* Glu, mem_usage_t* mem_usage, SuperLUStat_t* stat, int_t* info) {
    DNformat *Bstore, *Xstore;
    doublecomplex *Bmat, *Xmat;
    int ldb, ldx, nrhs;
    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;
    trans_t trant;
    char norm[1];
    int i, j, info1;
    double amax, anorm, bignum, smlnum, colcnd, rowcnd, rcmax, rcmin;
    int relax, panel_size;
    double t0; /* temporary time */
    double* utime;

    /* 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;

    *info = 0;
    nofact = (options->Fact != FACTORED);
    equil = (options->Equil == YES);
    notran = (options->Trans == NOTRANS);
    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;
    }

#if 0
printf("dgssvx: Fact=%4d, Trans=%4d, equed=%c\n",
       options->Fact, options->Trans, *equed);
#endif

    /* 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) *info = -13;
            else if(B->ncol > 0) { /* no checking if B->ncol=0 */
                if(Bstore->lda < SUPERLU_MAX(0, A->nrow) ||
                   B->Stype != SLU_DN || B->Dtype != SLU_Z ||
                   B->Mtype != SLU_GE)
                    *info = -13;
            }
            if(X->ncol < 0) *info = -14;
            else if(X->ncol > 0) { /* no checking if X->ncol=0 */
                if(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("zgssvx", &i);
        return;
    }

    /* Initialization for factor parameters */
    panel_size = sp_ienv(1);
    relax = sp_ienv(2);

    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 && equil) {
        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;

        /*	printf("Factor PA = LU ... relax %d\tw %d\tmaxsuper %d\trowblk %d\n",
                   relax, panel_size, sp_ienv(3), sp_ienv(4));
            fflush(stdout); */

        /* Compute the LU factorization of A*Pc. */
        t0 = SuperLU_timer_();
        zgstrf(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(*info > 0) {
        if(*info <= A->ncol) { /* singular */
            /* Compute the reciprocal pivot growth factor of the leading
               rank-deficient (*info) columns of A. */
            *recip_pivot_growth = zPivotGrowth(*info, AA, perm_c, L, U);
        }
        return;
    }

    /* *info == 0 at this point. */

    if(options->PivotGrowth) {
        /* 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) {
        /* Scale the right hand side if equilibration was performed. */
        if(notran) {
            if(rowequ) {
                for(j = 0; j < nrhs; ++j)
                    for(i = 0; i < A->nrow; ++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 < A->nrow; ++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;

        /* Use iterative refinement to improve the computed solution and compute
           error bounds and backward error estimates for it. */
        t0 = SuperLU_timer_();
        if(options->IterRefine != NOREFINE) {
            zgsrfs(trant, AA, L, U, perm_c, perm_r, equed, R, C, B, X, ferr, berr, stat, &info1);
        }
        else {
            for(j = 0; j < nrhs; ++j) ferr[j] = berr[j] = 1.0;
        }
        utime[REFINE] = 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 < A->nrow; ++i)
                        zd_mult(&Xmat[i + j * ldx], &Xmat[i + j * ldx], C[i]);
            }
        }
        else 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) {
        /* Set INFO = A->ncol+1 if the matrix is singular to working precision. */
        /*if ( *rcond < dlamch_("E") ) *info = A->ncol + 1;*/
        if(*rcond < dmach("E")) *info = A->ncol + 1;
    }

    if(nofact) {
        zQuerySpace(L, U, mem_usage);
        Destroy_CompCol_Permuted(&AC);
    }
    if(A->Stype == SLU_NR) {
        Destroy_SuperMatrix_Store(AA);
        SUPERLU_FREE(AA);
    }
}
