Date: 2013-07-02T14:41:20+00:00

Actual source code: aij.c
petsc-3.4.2 2013-07-02

      2: /*
      3:     Defines the basic matrix operations for the AIJ (compressed row)
      4:   matrix storage format.
      5: */

      8: #include <../src/mat/impls/aij/seq/aij.h>          /*I "petscmat.h" I*/
      9: #include <petscblaslapack.h>
     10: #include <petscbt.h>
     11: #include <petsc-private/kernels/blocktranspose.h>
     12: #if defined(PETSC_THREADCOMM_ACTIVE)
     13: #include <petscthreadcomm.h>
     14: #endif

     18: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
     19: {
     21:   PetscInt       i,m,n;
     22:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

     25:   MatGetSize(A,&m,&n);
     26:   PetscMemzero(norms,n*sizeof(PetscReal));
     27:   if (type == NORM_2) {
     28:     for (i=0; i<aij->i[m]; i++) {
     29:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
     30:     }
     31:   } else if (type == NORM_1) {
     32:     for (i=0; i<aij->i[m]; i++) {
     33:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
     34:     }
     35:   } else if (type == NORM_INFINITY) {
     36:     for (i=0; i<aij->i[m]; i++) {
     37:       norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
     38:     }
     39:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");

     41:   if (type == NORM_2) {
     42:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
     43:   }
     44:   return(0);
     45: }

     49: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
     50: {
     51:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
     52:   const MatScalar *aa = a->a;
     53:   PetscInt        i,m=A->rmap->n,cnt = 0;
     54:   const PetscInt  *jj = a->j,*diag;
     55:   PetscInt        *rows;
     56:   PetscErrorCode  ierr;

     59:   MatMarkDiagonal_SeqAIJ(A);
     60:   diag = a->diag;
     61:   for (i=0; i<m; i++) {
     62:     if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
     63:       cnt++;
     64:     }
     65:   }
     66:   PetscMalloc(cnt*sizeof(PetscInt),&rows);
     67:   cnt  = 0;
     68:   for (i=0; i<m; i++) {
     69:     if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
     70:       rows[cnt++] = i;
     71:     }
     72:   }
     73:   *nrows = cnt;
     74:   *zrows = rows;
     75:   return(0);
     76: }

     80: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
     81: {
     82:   PetscInt       nrows,*rows;

     86:   *zrows = NULL;
     87:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
     88:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
     89:   return(0);
     90: }

     94: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
     95: {
     96:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
     97:   const MatScalar *aa;
     98:   PetscInt        m=A->rmap->n,cnt = 0;
     99:   const PetscInt  *ii;
    100:   PetscInt        n,i,j,*rows;
    101:   PetscErrorCode  ierr;

    104:   *keptrows = 0;
    105:   ii        = a->i;
    106:   for (i=0; i<m; i++) {
    107:     n = ii[i+1] - ii[i];
    108:     if (!n) {
    109:       cnt++;
    110:       goto ok1;
    111:     }
    112:     aa = a->a + ii[i];
    113:     for (j=0; j<n; j++) {
    114:       if (aa[j] != 0.0) goto ok1;
    115:     }
    116:     cnt++;
    117: ok1:;
    118:   }
    119:   if (!cnt) return(0);
    120:   PetscMalloc((A->rmap->n-cnt)*sizeof(PetscInt),&rows);
    121:   cnt  = 0;
    122:   for (i=0; i<m; i++) {
    123:     n = ii[i+1] - ii[i];
    124:     if (!n) continue;
    125:     aa = a->a + ii[i];
    126:     for (j=0; j<n; j++) {
    127:       if (aa[j] != 0.0) {
    128:         rows[cnt++] = i;
    129:         break;
    130:       }
    131:     }
    132:   }
    133:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
    134:   return(0);
    135: }

    139: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
    140: {
    142:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) Y->data;
    143:   PetscInt       i,*diag, m = Y->rmap->n;
    144:   MatScalar      *aa = aij->a;
    145:   PetscScalar    *v;
    146:   PetscBool      missing;

    149:   if (Y->assembled) {
    150:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
    151:     if (!missing) {
    152:       diag = aij->diag;
    153:       VecGetArray(D,&v);
    154:       if (is == INSERT_VALUES) {
    155:         for (i=0; i<m; i++) {
    156:           aa[diag[i]] = v[i];
    157:         }
    158:       } else {
    159:         for (i=0; i<m; i++) {
    160:           aa[diag[i]] += v[i];
    161:         }
    162:       }
    163:       VecRestoreArray(D,&v);
    164:       return(0);
    165:     }
    166:     MatSeqAIJInvalidateDiagonal(Y);
    167:   }
    168:   MatDiagonalSet_Default(Y,D,is);
    169:   return(0);
    170: }

    174: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    175: {
    176:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    178:   PetscInt       i,ishift;

    181:   *m = A->rmap->n;
    182:   if (!ia) return(0);
    183:   ishift = 0;
    184:   if (symmetric && !A->structurally_symmetric) {
    185:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
    186:   } else if (oshift == 1) {
    187:     PetscInt *tia;
    188:     PetscInt nz = a->i[A->rmap->n];
    189:     /* malloc space and  add 1 to i and j indices */
    190:     PetscMalloc((A->rmap->n+1)*sizeof(PetscInt),&tia);
    191:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
    192:     *ia = tia;
    193:     if (ja) {
    194:       PetscInt *tja;
    195:       PetscMalloc((nz+1)*sizeof(PetscInt),&tja);
    196:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
    197:       *ja = tja;
    198:     }
    199:   } else {
    200:     *ia = a->i;
    201:     if (ja) *ja = a->j;
    202:   }
    203:   return(0);
    204: }

    208: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    209: {

    213:   if (!ia) return(0);
    214:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
    215:     PetscFree(*ia);
    216:     if (ja) {PetscFree(*ja);}
    217:   }
    218:   return(0);
    219: }

    223: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    224: {
    225:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    227:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
    228:   PetscInt       nz = a->i[m],row,*jj,mr,col;

    231:   *nn = n;
    232:   if (!ia) return(0);
    233:   if (symmetric) {
    234:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
    235:   } else {
    236:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
    237:     PetscMemzero(collengths,n*sizeof(PetscInt));
    238:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
    239:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
    240:     jj   = a->j;
    241:     for (i=0; i<nz; i++) {
    242:       collengths[jj[i]]++;
    243:     }
    244:     cia[0] = oshift;
    245:     for (i=0; i<n; i++) {
    246:       cia[i+1] = cia[i] + collengths[i];
    247:     }
    248:     PetscMemzero(collengths,n*sizeof(PetscInt));
    249:     jj   = a->j;
    250:     for (row=0; row<m; row++) {
    251:       mr = a->i[row+1] - a->i[row];
    252:       for (i=0; i<mr; i++) {
    253:         col = *jj++;

    255:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
    256:       }
    257:     }
    258:     PetscFree(collengths);
    259:     *ia  = cia; *ja = cja;
    260:   }
    261:   return(0);
    262: }

    266: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
    267: {

    271:   if (!ia) return(0);

    273:   PetscFree(*ia);
    274:   PetscFree(*ja);
    275:   return(0);
    276: }

    280: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
    281: {
    282:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    283:   PetscInt       *ai = a->i;

    287:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
    288:   return(0);
    289: }

    293: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    294: {
    295:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    296:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    297:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
    299:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
    300:   MatScalar      *ap,value,*aa = a->a;
    301:   PetscBool      ignorezeroentries = a->ignorezeroentries;
    302:   PetscBool      roworiented       = a->roworiented;

    306:   for (k=0; k<m; k++) { /* loop over added rows */
    307:     row = im[k];
    308:     if (row < 0) continue;
    309: #if defined(PETSC_USE_DEBUG)
    310:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
    311: #endif
    312:     rp   = aj + ai[row]; ap = aa + ai[row];
    313:     rmax = imax[row]; nrow = ailen[row];
    314:     low  = 0;
    315:     high = nrow;
    316:     for (l=0; l<n; l++) { /* loop over added columns */
    317:       if (in[l] < 0) continue;
    318: #if defined(PETSC_USE_DEBUG)
    319:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
    320: #endif
    321:       col = in[l];
    322:       if (v) {
    323:         if (roworiented) {
    324:           value = v[l + k*n];
    325:         } else {
    326:           value = v[k + l*m];
    327:         }
    328:       } else {
    329:         value = 0.;
    330:       }
    331:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    333:       if (col <= lastcol) low = 0;
    334:       else high = nrow;
    335:       lastcol = col;
    336:       while (high-low > 5) {
    337:         t = (low+high)/2;
    338:         if (rp[t] > col) high = t;
    339:         else low = t;
    340:       }
    341:       for (i=low; i<high; i++) {
    342:         if (rp[i] > col) break;
    343:         if (rp[i] == col) {
    344:           if (is == ADD_VALUES) ap[i] += value;
    345:           else ap[i] = value;
    346:           low = i + 1;
    347:           goto noinsert;
    348:         }
    349:       }
    350:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    351:       if (nonew == 1) goto noinsert;
    352:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
    353:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    354:       N = nrow++ - 1; a->nz++; high++;
    355:       /* shift up all the later entries in this row */
    356:       for (ii=N; ii>=i; ii--) {
    357:         rp[ii+1] = rp[ii];
    358:         ap[ii+1] = ap[ii];
    359:       }
    360:       rp[i] = col;
    361:       ap[i] = value;
    362:       low   = i + 1;
    363: noinsert:;
    364:     }
    365:     ailen[row] = nrow;
    366:   }
    367:   A->same_nonzero = PETSC_FALSE;
    368:   return(0);
    369: }

    374: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
    375: {
    376:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    377:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
    378:   PetscInt   *ai = a->i,*ailen = a->ilen;
    379:   MatScalar  *ap,*aa = a->a;

    382:   for (k=0; k<m; k++) { /* loop over rows */
    383:     row = im[k];
    384:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
    385:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
    386:     rp   = aj + ai[row]; ap = aa + ai[row];
    387:     nrow = ailen[row];
    388:     for (l=0; l<n; l++) { /* loop over columns */
    389:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
    390:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
    391:       col  = in[l];
    392:       high = nrow; low = 0; /* assume unsorted */
    393:       while (high-low > 5) {
    394:         t = (low+high)/2;
    395:         if (rp[t] > col) high = t;
    396:         else low = t;
    397:       }
    398:       for (i=low; i<high; i++) {
    399:         if (rp[i] > col) break;
    400:         if (rp[i] == col) {
    401:           *v++ = ap[i];
    402:           goto finished;
    403:         }
    404:       }
    405:       *v++ = 0.0;
    406: finished:;
    407:     }
    408:   }
    409:   return(0);
    410: }

    415: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
    416: {
    417:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    419:   PetscInt       i,*col_lens;
    420:   int            fd;
    421:   FILE           *file;

    424:   PetscViewerBinaryGetDescriptor(viewer,&fd);
    425:   PetscMalloc((4+A->rmap->n)*sizeof(PetscInt),&col_lens);

    427:   col_lens[0] = MAT_FILE_CLASSID;
    428:   col_lens[1] = A->rmap->n;
    429:   col_lens[2] = A->cmap->n;
    430:   col_lens[3] = a->nz;

    432:   /* store lengths of each row and write (including header) to file */
    433:   for (i=0; i<A->rmap->n; i++) {
    434:     col_lens[4+i] = a->i[i+1] - a->i[i];
    435:   }
    436:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
    437:   PetscFree(col_lens);

    439:   /* store column indices (zero start index) */
    440:   PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);

    442:   /* store nonzero values */
    443:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);

    445:   PetscViewerBinaryGetInfoPointer(viewer,&file);
    446:   if (file) {
    447:     fprintf(file,"-matload_block_size %d\n",(int)A->rmap->bs);
    448:   }
    449:   return(0);
    450: }

    452: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

    456: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
    457: {
    458:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    459:   PetscErrorCode    ierr;
    460:   PetscInt          i,j,m = A->rmap->n,shift=0;
    461:   const char        *name;
    462:   PetscViewerFormat format;

    465:   PetscViewerGetFormat(viewer,&format);
    466:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
    467:     PetscInt nofinalvalue = 0;
    468:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-!shift))) {
    469:       nofinalvalue = 1;
    470:     }
    471:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    472:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
    473:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
    474:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
    475:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

    477:     for (i=0; i<m; i++) {
    478:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    479: #if defined(PETSC_USE_COMPLEX)
    480:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    481: #else
    482:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
    483: #endif
    484:       }
    485:     }
    486:     if (nofinalvalue) {
    487:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
    488:     }
    489:     PetscObjectGetName((PetscObject)A,&name);
    490:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
    491:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    492:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
    493:     return(0);
    494:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
    495:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    496:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
    497:     for (i=0; i<m; i++) {
    498:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
    499:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    500: #if defined(PETSC_USE_COMPLEX)
    501:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    502:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    503:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    504:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    505:         } else if (PetscRealPart(a->a[j]) != 0.0) {
    506:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
    507:         }
    508: #else
    509:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);}
    510: #endif
    511:       }
    512:       PetscViewerASCIIPrintf(viewer,"\n");
    513:     }
    514:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    515:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
    516:     PetscInt nzd=0,fshift=1,*sptr;
    517:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    518:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
    519:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
    520:     for (i=0; i<m; i++) {
    521:       sptr[i] = nzd+1;
    522:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    523:         if (a->j[j] >= i) {
    524: #if defined(PETSC_USE_COMPLEX)
    525:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
    526: #else
    527:           if (a->a[j] != 0.0) nzd++;
    528: #endif
    529:         }
    530:       }
    531:     }
    532:     sptr[m] = nzd+1;
    533:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
    534:     for (i=0; i<m+1; i+=6) {
    535:       if (i+4<m) {
    536:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);
    537:       } else if (i+3<m) {
    538:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
    539:       } else if (i+2<m) {
    540:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
    541:       } else if (i+1<m) {
    542:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
    543:       } else if (i<m) {
    544:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
    545:       } else {
    546:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
    547:       }
    548:     }
    549:     PetscViewerASCIIPrintf(viewer,"\n");
    550:     PetscFree(sptr);
    551:     for (i=0; i<m; i++) {
    552:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    553:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
    554:       }
    555:       PetscViewerASCIIPrintf(viewer,"\n");
    556:     }
    557:     PetscViewerASCIIPrintf(viewer,"\n");
    558:     for (i=0; i<m; i++) {
    559:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    560:         if (a->j[j] >= i) {
    561: #if defined(PETSC_USE_COMPLEX)
    562:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
    563:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    564:           }
    565: #else
    566:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
    567: #endif
    568:         }
    569:       }
    570:       PetscViewerASCIIPrintf(viewer,"\n");
    571:     }
    572:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    573:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
    574:     PetscInt    cnt = 0,jcnt;
    575:     PetscScalar value;

    577:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    578:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
    579:     for (i=0; i<m; i++) {
    580:       jcnt = 0;
    581:       for (j=0; j<A->cmap->n; j++) {
    582:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
    583:           value = a->a[cnt++];
    584:           jcnt++;
    585:         } else {
    586:           value = 0.0;
    587:         }
    588: #if defined(PETSC_USE_COMPLEX)
    589:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
    590: #else
    591:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
    592: #endif
    593:       }
    594:       PetscViewerASCIIPrintf(viewer,"\n");
    595:     }
    596:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    597:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
    598:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    599:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
    600: #if defined(PETSC_USE_COMPLEX)
    601:     PetscViewerASCIIPrintf(viewer,"%%matrix complex general\n");
    602: #else
    603:     PetscViewerASCIIPrintf(viewer,"%%matrix real general\n");
    604: #endif
    605:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
    606:     for (i=0; i<m; i++) {
    607:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    608: #if defined(PETSC_USE_COMPLEX)
    609:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
    610:           PetscViewerASCIIPrintf(viewer,"%D %D, %g %g\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    611:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    612:           PetscViewerASCIIPrintf(viewer,"%D %D, %g -%g\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    613:         } else {
    614:           PetscViewerASCIIPrintf(viewer,"%D %D, %g\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]));
    615:         }
    616: #else
    617:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+shift, a->j[j]+shift, (double)a->a[j]);
    618: #endif
    619:       }
    620:     }
    621:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    622:   } else {
    623:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
    624:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
    625:     if (A->factortype) {
    626:       for (i=0; i<m; i++) {
    627:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
    628:         /* L part */
    629:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    630: #if defined(PETSC_USE_COMPLEX)
    631:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    632:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    633:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    634:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    635:           } else {
    636:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
    637:           }
    638: #else
    639:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);
    640: #endif
    641:         }
    642:         /* diagonal */
    643:         j = a->diag[i];
    644: #if defined(PETSC_USE_COMPLEX)
    645:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
    646:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),PetscImaginaryPart(1.0/a->a[j]));
    647:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    648:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),-PetscImaginaryPart(1.0/a->a[j]));
    649:         } else {
    650:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(1.0/a->a[j]));
    651:         }
    652: #else
    653:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)1.0/a->a[j]);
    654: #endif

    656:         /* U part */
    657:         for (j=a->diag[i+1]+1+shift; j<a->diag[i]+shift; j++) {
    658: #if defined(PETSC_USE_COMPLEX)
    659:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    660:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    661:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    662:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    663:           } else {
    664:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
    665:           }
    666: #else
    667:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);
    668: #endif
    669:         }
    670:         PetscViewerASCIIPrintf(viewer,"\n");
    671:       }
    672:     } else {
    673:       for (i=0; i<m; i++) {
    674:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
    675:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    676: #if defined(PETSC_USE_COMPLEX)
    677:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
    678:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    679:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    680:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    681:           } else {
    682:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
    683:           }
    684: #else
    685:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);
    686: #endif
    687:         }
    688:         PetscViewerASCIIPrintf(viewer,"\n");
    689:       }
    690:     }
    691:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
    692:   }
    693:   PetscViewerFlush(viewer);
    694:   return(0);
    695: }

    697: #include <petscdraw.h>
    700: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
    701: {
    702:   Mat               A  = (Mat) Aa;
    703:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    704:   PetscErrorCode    ierr;
    705:   PetscInt          i,j,m = A->rmap->n,color;
    706:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
    707:   PetscViewer       viewer;
    708:   PetscViewerFormat format;

    711:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
    712:   PetscViewerGetFormat(viewer,&format);

    714:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
    715:   /* loop over matrix elements drawing boxes */

    717:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
    718:     /* Blue for negative, Cyan for zero and  Red for positive */
    719:     color = PETSC_DRAW_BLUE;
    720:     for (i=0; i<m; i++) {
    721:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    722:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    723:         x_l = a->j[j]; x_r = x_l + 1.0;
    724:         if (PetscRealPart(a->a[j]) >=  0.) continue;
    725:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    726:       }
    727:     }
    728:     color = PETSC_DRAW_CYAN;
    729:     for (i=0; i<m; i++) {
    730:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    731:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    732:         x_l = a->j[j]; x_r = x_l + 1.0;
    733:         if (a->a[j] !=  0.) continue;
    734:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    735:       }
    736:     }
    737:     color = PETSC_DRAW_RED;
    738:     for (i=0; i<m; i++) {
    739:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    740:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    741:         x_l = a->j[j]; x_r = x_l + 1.0;
    742:         if (PetscRealPart(a->a[j]) <=  0.) continue;
    743:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    744:       }
    745:     }
    746:   } else {
    747:     /* use contour shading to indicate magnitude of values */
    748:     /* first determine max of all nonzero values */
    749:     PetscInt  nz = a->nz,count;
    750:     PetscDraw popup;
    751:     PetscReal scale;

    753:     for (i=0; i<nz; i++) {
    754:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
    755:     }
    756:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
    757:     PetscDrawGetPopup(draw,&popup);
    758:     if (popup) {
    759:       PetscDrawScalePopup(popup,0.0,maxv);
    760:     }
    761:     count = 0;
    762:     for (i=0; i<m; i++) {
    763:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    764:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    765:         x_l   = a->j[j]; x_r = x_l + 1.0;
    766:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
    767:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    768:         count++;
    769:       }
    770:     }
    771:   }
    772:   return(0);
    773: }

    775: #include <petscdraw.h>
    778: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
    779: {
    781:   PetscDraw      draw;
    782:   PetscReal      xr,yr,xl,yl,h,w;
    783:   PetscBool      isnull;

    786:   PetscViewerDrawGetDraw(viewer,0,&draw);
    787:   PetscDrawIsNull(draw,&isnull);
    788:   if (isnull) return(0);

    790:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
    791:   xr   = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
    792:   xr  += w;    yr += h;  xl = -w;     yl = -h;
    793:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
    794:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
    795:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
    796:   return(0);
    797: }

    801: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
    802: {
    804:   PetscBool      iascii,isbinary,isdraw;

    807:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
    808:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
    809:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
    810:   if (iascii) {
    811:     MatView_SeqAIJ_ASCII(A,viewer);
    812:   } else if (isbinary) {
    813:     MatView_SeqAIJ_Binary(A,viewer);
    814:   } else if (isdraw) {
    815:     MatView_SeqAIJ_Draw(A,viewer);
    816:   }
    817:   MatView_SeqAIJ_Inode(A,viewer);
    818:   return(0);
    819: }

    823: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
    824: {
    825:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    827:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
    828:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
    829:   MatScalar      *aa    = a->a,*ap;
    830:   PetscReal      ratio  = 0.6;

    833:   if (mode == MAT_FLUSH_ASSEMBLY) return(0);

    835:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    836:   for (i=1; i<m; i++) {
    837:     /* move each row back by the amount of empty slots (fshift) before it*/
    838:     fshift += imax[i-1] - ailen[i-1];
    839:     rmax    = PetscMax(rmax,ailen[i]);
    840:     if (fshift) {
    841:       ip = aj + ai[i];
    842:       ap = aa + ai[i];
    843:       N  = ailen[i];
    844:       for (j=0; j<N; j++) {
    845:         ip[j-fshift] = ip[j];
    846:         ap[j-fshift] = ap[j];
    847:       }
    848:     }
    849:     ai[i] = ai[i-1] + ailen[i-1];
    850:   }
    851:   if (m) {
    852:     fshift += imax[m-1] - ailen[m-1];
    853:     ai[m]   = ai[m-1] + ailen[m-1];
    854:   }
    855:   /* reset ilen and imax for each row */
    856:   for (i=0; i<m; i++) {
    857:     ailen[i] = imax[i] = ai[i+1] - ai[i];
    858:   }
    859:   a->nz = ai[m];
    860:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

    862:   MatMarkDiagonal_SeqAIJ(A);
    863:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
    864:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
    865:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);

    867:   A->info.mallocs    += a->reallocs;
    868:   a->reallocs         = 0;
    869:   A->info.nz_unneeded = (double)fshift;
    870:   a->rmax             = rmax;

    872:   MatCheckCompressedRow(A,&a->compressedrow,a->i,m,ratio);

    874:   A->same_nonzero = PETSC_TRUE;

    876:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);

    878:   MatSeqAIJInvalidateDiagonal(A);
    879:   return(0);
    880: }

    884: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
    885: {
    886:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    887:   PetscInt       i,nz = a->nz;
    888:   MatScalar      *aa = a->a;

    892:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
    893:   MatSeqAIJInvalidateDiagonal(A);
    894:   return(0);
    895: }

    899: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
    900: {
    901:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    902:   PetscInt       i,nz = a->nz;
    903:   MatScalar      *aa = a->a;

    907:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
    908:   MatSeqAIJInvalidateDiagonal(A);
    909:   return(0);
    910: }

    912: #if defined(PETSC_THREADCOMM_ACTIVE)
    913: PetscErrorCode MatZeroEntries_SeqAIJ_Kernel(PetscInt thread_id,Mat A)
    914: {
    916:   PetscInt       *trstarts=A->rmap->trstarts;
    917:   PetscInt       n,start,end;
    918:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    920:   start = trstarts[thread_id];
    921:   end   = trstarts[thread_id+1];
    922:   n     = a->i[end] - a->i[start];
    923:   PetscMemzero(a->a+a->i[start],n*sizeof(PetscScalar));
    924:   return 0;
    925: }

    929: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    930: {

    934:   PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatZeroEntries_SeqAIJ_Kernel,1,A);
    935:   MatSeqAIJInvalidateDiagonal(A);
    936:   return(0);
    937: }
    938: #else
    941: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    942: {
    943:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    947:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
    948:   MatSeqAIJInvalidateDiagonal(A);
    949:   return(0);
    950: }
    951: #endif

    955: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
    956: {
    957:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    961: #if defined(PETSC_USE_LOG)
    962:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
    963: #endif
    964:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
    965:   ISDestroy(&a->row);
    966:   ISDestroy(&a->col);
    967:   PetscFree(a->diag);
    968:   PetscFree(a->ibdiag);
    969:   PetscFree2(a->imax,a->ilen);
    970:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
    971:   PetscFree(a->solve_work);
    972:   ISDestroy(&a->icol);
    973:   PetscFree(a->saved_values);
    974:   ISColoringDestroy(&a->coloring);
    975:   PetscFree(a->xtoy);
    976:   MatDestroy(&a->XtoY);
    977:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
    978:   PetscFree(a->matmult_abdense);

    980:   MatDestroy_SeqAIJ_Inode(A);
    981:   PetscFree(A->data);

    983:   PetscObjectChangeTypeName((PetscObject)A,0);
    984:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
    985:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
    986:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
    987:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
    988:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
    989:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
    990:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
    991:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
    992:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
    993:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
    994:   return(0);
    995: }

    999: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
    1000: {
    1001:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    1005:   switch (op) {
    1006:   case MAT_ROW_ORIENTED:
    1007:     a->roworiented = flg;
    1008:     break;
    1009:   case MAT_KEEP_NONZERO_PATTERN:
    1010:     a->keepnonzeropattern = flg;
    1011:     break;
    1012:   case MAT_NEW_NONZERO_LOCATIONS:
    1013:     a->nonew = (flg ? 0 : 1);
    1014:     break;
    1015:   case MAT_NEW_NONZERO_LOCATION_ERR:
    1016:     a->nonew = (flg ? -1 : 0);
    1017:     break;
    1018:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
    1019:     a->nonew = (flg ? -2 : 0);
    1020:     break;
    1021:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
    1022:     a->nounused = (flg ? -1 : 0);
    1023:     break;
    1024:   case MAT_IGNORE_ZERO_ENTRIES:
    1025:     a->ignorezeroentries = flg;
    1026:     break;
    1027:   case MAT_CHECK_COMPRESSED_ROW:
    1028:     a->compressedrow.check = flg;
    1029:     break;
    1030:   case MAT_SPD:
    1031:   case MAT_SYMMETRIC:
    1032:   case MAT_STRUCTURALLY_SYMMETRIC:
    1033:   case MAT_HERMITIAN:
    1034:   case MAT_SYMMETRY_ETERNAL:
    1035:     /* These options are handled directly by MatSetOption() */
    1036:     break;
    1037:   case MAT_NEW_DIAGONALS:
    1038:   case MAT_IGNORE_OFF_PROC_ENTRIES:
    1039:   case MAT_USE_HASH_TABLE:
    1040:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
    1041:     break;
    1042:   case MAT_USE_INODES:
    1043:     /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
    1044:     break;
    1045:   default:
    1046:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
    1047:   }
    1048:   MatSetOption_SeqAIJ_Inode(A,op,flg);
    1049:   return(0);
    1050: }

    1054: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
    1055: {
    1056:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1058:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
    1059:   PetscScalar    *aa=a->a,*x,zero=0.0;

    1062:   VecGetLocalSize(v,&n);
    1063:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

    1065:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
    1066:     PetscInt *diag=a->diag;
    1067:     VecGetArray(v,&x);
    1068:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
    1069:     VecRestoreArray(v,&x);
    1070:     return(0);
    1071:   }

    1073:   VecSet(v,zero);
    1074:   VecGetArray(v,&x);
    1075:   for (i=0; i<n; i++) {
    1076:     nz = ai[i+1] - ai[i];
    1077:     if (!nz) x[i] = 0.0;
    1078:     for (j=ai[i]; j<ai[i+1]; j++) {
    1079:       if (aj[j] == i) {
    1080:         x[i] = aa[j];
    1081:         break;
    1082:       }
    1083:     }
    1084:   }
    1085:   VecRestoreArray(v,&x);
    1086:   return(0);
    1087: }

    1089: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
    1092: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
    1093: {
    1094:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1095:   PetscScalar    *x,*y;
    1097:   PetscInt       m = A->rmap->n;
    1098: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1099:   MatScalar         *v;
    1100:   PetscScalar       alpha;
    1101:   PetscInt          n,i,j,*idx,*ii,*ridx=NULL;
    1102:   Mat_CompressedRow cprow    = a->compressedrow;
    1103:   PetscBool         usecprow = cprow.use;
    1104: #endif

    1107:   if (zz != yy) {VecCopy(zz,yy);}
    1108:   VecGetArray(xx,&x);
    1109:   VecGetArray(yy,&y);

    1111: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    1112:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
    1113: #else
    1114:   if (usecprow) {
    1115:     m    = cprow.nrows;
    1116:     ii   = cprow.i;
    1117:     ridx = cprow.rindex;
    1118:   } else {
    1119:     ii = a->i;
    1120:   }
    1121:   for (i=0; i<m; i++) {
    1122:     idx = a->j + ii[i];
    1123:     v   = a->a + ii[i];
    1124:     n   = ii[i+1] - ii[i];
    1125:     if (usecprow) {
    1126:       alpha = x[ridx[i]];
    1127:     } else {
    1128:       alpha = x[i];
    1129:     }
    1130:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
    1131:   }
    1132: #endif
    1133:   PetscLogFlops(2.0*a->nz);
    1134:   VecRestoreArray(xx,&x);
    1135:   VecRestoreArray(yy,&y);
    1136:   return(0);
    1137: }

    1141: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
    1142: {

    1146:   VecSet(yy,0.0);
    1147:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
    1148:   return(0);
    1149: }

    1151: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
    1152: #if defined(PETSC_THREADCOMM_ACTIVE)
    1153: PetscErrorCode MatMult_SeqAIJ_Kernel(PetscInt thread_id,Mat A,Vec xx,Vec yy)
    1154: {
    1155:   PetscErrorCode    ierr;
    1156:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1157:   PetscScalar       *y;
    1158:   const PetscScalar *x;
    1159:   const MatScalar   *aa;
    1160:   PetscInt          *trstarts=A->rmap->trstarts;
    1161:   PetscInt          n,start,end,i;
    1162:   const PetscInt    *aj,*ai;
    1163:   PetscScalar       sum;

    1165:   VecGetArrayRead(xx,&x);
    1166:   VecGetArray(yy,&y);
    1167:   start = trstarts[thread_id];
    1168:   end   = trstarts[thread_id+1];
    1169:   aj    = a->j;
    1170:   aa    = a->a;
    1171:   ai    = a->i;
    1172:   for (i=start; i<end; i++) {
    1173:     n   = ai[i+1] - ai[i];
    1174:     aj  = a->j + ai[i];
    1175:     aa  = a->a + ai[i];
    1176:     sum = 0.0;
    1177:     PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1178:     y[i] = sum;
    1179:   }
    1180:   VecRestoreArrayRead(xx,&x);
    1181:   VecRestoreArray(yy,&y);
    1182:   return 0;
    1183: }

    1187: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
    1188: {
    1189:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1190:   PetscScalar       *y;
    1191:   const PetscScalar *x;
    1192:   const MatScalar   *aa;
    1193:   PetscErrorCode    ierr;
    1194:   PetscInt          m=A->rmap->n;
    1195:   const PetscInt    *aj,*ii,*ridx=NULL;
    1196:   PetscInt          n,i,nonzerorow=0;
    1197:   PetscScalar       sum;
    1198:   PetscBool         usecprow=a->compressedrow.use;

    1200: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1201: #pragma disjoint(*x,*y,*aa)
    1202: #endif

    1205:   aj = a->j;
    1206:   aa = a->a;
    1207:   ii = a->i;
    1208:   if (usecprow) { /* use compressed row format */
    1209:     VecGetArrayRead(xx,&x);
    1210:     VecGetArray(yy,&y);
    1211:     m    = a->compressedrow.nrows;
    1212:     ii   = a->compressedrow.i;
    1213:     ridx = a->compressedrow.rindex;
    1214:     for (i=0; i<m; i++) {
    1215:       n           = ii[i+1] - ii[i];
    1216:       aj          = a->j + ii[i];
    1217:       aa          = a->a + ii[i];
    1218:       sum         = 0.0;
    1219:       nonzerorow += (n>0);
    1220:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1221:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1222:       y[*ridx++] = sum;
    1223:     }
    1224:     VecRestoreArrayRead(xx,&x);
    1225:     VecRestoreArray(yy,&y);
    1226:   } else { /* do not use compressed row format */
    1227: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    1228:     fortranmultaij_(&m,x,ii,aj,aa,y);
    1229: #else
    1230:     PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatMult_SeqAIJ_Kernel,3,A,xx,yy);
    1231: #endif
    1232:   }
    1233:   PetscLogFlops(2.0*a->nz - nonzerorow);
    1234:   return(0);
    1235: }
    1236: #else
    1239: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
    1240: {
    1241:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1242:   PetscScalar       *y;
    1243:   const PetscScalar *x;
    1244:   const MatScalar   *aa;
    1245:   PetscErrorCode    ierr;
    1246:   PetscInt          m=A->rmap->n;
    1247:   const PetscInt    *aj,*ii,*ridx=NULL;
    1248:   PetscInt          n,i,nonzerorow=0;
    1249:   PetscScalar       sum;
    1250:   PetscBool         usecprow=a->compressedrow.use;

    1252: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    1253: #pragma disjoint(*x,*y,*aa)
    1254: #endif

    1257:   VecGetArrayRead(xx,&x);
    1258:   VecGetArray(yy,&y);
    1259:   aj   = a->j;
    1260:   aa   = a->a;
    1261:   ii   = a->i;
    1262:   if (usecprow) { /* use compressed row format */
    1263:     m    = a->compressedrow.nrows;
    1264:     ii   = a->compressedrow.i;
    1265:     ridx = a->compressedrow.rindex;
    1266:     for (i=0; i<m; i++) {
    1267:       n           = ii[i+1] - ii[i];
    1268:       aj          = a->j + ii[i];
    1269:       aa          = a->a + ii[i];
    1270:       sum         = 0.0;
    1271:       nonzerorow += (n>0);
    1272:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1273:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
    1274:       y[*ridx++] = sum;
    1275:     }
    1276:   } else { /* do not use compressed row format */
    1277: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    1278:     fortranmultaij_(&m,x,ii,aj,aa,y);
    1279: #else
    1280: #if defined(PETSC_THREADCOMM_ACTIVE)
    1281:     PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatMult_SeqAIJ_Kernel,3,A,xx,yy);
    1282: #else
    1283:     for (i=0; i<m; i++) {
    1284:       n           = ii[i+1] - ii[i];
    1285:       aj          = a->j + ii[i];
    1286:       aa          = a->a + ii[i];
    1287:       sum         = 0.0;
    1288:       nonzerorow += (n>0);
    1289:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1290:       y[i] = sum;
    1291:     }
    1292: #endif
    1293: #endif
    1294:   }
    1295:   PetscLogFlops(2.0*a->nz - nonzerorow);
    1296:   VecRestoreArrayRead(xx,&x);
    1297:   VecRestoreArray(yy,&y);
    1298:   return(0);
    1299: }
    1300: #endif

    1302: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
    1305: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    1306: {
    1307:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1308:   PetscScalar       *y,*z;
    1309:   const PetscScalar *x;
    1310:   const MatScalar   *aa;
    1311:   PetscErrorCode    ierr;
    1312:   PetscInt          m = A->rmap->n,*aj,*ii;
    1313:   PetscInt          n,i,*ridx=NULL;
    1314:   PetscScalar       sum;
    1315:   PetscBool         usecprow=a->compressedrow.use;

    1318:   VecGetArrayRead(xx,&x);
    1319:   VecGetArray(yy,&y);
    1320:   if (zz != yy) {
    1321:     VecGetArray(zz,&z);
    1322:   } else {
    1323:     z = y;
    1324:   }

    1326:   aj = a->j;
    1327:   aa = a->a;
    1328:   ii = a->i;
    1329:   if (usecprow) { /* use compressed row format */
    1330:     if (zz != yy) {
    1331:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
    1332:     }
    1333:     m    = a->compressedrow.nrows;
    1334:     ii   = a->compressedrow.i;
    1335:     ridx = a->compressedrow.rindex;
    1336:     for (i=0; i<m; i++) {
    1337:       n   = ii[i+1] - ii[i];
    1338:       aj  = a->j + ii[i];
    1339:       aa  = a->a + ii[i];
    1340:       sum = y[*ridx];
    1341:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1342:       z[*ridx++] = sum;
    1343:     }
    1344:   } else { /* do not use compressed row format */
    1345: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    1346:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
    1347: #else
    1348:     for (i=0; i<m; i++) {
    1349:       n   = ii[i+1] - ii[i];
    1350:       aj  = a->j + ii[i];
    1351:       aa  = a->a + ii[i];
    1352:       sum = y[i];
    1353:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
    1354:       z[i] = sum;
    1355:     }
    1356: #endif
    1357:   }
    1358:   PetscLogFlops(2.0*a->nz);
    1359:   VecRestoreArrayRead(xx,&x);
    1360:   VecRestoreArray(yy,&y);
    1361:   if (zz != yy) {
    1362:     VecRestoreArray(zz,&z);
    1363:   }
    1364: #if defined(PETSC_HAVE_CUSP)
    1365:   /*
    1366:   VecView(xx,0);
    1367:   VecView(zz,0);
    1368:   MatView(A,0);
    1369:   */
    1370: #endif
    1371:   return(0);
    1372: }

    1374: /*
    1375:      Adds diagonal pointers to sparse matrix structure.
    1376: */
    1379: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
    1380: {
    1381:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1383:   PetscInt       i,j,m = A->rmap->n;

    1386:   if (!a->diag) {
    1387:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
    1388:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
    1389:   }
    1390:   for (i=0; i<A->rmap->n; i++) {
    1391:     a->diag[i] = a->i[i+1];
    1392:     for (j=a->i[i]; j<a->i[i+1]; j++) {
    1393:       if (a->j[j] == i) {
    1394:         a->diag[i] = j;
    1395:         break;
    1396:       }
    1397:     }
    1398:   }
    1399:   return(0);
    1400: }

    1402: /*
    1403:      Checks for missing diagonals
    1404: */
    1407: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
    1408: {
    1409:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    1410:   PetscInt   *diag,*jj = a->j,i;

    1413:   *missing = PETSC_FALSE;
    1414:   if (A->rmap->n > 0 && !jj) {
    1415:     *missing = PETSC_TRUE;
    1416:     if (d) *d = 0;
    1417:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal");
    1418:   } else {
    1419:     diag = a->diag;
    1420:     for (i=0; i<A->rmap->n; i++) {
    1421:       if (jj[diag[i]] != i) {
    1422:         *missing = PETSC_TRUE;
    1423:         if (d) *d = i;
    1424:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
    1425:         break;
    1426:       }
    1427:     }
    1428:   }
    1429:   return(0);
    1430: }

    1434: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
    1435: {
    1436:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
    1438:   PetscInt       i,*diag,m = A->rmap->n;
    1439:   MatScalar      *v = a->a;
    1440:   PetscScalar    *idiag,*mdiag;

    1443:   if (a->idiagvalid) return(0);
    1444:   MatMarkDiagonal_SeqAIJ(A);
    1445:   diag = a->diag;
    1446:   if (!a->idiag) {
    1447:     PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
    1448:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
    1449:     v    = a->a;
    1450:   }
    1451:   mdiag = a->mdiag;
    1452:   idiag = a->idiag;

    1454:   if (omega == 1.0 && !PetscAbsScalar(fshift)) {
    1455:     for (i=0; i<m; i++) {
    1456:       mdiag[i] = v[diag[i]];
    1457:       if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
    1458:       idiag[i] = 1.0/v[diag[i]];
    1459:     }
    1460:     PetscLogFlops(m);
    1461:   } else {
    1462:     for (i=0; i<m; i++) {
    1463:       mdiag[i] = v[diag[i]];
    1464:       idiag[i] = omega/(fshift + v[diag[i]]);
    1465:     }
    1466:     PetscLogFlops(2.0*m);
    1467:   }
    1468:   a->idiagvalid = PETSC_TRUE;
    1469:   return(0);
    1470: }

    1472: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
    1475: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    1476: {
    1477:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1478:   PetscScalar       *x,d,sum,*t,scale;
    1479:   const MatScalar   *v = a->a,*idiag=0,*mdiag;
    1480:   const PetscScalar *b, *bs,*xb, *ts;
    1481:   PetscErrorCode    ierr;
    1482:   PetscInt          n = A->cmap->n,m = A->rmap->n,i;
    1483:   const PetscInt    *idx,*diag;

    1486:   its = its*lits;

    1488:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
    1489:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
    1490:   a->fshift = fshift;
    1491:   a->omega  = omega;

    1493:   diag  = a->diag;
    1494:   t     = a->ssor_work;
    1495:   idiag = a->idiag;
    1496:   mdiag = a->mdiag;

    1498:   VecGetArray(xx,&x);
    1499:   VecGetArrayRead(bb,&b);
    1500:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
    1501:   if (flag == SOR_APPLY_UPPER) {
    1502:     /* apply (U + D/omega) to the vector */
    1503:     bs = b;
    1504:     for (i=0; i<m; i++) {
    1505:       d   = fshift + mdiag[i];
    1506:       n   = a->i[i+1] - diag[i] - 1;
    1507:       idx = a->j + diag[i] + 1;
    1508:       v   = a->a + diag[i] + 1;
    1509:       sum = b[i]*d/omega;
    1510:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
    1511:       x[i] = sum;
    1512:     }
    1513:     VecRestoreArray(xx,&x);
    1514:     VecRestoreArrayRead(bb,&b);
    1515:     PetscLogFlops(a->nz);
    1516:     return(0);
    1517:   }

    1519:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
    1520:   else if (flag & SOR_EISENSTAT) {
    1521:     /* Let  A = L + U + D; where L is lower trianglar,
    1522:     U is upper triangular, E = D/omega; This routine applies

    1524:             (L + E)^{-1} A (U + E)^{-1}

    1526:     to a vector efficiently using Eisenstat's trick.
    1527:     */
    1528:     scale = (2.0/omega) - 1.0;

    1530:     /*  x = (E + U)^{-1} b */
    1531:     for (i=m-1; i>=0; i--) {
    1532:       n   = a->i[i+1] - diag[i] - 1;
    1533:       idx = a->j + diag[i] + 1;
    1534:       v   = a->a + diag[i] + 1;
    1535:       sum = b[i];
    1536:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1537:       x[i] = sum*idiag[i];
    1538:     }

    1540:     /*  t = b - (2*E - D)x */
    1541:     v = a->a;
    1542:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

    1544:     /*  t = (E + L)^{-1}t */
    1545:     ts   = t;
    1546:     diag = a->diag;
    1547:     for (i=0; i<m; i++) {
    1548:       n   = diag[i] - a->i[i];
    1549:       idx = a->j + a->i[i];
    1550:       v   = a->a + a->i[i];
    1551:       sum = t[i];
    1552:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
    1553:       t[i] = sum*idiag[i];
    1554:       /*  x = x + t */
    1555:       x[i] += t[i];
    1556:     }

    1558:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
    1559:     VecRestoreArray(xx,&x);
    1560:     VecRestoreArrayRead(bb,&b);
    1561:     return(0);
    1562:   }
    1563:   if (flag & SOR_ZERO_INITIAL_GUESS) {
    1564:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    1565:       for (i=0; i<m; i++) {
    1566:         n   = diag[i] - a->i[i];
    1567:         idx = a->j + a->i[i];
    1568:         v   = a->a + a->i[i];
    1569:         sum = b[i];
    1570:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1571:         t[i] = sum;
    1572:         x[i] = sum*idiag[i];
    1573:       }
    1574:       xb   = t;
    1575:       PetscLogFlops(a->nz);
    1576:     } else xb = b;
    1577:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    1578:       for (i=m-1; i>=0; i--) {
    1579:         n   = a->i[i+1] - diag[i] - 1;
    1580:         idx = a->j + diag[i] + 1;
    1581:         v   = a->a + diag[i] + 1;
    1582:         sum = xb[i];
    1583:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1584:         if (xb == b) {
    1585:           x[i] = sum*idiag[i];
    1586:         } else {
    1587:           x[i] = (1-omega)*x[i] + sum*idiag[i];
    1588:         }
    1589:       }
    1590:       PetscLogFlops(a->nz);
    1591:     }
    1592:     its--;
    1593:   }
    1594:   while (its--) {
    1595:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
    1596:       for (i=0; i<m; i++) {
    1597:         n   = a->i[i+1] - a->i[i];
    1598:         idx = a->j + a->i[i];
    1599:         v   = a->a + a->i[i];
    1600:         sum = b[i];
    1601:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1602:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    1603:       }
    1604:       PetscLogFlops(2.0*a->nz);
    1605:     }
    1606:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
    1607:       for (i=m-1; i>=0; i--) {
    1608:         n   = a->i[i+1] - a->i[i];
    1609:         idx = a->j + a->i[i];
    1610:         v   = a->a + a->i[i];
    1611:         sum = b[i];
    1612:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
    1613:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    1614:       }
    1615:       PetscLogFlops(2.0*a->nz);
    1616:     }
    1617:   }
    1618:   VecRestoreArray(xx,&x);
    1619:   VecRestoreArrayRead(bb,&b);
    1620:   return(0);
    1621: }

    1626: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
    1627: {
    1628:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    1631:   info->block_size   = 1.0;
    1632:   info->nz_allocated = (double)a->maxnz;
    1633:   info->nz_used      = (double)a->nz;
    1634:   info->nz_unneeded  = (double)(a->maxnz - a->nz);
    1635:   info->assemblies   = (double)A->num_ass;
    1636:   info->mallocs      = (double)A->info.mallocs;
    1637:   info->memory       = ((PetscObject)A)->mem;
    1638:   if (A->factortype) {
    1639:     info->fill_ratio_given  = A->info.fill_ratio_given;
    1640:     info->fill_ratio_needed = A->info.fill_ratio_needed;
    1641:     info->factor_mallocs    = A->info.factor_mallocs;
    1642:   } else {
    1643:     info->fill_ratio_given  = 0;
    1644:     info->fill_ratio_needed = 0;
    1645:     info->factor_mallocs    = 0;
    1646:   }
    1647:   return(0);
    1648: }

    1652: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    1653: {
    1654:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1655:   PetscInt          i,m = A->rmap->n - 1,d = 0;
    1656:   PetscErrorCode    ierr;
    1657:   const PetscScalar *xx;
    1658:   PetscScalar       *bb;
    1659:   PetscBool         missing;

    1662:   if (x && b) {
    1663:     VecGetArrayRead(x,&xx);
    1664:     VecGetArray(b,&bb);
    1665:     for (i=0; i<N; i++) {
    1666:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1667:       bb[rows[i]] = diag*xx[rows[i]];
    1668:     }
    1669:     VecRestoreArrayRead(x,&xx);
    1670:     VecRestoreArray(b,&bb);
    1671:   }

    1673:   if (a->keepnonzeropattern) {
    1674:     for (i=0; i<N; i++) {
    1675:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1676:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
    1677:     }
    1678:     if (diag != 0.0) {
    1679:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
    1680:       if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
    1681:       for (i=0; i<N; i++) {
    1682:         a->a[a->diag[rows[i]]] = diag;
    1683:       }
    1684:     }
    1685:     A->same_nonzero = PETSC_TRUE;
    1686:   } else {
    1687:     if (diag != 0.0) {
    1688:       for (i=0; i<N; i++) {
    1689:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1690:         if (a->ilen[rows[i]] > 0) {
    1691:           a->ilen[rows[i]]    = 1;
    1692:           a->a[a->i[rows[i]]] = diag;
    1693:           a->j[a->i[rows[i]]] = rows[i];
    1694:         } else { /* in case row was completely empty */
    1695:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    1696:         }
    1697:       }
    1698:     } else {
    1699:       for (i=0; i<N; i++) {
    1700:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1701:         a->ilen[rows[i]] = 0;
    1702:       }
    1703:     }
    1704:     A->same_nonzero = PETSC_FALSE;
    1705:   }
    1706:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
    1707:   return(0);
    1708: }

    1712: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
    1713: {
    1714:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    1715:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
    1716:   PetscErrorCode    ierr;
    1717:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
    1718:   const PetscScalar *xx;
    1719:   PetscScalar       *bb;

    1722:   if (x && b) {
    1723:     VecGetArrayRead(x,&xx);
    1724:     VecGetArray(b,&bb);
    1725:     vecs = PETSC_TRUE;
    1726:   }
    1727:   PetscMalloc(A->rmap->n*sizeof(PetscBool),&zeroed);
    1728:   PetscMemzero(zeroed,A->rmap->n*sizeof(PetscBool));
    1729:   for (i=0; i<N; i++) {
    1730:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1731:     PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));

    1733:     zeroed[rows[i]] = PETSC_TRUE;
    1734:   }
    1735:   for (i=0; i<A->rmap->n; i++) {
    1736:     if (!zeroed[i]) {
    1737:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    1738:         if (zeroed[a->j[j]]) {
    1739:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
    1740:           a->a[j] = 0.0;
    1741:         }
    1742:       }
    1743:     } else if (vecs) bb[i] = diag*xx[i];
    1744:   }
    1745:   if (x && b) {
    1746:     VecRestoreArrayRead(x,&xx);
    1747:     VecRestoreArray(b,&bb);
    1748:   }
    1749:   PetscFree(zeroed);
    1750:   if (diag != 0.0) {
    1751:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
    1752:     if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
    1753:     for (i=0; i<N; i++) {
    1754:       a->a[a->diag[rows[i]]] = diag;
    1755:     }
    1756:   }
    1757:   A->same_nonzero = PETSC_TRUE;
    1758:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
    1759:   return(0);
    1760: }

    1764: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1765: {
    1766:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    1767:   PetscInt   *itmp;

    1770:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

    1772:   *nz = a->i[row+1] - a->i[row];
    1773:   if (v) *v = a->a + a->i[row];
    1774:   if (idx) {
    1775:     itmp = a->j + a->i[row];
    1776:     if (*nz) *idx = itmp;
    1777:     else *idx = 0;
    1778:   }
    1779:   return(0);
    1780: }

    1782: /* remove this function? */
    1785: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1786: {
    1788:   return(0);
    1789: }

    1793: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
    1794: {
    1795:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
    1796:   MatScalar      *v  = a->a;
    1797:   PetscReal      sum = 0.0;
    1799:   PetscInt       i,j;

    1802:   if (type == NORM_FROBENIUS) {
    1803:     for (i=0; i<a->nz; i++) {
    1804:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    1805:     }
    1806:     *nrm = PetscSqrtReal(sum);
    1807:   } else if (type == NORM_1) {
    1808:     PetscReal *tmp;
    1809:     PetscInt  *jj = a->j;
    1810:     PetscMalloc((A->cmap->n+1)*sizeof(PetscReal),&tmp);
    1811:     PetscMemzero(tmp,A->cmap->n*sizeof(PetscReal));
    1812:     *nrm = 0.0;
    1813:     for (j=0; j<a->nz; j++) {
    1814:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
    1815:     }
    1816:     for (j=0; j<A->cmap->n; j++) {
    1817:       if (tmp[j] > *nrm) *nrm = tmp[j];
    1818:     }
    1819:     PetscFree(tmp);
    1820:   } else if (type == NORM_INFINITY) {
    1821:     *nrm = 0.0;
    1822:     for (j=0; j<A->rmap->n; j++) {
    1823:       v   = a->a + a->i[j];
    1824:       sum = 0.0;
    1825:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
    1826:         sum += PetscAbsScalar(*v); v++;
    1827:       }
    1828:       if (sum > *nrm) *nrm = sum;
    1829:     }
    1830:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
    1831:   return(0);
    1832: }

    1834: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
    1837: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
    1838: {
    1840:   PetscInt       i,j,anzj;
    1841:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
    1842:   PetscInt       an=A->cmap->N,am=A->rmap->N;
    1843:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

    1846:   /* Allocate space for symbolic transpose info and work array */
    1847:   PetscMalloc((an+1)*sizeof(PetscInt),&ati);
    1848:   PetscMalloc(ai[am]*sizeof(PetscInt),&atj);
    1849:   PetscMalloc(an*sizeof(PetscInt),&atfill);
    1850:   PetscMemzero(ati,(an+1)*sizeof(PetscInt));

    1852:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
    1853:   /* Note: offset by 1 for fast conversion into csr format. */
    1854:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
    1855:   /* Form ati for csr format of A^T. */
    1856:   for (i=0;i<an;i++) ati[i+1] += ati[i];

    1858:   /* Copy ati into atfill so we have locations of the next free space in atj */
    1859:   PetscMemcpy(atfill,ati,an*sizeof(PetscInt));

    1861:   /* Walk through A row-wise and mark nonzero entries of A^T. */
    1862:   for (i=0;i<am;i++) {
    1863:     anzj = ai[i+1] - ai[i];
    1864:     for (j=0;j<anzj;j++) {
    1865:       atj[atfill[*aj]] = i;
    1866:       atfill[*aj++]   += 1;
    1867:     }
    1868:   }

    1870:   /* Clean up temporary space and complete requests. */
    1871:   PetscFree(atfill);
    1872:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);

    1874:   (*B)->rmap->bs = A->cmap->bs;
    1875:   (*B)->cmap->bs = A->rmap->bs;

    1877:   b          = (Mat_SeqAIJ*)((*B)->data);
    1878:   b->free_a  = PETSC_FALSE;
    1879:   b->free_ij = PETSC_TRUE;
    1880:   b->nonew   = 0;
    1881:   return(0);
    1882: }

    1886: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
    1887: {
    1888:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1889:   Mat            C;
    1891:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
    1892:   MatScalar      *array = a->a;

    1895:   if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

    1897:   if (reuse == MAT_INITIAL_MATRIX || *B == A) {
    1898:     PetscMalloc((1+A->cmap->n)*sizeof(PetscInt),&col);
    1899:     PetscMemzero(col,(1+A->cmap->n)*sizeof(PetscInt));

    1901:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
    1902:     MatCreate(PetscObjectComm((PetscObject)A),&C);
    1903:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
    1904:     MatSetBlockSizes(C,A->cmap->bs,A->rmap->bs);
    1905:     MatSetType(C,((PetscObject)A)->type_name);
    1906:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
    1907:     PetscFree(col);
    1908:   } else {
    1909:     C = *B;
    1910:   }

    1912:   for (i=0; i<m; i++) {
    1913:     len    = ai[i+1]-ai[i];
    1914:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
    1915:     array += len;
    1916:     aj    += len;
    1917:   }
    1918:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    1919:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    1921:   if (reuse == MAT_INITIAL_MATRIX || *B != A) {
    1922:     *B = C;
    1923:   } else {
    1924:     MatHeaderMerge(A,C);
    1925:   }
    1926:   return(0);
    1927: }

    1931: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    1932: {
    1933:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
    1934:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
    1935:   MatScalar      *va,*vb;
    1937:   PetscInt       ma,na,mb,nb, i;

    1940:   bij = (Mat_SeqAIJ*) B->data;

    1942:   MatGetSize(A,&ma,&na);
    1943:   MatGetSize(B,&mb,&nb);
    1944:   if (ma!=nb || na!=mb) {
    1945:     *f = PETSC_FALSE;
    1946:     return(0);
    1947:   }
    1948:   aii  = aij->i; bii = bij->i;
    1949:   adx  = aij->j; bdx = bij->j;
    1950:   va   = aij->a; vb = bij->a;
    1951:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
    1952:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
    1953:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    1954:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    1956:   *f = PETSC_TRUE;
    1957:   for (i=0; i<ma; i++) {
    1958:     while (aptr[i]<aii[i+1]) {
    1959:       PetscInt    idc,idr;
    1960:       PetscScalar vc,vr;
    1961:       /* column/row index/value */
    1962:       idc = adx[aptr[i]];
    1963:       idr = bdx[bptr[idc]];
    1964:       vc  = va[aptr[i]];
    1965:       vr  = vb[bptr[idc]];
    1966:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
    1967:         *f = PETSC_FALSE;
    1968:         goto done;
    1969:       } else {
    1970:         aptr[i]++;
    1971:         if (B || i!=idc) bptr[idc]++;
    1972:       }
    1973:     }
    1974:   }
    1975: done:
    1976:   PetscFree(aptr);
    1977:   PetscFree(bptr);
    1978:   return(0);
    1979: }

    1983: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
    1984: {
    1985:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
    1986:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
    1987:   MatScalar      *va,*vb;
    1989:   PetscInt       ma,na,mb,nb, i;

    1992:   bij = (Mat_SeqAIJ*) B->data;

    1994:   MatGetSize(A,&ma,&na);
    1995:   MatGetSize(B,&mb,&nb);
    1996:   if (ma!=nb || na!=mb) {
    1997:     *f = PETSC_FALSE;
    1998:     return(0);
    1999:   }
    2000:   aii  = aij->i; bii = bij->i;
    2001:   adx  = aij->j; bdx = bij->j;
    2002:   va   = aij->a; vb = bij->a;
    2003:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
    2004:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
    2005:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    2006:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    2008:   *f = PETSC_TRUE;
    2009:   for (i=0; i<ma; i++) {
    2010:     while (aptr[i]<aii[i+1]) {
    2011:       PetscInt    idc,idr;
    2012:       PetscScalar vc,vr;
    2013:       /* column/row index/value */
    2014:       idc = adx[aptr[i]];
    2015:       idr = bdx[bptr[idc]];
    2016:       vc  = va[aptr[i]];
    2017:       vr  = vb[bptr[idc]];
    2018:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
    2019:         *f = PETSC_FALSE;
    2020:         goto done;
    2021:       } else {
    2022:         aptr[i]++;
    2023:         if (B || i!=idc) bptr[idc]++;
    2024:       }
    2025:     }
    2026:   }
    2027: done:
    2028:   PetscFree(aptr);
    2029:   PetscFree(bptr);
    2030:   return(0);
    2031: }

    2035: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2036: {

    2040:   MatIsTranspose_SeqAIJ(A,A,tol,f);
    2041:   return(0);
    2042: }

    2046: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
    2047: {

    2051:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
    2052:   return(0);
    2053: }

    2057: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
    2058: {
    2059:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2060:   PetscScalar    *l,*r,x;
    2061:   MatScalar      *v;
    2063:   PetscInt       i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj;

    2066:   if (ll) {
    2067:     /* The local size is used so that VecMPI can be passed to this routine
    2068:        by MatDiagonalScale_MPIAIJ */
    2069:     VecGetLocalSize(ll,&m);
    2070:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
    2071:     VecGetArray(ll,&l);
    2072:     v    = a->a;
    2073:     for (i=0; i<m; i++) {
    2074:       x = l[i];
    2075:       M = a->i[i+1] - a->i[i];
    2076:       for (j=0; j<M; j++) (*v++) *= x;
    2077:     }
    2078:     VecRestoreArray(ll,&l);
    2079:     PetscLogFlops(nz);
    2080:   }
    2081:   if (rr) {
    2082:     VecGetLocalSize(rr,&n);
    2083:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
    2084:     VecGetArray(rr,&r);
    2085:     v    = a->a; jj = a->j;
    2086:     for (i=0; i<nz; i++) (*v++) *= r[*jj++];
    2087:     VecRestoreArray(rr,&r);
    2088:     PetscLogFlops(nz);
    2089:   }
    2090:   MatSeqAIJInvalidateDiagonal(A);
    2091:   return(0);
    2092: }

    2096: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
    2097: {
    2098:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
    2100:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
    2101:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
    2102:   const PetscInt *irow,*icol;
    2103:   PetscInt       nrows,ncols;
    2104:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
    2105:   MatScalar      *a_new,*mat_a;
    2106:   Mat            C;
    2107:   PetscBool      stride,sorted;

    2110:   ISSorted(isrow,&sorted);
    2111:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
    2112:   ISSorted(iscol,&sorted);
    2113:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

    2115:   ISGetIndices(isrow,&irow);
    2116:   ISGetLocalSize(isrow,&nrows);
    2117:   ISGetLocalSize(iscol,&ncols);

    2119:   ISStrideGetInfo(iscol,&first,&step);
    2120:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
    2121:   if (stride && step == 1) {
    2122:     /* special case of contiguous rows */
    2123:     PetscMalloc2(nrows,PetscInt,&lens,nrows,PetscInt,&starts);
    2124:     /* loop over new rows determining lens and starting points */
    2125:     for (i=0; i<nrows; i++) {
    2126:       kstart = ai[irow[i]];
    2127:       kend   = kstart + ailen[irow[i]];
    2128:       for (k=kstart; k<kend; k++) {
    2129:         if (aj[k] >= first) {
    2130:           starts[i] = k;
    2131:           break;
    2132:         }
    2133:       }
    2134:       sum = 0;
    2135:       while (k < kend) {
    2136:         if (aj[k++] >= first+ncols) break;
    2137:         sum++;
    2138:       }
    2139:       lens[i] = sum;
    2140:     }
    2141:     /* create submatrix */
    2142:     if (scall == MAT_REUSE_MATRIX) {
    2143:       PetscInt n_cols,n_rows;
    2144:       MatGetSize(*B,&n_rows,&n_cols);
    2145:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
    2146:       MatZeroEntries(*B);
    2147:       C    = *B;
    2148:     } else {
    2149:       PetscInt rbs,cbs;
    2150:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2151:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2152:       ISGetBlockSize(isrow,&rbs);
    2153:       ISGetBlockSize(iscol,&cbs);
    2154:       MatSetBlockSizes(C,rbs,cbs);
    2155:       MatSetType(C,((PetscObject)A)->type_name);
    2156:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2157:     }
    2158:     c = (Mat_SeqAIJ*)C->data;

    2160:     /* loop over rows inserting into submatrix */
    2161:     a_new = c->a;
    2162:     j_new = c->j;
    2163:     i_new = c->i;

    2165:     for (i=0; i<nrows; i++) {
    2166:       ii    = starts[i];
    2167:       lensi = lens[i];
    2168:       for (k=0; k<lensi; k++) {
    2169:         *j_new++ = aj[ii+k] - first;
    2170:       }
    2171:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
    2172:       a_new     += lensi;
    2173:       i_new[i+1] = i_new[i] + lensi;
    2174:       c->ilen[i] = lensi;
    2175:     }
    2176:     PetscFree2(lens,starts);
    2177:   } else {
    2178:     ISGetIndices(iscol,&icol);
    2179:     PetscMalloc(oldcols*sizeof(PetscInt),&smap);
    2180:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
    2181:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
    2182:     for (i=0; i<ncols; i++) {
    2183: #if defined(PETSC_USE_DEBUG)
    2184:       if (icol[i] >= oldcols) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D <= A->cmap->n %D",i,icol[i],oldcols);
    2185: #endif
    2186:       smap[icol[i]] = i+1;
    2187:     }

    2189:     /* determine lens of each row */
    2190:     for (i=0; i<nrows; i++) {
    2191:       kstart  = ai[irow[i]];
    2192:       kend    = kstart + a->ilen[irow[i]];
    2193:       lens[i] = 0;
    2194:       for (k=kstart; k<kend; k++) {
    2195:         if (smap[aj[k]]) {
    2196:           lens[i]++;
    2197:         }
    2198:       }
    2199:     }
    2200:     /* Create and fill new matrix */
    2201:     if (scall == MAT_REUSE_MATRIX) {
    2202:       PetscBool equal;

    2204:       c = (Mat_SeqAIJ*)((*B)->data);
    2205:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
    2206:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
    2207:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
    2208:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
    2209:       C    = *B;
    2210:     } else {
    2211:       PetscInt rbs,cbs;
    2212:       MatCreate(PetscObjectComm((PetscObject)A),&C);
    2213:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    2214:       ISGetBlockSize(isrow,&rbs);
    2215:       ISGetBlockSize(iscol,&cbs);
    2216:       MatSetBlockSizes(C,rbs,cbs);
    2217:       MatSetType(C,((PetscObject)A)->type_name);
    2218:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    2219:     }
    2220:     c = (Mat_SeqAIJ*)(C->data);
    2221:     for (i=0; i<nrows; i++) {
    2222:       row      = irow[i];
    2223:       kstart   = ai[row];
    2224:       kend     = kstart + a->ilen[row];
    2225:       mat_i    = c->i[i];
    2226:       mat_j    = c->j + mat_i;
    2227:       mat_a    = c->a + mat_i;
    2228:       mat_ilen = c->ilen + i;
    2229:       for (k=kstart; k<kend; k++) {
    2230:         if ((tcol=smap[a->j[k]])) {
    2231:           *mat_j++ = tcol - 1;
    2232:           *mat_a++ = a->a[k];
    2233:           (*mat_ilen)++;

    2235:         }
    2236:       }
    2237:     }
    2238:     /* Free work space */
    2239:     ISRestoreIndices(iscol,&icol);
    2240:     PetscFree(smap);
    2241:     PetscFree(lens);
    2242:   }
    2243:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    2244:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    2246:   ISRestoreIndices(isrow,&irow);
    2247:   *B   = C;
    2248:   return(0);
    2249: }

    2253: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
    2254: {
    2256:   Mat            B;

    2259:   MatCreate(subComm,&B);
    2260:   MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
    2261:   MatSetBlockSizes(B,mat->rmap->bs,mat->cmap->bs);
    2262:   MatSetType(B,MATSEQAIJ);
    2263:   MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
    2264:   *subMat = B;
    2265:   return(0);
    2266: }

    2270: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
    2271: {
    2272:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    2274:   Mat            outA;
    2275:   PetscBool      row_identity,col_identity;

    2278:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

    2280:   ISIdentity(row,&row_identity);
    2281:   ISIdentity(col,&col_identity);

    2283:   outA             = inA;
    2284:   outA->factortype = MAT_FACTOR_LU;

    2286:   PetscObjectReference((PetscObject)row);
    2287:   ISDestroy(&a->row);

    2289:   a->row = row;

    2291:   PetscObjectReference((PetscObject)col);
    2292:   ISDestroy(&a->col);

    2294:   a->col = col;

    2296:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
    2297:   ISDestroy(&a->icol);
    2298:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
    2299:   PetscLogObjectParent(inA,a->icol);

    2301:   if (!a->solve_work) { /* this matrix may have been factored before */
    2302:     PetscMalloc((inA->rmap->n+1)*sizeof(PetscScalar),&a->solve_work);
    2303:     PetscLogObjectMemory(inA, (inA->rmap->n+1)*sizeof(PetscScalar));
    2304:   }

    2306:   MatMarkDiagonal_SeqAIJ(inA);
    2307:   if (row_identity && col_identity) {
    2308:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
    2309:   } else {
    2310:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
    2311:   }
    2312:   return(0);
    2313: }

    2317: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
    2318: {
    2319:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ*)inA->data;
    2320:   PetscScalar    oalpha = alpha;
    2322:   PetscBLASInt   one = 1,bnz;

    2325:   PetscBLASIntCast(a->nz,&bnz);
    2326:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
    2327:   PetscLogFlops(a->nz);
    2328:   MatSeqAIJInvalidateDiagonal(inA);
    2329:   return(0);
    2330: }

    2334: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
    2335: {
    2337:   PetscInt       i;

    2340:   if (scall == MAT_INITIAL_MATRIX) {
    2341:     PetscMalloc((n+1)*sizeof(Mat),B);
    2342:   }

    2344:   for (i=0; i<n; i++) {
    2345:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
    2346:   }
    2347:   return(0);
    2348: }

    2352: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
    2353: {
    2354:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2356:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
    2357:   const PetscInt *idx;
    2358:   PetscInt       start,end,*ai,*aj;
    2359:   PetscBT        table;

    2362:   m  = A->rmap->n;
    2363:   ai = a->i;
    2364:   aj = a->j;

    2366:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

    2368:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
    2369:   PetscBTCreate(m,&table);

    2371:   for (i=0; i<is_max; i++) {
    2372:     /* Initialize the two local arrays */
    2373:     isz  = 0;
    2374:     PetscBTMemzero(m,table);

    2376:     /* Extract the indices, assume there can be duplicate entries */
    2377:     ISGetIndices(is[i],&idx);
    2378:     ISGetLocalSize(is[i],&n);

    2380:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    2381:     for (j=0; j<n; ++j) {
    2382:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
    2383:     }
    2384:     ISRestoreIndices(is[i],&idx);
    2385:     ISDestroy(&is[i]);

    2387:     k = 0;
    2388:     for (j=0; j<ov; j++) { /* for each overlap */
    2389:       n = isz;
    2390:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
    2391:         row   = nidx[k];
    2392:         start = ai[row];
    2393:         end   = ai[row+1];
    2394:         for (l = start; l<end; l++) {
    2395:           val = aj[l];
    2396:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
    2397:         }
    2398:       }
    2399:     }
    2400:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
    2401:   }
    2402:   PetscBTDestroy(&table);
    2403:   PetscFree(nidx);
    2404:   return(0);
    2405: }

    2407: /* -------------------------------------------------------------- */
    2410: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
    2411: {
    2412:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2414:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
    2415:   const PetscInt *row,*col;
    2416:   PetscInt       *cnew,j,*lens;
    2417:   IS             icolp,irowp;
    2418:   PetscInt       *cwork = NULL;
    2419:   PetscScalar    *vwork = NULL;

    2422:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
    2423:   ISGetIndices(irowp,&row);
    2424:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
    2425:   ISGetIndices(icolp,&col);

    2427:   /* determine lengths of permuted rows */
    2428:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
    2429:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
    2430:   MatCreate(PetscObjectComm((PetscObject)A),B);
    2431:   MatSetSizes(*B,m,n,m,n);
    2432:   MatSetBlockSizes(*B,A->rmap->bs,A->cmap->bs);
    2433:   MatSetType(*B,((PetscObject)A)->type_name);
    2434:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
    2435:   PetscFree(lens);

    2437:   PetscMalloc(n*sizeof(PetscInt),&cnew);
    2438:   for (i=0; i<m; i++) {
    2439:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    2440:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
    2441:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
    2442:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    2443:   }
    2444:   PetscFree(cnew);

    2446:   (*B)->assembled = PETSC_FALSE;

    2448:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
    2449:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
    2450:   ISRestoreIndices(irowp,&row);
    2451:   ISRestoreIndices(icolp,&col);
    2452:   ISDestroy(&irowp);
    2453:   ISDestroy(&icolp);
    2454:   return(0);
    2455: }

    2459: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
    2460: {

    2464:   /* If the two matrices have the same copy implementation, use fast copy. */
    2465:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
    2466:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    2467:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

    2469:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
    2470:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
    2471:   } else {
    2472:     MatCopy_Basic(A,B,str);
    2473:   }
    2474:   return(0);
    2475: }

    2479: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
    2480: {

    2484:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
    2485:   return(0);
    2486: }

    2490: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
    2491: {
    2492:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    2495:   *array = a->a;
    2496:   return(0);
    2497: }

    2501: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
    2502: {
    2504:   return(0);
    2505: }

    2509: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
    2510: {
    2511:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void*))coloring->f;
    2513:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow;
    2514:   PetscScalar    dx,*y,*xx,*w3_array;
    2515:   PetscScalar    *vscale_array;
    2516:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
    2517:   Vec            w1,w2,w3;
    2518:   void           *fctx = coloring->fctx;
    2519:   PetscBool      flg   = PETSC_FALSE;

    2522:   if (!coloring->w1) {
    2523:     VecDuplicate(x1,&coloring->w1);
    2524:     PetscLogObjectParent(coloring,coloring->w1);
    2525:     VecDuplicate(x1,&coloring->w2);
    2526:     PetscLogObjectParent(coloring,coloring->w2);
    2527:     VecDuplicate(x1,&coloring->w3);
    2528:     PetscLogObjectParent(coloring,coloring->w3);
    2529:   }
    2530:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

    2532:   MatSetUnfactored(J);
    2533:   PetscOptionsGetBool(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg,NULL);
    2534:   if (flg) {
    2535:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
    2536:   } else {
    2537:     PetscBool assembled;
    2538:     MatAssembled(J,&assembled);
    2539:     if (assembled) {
    2540:       MatZeroEntries(J);
    2541:     }
    2542:   }

    2544:   VecGetOwnershipRange(x1,&start,&end);
    2545:   VecGetSize(x1,&N);

    2547:   if (!coloring->fset) {
    2548:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
    2549:     (*f)(sctx,x1,w1,fctx);
    2550:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
    2551:   } else {
    2552:     coloring->fset = PETSC_FALSE;
    2553:   }

    2555:   /*
    2556:       Compute all the scale factors and share with other processors
    2557:   */
    2558:   VecGetArray(x1,&xx);xx = xx - start;
    2559:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
    2560:   for (k=0; k<coloring->ncolors; k++) {
    2561:     /*
    2562:        Loop over each column associated with color adding the
    2563:        perturbation to the vector w3.
    2564:     */
    2565:     for (l=0; l<coloring->ncolumns[k]; l++) {
    2566:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
    2567:       dx  = xx[col];
    2568:       if (dx == 0.0) dx = 1.0;
    2569:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
    2570:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
    2571:       dx               *= epsilon;
    2572:       vscale_array[col] = 1.0/dx;
    2573:     }
    2574:   }
    2575:   vscale_array = vscale_array + start;

    2577:   VecRestoreArray(coloring->vscale,&vscale_array);
    2578:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
    2579:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

    2581:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
    2582:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

    2584:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
    2585:   else                        vscaleforrow = coloring->columnsforrow;

    2587:   VecGetArray(coloring->vscale,&vscale_array);
    2588:   /*
    2589:       Loop over each color
    2590:   */
    2591:   for (k=0; k<coloring->ncolors; k++) {
    2592:     coloring->currentcolor = k;

    2594:     VecCopy(x1,w3);
    2595:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
    2596:     /*
    2597:        Loop over each column associated with color adding the
    2598:        perturbation to the vector w3.
    2599:     */
    2600:     for (l=0; l<coloring->ncolumns[k]; l++) {
    2601:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
    2602:       dx  = xx[col];
    2603:       if (dx == 0.0) dx = 1.0;
    2604:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
    2605:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
    2606:       dx *= epsilon;
    2607:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Computed 0 differencing parameter");
    2608:       w3_array[col] += dx;
    2609:     }
    2610:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

    2612:     /*
    2613:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
    2614:     */

    2616:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
    2617:     (*f)(sctx,w3,w2,fctx);
    2618:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
    2619:     VecAXPY(w2,-1.0,w1);

    2621:     /*
    2622:        Loop over rows of vector, putting results into Jacobian matrix
    2623:     */
    2624:     VecGetArray(w2,&y);
    2625:     for (l=0; l<coloring->nrows[k]; l++) {
    2626:       row     = coloring->rows[k][l];
    2627:       col     = coloring->columnsforrow[k][l];
    2628:       y[row] *= vscale_array[vscaleforrow[k][l]];
    2629:       srow    = row + start;
    2630:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
    2631:     }
    2632:     VecRestoreArray(w2,&y);
    2633:   }
    2634:   coloring->currentcolor = k;

    2636:   VecRestoreArray(coloring->vscale,&vscale_array);
    2637:   xx   = xx + start;
    2638:   VecRestoreArray(x1,&xx);
    2639:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
    2640:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
    2641:   return(0);
    2642: }

    2644: /*
    2645:    Computes the number of nonzeros per row needed for preallocation when X and Y
    2646:    have different nonzero structure.
    2647: */
    2650: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
    2651: {
    2652:   PetscInt       i,m=Y->rmap->N;
    2653:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ*)X->data;
    2654:   Mat_SeqAIJ     *y  = (Mat_SeqAIJ*)Y->data;
    2655:   const PetscInt *xi = x->i,*yi = y->i;

    2658:   /* Set the number of nonzeros in the new matrix */
    2659:   for (i=0; i<m; i++) {
    2660:     PetscInt       j,k,nzx = xi[i+1] - xi[i],nzy = yi[i+1] - yi[i];
    2661:     const PetscInt *xj = x->j+xi[i],*yj = y->j+yi[i];
    2662:     nnz[i] = 0;
    2663:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
    2664:       for (; k<nzy && yj[k]<xj[j]; k++) nnz[i]++; /* Catch up to X */
    2665:       if (k<nzy && yj[k]==xj[j]) k++;             /* Skip duplicate */
    2666:       nnz[i]++;
    2667:     }
    2668:     for (; k<nzy; k++) nnz[i]++;
    2669:   }
    2670:   return(0);
    2671: }

    2675: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    2676: {
    2678:   PetscInt       i;
    2679:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
    2680:   PetscBLASInt   one=1,bnz;

    2683:   PetscBLASIntCast(x->nz,&bnz);
    2684:   if (str == SAME_NONZERO_PATTERN) {
    2685:     PetscScalar alpha = a;
    2686:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
    2687:     MatSeqAIJInvalidateDiagonal(Y);
    2688:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    2689:     if (y->xtoy && y->XtoY != X) {
    2690:       PetscFree(y->xtoy);
    2691:       MatDestroy(&y->XtoY);
    2692:     }
    2693:     if (!y->xtoy) { /* get xtoy */
    2694:       MatAXPYGetxtoy_Private(X->rmap->n,x->i,x->j,NULL, y->i,y->j,NULL, &y->xtoy);
    2695:       y->XtoY = X;
    2696:       PetscObjectReference((PetscObject)X);
    2697:     }
    2698:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
    2699:     PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(double)(PetscReal)(x->nz)/(y->nz+1));
    2700:   } else {
    2701:     Mat      B;
    2702:     PetscInt *nnz;
    2703:     PetscMalloc(Y->rmap->N*sizeof(PetscInt),&nnz);
    2704:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
    2705:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
    2706:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
    2707:     MatSetBlockSizes(B,Y->rmap->bs,Y->cmap->bs);
    2708:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
    2709:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
    2710:     MatSeqAIJSetPreallocation(B,0,nnz);
    2711:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
    2712:     MatHeaderReplace(Y,B);
    2713:     PetscFree(nnz);
    2714:   }
    2715:   return(0);
    2716: }

    2720: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
    2721: {
    2722: #if defined(PETSC_USE_COMPLEX)
    2723:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
    2724:   PetscInt    i,nz;
    2725:   PetscScalar *a;

    2728:   nz = aij->nz;
    2729:   a  = aij->a;
    2730:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
    2731: #else
    2733: #endif
    2734:   return(0);
    2735: }

    2739: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    2740: {
    2741:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2743:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    2744:   PetscReal      atmp;
    2745:   PetscScalar    *x;
    2746:   MatScalar      *aa;

    2749:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    2750:   aa = a->a;
    2751:   ai = a->i;
    2752:   aj = a->j;

    2754:   VecSet(v,0.0);
    2755:   VecGetArray(v,&x);
    2756:   VecGetLocalSize(v,&n);
    2757:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    2758:   for (i=0; i<m; i++) {
    2759:     ncols = ai[1] - ai[0]; ai++;
    2760:     x[i]  = 0.0;
    2761:     for (j=0; j<ncols; j++) {
    2762:       atmp = PetscAbsScalar(*aa);
    2763:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
    2764:       aa++; aj++;
    2765:     }
    2766:   }
    2767:   VecRestoreArray(v,&x);
    2768:   return(0);
    2769: }

    2773: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    2774: {
    2775:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2777:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    2778:   PetscScalar    *x;
    2779:   MatScalar      *aa;

    2782:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    2783:   aa = a->a;
    2784:   ai = a->i;
    2785:   aj = a->j;

    2787:   VecSet(v,0.0);
    2788:   VecGetArray(v,&x);
    2789:   VecGetLocalSize(v,&n);
    2790:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    2791:   for (i=0; i<m; i++) {
    2792:     ncols = ai[1] - ai[0]; ai++;
    2793:     if (ncols == A->cmap->n) { /* row is dense */
    2794:       x[i] = *aa; if (idx) idx[i] = 0;
    2795:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
    2796:       x[i] = 0.0;
    2797:       if (idx) {
    2798:         idx[i] = 0; /* in case ncols is zero */
    2799:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
    2800:           if (aj[j] > j) {
    2801:             idx[i] = j;
    2802:             break;
    2803:           }
    2804:         }
    2805:       }
    2806:     }
    2807:     for (j=0; j<ncols; j++) {
    2808:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    2809:       aa++; aj++;
    2810:     }
    2811:   }
    2812:   VecRestoreArray(v,&x);
    2813:   return(0);
    2814: }

    2818: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    2819: {
    2820:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2822:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    2823:   PetscReal      atmp;
    2824:   PetscScalar    *x;
    2825:   MatScalar      *aa;

    2828:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    2829:   aa = a->a;
    2830:   ai = a->i;
    2831:   aj = a->j;

    2833:   VecSet(v,0.0);
    2834:   VecGetArray(v,&x);
    2835:   VecGetLocalSize(v,&n);
    2836:   if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %d vs. %d rows", A->rmap->n, n);
    2837:   for (i=0; i<m; i++) {
    2838:     ncols = ai[1] - ai[0]; ai++;
    2839:     if (ncols) {
    2840:       /* Get first nonzero */
    2841:       for (j = 0; j < ncols; j++) {
    2842:         atmp = PetscAbsScalar(aa[j]);
    2843:         if (atmp > 1.0e-12) {
    2844:           x[i] = atmp;
    2845:           if (idx) idx[i] = aj[j];
    2846:           break;
    2847:         }
    2848:       }
    2849:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
    2850:     } else {
    2851:       x[i] = 0.0; if (idx) idx[i] = 0;
    2852:     }
    2853:     for (j = 0; j < ncols; j++) {
    2854:       atmp = PetscAbsScalar(*aa);
    2855:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
    2856:       aa++; aj++;
    2857:     }
    2858:   }
    2859:   VecRestoreArray(v,&x);
    2860:   return(0);
    2861: }

    2865: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
    2866: {
    2867:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2869:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
    2870:   PetscScalar    *x;
    2871:   MatScalar      *aa;

    2874:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    2875:   aa = a->a;
    2876:   ai = a->i;
    2877:   aj = a->j;

    2879:   VecSet(v,0.0);
    2880:   VecGetArray(v,&x);
    2881:   VecGetLocalSize(v,&n);
    2882:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    2883:   for (i=0; i<m; i++) {
    2884:     ncols = ai[1] - ai[0]; ai++;
    2885:     if (ncols == A->cmap->n) { /* row is dense */
    2886:       x[i] = *aa; if (idx) idx[i] = 0;
    2887:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
    2888:       x[i] = 0.0;
    2889:       if (idx) {   /* find first implicit 0.0 in the row */
    2890:         idx[i] = 0; /* in case ncols is zero */
    2891:         for (j=0; j<ncols; j++) {
    2892:           if (aj[j] > j) {
    2893:             idx[i] = j;
    2894:             break;
    2895:           }
    2896:         }
    2897:       }
    2898:     }
    2899:     for (j=0; j<ncols; j++) {
    2900:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
    2901:       aa++; aj++;
    2902:     }
    2903:   }
    2904:   VecRestoreArray(v,&x);
    2905:   return(0);
    2906: }

    2908: #include <petscblaslapack.h>
    2909: #include <petsc-private/kernels/blockinvert.h>

    2913: PetscErrorCode  MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
    2914: {
    2915:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
    2917:   PetscInt       i,bs = A->rmap->bs,mbs = A->rmap->n/A->rmap->bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
    2918:   MatScalar      *diag,work[25],*v_work;
    2919:   PetscReal      shift = 0.0;

    2922:   if (a->ibdiagvalid) {
    2923:     if (values) *values = a->ibdiag;
    2924:     return(0);
    2925:   }
    2926:   MatMarkDiagonal_SeqAIJ(A);
    2927:   if (!a->ibdiag) {
    2928:     PetscMalloc(bs2*mbs*sizeof(PetscScalar),&a->ibdiag);
    2929:     PetscLogObjectMemory(A,bs2*mbs*sizeof(PetscScalar));
    2930:   }
    2931:   diag = a->ibdiag;
    2932:   if (values) *values = a->ibdiag;
    2933:   /* factor and invert each block */
    2934:   switch (bs) {
    2935:   case 1:
    2936:     for (i=0; i<mbs; i++) {
    2937:       MatGetValues(A,1,&i,1,&i,diag+i);
    2938:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
    2939:     }
    2940:     break;
    2941:   case 2:
    2942:     for (i=0; i<mbs; i++) {
    2943:       ij[0] = 2*i; ij[1] = 2*i + 1;
    2944:       MatGetValues(A,2,ij,2,ij,diag);
    2945:       PetscKernel_A_gets_inverse_A_2(diag,shift);
    2946:       PetscKernel_A_gets_transpose_A_2(diag);
    2947:       diag += 4;
    2948:     }
    2949:     break;
    2950:   case 3:
    2951:     for (i=0; i<mbs; i++) {
    2952:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
    2953:       MatGetValues(A,3,ij,3,ij,diag);
    2954:       PetscKernel_A_gets_inverse_A_3(diag,shift);
    2955:       PetscKernel_A_gets_transpose_A_3(diag);
    2956:       diag += 9;
    2957:     }
    2958:     break;
    2959:   case 4:
    2960:     for (i=0; i<mbs; i++) {
    2961:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
    2962:       MatGetValues(A,4,ij,4,ij,diag);
    2963:       PetscKernel_A_gets_inverse_A_4(diag,shift);
    2964:       PetscKernel_A_gets_transpose_A_4(diag);
    2965:       diag += 16;
    2966:     }
    2967:     break;
    2968:   case 5:
    2969:     for (i=0; i<mbs; i++) {
    2970:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
    2971:       MatGetValues(A,5,ij,5,ij,diag);
    2972:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
    2973:       PetscKernel_A_gets_transpose_A_5(diag);
    2974:       diag += 25;
    2975:     }
    2976:     break;
    2977:   case 6:
    2978:     for (i=0; i<mbs; i++) {
    2979:       ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
    2980:       MatGetValues(A,6,ij,6,ij,diag);
    2981:       PetscKernel_A_gets_inverse_A_6(diag,shift);
    2982:       PetscKernel_A_gets_transpose_A_6(diag);
    2983:       diag += 36;
    2984:     }
    2985:     break;
    2986:   case 7:
    2987:     for (i=0; i<mbs; i++) {
    2988:       ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
    2989:       MatGetValues(A,7,ij,7,ij,diag);
    2990:       PetscKernel_A_gets_inverse_A_7(diag,shift);
    2991:       PetscKernel_A_gets_transpose_A_7(diag);
    2992:       diag += 49;
    2993:     }
    2994:     break;
    2995:   default:
    2996:     PetscMalloc3(bs,MatScalar,&v_work,bs,PetscInt,&v_pivots,bs,PetscInt,&IJ);
    2997:     for (i=0; i<mbs; i++) {
    2998:       for (j=0; j<bs; j++) {
    2999:         IJ[j] = bs*i + j;
    3000:       }
    3001:       MatGetValues(A,bs,IJ,bs,IJ,diag);
    3002:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
    3003:       PetscKernel_A_gets_transpose_A_N(diag,bs);
    3004:       diag += bs2;
    3005:     }
    3006:     PetscFree3(v_work,v_pivots,IJ);
    3007:   }
    3008:   a->ibdiagvalid = PETSC_TRUE;
    3009:   return(0);
    3010: }

    3014: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
    3015: {
    3017:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
    3018:   PetscScalar    a;
    3019:   PetscInt       m,n,i,j,col;

    3022:   if (!x->assembled) {
    3023:     MatGetSize(x,&m,&n);
    3024:     for (i=0; i<m; i++) {
    3025:       for (j=0; j<aij->imax[i]; j++) {
    3026:         PetscRandomGetValue(rctx,&a);
    3027:         col  = (PetscInt)(n*PetscRealPart(a));
    3028:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
    3029:       }
    3030:     }
    3031:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded");
    3032:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
    3033:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
    3034:   return(0);
    3035: }

    3037: extern PetscErrorCode  MatFDColoringApply_AIJ(Mat,MatFDColoring,Vec,MatStructure*,void*);
    3038: /* -------------------------------------------------------------------*/
    3039: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
    3040:                                         MatGetRow_SeqAIJ,
    3041:                                         MatRestoreRow_SeqAIJ,
    3042:                                         MatMult_SeqAIJ,
    3043:                                 /*  4*/ MatMultAdd_SeqAIJ,
    3044:                                         MatMultTranspose_SeqAIJ,
    3045:                                         MatMultTransposeAdd_SeqAIJ,
    3046:                                         0,
    3047:                                         0,
    3048:                                         0,
    3049:                                 /* 10*/ 0,
    3050:                                         MatLUFactor_SeqAIJ,
    3051:                                         0,
    3052:                                         MatSOR_SeqAIJ,
    3053:                                         MatTranspose_SeqAIJ,
    3054:                                 /*1 5*/ MatGetInfo_SeqAIJ,
    3055:                                         MatEqual_SeqAIJ,
    3056:                                         MatGetDiagonal_SeqAIJ,
    3057:                                         MatDiagonalScale_SeqAIJ,
    3058:                                         MatNorm_SeqAIJ,
    3059:                                 /* 20*/ 0,
    3060:                                         MatAssemblyEnd_SeqAIJ,
    3061:                                         MatSetOption_SeqAIJ,
    3062:                                         MatZeroEntries_SeqAIJ,
    3063:                                 /* 24*/ MatZeroRows_SeqAIJ,
    3064:                                         0,
    3065:                                         0,
    3066:                                         0,
    3067:                                         0,
    3068:                                 /* 29*/ MatSetUp_SeqAIJ,
    3069:                                         0,
    3070:                                         0,
    3071:                                         0,
    3072:                                         0,
    3073:                                 /* 34*/ MatDuplicate_SeqAIJ,
    3074:                                         0,
    3075:                                         0,
    3076:                                         MatILUFactor_SeqAIJ,
    3077:                                         0,
    3078:                                 /* 39*/ MatAXPY_SeqAIJ,
    3079:                                         MatGetSubMatrices_SeqAIJ,
    3080:                                         MatIncreaseOverlap_SeqAIJ,
    3081:                                         MatGetValues_SeqAIJ,
    3082:                                         MatCopy_SeqAIJ,
    3083:                                 /* 44*/ MatGetRowMax_SeqAIJ,
    3084:                                         MatScale_SeqAIJ,
    3085:                                         0,
    3086:                                         MatDiagonalSet_SeqAIJ,
    3087:                                         MatZeroRowsColumns_SeqAIJ,
    3088:                                 /* 49*/ MatSetRandom_SeqAIJ,
    3089:                                         MatGetRowIJ_SeqAIJ,
    3090:                                         MatRestoreRowIJ_SeqAIJ,
    3091:                                         MatGetColumnIJ_SeqAIJ,
    3092:                                         MatRestoreColumnIJ_SeqAIJ,
    3093:                                 /* 54*/ MatFDColoringCreate_SeqAIJ,
    3094:                                         0,
    3095:                                         0,
    3096:                                         MatPermute_SeqAIJ,
    3097:                                         0,
    3098:                                 /* 59*/ 0,
    3099:                                         MatDestroy_SeqAIJ,
    3100:                                         MatView_SeqAIJ,
    3101:                                         0,
    3102:                                         MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
    3103:                                 /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
    3104:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
    3105:                                         0,
    3106:                                         0,
    3107:                                         0,
    3108:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
    3109:                                         MatGetRowMinAbs_SeqAIJ,
    3110:                                         0,
    3111:                                         MatSetColoring_SeqAIJ,
    3112:                                         0,
    3113:                                 /* 74*/ MatSetValuesAdifor_SeqAIJ,
    3114:                                         MatFDColoringApply_AIJ,
    3115:                                         0,
    3116:                                         0,
    3117:                                         0,
    3118:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
    3119:                                         0,
    3120:                                         0,
    3121:                                         0,
    3122:                                         MatLoad_SeqAIJ,
    3123:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
    3124:                                         MatIsHermitian_SeqAIJ,
    3125:                                         0,
    3126:                                         0,
    3127:                                         0,
    3128:                                 /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
    3129:                                         MatMatMultSymbolic_SeqAIJ_SeqAIJ,
    3130:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
    3131:                                         MatPtAP_SeqAIJ_SeqAIJ,
    3132:                                         MatPtAPSymbolic_SeqAIJ_SeqAIJ,
    3133:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
    3134:                                         MatMatTransposeMult_SeqAIJ_SeqAIJ,
    3135:                                         MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
    3136:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
    3137:                                         0,
    3138:                                 /* 99*/ 0,
    3139:                                         0,
    3140:                                         0,
    3141:                                         MatConjugate_SeqAIJ,
    3142:                                         0,
    3143:                                 /*104*/ MatSetValuesRow_SeqAIJ,
    3144:                                         MatRealPart_SeqAIJ,
    3145:                                         MatImaginaryPart_SeqAIJ,
    3146:                                         0,
    3147:                                         0,
    3148:                                 /*109*/ MatMatSolve_SeqAIJ,
    3149:                                         0,
    3150:                                         MatGetRowMin_SeqAIJ,
    3151:                                         0,
    3152:                                         MatMissingDiagonal_SeqAIJ,
    3153:                                 /*114*/ 0,
    3154:                                         0,
    3155:                                         0,
    3156:                                         0,
    3157:                                         0,
    3158:                                 /*119*/ 0,
    3159:                                         0,
    3160:                                         0,
    3161:                                         0,
    3162:                                         MatGetMultiProcBlock_SeqAIJ,
    3163:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
    3164:                                         MatGetColumnNorms_SeqAIJ,
    3165:                                         MatInvertBlockDiagonal_SeqAIJ,
    3166:                                         0,
    3167:                                         0,
    3168:                                 /*129*/ 0,
    3169:                                         MatTransposeMatMult_SeqAIJ_SeqAIJ,
    3170:                                         MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
    3171:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
    3172:                                         MatTransposeColoringCreate_SeqAIJ,
    3173:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
    3174:                                         MatTransColoringApplyDenToSp_SeqAIJ,
    3175:                                         MatRARt_SeqAIJ_SeqAIJ,
    3176:                                         MatRARtSymbolic_SeqAIJ_SeqAIJ,
    3177:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
    3178:                                  /*139*/0,
    3179:                                         0
    3180: };

    3184: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
    3185: {
    3186:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
    3187:   PetscInt   i,nz,n;

    3190:   nz = aij->maxnz;
    3191:   n  = mat->rmap->n;
    3192:   for (i=0; i<nz; i++) {
    3193:     aij->j[i] = indices[i];
    3194:   }
    3195:   aij->nz = nz;
    3196:   for (i=0; i<n; i++) {
    3197:     aij->ilen[i] = aij->imax[i];
    3198:   }
    3199:   return(0);
    3200: }

    3204: /*@
    3205:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
    3206:        in the matrix.

    3208:   Input Parameters:
    3209: +  mat - the SeqAIJ matrix
    3210: -  indices - the column indices

    3212:   Level: advanced

    3214:   Notes:
    3215:     This can be called if you have precomputed the nonzero structure of the
    3216:   matrix and want to provide it to the matrix object to improve the performance
    3217:   of the MatSetValues() operation.

    3219:     You MUST have set the correct numbers of nonzeros per row in the call to
    3220:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

    3222:     MUST be called before any calls to MatSetValues();

    3224:     The indices should start with zero, not one.

    3226: @*/
    3227: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
    3228: {

    3234:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
    3235:   return(0);
    3236: }

    3238: /* ----------------------------------------------------------------------------------------*/

    3242: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
    3243: {
    3244:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3246:   size_t         nz = aij->i[mat->rmap->n];

    3249:   if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

    3251:   /* allocate space for values if not already there */
    3252:   if (!aij->saved_values) {
    3253:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
    3254:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
    3255:   }

    3257:   /* copy values over */
    3258:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
    3259:   return(0);
    3260: }

    3264: /*@
    3265:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
    3266:        example, reuse of the linear part of a Jacobian, while recomputing the
    3267:        nonlinear portion.

    3269:    Collect on Mat

    3271:   Input Parameters:
    3272: .  mat - the matrix (currently only AIJ matrices support this option)

    3274:   Level: advanced

    3276:   Common Usage, with SNESSolve():
    3277: $    Create Jacobian matrix
    3278: $    Set linear terms into matrix
    3279: $    Apply boundary conditions to matrix, at this time matrix must have
    3280: $      final nonzero structure (i.e. setting the nonlinear terms and applying
    3281: $      boundary conditions again will not change the nonzero structure
    3282: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3283: $    MatStoreValues(mat);
    3284: $    Call SNESSetJacobian() with matrix
    3285: $    In your Jacobian routine
    3286: $      MatRetrieveValues(mat);
    3287: $      Set nonlinear terms in matrix

    3289:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
    3290: $    // build linear portion of Jacobian
    3291: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
    3292: $    MatStoreValues(mat);
    3293: $    loop over nonlinear iterations
    3294: $       MatRetrieveValues(mat);
    3295: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
    3296: $       // call MatAssemblyBegin/End() on matrix
    3297: $       Solve linear system with Jacobian
    3298: $    endloop

    3300:   Notes:
    3301:     Matrix must already be assemblied before calling this routine
    3302:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
    3303:     calling this routine.

    3305:     When this is called multiple times it overwrites the previous set of stored values
    3306:     and does not allocated additional space.

    3308: .seealso: MatRetrieveValues()

    3310: @*/
    3311: PetscErrorCode  MatStoreValues(Mat mat)
    3312: {

    3317:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    3318:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3319:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
    3320:   return(0);
    3321: }

    3325: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
    3326: {
    3327:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
    3329:   PetscInt       nz = aij->i[mat->rmap->n];

    3332:   if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
    3333:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
    3334:   /* copy values over */
    3335:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
    3336:   return(0);
    3337: }

    3341: /*@
    3342:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
    3343:        example, reuse of the linear part of a Jacobian, while recomputing the
    3344:        nonlinear portion.

    3346:    Collect on Mat

    3348:   Input Parameters:
    3349: .  mat - the matrix (currently on AIJ matrices support this option)

    3351:   Level: advanced

    3353: .seealso: MatStoreValues()

    3355: @*/
    3356: PetscErrorCode  MatRetrieveValues(Mat mat)
    3357: {

    3362:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    3363:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    3364:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
    3365:   return(0);
    3366: }

    3369: /* --------------------------------------------------------------------------------*/
    3372: /*@C
    3373:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
    3374:    (the default parallel PETSc format).  For good matrix assembly performance
    3375:    the user should preallocate the matrix storage by setting the parameter nz
    3376:    (or the array nnz).  By setting these parameters accurately, performance
    3377:    during matrix assembly can be increased by more than a factor of 50.

    3379:    Collective on MPI_Comm

    3381:    Input Parameters:
    3382: +  comm - MPI communicator, set to PETSC_COMM_SELF
    3383: .  m - number of rows
    3384: .  n - number of columns
    3385: .  nz - number of nonzeros per row (same for all rows)
    3386: -  nnz - array containing the number of nonzeros in the various rows
    3387:          (possibly different for each row) or NULL

    3389:    Output Parameter:
    3390: .  A - the matrix

    3392:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    3393:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
    3394:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    3396:    Notes:
    3397:    If nnz is given then nz is ignored

    3399:    The AIJ format (also called the Yale sparse matrix format or
    3400:    compressed row storage), is fully compatible with standard Fortran 77
    3401:    storage.  That is, the stored row and column indices can begin at
    3402:    either one (as in Fortran) or zero.  See the users' manual for details.

    3404:    Specify the preallocated storage with either nz or nnz (not both).
    3405:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    3406:    allocation.  For large problems you MUST preallocate memory or you
    3407:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    3409:    By default, this format uses inodes (identical nodes) when possible, to
    3410:    improve numerical efficiency of matrix-vector products and solves. We
    3411:    search for consecutive rows with the same nonzero structure, thereby
    3412:    reusing matrix information to achieve increased efficiency.

    3414:    Options Database Keys:
    3415: +  -mat_no_inode  - Do not use inodes
    3416: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

    3418:    Level: intermediate

    3420: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

    3422: @*/
    3423: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
    3424: {

    3428:   MatCreate(comm,A);
    3429:   MatSetSizes(*A,m,n,m,n);
    3430:   MatSetType(*A,MATSEQAIJ);
    3431:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
    3432:   return(0);
    3433: }

    3437: /*@C
    3438:    MatSeqAIJSetPreallocation - For good matrix assembly performance
    3439:    the user should preallocate the matrix storage by setting the parameter nz
    3440:    (or the array nnz).  By setting these parameters accurately, performance
    3441:    during matrix assembly can be increased by more than a factor of 50.

    3443:    Collective on MPI_Comm

    3445:    Input Parameters:
    3446: +  B - The matrix-free
    3447: .  nz - number of nonzeros per row (same for all rows)
    3448: -  nnz - array containing the number of nonzeros in the various rows
    3449:          (possibly different for each row) or NULL

    3451:    Notes:
    3452:      If nnz is given then nz is ignored

    3454:     The AIJ format (also called the Yale sparse matrix format or
    3455:    compressed row storage), is fully compatible with standard Fortran 77
    3456:    storage.  That is, the stored row and column indices can begin at
    3457:    either one (as in Fortran) or zero.  See the users' manual for details.

    3459:    Specify the preallocated storage with either nz or nnz (not both).
    3460:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    3461:    allocation.  For large problems you MUST preallocate memory or you
    3462:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    3464:    You can call MatGetInfo() to get information on how effective the preallocation was;
    3465:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
    3466:    You can also run with the option -info and look for messages with the string
    3467:    malloc in them to see if additional memory allocation was needed.

    3469:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
    3470:    entries or columns indices

    3472:    By default, this format uses inodes (identical nodes) when possible, to
    3473:    improve numerical efficiency of matrix-vector products and solves. We
    3474:    search for consecutive rows with the same nonzero structure, thereby
    3475:    reusing matrix information to achieve increased efficiency.

    3477:    Options Database Keys:
    3478: +  -mat_no_inode  - Do not use inodes
    3479: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
    3480: -  -mat_aij_oneindex - Internally use indexing starting at 1
    3481:         rather than 0.  Note that when calling MatSetValues(),
    3482:         the user still MUST index entries starting at 0!

    3484:    Level: intermediate

    3486: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

    3488: @*/
    3489: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
    3490: {

    3496:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
    3497:   return(0);
    3498: }

    3502: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
    3503: {
    3504:   Mat_SeqAIJ     *b;
    3505:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
    3507:   PetscInt       i;

    3510:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
    3511:   if (nz == MAT_SKIP_ALLOCATION) {
    3512:     skipallocation = PETSC_TRUE;
    3513:     nz             = 0;
    3514:   }

    3516:   PetscLayoutSetUp(B->rmap);
    3517:   PetscLayoutSetUp(B->cmap);

    3519:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
    3520:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
    3521:   if (nnz) {
    3522:     for (i=0; i<B->rmap->n; i++) {
    3523:       if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
    3524:       if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->cmap->n);
    3525:     }
    3526:   }

    3528:   B->preallocated = PETSC_TRUE;

    3530:   b = (Mat_SeqAIJ*)B->data;

    3532:   if (!skipallocation) {
    3533:     if (!b->imax) {
    3534:       PetscMalloc2(B->rmap->n,PetscInt,&b->imax,B->rmap->n,PetscInt,&b->ilen);
    3535:       PetscLogObjectMemory(B,2*B->rmap->n*sizeof(PetscInt));
    3536:     }
    3537:     if (!nnz) {
    3538:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
    3539:       else if (nz < 0) nz = 1;
    3540:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
    3541:       nz = nz*B->rmap->n;
    3542:     } else {
    3543:       nz = 0;
    3544:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
    3545:     }
    3546:     /* b->ilen will count nonzeros in each row so far. */
    3547:     for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;

    3549:     /* allocate the matrix space */
    3550:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
    3551:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap->n+1,PetscInt,&b->i);
    3552:     PetscLogObjectMemory(B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
    3553:     b->i[0] = 0;
    3554:     for (i=1; i<B->rmap->n+1; i++) {
    3555:       b->i[i] = b->i[i-1] + b->imax[i-1];
    3556:     }
    3557:     b->singlemalloc = PETSC_TRUE;
    3558:     b->free_a       = PETSC_TRUE;
    3559:     b->free_ij      = PETSC_TRUE;
    3560: #if defined(PETSC_THREADCOMM_ACTIVE)
    3561:     MatZeroEntries_SeqAIJ(B);
    3562: #endif
    3563:   } else {
    3564:     b->free_a  = PETSC_FALSE;
    3565:     b->free_ij = PETSC_FALSE;
    3566:   }

    3568:   b->nz               = 0;
    3569:   b->maxnz            = nz;
    3570:   B->info.nz_unneeded = (double)b->maxnz;
    3571:   if (realalloc) {
    3572:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
    3573:   }
    3574:   return(0);
    3575: }

    3577: #undef  __FUNCT__
    3579: /*@
    3580:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

    3582:    Input Parameters:
    3583: +  B - the matrix
    3584: .  i - the indices into j for the start of each row (starts with zero)
    3585: .  j - the column indices for each row (starts with zero) these must be sorted for each row
    3586: -  v - optional values in the matrix

    3588:    Level: developer

    3590:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

    3592: .keywords: matrix, aij, compressed row, sparse, sequential

    3594: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
    3595: @*/
    3596: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
    3597: {

    3603:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
    3604:   return(0);
    3605: }

    3607: #undef  __FUNCT__
    3609: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
    3610: {
    3611:   PetscInt       i;
    3612:   PetscInt       m,n;
    3613:   PetscInt       nz;
    3614:   PetscInt       *nnz, nz_max = 0;
    3615:   PetscScalar    *values;

    3619:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

    3621:   PetscLayoutSetUp(B->rmap);
    3622:   PetscLayoutSetUp(B->cmap);

    3624:   MatGetSize(B, &m, &n);
    3625:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
    3626:   for (i = 0; i < m; i++) {
    3627:     nz     = Ii[i+1]- Ii[i];
    3628:     nz_max = PetscMax(nz_max, nz);
    3629:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
    3630:     nnz[i] = nz;
    3631:   }
    3632:   MatSeqAIJSetPreallocation(B, 0, nnz);
    3633:   PetscFree(nnz);

    3635:   if (v) {
    3636:     values = (PetscScalar*) v;
    3637:   } else {
    3638:     PetscMalloc(nz_max*sizeof(PetscScalar), &values);
    3639:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
    3640:   }

    3642:   for (i = 0; i < m; i++) {
    3643:     nz   = Ii[i+1] - Ii[i];
    3644:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
    3645:   }

    3647:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    3648:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

    3650:   if (!v) {
    3651:     PetscFree(values);
    3652:   }
    3653:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
    3654:   return(0);
    3655: }

    3657: #include <../src/mat/impls/dense/seq/dense.h>
    3658: #include <petsc-private/kernels/petscaxpy.h>

    3662: /*
    3663:     Computes (B'*A')' since computing B*A directly is untenable

    3665:                n                       p                          p
    3666:         (              )       (              )         (                  )
    3667:       m (      A       )  *  n (       B      )   =   m (         C        )
    3668:         (              )       (              )         (                  )

    3670: */
    3671: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
    3672: {
    3673:   PetscErrorCode    ierr;
    3674:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
    3675:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
    3676:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
    3677:   PetscInt          i,n,m,q,p;
    3678:   const PetscInt    *ii,*idx;
    3679:   const PetscScalar *b,*a,*a_q;
    3680:   PetscScalar       *c,*c_q;

    3683:   m    = A->rmap->n;
    3684:   n    = A->cmap->n;
    3685:   p    = B->cmap->n;
    3686:   a    = sub_a->v;
    3687:   b    = sub_b->a;
    3688:   c    = sub_c->v;
    3689:   PetscMemzero(c,m*p*sizeof(PetscScalar));

    3691:   ii  = sub_b->i;
    3692:   idx = sub_b->j;
    3693:   for (i=0; i<n; i++) {
    3694:     q = ii[i+1] - ii[i];
    3695:     while (q-->0) {
    3696:       c_q = c + m*(*idx);
    3697:       a_q = a + m*i;
    3698:       PetscKernelAXPY(c_q,*b,a_q,m);
    3699:       idx++;
    3700:       b++;
    3701:     }
    3702:   }
    3703:   return(0);
    3704: }

    3708: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
    3709: {
    3711:   PetscInt       m=A->rmap->n,n=B->cmap->n;
    3712:   Mat            Cmat;

    3715:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
    3716:   MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
    3717:   MatSetSizes(Cmat,m,n,m,n);
    3718:   MatSetBlockSizes(Cmat,A->rmap->bs,B->cmap->bs);
    3719:   MatSetType(Cmat,MATSEQDENSE);
    3720:   MatSeqDenseSetPreallocation(Cmat,NULL);

    3722:   Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;

    3724:   *C = Cmat;
    3725:   return(0);
    3726: }

    3728: /* ----------------------------------------------------------------*/
    3731: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
    3732: {

    3736:   if (scall == MAT_INITIAL_MATRIX) {
    3737:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
    3738:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
    3739:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
    3740:   }
    3741:   PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
    3742:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
    3743:   PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
    3744:   return(0);
    3745: }

    3748: /*MC
    3749:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
    3750:    based on compressed sparse row format.

    3752:    Options Database Keys:
    3753: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

    3755:   Level: beginner

    3757: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
    3758: M*/

    3760: /*MC
    3761:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

    3763:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
    3764:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
    3765:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
    3766:   for communicators controlling multiple processes.  It is recommended that you call both of
    3767:   the above preallocation routines for simplicity.

    3769:    Options Database Keys:
    3770: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

    3772:   Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
    3773:    enough exist.

    3775:   Level: beginner

    3777: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
    3778: M*/

    3780: /*MC
    3781:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

    3783:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
    3784:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
    3785:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
    3786:   for communicators controlling multiple processes.  It is recommended that you call both of
    3787:   the above preallocation routines for simplicity.

    3789:    Options Database Keys:
    3790: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

    3792:   Level: beginner

    3794: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
    3795: M*/

    3797: #if defined(PETSC_HAVE_PASTIX)
    3798: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_pastix(Mat,MatFactorType,Mat*);
    3799: #endif
    3800: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
    3801: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_essl(Mat,MatFactorType,Mat*);
    3802: #endif
    3803: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
    3804: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_petsc(Mat,MatFactorType,Mat*);
    3805: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_bas(Mat,MatFactorType,Mat*);
    3806: extern PetscErrorCode  MatGetFactorAvailable_seqaij_petsc(Mat,MatFactorType,PetscBool*);
    3807: #if defined(PETSC_HAVE_MUMPS)
    3808: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mumps(Mat,MatFactorType,Mat*);
    3809: #endif
    3810: #if defined(PETSC_HAVE_SUPERLU)
    3811: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_superlu(Mat,MatFactorType,Mat*);
    3812: #endif
    3813: #if defined(PETSC_HAVE_SUPERLU_DIST)
    3814: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_superlu_dist(Mat,MatFactorType,Mat*);
    3815: #endif
    3816: #if defined(PETSC_HAVE_UMFPACK)
    3817: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_umfpack(Mat,MatFactorType,Mat*);
    3818: #endif
    3819: #if defined(PETSC_HAVE_CHOLMOD)
    3820: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_cholmod(Mat,MatFactorType,Mat*);
    3821: #endif
    3822: #if defined(PETSC_HAVE_LUSOL)
    3823: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_lusol(Mat,MatFactorType,Mat*);
    3824: #endif
    3825: #if defined(PETSC_HAVE_MATLAB_ENGINE)
    3826: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_matlab(Mat,MatFactorType,Mat*);
    3827: extern PetscErrorCode  MatlabEnginePut_SeqAIJ(PetscObject,void*);
    3828: extern PetscErrorCode  MatlabEngineGet_SeqAIJ(PetscObject,void*);
    3829: #endif
    3830: #if defined(PETSC_HAVE_CLIQUE)
    3831: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_clique(Mat,MatFactorType,Mat*);
    3832: #endif

    3837: /*@C
    3838:    MatSeqAIJGetArray - gives access to the array where the data for a SeqSeqAIJ matrix is stored

    3840:    Not Collective

    3842:    Input Parameter:
    3843: .  mat - a MATSEQDENSE matrix

    3845:    Output Parameter:
    3846: .   array - pointer to the data

    3848:    Level: intermediate

    3850: .seealso: MatSeqAIJRestoreArray()
    3851: @*/
    3852: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
    3853: {

    3857:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
    3858:   return(0);
    3859: }

    3863: /*@C
    3864:    MatSeqAIJRestoreArray - returns access to the array where the data for a SeqSeqAIJ matrix is stored obtained by MatSeqAIJGetArray()

    3866:    Not Collective

    3868:    Input Parameters:
    3869: .  mat - a MATSEQDENSE matrix
    3870: .  array - pointer to the data

    3872:    Level: intermediate

    3874: .seealso: MatSeqAIJGetArray()
    3875: @*/
    3876: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
    3877: {

    3881:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
    3882:   return(0);
    3883: }

    3887: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
    3888: {
    3889:   Mat_SeqAIJ     *b;
    3891:   PetscMPIInt    size;

    3894:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
    3895:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

    3897:   PetscNewLog(B,Mat_SeqAIJ,&b);

    3899:   B->data = (void*)b;

    3901:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));

    3903:   b->row                = 0;
    3904:   b->col                = 0;
    3905:   b->icol               = 0;
    3906:   b->reallocs           = 0;
    3907:   b->ignorezeroentries  = PETSC_FALSE;
    3908:   b->roworiented        = PETSC_TRUE;
    3909:   b->nonew              = 0;
    3910:   b->diag               = 0;
    3911:   b->solve_work         = 0;
    3912:   B->spptr              = 0;
    3913:   b->saved_values       = 0;
    3914:   b->idiag              = 0;
    3915:   b->mdiag              = 0;
    3916:   b->ssor_work          = 0;
    3917:   b->omega              = 1.0;
    3918:   b->fshift             = 0.0;
    3919:   b->idiagvalid         = PETSC_FALSE;
    3920:   b->ibdiagvalid        = PETSC_FALSE;
    3921:   b->keepnonzeropattern = PETSC_FALSE;
    3922:   b->xtoy               = 0;
    3923:   b->XtoY               = 0;
    3924:   B->same_nonzero       = PETSC_FALSE;

    3926:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    3927:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
    3928:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

    3930: #if defined(PETSC_HAVE_MATLAB_ENGINE)
    3931:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_matlab_C",MatGetFactor_seqaij_matlab);
    3932:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
    3933:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
    3934: #endif
    3935: #if defined(PETSC_HAVE_PASTIX)
    3936:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_pastix_C",MatGetFactor_seqaij_pastix);
    3937: #endif
    3938: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
    3939:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_essl_C",MatGetFactor_seqaij_essl);
    3940: #endif
    3941: #if defined(PETSC_HAVE_SUPERLU)
    3942:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_C",MatGetFactor_seqaij_superlu);
    3943: #endif
    3944: #if defined(PETSC_HAVE_SUPERLU_DIST)
    3945:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_dist_C",MatGetFactor_seqaij_superlu_dist);
    3946: #endif
    3947: #if defined(PETSC_HAVE_MUMPS)
    3948:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_aij_mumps);
    3949: #endif
    3950: #if defined(PETSC_HAVE_UMFPACK)
    3951:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_umfpack_C",MatGetFactor_seqaij_umfpack);
    3952: #endif
    3953: #if defined(PETSC_HAVE_CHOLMOD)
    3954:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_cholmod_C",MatGetFactor_seqaij_cholmod);
    3955: #endif
    3956: #if defined(PETSC_HAVE_LUSOL)
    3957:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_lusol_C",MatGetFactor_seqaij_lusol);
    3958: #endif
    3959: #if defined(PETSC_HAVE_CLIQUE)
    3960:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_clique_C",MatGetFactor_aij_clique);
    3961: #endif

    3963:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_petsc_C",MatGetFactor_seqaij_petsc);
    3964:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactorAvailable_petsc_C",MatGetFactorAvailable_seqaij_petsc);
    3965:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_bas_C",MatGetFactor_seqaij_bas);
    3966:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
    3967:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
    3968:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
    3969:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
    3970:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
    3971:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
    3972:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
    3973:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
    3974:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
    3975:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
    3976:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
    3977:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
    3978:   PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
    3979:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
    3980:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
    3981:   MatCreate_SeqAIJ_Inode(B);
    3982:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
    3983:   return(0);
    3984: }

    3988: /*
    3989:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
    3990: */
    3991: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
    3992: {
    3993:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
    3995:   PetscInt       i,m = A->rmap->n;

    3998:   c = (Mat_SeqAIJ*)C->data;

    4000:   C->factortype = A->factortype;
    4001:   c->row        = 0;
    4002:   c->col        = 0;
    4003:   c->icol       = 0;
    4004:   c->reallocs   = 0;

    4006:   C->assembled = PETSC_TRUE;

    4008:   PetscLayoutReference(A->rmap,&C->rmap);
    4009:   PetscLayoutReference(A->cmap,&C->cmap);

    4011:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
    4012:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
    4013:   for (i=0; i<m; i++) {
    4014:     c->imax[i] = a->imax[i];
    4015:     c->ilen[i] = a->ilen[i];
    4016:   }

    4018:   /* allocate the matrix space */
    4019:   if (mallocmatspace) {
    4020:     PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
    4021:     PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

    4023:     c->singlemalloc = PETSC_TRUE;

    4025:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
    4026:     if (m > 0) {
    4027:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
    4028:       if (cpvalues == MAT_COPY_VALUES) {
    4029:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
    4030:       } else {
    4031:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
    4032:       }
    4033:     }
    4034:   }

    4036:   c->ignorezeroentries = a->ignorezeroentries;
    4037:   c->roworiented       = a->roworiented;
    4038:   c->nonew             = a->nonew;
    4039:   if (a->diag) {
    4040:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
    4041:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
    4042:     for (i=0; i<m; i++) {
    4043:       c->diag[i] = a->diag[i];
    4044:     }
    4045:   } else c->diag = 0;

    4047:   c->solve_work         = 0;
    4048:   c->saved_values       = 0;
    4049:   c->idiag              = 0;
    4050:   c->ssor_work          = 0;
    4051:   c->keepnonzeropattern = a->keepnonzeropattern;
    4052:   c->free_a             = PETSC_TRUE;
    4053:   c->free_ij            = PETSC_TRUE;
    4054:   c->xtoy               = 0;
    4055:   c->XtoY               = 0;

    4057:   c->rmax         = a->rmax;
    4058:   c->nz           = a->nz;
    4059:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
    4060:   C->preallocated = PETSC_TRUE;

    4062:   c->compressedrow.use   = a->compressedrow.use;
    4063:   c->compressedrow.nrows = a->compressedrow.nrows;
    4064:   c->compressedrow.check = a->compressedrow.check;
    4065:   if (a->compressedrow.use) {
    4066:     i    = a->compressedrow.nrows;
    4067:     PetscMalloc2(i+1,PetscInt,&c->compressedrow.i,i,PetscInt,&c->compressedrow.rindex);
    4068:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
    4069:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
    4070:   } else {
    4071:     c->compressedrow.use    = PETSC_FALSE;
    4072:     c->compressedrow.i      = NULL;
    4073:     c->compressedrow.rindex = NULL;
    4074:   }
    4075:   C->same_nonzero = A->same_nonzero;

    4077:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
    4078:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
    4079:   return(0);
    4080: }

    4084: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
    4085: {

    4089:   MatCreate(PetscObjectComm((PetscObject)A),B);
    4090:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
    4091:   MatSetBlockSizes(*B,A->rmap->bs,A->cmap->bs);
    4092:   MatSetType(*B,((PetscObject)A)->type_name);
    4093:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
    4094:   return(0);
    4095: }

    4099: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
    4100: {
    4101:   Mat_SeqAIJ     *a;
    4103:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
    4104:   int            fd;
    4105:   PetscMPIInt    size;
    4106:   MPI_Comm       comm;
    4107:   PetscInt       bs = 1;

    4110:   PetscObjectGetComm((PetscObject)viewer,&comm);
    4111:   MPI_Comm_size(comm,&size);
    4112:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");

    4114:   PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
    4115:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
    4116:   PetscOptionsEnd();
    4117:   if (bs > 1) {MatSetBlockSize(newMat,bs);}

    4119:   PetscViewerBinaryGetDescriptor(viewer,&fd);
    4120:   PetscBinaryRead(fd,header,4,PETSC_INT);
    4121:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
    4122:   M = header[1]; N = header[2]; nz = header[3];

    4124:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");

    4126:   /* read in row lengths */
    4127:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
    4128:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

    4130:   /* check if sum of rowlengths is same as nz */
    4131:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
    4132:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

    4134:   /* set global size if not set already*/
    4135:   if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
    4136:     MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
    4137:   } else {
    4138:     /* if sizes and type are already set, check if the vector global sizes are correct */
    4139:     MatGetSize(newMat,&rows,&cols);
    4140:     if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
    4141:       MatGetLocalSize(newMat,&rows,&cols);
    4142:     }
    4143:     if (M != rows ||  N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%d, %d) than the input matrix (%d, %d)",M,N,rows,cols);
    4144:   }
    4145:   MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
    4146:   a    = (Mat_SeqAIJ*)newMat->data;

    4148:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

    4150:   /* read in nonzero values */
    4151:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

    4153:   /* set matrix "i" values */
    4154:   a->i[0] = 0;
    4155:   for (i=1; i<= M; i++) {
    4156:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
    4157:     a->ilen[i-1] = rowlengths[i-1];
    4158:   }
    4159:   PetscFree(rowlengths);

    4161:   MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
    4162:   MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
    4163:   return(0);
    4164: }

    4168: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
    4169: {
    4170:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
    4172: #if defined(PETSC_USE_COMPLEX)
    4173:   PetscInt k;
    4174: #endif

    4177:   /* If the  matrix dimensions are not equal,or no of nonzeros */
    4178:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
    4179:     *flg = PETSC_FALSE;
    4180:     return(0);
    4181:   }

    4183:   /* if the a->i are the same */
    4184:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
    4185:   if (!*flg) return(0);

    4187:   /* if a->j are the same */
    4188:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
    4189:   if (!*flg) return(0);

    4191:   /* if a->a are the same */
    4192: #if defined(PETSC_USE_COMPLEX)
    4193:   for (k=0; k<a->nz; k++) {
    4194:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
    4195:       *flg = PETSC_FALSE;
    4196:       return(0);
    4197:     }
    4198:   }
    4199: #else
    4200:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
    4201: #endif
    4202:   return(0);
    4203: }

    4207: /*@
    4208:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
    4209:               provided by the user.

    4211:       Collective on MPI_Comm

    4213:    Input Parameters:
    4214: +   comm - must be an MPI communicator of size 1
    4215: .   m - number of rows
    4216: .   n - number of columns
    4217: .   i - row indices
    4218: .   j - column indices
    4219: -   a - matrix values

    4221:    Output Parameter:
    4222: .   mat - the matrix

    4224:    Level: intermediate

    4226:    Notes:
    4227:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
    4228:     once the matrix is destroyed and not before

    4230:        You cannot set new nonzero locations into this matrix, that will generate an error.

    4232:        The i and j indices are 0 based

    4234:        The format which is used for the sparse matrix input, is equivalent to a
    4235:     row-major ordering.. i.e for the following matrix, the input data expected is
    4236:     as shown:

    4238:         1 0 0
    4239:         2 0 3
    4240:         4 5 6

    4242:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
    4243:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
    4244:         v =  {1,2,3,4,5,6}  [size = nz = 6]

    4247: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    4249: @*/
    4250: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat)
    4251: {
    4253:   PetscInt       ii;
    4254:   Mat_SeqAIJ     *aij;
    4255: #if defined(PETSC_USE_DEBUG)
    4256:   PetscInt jj;
    4257: #endif

    4260:   if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
    4261:   MatCreate(comm,mat);
    4262:   MatSetSizes(*mat,m,n,m,n);
    4263:   /* MatSetBlockSizes(*mat,,); */
    4264:   MatSetType(*mat,MATSEQAIJ);
    4265:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
    4266:   aij  = (Mat_SeqAIJ*)(*mat)->data;
    4267:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

    4269:   aij->i            = i;
    4270:   aij->j            = j;
    4271:   aij->a            = a;
    4272:   aij->singlemalloc = PETSC_FALSE;
    4273:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
    4274:   aij->free_a       = PETSC_FALSE;
    4275:   aij->free_ij      = PETSC_FALSE;

    4277:   for (ii=0; ii<m; ii++) {
    4278:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
    4279: #if defined(PETSC_USE_DEBUG)
    4280:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
    4281:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
    4282:       if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
    4283:       if (j[jj] == j[jj]-1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
    4284:     }
    4285: #endif
    4286:   }
    4287: #if defined(PETSC_USE_DEBUG)
    4288:   for (ii=0; ii<aij->i[m]; ii++) {
    4289:     if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
    4290:     if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
    4291:   }
    4292: #endif

    4294:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    4295:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    4296:   return(0);
    4297: }
    4300: /*@C
    4301:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
    4302:               provided by the user.

    4304:       Collective on MPI_Comm

    4306:    Input Parameters:
    4307: +   comm - must be an MPI communicator of size 1
    4308: .   m   - number of rows
    4309: .   n   - number of columns
    4310: .   i   - row indices
    4311: .   j   - column indices
    4312: .   a   - matrix values
    4313: .   nz  - number of nonzeros
    4314: -   idx - 0 or 1 based

    4316:    Output Parameter:
    4317: .   mat - the matrix

    4319:    Level: intermediate

    4321:    Notes:
    4322:        The i and j indices are 0 based

    4324:        The format which is used for the sparse matrix input, is equivalent to a
    4325:     row-major ordering.. i.e for the following matrix, the input data expected is
    4326:     as shown:

    4328:         1 0 0
    4329:         2 0 3
    4330:         4 5 6

    4332:         i =  {0,1,1,2,2,2}
    4333:         j =  {0,0,2,0,1,2}
    4334:         v =  {1,2,3,4,5,6}

    4337: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

    4339: @*/
    4340: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx)
    4341: {
    4343:   PetscInt       ii, *nnz, one = 1,row,col;

    4347:   PetscMalloc(m*sizeof(PetscInt),&nnz);
    4348:   PetscMemzero(nnz,m*sizeof(PetscInt));
    4349:   for (ii = 0; ii < nz; ii++) {
    4350:     nnz[i[ii]] += 1;
    4351:   }
    4352:   MatCreate(comm,mat);
    4353:   MatSetSizes(*mat,m,n,m,n);
    4354:   /* MatSetBlockSizes(*mat,,); */
    4355:   MatSetType(*mat,MATSEQAIJ);
    4356:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
    4357:   for (ii = 0; ii < nz; ii++) {
    4358:     if (idx) {
    4359:       row = i[ii] - 1;
    4360:       col = j[ii] - 1;
    4361:     } else {
    4362:       row = i[ii];
    4363:       col = j[ii];
    4364:     }
    4365:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
    4366:   }
    4367:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    4368:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    4369:   PetscFree(nnz);
    4370:   return(0);
    4371: }

    4375: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
    4376: {
    4378:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    4381:   if (coloring->ctype == IS_COLORING_GLOBAL) {
    4382:     ISColoringReference(coloring);
    4383:     a->coloring = coloring;
    4384:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
    4385:     PetscInt        i,*larray;
    4386:     ISColoring      ocoloring;
    4387:     ISColoringValue *colors;

    4389:     /* set coloring for diagonal portion */
    4390:     PetscMalloc(A->cmap->n*sizeof(PetscInt),&larray);
    4391:     for (i=0; i<A->cmap->n; i++) larray[i] = i;
    4392:     ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,NULL,larray);
    4393:     PetscMalloc(A->cmap->n*sizeof(ISColoringValue),&colors);
    4394:     for (i=0; i<A->cmap->n; i++) colors[i] = coloring->colors[larray[i]];
    4395:     PetscFree(larray);
    4396:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,&ocoloring);
    4397:     a->coloring = ocoloring;
    4398:   }
    4399:   return(0);
    4400: }

    4404: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
    4405: {
    4406:   Mat_SeqAIJ      *a      = (Mat_SeqAIJ*)A->data;
    4407:   PetscInt        m       = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
    4408:   MatScalar       *v      = a->a;
    4409:   PetscScalar     *values = (PetscScalar*)advalues;
    4410:   ISColoringValue *color;

    4413:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
    4414:   color = a->coloring->colors;
    4415:   /* loop over rows */
    4416:   for (i=0; i<m; i++) {
    4417:     nz = ii[i+1] - ii[i];
    4418:     /* loop over columns putting computed value into matrix */
    4419:     for (j=0; j<nz; j++) *v++ = values[color[*jj++]];
    4420:     values += nl; /* jump to next row of derivatives */
    4421:   }
    4422:   return(0);
    4423: }

    4427: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
    4428: {
    4429:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

    4433:   a->idiagvalid  = PETSC_FALSE;
    4434:   a->ibdiagvalid = PETSC_FALSE;

    4436:   MatSeqAIJInvalidateDiagonal_Inode(A);
    4437:   return(0);
    4438: }

    4440: /*
    4441:     Special version for direct calls from Fortran
    4442: */
    4443: #include <petsc-private/fortranimpl.h>
    4444: #if defined(PETSC_HAVE_FORTRAN_CAPS)
    4445: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
    4446: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
    4447: #define matsetvaluesseqaij_ matsetvaluesseqaij
    4448: #endif

    4450: /* Change these macros so can be used in void function */
    4451: #undef CHKERRQ
    4452: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
    4453: #undef SETERRQ2
    4454: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
    4455: #undef SETERRQ3
    4456: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

    4460: PETSC_EXTERN void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
    4461: {
    4462:   Mat            A  = *AA;
    4463:   PetscInt       m  = *mm, n = *nn;
    4464:   InsertMode     is = *isis;
    4465:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    4466:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    4467:   PetscInt       *imax,*ai,*ailen;
    4469:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
    4470:   MatScalar      *ap,value,*aa;
    4471:   PetscBool      ignorezeroentries = a->ignorezeroentries;
    4472:   PetscBool      roworiented       = a->roworiented;

    4475:   MatCheckPreallocated(A,1);
    4476:   imax  = a->imax;
    4477:   ai    = a->i;
    4478:   ailen = a->ilen;
    4479:   aj    = a->j;
    4480:   aa    = a->a;

    4482:   for (k=0; k<m; k++) { /* loop over added rows */
    4483:     row = im[k];
    4484:     if (row < 0) continue;
    4485: #if defined(PETSC_USE_DEBUG)
    4486:     if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
    4487: #endif
    4488:     rp   = aj + ai[row]; ap = aa + ai[row];
    4489:     rmax = imax[row]; nrow = ailen[row];
    4490:     low  = 0;
    4491:     high = nrow;
    4492:     for (l=0; l<n; l++) { /* loop over added columns */
    4493:       if (in[l] < 0) continue;
    4494: #if defined(PETSC_USE_DEBUG)
    4495:       if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
    4496: #endif
    4497:       col = in[l];
    4498:       if (roworiented) value = v[l + k*n];
    4499:       else value = v[k + l*m];

    4501:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    4503:       if (col <= lastcol) low = 0;
    4504:       else high = nrow;
    4505:       lastcol = col;
    4506:       while (high-low > 5) {
    4507:         t = (low+high)/2;
    4508:         if (rp[t] > col) high = t;
    4509:         else             low  = t;
    4510:       }
    4511:       for (i=low; i<high; i++) {
    4512:         if (rp[i] > col) break;
    4513:         if (rp[i] == col) {
    4514:           if (is == ADD_VALUES) ap[i] += value;
    4515:           else                  ap[i] = value;
    4516:           goto noinsert;
    4517:         }
    4518:       }
    4519:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    4520:       if (nonew == 1) goto noinsert;
    4521:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
    4522:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
    4523:       N = nrow++ - 1; a->nz++; high++;
    4524:       /* shift up all the later entries in this row */
    4525:       for (ii=N; ii>=i; ii--) {
    4526:         rp[ii+1] = rp[ii];
    4527:         ap[ii+1] = ap[ii];
    4528:       }
    4529:       rp[i] = col;
    4530:       ap[i] = value;
    4531: noinsert:;
    4532:       low = i + 1;
    4533:     }
    4534:     ailen[row] = nrow;
    4535:   }
    4536:   A->same_nonzero = PETSC_FALSE;
    4537:   PetscFunctionReturnVoid();
    4538: }