Date: 2026-07-30T21:14:13+00:00

Actual source code: aijfact.c

      1: #include <../src/mat/impls/aij/seq/aij.h>
      2: #include <../src/mat/impls/sbaij/seq/sbaij.h>
      3: #include <petscbt.h>
      4: #include <../src/mat/utils/freespace.h>

      6: static PetscErrorCode MatFactorGetSolverType_petsc(Mat A, MatSolverType *type)
      7: {
      8:   PetscFunctionBegin;
      9:   *type = MATSOLVERPETSC;
     10:   PetscFunctionReturn(PETSC_SUCCESS);
     11: }

     13: PETSC_INTERN PetscErrorCode MatGetFactor_seqaij_petsc(Mat A, MatFactorType ftype, Mat *B)
     14: {
     15:   PetscInt n = A->rmap->n;

     17:   PetscFunctionBegin;
     18:   if (PetscDefined(USE_COMPLEX) && (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) && A->hermitian == PETSC_BOOL3_TRUE && A->symmetric != PETSC_BOOL3_TRUE) {
     19:     PetscCall(PetscInfo(A, "Hermitian MAT_FACTOR_CHOLESKY or MAT_FACTOR_ICC are not supported. Use MAT_FACTOR_LU instead.\n"));
     20:     *B = NULL;
     21:     PetscFunctionReturn(PETSC_SUCCESS);
     22:   }

     24:   PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
     25:   PetscCall(MatSetSizes(*B, n, n, n, n));
     26:   if (ftype == MAT_FACTOR_LU || ftype == MAT_FACTOR_ILU || ftype == MAT_FACTOR_ILUDT) {
     27:     PetscCall(MatSetType(*B, MATSEQAIJ));

     29:     (*B)->ops->ilufactorsymbolic = MatILUFactorSymbolic_SeqAIJ;
     30:     (*B)->ops->lufactorsymbolic  = MatLUFactorSymbolic_SeqAIJ;

     32:     PetscCall(MatSetBlockSizesFromMats(*B, A, A));
     33:     PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_LU]));
     34:     PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILU]));
     35:     PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ILUDT]));
     36:   } else if (ftype == MAT_FACTOR_CHOLESKY || ftype == MAT_FACTOR_ICC) {
     37:     PetscCall(MatSetType(*B, MATSEQSBAIJ));
     38:     PetscCall(MatSeqSBAIJSetPreallocation(*B, 1, MAT_SKIP_ALLOCATION, NULL));

     40:     (*B)->ops->iccfactorsymbolic      = MatICCFactorSymbolic_SeqAIJ;
     41:     (*B)->ops->choleskyfactorsymbolic = MatCholeskyFactorSymbolic_SeqAIJ;
     42:     PetscCall(PetscStrallocpy(MATORDERINGND, (char **)&(*B)->preferredordering[MAT_FACTOR_CHOLESKY]));
     43:     PetscCall(PetscStrallocpy(MATORDERINGNATURAL, (char **)&(*B)->preferredordering[MAT_FACTOR_ICC]));
     44:   } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Factor type not supported");
     45:   (*B)->factortype = ftype;

     47:   PetscCall(PetscFree((*B)->solvertype));
     48:   PetscCall(PetscStrallocpy(MATSOLVERPETSC, &(*B)->solvertype));
     49:   (*B)->canuseordering = PETSC_TRUE;
     50:   PetscCall(PetscObjectComposeFunction((PetscObject)*B, "MatFactorGetSolverType_C", MatFactorGetSolverType_petsc));
     51:   PetscFunctionReturn(PETSC_SUCCESS);
     52: }

     54: PetscErrorCode MatLUFactorSymbolic_SeqAIJ(Mat B, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
     55: {
     56:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *b;
     57:   IS                 isicol;
     58:   const PetscInt    *r, *ic, *ai = a->i, *aj = a->j, *ajtmp;
     59:   PetscInt           i, n = A->rmap->n;
     60:   PetscInt          *bi, *bj;
     61:   PetscInt          *bdiag, row, nnz, nzi, reallocs = 0, nzbd, *im;
     62:   PetscReal          f;
     63:   PetscInt           nlnk, *lnk, k, **bi_ptr;
     64:   PetscFreeSpaceList free_space = NULL, current_space = NULL;
     65:   PetscBT            lnkbt;
     66:   PetscBool          diagDense;

     68:   PetscFunctionBegin;
     69:   PetscCheck(A->rmap->N == A->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "matrix must be square");
     70:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, NULL, &diagDense));
     71:   PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entries");

     73:   PetscCall(ISInvertPermutation(iscol, PETSC_DECIDE, &isicol));
     74:   PetscCall(ISGetIndices(isrow, &r));
     75:   PetscCall(ISGetIndices(isicol, &ic));

     77:   /* get new row and diagonal pointers, must be allocated separately because they will be given to the Mat_SeqAIJ and freed separately */
     78:   PetscCall(PetscShmgetAllocateArray(n + 1, sizeof(PetscInt), (void **)&bi));
     79:   PetscCall(PetscMalloc1(n + 1, &bdiag));
     80:   bi[0] = bdiag[0] = 0;

     82:   /* linked list for storing column indices of the active row */
     83:   nlnk = n + 1;
     84:   PetscCall(PetscLLCreate(n, n, nlnk, lnk, lnkbt));

     86:   PetscCall(PetscMalloc2(n + 1, &bi_ptr, n + 1, &im));

     88:   /* initial FreeSpace size is f*(ai[n]+1) */
     89:   f = info->fill;
     90:   if (n == 1) f = 1; /* prevent failure in corner case of 1x1 matrix with fill < 0.5 */
     91:   PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(f, ai[n] + 1), &free_space));
     92:   current_space = free_space;

     94:   for (i = 0; i < n; i++) {
     95:     /* copy previous fill into linked list */
     96:     nzi   = 0;
     97:     nnz   = ai[r[i] + 1] - ai[r[i]];
     98:     ajtmp = aj + ai[r[i]];
     99:     PetscCall(PetscLLAddPerm(nnz, ajtmp, ic, n, &nlnk, lnk, lnkbt));
    100:     nzi += nlnk;

    102:     /* add pivot rows into linked list */
    103:     row = lnk[n];
    104:     while (row < i) {
    105:       nzbd  = bdiag[row] + 1;     /* num of entries in the row with column index <= row */
    106:       ajtmp = bi_ptr[row] + nzbd; /* points to the entry next to the diagonal */
    107:       PetscCall(PetscLLAddSortedLU(ajtmp, row, &nlnk, lnk, lnkbt, i, nzbd, im));
    108:       nzi += nlnk;
    109:       row = lnk[row];
    110:     }
    111:     bi[i + 1] = bi[i] + nzi;
    112:     im[i]     = nzi;

    114:     /* mark bdiag */
    115:     nzbd = 0;
    116:     nnz  = nzi;
    117:     k    = lnk[n];
    118:     while (nnz-- && k < i) {
    119:       nzbd++;
    120:       k = lnk[k];
    121:     }
    122:     bdiag[i] = nzbd; /* note: bdiag[i] = nnzL as input for PetscFreeSpaceContiguous_LU() */

    124:     /* if free space is not available, make more free space */
    125:     if (current_space->local_remaining < nzi) {
    126:       /* estimated additional space needed */
    127:       nnz = PetscIntMultTruncate(2, PetscIntMultTruncate(n - 1, nzi));
    128:       PetscCall(PetscFreeSpaceGet(nnz, &current_space));
    129:       reallocs++;
    130:     }

    132:     /* copy data into free space, then initialize lnk */
    133:     PetscCall(PetscLLClean(n, n, nzi, lnk, current_space->array, lnkbt));

    135:     bi_ptr[i] = current_space->array;
    136:     current_space->array += nzi;
    137:     current_space->local_used += nzi;
    138:     current_space->local_remaining -= nzi;
    139:   }

    141:   PetscCall(ISRestoreIndices(isrow, &r));
    142:   PetscCall(ISRestoreIndices(isicol, &ic));

    144:   /*   copy free_space into bj and free free_space; set bi, bj, bdiag in new datastructure; */
    145:   PetscCall(PetscShmgetAllocateArray(bi[n], sizeof(PetscInt), (void **)&bj));
    146:   PetscCall(PetscFreeSpaceContiguous_LU(&free_space, bj, n, bi, bdiag));
    147:   PetscCall(PetscLLDestroy(lnk, lnkbt));
    148:   PetscCall(PetscFree2(bi_ptr, im));

    150:   /* put together the new matrix */
    151:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(B, MAT_SKIP_ALLOCATION, NULL));
    152:   b          = (Mat_SeqAIJ *)B->data;
    153:   b->free_ij = PETSC_TRUE;
    154:   PetscCall(PetscShmgetAllocateArray(bdiag[0] + 1, sizeof(PetscScalar), (void **)&b->a));
    155:   b->free_a = PETSC_TRUE;
    156:   b->j      = bj;
    157:   b->i      = bi;
    158:   b->diag   = bdiag;
    159:   b->ilen   = NULL;
    160:   b->imax   = NULL;
    161:   b->row    = isrow;
    162:   b->col    = iscol;
    163:   PetscCall(PetscObjectReference((PetscObject)isrow));
    164:   PetscCall(PetscObjectReference((PetscObject)iscol));
    165:   b->icol = isicol;
    166:   PetscCall(PetscMalloc1(n, &b->solve_work));

    168:   /* In b structure:  Free imax, ilen, old a, old j.  Allocate solve_work, new a, new j */
    169:   b->maxnz = b->nz = bdiag[0] + 1;

    171:   B->factortype            = MAT_FACTOR_LU;
    172:   B->info.factor_mallocs   = reallocs;
    173:   B->info.fill_ratio_given = f;

    175:   if (ai[n]) {
    176:     B->info.fill_ratio_needed = ((PetscReal)(bdiag[0] + 1)) / ((PetscReal)ai[n]);
    177:   } else {
    178:     B->info.fill_ratio_needed = 0.0;
    179:   }
    180: #if defined(PETSC_USE_INFO)
    181:   if (ai[n] != 0) {
    182:     PetscReal af = B->info.fill_ratio_needed;
    183:     PetscCall(PetscInfo(A, "Reallocs %" PetscInt_FMT " Fill ratio:given %g needed %g\n", reallocs, (double)f, (double)af));
    184:     PetscCall(PetscInfo(A, "Run with -pc_factor_fill %g or use \n", (double)af));
    185:     PetscCall(PetscInfo(A, "PCFactorSetFill(pc,%g);\n", (double)af));
    186:     PetscCall(PetscInfo(A, "for best performance.\n"));
    187:   } else {
    188:     PetscCall(PetscInfo(A, "Empty matrix\n"));
    189:   }
    190: #endif
    191:   B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJ;
    192:   if (a->inode.size_csr) B->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJ_Inode;
    193:   PetscCall(MatSeqAIJCheckInode_FactorLU(B));
    194:   PetscFunctionReturn(PETSC_SUCCESS);
    195: }

    197: /*
    198:     Trouble in factorization, should we dump the original matrix?
    199: */
    200: PetscErrorCode MatFactorDumpMatrix(Mat A)
    201: {
    202:   PetscBool flg = PETSC_FALSE;

    204:   PetscFunctionBegin;
    205:   PetscCall(PetscOptionsGetBool(((PetscObject)A)->options, NULL, "-mat_factor_dump_on_error", &flg, NULL));
    206:   if (flg) {
    207:     PetscViewer viewer;
    208:     char        filename[PETSC_MAX_PATH_LEN];

    210:     PetscCall(PetscSNPrintf(filename, PETSC_MAX_PATH_LEN, "matrix_factor_error.%d", PetscGlobalRank));
    211:     PetscCall(PetscViewerBinaryOpen(PetscObjectComm((PetscObject)A), filename, FILE_MODE_WRITE, &viewer));
    212:     PetscCall(MatView(A, viewer));
    213:     PetscCall(PetscViewerDestroy(&viewer));
    214:   }
    215:   PetscFunctionReturn(PETSC_SUCCESS);
    216: }

    218: PetscErrorCode MatLUFactorNumeric_SeqAIJ(Mat B, Mat A, const MatFactorInfo *info)
    219: {
    220:   Mat              C = B;
    221:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)C->data;
    222:   IS               isrow = b->row, isicol = b->icol;
    223:   const PetscInt  *r, *ic, *ics;
    224:   const PetscInt   n = A->rmap->n, *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j, *bdiag = b->diag;
    225:   PetscInt         i, j, k, nz, nzL, row, *pj;
    226:   const PetscInt  *ajtmp, *bjtmp;
    227:   MatScalar       *rtmp, *pc, multiplier, *pv;
    228:   const MatScalar *aa, *v;
    229:   MatScalar       *ba;
    230:   PetscBool        row_identity, col_identity;
    231:   FactorShiftCtx   sctx;
    232:   const PetscInt  *ddiag;
    233:   PetscReal        rs;
    234:   MatScalar        d;

    236:   PetscFunctionBegin;
    237:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    238:   PetscCall(MatSeqAIJGetArrayWrite(B, &ba));
    239:   /* MatPivotSetUp(): initialize shift context sctx */
    240:   PetscCall(PetscMemzero(&sctx, sizeof(FactorShiftCtx)));

    242:   if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    243:     PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &ddiag, NULL));
    244:     sctx.shift_top = info->zeropivot;
    245:     for (i = 0; i < n; i++) {
    246:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    247:       d  = aa[ddiag[i]];
    248:       rs = -PetscAbsScalar(d) - PetscRealPart(d);
    249:       v  = aa + ai[i];
    250:       nz = ai[i + 1] - ai[i];
    251:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(v[j]);
    252:       if (rs > sctx.shift_top) sctx.shift_top = rs;
    253:     }
    254:     sctx.shift_top *= 1.1;
    255:     sctx.nshift_max = 5;
    256:     sctx.shift_lo   = 0.;
    257:     sctx.shift_hi   = 1.;
    258:   }

    260:   PetscCall(ISGetIndices(isrow, &r));
    261:   PetscCall(ISGetIndices(isicol, &ic));
    262:   PetscCall(PetscMalloc1(n + 1, &rtmp));
    263:   ics = ic;

    265:   do {
    266:     sctx.newshift = PETSC_FALSE;
    267:     for (i = 0; i < n; i++) {
    268:       /* zero rtmp */
    269:       /* L part */
    270:       nz    = bi[i + 1] - bi[i];
    271:       bjtmp = bj + bi[i];
    272:       for (j = 0; j < nz; j++) rtmp[bjtmp[j]] = 0.0;

    274:       /* U part */
    275:       nz    = bdiag[i] - bdiag[i + 1];
    276:       bjtmp = bj + bdiag[i + 1] + 1;
    277:       for (j = 0; j < nz; j++) rtmp[bjtmp[j]] = 0.0;

    279:       /* load in initial (unfactored row) */
    280:       nz    = ai[r[i] + 1] - ai[r[i]];
    281:       ajtmp = aj + ai[r[i]];
    282:       v     = aa + ai[r[i]];
    283:       for (j = 0; j < nz; j++) rtmp[ics[ajtmp[j]]] = v[j];
    284:       /* ZeropivotApply() */
    285:       rtmp[i] += sctx.shift_amount; /* shift the diagonal of the matrix */

    287:       /* elimination */
    288:       bjtmp = bj + bi[i];
    289:       row   = *bjtmp++;
    290:       nzL   = bi[i + 1] - bi[i];
    291:       for (k = 0; k < nzL; k++) {
    292:         pc = rtmp + row;
    293:         if (*pc != 0.0) {
    294:           pv         = ba + bdiag[row];
    295:           multiplier = *pc * (*pv);
    296:           *pc        = multiplier;

    298:           pj = b->j + bdiag[row + 1] + 1; /* beginning of U(row,:) */
    299:           pv = ba + bdiag[row + 1] + 1;
    300:           nz = bdiag[row] - bdiag[row + 1] - 1; /* num of entries in U(row,:) excluding diag */

    302:           for (j = 0; j < nz; j++) rtmp[pj[j]] -= multiplier * pv[j];
    303:           PetscCall(PetscLogFlops(1 + 2.0 * nz));
    304:         }
    305:         row = *bjtmp++;
    306:       }

    308:       /* finished row so stick it into b->a */
    309:       rs = 0.0;
    310:       /* L part */
    311:       pv = ba + bi[i];
    312:       pj = b->j + bi[i];
    313:       nz = bi[i + 1] - bi[i];
    314:       for (j = 0; j < nz; j++) {
    315:         pv[j] = rtmp[pj[j]];
    316:         rs += PetscAbsScalar(pv[j]);
    317:       }

    319:       /* U part */
    320:       pv = ba + bdiag[i + 1] + 1;
    321:       pj = b->j + bdiag[i + 1] + 1;
    322:       nz = bdiag[i] - bdiag[i + 1] - 1;
    323:       for (j = 0; j < nz; j++) {
    324:         pv[j] = rtmp[pj[j]];
    325:         rs += PetscAbsScalar(pv[j]);
    326:       }

    328:       sctx.rs = rs;
    329:       sctx.pv = rtmp[i];
    330:       PetscCall(MatPivotCheck(B, A, info, &sctx, i));
    331:       if (sctx.newshift) break; /* break for-loop */
    332:       rtmp[i] = sctx.pv;        /* sctx.pv might be updated in the case of MAT_SHIFT_INBLOCKS */

    334:       /* Mark diagonal and invert diagonal for simpler triangular solves */
    335:       pv  = ba + bdiag[i];
    336:       *pv = 1.0 / rtmp[i];

    338:     } /* endof for (i=0; i<n; i++) { */

    340:     /* MatPivotRefine() */
    341:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE && !sctx.newshift && sctx.shift_fraction > 0 && sctx.nshift < sctx.nshift_max) {
    342:       /*
    343:        * if no shift in this attempt & shifting & started shifting & can refine,
    344:        * then try lower shift
    345:        */
    346:       sctx.shift_hi       = sctx.shift_fraction;
    347:       sctx.shift_fraction = (sctx.shift_hi + sctx.shift_lo) / 2.;
    348:       sctx.shift_amount   = sctx.shift_fraction * sctx.shift_top;
    349:       sctx.newshift       = PETSC_TRUE;
    350:       sctx.nshift++;
    351:     }
    352:   } while (sctx.newshift);

    354:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    355:   PetscCall(MatSeqAIJRestoreArrayWrite(B, &ba));

    357:   PetscCall(PetscFree(rtmp));
    358:   PetscCall(ISRestoreIndices(isicol, &ic));
    359:   PetscCall(ISRestoreIndices(isrow, &r));

    361:   PetscCall(ISIdentity(isrow, &row_identity));
    362:   PetscCall(ISIdentity(isicol, &col_identity));
    363:   if (b->inode.size_csr) {
    364:     C->ops->solve = MatSolve_SeqAIJ_Inode;
    365:   } else if (row_identity && col_identity) {
    366:     C->ops->solve = MatSolve_SeqAIJ_NaturalOrdering;
    367:   } else {
    368:     C->ops->solve = MatSolve_SeqAIJ;
    369:   }
    370:   C->ops->solveadd          = MatSolveAdd_SeqAIJ;
    371:   C->ops->solvetranspose    = MatSolveTranspose_SeqAIJ;
    372:   C->ops->solvetransposeadd = MatSolveTransposeAdd_SeqAIJ;
    373:   C->ops->matsolve          = MatMatSolve_SeqAIJ;
    374:   C->ops->matsolvetranspose = MatMatSolveTranspose_SeqAIJ;
    375:   C->assembled              = PETSC_TRUE;
    376:   C->preallocated           = PETSC_TRUE;

    378:   PetscCall(PetscLogFlops(C->cmap->n));

    380:   /* MatShiftView(A,info,&sctx) */
    381:   if (sctx.nshift) {
    382:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    383:       PetscCall(PetscInfo(A, "number of shift_pd tries %" PetscInt_FMT ", shift_amount %g, diagonal shifted up by %e fraction top_value %e\n", sctx.nshift, (double)sctx.shift_amount, (double)sctx.shift_fraction, (double)sctx.shift_top));
    384:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    385:       PetscCall(PetscInfo(A, "number of shift_nz tries %" PetscInt_FMT ", shift_amount %g\n", sctx.nshift, (double)sctx.shift_amount));
    386:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_INBLOCKS) {
    387:       PetscCall(PetscInfo(A, "number of shift_inblocks applied %" PetscInt_FMT ", each shift_amount %g\n", sctx.nshift, (double)info->shiftamount));
    388:     }
    389:   }
    390:   PetscFunctionReturn(PETSC_SUCCESS);
    391: }

    393: static PetscErrorCode MatMatSolve_SeqAIJ_inplace(Mat, Mat, Mat);
    394: static PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering_inplace(Mat, Vec, Vec);
    395: static PetscErrorCode MatSolveAdd_SeqAIJ_inplace(Mat, Vec, Vec, Vec);

    397: PetscErrorCode MatLUFactorNumeric_SeqAIJ_inplace(Mat B, Mat A, const MatFactorInfo *info)
    398: {
    399:   Mat              C = B;
    400:   Mat_SeqAIJ      *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)C->data;
    401:   IS               isrow = b->row, isicol = b->icol;
    402:   const PetscInt  *r, *ic, *ics;
    403:   PetscInt         nz, row, i, j, n = A->rmap->n, diag;
    404:   const PetscInt  *ai = a->i, *aj = a->j, *bi = b->i, *bj = b->j;
    405:   const PetscInt  *ajtmp, *bjtmp, *ddiag, *pj;
    406:   MatScalar       *pv, *rtmp, *pc, multiplier, d;
    407:   const MatScalar *v, *aa;
    408:   MatScalar       *ba;
    409:   PetscReal        rs = 0.0;
    410:   FactorShiftCtx   sctx;
    411:   PetscBool        row_identity, col_identity;

    413:   PetscFunctionBegin;
    414:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &ddiag, NULL));

    416:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    417:   PetscCall(MatSeqAIJGetArrayWrite(B, &ba));
    418:   /* MatPivotSetUp(): initialize shift context sctx */
    419:   PetscCall(PetscMemzero(&sctx, sizeof(FactorShiftCtx)));

    421:   if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    422:     PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &ddiag, NULL));
    423:     sctx.shift_top = info->zeropivot;
    424:     for (i = 0; i < n; i++) {
    425:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    426:       d  = aa[ddiag[i]];
    427:       rs = -PetscAbsScalar(d) - PetscRealPart(d);
    428:       v  = aa + ai[i];
    429:       nz = ai[i + 1] - ai[i];
    430:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(v[j]);
    431:       if (rs > sctx.shift_top) sctx.shift_top = rs;
    432:     }
    433:     sctx.shift_top *= 1.1;
    434:     sctx.nshift_max = 5;
    435:     sctx.shift_lo   = 0.;
    436:     sctx.shift_hi   = 1.;
    437:   }

    439:   PetscCall(ISGetIndices(isrow, &r));
    440:   PetscCall(ISGetIndices(isicol, &ic));
    441:   PetscCall(PetscMalloc1(n + 1, &rtmp));
    442:   ics = ic;

    444:   do {
    445:     sctx.newshift = PETSC_FALSE;
    446:     for (i = 0; i < n; i++) {
    447:       nz    = bi[i + 1] - bi[i];
    448:       bjtmp = bj + bi[i];
    449:       for (j = 0; j < nz; j++) rtmp[bjtmp[j]] = 0.0;

    451:       /* load in initial (unfactored row) */
    452:       nz    = ai[r[i] + 1] - ai[r[i]];
    453:       ajtmp = aj + ai[r[i]];
    454:       v     = aa + ai[r[i]];
    455:       for (j = 0; j < nz; j++) rtmp[ics[ajtmp[j]]] = v[j];
    456:       rtmp[ics[r[i]]] += sctx.shift_amount; /* shift the diagonal of the matrix */

    458:       row = *bjtmp++;
    459:       while (row < i) {
    460:         pc = rtmp + row;
    461:         if (*pc != 0.0) {
    462:           pv         = ba + ddiag[row];
    463:           pj         = b->j + ddiag[row] + 1;
    464:           multiplier = *pc / *pv++;
    465:           *pc        = multiplier;
    466:           nz         = bi[row + 1] - ddiag[row] - 1;
    467:           for (j = 0; j < nz; j++) rtmp[pj[j]] -= multiplier * pv[j];
    468:           PetscCall(PetscLogFlops(1 + 2.0 * nz));
    469:         }
    470:         row = *bjtmp++;
    471:       }
    472:       /* finished row so stick it into b->a */
    473:       pv   = ba + bi[i];
    474:       pj   = b->j + bi[i];
    475:       nz   = bi[i + 1] - bi[i];
    476:       diag = ddiag[i] - bi[i];
    477:       rs   = 0.0;
    478:       for (j = 0; j < nz; j++) {
    479:         pv[j] = rtmp[pj[j]];
    480:         rs += PetscAbsScalar(pv[j]);
    481:       }
    482:       rs -= PetscAbsScalar(pv[diag]);

    484:       sctx.rs = rs;
    485:       sctx.pv = pv[diag];
    486:       PetscCall(MatPivotCheck(B, A, info, &sctx, i));
    487:       if (sctx.newshift) break;
    488:       pv[diag] = sctx.pv;
    489:     }

    491:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE && !sctx.newshift && sctx.shift_fraction > 0 && sctx.nshift < sctx.nshift_max) {
    492:       /*
    493:        * if no shift in this attempt & shifting & started shifting & can refine,
    494:        * then try lower shift
    495:        */
    496:       sctx.shift_hi       = sctx.shift_fraction;
    497:       sctx.shift_fraction = (sctx.shift_hi + sctx.shift_lo) / 2.;
    498:       sctx.shift_amount   = sctx.shift_fraction * sctx.shift_top;
    499:       sctx.newshift       = PETSC_TRUE;
    500:       sctx.nshift++;
    501:     }
    502:   } while (sctx.newshift);

    504:   /* invert diagonal entries for simpler triangular solves */
    505:   for (i = 0; i < n; i++) ba[ddiag[i]] = 1.0 / ba[ddiag[i]];
    506:   PetscCall(PetscFree(rtmp));
    507:   PetscCall(ISRestoreIndices(isicol, &ic));
    508:   PetscCall(ISRestoreIndices(isrow, &r));
    509:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    510:   PetscCall(MatSeqAIJRestoreArrayWrite(B, &ba));

    512:   PetscCall(ISIdentity(isrow, &row_identity));
    513:   PetscCall(ISIdentity(isicol, &col_identity));
    514:   if (row_identity && col_identity) {
    515:     C->ops->solve = MatSolve_SeqAIJ_NaturalOrdering_inplace;
    516:   } else {
    517:     C->ops->solve = MatSolve_SeqAIJ_inplace;
    518:   }
    519:   C->ops->solveadd          = MatSolveAdd_SeqAIJ_inplace;
    520:   C->ops->solvetranspose    = MatSolveTranspose_SeqAIJ_inplace;
    521:   C->ops->solvetransposeadd = MatSolveTransposeAdd_SeqAIJ_inplace;
    522:   C->ops->matsolve          = MatMatSolve_SeqAIJ_inplace;
    523:   C->ops->matsolvetranspose = NULL;

    525:   C->assembled    = PETSC_TRUE;
    526:   C->preallocated = PETSC_TRUE;

    528:   PetscCall(PetscLogFlops(C->cmap->n));
    529:   if (sctx.nshift) {
    530:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    531:       PetscCall(PetscInfo(A, "number of shift_pd tries %" PetscInt_FMT ", shift_amount %g, diagonal shifted up by %e fraction top_value %e\n", sctx.nshift, (double)sctx.shift_amount, (double)sctx.shift_fraction, (double)sctx.shift_top));
    532:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    533:       PetscCall(PetscInfo(A, "number of shift_nz tries %" PetscInt_FMT ", shift_amount %g\n", sctx.nshift, (double)sctx.shift_amount));
    534:     }
    535:   }
    536:   C->ops->solve          = MatSolve_SeqAIJ_inplace;
    537:   C->ops->solvetranspose = MatSolveTranspose_SeqAIJ_inplace;

    539:   PetscCall(MatSeqAIJCheckInode(C));
    540:   PetscFunctionReturn(PETSC_SUCCESS);
    541: }

    543: static PetscErrorCode MatSolve_SeqAIJ_InplaceWithPerm(Mat, Vec, Vec);

    545: /*
    546:    This routine implements inplace ILU(0) with row or/and column permutations.
    547:    Input:
    548:      A - original matrix
    549:    Output;
    550:      A - a->i (rowptr) is same as original rowptr, but factored i-the row is stored in rowperm[i]
    551:          a->j (col index) is permuted by the inverse of colperm, then sorted
    552:          a->a reordered accordingly with a->j
    553:          a->diag (ptr to diagonal elements) is updated.
    554: */
    555: PetscErrorCode MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(Mat B, Mat A, const MatFactorInfo *info)
    556: {
    557:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ *)A->data;
    558:   IS              isrow = a->row, isicol = a->icol;
    559:   const PetscInt *r, *ic, *ics;
    560:   PetscInt        i, j, n = A->rmap->n, *ai = a->i, *aj = a->j;
    561:   PetscInt       *ajtmp, nz, row;
    562:   PetscInt        nbdiag, *pj;
    563:   PetscScalar    *rtmp, *pc, multiplier, d;
    564:   MatScalar      *pv, *v;
    565:   PetscReal       rs;
    566:   FactorShiftCtx  sctx;
    567:   MatScalar      *aa, *vtmp;
    568:   PetscInt       *diag;

    570:   PetscFunctionBegin;
    571:   PetscCheck(A == B, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "input and output matrix must have same address");

    573:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, (const PetscInt **)&diag, NULL));
    574:   PetscCall(MatSeqAIJGetArray(A, &aa));
    575:   /* MatPivotSetUp(): initialize shift context sctx */
    576:   PetscCall(PetscMemzero(&sctx, sizeof(FactorShiftCtx)));

    578:   if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    579:     const PetscInt *ddiag;

    581:     PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &ddiag, NULL));
    582:     sctx.shift_top = info->zeropivot;
    583:     for (i = 0; i < n; i++) {
    584:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    585:       d    = aa[ddiag[i]];
    586:       rs   = -PetscAbsScalar(d) - PetscRealPart(d);
    587:       vtmp = aa + ai[i];
    588:       nz   = ai[i + 1] - ai[i];
    589:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(vtmp[j]);
    590:       if (rs > sctx.shift_top) sctx.shift_top = rs;
    591:     }
    592:     sctx.shift_top *= 1.1;
    593:     sctx.nshift_max = 5;
    594:     sctx.shift_lo   = 0.;
    595:     sctx.shift_hi   = 1.;
    596:   }

    598:   PetscCall(ISGetIndices(isrow, &r));
    599:   PetscCall(ISGetIndices(isicol, &ic));
    600:   PetscCall(PetscMalloc1(n + 1, &rtmp));
    601:   PetscCall(PetscArrayzero(rtmp, n + 1));
    602:   ics = ic;

    604: #if defined(MV)
    605:   sctx.shift_top      = 0.;
    606:   sctx.nshift_max     = 0;
    607:   sctx.shift_lo       = 0.;
    608:   sctx.shift_hi       = 0.;
    609:   sctx.shift_fraction = 0.;

    611:   if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    612:     sctx.shift_top = 0.;
    613:     for (i = 0; i < n; i++) {
    614:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    615:       d  = aa[diag[i]];
    616:       rs = -PetscAbsScalar(d) - PetscRealPart(d);
    617:       v  = aa + ai[i];
    618:       nz = ai[i + 1] - ai[i];
    619:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(v[j]);
    620:       if (rs > sctx.shift_top) sctx.shift_top = rs;
    621:     }
    622:     if (sctx.shift_top < info->zeropivot) sctx.shift_top = info->zeropivot;
    623:     sctx.shift_top *= 1.1;
    624:     sctx.nshift_max = 5;
    625:     sctx.shift_lo   = 0.;
    626:     sctx.shift_hi   = 1.;
    627:   }

    629:   sctx.shift_amount = 0.;
    630:   sctx.nshift       = 0;
    631: #endif

    633:   do {
    634:     sctx.newshift = PETSC_FALSE;
    635:     for (i = 0; i < n; i++) {
    636:       /* load in initial unfactored row */
    637:       nz    = ai[r[i] + 1] - ai[r[i]];
    638:       ajtmp = aj + ai[r[i]];
    639:       v     = aa + ai[r[i]];
    640:       /* sort permuted ajtmp and values v accordingly */
    641:       for (j = 0; j < nz; j++) ajtmp[j] = ics[ajtmp[j]];
    642:       PetscCall(PetscSortIntWithScalarArray(nz, ajtmp, v));

    644:       diag[r[i]] = ai[r[i]];
    645:       for (j = 0; j < nz; j++) {
    646:         rtmp[ajtmp[j]] = v[j];
    647:         if (ajtmp[j] < i) diag[r[i]]++; /* update a->diag */
    648:       }
    649:       rtmp[r[i]] += sctx.shift_amount; /* shift the diagonal of the matrix */

    651:       row = *ajtmp++;
    652:       while (row < i) {
    653:         pc = rtmp + row;
    654:         if (*pc != 0.0) {
    655:           pv = aa + diag[r[row]];
    656:           pj = aj + diag[r[row]] + 1;

    658:           multiplier = *pc / *pv++;
    659:           *pc        = multiplier;
    660:           nz         = ai[r[row] + 1] - diag[r[row]] - 1;
    661:           for (j = 0; j < nz; j++) rtmp[pj[j]] -= multiplier * pv[j];
    662:           PetscCall(PetscLogFlops(1 + 2.0 * nz));
    663:         }
    664:         row = *ajtmp++;
    665:       }
    666:       /* finished row so overwrite it onto aa */
    667:       pv     = aa + ai[r[i]];
    668:       pj     = aj + ai[r[i]];
    669:       nz     = ai[r[i] + 1] - ai[r[i]];
    670:       nbdiag = diag[r[i]] - ai[r[i]]; /* num of entries before the diagonal */

    672:       rs = 0.0;
    673:       for (j = 0; j < nz; j++) {
    674:         pv[j] = rtmp[pj[j]];
    675:         if (j != nbdiag) rs += PetscAbsScalar(pv[j]);
    676:       }

    678:       sctx.rs = rs;
    679:       sctx.pv = pv[nbdiag];
    680:       PetscCall(MatPivotCheck(B, A, info, &sctx, i));
    681:       if (sctx.newshift) break;
    682:       pv[nbdiag] = sctx.pv;
    683:     }

    685:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE && !sctx.newshift && sctx.shift_fraction > 0 && sctx.nshift < sctx.nshift_max) {
    686:       /*
    687:        * if no shift in this attempt & shifting & started shifting & can refine,
    688:        * then try lower shift
    689:        */
    690:       sctx.shift_hi       = sctx.shift_fraction;
    691:       sctx.shift_fraction = (sctx.shift_hi + sctx.shift_lo) / 2.;
    692:       sctx.shift_amount   = sctx.shift_fraction * sctx.shift_top;
    693:       sctx.newshift       = PETSC_TRUE;
    694:       sctx.nshift++;
    695:     }
    696:   } while (sctx.newshift);

    698:   /* invert diagonal entries for simpler triangular solves */
    699:   for (i = 0; i < n; i++) aa[diag[r[i]]] = 1.0 / aa[diag[r[i]]];

    701:   PetscCall(MatSeqAIJRestoreArray(A, &aa));
    702:   PetscCall(PetscFree(rtmp));
    703:   PetscCall(ISRestoreIndices(isicol, &ic));
    704:   PetscCall(ISRestoreIndices(isrow, &r));

    706:   A->ops->solve             = MatSolve_SeqAIJ_InplaceWithPerm;
    707:   A->ops->solveadd          = MatSolveAdd_SeqAIJ_inplace;
    708:   A->ops->solvetranspose    = MatSolveTranspose_SeqAIJ_inplace;
    709:   A->ops->solvetransposeadd = MatSolveTransposeAdd_SeqAIJ_inplace;

    711:   A->assembled    = PETSC_TRUE;
    712:   A->preallocated = PETSC_TRUE;

    714:   PetscCall(PetscLogFlops(A->cmap->n));
    715:   if (sctx.nshift) {
    716:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    717:       PetscCall(PetscInfo(A, "number of shift_pd tries %" PetscInt_FMT ", shift_amount %g, diagonal shifted up by %e fraction top_value %e\n", sctx.nshift, (double)sctx.shift_amount, (double)sctx.shift_fraction, (double)sctx.shift_top));
    718:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    719:       PetscCall(PetscInfo(A, "number of shift_nz tries %" PetscInt_FMT ", shift_amount %g\n", sctx.nshift, (double)sctx.shift_amount));
    720:     }
    721:   }
    722:   PetscFunctionReturn(PETSC_SUCCESS);
    723: }

    725: PetscErrorCode MatLUFactor_SeqAIJ(Mat A, IS row, IS col, const MatFactorInfo *info)
    726: {
    727:   Mat C;

    729:   PetscFunctionBegin;
    730:   PetscCall(MatGetFactor(A, MATSOLVERPETSC, MAT_FACTOR_LU, &C));
    731:   PetscCall(MatLUFactorSymbolic(C, A, row, col, info));
    732:   PetscCall(MatLUFactorNumeric(C, A, info));

    734:   A->ops->solve          = C->ops->solve;
    735:   A->ops->solvetranspose = C->ops->solvetranspose;

    737:   PetscCall(MatHeaderMerge(A, &C));
    738:   PetscFunctionReturn(PETSC_SUCCESS);
    739: }

    741: PetscErrorCode MatSolve_SeqAIJ_inplace(Mat A, Vec bb, Vec xx)
    742: {
    743:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    744:   IS                 iscol = a->col, isrow = a->row;
    745:   PetscInt           i, n = A->rmap->n, *vi, *ai = a->i, *aj = a->j;
    746:   PetscInt           nz;
    747:   const PetscInt    *rout, *cout, *r, *c;
    748:   PetscScalar       *x, *tmp, *tmps, sum;
    749:   const PetscScalar *b;
    750:   const MatScalar   *aa, *v;
    751:   const PetscInt    *adiag;

    753:   PetscFunctionBegin;
    754:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);

    756:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    757:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    758:   PetscCall(VecGetArrayRead(bb, &b));
    759:   PetscCall(VecGetArrayWrite(xx, &x));
    760:   tmp = a->solve_work;

    762:   PetscCall(ISGetIndices(isrow, &rout));
    763:   r = rout;
    764:   PetscCall(ISGetIndices(iscol, &cout));
    765:   c = cout + (n - 1);

    767:   /* forward solve the lower triangular */
    768:   tmp[0] = b[*r++];
    769:   tmps   = tmp;
    770:   for (i = 1; i < n; i++) {
    771:     v   = aa + ai[i];
    772:     vi  = aj + ai[i];
    773:     nz  = adiag[i] - ai[i];
    774:     sum = b[*r++];
    775:     PetscSparseDenseMinusDot(sum, tmps, v, vi, nz);
    776:     tmp[i] = sum;
    777:   }

    779:   /* backward solve the upper triangular */
    780:   for (i = n - 1; i >= 0; i--) {
    781:     v   = aa + adiag[i] + 1;
    782:     vi  = aj + adiag[i] + 1;
    783:     nz  = ai[i + 1] - adiag[i] - 1;
    784:     sum = tmp[i];
    785:     PetscSparseDenseMinusDot(sum, tmps, v, vi, nz);
    786:     x[*c--] = tmp[i] = sum * aa[adiag[i]];
    787:   }
    788:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    789:   PetscCall(ISRestoreIndices(isrow, &rout));
    790:   PetscCall(ISRestoreIndices(iscol, &cout));
    791:   PetscCall(VecRestoreArrayRead(bb, &b));
    792:   PetscCall(VecRestoreArrayWrite(xx, &x));
    793:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    794:   PetscFunctionReturn(PETSC_SUCCESS);
    795: }

    797: static PetscErrorCode MatMatSolve_SeqAIJ_inplace(Mat A, Mat B, Mat X)
    798: {
    799:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    800:   IS                 iscol = a->col, isrow = a->row;
    801:   PetscInt           i, n = A->rmap->n, *vi, *ai = a->i, *aj = a->j;
    802:   PetscInt           nz, neq, ldb, ldx;
    803:   const PetscInt    *rout, *cout, *r, *c;
    804:   PetscScalar       *x, *tmp = a->solve_work, *tmps, sum;
    805:   const PetscScalar *b, *aa, *v;
    806:   PetscBool          isdense;
    807:   const PetscInt    *adiag;

    809:   PetscFunctionBegin;
    810:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
    811:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSE, &isdense));
    812:   PetscCheck(isdense, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "B matrix must be a SeqDense matrix");
    813:   if (X != B) {
    814:     PetscCall(PetscObjectTypeCompare((PetscObject)X, MATSEQDENSE, &isdense));
    815:     PetscCheck(isdense, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "X matrix must be a SeqDense matrix");
    816:   }
    817:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    818:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    819:   PetscCall(MatDenseGetArrayRead(B, &b));
    820:   PetscCall(MatDenseGetLDA(B, &ldb));
    821:   PetscCall(MatDenseGetArray(X, &x));
    822:   PetscCall(MatDenseGetLDA(X, &ldx));
    823:   PetscCall(ISGetIndices(isrow, &rout));
    824:   r = rout;
    825:   PetscCall(ISGetIndices(iscol, &cout));
    826:   c = cout;
    827:   for (neq = 0; neq < B->cmap->n; neq++) {
    828:     /* forward solve the lower triangular */
    829:     tmp[0] = b[r[0]];
    830:     tmps   = tmp;
    831:     for (i = 1; i < n; i++) {
    832:       v   = aa + ai[i];
    833:       vi  = aj + ai[i];
    834:       nz  = adiag[i] - ai[i];
    835:       sum = b[r[i]];
    836:       PetscSparseDenseMinusDot(sum, tmps, v, vi, nz);
    837:       tmp[i] = sum;
    838:     }
    839:     /* backward solve the upper triangular */
    840:     for (i = n - 1; i >= 0; i--) {
    841:       v   = aa + adiag[i] + 1;
    842:       vi  = aj + adiag[i] + 1;
    843:       nz  = ai[i + 1] - adiag[i] - 1;
    844:       sum = tmp[i];
    845:       PetscSparseDenseMinusDot(sum, tmps, v, vi, nz);
    846:       x[c[i]] = tmp[i] = sum * aa[adiag[i]];
    847:     }
    848:     b += ldb;
    849:     x += ldx;
    850:   }
    851:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    852:   PetscCall(ISRestoreIndices(isrow, &rout));
    853:   PetscCall(ISRestoreIndices(iscol, &cout));
    854:   PetscCall(MatDenseRestoreArrayRead(B, &b));
    855:   PetscCall(MatDenseRestoreArray(X, &x));
    856:   PetscCall(PetscLogFlops(B->cmap->n * (2.0 * a->nz - n)));
    857:   PetscFunctionReturn(PETSC_SUCCESS);
    858: }

    860: PetscErrorCode MatMatSolve_SeqAIJ(Mat A, Mat B, Mat X)
    861: {
    862:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    863:   IS                 iscol = a->col, isrow = a->row;
    864:   PetscInt           i, n = A->rmap->n, *vi, *ai = a->i, *aj = a->j;
    865:   const PetscInt    *adiag;
    866:   PetscInt           nz, neq, ldb, ldx;
    867:   const PetscInt    *rout, *cout, *r, *c;
    868:   PetscScalar       *x, *tmp = a->solve_work, sum;
    869:   const PetscScalar *b, *aa, *v;
    870:   PetscBool          isdense;

    872:   PetscFunctionBegin;
    873:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
    874:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSE, &isdense));
    875:   PetscCheck(isdense, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "B matrix must be a SeqDense matrix");
    876:   if (X != B) {
    877:     PetscCall(PetscObjectTypeCompare((PetscObject)X, MATSEQDENSE, &isdense));
    878:     PetscCheck(isdense, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "X matrix must be a SeqDense matrix");
    879:   }
    880:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    881:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    882:   PetscCall(MatDenseGetArrayRead(B, &b));
    883:   PetscCall(MatDenseGetLDA(B, &ldb));
    884:   PetscCall(MatDenseGetArray(X, &x));
    885:   PetscCall(MatDenseGetLDA(X, &ldx));
    886:   PetscCall(ISGetIndices(isrow, &rout));
    887:   r = rout;
    888:   PetscCall(ISGetIndices(iscol, &cout));
    889:   c = cout;
    890:   for (neq = 0; neq < B->cmap->n; neq++) {
    891:     /* forward solve the lower triangular */
    892:     tmp[0] = b[r[0]];
    893:     v      = aa;
    894:     vi     = aj;
    895:     for (i = 1; i < n; i++) {
    896:       nz  = ai[i + 1] - ai[i];
    897:       sum = b[r[i]];
    898:       PetscSparseDenseMinusDot(sum, tmp, v, vi, nz);
    899:       tmp[i] = sum;
    900:       v += nz;
    901:       vi += nz;
    902:     }
    903:     /* backward solve the upper triangular */
    904:     for (i = n - 1; i >= 0; i--) {
    905:       v   = aa + adiag[i + 1] + 1;
    906:       vi  = aj + adiag[i + 1] + 1;
    907:       nz  = adiag[i] - adiag[i + 1] - 1;
    908:       sum = tmp[i];
    909:       PetscSparseDenseMinusDot(sum, tmp, v, vi, nz);
    910:       x[c[i]] = tmp[i] = sum * v[nz]; /* v[nz] = aa[adiag[i]] */
    911:     }
    912:     b += ldb;
    913:     x += ldx;
    914:   }
    915:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    916:   PetscCall(ISRestoreIndices(isrow, &rout));
    917:   PetscCall(ISRestoreIndices(iscol, &cout));
    918:   PetscCall(MatDenseRestoreArrayRead(B, &b));
    919:   PetscCall(MatDenseRestoreArray(X, &x));
    920:   PetscCall(PetscLogFlops(B->cmap->n * (2.0 * a->nz - n)));
    921:   PetscFunctionReturn(PETSC_SUCCESS);
    922: }

    924: PetscErrorCode MatMatSolveTranspose_SeqAIJ(Mat A, Mat B, Mat X)
    925: {
    926:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    927:   IS                 iscol = a->col, isrow = a->row;
    928:   PetscInt           i, n = A->rmap->n, *vi, *ai = a->i, *aj = a->j, j;
    929:   const PetscInt    *adiag = a->diag;
    930:   PetscInt           nz, neq, ldb, ldx;
    931:   const PetscInt    *rout, *cout, *r, *c;
    932:   PetscScalar       *x, *tmp = a->solve_work, s1;
    933:   const PetscScalar *b, *aa, *v;
    934:   PetscBool          isdense;

    936:   PetscFunctionBegin;
    937:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
    938:   PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQDENSE, &isdense));
    939:   PetscCheck(isdense, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "B matrix must be a SeqDense matrix");
    940:   if (X != B) {
    941:     PetscCall(PetscObjectTypeCompare((PetscObject)X, MATSEQDENSE, &isdense));
    942:     PetscCheck(isdense, PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "X matrix must be a SeqDense matrix");
    943:   }
    944:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    945:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    946:   PetscCall(MatDenseGetArrayRead(B, &b));
    947:   PetscCall(MatDenseGetLDA(B, &ldb));
    948:   PetscCall(MatDenseGetArray(X, &x));
    949:   PetscCall(MatDenseGetLDA(X, &ldx));
    950:   PetscCall(ISGetIndices(isrow, &rout));
    951:   r = rout;
    952:   PetscCall(ISGetIndices(iscol, &cout));
    953:   c = cout;
    954:   for (neq = 0; neq < B->cmap->n; neq++) {
    955:     /* copy the b into temp work space according to permutation */
    956:     for (i = 0; i < n; i++) tmp[i] = b[c[i]];

    958:     /* forward solve the U^T */
    959:     for (i = 0; i < n; i++) {
    960:       v  = aa + adiag[i + 1] + 1;
    961:       vi = aj + adiag[i + 1] + 1;
    962:       nz = adiag[i] - adiag[i + 1] - 1;
    963:       s1 = tmp[i];
    964:       s1 *= v[nz]; /* multiply by inverse of diagonal entry */
    965:       for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    966:       tmp[i] = s1;
    967:     }

    969:     /* backward solve the L^T */
    970:     for (i = n - 1; i >= 0; i--) {
    971:       v  = aa + ai[i];
    972:       vi = aj + ai[i];
    973:       nz = ai[i + 1] - ai[i];
    974:       s1 = tmp[i];
    975:       for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    976:     }

    978:     /* copy tmp into x according to permutation */
    979:     for (i = 0; i < n; i++) x[r[i]] = tmp[i];
    980:     b += ldb;
    981:     x += ldx;
    982:   }
    983:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    984:   PetscCall(ISRestoreIndices(isrow, &rout));
    985:   PetscCall(ISRestoreIndices(iscol, &cout));
    986:   PetscCall(MatDenseRestoreArrayRead(B, &b));
    987:   PetscCall(MatDenseRestoreArray(X, &x));
    988:   PetscCall(PetscLogFlops(B->cmap->n * (2.0 * a->nz - n)));
    989:   PetscFunctionReturn(PETSC_SUCCESS);
    990: }

    992: static PetscErrorCode MatSolve_SeqAIJ_InplaceWithPerm(Mat A, Vec bb, Vec xx)
    993: {
    994:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    995:   IS                 iscol = a->col, isrow = a->row;
    996:   const PetscInt    *r, *c, *rout, *cout, *adiag;
    997:   PetscInt           i, n = A->rmap->n, *vi, *ai = a->i, *aj = a->j;
    998:   PetscInt           nz, row;
    999:   PetscScalar       *x, *tmp, *tmps, sum;
    1000:   const PetscScalar *b;
    1001:   const MatScalar   *aa, *v;

    1003:   PetscFunctionBegin;
    1004:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);

    1006:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1007:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1008:   PetscCall(VecGetArrayRead(bb, &b));
    1009:   PetscCall(VecGetArrayWrite(xx, &x));
    1010:   tmp = a->solve_work;

    1012:   PetscCall(ISGetIndices(isrow, &rout));
    1013:   r = rout;
    1014:   PetscCall(ISGetIndices(iscol, &cout));
    1015:   c = cout + (n - 1);

    1017:   /* forward solve the lower triangular */
    1018:   tmp[0] = b[*r++];
    1019:   tmps   = tmp;
    1020:   for (row = 1; row < n; row++) {
    1021:     i   = rout[row]; /* permuted row */
    1022:     v   = aa + ai[i];
    1023:     vi  = aj + ai[i];
    1024:     nz  = adiag[i] - ai[i];
    1025:     sum = b[*r++];
    1026:     PetscSparseDenseMinusDot(sum, tmps, v, vi, nz);
    1027:     tmp[row] = sum;
    1028:   }

    1030:   /* backward solve the upper triangular */
    1031:   for (row = n - 1; row >= 0; row--) {
    1032:     i   = rout[row]; /* permuted row */
    1033:     v   = aa + adiag[i] + 1;
    1034:     vi  = aj + adiag[i] + 1;
    1035:     nz  = ai[i + 1] - adiag[i] - 1;
    1036:     sum = tmp[row];
    1037:     PetscSparseDenseMinusDot(sum, tmps, v, vi, nz);
    1038:     x[*c--] = tmp[row] = sum * aa[adiag[i]];
    1039:   }
    1040:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1041:   PetscCall(ISRestoreIndices(isrow, &rout));
    1042:   PetscCall(ISRestoreIndices(iscol, &cout));
    1043:   PetscCall(VecRestoreArrayRead(bb, &b));
    1044:   PetscCall(VecRestoreArrayWrite(xx, &x));
    1045:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    1046:   PetscFunctionReturn(PETSC_SUCCESS);
    1047: }

    1049: #include <../src/mat/impls/aij/seq/ftn-kernels/fsolve.h>
    1050: static PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering_inplace(Mat A, Vec bb, Vec xx)
    1051: {
    1052:   Mat_SeqAIJ        *a  = (Mat_SeqAIJ *)A->data;
    1053:   PetscInt           n  = A->rmap->n;
    1054:   const PetscInt    *ai = a->i, *aj = a->j, *adiag;
    1055:   PetscScalar       *x;
    1056:   const PetscScalar *b;
    1057:   const MatScalar   *aa;
    1058: #if !defined(PETSC_USE_FORTRAN_KERNEL_SOLVEAIJ)
    1059:   PetscInt         adiag_i, i, nz, ai_i;
    1060:   const PetscInt  *vi;
    1061:   const MatScalar *v;
    1062:   PetscScalar      sum;
    1063: #endif

    1065:   PetscFunctionBegin;
    1066:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);
    1067:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1068:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1069:   PetscCall(VecGetArrayRead(bb, &b));
    1070:   PetscCall(VecGetArrayWrite(xx, &x));

    1072: #if defined(PETSC_USE_FORTRAN_KERNEL_SOLVEAIJ)
    1073:   fortransolveaij_(&n, x, ai, aj, adiag, aa, b);
    1074: #else
    1075:   /* forward solve the lower triangular */
    1076:   x[0] = b[0];
    1077:   for (i = 1; i < n; i++) {
    1078:     ai_i = ai[i];
    1079:     v    = aa + ai_i;
    1080:     vi   = aj + ai_i;
    1081:     nz   = adiag[i] - ai_i;
    1082:     sum  = b[i];
    1083:     PetscSparseDenseMinusDot(sum, x, v, vi, nz);
    1084:     x[i] = sum;
    1085:   }

    1087:   /* backward solve the upper triangular */
    1088:   for (i = n - 1; i >= 0; i--) {
    1089:     adiag_i = adiag[i];
    1090:     v       = aa + adiag_i + 1;
    1091:     vi      = aj + adiag_i + 1;
    1092:     nz      = ai[i + 1] - adiag_i - 1;
    1093:     sum     = x[i];
    1094:     PetscSparseDenseMinusDot(sum, x, v, vi, nz);
    1095:     x[i] = sum * aa[adiag_i];
    1096:   }
    1097: #endif
    1098:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    1099:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1100:   PetscCall(VecRestoreArrayRead(bb, &b));
    1101:   PetscCall(VecRestoreArrayWrite(xx, &x));
    1102:   PetscFunctionReturn(PETSC_SUCCESS);
    1103: }

    1105: static PetscErrorCode MatSolveAdd_SeqAIJ_inplace(Mat A, Vec bb, Vec yy, Vec xx)
    1106: {
    1107:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    1108:   IS                 iscol = a->col, isrow = a->row;
    1109:   PetscInt           i, n                  = A->rmap->n, j;
    1110:   PetscInt           nz;
    1111:   const PetscInt    *rout, *cout, *r, *c, *vi, *ai = a->i, *aj = a->j, *adiag;
    1112:   PetscScalar       *x, *tmp, sum;
    1113:   const PetscScalar *b;
    1114:   const MatScalar   *aa, *v;

    1116:   PetscFunctionBegin;
    1117:   if (yy != xx) PetscCall(VecCopy(yy, xx));

    1119:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1120:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1121:   PetscCall(VecGetArrayRead(bb, &b));
    1122:   PetscCall(VecGetArray(xx, &x));
    1123:   tmp = a->solve_work;

    1125:   PetscCall(ISGetIndices(isrow, &rout));
    1126:   r = rout;
    1127:   PetscCall(ISGetIndices(iscol, &cout));
    1128:   c = cout + (n - 1);

    1130:   /* forward solve the lower triangular */
    1131:   tmp[0] = b[*r++];
    1132:   for (i = 1; i < n; i++) {
    1133:     v   = aa + ai[i];
    1134:     vi  = aj + ai[i];
    1135:     nz  = adiag[i] - ai[i];
    1136:     sum = b[*r++];
    1137:     for (j = 0; j < nz; j++) sum -= v[j] * tmp[vi[j]];
    1138:     tmp[i] = sum;
    1139:   }

    1141:   /* backward solve the upper triangular */
    1142:   for (i = n - 1; i >= 0; i--) {
    1143:     v   = aa + adiag[i] + 1;
    1144:     vi  = aj + adiag[i] + 1;
    1145:     nz  = ai[i + 1] - adiag[i] - 1;
    1146:     sum = tmp[i];
    1147:     for (j = 0; j < nz; j++) sum -= v[j] * tmp[vi[j]];
    1148:     tmp[i] = sum * aa[adiag[i]];
    1149:     x[*c--] += tmp[i];
    1150:   }

    1152:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1153:   PetscCall(ISRestoreIndices(isrow, &rout));
    1154:   PetscCall(ISRestoreIndices(iscol, &cout));
    1155:   PetscCall(VecRestoreArrayRead(bb, &b));
    1156:   PetscCall(VecRestoreArray(xx, &x));
    1157:   PetscCall(PetscLogFlops(2.0 * a->nz));
    1158:   PetscFunctionReturn(PETSC_SUCCESS);
    1159: }

    1161: PetscErrorCode MatSolveAdd_SeqAIJ(Mat A, Vec bb, Vec yy, Vec xx)
    1162: {
    1163:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    1164:   IS                 iscol = a->col, isrow = a->row;
    1165:   PetscInt           i, n                  = A->rmap->n, j;
    1166:   PetscInt           nz;
    1167:   const PetscInt    *rout, *cout, *r, *c, *vi, *ai = a->i, *aj = a->j, *adiag;
    1168:   PetscScalar       *x, *tmp, sum;
    1169:   const PetscScalar *b;
    1170:   const MatScalar   *aa, *v;

    1172:   PetscFunctionBegin;
    1173:   if (yy != xx) PetscCall(VecCopy(yy, xx));

    1175:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1176:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1177:   PetscCall(VecGetArrayRead(bb, &b));
    1178:   PetscCall(VecGetArray(xx, &x));
    1179:   tmp = a->solve_work;

    1181:   PetscCall(ISGetIndices(isrow, &rout));
    1182:   r = rout;
    1183:   PetscCall(ISGetIndices(iscol, &cout));
    1184:   c = cout;

    1186:   /* forward solve the lower triangular */
    1187:   tmp[0] = b[r[0]];
    1188:   v      = aa;
    1189:   vi     = aj;
    1190:   for (i = 1; i < n; i++) {
    1191:     nz  = ai[i + 1] - ai[i];
    1192:     sum = b[r[i]];
    1193:     for (j = 0; j < nz; j++) sum -= v[j] * tmp[vi[j]];
    1194:     tmp[i] = sum;
    1195:     v += nz;
    1196:     vi += nz;
    1197:   }

    1199:   /* backward solve the upper triangular */
    1200:   v  = aa + adiag[n - 1];
    1201:   vi = aj + adiag[n - 1];
    1202:   for (i = n - 1; i >= 0; i--) {
    1203:     nz  = adiag[i] - adiag[i + 1] - 1;
    1204:     sum = tmp[i];
    1205:     for (j = 0; j < nz; j++) sum -= v[j] * tmp[vi[j]];
    1206:     tmp[i] = sum * v[nz];
    1207:     x[c[i]] += tmp[i];
    1208:     v += nz + 1;
    1209:     vi += nz + 1;
    1210:   }

    1212:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1213:   PetscCall(ISRestoreIndices(isrow, &rout));
    1214:   PetscCall(ISRestoreIndices(iscol, &cout));
    1215:   PetscCall(VecRestoreArrayRead(bb, &b));
    1216:   PetscCall(VecRestoreArray(xx, &x));
    1217:   PetscCall(PetscLogFlops(2.0 * a->nz));
    1218:   PetscFunctionReturn(PETSC_SUCCESS);
    1219: }

    1221: PetscErrorCode MatSolveTranspose_SeqAIJ_inplace(Mat A, Vec bb, Vec xx)
    1222: {
    1223:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    1224:   IS                 iscol = a->col, isrow = a->row;
    1225:   const PetscInt    *rout, *cout, *r, *c, *diag = a->diag, *ai = a->i, *aj = a->j, *vi;
    1226:   PetscInt           i, n = A->rmap->n, j;
    1227:   PetscInt           nz;
    1228:   PetscScalar       *x, *tmp, s1;
    1229:   const MatScalar   *aa, *v;
    1230:   const PetscScalar *b;

    1232:   PetscFunctionBegin;
    1233:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1234:   PetscCall(VecGetArrayRead(bb, &b));
    1235:   PetscCall(VecGetArrayWrite(xx, &x));
    1236:   tmp = a->solve_work;

    1238:   PetscCall(ISGetIndices(isrow, &rout));
    1239:   r = rout;
    1240:   PetscCall(ISGetIndices(iscol, &cout));
    1241:   c = cout;

    1243:   /* copy the b into temp work space according to permutation */
    1244:   for (i = 0; i < n; i++) tmp[i] = b[c[i]];

    1246:   /* forward solve the U^T */
    1247:   for (i = 0; i < n; i++) {
    1248:     v  = aa + diag[i];
    1249:     vi = aj + diag[i] + 1;
    1250:     nz = ai[i + 1] - diag[i] - 1;
    1251:     s1 = tmp[i];
    1252:     s1 *= (*v++); /* multiply by inverse of diagonal entry */
    1253:     for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    1254:     tmp[i] = s1;
    1255:   }

    1257:   /* backward solve the L^T */
    1258:   for (i = n - 1; i >= 0; i--) {
    1259:     v  = aa + diag[i] - 1;
    1260:     vi = aj + diag[i] - 1;
    1261:     nz = diag[i] - ai[i];
    1262:     s1 = tmp[i];
    1263:     for (j = 0; j > -nz; j--) tmp[vi[j]] -= s1 * v[j];
    1264:   }

    1266:   /* copy tmp into x according to permutation */
    1267:   for (i = 0; i < n; i++) x[r[i]] = tmp[i];

    1269:   PetscCall(ISRestoreIndices(isrow, &rout));
    1270:   PetscCall(ISRestoreIndices(iscol, &cout));
    1271:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1272:   PetscCall(VecRestoreArrayRead(bb, &b));
    1273:   PetscCall(VecRestoreArrayWrite(xx, &x));

    1275:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    1276:   PetscFunctionReturn(PETSC_SUCCESS);
    1277: }

    1279: PetscErrorCode MatSolveTranspose_SeqAIJ(Mat A, Vec bb, Vec xx)
    1280: {
    1281:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    1282:   IS                 iscol = a->col, isrow = a->row;
    1283:   const PetscInt    *rout, *cout, *r, *c, *adiag, *ai = a->i, *aj = a->j, *vi;
    1284:   PetscInt           i, n = A->rmap->n, j;
    1285:   PetscInt           nz;
    1286:   PetscScalar       *x, *tmp, s1;
    1287:   const MatScalar   *aa, *v;
    1288:   const PetscScalar *b;

    1290:   PetscFunctionBegin;
    1291:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1292:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1293:   PetscCall(VecGetArrayRead(bb, &b));
    1294:   PetscCall(VecGetArrayWrite(xx, &x));
    1295:   tmp = a->solve_work;

    1297:   PetscCall(ISGetIndices(isrow, &rout));
    1298:   r = rout;
    1299:   PetscCall(ISGetIndices(iscol, &cout));
    1300:   c = cout;

    1302:   /* copy the b into temp work space according to permutation */
    1303:   for (i = 0; i < n; i++) tmp[i] = b[c[i]];

    1305:   /* forward solve the U^T */
    1306:   for (i = 0; i < n; i++) {
    1307:     v  = aa + adiag[i + 1] + 1;
    1308:     vi = aj + adiag[i + 1] + 1;
    1309:     nz = adiag[i] - adiag[i + 1] - 1;
    1310:     s1 = tmp[i];
    1311:     s1 *= v[nz]; /* multiply by inverse of diagonal entry */
    1312:     for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    1313:     tmp[i] = s1;
    1314:   }

    1316:   /* backward solve the L^T */
    1317:   for (i = n - 1; i >= 0; i--) {
    1318:     v  = aa + ai[i];
    1319:     vi = aj + ai[i];
    1320:     nz = ai[i + 1] - ai[i];
    1321:     s1 = tmp[i];
    1322:     for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    1323:   }

    1325:   /* copy tmp into x according to permutation */
    1326:   for (i = 0; i < n; i++) x[r[i]] = tmp[i];

    1328:   PetscCall(ISRestoreIndices(isrow, &rout));
    1329:   PetscCall(ISRestoreIndices(iscol, &cout));
    1330:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1331:   PetscCall(VecRestoreArrayRead(bb, &b));
    1332:   PetscCall(VecRestoreArrayWrite(xx, &x));

    1334:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    1335:   PetscFunctionReturn(PETSC_SUCCESS);
    1336: }

    1338: PetscErrorCode MatSolveTransposeAdd_SeqAIJ_inplace(Mat A, Vec bb, Vec zz, Vec xx)
    1339: {
    1340:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    1341:   IS                 iscol = a->col, isrow = a->row;
    1342:   const PetscInt    *rout, *cout, *r, *c, *diag = a->diag, *ai = a->i, *aj = a->j, *vi;
    1343:   PetscInt           i, n = A->rmap->n, j;
    1344:   PetscInt           nz;
    1345:   PetscScalar       *x, *tmp, s1;
    1346:   const MatScalar   *aa, *v;
    1347:   const PetscScalar *b;

    1349:   PetscFunctionBegin;
    1350:   if (zz != xx) PetscCall(VecCopy(zz, xx));
    1351:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1352:   PetscCall(VecGetArrayRead(bb, &b));
    1353:   PetscCall(VecGetArray(xx, &x));
    1354:   tmp = a->solve_work;

    1356:   PetscCall(ISGetIndices(isrow, &rout));
    1357:   r = rout;
    1358:   PetscCall(ISGetIndices(iscol, &cout));
    1359:   c = cout;

    1361:   /* copy the b into temp work space according to permutation */
    1362:   for (i = 0; i < n; i++) tmp[i] = b[c[i]];

    1364:   /* forward solve the U^T */
    1365:   for (i = 0; i < n; i++) {
    1366:     v  = aa + diag[i];
    1367:     vi = aj + diag[i] + 1;
    1368:     nz = ai[i + 1] - diag[i] - 1;
    1369:     s1 = tmp[i];
    1370:     s1 *= (*v++); /* multiply by inverse of diagonal entry */
    1371:     for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    1372:     tmp[i] = s1;
    1373:   }

    1375:   /* backward solve the L^T */
    1376:   for (i = n - 1; i >= 0; i--) {
    1377:     v  = aa + diag[i] - 1;
    1378:     vi = aj + diag[i] - 1;
    1379:     nz = diag[i] - ai[i];
    1380:     s1 = tmp[i];
    1381:     for (j = 0; j > -nz; j--) tmp[vi[j]] -= s1 * v[j];
    1382:   }

    1384:   /* copy tmp into x according to permutation */
    1385:   for (i = 0; i < n; i++) x[r[i]] += tmp[i];

    1387:   PetscCall(ISRestoreIndices(isrow, &rout));
    1388:   PetscCall(ISRestoreIndices(iscol, &cout));
    1389:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1390:   PetscCall(VecRestoreArrayRead(bb, &b));
    1391:   PetscCall(VecRestoreArray(xx, &x));

    1393:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    1394:   PetscFunctionReturn(PETSC_SUCCESS);
    1395: }

    1397: PetscErrorCode MatSolveTransposeAdd_SeqAIJ(Mat A, Vec bb, Vec zz, Vec xx)
    1398: {
    1399:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    1400:   IS                 iscol = a->col, isrow = a->row;
    1401:   const PetscInt    *rout, *cout, *r, *c, *adiag, *ai = a->i, *aj = a->j, *vi;
    1402:   PetscInt           i, n = A->rmap->n, j;
    1403:   PetscInt           nz;
    1404:   PetscScalar       *x, *tmp, s1;
    1405:   const MatScalar   *aa, *v;
    1406:   const PetscScalar *b;

    1408:   PetscFunctionBegin;
    1409:   if (zz != xx) PetscCall(VecCopy(zz, xx));
    1410:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1411:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1412:   PetscCall(VecGetArrayRead(bb, &b));
    1413:   PetscCall(VecGetArray(xx, &x));
    1414:   tmp = a->solve_work;

    1416:   PetscCall(ISGetIndices(isrow, &rout));
    1417:   r = rout;
    1418:   PetscCall(ISGetIndices(iscol, &cout));
    1419:   c = cout;

    1421:   /* copy the b into temp work space according to permutation */
    1422:   for (i = 0; i < n; i++) tmp[i] = b[c[i]];

    1424:   /* forward solve the U^T */
    1425:   for (i = 0; i < n; i++) {
    1426:     v  = aa + adiag[i + 1] + 1;
    1427:     vi = aj + adiag[i + 1] + 1;
    1428:     nz = adiag[i] - adiag[i + 1] - 1;
    1429:     s1 = tmp[i];
    1430:     s1 *= v[nz]; /* multiply by inverse of diagonal entry */
    1431:     for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    1432:     tmp[i] = s1;
    1433:   }

    1435:   /* backward solve the L^T */
    1436:   for (i = n - 1; i >= 0; i--) {
    1437:     v  = aa + ai[i];
    1438:     vi = aj + ai[i];
    1439:     nz = ai[i + 1] - ai[i];
    1440:     s1 = tmp[i];
    1441:     for (j = 0; j < nz; j++) tmp[vi[j]] -= s1 * v[j];
    1442:   }

    1444:   /* copy tmp into x according to permutation */
    1445:   for (i = 0; i < n; i++) x[r[i]] += tmp[i];

    1447:   PetscCall(ISRestoreIndices(isrow, &rout));
    1448:   PetscCall(ISRestoreIndices(iscol, &cout));
    1449:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1450:   PetscCall(VecRestoreArrayRead(bb, &b));
    1451:   PetscCall(VecRestoreArray(xx, &x));

    1453:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    1454:   PetscFunctionReturn(PETSC_SUCCESS);
    1455: }

    1457: /*
    1458:    ilu() under revised new data structure.
    1459:    Factored arrays bj and ba are stored as
    1460:      L(0,:), L(1,:), ...,L(n-1,:),  U(n-1,:),...,U(i,:),U(i-1,:),...,U(0,:)

    1462:    bi=fact->i is an array of size n+1, in which
    1463:      bi[i]:  points to 1st entry of L(i,:),i=0,...,n-1
    1464:      bi[n]:  points to L(n-1,n-1)+1

    1466:   bdiag=fact->diag is an array of size n+1,in which
    1467:      bdiag[i]: points to diagonal of U(i,:), i=0,...,n-1
    1468:      bdiag[n]: points to entry of U(n-1,0)-1

    1470:    U(i,:) contains bdiag[i] as its last entry, i.e.,
    1471:     U(i,:) = (u[i,i+1],...,u[i,n-1],diag[i])
    1472: */
    1473: PetscErrorCode MatILUFactorSymbolic_SeqAIJ_ilu0(Mat fact, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
    1474: {
    1475:   Mat_SeqAIJ    *a = (Mat_SeqAIJ *)A->data, *b;
    1476:   const PetscInt n = A->rmap->n, *ai = a->i, *aj, *adiag;
    1477:   PetscInt       i, j, k = 0, nz, *bi, *bj, *bdiag;
    1478:   IS             isicol;

    1480:   PetscFunctionBegin;
    1481:   PetscCall(ISInvertPermutation(iscol, PETSC_DECIDE, &isicol));
    1482:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1483:   PetscCall(MatDuplicateNoCreate_SeqAIJ(fact, A, MAT_DO_NOT_COPY_VALUES, PETSC_FALSE));
    1484:   b = (Mat_SeqAIJ *)fact->data;

    1486:   /* allocate matrix arrays for new data structure */
    1487:   PetscCall(PetscShmgetAllocateArray(ai[n], sizeof(PetscScalar), (void **)&b->a));
    1488:   PetscCall(PetscShmgetAllocateArray(ai[n], sizeof(PetscInt), (void **)&b->j));
    1489:   PetscCall(PetscShmgetAllocateArray(n + 1, sizeof(PetscInt), (void **)&b->i));
    1490:   if (n > 0) PetscCall(PetscArrayzero(b->a, ai[n]));
    1491:   b->free_a  = PETSC_TRUE;
    1492:   b->free_ij = PETSC_TRUE;

    1494:   if (!b->diag) PetscCall(PetscMalloc1(n + 1, &b->diag));
    1495:   bdiag = b->diag;

    1497:   /* set bi and bj with new data structure */
    1498:   bi = b->i;
    1499:   bj = b->j;

    1501:   /* L part */
    1502:   bi[0] = 0;
    1503:   for (i = 0; i < n; i++) {
    1504:     nz        = adiag[i] - ai[i];
    1505:     bi[i + 1] = bi[i] + nz;
    1506:     aj        = a->j + ai[i];
    1507:     for (j = 0; j < nz; j++) bj[k++] = aj[j];
    1508:   }

    1510:   /* U part */
    1511:   bdiag[n] = bi[n] - 1;
    1512:   for (i = n - 1; i >= 0; i--) {
    1513:     nz = ai[i + 1] - adiag[i] - 1;
    1514:     aj = a->j + adiag[i] + 1;
    1515:     for (j = 0; j < nz; j++) bj[k++] = aj[j];
    1516:     /* diag[i] */
    1517:     bj[k++]  = i;
    1518:     bdiag[i] = bdiag[i + 1] + nz + 1;
    1519:   }

    1521:   fact->factortype             = MAT_FACTOR_ILU;
    1522:   fact->info.factor_mallocs    = 0;
    1523:   fact->info.fill_ratio_given  = info->fill;
    1524:   fact->info.fill_ratio_needed = 1.0;
    1525:   fact->ops->lufactornumeric   = MatLUFactorNumeric_SeqAIJ;
    1526:   PetscCall(MatSeqAIJCheckInode_FactorLU(fact));

    1528:   b       = (Mat_SeqAIJ *)fact->data;
    1529:   b->row  = isrow;
    1530:   b->col  = iscol;
    1531:   b->icol = isicol;
    1532:   PetscCall(PetscMalloc1(fact->rmap->n, &b->solve_work));
    1533:   PetscCall(PetscObjectReference((PetscObject)isrow));
    1534:   PetscCall(PetscObjectReference((PetscObject)iscol));
    1535:   PetscFunctionReturn(PETSC_SUCCESS);
    1536: }

    1538: PetscErrorCode MatILUFactorSymbolic_SeqAIJ(Mat fact, Mat A, IS isrow, IS iscol, const MatFactorInfo *info)
    1539: {
    1540:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data, *b;
    1541:   IS                 isicol;
    1542:   const PetscInt    *r, *ic;
    1543:   PetscInt           n = A->rmap->n, *ai = a->i, *aj = a->j;
    1544:   PetscInt          *bi, *cols, nnz, *cols_lvl;
    1545:   PetscInt          *bdiag, prow, fm, nzbd, reallocs = 0, dcount = 0;
    1546:   PetscInt           i, levels, diagonal_fill;
    1547:   PetscBool          col_identity, row_identity;
    1548:   PetscReal          f;
    1549:   PetscInt           nlnk, *lnk, *lnk_lvl = NULL;
    1550:   PetscBT            lnkbt;
    1551:   PetscInt           nzi, *bj, **bj_ptr, **bjlvl_ptr;
    1552:   PetscFreeSpaceList free_space = NULL, current_space = NULL;
    1553:   PetscFreeSpaceList free_space_lvl = NULL, current_space_lvl = NULL;
    1554:   PetscBool          diagDense;

    1556:   PetscFunctionBegin;
    1557:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Must be square matrix, rows %" PetscInt_FMT " columns %" PetscInt_FMT, A->rmap->n, A->cmap->n);
    1558:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, NULL, &diagDense));
    1559:   PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entries");

    1561:   levels = (PetscInt)info->levels;
    1562:   PetscCall(ISIdentity(isrow, &row_identity));
    1563:   PetscCall(ISIdentity(iscol, &col_identity));
    1564:   if (!levels && row_identity && col_identity) {
    1565:     /* special case: ilu(0) with natural ordering */
    1566:     PetscCall(MatILUFactorSymbolic_SeqAIJ_ilu0(fact, A, isrow, iscol, info));
    1567:     if (a->inode.size_csr) fact->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJ_Inode;
    1568:     PetscFunctionReturn(PETSC_SUCCESS);
    1569:   }

    1571:   PetscCall(ISInvertPermutation(iscol, PETSC_DECIDE, &isicol));
    1572:   PetscCall(ISGetIndices(isrow, &r));
    1573:   PetscCall(ISGetIndices(isicol, &ic));

    1575:   /* get new row and diagonal pointers, must be allocated separately because they will be given to the Mat_SeqAIJ and freed separately */
    1576:   PetscCall(PetscShmgetAllocateArray(n + 1, sizeof(PetscInt), (void **)&bi));
    1577:   PetscCall(PetscMalloc1(n + 1, &bdiag));
    1578:   bi[0] = bdiag[0] = 0;
    1579:   PetscCall(PetscMalloc2(n, &bj_ptr, n, &bjlvl_ptr));

    1581:   /* create a linked list for storing column indices of the active row */
    1582:   nlnk = n + 1;
    1583:   PetscCall(PetscIncompleteLLCreate(n, n, nlnk, lnk, lnk_lvl, lnkbt));

    1585:   /* initial FreeSpace size is f*(ai[n]+1) */
    1586:   f             = info->fill;
    1587:   diagonal_fill = (PetscInt)info->diagonal_fill;
    1588:   PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(f, ai[n] + 1), &free_space));
    1589:   current_space = free_space;
    1590:   PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(f, ai[n] + 1), &free_space_lvl));
    1591:   current_space_lvl = free_space_lvl;
    1592:   for (i = 0; i < n; i++) {
    1593:     nzi = 0;
    1594:     /* copy current row into linked list */
    1595:     nnz = ai[r[i] + 1] - ai[r[i]];
    1596:     PetscCheck(nnz, PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Empty row in matrix: row in original ordering %" PetscInt_FMT " in permuted ordering %" PetscInt_FMT, r[i], i);
    1597:     cols   = aj + ai[r[i]];
    1598:     lnk[i] = -1; /* marker to indicate if diagonal exists */
    1599:     PetscCall(PetscIncompleteLLInit(nnz, cols, n, ic, &nlnk, lnk, lnk_lvl, lnkbt));
    1600:     nzi += nlnk;

    1602:     /* make sure diagonal entry is included */
    1603:     if (diagonal_fill && lnk[i] == -1) {
    1604:       fm = n;
    1605:       while (lnk[fm] < i) fm = lnk[fm];
    1606:       lnk[i]     = lnk[fm]; /* insert diagonal into linked list */
    1607:       lnk[fm]    = i;
    1608:       lnk_lvl[i] = 0;
    1609:       nzi++;
    1610:       dcount++;
    1611:     }

    1613:     /* add pivot rows into the active row */
    1614:     nzbd = 0;
    1615:     prow = lnk[n];
    1616:     while (prow < i) {
    1617:       nnz      = bdiag[prow];
    1618:       cols     = bj_ptr[prow] + nnz + 1;
    1619:       cols_lvl = bjlvl_ptr[prow] + nnz + 1;
    1620:       nnz      = bi[prow + 1] - bi[prow] - nnz - 1;
    1621:       PetscCall(PetscILULLAddSorted(nnz, cols, levels, cols_lvl, prow, &nlnk, lnk, lnk_lvl, lnkbt, prow));
    1622:       nzi += nlnk;
    1623:       prow = lnk[prow];
    1624:       nzbd++;
    1625:     }
    1626:     bdiag[i]  = nzbd;
    1627:     bi[i + 1] = bi[i] + nzi;
    1628:     /* if free space is not available, make more free space */
    1629:     if (current_space->local_remaining < nzi) {
    1630:       nnz = PetscIntMultTruncate(2, PetscIntMultTruncate(nzi, n - i)); /* estimated and max additional space needed */
    1631:       PetscCall(PetscFreeSpaceGet(nnz, &current_space));
    1632:       PetscCall(PetscFreeSpaceGet(nnz, &current_space_lvl));
    1633:       reallocs++;
    1634:     }

    1636:     /* copy data into free_space and free_space_lvl, then initialize lnk */
    1637:     PetscCall(PetscIncompleteLLClean(n, n, nzi, lnk, lnk_lvl, current_space->array, current_space_lvl->array, lnkbt));
    1638:     bj_ptr[i]    = current_space->array;
    1639:     bjlvl_ptr[i] = current_space_lvl->array;

    1641:     /* make sure the active row i has diagonal entry */
    1642:     PetscCheck(*(bj_ptr[i] + bdiag[i]) == i, PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Row %" PetscInt_FMT " has missing diagonal in factored matrix, try running with -pc_factor_nonzeros_along_diagonal or -pc_factor_diagonal_fill", i);

    1644:     current_space->array += nzi;
    1645:     current_space->local_used += nzi;
    1646:     current_space->local_remaining -= nzi;
    1647:     current_space_lvl->array += nzi;
    1648:     current_space_lvl->local_used += nzi;
    1649:     current_space_lvl->local_remaining -= nzi;
    1650:   }

    1652:   PetscCall(ISRestoreIndices(isrow, &r));
    1653:   PetscCall(ISRestoreIndices(isicol, &ic));
    1654:   /* copy free_space into bj and free free_space; set bi, bj, bdiag in new datastructure; */
    1655:   PetscCall(PetscShmgetAllocateArray(bi[n], sizeof(PetscInt), (void **)&bj));
    1656:   PetscCall(PetscFreeSpaceContiguous_LU(&free_space, bj, n, bi, bdiag));

    1658:   PetscCall(PetscIncompleteLLDestroy(lnk, lnkbt));
    1659:   PetscCall(PetscFreeSpaceDestroy(free_space_lvl));
    1660:   PetscCall(PetscFree2(bj_ptr, bjlvl_ptr));

    1662: #if defined(PETSC_USE_INFO)
    1663:   {
    1664:     PetscReal af = ((PetscReal)(bdiag[0] + 1)) / ((PetscReal)ai[n]);
    1665:     PetscCall(PetscInfo(A, "Reallocs %" PetscInt_FMT " Fill ratio:given %g needed %g\n", reallocs, (double)f, (double)af));
    1666:     PetscCall(PetscInfo(A, "Run with -[sub_]pc_factor_fill %g or use \n", (double)af));
    1667:     PetscCall(PetscInfo(A, "PCFactorSetFill([sub]pc,%g);\n", (double)af));
    1668:     PetscCall(PetscInfo(A, "for best performance.\n"));
    1669:     if (diagonal_fill) PetscCall(PetscInfo(A, "Detected and replaced %" PetscInt_FMT " missing diagonals\n", dcount));
    1670:   }
    1671: #endif
    1672:   /* put together the new matrix */
    1673:   PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(fact, MAT_SKIP_ALLOCATION, NULL));
    1674:   b          = (Mat_SeqAIJ *)fact->data;
    1675:   b->free_ij = PETSC_TRUE;
    1676:   PetscCall(PetscShmgetAllocateArray(bdiag[0] + 1, sizeof(PetscScalar), (void **)&b->a));
    1677:   b->free_a = PETSC_TRUE;
    1678:   b->j      = bj;
    1679:   b->i      = bi;
    1680:   b->diag   = bdiag;
    1681:   b->ilen   = NULL;
    1682:   b->imax   = NULL;
    1683:   b->row    = isrow;
    1684:   b->col    = iscol;
    1685:   PetscCall(PetscObjectReference((PetscObject)isrow));
    1686:   PetscCall(PetscObjectReference((PetscObject)iscol));
    1687:   b->icol = isicol;

    1689:   PetscCall(PetscMalloc1(n, &b->solve_work));
    1690:   /* In b structure:  Free imax, ilen, old a, old j.
    1691:      Allocate bdiag, solve_work, new a, new j */
    1692:   b->maxnz = b->nz = bdiag[0] + 1;

    1694:   fact->info.factor_mallocs    = reallocs;
    1695:   fact->info.fill_ratio_given  = f;
    1696:   fact->info.fill_ratio_needed = ((PetscReal)(bdiag[0] + 1)) / ((PetscReal)ai[n]);
    1697:   fact->ops->lufactornumeric   = MatLUFactorNumeric_SeqAIJ;
    1698:   if (a->inode.size_csr) fact->ops->lufactornumeric = MatLUFactorNumeric_SeqAIJ_Inode;
    1699:   PetscCall(MatSeqAIJCheckInode_FactorLU(fact));
    1700:   PetscFunctionReturn(PETSC_SUCCESS);
    1701: }

    1703: PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ(Mat B, Mat A, const MatFactorInfo *info)
    1704: {
    1705:   Mat              C  = B;
    1706:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ *)A->data;
    1707:   Mat_SeqSBAIJ    *b  = (Mat_SeqSBAIJ *)C->data;
    1708:   IS               ip = b->row, iip = b->icol;
    1709:   const PetscInt  *rip, *riip;
    1710:   PetscInt         i, j, mbs = A->rmap->n, *bi = b->i, *bj = b->j, *bdiag = b->diag, *bjtmp;
    1711:   PetscInt        *ai = a->i, *aj = a->j;
    1712:   PetscInt         k, jmin, jmax, *c2r, *il, col, nexti, ili, nz;
    1713:   MatScalar       *rtmp, *ba = b->a, *bval, dk, uikdi;
    1714:   PetscBool        perm_identity;
    1715:   FactorShiftCtx   sctx;
    1716:   PetscReal        rs;
    1717:   const MatScalar *aa, *v;
    1718:   MatScalar        d;
    1719:   const PetscInt  *adiag;

    1721:   PetscFunctionBegin;
    1722:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1723:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1724:   /* MatPivotSetUp(): initialize shift context sctx */
    1725:   PetscCall(PetscMemzero(&sctx, sizeof(FactorShiftCtx)));

    1727:   if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    1728:     sctx.shift_top = info->zeropivot;
    1729:     for (i = 0; i < mbs; i++) {
    1730:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    1731:       d  = aa[adiag[i]];
    1732:       rs = -PetscAbsScalar(d) - PetscRealPart(d);
    1733:       v  = aa + ai[i];
    1734:       nz = ai[i + 1] - ai[i];
    1735:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(v[j]);
    1736:       if (rs > sctx.shift_top) sctx.shift_top = rs;
    1737:     }
    1738:     sctx.shift_top *= 1.1;
    1739:     sctx.nshift_max = 5;
    1740:     sctx.shift_lo   = 0.;
    1741:     sctx.shift_hi   = 1.;
    1742:   }

    1744:   PetscCall(ISGetIndices(ip, &rip));
    1745:   PetscCall(ISGetIndices(iip, &riip));

    1747:   /* allocate working arrays
    1748:      c2r: linked list, keep track of pivot rows for a given column. c2r[col]: head of the list for a given col
    1749:      il:  for active k row, il[i] gives the index of the 1st nonzero entry in U[i,k:n-1] in bj and ba arrays
    1750:   */
    1751:   PetscCall(PetscMalloc3(mbs, &rtmp, mbs, &il, mbs, &c2r));

    1753:   do {
    1754:     sctx.newshift = PETSC_FALSE;

    1756:     for (i = 0; i < mbs; i++) c2r[i] = mbs;
    1757:     if (mbs) il[0] = 0;

    1759:     for (k = 0; k < mbs; k++) {
    1760:       /* zero rtmp */
    1761:       nz    = bi[k + 1] - bi[k];
    1762:       bjtmp = bj + bi[k];
    1763:       for (j = 0; j < nz; j++) rtmp[bjtmp[j]] = 0.0;

    1765:       /* load in initial unfactored row */
    1766:       bval = ba + bi[k];
    1767:       jmin = ai[rip[k]];
    1768:       jmax = ai[rip[k] + 1];
    1769:       for (j = jmin; j < jmax; j++) {
    1770:         col = riip[aj[j]];
    1771:         if (col >= k) { /* only take upper triangular entry */
    1772:           rtmp[col] = aa[j];
    1773:           *bval++   = 0.0; /* for in-place factorization */
    1774:         }
    1775:       }
    1776:       /* shift the diagonal of the matrix: ZeropivotApply() */
    1777:       rtmp[k] += sctx.shift_amount; /* shift the diagonal of the matrix */

    1779:       /* modify k-th row by adding in those rows i with U(i,k)!=0 */
    1780:       dk = rtmp[k];
    1781:       i  = c2r[k]; /* first row to be added to k_th row  */

    1783:       while (i < k) {
    1784:         nexti = c2r[i]; /* next row to be added to k_th row */

    1786:         /* compute multiplier, update diag(k) and U(i,k) */
    1787:         ili   = il[i];                   /* index of first nonzero element in U(i,k:bms-1) */
    1788:         uikdi = -ba[ili] * ba[bdiag[i]]; /* diagonal(k) */
    1789:         dk += uikdi * ba[ili];           /* update diag[k] */
    1790:         ba[ili] = uikdi;                 /* -U(i,k) */

    1792:         /* add multiple of row i to k-th row */
    1793:         jmin = ili + 1;
    1794:         jmax = bi[i + 1];
    1795:         if (jmin < jmax) {
    1796:           for (j = jmin; j < jmax; j++) rtmp[bj[j]] += uikdi * ba[j];
    1797:           /* update il and c2r for row i */
    1798:           il[i]  = jmin;
    1799:           j      = bj[jmin];
    1800:           c2r[i] = c2r[j];
    1801:           c2r[j] = i;
    1802:         }
    1803:         i = nexti;
    1804:       }

    1806:       /* copy data into U(k,:) */
    1807:       rs   = 0.0;
    1808:       jmin = bi[k];
    1809:       jmax = bi[k + 1] - 1;
    1810:       if (jmin < jmax) {
    1811:         for (j = jmin; j < jmax; j++) {
    1812:           col   = bj[j];
    1813:           ba[j] = rtmp[col];
    1814:           rs += PetscAbsScalar(ba[j]);
    1815:         }
    1816:         /* add the k-th row into il and c2r */
    1817:         il[k]  = jmin;
    1818:         i      = bj[jmin];
    1819:         c2r[k] = c2r[i];
    1820:         c2r[i] = k;
    1821:       }

    1823:       /* MatPivotCheck() */
    1824:       sctx.rs = rs;
    1825:       sctx.pv = dk;
    1826:       PetscCall(MatPivotCheck(B, A, info, &sctx, i));
    1827:       if (sctx.newshift) break;
    1828:       dk = sctx.pv;

    1830:       ba[bdiag[k]] = 1.0 / dk; /* U(k,k) */
    1831:     }
    1832:   } while (sctx.newshift);

    1834:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    1835:   PetscCall(PetscFree3(rtmp, il, c2r));
    1836:   PetscCall(ISRestoreIndices(ip, &rip));
    1837:   PetscCall(ISRestoreIndices(iip, &riip));

    1839:   PetscCall(ISIdentity(ip, &perm_identity));
    1840:   if (perm_identity) {
    1841:     B->ops->solve          = MatSolve_SeqSBAIJ_1_NaturalOrdering;
    1842:     B->ops->solvetranspose = MatSolve_SeqSBAIJ_1_NaturalOrdering;
    1843:     B->ops->forwardsolve   = MatForwardSolve_SeqSBAIJ_1_NaturalOrdering;
    1844:     B->ops->backwardsolve  = MatBackwardSolve_SeqSBAIJ_1_NaturalOrdering;
    1845:   } else {
    1846:     B->ops->solve          = MatSolve_SeqSBAIJ_1;
    1847:     B->ops->solvetranspose = MatSolve_SeqSBAIJ_1;
    1848:     B->ops->forwardsolve   = MatForwardSolve_SeqSBAIJ_1;
    1849:     B->ops->backwardsolve  = MatBackwardSolve_SeqSBAIJ_1;
    1850:   }

    1852:   C->assembled    = PETSC_TRUE;
    1853:   C->preallocated = PETSC_TRUE;

    1855:   PetscCall(PetscLogFlops(C->rmap->n));

    1857:   /* MatPivotView() */
    1858:   if (sctx.nshift) {
    1859:     if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    1860:       PetscCall(PetscInfo(A, "number of shift_pd tries %" PetscInt_FMT ", shift_amount %g, diagonal shifted up by %e fraction top_value %e\n", sctx.nshift, (double)sctx.shift_amount, (double)sctx.shift_fraction, (double)sctx.shift_top));
    1861:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    1862:       PetscCall(PetscInfo(A, "number of shift_nz tries %" PetscInt_FMT ", shift_amount %g\n", sctx.nshift, (double)sctx.shift_amount));
    1863:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_INBLOCKS) {
    1864:       PetscCall(PetscInfo(A, "number of shift_inblocks applied %" PetscInt_FMT ", each shift_amount %g\n", sctx.nshift, (double)info->shiftamount));
    1865:     }
    1866:   }
    1867:   PetscFunctionReturn(PETSC_SUCCESS);
    1868: }

    1870: PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ_inplace(Mat B, Mat A, const MatFactorInfo *info)
    1871: {
    1872:   Mat              C  = B;
    1873:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ *)A->data;
    1874:   Mat_SeqSBAIJ    *b  = (Mat_SeqSBAIJ *)C->data;
    1875:   IS               ip = b->row, iip = b->icol;
    1876:   const PetscInt  *rip, *riip;
    1877:   PetscInt         i, j, mbs = A->rmap->n, *bi = b->i, *bj = b->j, *bcol, *bjtmp;
    1878:   PetscInt        *ai = a->i, *aj = a->j;
    1879:   PetscInt         k, jmin, jmax, *jl, *il, col, nexti, ili, nz;
    1880:   MatScalar       *rtmp, *ba = b->a, *bval, dk, uikdi;
    1881:   const MatScalar *aa, *v;
    1882:   PetscBool        perm_identity;
    1883:   FactorShiftCtx   sctx;
    1884:   PetscReal        rs;
    1885:   MatScalar        d;
    1886:   const PetscInt  *adiag;

    1888:   PetscFunctionBegin;
    1889:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, NULL));
    1890:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    1891:   /* MatPivotSetUp(): initialize shift context sctx */
    1892:   PetscCall(PetscMemzero(&sctx, sizeof(FactorShiftCtx)));

    1894:   if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) { /* set sctx.shift_top=max{rs} */
    1895:     sctx.shift_top = info->zeropivot;
    1896:     for (i = 0; i < mbs; i++) {
    1897:       /* calculate sum(|aij|)-RealPart(aii), amt of shift needed for this row */
    1898:       d  = aa[adiag[i]];
    1899:       rs = -PetscAbsScalar(d) - PetscRealPart(d);
    1900:       v  = aa + ai[i];
    1901:       nz = ai[i + 1] - ai[i];
    1902:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(v[j]);
    1903:       if (rs > sctx.shift_top) sctx.shift_top = rs;
    1904:     }
    1905:     sctx.shift_top *= 1.1;
    1906:     sctx.nshift_max = 5;
    1907:     sctx.shift_lo   = 0.;
    1908:     sctx.shift_hi   = 1.;
    1909:   }

    1911:   PetscCall(ISGetIndices(ip, &rip));
    1912:   PetscCall(ISGetIndices(iip, &riip));

    1914:   /* initialization */
    1915:   PetscCall(PetscMalloc3(mbs, &rtmp, mbs, &il, mbs, &jl));

    1917:   do {
    1918:     sctx.newshift = PETSC_FALSE;

    1920:     for (i = 0; i < mbs; i++) jl[i] = mbs;
    1921:     il[0] = 0;

    1923:     for (k = 0; k < mbs; k++) {
    1924:       /* zero rtmp */
    1925:       nz    = bi[k + 1] - bi[k];
    1926:       bjtmp = bj + bi[k];
    1927:       for (j = 0; j < nz; j++) rtmp[bjtmp[j]] = 0.0;

    1929:       bval = ba + bi[k];
    1930:       /* initialize k-th row by the perm[k]-th row of A */
    1931:       jmin = ai[rip[k]];
    1932:       jmax = ai[rip[k] + 1];
    1933:       for (j = jmin; j < jmax; j++) {
    1934:         col = riip[aj[j]];
    1935:         if (col >= k) { /* only take upper triangular entry */
    1936:           rtmp[col] = aa[j];
    1937:           *bval++   = 0.0; /* for in-place factorization */
    1938:         }
    1939:       }
    1940:       /* shift the diagonal of the matrix */
    1941:       if (sctx.nshift) rtmp[k] += sctx.shift_amount;

    1943:       /* modify k-th row by adding in those rows i with U(i,k)!=0 */
    1944:       dk = rtmp[k];
    1945:       i  = jl[k]; /* first row to be added to k_th row  */

    1947:       while (i < k) {
    1948:         nexti = jl[i]; /* next row to be added to k_th row */

    1950:         /* compute multiplier, update diag(k) and U(i,k) */
    1951:         ili   = il[i];                /* index of first nonzero element in U(i,k:bms-1) */
    1952:         uikdi = -ba[ili] * ba[bi[i]]; /* diagonal(k) */
    1953:         dk += uikdi * ba[ili];
    1954:         ba[ili] = uikdi; /* -U(i,k) */

    1956:         /* add multiple of row i to k-th row */
    1957:         jmin = ili + 1;
    1958:         jmax = bi[i + 1];
    1959:         if (jmin < jmax) {
    1960:           for (j = jmin; j < jmax; j++) rtmp[bj[j]] += uikdi * ba[j];
    1961:           /* update il and jl for row i */
    1962:           il[i] = jmin;
    1963:           j     = bj[jmin];
    1964:           jl[i] = jl[j];
    1965:           jl[j] = i;
    1966:         }
    1967:         i = nexti;
    1968:       }

    1970:       /* shift the diagonals when zero pivot is detected */
    1971:       /* compute rs=sum of abs(off-diagonal) */
    1972:       rs   = 0.0;
    1973:       jmin = bi[k] + 1;
    1974:       nz   = bi[k + 1] - jmin;
    1975:       bcol = bj + jmin;
    1976:       for (j = 0; j < nz; j++) rs += PetscAbsScalar(rtmp[bcol[j]]);

    1978:       sctx.rs = rs;
    1979:       sctx.pv = dk;
    1980:       PetscCall(MatPivotCheck(B, A, info, &sctx, k));
    1981:       if (sctx.newshift) break;
    1982:       dk = sctx.pv;

    1984:       /* copy data into U(k,:) */
    1985:       ba[bi[k]] = 1.0 / dk; /* U(k,k) */
    1986:       jmin      = bi[k] + 1;
    1987:       jmax      = bi[k + 1];
    1988:       if (jmin < jmax) {
    1989:         for (j = jmin; j < jmax; j++) {
    1990:           col   = bj[j];
    1991:           ba[j] = rtmp[col];
    1992:         }
    1993:         /* add the k-th row into il and jl */
    1994:         il[k] = jmin;
    1995:         i     = bj[jmin];
    1996:         jl[k] = jl[i];
    1997:         jl[i] = k;
    1998:       }
    1999:     }
    2000:   } while (sctx.newshift);

    2002:   PetscCall(PetscFree3(rtmp, il, jl));
    2003:   PetscCall(ISRestoreIndices(ip, &rip));
    2004:   PetscCall(ISRestoreIndices(iip, &riip));
    2005:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));

    2007:   PetscCall(ISIdentity(ip, &perm_identity));
    2008:   if (perm_identity) {
    2009:     B->ops->solve          = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    2010:     B->ops->solvetranspose = MatSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    2011:     B->ops->forwardsolve   = MatForwardSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    2012:     B->ops->backwardsolve  = MatBackwardSolve_SeqSBAIJ_1_NaturalOrdering_inplace;
    2013:   } else {
    2014:     B->ops->solve          = MatSolve_SeqSBAIJ_1_inplace;
    2015:     B->ops->solvetranspose = MatSolve_SeqSBAIJ_1_inplace;
    2016:     B->ops->forwardsolve   = MatForwardSolve_SeqSBAIJ_1_inplace;
    2017:     B->ops->backwardsolve  = MatBackwardSolve_SeqSBAIJ_1_inplace;
    2018:   }

    2020:   C->assembled    = PETSC_TRUE;
    2021:   C->preallocated = PETSC_TRUE;

    2023:   PetscCall(PetscLogFlops(C->rmap->n));
    2024:   if (sctx.nshift) {
    2025:     if (info->shifttype == (PetscReal)MAT_SHIFT_NONZERO) {
    2026:       PetscCall(PetscInfo(A, "number of shiftnz tries %" PetscInt_FMT ", shift_amount %g\n", sctx.nshift, (double)sctx.shift_amount));
    2027:     } else if (info->shifttype == (PetscReal)MAT_SHIFT_POSITIVE_DEFINITE) {
    2028:       PetscCall(PetscInfo(A, "number of shiftpd tries %" PetscInt_FMT ", shift_amount %g\n", sctx.nshift, (double)sctx.shift_amount));
    2029:     }
    2030:   }
    2031:   PetscFunctionReturn(PETSC_SUCCESS);
    2032: }

    2034: /*
    2035:    icc() under revised new data structure.
    2036:    Factored arrays bj and ba are stored as
    2037:      U(0,:),...,U(i,:),U(n-1,:)

    2039:    ui=fact->i is an array of size n+1, in which
    2040:    ui+
    2041:      ui[i]:  points to 1st entry of U(i,:),i=0,...,n-1
    2042:      ui[n]:  points to U(n-1,n-1)+1

    2044:   udiag=fact->diag is an array of size n,in which
    2045:      udiag[i]: points to diagonal of U(i,:), i=0,...,n-1

    2047:    U(i,:) contains udiag[i] as its last entry, i.e.,
    2048:     U(i,:) = (u[i,i+1],...,u[i,n-1],diag[i])
    2049: */

    2051: PetscErrorCode MatICCFactorSymbolic_SeqAIJ(Mat fact, Mat A, IS perm, const MatFactorInfo *info)
    2052: {
    2053:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2054:   Mat_SeqSBAIJ      *b;
    2055:   PetscBool          perm_identity;
    2056:   PetscInt           reallocs = 0, i, *ai = a->i, *aj = a->j, am = A->rmap->n, *ui, *udiag;
    2057:   const PetscInt    *rip, *riip, *adiag;
    2058:   PetscInt           jmin, jmax, nzk, k, j, *jl, prow, *il, nextprow;
    2059:   PetscInt           nlnk, *lnk, *lnk_lvl = NULL;
    2060:   PetscInt           ncols, ncols_upper, *cols, *ajtmp, *uj, **uj_ptr, **uj_lvl_ptr;
    2061:   PetscReal          fill = info->fill, levels = info->levels;
    2062:   PetscFreeSpaceList free_space = NULL, current_space = NULL;
    2063:   PetscFreeSpaceList free_space_lvl = NULL, current_space_lvl = NULL;
    2064:   PetscBT            lnkbt;
    2065:   IS                 iperm;
    2066:   PetscBool          diagDense;

    2068:   PetscFunctionBegin;
    2069:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Must be square matrix, rows %" PetscInt_FMT " columns %" PetscInt_FMT, A->rmap->n, A->cmap->n);
    2070:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, &adiag, &diagDense));
    2071:   PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entries");
    2072:   PetscCall(ISIdentity(perm, &perm_identity));
    2073:   PetscCall(ISInvertPermutation(perm, PETSC_DECIDE, &iperm));

    2075:   PetscCall(PetscShmgetAllocateArray(am + 1, sizeof(PetscInt), (void **)&ui));
    2076:   PetscCall(PetscMalloc1(am + 1, &udiag));
    2077:   ui[0] = 0;

    2079:   /* ICC(0) without matrix ordering: simply rearrange column indices */
    2080:   if (!levels && perm_identity) {
    2081:     for (i = 0; i < am; i++) {
    2082:       ncols     = ai[i + 1] - adiag[i];
    2083:       ui[i + 1] = ui[i] + ncols;
    2084:       udiag[i]  = ui[i + 1] - 1; /* points to the last entry of U(i,:) */
    2085:     }
    2086:     PetscCall(PetscMalloc1(ui[am] + 1, &uj));
    2087:     cols = uj;
    2088:     for (i = 0; i < am; i++) {
    2089:       aj    = a->j + adiag[i] + 1; /* 1st entry of U(i,:) without diagonal */
    2090:       ncols = ai[i + 1] - adiag[i] - 1;
    2091:       for (j = 0; j < ncols; j++) *cols++ = aj[j];
    2092:       *cols++ = i; /* diagonal is located as the last entry of U(i,:) */
    2093:     }
    2094:   } else { /* case: levels>0 || (levels=0 && !perm_identity) */
    2095:     PetscCall(ISGetIndices(iperm, &riip));
    2096:     PetscCall(ISGetIndices(perm, &rip));

    2098:     /* initialization */
    2099:     PetscCall(PetscMalloc1(am + 1, &ajtmp));

    2101:     /* jl: linked list for storing indices of the pivot rows
    2102:        il: il[i] points to the 1st nonzero entry of U(i,k:am-1) */
    2103:     PetscCall(PetscMalloc4(am, &uj_ptr, am, &uj_lvl_ptr, am, &jl, am, &il));
    2104:     for (i = 0; i < am; i++) {
    2105:       jl[i] = am;
    2106:       il[i] = 0;
    2107:     }

    2109:     /* create and initialize a linked list for storing column indices of the active row k */
    2110:     nlnk = am + 1;
    2111:     PetscCall(PetscIncompleteLLCreate(am, am, nlnk, lnk, lnk_lvl, lnkbt));

    2113:     /* initial FreeSpace size is fill*(ai[am]+am)/2 */
    2114:     PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, (ai[am] + am) / 2), &free_space));
    2115:     current_space = free_space;
    2116:     PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, (ai[am] + am) / 2), &free_space_lvl));
    2117:     current_space_lvl = free_space_lvl;

    2119:     for (k = 0; k < am; k++) { /* for each active row k */
    2120:       /* initialize lnk by the column indices of row rip[k] of A */
    2121:       nzk   = 0;
    2122:       ncols = ai[rip[k] + 1] - ai[rip[k]];
    2123:       PetscCheck(ncols, PETSC_COMM_SELF, PETSC_ERR_MAT_CH_ZRPVT, "Empty row in matrix: row in original ordering %" PetscInt_FMT " in permuted ordering %" PetscInt_FMT, rip[k], k);
    2124:       ncols_upper = 0;
    2125:       for (j = 0; j < ncols; j++) {
    2126:         i = *(aj + ai[rip[k]] + j); /* unpermuted column index */
    2127:         if (riip[i] >= k) {         /* only take upper triangular entry */
    2128:           ajtmp[ncols_upper] = i;
    2129:           ncols_upper++;
    2130:         }
    2131:       }
    2132:       PetscCall(PetscIncompleteLLInit(ncols_upper, ajtmp, am, riip, &nlnk, lnk, lnk_lvl, lnkbt));
    2133:       nzk += nlnk;

    2135:       /* update lnk by computing fill-in for each pivot row to be merged in */
    2136:       prow = jl[k]; /* 1st pivot row */

    2138:       while (prow < k) {
    2139:         nextprow = jl[prow];

    2141:         /* merge prow into k-th row */
    2142:         jmin  = il[prow] + 1; /* index of the 2nd nzero entry in U(prow,k:am-1) */
    2143:         jmax  = ui[prow + 1];
    2144:         ncols = jmax - jmin;
    2145:         i     = jmin - ui[prow];
    2146:         cols  = uj_ptr[prow] + i;     /* points to the 2nd nzero entry in U(prow,k:am-1) */
    2147:         uj    = uj_lvl_ptr[prow] + i; /* levels of cols */
    2148:         j     = *(uj - 1);
    2149:         PetscCall(PetscICCLLAddSorted(ncols, cols, levels, uj, am, &nlnk, lnk, lnk_lvl, lnkbt, j));
    2150:         nzk += nlnk;

    2152:         /* update il and jl for prow */
    2153:         if (jmin < jmax) {
    2154:           il[prow] = jmin;
    2155:           j        = *cols;
    2156:           jl[prow] = jl[j];
    2157:           jl[j]    = prow;
    2158:         }
    2159:         prow = nextprow;
    2160:       }

    2162:       /* if free space is not available, make more free space */
    2163:       if (current_space->local_remaining < nzk) {
    2164:         i = am - k + 1;                                    /* num of unfactored rows */
    2165:         i = PetscIntMultTruncate(i, PetscMin(nzk, i - 1)); /* i*nzk, i*(i-1): estimated and max additional space needed */
    2166:         PetscCall(PetscFreeSpaceGet(i, &current_space));
    2167:         PetscCall(PetscFreeSpaceGet(i, &current_space_lvl));
    2168:         reallocs++;
    2169:       }

    2171:       /* copy data into free_space and free_space_lvl, then initialize lnk */
    2172:       PetscCheck(nzk != 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Empty row %" PetscInt_FMT " in ICC matrix factor", k);
    2173:       PetscCall(PetscIncompleteLLClean(am, am, nzk, lnk, lnk_lvl, current_space->array, current_space_lvl->array, lnkbt));

    2175:       /* add the k-th row into il and jl */
    2176:       if (nzk > 1) {
    2177:         i     = current_space->array[1]; /* col value of the first nonzero element in U(k, k+1:am-1) */
    2178:         jl[k] = jl[i];
    2179:         jl[i] = k;
    2180:         il[k] = ui[k] + 1;
    2181:       }
    2182:       uj_ptr[k]     = current_space->array;
    2183:       uj_lvl_ptr[k] = current_space_lvl->array;

    2185:       current_space->array += nzk;
    2186:       current_space->local_used += nzk;
    2187:       current_space->local_remaining -= nzk;

    2189:       current_space_lvl->array += nzk;
    2190:       current_space_lvl->local_used += nzk;
    2191:       current_space_lvl->local_remaining -= nzk;

    2193:       ui[k + 1] = ui[k] + nzk;
    2194:     }

    2196:     PetscCall(ISRestoreIndices(perm, &rip));
    2197:     PetscCall(ISRestoreIndices(iperm, &riip));
    2198:     PetscCall(PetscFree4(uj_ptr, uj_lvl_ptr, jl, il));
    2199:     PetscCall(PetscFree(ajtmp));

    2201:     /* copy free_space into uj and free free_space; set ui, uj, udiag in new datastructure; */
    2202:     PetscCall(PetscShmgetAllocateArray(ui[am] + 1, sizeof(PetscInt), (void **)&uj));
    2203:     PetscCall(PetscFreeSpaceContiguous_Cholesky(&free_space, uj, am, ui, udiag)); /* store matrix factor  */
    2204:     PetscCall(PetscIncompleteLLDestroy(lnk, lnkbt));
    2205:     PetscCall(PetscFreeSpaceDestroy(free_space_lvl));

    2207:   } /* end of case: levels>0 || (levels=0 && !perm_identity) */

    2209:   /* put together the new matrix in MATSEQSBAIJ format */
    2210:   b          = (Mat_SeqSBAIJ *)fact->data;
    2211:   b->free_ij = PETSC_TRUE;
    2212:   PetscCall(PetscShmgetAllocateArray(ui[am], sizeof(PetscScalar), (void **)&b->a));
    2213:   b->free_a = PETSC_TRUE;
    2214:   b->j      = uj;
    2215:   b->i      = ui;
    2216:   b->diag   = udiag;
    2217:   b->ilen   = NULL;
    2218:   b->imax   = NULL;
    2219:   b->row    = perm;
    2220:   b->col    = perm;
    2221:   PetscCall(PetscObjectReference((PetscObject)perm));
    2222:   PetscCall(PetscObjectReference((PetscObject)perm));
    2223:   b->icol          = iperm;
    2224:   b->pivotinblocks = PETSC_FALSE; /* need to get from MatFactorInfo */

    2226:   PetscCall(PetscMalloc1(am, &b->solve_work));

    2228:   b->maxnz = b->nz = ui[am];

    2230:   fact->info.factor_mallocs   = reallocs;
    2231:   fact->info.fill_ratio_given = fill;
    2232:   if (ai[am] != 0) {
    2233:     /* nonzeros in lower triangular part of A (including diagonals) = (ai[am]+am)/2 */
    2234:     fact->info.fill_ratio_needed = ((PetscReal)2 * ui[am]) / (ai[am] + am);
    2235:   } else {
    2236:     fact->info.fill_ratio_needed = 0.0;
    2237:   }
    2238: #if defined(PETSC_USE_INFO)
    2239:   if (ai[am] != 0) {
    2240:     PetscReal af = fact->info.fill_ratio_needed;
    2241:     PetscCall(PetscInfo(A, "Reallocs %" PetscInt_FMT " Fill ratio:given %g needed %g\n", reallocs, (double)fill, (double)af));
    2242:     PetscCall(PetscInfo(A, "Run with -pc_factor_fill %g or use \n", (double)af));
    2243:     PetscCall(PetscInfo(A, "PCFactorSetFill(pc,%g) for best performance.\n", (double)af));
    2244:   } else {
    2245:     PetscCall(PetscInfo(A, "Empty matrix\n"));
    2246:   }
    2247: #endif
    2248:   fact->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJ;
    2249:   PetscFunctionReturn(PETSC_SUCCESS);
    2250: }

    2252: PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJ(Mat fact, Mat A, IS perm, const MatFactorInfo *info)
    2253: {
    2254:   Mat_SeqAIJ        *a = (Mat_SeqAIJ *)A->data;
    2255:   Mat_SeqSBAIJ      *b;
    2256:   PetscBool          perm_identity;
    2257:   PetscReal          fill = info->fill;
    2258:   const PetscInt    *rip, *riip;
    2259:   PetscInt           i, am = A->rmap->n, *ai = a->i, *aj = a->j, reallocs = 0, prow;
    2260:   PetscInt          *jl, jmin, jmax, nzk, *ui, k, j, *il, nextprow;
    2261:   PetscInt           nlnk, *lnk, ncols, ncols_upper, *cols, *uj, **ui_ptr, *uj_ptr, *udiag;
    2262:   PetscFreeSpaceList free_space = NULL, current_space = NULL;
    2263:   PetscBT            lnkbt;
    2264:   IS                 iperm;
    2265:   PetscBool          diagDense;

    2267:   PetscFunctionBegin;
    2268:   PetscCheck(A->rmap->n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Must be square matrix, rows %" PetscInt_FMT " columns %" PetscInt_FMT, A->rmap->n, A->cmap->n);
    2269:   PetscCall(MatGetDiagonalMarkers_SeqAIJ(A, NULL, &diagDense));
    2270:   PetscCheck(diagDense, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entries");

    2272:   /* check whether perm is the identity mapping */
    2273:   PetscCall(ISIdentity(perm, &perm_identity));
    2274:   PetscCall(ISInvertPermutation(perm, PETSC_DECIDE, &iperm));
    2275:   PetscCall(ISGetIndices(iperm, &riip));
    2276:   PetscCall(ISGetIndices(perm, &rip));

    2278:   /* initialization */
    2279:   PetscCall(PetscShmgetAllocateArray(am + 1, sizeof(PetscInt), (void **)&ui));
    2280:   PetscCall(PetscMalloc1(am + 1, &udiag));
    2281:   ui[0] = 0;

    2283:   /* jl: linked list for storing indices of the pivot rows
    2284:      il: il[i] points to the 1st nonzero entry of U(i,k:am-1) */
    2285:   PetscCall(PetscMalloc4(am, &ui_ptr, am, &jl, am, &il, am, &cols));
    2286:   for (i = 0; i < am; i++) {
    2287:     jl[i] = am;
    2288:     il[i] = 0;
    2289:   }

    2291:   /* create and initialize a linked list for storing column indices of the active row k */
    2292:   nlnk = am + 1;
    2293:   PetscCall(PetscLLCreate(am, am, nlnk, lnk, lnkbt));

    2295:   /* initial FreeSpace size is fill*(ai[am]+am)/2 */
    2296:   PetscCall(PetscFreeSpaceGet(PetscRealIntMultTruncate(fill, (ai[am] + am) / 2), &free_space));
    2297:   current_space = free_space;

    2299:   for (k = 0; k < am; k++) { /* for each active row k */
    2300:     /* initialize lnk by the column indices of row rip[k] of A */
    2301:     nzk   = 0;
    2302:     ncols = ai[rip[k] + 1] - ai[rip[k]];
    2303:     PetscCheck(ncols, PETSC_COMM_SELF, PETSC_ERR_MAT_CH_ZRPVT, "Empty row in matrix: row in original ordering %" PetscInt_FMT " in permuted ordering %" PetscInt_FMT, rip[k], k);
    2304:     ncols_upper = 0;
    2305:     for (j = 0; j < ncols; j++) {
    2306:       i = riip[*(aj + ai[rip[k]] + j)];
    2307:       if (i >= k) { /* only take upper triangular entry */
    2308:         cols[ncols_upper] = i;
    2309:         ncols_upper++;
    2310:       }
    2311:     }
    2312:     PetscCall(PetscLLAdd(ncols_upper, cols, am, &nlnk, lnk, lnkbt));
    2313:     nzk += nlnk;

    2315:     /* update lnk by computing fill-in for each pivot row to be merged in */
    2316:     prow = jl[k]; /* 1st pivot row */

    2318:     while (prow < k) {
    2319:       nextprow = jl[prow];
    2320:       /* merge prow into k-th row */
    2321:       jmin   = il[prow] + 1; /* index of the 2nd nzero entry in U(prow,k:am-1) */
    2322:       jmax   = ui[prow + 1];
    2323:       ncols  = jmax - jmin;
    2324:       uj_ptr = ui_ptr[prow] + jmin - ui[prow]; /* points to the 2nd nzero entry in U(prow,k:am-1) */
    2325:       PetscCall(PetscLLAddSorted(ncols, uj_ptr, am, &nlnk, lnk, lnkbt));
    2326:       nzk += nlnk;

    2328:       /* update il and jl for prow */
    2329:       if (jmin < jmax) {
    2330:         il[prow] = jmin;
    2331:         j        = *uj_ptr;
    2332:         jl[prow] = jl[j];
    2333:         jl[j]    = prow;
    2334:       }
    2335:       prow = nextprow;
    2336:     }

    2338:     /* if free space is not available, make more free space */
    2339:     if (current_space->local_remaining < nzk) {
    2340:       i = am - k + 1;                                    /* num of unfactored rows */
    2341:       i = PetscIntMultTruncate(i, PetscMin(nzk, i - 1)); /* i*nzk, i*(i-1): estimated and max additional space needed */
    2342:       PetscCall(PetscFreeSpaceGet(i, &current_space));
    2343:       reallocs++;
    2344:     }

    2346:     /* copy data into free space, then initialize lnk */
    2347:     PetscCall(PetscLLClean(am, am, nzk, lnk, current_space->array, lnkbt));

    2349:     /* add the k-th row into il and jl */
    2350:     if (nzk > 1) {
    2351:       i     = current_space->array[1]; /* col value of the first nonzero element in U(k, k+1:am-1) */
    2352:       jl[k] = jl[i];
    2353:       jl[i] = k;
    2354:       il[k] = ui[k] + 1;
    2355:     }
    2356:     ui_ptr[k] = current_space->array;

    2358:     current_space->array += nzk;
    2359:     current_space->local_used += nzk;
    2360:     current_space->local_remaining -= nzk;

    2362:     ui[k + 1] = ui[k] + nzk;
    2363:   }

    2365:   PetscCall(ISRestoreIndices(perm, &rip));
    2366:   PetscCall(ISRestoreIndices(iperm, &riip));
    2367:   PetscCall(PetscFree4(ui_ptr, jl, il, cols));

    2369:   /* copy free_space into uj and free free_space; set ui, uj, udiag in new datastructure; */
    2370:   PetscCall(PetscShmgetAllocateArray(ui[am], sizeof(PetscInt), (void **)&uj));
    2371:   PetscCall(PetscFreeSpaceContiguous_Cholesky(&free_space, uj, am, ui, udiag)); /* store matrix factor */
    2372:   PetscCall(PetscLLDestroy(lnk, lnkbt));

    2374:   /* put together the new matrix in MATSEQSBAIJ format */
    2375:   b          = (Mat_SeqSBAIJ *)fact->data;
    2376:   b->free_ij = PETSC_TRUE;
    2377:   PetscCall(PetscShmgetAllocateArray(ui[am], sizeof(PetscScalar), (void **)&b->a));
    2378:   b->free_a = PETSC_TRUE;
    2379:   b->j      = uj;
    2380:   b->i      = ui;
    2381:   b->diag   = udiag;
    2382:   b->ilen   = NULL;
    2383:   b->imax   = NULL;
    2384:   b->row    = perm;
    2385:   b->col    = perm;

    2387:   PetscCall(PetscObjectReference((PetscObject)perm));
    2388:   PetscCall(PetscObjectReference((PetscObject)perm));

    2390:   b->icol          = iperm;
    2391:   b->pivotinblocks = PETSC_FALSE; /* need to get from MatFactorInfo */

    2393:   PetscCall(PetscMalloc1(am, &b->solve_work));

    2395:   b->maxnz = b->nz = ui[am];

    2397:   fact->info.factor_mallocs   = reallocs;
    2398:   fact->info.fill_ratio_given = fill;
    2399:   if (ai[am] != 0) {
    2400:     /* nonzeros in lower triangular part of A (including diagonals) = (ai[am]+am)/2 */
    2401:     fact->info.fill_ratio_needed = ((PetscReal)2 * ui[am]) / (ai[am] + am);
    2402:   } else {
    2403:     fact->info.fill_ratio_needed = 0.0;
    2404:   }
    2405: #if defined(PETSC_USE_INFO)
    2406:   if (ai[am] != 0) {
    2407:     PetscReal af = fact->info.fill_ratio_needed;
    2408:     PetscCall(PetscInfo(A, "Reallocs %" PetscInt_FMT " Fill ratio:given %g needed %g\n", reallocs, (double)fill, (double)af));
    2409:     PetscCall(PetscInfo(A, "Run with -pc_factor_fill %g or use \n", (double)af));
    2410:     PetscCall(PetscInfo(A, "PCFactorSetFill(pc,%g) for best performance.\n", (double)af));
    2411:   } else {
    2412:     PetscCall(PetscInfo(A, "Empty matrix\n"));
    2413:   }
    2414: #endif
    2415:   fact->ops->choleskyfactornumeric = MatCholeskyFactorNumeric_SeqAIJ;
    2416:   PetscFunctionReturn(PETSC_SUCCESS);
    2417: }

    2419: PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering(Mat A, Vec bb, Vec xx)
    2420: {
    2421:   Mat_SeqAIJ        *a  = (Mat_SeqAIJ *)A->data;
    2422:   PetscInt           n  = A->rmap->n;
    2423:   const PetscInt    *ai = a->i, *aj = a->j, *adiag = a->diag, *vi;
    2424:   PetscScalar       *x, sum;
    2425:   const PetscScalar *b;
    2426:   const MatScalar   *aa, *v;
    2427:   PetscInt           i, nz;

    2429:   PetscFunctionBegin;
    2430:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);

    2432:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    2433:   PetscCall(VecGetArrayRead(bb, &b));
    2434:   PetscCall(VecGetArrayWrite(xx, &x));

    2436:   /* forward solve the lower triangular */
    2437:   x[0] = b[0];
    2438:   v    = aa;
    2439:   vi   = aj;
    2440:   for (i = 1; i < n; i++) {
    2441:     nz  = ai[i + 1] - ai[i];
    2442:     sum = b[i];
    2443:     PetscSparseDenseMinusDot(sum, x, v, vi, nz);
    2444:     v += nz;
    2445:     vi += nz;
    2446:     x[i] = sum;
    2447:   }

    2449:   /* backward solve the upper triangular */
    2450:   for (i = n - 1; i >= 0; i--) {
    2451:     v   = aa + adiag[i + 1] + 1;
    2452:     vi  = aj + adiag[i + 1] + 1;
    2453:     nz  = adiag[i] - adiag[i + 1] - 1;
    2454:     sum = x[i];
    2455:     PetscSparseDenseMinusDot(sum, x, v, vi, nz);
    2456:     x[i] = sum * v[nz]; /* x[i]=aa[adiag[i]]*sum; v++; */
    2457:   }

    2459:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    2460:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2461:   PetscCall(VecRestoreArrayRead(bb, &b));
    2462:   PetscCall(VecRestoreArrayWrite(xx, &x));
    2463:   PetscFunctionReturn(PETSC_SUCCESS);
    2464: }

    2466: PetscErrorCode MatSolve_SeqAIJ(Mat A, Vec bb, Vec xx)
    2467: {
    2468:   Mat_SeqAIJ        *a     = (Mat_SeqAIJ *)A->data;
    2469:   IS                 iscol = a->col, isrow = a->row;
    2470:   PetscInt           i, n = A->rmap->n, *vi, *ai = a->i, *aj = a->j, *adiag = a->diag, nz;
    2471:   const PetscInt    *rout, *cout, *r, *c;
    2472:   PetscScalar       *x, *tmp, sum;
    2473:   const PetscScalar *b;
    2474:   const MatScalar   *aa, *v;

    2476:   PetscFunctionBegin;
    2477:   if (!n) PetscFunctionReturn(PETSC_SUCCESS);

    2479:   PetscCall(MatSeqAIJGetArrayRead(A, &aa));
    2480:   PetscCall(VecGetArrayRead(bb, &b));
    2481:   PetscCall(VecGetArrayWrite(xx, &x));
    2482:   tmp = a->solve_work;

    2484:   PetscCall(ISGetIndices(isrow, &rout));
    2485:   r = rout;
    2486:   PetscCall(ISGetIndices(iscol, &cout));
    2487:   c = cout;

    2489:   /* forward solve the lower triangular */
    2490:   tmp[0] = b[r[0]];
    2491:   v      = aa;
    2492:   vi     = aj;
    2493:   for (i = 1; i < n; i++) {
    2494:     nz  = ai[i + 1] - ai[i];
    2495:     sum = b[r[i]];
    2496:     PetscSparseDenseMinusDot(sum, tmp, v, vi, nz);
    2497:     tmp[i] = sum;
    2498:     v += nz;
    2499:     vi += nz;
    2500:   }

    2502:   /* backward solve the upper triangular */
    2503:   for (i = n - 1; i >= 0; i--) {
    2504:     v   = aa + adiag[i + 1] + 1;
    2505:     vi  = aj + adiag[i + 1] + 1;
    2506:     nz  = adiag[i] - adiag[i + 1] - 1;
    2507:     sum = tmp[i];
    2508:     PetscSparseDenseMinusDot(sum, tmp, v, vi, nz);
    2509:     x[c[i]] = tmp[i] = sum * v[nz]; /* v[nz] = aa[adiag[i]] */
    2510:   }

    2512:   PetscCall(ISRestoreIndices(isrow, &rout));
    2513:   PetscCall(ISRestoreIndices(iscol, &cout));
    2514:   PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
    2515:   PetscCall(VecRestoreArrayRead(bb, &b));
    2516:   PetscCall(VecRestoreArrayWrite(xx, &x));
    2517:   PetscCall(PetscLogFlops(2.0 * a->nz - A->cmap->n));
    2518:   PetscFunctionReturn(PETSC_SUCCESS);
    2519: }