Actual source code: aij.c

      1: #define PETSCMAT_DLL

      3: /*
      4:     Defines the basic matrix operations for the AIJ (compressed row)
      5:   matrix storage format.
      6: */

     8:  #include src/mat/impls/aij/seq/aij.h
     9:  #include src/inline/spops.h
     10:  #include src/inline/dot.h
     11:  #include petscbt.h

     15: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
     16: {
     17:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
     19:   PetscInt       i,ishift;
     20:
     22:   *m     = A->m;
     23:   if (!ia) return(0);
     24:   ishift = 0;
     25:   if (symmetric && !A->structurally_symmetric) {
     26:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
     27:   } else if (oshift == 1) {
     28:     PetscInt nz = a->i[A->m];
     29:     /* malloc space and  add 1 to i and j indices */
     30:     PetscMalloc((A->m+1)*sizeof(PetscInt),ia);
     31:     PetscMalloc((nz+1)*sizeof(PetscInt),ja);
     32:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
     33:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1;
     34:   } else {
     35:     *ia = a->i; *ja = a->j;
     36:   }
     37:   return(0);
     38: }

     42: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
     43: {
     45:
     47:   if (!ia) return(0);
     48:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
     49:     PetscFree(*ia);
     50:     PetscFree(*ja);
     51:   }
     52:   return(0);
     53: }

     57: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
     58: {
     59:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
     61:   PetscInt       i,*collengths,*cia,*cja,n = A->n,m = A->m;
     62:   PetscInt       nz = a->i[m],row,*jj,mr,col;
     63:
     65:   *nn     = A->n;
     66:   if (!ia) return(0);
     67:   if (symmetric) {
     68:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,0,oshift,ia,ja);
     69:   } else {
     70:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
     71:     PetscMemzero(collengths,n*sizeof(PetscInt));
     72:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
     73:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
     74:     jj = a->j;
     75:     for (i=0; i<nz; i++) {
     76:       collengths[jj[i]]++;
     77:     }
     78:     cia[0] = oshift;
     79:     for (i=0; i<n; i++) {
     80:       cia[i+1] = cia[i] + collengths[i];
     81:     }
     82:     PetscMemzero(collengths,n*sizeof(PetscInt));
     83:     jj   = a->j;
     84:     for (row=0; row<m; row++) {
     85:       mr = a->i[row+1] - a->i[row];
     86:       for (i=0; i<mr; i++) {
     87:         col = *jj++;
     88:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
     89:       }
     90:     }
     91:     PetscFree(collengths);
     92:     *ia = cia; *ja = cja;
     93:   }
     94:   return(0);
     95: }

     99: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
    100: {

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

    106:   PetscFree(*ia);
    107:   PetscFree(*ja);
    108:
    109:   return(0);
    110: }

    112: #define CHUNKSIZE   15

    116: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
    117: {
    118:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    119:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
    120:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
    122:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
    123:   PetscScalar    *ap,value,*aa = a->a;
    124:   PetscTruth     ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE);
    125:   PetscTruth     roworiented = a->roworiented;

    128:   for (k=0; k<m; k++) { /* loop over added rows */
    129:     row  = im[k];
    130:     if (row < 0) continue;
    131: #if defined(PETSC_USE_DEBUG)
    132:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->m-1);
    133: #endif
    134:     rp   = aj + ai[row]; ap = aa + ai[row];
    135:     rmax = imax[row]; nrow = ailen[row];
    136:     low  = 0;
    137:     high = nrow;
    138:     for (l=0; l<n; l++) { /* loop over added columns */
    139:       if (in[l] < 0) continue;
    140: #if defined(PETSC_USE_DEBUG)
    141:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->n-1);
    142: #endif
    143:       col = in[l];
    144:       if (roworiented) {
    145:         value = v[l + k*n];
    146:       } else {
    147:         value = v[k + l*m];
    148:       }
    149:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

    151:       if (col <= lastcol) low = 0; else high = nrow;
    152:       lastcol = col;
    153:       while (high-low > 5) {
    154:         t = (low+high)/2;
    155:         if (rp[t] > col) high = t;
    156:         else             low  = t;
    157:       }
    158:       for (i=low; i<high; i++) {
    159:         if (rp[i] > col) break;
    160:         if (rp[i] == col) {
    161:           if (is == ADD_VALUES) ap[i] += value;
    162:           else                  ap[i] = value;
    163:           goto noinsert;
    164:         }
    165:       }
    166:       if (value == 0.0 && ignorezeroentries) goto noinsert;
    167:       if (nonew == 1) goto noinsert;
    168:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
    169:       MatSeqXAIJReallocateAIJ(a,1,nrow,row,col,rmax,aa,ai,aj,A->m,rp,ap,imax,nonew);
    170:       N = nrow++ - 1; a->nz++; high++;
    171:       /* shift up all the later entries in this row */
    172:       for (ii=N; ii>=i; ii--) {
    173:         rp[ii+1] = rp[ii];
    174:         ap[ii+1] = ap[ii];
    175:       }
    176:       rp[i] = col;
    177:       ap[i] = value;
    178:       noinsert:;
    179:       low = i + 1;
    180:     }
    181:     ailen[row] = nrow;
    182:   }
    183:   A->same_nonzero = PETSC_FALSE;
    184:   return(0);
    185: }

    189: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
    190: {
    191:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
    192:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
    193:   PetscInt     *ai = a->i,*ailen = a->ilen;
    194:   PetscScalar  *ap,*aa = a->a,zero = 0.0;

    197:   for (k=0; k<m; k++) { /* loop over rows */
    198:     row  = im[k];
    199:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row);
    200:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->m-1);
    201:     rp   = aj + ai[row]; ap = aa + ai[row];
    202:     nrow = ailen[row];
    203:     for (l=0; l<n; l++) { /* loop over columns */
    204:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]);
    205:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->n-1);
    206:       col = in[l] ;
    207:       high = nrow; low = 0; /* assume unsorted */
    208:       while (high-low > 5) {
    209:         t = (low+high)/2;
    210:         if (rp[t] > col) high = t;
    211:         else             low  = t;
    212:       }
    213:       for (i=low; i<high; i++) {
    214:         if (rp[i] > col) break;
    215:         if (rp[i] == col) {
    216:           *v++ = ap[i];
    217:           goto finished;
    218:         }
    219:       }
    220:       *v++ = zero;
    221:       finished:;
    222:     }
    223:   }
    224:   return(0);
    225: }

    230: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
    231: {
    232:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    234:   PetscInt       i,*col_lens;
    235:   int            fd;

    238:   PetscViewerBinaryGetDescriptor(viewer,&fd);
    239:   PetscMalloc((4+A->m)*sizeof(PetscInt),&col_lens);
    240:   col_lens[0] = MAT_FILE_COOKIE;
    241:   col_lens[1] = A->m;
    242:   col_lens[2] = A->n;
    243:   col_lens[3] = a->nz;

    245:   /* store lengths of each row and write (including header) to file */
    246:   for (i=0; i<A->m; i++) {
    247:     col_lens[4+i] = a->i[i+1] - a->i[i];
    248:   }
    249:   PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,PETSC_TRUE);
    250:   PetscFree(col_lens);

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

    255:   /* store nonzero values */
    256:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
    257:   return(0);
    258: }

    260: EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

    264: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
    265: {
    266:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    267:   PetscErrorCode    ierr;
    268:   PetscInt          i,j,m = A->m,shift=0;
    269:   const char        *name;
    270:   PetscViewerFormat format;

    273:   PetscObjectGetName((PetscObject)A,&name);
    274:   PetscViewerGetFormat(viewer,&format);
    275:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
    276:     PetscInt nofinalvalue = 0;
    277:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) {
    278:       nofinalvalue = 1;
    279:     }
    280:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
    281:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->n);
    282:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
    283:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
    284:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

    286:     for (i=0; i<m; i++) {
    287:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    288: #if defined(PETSC_USE_COMPLEX)
    289:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    290: #else
    291:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
    292: #endif
    293:       }
    294:     }
    295:     if (nofinalvalue) {
    296:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->n,0.0);
    297:     }
    298:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
    299:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
    300:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
    301:      return(0);
    302:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
    303:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
    304:     for (i=0; i<m; i++) {
    305:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
    306:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    307: #if defined(PETSC_USE_COMPLEX)
    308:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    309:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    310:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
    311:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    312:         } else if (PetscRealPart(a->a[j]) != 0.0) {
    313:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
    314:         }
    315: #else
    316:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,a->a[j]);}
    317: #endif
    318:       }
    319:       PetscViewerASCIIPrintf(viewer,"\n");
    320:     }
    321:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
    322:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
    323:     PetscInt nzd=0,fshift=1,*sptr;
    324:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
    325:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
    326:     for (i=0; i<m; i++) {
    327:       sptr[i] = nzd+1;
    328:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    329:         if (a->j[j] >= i) {
    330: #if defined(PETSC_USE_COMPLEX)
    331:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
    332: #else
    333:           if (a->a[j] != 0.0) nzd++;
    334: #endif
    335:         }
    336:       }
    337:     }
    338:     sptr[m] = nzd+1;
    339:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
    340:     for (i=0; i<m+1; i+=6) {
    341:       if (i+4<m) {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]);}
    342:       else if (i+3<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);}
    343:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
    344:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
    345:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
    346:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
    347:     }
    348:     PetscViewerASCIIPrintf(viewer,"\n");
    349:     PetscFree(sptr);
    350:     for (i=0; i<m; i++) {
    351:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    352:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
    353:       }
    354:       PetscViewerASCIIPrintf(viewer,"\n");
    355:     }
    356:     PetscViewerASCIIPrintf(viewer,"\n");
    357:     for (i=0; i<m; i++) {
    358:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    359:         if (a->j[j] >= i) {
    360: #if defined(PETSC_USE_COMPLEX)
    361:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
    362:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    363:           }
    364: #else
    365:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
    366: #endif
    367:         }
    368:       }
    369:       PetscViewerASCIIPrintf(viewer,"\n");
    370:     }
    371:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
    372:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
    373:     PetscInt         cnt = 0,jcnt;
    374:     PetscScalar value;

    376:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
    377:     for (i=0; i<m; i++) {
    378:       jcnt = 0;
    379:       for (j=0; j<A->n; j++) {
    380:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
    381:           value = a->a[cnt++];
    382:           jcnt++;
    383:         } else {
    384:           value = 0.0;
    385:         }
    386: #if defined(PETSC_USE_COMPLEX)
    387:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
    388: #else
    389:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
    390: #endif
    391:       }
    392:       PetscViewerASCIIPrintf(viewer,"\n");
    393:     }
    394:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
    395:   } else {
    396:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
    397:     for (i=0; i<m; i++) {
    398:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
    399:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
    400: #if defined(PETSC_USE_COMPLEX)
    401:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
    402:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
    403:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
    404:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
    405:         } else {
    406:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
    407:         }
    408: #else
    409:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,a->a[j]);
    410: #endif
    411:       }
    412:       PetscViewerASCIIPrintf(viewer,"\n");
    413:     }
    414:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
    415:   }
    416:   PetscViewerFlush(viewer);
    417:   return(0);
    418: }

    422: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
    423: {
    424:   Mat               A = (Mat) Aa;
    425:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    426:   PetscErrorCode    ierr;
    427:   PetscInt          i,j,m = A->m,color;
    428:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
    429:   PetscViewer       viewer;
    430:   PetscViewerFormat format;

    433:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
    434:   PetscViewerGetFormat(viewer,&format);

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

    439:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
    440:     /* Blue for negative, Cyan for zero and  Red for positive */
    441:     color = PETSC_DRAW_BLUE;
    442:     for (i=0; i<m; i++) {
    443:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    444:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    445:         x_l = a->j[j] ; x_r = x_l + 1.0;
    446: #if defined(PETSC_USE_COMPLEX)
    447:         if (PetscRealPart(a->a[j]) >=  0.) continue;
    448: #else
    449:         if (a->a[j] >=  0.) continue;
    450: #endif
    451:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    452:       }
    453:     }
    454:     color = PETSC_DRAW_CYAN;
    455:     for (i=0; i<m; i++) {
    456:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    457:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    458:         x_l = a->j[j]; x_r = x_l + 1.0;
    459:         if (a->a[j] !=  0.) continue;
    460:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    461:       }
    462:     }
    463:     color = PETSC_DRAW_RED;
    464:     for (i=0; i<m; i++) {
    465:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    466:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    467:         x_l = a->j[j]; x_r = x_l + 1.0;
    468: #if defined(PETSC_USE_COMPLEX)
    469:         if (PetscRealPart(a->a[j]) <=  0.) continue;
    470: #else
    471:         if (a->a[j] <=  0.) continue;
    472: #endif
    473:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    474:       }
    475:     }
    476:   } else {
    477:     /* use contour shading to indicate magnitude of values */
    478:     /* first determine max of all nonzero values */
    479:     PetscInt    nz = a->nz,count;
    480:     PetscDraw   popup;
    481:     PetscReal scale;

    483:     for (i=0; i<nz; i++) {
    484:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
    485:     }
    486:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
    487:     PetscDrawGetPopup(draw,&popup);
    488:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
    489:     count = 0;
    490:     for (i=0; i<m; i++) {
    491:       y_l = m - i - 1.0; y_r = y_l + 1.0;
    492:       for (j=a->i[i]; j<a->i[i+1]; j++) {
    493:         x_l = a->j[j]; x_r = x_l + 1.0;
    494:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
    495:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
    496:         count++;
    497:       }
    498:     }
    499:   }
    500:   return(0);
    501: }

    505: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
    506: {
    508:   PetscDraw      draw;
    509:   PetscReal      xr,yr,xl,yl,h,w;
    510:   PetscTruth     isnull;

    513:   PetscViewerDrawGetDraw(viewer,0,&draw);
    514:   PetscDrawIsNull(draw,&isnull);
    515:   if (isnull) return(0);

    517:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
    518:   xr  = A->n; yr = A->m; h = yr/10.0; w = xr/10.0;
    519:   xr += w;    yr += h;  xl = -w;     yl = -h;
    520:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
    521:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
    522:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
    523:   return(0);
    524: }

    528: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
    529: {
    531:   PetscTruth     issocket,iascii,isbinary,isdraw;

    534:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
    535:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
    536:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
    537:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
    538:   if (iascii) {
    539:     MatView_SeqAIJ_ASCII(A,viewer);
    540: #if defined(PETSC_USE_SOCKET_VIEWER)
    541:   } else if (issocket) {
    542:     Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    543:     PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);
    544: #endif
    545:   } else if (isbinary) {
    546:     MatView_SeqAIJ_Binary(A,viewer);
    547:   } else if (isdraw) {
    548:     MatView_SeqAIJ_Draw(A,viewer);
    549:   } else {
    550:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
    551:   }
    552:   MatView_Inode(A,viewer);
    553:   return(0);
    554: }

    558: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
    559: {
    560:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    562:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
    563:   PetscInt       m = A->m,*ip,N,*ailen = a->ilen,rmax = 0;
    564:   PetscScalar    *aa = a->a,*ap;
    565:   PetscReal      ratio=0.6;

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

    570:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
    571:   for (i=1; i<m; i++) {
    572:     /* move each row back by the amount of empty slots (fshift) before it*/
    573:     fshift += imax[i-1] - ailen[i-1];
    574:     rmax   = PetscMax(rmax,ailen[i]);
    575:     if (fshift) {
    576:       ip = aj + ai[i] ;
    577:       ap = aa + ai[i] ;
    578:       N  = ailen[i];
    579:       for (j=0; j<N; j++) {
    580:         ip[j-fshift] = ip[j];
    581:         ap[j-fshift] = ap[j];
    582:       }
    583:     }
    584:     ai[i] = ai[i-1] + ailen[i-1];
    585:   }
    586:   if (m) {
    587:     fshift += imax[m-1] - ailen[m-1];
    588:     ai[m]  = ai[m-1] + ailen[m-1];
    589:   }
    590:   /* reset ilen and imax for each row */
    591:   for (i=0; i<m; i++) {
    592:     ailen[i] = imax[i] = ai[i+1] - ai[i];
    593:   }
    594:   a->nz = ai[m];

    596:   /* diagonals may have moved, so kill the diagonal pointers */
    597:   if (fshift && a->diag) {
    598:     PetscFree(a->diag);
    599:     PetscLogObjectMemory(A,-(m+1)*sizeof(PetscInt));
    600:     a->diag = 0;
    601:   }
    602:   PetscLogInfo((A,"MatAssemblyEnd_SeqAIJ:Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->n,fshift,a->nz));
    603:   PetscLogInfo((A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %D\n",a->reallocs));
    604:   PetscLogInfo((A,"MatAssemblyEnd_SeqAIJ:Maximum nonzeros in any row is %D\n",rmax));
    605:   a->reallocs          = 0;
    606:   A->info.nz_unneeded  = (double)fshift;
    607:   a->rmax              = rmax;

    609:   /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
    610:   Mat_CheckCompressedRow(A,&a->compressedrow,a->i,m,ratio);
    611:   A->same_nonzero = PETSC_TRUE;

    613:   MatAssemblyEnd_Inode(A,mode);
    614:   return(0);
    615: }

    619: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
    620: {
    621:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    625:   PetscMemzero(a->a,(a->i[A->m])*sizeof(PetscScalar));
    626:   return(0);
    627: }

    631: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
    632: {
    633:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    637: #if defined(PETSC_USE_LOG)
    638:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->m,A->n,a->nz);
    639: #endif
    640:   if (a->freedata){
    641:     MatSeqXAIJFreeAIJ(a->singlemalloc,&a->a,&a->j,&a->i);
    642:   }
    643:   if (a->row) {
    644:     ISDestroy(a->row);
    645:   }
    646:   if (a->col) {
    647:     ISDestroy(a->col);
    648:   }
    649:   if (a->diag) {PetscFree(a->diag);}
    650:   if (a->ilen) {PetscFree2(a->imax,a->ilen);}
    651:   if (a->idiag) {PetscFree(a->idiag);}
    652:   if (a->solve_work) {PetscFree(a->solve_work);}
    653:   if (a->icol) {ISDestroy(a->icol);}
    654:   if (a->saved_values) {PetscFree(a->saved_values);}
    655:   if (a->coloring) {ISColoringDestroy(a->coloring);}
    656:   if (a->xtoy) {PetscFree(a->xtoy);}
    657:   if (a->compressedrow.use){PetscFree(a->compressedrow.i);}

    659:   MatDestroy_Inode(A);

    661:   PetscFree(a);

    663:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
    664:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
    665:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
    666:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
    667:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
    668:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
    669:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
    670:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
    671:   return(0);
    672: }

    676: PetscErrorCode MatCompress_SeqAIJ(Mat A)
    677: {
    679:   return(0);
    680: }

    684: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op)
    685: {
    686:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    690:   switch (op) {
    691:     case MAT_ROW_ORIENTED:
    692:       a->roworiented       = PETSC_TRUE;
    693:       break;
    694:     case MAT_KEEP_ZEROED_ROWS:
    695:       a->keepzeroedrows    = PETSC_TRUE;
    696:       break;
    697:     case MAT_COLUMN_ORIENTED:
    698:       a->roworiented       = PETSC_FALSE;
    699:       break;
    700:     case MAT_COLUMNS_SORTED:
    701:       a->sorted            = PETSC_TRUE;
    702:       break;
    703:     case MAT_COLUMNS_UNSORTED:
    704:       a->sorted            = PETSC_FALSE;
    705:       break;
    706:     case MAT_NO_NEW_NONZERO_LOCATIONS:
    707:       a->nonew             = 1;
    708:       break;
    709:     case MAT_NEW_NONZERO_LOCATION_ERR:
    710:       a->nonew             = -1;
    711:       break;
    712:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
    713:       a->nonew             = -2;
    714:       break;
    715:     case MAT_YES_NEW_NONZERO_LOCATIONS:
    716:       a->nonew             = 0;
    717:       break;
    718:     case MAT_IGNORE_ZERO_ENTRIES:
    719:       a->ignorezeroentries = PETSC_TRUE;
    720:       break;
    721:     case MAT_USE_COMPRESSEDROW:
    722:       a->compressedrow.use = PETSC_TRUE;
    723:       break;
    724:     case MAT_DO_NOT_USE_COMPRESSEDROW:
    725:       a->compressedrow.use = PETSC_FALSE;
    726:       break;
    727:     case MAT_ROWS_SORTED:
    728:     case MAT_ROWS_UNSORTED:
    729:     case MAT_YES_NEW_DIAGONALS:
    730:     case MAT_IGNORE_OFF_PROC_ENTRIES:
    731:     case MAT_USE_HASH_TABLE:
    732:       PetscLogInfo((A,"MatSetOption_SeqAIJ:Option ignored\n"));
    733:       break;
    734:     case MAT_NO_NEW_DIAGONALS:
    735:       SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
    736:     default:
    737:       break;
    738:   }
    739:   MatSetOption_Inode(A,op);
    740:   return(0);
    741: }

    745: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
    746: {
    747:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    749:   PetscInt       i,j,n;
    750:   PetscScalar    *x,zero = 0.0;

    753:   VecSet(v,zero);
    754:   VecGetArray(v,&x);
    755:   VecGetLocalSize(v,&n);
    756:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    757:   for (i=0; i<A->m; i++) {
    758:     for (j=a->i[i]; j<a->i[i+1]; j++) {
    759:       if (a->j[j] == i) {
    760:         x[i] = a->a[j];
    761:         break;
    762:       }
    763:     }
    764:   }
    765:   VecRestoreArray(v,&x);
    766:   return(0);
    767: }

    771: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
    772: {
    773:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
    774:   PetscScalar       *x,*y;
    775:   PetscErrorCode    ierr;
    776:   PetscInt          m = A->m;
    777: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    778:   PetscScalar       *v,alpha;
    779:   PetscInt          n,i,*idx,*ii,*ridx=PETSC_NULL;
    780:   Mat_CompressedRow cprow = a->compressedrow;
    781:   PetscTruth        usecprow = cprow.use;
    782: #endif

    785:   if (zz != yy) {VecCopy(zz,yy);}
    786:   VecGetArray(xx,&x);
    787:   VecGetArray(yy,&y);

    789: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
    790:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
    791: #else
    792:   if (usecprow){
    793:     m    = cprow.nrows;
    794:     ii   = cprow.i;
    795:     ridx = cprow.rindex;
    796:   } else {
    797:     ii = a->i;
    798:   }
    799:   for (i=0; i<m; i++) {
    800:     idx   = a->j + ii[i] ;
    801:     v     = a->a + ii[i] ;
    802:     n     = ii[i+1] - ii[i];
    803:     if (usecprow){
    804:       alpha = x[ridx[i]];
    805:     } else {
    806:       alpha = x[i];
    807:     }
    808:     while (n-->0) {y[*idx++] += alpha * *v++;}
    809:   }
    810: #endif
    811:   PetscLogFlops(2*a->nz);
    812:   VecRestoreArray(xx,&x);
    813:   VecRestoreArray(yy,&y);
    814:   return(0);
    815: }

    819: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
    820: {
    821:   PetscScalar    zero = 0.0;

    825:   VecSet(yy,zero);
    826:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
    827:   return(0);
    828: }

    833: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
    834: {
    835:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    836:   PetscScalar    *x,*y,*aa;
    838:   PetscInt       m=A->m,*aj,*ii;
    839: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    840:   PetscInt       n,i,jrow,j,*ridx=PETSC_NULL;
    841:   PetscScalar    sum;
    842:   PetscTruth     usecprow=a->compressedrow.use;
    843: #endif

    845: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
    846: #pragma disjoint(*x,*y,*aa)
    847: #endif

    850:   VecGetArray(xx,&x);
    851:   VecGetArray(yy,&y);
    852:   aj  = a->j;
    853:   aa    = a->a;
    854:   ii   = a->i;
    855: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
    856:   fortranmultaij_(&m,x,ii,aj,aa,y);
    857: #else
    858:   if (usecprow){ /* use compressed row format */
    859:     m    = a->compressedrow.nrows;
    860:     ii   = a->compressedrow.i;
    861:     ridx = a->compressedrow.rindex;
    862:     for (i=0; i<m; i++){
    863:       n   = ii[i+1] - ii[i];
    864:       aj  = a->j + ii[i];
    865:       aa  = a->a + ii[i];
    866:       sum = 0.0;
    867:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
    868:       y[*ridx++] = sum;
    869:     }
    870:   } else { /* do not use compressed row format */
    871:     for (i=0; i<m; i++) {
    872:       jrow = ii[i];
    873:       n    = ii[i+1] - jrow;
    874:       sum  = 0.0;
    875:       for (j=0; j<n; j++) {
    876:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
    877:       }
    878:       y[i] = sum;
    879:     }
    880:   }
    881: #endif
    882:   PetscLogFlops(2*a->nz - m);
    883:   VecRestoreArray(xx,&x);
    884:   VecRestoreArray(yy,&y);
    885:   return(0);
    886: }

    890: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
    891: {
    892:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    893:   PetscScalar    *x,*y,*z,*aa;
    895:   PetscInt       m = A->m,*aj,*ii;
    896: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    897:   PetscInt       n,i,jrow,j,*ridx=PETSC_NULL;
    898:   PetscScalar    sum;
    899:   PetscTruth     usecprow=a->compressedrow.use;
    900: #endif

    903:   VecGetArray(xx,&x);
    904:   VecGetArray(yy,&y);
    905:   if (zz != yy) {
    906:     VecGetArray(zz,&z);
    907:   } else {
    908:     z = y;
    909:   }

    911:   aj  = a->j;
    912:   aa  = a->a;
    913:   ii  = a->i;
    914: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
    915:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
    916: #else
    917:   if (usecprow){ /* use compressed row format */
    918:     if (zz != yy){
    919:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
    920:     }
    921:     m    = a->compressedrow.nrows;
    922:     ii   = a->compressedrow.i;
    923:     ridx = a->compressedrow.rindex;
    924:     for (i=0; i<m; i++){
    925:       n  = ii[i+1] - ii[i];
    926:       aj  = a->j + ii[i];
    927:       aa  = a->a + ii[i];
    928:       sum = y[*ridx];
    929:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
    930:       z[*ridx++] = sum;
    931:     }
    932:   } else { /* do not use compressed row format */
    933:     for (i=0; i<m; i++) {
    934:       jrow = ii[i];
    935:       n    = ii[i+1] - jrow;
    936:       sum  = y[i];
    937:       for (j=0; j<n; j++) {
    938:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
    939:       }
    940:       z[i] = sum;
    941:     }
    942:   }
    943: #endif
    944:   PetscLogFlops(2*a->nz);
    945:   VecRestoreArray(xx,&x);
    946:   VecRestoreArray(yy,&y);
    947:   if (zz != yy) {
    948:     VecRestoreArray(zz,&z);
    949:   }
    950:   return(0);
    951: }

    953: /*
    954:      Adds diagonal pointers to sparse matrix structure.
    955: */
    958: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
    959: {
    960:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    962:   PetscInt       i,j,*diag,m = A->m;

    965:   if (a->diag) return(0);

    967:   PetscMalloc((m+1)*sizeof(PetscInt),&diag);
    968:   PetscLogObjectMemory(A,(m+1)*sizeof(PetscInt));
    969:   for (i=0; i<A->m; i++) {
    970:     diag[i] = a->i[i+1];
    971:     for (j=a->i[i]; j<a->i[i+1]; j++) {
    972:       if (a->j[j] == i) {
    973:         diag[i] = j;
    974:         break;
    975:       }
    976:     }
    977:   }
    978:   a->diag = diag;
    979:   return(0);
    980: }

    982: /*
    983:      Checks for missing diagonals
    984: */
    987: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A)
    988: {
    989:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    991:   PetscInt       *diag,*jj = a->j,i;

    994:   MatMarkDiagonal_SeqAIJ(A);
    995:   diag = a->diag;
    996:   for (i=0; i<A->m; i++) {
    997:     if (jj[diag[i]] != i) {
    998:       SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %D",i);
    999:     }
    1000:   }
    1001:   return(0);
    1002: }

    1006: PetscErrorCode MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
    1007: {
    1008:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
    1009:   PetscScalar        *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag;
    1010:   const PetscScalar  *v = a->a, *b, *bs,*xb, *ts;
    1011:   PetscErrorCode     ierr;
    1012:   PetscInt           n = A->n,m = A->m,i;
    1013:   const PetscInt     *idx,*diag;

    1016:   its = its*lits;
    1017:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);

    1019:   if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
    1020:   diag = a->diag;
    1021:   if (!a->idiag) {
    1022:     PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);
    1023:     a->ssor  = a->idiag + m;
    1024:     mdiag    = a->ssor + m;

    1026:     v        = a->a;

    1028:     /* this is wrong when fshift omega changes each iteration */
    1029:     if (omega == 1.0 && !fshift) {
    1030:       for (i=0; i<m; i++) {
    1031:         mdiag[i]    = v[diag[i]];
    1032:         a->idiag[i] = 1.0/v[diag[i]];
    1033:       }
    1034:       PetscLogFlops(m);
    1035:     } else {
    1036:       for (i=0; i<m; i++) {
    1037:         mdiag[i]    = v[diag[i]];
    1038:         a->idiag[i] = omega/(fshift + v[diag[i]]);
    1039:       }
    1040:       PetscLogFlops(2*m);
    1041:     }
    1042:   }
    1043:   t     = a->ssor;
    1044:   idiag = a->idiag;
    1045:   mdiag = a->idiag + 2*m;

    1047:   VecGetArray(xx,&x);
    1048:   if (xx != bb) {
    1049:     VecGetArray(bb,(PetscScalar**)&b);
    1050:   } else {
    1051:     b = x;
    1052:   }

    1054:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
    1055:   xs   = x;
    1056:   if (flag == SOR_APPLY_UPPER) {
    1057:    /* apply (U + D/omega) to the vector */
    1058:     bs = b;
    1059:     for (i=0; i<m; i++) {
    1060:         d    = fshift + a->a[diag[i]];
    1061:         n    = a->i[i+1] - diag[i] - 1;
    1062:         idx  = a->j + diag[i] + 1;
    1063:         v    = a->a + diag[i] + 1;
    1064:         sum  = b[i]*d/omega;
    1065:         SPARSEDENSEDOT(sum,bs,v,idx,n);
    1066:         x[i] = sum;
    1067:     }
    1068:     VecRestoreArray(xx,&x);
    1069:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
    1070:     PetscLogFlops(a->nz);
    1071:     return(0);
    1072:   }

    1075:     /* Let  A = L + U + D; where L is lower trianglar,
    1076:     U is upper triangular, E is diagonal; This routine applies

    1078:             (L + E)^{-1} A (U + E)^{-1}

    1080:     to a vector efficiently using Eisenstat's trick. This is for
    1081:     the case of SSOR preconditioner, so E is D/omega where omega
    1082:     is the relaxation factor.
    1083:     */

    1085:   if (flag == SOR_APPLY_LOWER) {
    1086:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
    1087:   } else if (flag & SOR_EISENSTAT) {
    1088:     /* Let  A = L + U + D; where L is lower trianglar,
    1089:     U is upper triangular, E is diagonal; This routine applies

    1091:             (L + E)^{-1} A (U + E)^{-1}

    1093:     to a vector efficiently using Eisenstat's trick. This is for
    1094:     the case of SSOR preconditioner, so E is D/omega where omega
    1095:     is the relaxation factor.
    1096:     */
    1097:     scale = (2.0/omega) - 1.0;

    1099:     /*  x = (E + U)^{-1} b */
    1100:     for (i=m-1; i>=0; i--) {
    1101:       n    = a->i[i+1] - diag[i] - 1;
    1102:       idx  = a->j + diag[i] + 1;
    1103:       v    = a->a + diag[i] + 1;
    1104:       sum  = b[i];
    1105:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
    1106:       x[i] = sum*idiag[i];
    1107:     }

    1109:     /*  t = b - (2*E - D)x */
    1110:     v = a->a;
    1111:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

    1113:     /*  t = (E + L)^{-1}t */
    1114:     ts = t;
    1115:     diag = a->diag;
    1116:     for (i=0; i<m; i++) {
    1117:       n    = diag[i] - a->i[i];
    1118:       idx  = a->j + a->i[i];
    1119:       v    = a->a + a->i[i];
    1120:       sum  = t[i];
    1121:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
    1122:       t[i] = sum*idiag[i];
    1123:       /*  x = x + t */
    1124:       x[i] += t[i];
    1125:     }

    1127:     PetscLogFlops(6*m-1 + 2*a->nz);
    1128:     VecRestoreArray(xx,&x);
    1129:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
    1130:     return(0);
    1131:   }
    1132:   if (flag & SOR_ZERO_INITIAL_GUESS) {
    1133:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
    1134: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
    1135:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)b);
    1136: #else
    1137:       for (i=0; i<m; i++) {
    1138:         n    = diag[i] - a->i[i];
    1139:         idx  = a->j + a->i[i];
    1140:         v    = a->a + a->i[i];
    1141:         sum  = b[i];
    1142:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
    1143:         x[i] = sum*idiag[i];
    1144:       }
    1145: #endif
    1146:       xb = x;
    1147:       PetscLogFlops(a->nz);
    1148:     } else xb = b;
    1149:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
    1150:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
    1151:       for (i=0; i<m; i++) {
    1152:         x[i] *= mdiag[i];
    1153:       }
    1154:       PetscLogFlops(m);
    1155:     }
    1156:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
    1157: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
    1158:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)xb);
    1159: #else
    1160:       for (i=m-1; i>=0; i--) {
    1161:         n    = a->i[i+1] - diag[i] - 1;
    1162:         idx  = a->j + diag[i] + 1;
    1163:         v    = a->a + diag[i] + 1;
    1164:         sum  = xb[i];
    1165:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
    1166:         x[i] = sum*idiag[i];
    1167:       }
    1168: #endif
    1169:       PetscLogFlops(a->nz);
    1170:     }
    1171:     its--;
    1172:   }
    1173:   while (its--) {
    1174:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
    1175: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
    1176:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
    1177: #else
    1178:       for (i=0; i<m; i++) {
    1179:         d    = fshift + a->a[diag[i]];
    1180:         n    = a->i[i+1] - a->i[i];
    1181:         idx  = a->j + a->i[i];
    1182:         v    = a->a + a->i[i];
    1183:         sum  = b[i];
    1184:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
    1185:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    1186:       }
    1187: #endif
    1188:       PetscLogFlops(a->nz);
    1189:     }
    1190:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
    1191: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
    1192:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
    1193: #else
    1194:       for (i=m-1; i>=0; i--) {
    1195:         d    = fshift + a->a[diag[i]];
    1196:         n    = a->i[i+1] - a->i[i];
    1197:         idx  = a->j + a->i[i];
    1198:         v    = a->a + a->i[i];
    1199:         sum  = b[i];
    1200:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
    1201:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
    1202:       }
    1203: #endif
    1204:       PetscLogFlops(a->nz);
    1205:     }
    1206:   }
    1207:   VecRestoreArray(xx,&x);
    1208:   if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
    1209:   return(0);
    1210: }

    1214: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
    1215: {
    1216:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

    1219:   info->rows_global    = (double)A->m;
    1220:   info->columns_global = (double)A->n;
    1221:   info->rows_local     = (double)A->m;
    1222:   info->columns_local  = (double)A->n;
    1223:   info->block_size     = 1.0;
    1224:   info->nz_allocated   = (double)a->maxnz;
    1225:   info->nz_used        = (double)a->nz;
    1226:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
    1227:   info->assemblies     = (double)A->num_ass;
    1228:   info->mallocs        = (double)a->reallocs;
    1229:   info->memory         = A->mem;
    1230:   if (A->factor) {
    1231:     info->fill_ratio_given  = A->info.fill_ratio_given;
    1232:     info->fill_ratio_needed = A->info.fill_ratio_needed;
    1233:     info->factor_mallocs    = A->info.factor_mallocs;
    1234:   } else {
    1235:     info->fill_ratio_given  = 0;
    1236:     info->fill_ratio_needed = 0;
    1237:     info->factor_mallocs    = 0;
    1238:   }
    1239:   return(0);
    1240: }

    1244: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
    1245: {
    1246:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1247:   PetscInt       i,m = A->m - 1;

    1251:   if (a->keepzeroedrows) {
    1252:     for (i=0; i<N; i++) {
    1253:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1254:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
    1255:     }
    1256:     if (diag != 0.0) {
    1257:       MatMissingDiagonal_SeqAIJ(A);
    1258:       MatMarkDiagonal_SeqAIJ(A);
    1259:       for (i=0; i<N; i++) {
    1260:         a->a[a->diag[rows[i]]] = diag;
    1261:       }
    1262:     }
    1263:     A->same_nonzero = PETSC_TRUE;
    1264:   } else {
    1265:     if (diag != 0.0) {
    1266:       for (i=0; i<N; i++) {
    1267:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1268:         if (a->ilen[rows[i]] > 0) {
    1269:           a->ilen[rows[i]]          = 1;
    1270:           a->a[a->i[rows[i]]] = diag;
    1271:           a->j[a->i[rows[i]]] = rows[i];
    1272:         } else { /* in case row was completely empty */
    1273:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
    1274:         }
    1275:       }
    1276:     } else {
    1277:       for (i=0; i<N; i++) {
    1278:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
    1279:         a->ilen[rows[i]] = 0;
    1280:       }
    1281:     }
    1282:     A->same_nonzero = PETSC_FALSE;
    1283:   }
    1284:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
    1285:   return(0);
    1286: }

    1290: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1291: {
    1292:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    1293:   PetscInt   *itmp;

    1296:   if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

    1298:   *nz = a->i[row+1] - a->i[row];
    1299:   if (v) *v = a->a + a->i[row];
    1300:   if (idx) {
    1301:     itmp = a->j + a->i[row];
    1302:     if (*nz) {
    1303:       *idx = itmp;
    1304:     }
    1305:     else *idx = 0;
    1306:   }
    1307:   return(0);
    1308: }

    1310: /* remove this function? */
    1313: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
    1314: {
    1316:   return(0);
    1317: }

    1321: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
    1322: {
    1323:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1324:   PetscScalar    *v = a->a;
    1325:   PetscReal      sum = 0.0;
    1327:   PetscInt       i,j;

    1330:   if (type == NORM_FROBENIUS) {
    1331:     for (i=0; i<a->nz; i++) {
    1332: #if defined(PETSC_USE_COMPLEX)
    1333:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
    1334: #else
    1335:       sum += (*v)*(*v); v++;
    1336: #endif
    1337:     }
    1338:     *nrm = sqrt(sum);
    1339:   } else if (type == NORM_1) {
    1340:     PetscReal *tmp;
    1341:     PetscInt    *jj = a->j;
    1342:     PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);
    1343:     PetscMemzero(tmp,A->n*sizeof(PetscReal));
    1344:     *nrm = 0.0;
    1345:     for (j=0; j<a->nz; j++) {
    1346:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
    1347:     }
    1348:     for (j=0; j<A->n; j++) {
    1349:       if (tmp[j] > *nrm) *nrm = tmp[j];
    1350:     }
    1351:     PetscFree(tmp);
    1352:   } else if (type == NORM_INFINITY) {
    1353:     *nrm = 0.0;
    1354:     for (j=0; j<A->m; j++) {
    1355:       v = a->a + a->i[j];
    1356:       sum = 0.0;
    1357:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
    1358:         sum += PetscAbsScalar(*v); v++;
    1359:       }
    1360:       if (sum > *nrm) *nrm = sum;
    1361:     }
    1362:   } else {
    1363:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
    1364:   }
    1365:   return(0);
    1366: }

    1370: PetscErrorCode MatTranspose_SeqAIJ(Mat A,Mat *B)
    1371: {
    1372:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1373:   Mat            C;
    1375:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->m,len,*col;
    1376:   PetscScalar    *array = a->a;

    1379:   if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
    1380:   PetscMalloc((1+A->n)*sizeof(PetscInt),&col);
    1381:   PetscMemzero(col,(1+A->n)*sizeof(PetscInt));
    1382:
    1383:   for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
    1384:   MatCreate(A->comm,&C);
    1385:   MatSetSizes(C,A->n,m,A->n,m);
    1386:   MatSetType(C,A->type_name);
    1387:   MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
    1388:   PetscFree(col);
    1389:   for (i=0; i<m; i++) {
    1390:     len    = ai[i+1]-ai[i];
    1391:     MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);
    1392:     array += len;
    1393:     aj    += len;
    1394:   }

    1396:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    1397:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    1399:   if (B) {
    1400:     *B = C;
    1401:   } else {
    1402:     MatHeaderCopy(A,C);
    1403:   }
    1404:   return(0);
    1405: }

    1410: PetscErrorCode PETSCMAT_DLLEXPORT MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
    1411: {
    1412:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
    1413:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
    1415:   PetscInt       ma,na,mb,nb, i;

    1418:   bij = (Mat_SeqAIJ *) B->data;
    1419:
    1420:   MatGetSize(A,&ma,&na);
    1421:   MatGetSize(B,&mb,&nb);
    1422:   if (ma!=nb || na!=mb){
    1423:     *f = PETSC_FALSE;
    1424:     return(0);
    1425:   }
    1426:   aii = aij->i; bii = bij->i;
    1427:   adx = aij->j; bdx = bij->j;
    1428:   va  = aij->a; vb = bij->a;
    1429:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
    1430:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
    1431:   for (i=0; i<ma; i++) aptr[i] = aii[i];
    1432:   for (i=0; i<mb; i++) bptr[i] = bii[i];

    1434:   *f = PETSC_TRUE;
    1435:   for (i=0; i<ma; i++) {
    1436:     while (aptr[i]<aii[i+1]) {
    1437:       PetscInt         idc,idr;
    1438:       PetscScalar vc,vr;
    1439:       /* column/row index/value */
    1440:       idc = adx[aptr[i]];
    1441:       idr = bdx[bptr[idc]];
    1442:       vc  = va[aptr[i]];
    1443:       vr  = vb[bptr[idc]];
    1444:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
    1445:         *f = PETSC_FALSE;
    1446:         goto done;
    1447:       } else {
    1448:         aptr[i]++;
    1449:         if (B || i!=idc) bptr[idc]++;
    1450:       }
    1451:     }
    1452:   }
    1453:  done:
    1454:   PetscFree(aptr);
    1455:   if (B) {
    1456:     PetscFree(bptr);
    1457:   }
    1458:   return(0);
    1459: }

    1464: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
    1465: {
    1468:   MatIsTranspose_SeqAIJ(A,A,tol,f);
    1469:   return(0);
    1470: }

    1474: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
    1475: {
    1476:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1477:   PetscScalar    *l,*r,x,*v;
    1479:   PetscInt       i,j,m = A->m,n = A->n,M,nz = a->nz,*jj;

    1482:   if (ll) {
    1483:     /* The local size is used so that VecMPI can be passed to this routine
    1484:        by MatDiagonalScale_MPIAIJ */
    1485:     VecGetLocalSize(ll,&m);
    1486:     if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
    1487:     VecGetArray(ll,&l);
    1488:     v = a->a;
    1489:     for (i=0; i<m; i++) {
    1490:       x = l[i];
    1491:       M = a->i[i+1] - a->i[i];
    1492:       for (j=0; j<M; j++) { (*v++) *= x;}
    1493:     }
    1494:     VecRestoreArray(ll,&l);
    1495:     PetscLogFlops(nz);
    1496:   }
    1497:   if (rr) {
    1498:     VecGetLocalSize(rr,&n);
    1499:     if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
    1500:     VecGetArray(rr,&r);
    1501:     v = a->a; jj = a->j;
    1502:     for (i=0; i<nz; i++) {
    1503:       (*v++) *= r[*jj++];
    1504:     }
    1505:     VecRestoreArray(rr,&r);
    1506:     PetscLogFlops(nz);
    1507:   }
    1508:   return(0);
    1509: }

    1513: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
    1514: {
    1515:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
    1517:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->n,*lens;
    1518:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
    1519:   PetscInt       *irow,*icol,nrows,ncols;
    1520:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
    1521:   PetscScalar    *a_new,*mat_a;
    1522:   Mat            C;
    1523:   PetscTruth     stride;

    1526:   ISSorted(isrow,(PetscTruth*)&i);
    1527:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
    1528:   ISSorted(iscol,(PetscTruth*)&i);
    1529:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

    1531:   ISGetIndices(isrow,&irow);
    1532:   ISGetLocalSize(isrow,&nrows);
    1533:   ISGetLocalSize(iscol,&ncols);

    1535:   ISStrideGetInfo(iscol,&first,&step);
    1536:   ISStride(iscol,&stride);
    1537:   if (stride && step == 1) {
    1538:     /* special case of contiguous rows */
    1539:     PetscMalloc((2*nrows+1)*sizeof(PetscInt),&lens);
    1540:     starts = lens + nrows;
    1541:     /* loop over new rows determining lens and starting points */
    1542:     for (i=0; i<nrows; i++) {
    1543:       kstart  = ai[irow[i]];
    1544:       kend    = kstart + ailen[irow[i]];
    1545:       for (k=kstart; k<kend; k++) {
    1546:         if (aj[k] >= first) {
    1547:           starts[i] = k;
    1548:           break;
    1549:         }
    1550:       }
    1551:       sum = 0;
    1552:       while (k < kend) {
    1553:         if (aj[k++] >= first+ncols) break;
    1554:         sum++;
    1555:       }
    1556:       lens[i] = sum;
    1557:     }
    1558:     /* create submatrix */
    1559:     if (scall == MAT_REUSE_MATRIX) {
    1560:       PetscInt n_cols,n_rows;
    1561:       MatGetSize(*B,&n_rows,&n_cols);
    1562:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
    1563:       MatZeroEntries(*B);
    1564:       C = *B;
    1565:     } else {
    1566:       MatCreate(A->comm,&C);
    1567:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    1568:       MatSetType(C,A->type_name);
    1569:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    1570:     }
    1571:     c = (Mat_SeqAIJ*)C->data;

    1573:     /* loop over rows inserting into submatrix */
    1574:     a_new    = c->a;
    1575:     j_new    = c->j;
    1576:     i_new    = c->i;

    1578:     for (i=0; i<nrows; i++) {
    1579:       ii    = starts[i];
    1580:       lensi = lens[i];
    1581:       for (k=0; k<lensi; k++) {
    1582:         *j_new++ = aj[ii+k] - first;
    1583:       }
    1584:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
    1585:       a_new      += lensi;
    1586:       i_new[i+1]  = i_new[i] + lensi;
    1587:       c->ilen[i]  = lensi;
    1588:     }
    1589:     PetscFree(lens);
    1590:   } else {
    1591:     ISGetIndices(iscol,&icol);
    1592:     PetscMalloc((1+oldcols)*sizeof(PetscInt),&smap);
    1593:
    1594:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
    1595:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
    1596:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
    1597:     /* determine lens of each row */
    1598:     for (i=0; i<nrows; i++) {
    1599:       kstart  = ai[irow[i]];
    1600:       kend    = kstart + a->ilen[irow[i]];
    1601:       lens[i] = 0;
    1602:       for (k=kstart; k<kend; k++) {
    1603:         if (smap[aj[k]]) {
    1604:           lens[i]++;
    1605:         }
    1606:       }
    1607:     }
    1608:     /* Create and fill new matrix */
    1609:     if (scall == MAT_REUSE_MATRIX) {
    1610:       PetscTruth equal;

    1612:       c = (Mat_SeqAIJ *)((*B)->data);
    1613:       if ((*B)->m  != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
    1614:       PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(PetscInt),&equal);
    1615:       if (!equal) {
    1616:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
    1617:       }
    1618:       PetscMemzero(c->ilen,(*B)->m*sizeof(PetscInt));
    1619:       C = *B;
    1620:     } else {
    1621:       MatCreate(A->comm,&C);
    1622:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
    1623:       MatSetType(C,A->type_name);
    1624:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
    1625:     }
    1626:     c = (Mat_SeqAIJ *)(C->data);
    1627:     for (i=0; i<nrows; i++) {
    1628:       row    = irow[i];
    1629:       kstart = ai[row];
    1630:       kend   = kstart + a->ilen[row];
    1631:       mat_i  = c->i[i];
    1632:       mat_j  = c->j + mat_i;
    1633:       mat_a  = c->a + mat_i;
    1634:       mat_ilen = c->ilen + i;
    1635:       for (k=kstart; k<kend; k++) {
    1636:         if ((tcol=smap[a->j[k]])) {
    1637:           *mat_j++ = tcol - 1;
    1638:           *mat_a++ = a->a[k];
    1639:           (*mat_ilen)++;

    1641:         }
    1642:       }
    1643:     }
    1644:     /* Free work space */
    1645:     ISRestoreIndices(iscol,&icol);
    1646:     PetscFree(smap);
    1647:     PetscFree(lens);
    1648:   }
    1649:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
    1650:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

    1652:   ISRestoreIndices(isrow,&irow);
    1653:   *B = C;
    1654:   return(0);
    1655: }

    1657: /*
    1658: */
    1661: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
    1662: {
    1663:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
    1665:   Mat            outA;
    1666:   PetscTruth     row_identity,col_identity;

    1669:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
    1670:   ISIdentity(row,&row_identity);
    1671:   ISIdentity(col,&col_identity);
    1672:   if (!row_identity || !col_identity) {
    1673:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
    1674:   }

    1676:   outA          = inA;
    1677:   inA->factor   = FACTOR_LU;
    1678:   a->row        = row;
    1679:   a->col        = col;
    1680:   PetscObjectReference((PetscObject)row);
    1681:   PetscObjectReference((PetscObject)col);

    1683:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
    1684:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
    1685:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
    1686:   PetscLogObjectParent(inA,a->icol);

    1688:   if (!a->solve_work) { /* this matrix may have been factored before */
    1689:      PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
    1690:   }

    1692:   if (!a->diag) {
    1693:     MatMarkDiagonal_SeqAIJ(inA);
    1694:   }
    1695:   MatLUFactorNumeric_SeqAIJ(inA,info,&outA);
    1696:   return(0);
    1697: }

    1699:  #include petscblaslapack.h
    1702: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
    1703: {
    1704:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)inA->data;
    1705:   PetscBLASInt bnz = (PetscBLASInt)a->nz,one = 1;
    1706:   PetscScalar oalpha = alpha;

    1711:   BLASscal_(&bnz,&oalpha,a->a,&one);
    1712:   PetscLogFlops(a->nz);
    1713:   return(0);
    1714: }

    1718: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
    1719: {
    1721:   PetscInt       i;

    1724:   if (scall == MAT_INITIAL_MATRIX) {
    1725:     PetscMalloc((n+1)*sizeof(Mat),B);
    1726:   }

    1728:   for (i=0; i<n; i++) {
    1729:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
    1730:   }
    1731:   return(0);
    1732: }

    1736: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
    1737: {
    1738:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1740:   PetscInt       row,i,j,k,l,m,n,*idx,*nidx,isz,val;
    1741:   PetscInt       start,end,*ai,*aj;
    1742:   PetscBT        table;

    1745:   m     = A->m;
    1746:   ai    = a->i;
    1747:   aj    = a->j;

    1749:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

    1751:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
    1752:   PetscBTCreate(m,table);

    1754:   for (i=0; i<is_max; i++) {
    1755:     /* Initialize the two local arrays */
    1756:     isz  = 0;
    1757:     PetscBTMemzero(m,table);
    1758:
    1759:     /* Extract the indices, assume there can be duplicate entries */
    1760:     ISGetIndices(is[i],&idx);
    1761:     ISGetLocalSize(is[i],&n);
    1762:
    1763:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
    1764:     for (j=0; j<n ; ++j){
    1765:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
    1766:     }
    1767:     ISRestoreIndices(is[i],&idx);
    1768:     ISDestroy(is[i]);
    1769:
    1770:     k = 0;
    1771:     for (j=0; j<ov; j++){ /* for each overlap */
    1772:       n = isz;
    1773:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
    1774:         row   = nidx[k];
    1775:         start = ai[row];
    1776:         end   = ai[row+1];
    1777:         for (l = start; l<end ; l++){
    1778:           val = aj[l] ;
    1779:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
    1780:         }
    1781:       }
    1782:     }
    1783:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
    1784:   }
    1785:   PetscBTDestroy(table);
    1786:   PetscFree(nidx);
    1787:   return(0);
    1788: }

    1790: /* -------------------------------------------------------------- */
    1793: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
    1794: {
    1795:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    1797:   PetscInt       i,nz,m = A->m,n = A->n,*col;
    1798:   PetscInt       *row,*cnew,j,*lens;
    1799:   IS             icolp,irowp;
    1800:   PetscInt       *cwork;
    1801:   PetscScalar    *vwork;

    1804:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
    1805:   ISGetIndices(irowp,&row);
    1806:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
    1807:   ISGetIndices(icolp,&col);
    1808:
    1809:   /* determine lengths of permuted rows */
    1810:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
    1811:   for (i=0; i<m; i++) {
    1812:     lens[row[i]] = a->i[i+1] - a->i[i];
    1813:   }
    1814:   MatCreate(A->comm,B);
    1815:   MatSetSizes(*B,m,n,m,n);
    1816:   MatSetType(*B,A->type_name);
    1817:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
    1818:   PetscFree(lens);

    1820:   PetscMalloc(n*sizeof(PetscInt),&cnew);
    1821:   for (i=0; i<m; i++) {
    1822:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    1823:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
    1824:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
    1825:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
    1826:   }
    1827:   PetscFree(cnew);
    1828:   (*B)->assembled     = PETSC_FALSE;
    1829:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
    1830:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
    1831:   ISRestoreIndices(irowp,&row);
    1832:   ISRestoreIndices(icolp,&col);
    1833:   ISDestroy(irowp);
    1834:   ISDestroy(icolp);
    1835:   return(0);
    1836: }

    1840: PetscErrorCode MatPrintHelp_SeqAIJ(Mat A)
    1841: {
    1842:   static PetscTruth called = PETSC_FALSE;
    1843:   MPI_Comm          comm = A->comm;
    1844:   PetscErrorCode    ierr;

    1847:   MatPrintHelp_Inode(A);
    1848:   if (called) {return(0);} else called = PETSC_TRUE;
    1849:   (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");
    1850:   (*PetscHelpPrintf)(comm,"  -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");
    1851:   (*PetscHelpPrintf)(comm,"  -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");
    1852:   (*PetscHelpPrintf)(comm,"  -mat_no_compressedrow: Do not use compressedrow\n");
    1853:   return(0);
    1854: }

    1858: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
    1859: {

    1863:   /* If the two matrices have the same copy implementation, use fast copy. */
    1864:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
    1865:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    1866:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

    1868:     if (a->i[A->m] != b->i[B->m]) {
    1869:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
    1870:     }
    1871:     PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));
    1872:   } else {
    1873:     MatCopy_Basic(A,B,str);
    1874:   }
    1875:   return(0);
    1876: }

    1880: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
    1881: {

    1885:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
    1886:   return(0);
    1887: }

    1891: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
    1892: {
    1893:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
    1895:   *array = a->a;
    1896:   return(0);
    1897: }

    1901: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
    1902: {
    1904:   return(0);
    1905: }

    1909: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
    1910: {
    1911:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
    1913:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
    1914:   PetscScalar    dx,mone = -1.0,*y,*xx,*w3_array;
    1915:   PetscScalar    *vscale_array;
    1916:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
    1917:   Vec            w1,w2,w3;
    1918:   void           *fctx = coloring->fctx;
    1919:   PetscTruth     flg;

    1922:   if (!coloring->w1) {
    1923:     VecDuplicate(x1,&coloring->w1);
    1924:     PetscLogObjectParent(coloring,coloring->w1);
    1925:     VecDuplicate(x1,&coloring->w2);
    1926:     PetscLogObjectParent(coloring,coloring->w2);
    1927:     VecDuplicate(x1,&coloring->w3);
    1928:     PetscLogObjectParent(coloring,coloring->w3);
    1929:   }
    1930:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

    1932:   MatSetUnfactored(J);
    1933:   PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);
    1934:   if (flg) {
    1935:     PetscLogInfo((coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"));
    1936:   } else {
    1937:     PetscTruth assembled;
    1938:     MatAssembled(J,&assembled);
    1939:     if (assembled) {
    1940:       MatZeroEntries(J);
    1941:     }
    1942:   }

    1944:   VecGetOwnershipRange(x1,&start,&end);
    1945:   VecGetSize(x1,&N);

    1947:   /*
    1948:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
    1949:      coloring->F for the coarser grids from the finest
    1950:   */
    1951:   if (coloring->F) {
    1952:     VecGetLocalSize(coloring->F,&m1);
    1953:     VecGetLocalSize(w1,&m2);
    1954:     if (m1 != m2) {
    1955:       coloring->F = 0;
    1956:     }
    1957:   }

    1959:   if (coloring->F) {
    1960:     w1          = coloring->F;
    1961:     coloring->F = 0;
    1962:   } else {
    1963:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
    1964:     (*f)(sctx,x1,w1,fctx);
    1965:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
    1966:   }

    1968:   /*
    1969:       Compute all the scale factors and share with other processors
    1970:   */
    1971:   VecGetArray(x1,&xx);xx = xx - start;
    1972:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
    1973:   for (k=0; k<coloring->ncolors; k++) {
    1974:     /*
    1975:        Loop over each column associated with color adding the
    1976:        perturbation to the vector w3.
    1977:     */
    1978:     for (l=0; l<coloring->ncolumns[k]; l++) {
    1979:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
    1980:       dx  = xx[col];
    1981:       if (dx == 0.0) dx = 1.0;
    1982: #if !defined(PETSC_USE_COMPLEX)
    1983:       if (dx < umin && dx >= 0.0)      dx = umin;
    1984:       else if (dx < 0.0 && dx > -umin) dx = -umin;
    1985: #else
    1986:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
    1987:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
    1988: #endif
    1989:       dx                *= epsilon;
    1990:       vscale_array[col] = 1.0/dx;
    1991:     }
    1992:   }
    1993:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
    1994:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
    1995:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

    1997:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
    1998:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

    2000:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
    2001:   else                        vscaleforrow = coloring->columnsforrow;

    2003:   VecGetArray(coloring->vscale,&vscale_array);
    2004:   /*
    2005:       Loop over each color
    2006:   */
    2007:   for (k=0; k<coloring->ncolors; k++) {
    2008:     coloring->currentcolor = k;
    2009:     VecCopy(x1,w3);
    2010:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
    2011:     /*
    2012:        Loop over each column associated with color adding the
    2013:        perturbation to the vector w3.
    2014:     */
    2015:     for (l=0; l<coloring->ncolumns[k]; l++) {
    2016:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
    2017:       dx  = xx[col];
    2018:       if (dx == 0.0) dx = 1.0;
    2019: #if !defined(PETSC_USE_COMPLEX)
    2020:       if (dx < umin && dx >= 0.0)      dx = umin;
    2021:       else if (dx < 0.0 && dx > -umin) dx = -umin;
    2022: #else
    2023:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
    2024:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
    2025: #endif
    2026:       dx            *= epsilon;
    2027:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter");
    2028:       w3_array[col] += dx;
    2029:     }
    2030:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

    2032:     /*
    2033:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
    2034:     */

    2036:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
    2037:     (*f)(sctx,w3,w2,fctx);
    2038:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
    2039:     VecAXPY(w2,mone,w1);

    2041:     /*
    2042:        Loop over rows of vector, putting results into Jacobian matrix
    2043:     */
    2044:     VecGetArray(w2,&y);
    2045:     for (l=0; l<coloring->nrows[k]; l++) {
    2046:       row    = coloring->rows[k][l];
    2047:       col    = coloring->columnsforrow[k][l];
    2048:       y[row] *= vscale_array[vscaleforrow[k][l]];
    2049:       srow   = row + start;
    2050:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
    2051:     }
    2052:     VecRestoreArray(w2,&y);
    2053:   }
    2054:   coloring->currentcolor = k;
    2055:   VecRestoreArray(coloring->vscale,&vscale_array);
    2056:   xx = xx + start; VecRestoreArray(x1,&xx);
    2057:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
    2058:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
    2059:   return(0);
    2060: }

    2062:  #include petscblaslapack.h
    2065: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
    2066: {
    2068:   PetscInt       i;
    2069:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
    2070:   PetscBLASInt   one=1,bnz = (PetscBLASInt)x->nz;

    2073:   if (str == SAME_NONZERO_PATTERN) {
    2074:     PetscScalar alpha = a;
    2075:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
    2076:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
    2077:     if (y->xtoy && y->XtoY != X) {
    2078:       PetscFree(y->xtoy);
    2079:       MatDestroy(y->XtoY);
    2080:     }
    2081:     if (!y->xtoy) { /* get xtoy */
    2082:       MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
    2083:       y->XtoY = X;
    2084:     }
    2085:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
    2086:     PetscLogInfo((0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz));
    2087:   } else {
    2088:     MatAXPY_Basic(Y,a,X,str);
    2089:   }
    2090:   return(0);
    2091: }

    2095: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
    2096: {
    2098:   return(0);
    2099: }

    2103: PetscErrorCode PETSCMAT_DLLEXPORT MatConjugate_SeqAIJ(Mat mat)
    2104: {
    2105: #if defined(PETSC_USE_COMPLEX)
    2106:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
    2107:   PetscInt    i,nz;
    2108:   PetscScalar *a;

    2111:   nz = aij->nz;
    2112:   a  = aij->a;
    2113:   for (i=0; i<nz; i++) {
    2114:     a[i] = PetscConj(a[i]);
    2115:   }
    2116: #else
    2118: #endif
    2119:   return(0);
    2120: }

    2124: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v)
    2125: {
    2126:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
    2128:   PetscInt       i,j,m = A->m,*ai,*aj,ncols,n;
    2129:   PetscReal      atmp;
    2130:   PetscScalar    *x,zero = 0.0;
    2131:   MatScalar      *aa;

    2134:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
    2135:   aa   = a->a;
    2136:   ai   = a->i;
    2137:   aj   = a->j;

    2139:   VecSet(v,zero);
    2140:   VecGetArray(v,&x);
    2141:   VecGetLocalSize(v,&n);
    2142:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
    2143:   for (i=0; i<m; i++) {
    2144:     ncols = ai[1] - ai[0]; ai++;
    2145:     for (j=0; j<ncols; j++){
    2146:       atmp = PetscAbsScalar(*aa); aa++;
    2147:       if (PetscAbsScalar(x[i]) < atmp) x[i] = atmp;
    2148:       aj++;
    2149:     }
    2150:   }
    2151:   VecRestoreArray(v,&x);
    2152:   return(0);
    2153: }

    2155: /* -------------------------------------------------------------------*/
    2156: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
    2157:        MatGetRow_SeqAIJ,
    2158:        MatRestoreRow_SeqAIJ,
    2159:        MatMult_SeqAIJ,
    2160: /* 4*/ MatMultAdd_SeqAIJ,
    2161:        MatMultTranspose_SeqAIJ,
    2162:        MatMultTransposeAdd_SeqAIJ,
    2163:        MatSolve_SeqAIJ,
    2164:        MatSolveAdd_SeqAIJ,
    2165:        MatSolveTranspose_SeqAIJ,
    2166: /*10*/ MatSolveTransposeAdd_SeqAIJ,
    2167:        MatLUFactor_SeqAIJ,
    2168:        0,
    2169:        MatRelax_SeqAIJ,
    2170:        MatTranspose_SeqAIJ,
    2171: /*15*/ MatGetInfo_SeqAIJ,
    2172:        MatEqual_SeqAIJ,
    2173:        MatGetDiagonal_SeqAIJ,
    2174:        MatDiagonalScale_SeqAIJ,
    2175:        MatNorm_SeqAIJ,
    2176: /*20*/ 0,
    2177:        MatAssemblyEnd_SeqAIJ,
    2178:        MatCompress_SeqAIJ,
    2179:        MatSetOption_SeqAIJ,
    2180:        MatZeroEntries_SeqAIJ,
    2181: /*25*/ MatZeroRows_SeqAIJ,
    2182:        MatLUFactorSymbolic_SeqAIJ,
    2183:        MatLUFactorNumeric_SeqAIJ,
    2184:        MatCholeskyFactorSymbolic_SeqAIJ,
    2185:        MatCholeskyFactorNumeric_SeqAIJ,
    2186: /*30*/ MatSetUpPreallocation_SeqAIJ,
    2187:        MatILUFactorSymbolic_SeqAIJ,
    2188:        MatICCFactorSymbolic_SeqAIJ,
    2189:        MatGetArray_SeqAIJ,
    2190:        MatRestoreArray_SeqAIJ,
    2191: /*35*/ MatDuplicate_SeqAIJ,
    2192:        0,
    2193:        0,
    2194:        MatILUFactor_SeqAIJ,
    2195:        0,
    2196: /*40*/ MatAXPY_SeqAIJ,
    2197:        MatGetSubMatrices_SeqAIJ,
    2198:        MatIncreaseOverlap_SeqAIJ,
    2199:        MatGetValues_SeqAIJ,
    2200:        MatCopy_SeqAIJ,
    2201: /*45*/ MatPrintHelp_SeqAIJ,
    2202:        MatScale_SeqAIJ,
    2203:        0,
    2204:        0,
    2205:        MatILUDTFactor_SeqAIJ,
    2206: /*50*/ MatSetBlockSize_SeqAIJ,
    2207:        MatGetRowIJ_SeqAIJ,
    2208:        MatRestoreRowIJ_SeqAIJ,
    2209:        MatGetColumnIJ_SeqAIJ,
    2210:        MatRestoreColumnIJ_SeqAIJ,
    2211: /*55*/ MatFDColoringCreate_SeqAIJ,
    2212:        0,
    2213:        0,
    2214:        MatPermute_SeqAIJ,
    2215:        0,
    2216: /*60*/ 0,
    2217:        MatDestroy_SeqAIJ,
    2218:        MatView_SeqAIJ,
    2219:        MatGetPetscMaps_Petsc,
    2220:        0,
    2221: /*65*/ 0,
    2222:        0,
    2223:        0,
    2224:        0,
    2225:        0,
    2226: /*70*/ MatGetRowMax_SeqAIJ,
    2227:        0,
    2228:        MatSetColoring_SeqAIJ,
    2229: #if defined(PETSC_HAVE_ADIC)
    2230:        MatSetValuesAdic_SeqAIJ,
    2231: #else
    2232:        0,
    2233: #endif
    2234:        MatSetValuesAdifor_SeqAIJ,
    2235: /*75*/ MatFDColoringApply_SeqAIJ,
    2236:        0,
    2237:        0,
    2238:        0,
    2239:        0,
    2240: /*80*/ 0,
    2241:        0,
    2242:        0,
    2243:        0,
    2244:        MatLoad_SeqAIJ,
    2245: /*85*/ MatIsSymmetric_SeqAIJ,
    2246:        0,
    2247:        0,
    2248:        0,
    2249:        0,
    2250: /*90*/ MatMatMult_SeqAIJ_SeqAIJ,
    2251:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
    2252:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
    2253:        MatPtAP_Basic,
    2254:        MatPtAPSymbolic_SeqAIJ,
    2255: /*95*/ MatPtAPNumeric_SeqAIJ,
    2256:        MatMatMultTranspose_SeqAIJ_SeqAIJ,
    2257:        MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
    2258:        MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
    2259:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
    2260: /*100*/MatPtAPNumeric_SeqAIJ_SeqAIJ,
    2261:        0,
    2262:        0,
    2263:        MatConjugate_SeqAIJ
    2264: };

    2269: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
    2270: {
    2271:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
    2272:   PetscInt   i,nz,n;

    2276:   nz = aij->maxnz;
    2277:   n  = mat->n;
    2278:   for (i=0; i<nz; i++) {
    2279:     aij->j[i] = indices[i];
    2280:   }
    2281:   aij->nz = nz;
    2282:   for (i=0; i<n; i++) {
    2283:     aij->ilen[i] = aij->imax[i];
    2284:   }

    2286:   return(0);
    2287: }

    2292: /*@
    2293:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
    2294:        in the matrix.

    2296:   Input Parameters:
    2297: +  mat - the SeqAIJ matrix
    2298: -  indices - the column indices

    2300:   Level: advanced

    2302:   Notes:
    2303:     This can be called if you have precomputed the nonzero structure of the
    2304:   matrix and want to provide it to the matrix object to improve the performance
    2305:   of the MatSetValues() operation.

    2307:     You MUST have set the correct numbers of nonzeros per row in the call to
    2308:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

    2310:     MUST be called before any calls to MatSetValues();

    2312:     The indices should start with zero, not one.

    2314: @*/
    2315: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
    2316: {
    2317:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

    2322:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
    2323:   if (f) {
    2324:     (*f)(mat,indices);
    2325:   } else {
    2326:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
    2327:   }
    2328:   return(0);
    2329: }

    2331: /* ----------------------------------------------------------------------------------------*/

    2336: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues_SeqAIJ(Mat mat)
    2337: {
    2338:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
    2340:   size_t         nz = aij->i[mat->m];

    2343:   if (aij->nonew != 1) {
    2344:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
    2345:   }

    2347:   /* allocate space for values if not already there */
    2348:   if (!aij->saved_values) {
    2349:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
    2350:   }

    2352:   /* copy values over */
    2353:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
    2354:   return(0);
    2355: }

    2360: /*@
    2361:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
    2362:        example, reuse of the linear part of a Jacobian, while recomputing the
    2363:        nonlinear portion.

    2365:    Collect on Mat

    2367:   Input Parameters:
    2368: .  mat - the matrix (currently only AIJ matrices support this option)

    2370:   Level: advanced

    2372:   Common Usage, with SNESSolve():
    2373: $    Create Jacobian matrix
    2374: $    Set linear terms into matrix
    2375: $    Apply boundary conditions to matrix, at this time matrix must have
    2376: $      final nonzero structure (i.e. setting the nonlinear terms and applying
    2377: $      boundary conditions again will not change the nonzero structure
    2378: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
    2379: $    MatStoreValues(mat);
    2380: $    Call SNESSetJacobian() with matrix
    2381: $    In your Jacobian routine
    2382: $      MatRetrieveValues(mat);
    2383: $      Set nonlinear terms in matrix
    2384:
    2385:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
    2386: $    // build linear portion of Jacobian
    2387: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
    2388: $    MatStoreValues(mat);
    2389: $    loop over nonlinear iterations
    2390: $       MatRetrieveValues(mat);
    2391: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
    2392: $       // call MatAssemblyBegin/End() on matrix
    2393: $       Solve linear system with Jacobian
    2394: $    endloop

    2396:   Notes:
    2397:     Matrix must already be assemblied before calling this routine
    2398:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before
    2399:     calling this routine.

    2401:     When this is called multiple times it overwrites the previous set of stored values
    2402:     and does not allocated additional space.

    2404: .seealso: MatRetrieveValues()

    2406: @*/
    2407: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues(Mat mat)
    2408: {
    2409:   PetscErrorCode ierr,(*f)(Mat);

    2413:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    2414:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

    2416:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
    2417:   if (f) {
    2418:     (*f)(mat);
    2419:   } else {
    2420:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values");
    2421:   }
    2422:   return(0);
    2423: }

    2428: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues_SeqAIJ(Mat mat)
    2429: {
    2430:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
    2432:   PetscInt       nz = aij->i[mat->m];

    2435:   if (aij->nonew != 1) {
    2436:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
    2437:   }
    2438:   if (!aij->saved_values) {
    2439:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
    2440:   }
    2441:   /* copy values over */
    2442:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
    2443:   return(0);
    2444: }

    2449: /*@
    2450:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
    2451:        example, reuse of the linear part of a Jacobian, while recomputing the
    2452:        nonlinear portion.

    2454:    Collect on Mat

    2456:   Input Parameters:
    2457: .  mat - the matrix (currently on AIJ matrices support this option)

    2459:   Level: advanced

    2461: .seealso: MatStoreValues()

    2463: @*/
    2464: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues(Mat mat)
    2465: {
    2466:   PetscErrorCode ierr,(*f)(Mat);

    2470:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
    2471:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

    2473:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
    2474:   if (f) {
    2475:     (*f)(mat);
    2476:   } else {
    2477:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values");
    2478:   }
    2479:   return(0);
    2480: }

    2483: /* --------------------------------------------------------------------------------*/
    2486: /*@C
    2487:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
    2488:    (the default parallel PETSc format).  For good matrix assembly performance
    2489:    the user should preallocate the matrix storage by setting the parameter nz
    2490:    (or the array nnz).  By setting these parameters accurately, performance
    2491:    during matrix assembly can be increased by more than a factor of 50.

    2493:    Collective on MPI_Comm

    2495:    Input Parameters:
    2496: +  comm - MPI communicator, set to PETSC_COMM_SELF
    2497: .  m - number of rows
    2498: .  n - number of columns
    2499: .  nz - number of nonzeros per row (same for all rows)
    2500: -  nnz - array containing the number of nonzeros in the various rows
    2501:          (possibly different for each row) or PETSC_NULL

    2503:    Output Parameter:
    2504: .  A - the matrix

    2506:    Notes:
    2507:    If nnz is given then nz is ignored

    2509:    The AIJ format (also called the Yale sparse matrix format or
    2510:    compressed row storage), is fully compatible with standard Fortran 77
    2511:    storage.  That is, the stored row and column indices can begin at
    2512:    either one (as in Fortran) or zero.  See the users' manual for details.

    2514:    Specify the preallocated storage with either nz or nnz (not both).
    2515:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
    2516:    allocation.  For large problems you MUST preallocate memory or you
    2517:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    2519:    By default, this format uses inodes (identical nodes) when possible, to
    2520:    improve numerical efficiency of matrix-vector products and solves. We
    2521:    search for consecutive rows with the same nonzero structure, thereby
    2522:    reusing matrix information to achieve increased efficiency.

    2524:    Options Database Keys:
    2525: +  -mat_no_inode  - Do not use inodes
    2526: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
    2527: -  -mat_aij_oneindex - Internally use indexing starting at 1
    2528:         rather than 0.  Note that when calling MatSetValues(),
    2529:         the user still MUST index entries starting at 0!

    2531:    Level: intermediate

    2533: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

    2535: @*/
    2536: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
    2537: {

    2541:   MatCreate(comm,A);
    2542:   MatSetSizes(*A,m,n,m,n);
    2543:   MatSetType(*A,MATSEQAIJ);
    2544:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
    2545:   return(0);
    2546: }

    2550: /*@C
    2551:    MatSeqAIJSetPreallocation - For good matrix assembly performance
    2552:    the user should preallocate the matrix storage by setting the parameter nz
    2553:    (or the array nnz).  By setting these parameters accurately, performance
    2554:    during matrix assembly can be increased by more than a factor of 50.

    2556:    Collective on MPI_Comm

    2558:    Input Parameters:
    2559: +  B - The matrix
    2560: .  nz - number of nonzeros per row (same for all rows)
    2561: -  nnz - array containing the number of nonzeros in the various rows
    2562:          (possibly different for each row) or PETSC_NULL

    2564:    Notes:
    2565:      If nnz is given then nz is ignored

    2567:     The AIJ format (also called the Yale sparse matrix format or
    2568:    compressed row storage), is fully compatible with standard Fortran 77
    2569:    storage.  That is, the stored row and column indices can begin at
    2570:    either one (as in Fortran) or zero.  See the users' manual for details.

    2572:    Specify the preallocated storage with either nz or nnz (not both).
    2573:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
    2574:    allocation.  For large problems you MUST preallocate memory or you
    2575:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    2577:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
    2578:    entries or columns indices

    2580:    By default, this format uses inodes (identical nodes) when possible, to
    2581:    improve numerical efficiency of matrix-vector products and solves. We
    2582:    search for consecutive rows with the same nonzero structure, thereby
    2583:    reusing matrix information to achieve increased efficiency.

    2585:    Options Database Keys:
    2586: +  -mat_no_inode  - Do not use inodes
    2587: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
    2588: -  -mat_aij_oneindex - Internally use indexing starting at 1
    2589:         rather than 0.  Note that when calling MatSetValues(),
    2590:         the user still MUST index entries starting at 0!

    2592:    Level: intermediate

    2594: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

    2596: @*/
    2597: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
    2598: {
    2599:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]);

    2602:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
    2603:   if (f) {
    2604:     (*f)(B,nz,nnz);
    2605:   }
    2606:   return(0);
    2607: }

    2612: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz)
    2613: {
    2614:   Mat_SeqAIJ     *b;
    2615:   PetscTruth     skipallocation = PETSC_FALSE;
    2617:   PetscInt       i;

    2620:
    2621:   if (nz == MAT_SKIP_ALLOCATION) {
    2622:     skipallocation = PETSC_TRUE;
    2623:     nz             = 0;
    2624:   }

    2626:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
    2627:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
    2628:   if (nnz) {
    2629:     for (i=0; i<B->m; i++) {
    2630:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
    2631:       if (nnz[i] > B->n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->n);
    2632:     }
    2633:   }

    2635:   B->preallocated = PETSC_TRUE;
    2636:   b = (Mat_SeqAIJ*)B->data;

    2638:   if (!skipallocation) {
    2639:     PetscMalloc2(B->m,PetscInt,&b->imax,B->m,PetscInt,&b->ilen);
    2640:     if (!nnz) {
    2641:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
    2642:       else if (nz <= 0)        nz = 1;
    2643:       for (i=0; i<B->m; i++) b->imax[i] = nz;
    2644:       nz = nz*B->m;
    2645:     } else {
    2646:       nz = 0;
    2647:       for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
    2648:     }

    2650:     /* b->ilen will count nonzeros in each row so far. */
    2651:     for (i=0; i<B->m; i++) { b->ilen[i] = 0;}

    2653:     /* allocate the matrix space */
    2654:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->m+1,PetscInt,&b->i);
    2655:     b->i[0] = 0;
    2656:     for (i=1; i<B->m+1; i++) {
    2657:       b->i[i] = b->i[i-1] + b->imax[i-1];
    2658:     }
    2659:     b->singlemalloc = PETSC_TRUE;
    2660:     b->freedata     = PETSC_TRUE;
    2661:   } else {
    2662:     b->freedata     = PETSC_FALSE;
    2663:   }

    2665:   b->nz                = 0;
    2666:   b->maxnz             = nz;
    2667:   B->info.nz_unneeded  = (double)b->maxnz;
    2668:   return(0);
    2669: }

    2672: /*MC
    2673:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
    2674:    based on compressed sparse row format.

    2676:    Options Database Keys:
    2677: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

    2679:   Level: beginner

    2681: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
    2682: M*/

    2687: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SeqAIJ(Mat B)
    2688: {
    2689:   Mat_SeqAIJ     *b;
    2691:   PetscMPIInt    size;

    2694:   MPI_Comm_size(B->comm,&size);
    2695:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

    2697:   B->m = B->M = PetscMax(B->m,B->M);
    2698:   B->n = B->N = PetscMax(B->n,B->N);

    2700:   PetscNew(Mat_SeqAIJ,&b);
    2701:   B->data             = (void*)b;
    2702:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
    2703:   B->factor           = 0;
    2704:   B->mapping          = 0;
    2705:   b->row              = 0;
    2706:   b->col              = 0;
    2707:   b->icol             = 0;
    2708:   b->reallocs         = 0;
    2709:
    2710:   PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
    2711:   PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);

    2713:   b->sorted            = PETSC_FALSE;
    2714:   b->ignorezeroentries = PETSC_FALSE;
    2715:   b->roworiented       = PETSC_TRUE;
    2716:   b->nonew             = 0;
    2717:   b->diag              = 0;
    2718:   b->solve_work        = 0;
    2719:   B->spptr             = 0;
    2720:   b->saved_values      = 0;
    2721:   b->idiag             = 0;
    2722:   b->ssor              = 0;
    2723:   b->keepzeroedrows    = PETSC_FALSE;
    2724:   b->xtoy              = 0;
    2725:   b->XtoY              = 0;
    2726:   b->compressedrow.use     = PETSC_FALSE;
    2727:   b->compressedrow.nrows   = B->m;
    2728:   b->compressedrow.i       = PETSC_NULL;
    2729:   b->compressedrow.rindex  = PETSC_NULL;
    2730:   b->compressedrow.checked = PETSC_FALSE;
    2731:   B->same_nonzero          = PETSC_FALSE;

    2733:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

    2735:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
    2736:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
    2737:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
    2738:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
    2739:                                      "MatStoreValues_SeqAIJ",
    2740:                                      MatStoreValues_SeqAIJ);
    2741:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
    2742:                                      "MatRetrieveValues_SeqAIJ",
    2743:                                      MatRetrieveValues_SeqAIJ);
    2744:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
    2745:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
    2746:                                       MatConvert_SeqAIJ_SeqSBAIJ);
    2747:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
    2748:                                      "MatConvert_SeqAIJ_SeqBAIJ",
    2749:                                       MatConvert_SeqAIJ_SeqBAIJ);
    2750:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
    2751:                                      "MatIsTranspose_SeqAIJ",
    2752:                                       MatIsTranspose_SeqAIJ);
    2753:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
    2754:                                      "MatSeqAIJSetPreallocation_SeqAIJ",
    2755:                                       MatSeqAIJSetPreallocation_SeqAIJ);
    2756:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
    2757:                                      "MatReorderForNonzeroDiagonal_SeqAIJ",
    2758:                                       MatReorderForNonzeroDiagonal_SeqAIJ);
    2759:   MatCreate_Inode(B);
    2760:   return(0);
    2761: }

    2766: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
    2767: {
    2768:   Mat            C;
    2769:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
    2771:   PetscInt       i,m = A->m;

    2774:   *B = 0;
    2775:   MatCreate(A->comm,&C);
    2776:   MatSetSizes(C,A->m,A->n,A->m,A->n);
    2777:   MatSetType(C,A->type_name);
    2778:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
    2779:
    2780:   c = (Mat_SeqAIJ*)C->data;

    2782:   C->factor           = A->factor;

    2784:   c->row            = 0;
    2785:   c->col            = 0;
    2786:   c->icol           = 0;
    2787:   c->reallocs       = 0;

    2789:   C->assembled      = PETSC_TRUE;
    2790:
    2791:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
    2792:   for (i=0; i<m; i++) {
    2793:     c->imax[i] = a->imax[i];
    2794:     c->ilen[i] = a->ilen[i];
    2795:   }

    2797:   /* allocate the matrix space */
    2798:   PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
    2799:   c->singlemalloc = PETSC_TRUE;
    2800:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
    2801:   if (m > 0) {
    2802:     PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
    2803:     if (cpvalues == MAT_COPY_VALUES) {
    2804:       PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
    2805:     } else {
    2806:       PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
    2807:     }
    2808:   }

    2810:   c->sorted            = a->sorted;
    2811:   c->ignorezeroentries = a->ignorezeroentries;
    2812:   c->roworiented       = a->roworiented;
    2813:   c->nonew             = a->nonew;
    2814:   if (a->diag) {
    2815:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
    2816:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
    2817:     for (i=0; i<m; i++) {
    2818:       c->diag[i] = a->diag[i];
    2819:     }
    2820:   } else c->diag        = 0;
    2821:   c->solve_work         = 0;
    2822:   c->saved_values          = 0;
    2823:   c->idiag                 = 0;
    2824:   c->ssor                  = 0;
    2825:   c->keepzeroedrows        = a->keepzeroedrows;
    2826:   c->freedata              = PETSC_TRUE;
    2827:   c->xtoy                  = 0;
    2828:   c->XtoY                  = 0;

    2830:   c->nz                 = a->nz;
    2831:   c->maxnz              = a->maxnz;
    2832:   C->preallocated       = PETSC_TRUE;

    2834:   c->compressedrow.use     = a->compressedrow.use;
    2835:   c->compressedrow.nrows   = a->compressedrow.nrows;
    2836:   c->compressedrow.checked = a->compressedrow.checked;
    2837:   if ( a->compressedrow.checked && a->compressedrow.use){
    2838:     i = a->compressedrow.nrows;
    2839:     PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
    2840:     c->compressedrow.rindex = c->compressedrow.i + i + 1;
    2841:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
    2842:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
    2843:   } else {
    2844:     c->compressedrow.use    = PETSC_FALSE;
    2845:     c->compressedrow.i      = PETSC_NULL;
    2846:     c->compressedrow.rindex = PETSC_NULL;
    2847:   }
    2848:   C->same_nonzero = A->same_nonzero;

    2850:   MatDuplicate_Inode(A,cpvalues,&C);

    2852:   *B = C;
    2853:   PetscFListDuplicate(A->qlist,&C->qlist);
    2854:   return(0);
    2855: }

    2859: PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer, MatType type,Mat *A)
    2860: {
    2861:   Mat_SeqAIJ     *a;
    2862:   Mat            B;
    2864:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N;
    2865:   int            fd;
    2866:   PetscMPIInt    size;
    2867:   MPI_Comm       comm;
    2868:
    2870:   PetscObjectGetComm((PetscObject)viewer,&comm);
    2871:   MPI_Comm_size(comm,&size);
    2872:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
    2873:   PetscViewerBinaryGetDescriptor(viewer,&fd);
    2874:   PetscBinaryRead(fd,header,4,PETSC_INT);
    2875:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
    2876:   M = header[1]; N = header[2]; nz = header[3];

    2878:   if (nz < 0) {
    2879:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
    2880:   }

    2882:   /* read in row lengths */
    2883:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
    2884:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

    2886:   /* check if sum of rowlengths is same as nz */
    2887:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
    2888:   if (sum != nz) SETERRQ2(PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

    2890:   /* create our matrix */
    2891:   MatCreate(comm,&B);
    2892:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M,N);
    2893:   MatSetType(B,type);
    2894:   MatSeqAIJSetPreallocation_SeqAIJ(B,0,rowlengths);
    2895:   a = (Mat_SeqAIJ*)B->data;

    2897:   /* read in column indices and adjust for Fortran indexing*/
    2898:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

    2900:   /* read in nonzero values */
    2901:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

    2903:   /* set matrix "i" values */
    2904:   a->i[0] = 0;
    2905:   for (i=1; i<= M; i++) {
    2906:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
    2907:     a->ilen[i-1] = rowlengths[i-1];
    2908:   }
    2909:   PetscFree(rowlengths);

    2911:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
    2912:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
    2913:   *A = B;
    2914:   return(0);
    2915: }

    2919: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
    2920: {
    2921:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;

    2925:   /* If the  matrix dimensions are not equal,or no of nonzeros */
    2926:   if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) {
    2927:     *flg = PETSC_FALSE;
    2928:     return(0);
    2929:   }
    2930:
    2931:   /* if the a->i are the same */
    2932:   PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(PetscInt),flg);
    2933:   if (!*flg) return(0);
    2934:
    2935:   /* if a->j are the same */
    2936:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
    2937:   if (!*flg) return(0);
    2938:
    2939:   /* if a->a are the same */
    2940:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

    2942:   return(0);
    2943:
    2944: }

    2948: /*@C
    2949:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
    2950:               provided by the user.

    2952:       Coolective on MPI_Comm

    2954:    Input Parameters:
    2955: +   comm - must be an MPI communicator of size 1
    2956: .   m - number of rows
    2957: .   n - number of columns
    2958: .   i - row indices
    2959: .   j - column indices
    2960: -   a - matrix values

    2962:    Output Parameter:
    2963: .   mat - the matrix

    2965:    Level: intermediate

    2967:    Notes:
    2968:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
    2969:     once the matrix is destroyed

    2971:        You cannot set new nonzero locations into this matrix, that will generate an error.

    2973:        The i and j indices are 0 based

    2975: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()

    2977: @*/
    2978: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
    2979: {
    2981:   PetscInt       ii;
    2982:   Mat_SeqAIJ     *aij;

    2985:   if (i[0]) {
    2986:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
    2987:   }
    2988:   MatCreate(comm,mat);
    2989:   MatSetSizes(*mat,m,n,m,n);
    2990:   MatSetType(*mat,MATSEQAIJ);
    2991:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
    2992:   aij  = (Mat_SeqAIJ*)(*mat)->data;
    2993:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

    2995:   aij->i = i;
    2996:   aij->j = j;
    2997:   aij->a = a;
    2998:   aij->singlemalloc = PETSC_FALSE;
    2999:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
    3000:   aij->freedata     = PETSC_FALSE;

    3002:   for (ii=0; ii<m; ii++) {
    3003:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
    3004: #if defined(PETSC_USE_DEBUG)
    3005:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
    3006: #endif
    3007:   }
    3008: #if defined(PETSC_USE_DEBUG)
    3009:   for (ii=0; ii<aij->i[m]; ii++) {
    3010:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
    3011:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
    3012:   }
    3013: #endif

    3015:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
    3016:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
    3017:   return(0);
    3018: }

    3022: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
    3023: {
    3025:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

    3028:   if (coloring->ctype == IS_COLORING_LOCAL) {
    3029:     ISColoringReference(coloring);
    3030:     a->coloring = coloring;
    3031:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
    3032:     PetscInt             i,*larray;
    3033:     ISColoring      ocoloring;
    3034:     ISColoringValue *colors;

    3036:     /* set coloring for diagonal portion */
    3037:     PetscMalloc((A->n+1)*sizeof(PetscInt),&larray);
    3038:     for (i=0; i<A->n; i++) {
    3039:       larray[i] = i;
    3040:     }
    3041:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);
    3042:     PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);
    3043:     for (i=0; i<A->n; i++) {
    3044:       colors[i] = coloring->colors[larray[i]];
    3045:     }
    3046:     PetscFree(larray);
    3047:     ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);
    3048:     a->coloring = ocoloring;
    3049:   }
    3050:   return(0);
    3051: }

    3053: #if defined(PETSC_HAVE_ADIC)
    3055: #include "adic/ad_utils.h"

    3060: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
    3061: {
    3062:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3063:   PetscInt        m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen;
    3064:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
    3065:   ISColoringValue *color;

    3068:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
    3069:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
    3070:   color = a->coloring->colors;
    3071:   /* loop over rows */
    3072:   for (i=0; i<m; i++) {
    3073:     nz = ii[i+1] - ii[i];
    3074:     /* loop over columns putting computed value into matrix */
    3075:     for (j=0; j<nz; j++) {
    3076:       *v++ = values[color[*jj++]];
    3077:     }
    3078:     values += nlen; /* jump to next row of derivatives */
    3079:   }
    3080:   return(0);
    3081: }
    3082: #endif

    3086: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
    3087: {
    3088:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
    3089:   PetscInt             m = A->m,*ii = a->i,*jj = a->j,nz,i,j;
    3090:   PetscScalar     *v = a->a,*values = (PetscScalar *)advalues;
    3091:   ISColoringValue *color;

    3094:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
    3095:   color = a->coloring->colors;
    3096:   /* loop over rows */
    3097:   for (i=0; i<m; i++) {
    3098:     nz = ii[i+1] - ii[i];
    3099:     /* loop over columns putting computed value into matrix */
    3100:     for (j=0; j<nz; j++) {
    3101:       *v++ = values[color[*jj++]];
    3102:     }
    3103:     values += nl; /* jump to next row of derivatives */
    3104:   }
    3105:   return(0);
    3106: }