Actual source code: aij.c

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


  8: #include <../src/mat/impls/aij/seq/aij.h>          /*I "petscmat.h" I*/
  9: #include <petscblaslapack.h>
 10: #include <petscbt.h>
 11: #include <../src/mat/blocktranspose.h>

 15: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
 16: {
 18:   PetscInt       i,m,n;
 19:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)A->data;

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

 38:   if (type == NORM_2) {
 39:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
 40:   }
 41:   return(0);
 42: }

 46: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
 47: {
 48:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
 49:   const MatScalar   *aa = a->a;
 50:   PetscInt          i,m=A->rmap->n,cnt = 0;
 51:   const PetscInt    *jj = a->j,*diag;
 52:   PetscInt          *rows;
 53:   PetscErrorCode    ierr;

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

 76: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
 77: {
 78:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
 79:   const MatScalar   *aa;
 80:   PetscInt          m=A->rmap->n,cnt = 0;
 81:   const PetscInt    *ii;
 82:   PetscInt          n,i,j,*rows;
 83:   PetscErrorCode    ierr;

 86:   *keptrows = 0;
 87:   ii        = a->i;
 88:   for (i=0; i<m; i++) {
 89:     n   = ii[i+1] - ii[i];
 90:     if (!n) {
 91:       cnt++;
 92:       goto ok1;
 93:     }
 94:     aa  = a->a + ii[i];
 95:     for (j=0; j<n; j++) {
 96:       if (aa[j] != 0.0) goto ok1;
 97:     }
 98:     cnt++;
 99:     ok1:;
100:   }
101:   if (!cnt) return(0);
102:   PetscMalloc((A->rmap->n-cnt)*sizeof(PetscInt),&rows);
103:   cnt  = 0;
104:   for (i=0; i<m; i++) {
105:     n   = ii[i+1] - ii[i];
106:     if (!n) continue;
107:     aa  = a->a + ii[i];
108:     for (j=0; j<n; j++) {
109:       if (aa[j] != 0.0) {
110:         rows[cnt++] = i;
111:         break;
112:       }
113:     }
114:   }
115:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
116:   return(0);
117: }

121: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
122: {
124:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) Y->data;
125:   PetscInt       i,*diag, m = Y->rmap->n;
126:   MatScalar      *aa = aij->a;
127:   PetscScalar    *v;
128:   PetscBool      missing;

131:   if (Y->assembled) {
132:     MatMissingDiagonal_SeqAIJ(Y,&missing,PETSC_NULL);
133:     if (!missing) {
134:       diag = aij->diag;
135:       VecGetArray(D,&v);
136:       if (is == INSERT_VALUES) {
137:         for (i=0; i<m; i++) {
138:           aa[diag[i]] = v[i];
139:         }
140:       } else {
141:         for (i=0; i<m; i++) {
142:           aa[diag[i]] += v[i];
143:         }
144:       }
145:       VecRestoreArray(D,&v);
146:       return(0);
147:     }
148:     aij->idiagvalid  = PETSC_FALSE;
149:     aij->ibdiagvalid = PETSC_FALSE;
150:   }
151:   MatDiagonalSet_Default(Y,D,is);
152:   return(0);
153: }

157: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
158: {
159:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
161:   PetscInt       i,ishift;
162: 
164:   *m     = A->rmap->n;
165:   if (!ia) return(0);
166:   ishift = 0;
167:   if (symmetric && !A->structurally_symmetric) {
168:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,ia,ja);
169:   } else if (oshift == 1) {
170:     PetscInt nz = a->i[A->rmap->n];
171:     /* malloc space and  add 1 to i and j indices */
172:     PetscMalloc((A->rmap->n+1)*sizeof(PetscInt),ia);
173:     for (i=0; i<A->rmap->n+1; i++) (*ia)[i] = a->i[i] + 1;
174:     if (ja) {
175:       PetscMalloc((nz+1)*sizeof(PetscInt),ja);
176:       for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
177:     }
178:   } else {
179:     *ia = a->i;
180:     if (ja) *ja = a->j;
181:   }
182:   return(0);
183: }

187: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
188: {
190: 
192:   if (!ia) return(0);
193:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
194:     PetscFree(*ia);
195:     if (ja) {PetscFree(*ja);}
196:   }
197:   return(0);
198: }

202: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
203: {
204:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
206:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
207:   PetscInt       nz = a->i[m],row,*jj,mr,col;

210:   *nn = n;
211:   if (!ia) return(0);
212:   if (symmetric) {
213:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,ia,ja);
214:   } else {
215:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
216:     PetscMemzero(collengths,n*sizeof(PetscInt));
217:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
218:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
219:     jj = a->j;
220:     for (i=0; i<nz; i++) {
221:       collengths[jj[i]]++;
222:     }
223:     cia[0] = oshift;
224:     for (i=0; i<n; i++) {
225:       cia[i+1] = cia[i] + collengths[i];
226:     }
227:     PetscMemzero(collengths,n*sizeof(PetscInt));
228:     jj   = a->j;
229:     for (row=0; row<m; row++) {
230:       mr = a->i[row+1] - a->i[row];
231:       for (i=0; i<mr; i++) {
232:         col = *jj++;
233:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
234:       }
235:     }
236:     PetscFree(collengths);
237:     *ia = cia; *ja = cja;
238:   }
239:   return(0);
240: }

244: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
245: {

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

251:   PetscFree(*ia);
252:   PetscFree(*ja);
253: 
254:   return(0);
255: }

259: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
260: {
261:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
262:   PetscInt       *ai = a->i;

266:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
267:   return(0);
268: }

272: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
273: {
274:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
275:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
276:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
278:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
279:   MatScalar      *ap,value,*aa = a->a;
280:   PetscBool      ignorezeroentries = a->ignorezeroentries;
281:   PetscBool      roworiented = a->roworiented;

285:   for (k=0; k<m; k++) { /* loop over added rows */
286:     row  = im[k];
287:     if (row < 0) continue;
288: #if defined(PETSC_USE_DEBUG)  
289:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
290: #endif
291:     rp   = aj + ai[row]; ap = aa + ai[row];
292:     rmax = imax[row]; nrow = ailen[row];
293:     low  = 0;
294:     high = nrow;
295:     for (l=0; l<n; l++) { /* loop over added columns */
296:       if (in[l] < 0) continue;
297: #if defined(PETSC_USE_DEBUG)  
298:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
299: #endif
300:       col = in[l];
301:       if (v) {
302:         if (roworiented) {
303:           value = v[l + k*n];
304:         } else {
305:           value = v[k + l*m];
306:         }
307:       } else {
308:         value = 0.;
309:       }
310:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

312:       if (col <= lastcol) low = 0; else high = nrow;
313:       lastcol = col;
314:       while (high-low > 5) {
315:         t = (low+high)/2;
316:         if (rp[t] > col) high = t;
317:         else             low  = t;
318:       }
319:       for (i=low; i<high; i++) {
320:         if (rp[i] > col) break;
321:         if (rp[i] == col) {
322:           if (is == ADD_VALUES) ap[i] += value;
323:           else                  ap[i] = value;
324:           low = i + 1;
325:           goto noinsert;
326:         }
327:       }
328:       if (value == 0.0 && ignorezeroentries) goto noinsert;
329:       if (nonew == 1) goto noinsert;
330:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
331:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
332:       N = nrow++ - 1; a->nz++; high++;
333:       /* shift up all the later entries in this row */
334:       for (ii=N; ii>=i; ii--) {
335:         rp[ii+1] = rp[ii];
336:         ap[ii+1] = ap[ii];
337:       }
338:       rp[i] = col;
339:       ap[i] = value;
340:       low   = i + 1;
341:       noinsert:;
342:     }
343:     ailen[row] = nrow;
344:   }
345:   A->same_nonzero = PETSC_FALSE;
346:   return(0);
347: }


352: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
353: {
354:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
355:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
356:   PetscInt     *ai = a->i,*ailen = a->ilen;
357:   MatScalar    *ap,*aa = a->a;

360:   for (k=0; k<m; k++) { /* loop over rows */
361:     row  = im[k];
362:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
363:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
364:     rp   = aj + ai[row]; ap = aa + ai[row];
365:     nrow = ailen[row];
366:     for (l=0; l<n; l++) { /* loop over columns */
367:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
368:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
369:       col = in[l] ;
370:       high = nrow; low = 0; /* assume unsorted */
371:       while (high-low > 5) {
372:         t = (low+high)/2;
373:         if (rp[t] > col) high = t;
374:         else             low  = t;
375:       }
376:       for (i=low; i<high; i++) {
377:         if (rp[i] > col) break;
378:         if (rp[i] == col) {
379:           *v++ = ap[i];
380:           goto finished;
381:         }
382:       }
383:       *v++ = 0.0;
384:       finished:;
385:     }
386:   }
387:   return(0);
388: }


393: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
394: {
395:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
397:   PetscInt       i,*col_lens;
398:   int            fd;

401:   PetscViewerBinaryGetDescriptor(viewer,&fd);
402:   PetscMalloc((4+A->rmap->n)*sizeof(PetscInt),&col_lens);
403:   col_lens[0] = MAT_FILE_CLASSID;
404:   col_lens[1] = A->rmap->n;
405:   col_lens[2] = A->cmap->n;
406:   col_lens[3] = a->nz;

408:   /* store lengths of each row and write (including header) to file */
409:   for (i=0; i<A->rmap->n; i++) {
410:     col_lens[4+i] = a->i[i+1] - a->i[i];
411:   }
412:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
413:   PetscFree(col_lens);

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

418:   /* store nonzero values */
419:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
420:   return(0);
421: }


427: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
428: {
429:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
430:   PetscErrorCode    ierr;
431:   PetscInt          i,j,m = A->rmap->n,shift=0;
432:   const char        *name;
433:   PetscViewerFormat format;

436:   PetscViewerGetFormat(viewer,&format);
437:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
438:     PetscInt nofinalvalue = 0;
439:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-!shift)) {
440:       nofinalvalue = 1;
441:     }
442:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
443:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
444:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
445:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
446:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

448:     for (i=0; i<m; i++) {
449:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
450: #if defined(PETSC_USE_COMPLEX)
451:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
452: #else
453:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
454: #endif
455:       }
456:     }
457:     if (nofinalvalue) {
458:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
459:     }
460:     PetscObjectGetName((PetscObject)A,&name);
461:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
462:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
463:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
464:      return(0);
465:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
466:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
467:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
468:     for (i=0; i<m; i++) {
469:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
470:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
471: #if defined(PETSC_USE_COMPLEX)
472:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
473:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
474:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
475:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
476:         } else if (PetscRealPart(a->a[j]) != 0.0) {
477:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
478:         }
479: #else
480:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);}
481: #endif
482:       }
483:       PetscViewerASCIIPrintf(viewer,"\n");
484:     }
485:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
486:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
487:     PetscInt nzd=0,fshift=1,*sptr;
488:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
489:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
490:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
491:     for (i=0; i<m; i++) {
492:       sptr[i] = nzd+1;
493:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
494:         if (a->j[j] >= i) {
495: #if defined(PETSC_USE_COMPLEX)
496:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
497: #else
498:           if (a->a[j] != 0.0) nzd++;
499: #endif
500:         }
501:       }
502:     }
503:     sptr[m] = nzd+1;
504:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
505:     for (i=0; i<m+1; i+=6) {
506:       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]);}
507:       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]);}
508:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
509:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
510:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
511:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
512:     }
513:     PetscViewerASCIIPrintf(viewer,"\n");
514:     PetscFree(sptr);
515:     for (i=0; i<m; i++) {
516:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
517:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
518:       }
519:       PetscViewerASCIIPrintf(viewer,"\n");
520:     }
521:     PetscViewerASCIIPrintf(viewer,"\n");
522:     for (i=0; i<m; i++) {
523:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
524:         if (a->j[j] >= i) {
525: #if defined(PETSC_USE_COMPLEX)
526:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
527:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
528:           }
529: #else
530:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
531: #endif
532:         }
533:       }
534:       PetscViewerASCIIPrintf(viewer,"\n");
535:     }
536:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
537:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
538:     PetscInt         cnt = 0,jcnt;
539:     PetscScalar value;

541:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
542:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
543:     for (i=0; i<m; i++) {
544:       jcnt = 0;
545:       for (j=0; j<A->cmap->n; j++) {
546:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
547:           value = a->a[cnt++];
548:           jcnt++;
549:         } else {
550:           value = 0.0;
551:         }
552: #if defined(PETSC_USE_COMPLEX)
553:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
554: #else
555:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
556: #endif
557:       }
558:       PetscViewerASCIIPrintf(viewer,"\n");
559:     }
560:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
561:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
562:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
563:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
564: #if defined(PETSC_USE_COMPLEX)
565:     PetscViewerASCIIPrintf(viewer,"%%matrix complex general\n");
566: #else
567:     PetscViewerASCIIPrintf(viewer,"%%matrix real general\n");
568: #endif
569:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
570:     for (i=0; i<m; i++) {
571:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
572: #if defined(PETSC_USE_COMPLEX)
573:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
574:           PetscViewerASCIIPrintf(viewer,"%D %D, %G %G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
575:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
576:           PetscViewerASCIIPrintf(viewer,"%D %D, %G -%G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
577:         } else {
578:           PetscViewerASCIIPrintf(viewer,"%D %D, %G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]));
579:         }
580: #else
581:         PetscViewerASCIIPrintf(viewer,"%D %D %G\n", i+shift, a->j[j]+shift, a->a[j]);
582: #endif
583:       }
584:     }
585:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
586:   } else {
587:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
588:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
589:     if (A->factortype){
590:       for (i=0; i<m; i++) {
591:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
592:         /* L part */
593:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
594: #if defined(PETSC_USE_COMPLEX)
595:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
596:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
597:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
598:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
599:           } else {
600:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
601:           }
602: #else
603:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
604: #endif
605:         }
606:         /* diagonal */
607:         j = a->diag[i];
608: #if defined(PETSC_USE_COMPLEX)
609:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
610:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),PetscImaginaryPart(1.0/a->a[j]));
611:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
612:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),-PetscImaginaryPart(1.0/a->a[j]));
613:           } else {
614:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(1.0/a->a[j]));
615:           }
616: #else
617:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,1.0/a->a[j]);
618: #endif

620:         /* U part */
621:         for (j=a->diag[i+1]+1+shift; j<a->diag[i]+shift; j++) {
622: #if defined(PETSC_USE_COMPLEX)
623:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
624:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
625:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
626:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
627:           } else {
628:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
629:           }
630: #else
631:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
632: #endif
633: }
634:           PetscViewerASCIIPrintf(viewer,"\n");
635:         }
636:     } else {
637:       for (i=0; i<m; i++) {
638:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
639:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
640: #if defined(PETSC_USE_COMPLEX)
641:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
642:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
643:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
644:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
645:           } else {
646:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
647:           }
648: #else
649:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
650: #endif
651:         }
652:         PetscViewerASCIIPrintf(viewer,"\n");
653:       }
654:     }
655:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
656:   }
657:   PetscViewerFlush(viewer);
658:   return(0);
659: }

663: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
664: {
665:   Mat               A = (Mat) Aa;
666:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
667:   PetscErrorCode    ierr;
668:   PetscInt          i,j,m = A->rmap->n,color;
669:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
670:   PetscViewer       viewer;
671:   PetscViewerFormat format;

674:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
675:   PetscViewerGetFormat(viewer,&format);

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

680:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
681:     /* Blue for negative, Cyan for zero and  Red for positive */
682:     color = PETSC_DRAW_BLUE;
683:     for (i=0; i<m; i++) {
684:       y_l = m - i - 1.0; y_r = y_l + 1.0;
685:       for (j=a->i[i]; j<a->i[i+1]; j++) {
686:         x_l = a->j[j] ; x_r = x_l + 1.0;
687: #if defined(PETSC_USE_COMPLEX)
688:         if (PetscRealPart(a->a[j]) >=  0.) continue;
689: #else
690:         if (a->a[j] >=  0.) continue;
691: #endif
692:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
693:       }
694:     }
695:     color = PETSC_DRAW_CYAN;
696:     for (i=0; i<m; i++) {
697:       y_l = m - i - 1.0; y_r = y_l + 1.0;
698:       for (j=a->i[i]; j<a->i[i+1]; j++) {
699:         x_l = a->j[j]; x_r = x_l + 1.0;
700:         if (a->a[j] !=  0.) continue;
701:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
702:       }
703:     }
704:     color = PETSC_DRAW_RED;
705:     for (i=0; i<m; i++) {
706:       y_l = m - i - 1.0; y_r = y_l + 1.0;
707:       for (j=a->i[i]; j<a->i[i+1]; j++) {
708:         x_l = a->j[j]; x_r = x_l + 1.0;
709: #if defined(PETSC_USE_COMPLEX)
710:         if (PetscRealPart(a->a[j]) <=  0.) continue;
711: #else
712:         if (a->a[j] <=  0.) continue;
713: #endif
714:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
715:       }
716:     }
717:   } else {
718:     /* use contour shading to indicate magnitude of values */
719:     /* first determine max of all nonzero values */
720:     PetscInt    nz = a->nz,count;
721:     PetscDraw   popup;
722:     PetscReal scale;

724:     for (i=0; i<nz; i++) {
725:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
726:     }
727:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
728:     PetscDrawGetPopup(draw,&popup);
729:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
730:     count = 0;
731:     for (i=0; i<m; i++) {
732:       y_l = m - i - 1.0; y_r = y_l + 1.0;
733:       for (j=a->i[i]; j<a->i[i+1]; j++) {
734:         x_l = a->j[j]; x_r = x_l + 1.0;
735:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
736:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
737:         count++;
738:       }
739:     }
740:   }
741:   return(0);
742: }

746: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
747: {
749:   PetscDraw      draw;
750:   PetscReal      xr,yr,xl,yl,h,w;
751:   PetscBool      isnull;

754:   PetscViewerDrawGetDraw(viewer,0,&draw);
755:   PetscDrawIsNull(draw,&isnull);
756:   if (isnull) return(0);

758:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
759:   xr  = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
760:   xr += w;    yr += h;  xl = -w;     yl = -h;
761:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
762:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
763:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
764:   return(0);
765: }

769: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
770: {
772:   PetscBool      iascii,isbinary,isdraw;

775:   PetscTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
776:   PetscTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
777:   PetscTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
778:   if (iascii) {
779:     MatView_SeqAIJ_ASCII(A,viewer);
780:   } else if (isbinary) {
781:     MatView_SeqAIJ_Binary(A,viewer);
782:   } else if (isdraw) {
783:     MatView_SeqAIJ_Draw(A,viewer);
784:   } else {
785:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
786:   }
787:   MatView_SeqAIJ_Inode(A,viewer);
788:   return(0);
789: }

793: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
794: {
795:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
797:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
798:   PetscInt       m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
799:   MatScalar      *aa = a->a,*ap;
800:   PetscReal      ratio=0.6;

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

805:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
806:   for (i=1; i<m; i++) {
807:     /* move each row back by the amount of empty slots (fshift) before it*/
808:     fshift += imax[i-1] - ailen[i-1];
809:     rmax   = PetscMax(rmax,ailen[i]);
810:     if (fshift) {
811:       ip = aj + ai[i] ;
812:       ap = aa + ai[i] ;
813:       N  = ailen[i];
814:       for (j=0; j<N; j++) {
815:         ip[j-fshift] = ip[j];
816:         ap[j-fshift] = ap[j];
817:       }
818:     }
819:     ai[i] = ai[i-1] + ailen[i-1];
820:   }
821:   if (m) {
822:     fshift += imax[m-1] - ailen[m-1];
823:     ai[m]  = ai[m-1] + ailen[m-1];
824:   }
825:   /* reset ilen and imax for each row */
826:   for (i=0; i<m; i++) {
827:     ailen[i] = imax[i] = ai[i+1] - ai[i];
828:   }
829:   a->nz = ai[m];
830:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

832:   MatMarkDiagonal_SeqAIJ(A);
833:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
834:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
835:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);
836:   A->info.mallocs     += a->reallocs;
837:   a->reallocs          = 0;
838:   A->info.nz_unneeded  = (double)fshift;
839:   a->rmax              = rmax;

841:   MatCheckCompressedRow(A,&a->compressedrow,a->i,m,ratio);
842:   A->same_nonzero = PETSC_TRUE;

844:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);

846:   a->idiagvalid  = PETSC_FALSE;
847:   a->ibdiagvalid = PETSC_FALSE;
848:   return(0);
849: }

853: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
854: {
855:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
856:   PetscInt       i,nz = a->nz;
857:   MatScalar      *aa = a->a;

860:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
861:   a->idiagvalid  = PETSC_FALSE;
862:   a->ibdiagvalid = PETSC_FALSE;
863:   return(0);
864: }

868: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
869: {
870:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
871:   PetscInt       i,nz = a->nz;
872:   MatScalar      *aa = a->a;

875:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
876:   a->idiagvalid  = PETSC_FALSE;
877:   a->ibdiagvalid = PETSC_FALSE;
878:   return(0);
879: }

883: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
884: {
885:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

889:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
890:   a->idiagvalid  = PETSC_FALSE;
891:   a->ibdiagvalid = PETSC_FALSE;
892:   return(0);
893: }

897: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
898: {
899:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

903: #if defined(PETSC_USE_LOG)
904:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
905: #endif
906:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
907:   ISDestroy(&a->row);
908:   ISDestroy(&a->col);
909:   PetscFree(a->diag);
910:   PetscFree(a->ibdiag);
911:   PetscFree2(a->imax,a->ilen);
912:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
913:   PetscFree(a->solve_work);
914:   ISDestroy(&a->icol);
915:   PetscFree(a->saved_values);
916:   ISColoringDestroy(&a->coloring);
917:   PetscFree(a->xtoy);
918:   MatDestroy(&a->XtoY);
919:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);

921:   MatDestroy_SeqAIJ_Inode(A);
922:   PetscFree(A->data);

924:   PetscObjectChangeTypeName((PetscObject)A,0);
925:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
926:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
927:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
928:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
929:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
930:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqaijperm_C","",PETSC_NULL);
931:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
932:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
933:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C","",PETSC_NULL);
934:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
935:   return(0);
936: }

940: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool  flg)
941: {
942:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

946:   switch (op) {
947:     case MAT_ROW_ORIENTED:
948:       a->roworiented       = flg;
949:       break;
950:     case MAT_KEEP_NONZERO_PATTERN:
951:       a->keepnonzeropattern    = flg;
952:       break;
953:     case MAT_NEW_NONZERO_LOCATIONS:
954:       a->nonew             = (flg ? 0 : 1);
955:       break;
956:     case MAT_NEW_NONZERO_LOCATION_ERR:
957:       a->nonew             = (flg ? -1 : 0);
958:       break;
959:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
960:       a->nonew             = (flg ? -2 : 0);
961:       break;
962:     case MAT_UNUSED_NONZERO_LOCATION_ERR:
963:       a->nounused          = (flg ? -1 : 0);
964:       break;
965:     case MAT_IGNORE_ZERO_ENTRIES:
966:       a->ignorezeroentries = flg;
967:       break;
968:     case MAT_CHECK_COMPRESSED_ROW:
969:       a->compressedrow.check = flg;
970:       break;
971:     case MAT_SPD:
972:       A->spd_set                         = PETSC_TRUE;
973:       A->spd                             = flg;
974:       if (flg) {
975:         A->symmetric                     = PETSC_TRUE;
976:         A->structurally_symmetric        = PETSC_TRUE;
977:         A->symmetric_set                 = PETSC_TRUE;
978:         A->structurally_symmetric_set    = PETSC_TRUE;
979:       }
980:       break;
981:     case MAT_SYMMETRIC:
982:     case MAT_STRUCTURALLY_SYMMETRIC:
983:     case MAT_HERMITIAN:
984:     case MAT_SYMMETRY_ETERNAL:
985:     case MAT_NEW_DIAGONALS:
986:     case MAT_IGNORE_OFF_PROC_ENTRIES:
987:     case MAT_USE_HASH_TABLE:
988:       PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
989:       break;
990:     case MAT_USE_INODES:
991:       /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
992:       break;
993:     default:
994:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
995:   }
996:   MatSetOption_SeqAIJ_Inode(A,op,flg);
997:   return(0);
998: }

1002: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1003: {
1004:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1006:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
1007:   PetscScalar    *aa=a->a,*x,zero=0.0;

1010:   VecGetLocalSize(v,&n);
1011:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1013:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU){
1014:     PetscInt *diag=a->diag;
1015:     VecGetArray(v,&x);
1016:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1017:     VecRestoreArray(v,&x);
1018:     return(0);
1019:   }

1021:   VecSet(v,zero);
1022:   VecGetArray(v,&x);
1023:   for (i=0; i<n; i++) {
1024:     nz = ai[i+1] - ai[i];
1025:     if (!nz) x[i] = 0.0;
1026:     for (j=ai[i]; j<ai[i+1]; j++){
1027:       if (aj[j] == i) {
1028:         x[i] = aa[j];
1029:         break;
1030:       }
1031:     }
1032:   }
1033:   VecRestoreArray(v,&x);
1034:   return(0);
1035: }

1037: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1040: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1041: {
1042:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1043:   PetscScalar       *x,*y;
1044:   PetscErrorCode    ierr;
1045:   PetscInt          m = A->rmap->n;
1046: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1047:   MatScalar         *v;
1048:   PetscScalar       alpha;
1049:   PetscInt          n,i,j,*idx,*ii,*ridx=PETSC_NULL;
1050:   Mat_CompressedRow cprow = a->compressedrow;
1051:   PetscBool         usecprow = cprow.use;
1052: #endif

1055:   if (zz != yy) {VecCopy(zz,yy);}
1056:   VecGetArray(xx,&x);
1057:   VecGetArray(yy,&y);

1059: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1060:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1061: #else
1062:   if (usecprow){
1063:     m    = cprow.nrows;
1064:     ii   = cprow.i;
1065:     ridx = cprow.rindex;
1066:   } else {
1067:     ii = a->i;
1068:   }
1069:   for (i=0; i<m; i++) {
1070:     idx   = a->j + ii[i] ;
1071:     v     = a->a + ii[i] ;
1072:     n     = ii[i+1] - ii[i];
1073:     if (usecprow){
1074:       alpha = x[ridx[i]];
1075:     } else {
1076:       alpha = x[i];
1077:     }
1078:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1079:   }
1080: #endif
1081:   PetscLogFlops(2.0*a->nz);
1082:   VecRestoreArray(xx,&x);
1083:   VecRestoreArray(yy,&y);
1084:   return(0);
1085: }

1089: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1090: {

1094:   VecSet(yy,0.0);
1095:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1096:   return(0);
1097: }

1099: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1102: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1103: {
1104:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1105:   PetscScalar       *y;
1106:   const PetscScalar *x;
1107:   const MatScalar   *aa;
1108:   PetscErrorCode    ierr;
1109:   PetscInt          m=A->rmap->n;
1110:   const PetscInt    *aj,*ii,*ridx=PETSC_NULL;
1111:   PetscInt          n,i,nonzerorow=0;
1112:   PetscScalar       sum;
1113:   PetscBool         usecprow=a->compressedrow.use;

1115: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1116: #pragma disjoint(*x,*y,*aa)
1117: #endif

1120:   VecGetArrayRead(xx,&x);
1121:   VecGetArray(yy,&y);
1122:   aj  = a->j;
1123:   aa  = a->a;
1124:   ii  = a->i;
1125:   if (usecprow){ /* use compressed row format */
1126:     m    = a->compressedrow.nrows;
1127:     ii   = a->compressedrow.i;
1128:     ridx = a->compressedrow.rindex;
1129:     for (i=0; i<m; i++){
1130:       n   = ii[i+1] - ii[i];
1131:       aj  = a->j + ii[i];
1132:       aa  = a->a + ii[i];
1133:       sum = 0.0;
1134:       nonzerorow += (n>0);
1135:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1136:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1137:       y[*ridx++] = sum;
1138:     }
1139:   } else { /* do not use compressed row format */
1140: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1141:     fortranmultaij_(&m,x,ii,aj,aa,y);
1142: #else
1143:     for (i=0; i<m; i++) {
1144:       n   = ii[i+1] - ii[i];
1145:       aj  = a->j + ii[i];
1146:       aa  = a->a + ii[i];
1147:       sum  = 0.0;
1148:       nonzerorow += (n>0);
1149:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1150:       y[i] = sum;
1151:     }
1152: #endif
1153:   }
1154:   PetscLogFlops(2.0*a->nz - nonzerorow);
1155:   VecRestoreArrayRead(xx,&x);
1156:   VecRestoreArray(yy,&y);
1157:   return(0);
1158: }

1160: #if defined(PETSC_HAVE_PTHREADCLASSES)

1162: /* ******************* */
1163: #if defined(PETSC_HAVE_PTHREADCLASSES)
1165: void* DoCoreAffinity(void);

1167: typedef struct {
1168:   const MatScalar* matdata;
1169:   const PetscScalar* vecdata;
1170:   PetscScalar* vecout;
1171:   const PetscInt* colindnz;
1172:   const PetscInt* rownumnz;
1173:   PetscInt numrows;
1174:   const PetscInt* specidx;
1175:   PetscInt nzr;
1176: } MatMult_KernelData;

1178: void* MatMult_Kernel(void *arg)
1179: {
1180:   if(PetscUseThreadPool==PETSC_FALSE) {
1181:     DoCoreAffinity();
1182:   }
1183:   MatMult_KernelData *data = (MatMult_KernelData*)arg;
1184:   PetscScalar       sum;
1185:   const MatScalar   *aabase = data->matdata,*aa;
1186:   const PetscScalar *x = data->vecdata;
1187:   PetscScalar       *y = data->vecout;
1188:   const PetscInt    *ajbase = data->colindnz,*aj;
1189:   const PetscInt    *ii = data->rownumnz;
1190:   PetscInt          m  = data->numrows;
1191:   const PetscInt    *ridx = data->specidx;
1192:   PetscInt          i,n,nonzerorow = 0;

1194:   if(ridx!=NULL) {
1195:     for (i=0; i<m; i++){
1196:       n   = ii[i+1] - ii[i];
1197:       aj  = ajbase + ii[i];
1198:       aa  = aabase + ii[i];
1199:       sum = 0.0;
1200:       if(n>0) {
1201:         PetscSparseDensePlusDot(sum,x,aa,aj,n);
1202:         nonzerorow++;
1203:       }
1204:       y[*ridx++] = sum;
1205:     }
1206:   }
1207:   else {
1208:     PetscInt ibase = data->nzr;
1209:     for (i=0; i<m; i++) {
1210:       n   = ii[i+1] - ii[i];
1211:       aj  = ajbase + ii[i];
1212:       aa  = aabase + ii[i];
1213:       sum  = 0.0;
1214:       if(n>0) {
1215:         PetscSparseDensePlusDot(sum,x,aa,aj,n);
1216:         nonzerorow++;
1217:       }
1218:       y[i+ibase] = sum;
1219:     }
1220:   }
1221:   data->nzr = nonzerorow;
1222:   return NULL;
1223: }
1224: #endif


1231: PetscErrorCode MatMult_SeqAIJPThread(Mat A,Vec xx,Vec yy)
1232: {
1233:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1234:   PetscScalar       *y;
1235:   const PetscScalar *x;
1236:   PetscErrorCode    ierr;
1237:   PetscInt          m=A->rmap->n,nonzerorow=0;
1238:   PetscBool         usecprow=a->compressedrow.use;

1240: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1241: #pragma disjoint(*x,*y,*aa)
1242: #endif

1245:   VecGetArrayRead(xx,&x);
1246:   VecGetArray(yy,&y);

1248:   if(usecprow) {
1249:     PetscInt          NumPerThread,iindex;
1250:     const MatScalar   *aa = a->a;
1251:     const PetscInt    *aj = a->j,*ii = a->compressedrow.i,*ridx=a->compressedrow.rindex;
1252:     PetscInt          i,iStartVal,iEndVal,iStartIndex,iEndIndex;
1253:     const PetscInt    iNumThreads = PetscMaxThreads;  /* this number could be different */
1254:     MatMult_KernelData* kerneldatap = (MatMult_KernelData*)malloc(iNumThreads*sizeof(MatMult_KernelData));
1255:     MatMult_KernelData** pdata = (MatMult_KernelData**)malloc(iNumThreads*sizeof(MatMult_KernelData*));

1257:     m    = a->compressedrow.nrows;
1258:     NumPerThread = ii[m]/iNumThreads;
1259:     iindex = 0;
1260:     for(i=0; i<iNumThreads;i++) {
1261:       iStartIndex = iindex;
1262:       iStartVal = ii[iStartIndex];
1263:       iEndVal = iStartVal;
1264:       /* determine number of rows to process */
1265:       while(iEndVal-iStartVal<NumPerThread) {
1266:         iindex++;
1267:         iEndVal = ii[iindex];
1268:       }
1269:       /* determine whether to go back 1 */
1270:       if(iEndVal-iStartVal-NumPerThread>NumPerThread-(ii[iindex-1]-iStartVal)) {
1271:         iindex--;
1272:         iEndVal = ii[iindex];
1273:       }
1274:       iEndIndex = iindex;
1275:       kerneldatap[i].matdata  = aa;
1276:       kerneldatap[i].vecdata  = x;
1277:       kerneldatap[i].vecout   = y;
1278:       kerneldatap[i].colindnz = aj;
1279:       kerneldatap[i].rownumnz = ii + iStartIndex;
1280:       kerneldatap[i].numrows  = iEndIndex - iStartIndex + 1;
1281:       kerneldatap[i].specidx  = ridx + iStartVal;
1282:       kerneldatap[i].nzr      = 0;
1283:       pdata[i] = &kerneldatap[i];
1284:       iindex++;
1285:     }
1286:     MainJob(MatMult_Kernel,(void**)pdata,iNumThreads);
1287:     /* collect results */
1288:     for(i=0; i<iNumThreads; i++) {
1289:       nonzerorow += kerneldatap[i].nzr;
1290:     }
1291:     free(kerneldatap);
1292:     free(pdata);
1293:   }
1294:   else {
1295: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1296:   fortranmultaij_(&m,x,a->i,a->j,a->a,y);
1297: #else
1298:   PetscInt            i,iindex;
1299:     const MatScalar   *aa = a->a;
1300:     const PetscInt    *aj = a->j,*ii = a->i;
1301:     const PetscInt    iNumThreads = PetscMaxThreads;  /* this number could be different */
1302:     PetscInt          Q = m/iNumThreads;
1303:     PetscInt          R = m-Q*iNumThreads;
1304:     PetscBool         S;

1306:     MatMult_KernelData* kerneldatap = (MatMult_KernelData*)malloc(iNumThreads*sizeof(MatMult_KernelData));
1307:     MatMult_KernelData** pdata = (MatMult_KernelData**)malloc(iNumThreads*sizeof(MatMult_KernelData*));

1309:     iindex = 0;
1310:     for(i=0; i<iNumThreads;i++) {
1311:       S = (PetscBool)(i<R);
1312:       kerneldatap[i].matdata  = aa;
1313:       kerneldatap[i].vecdata  = x;
1314:       kerneldatap[i].vecout   = y;
1315:       kerneldatap[i].colindnz = aj;
1316:       kerneldatap[i].rownumnz = ii + iindex;
1317:       kerneldatap[i].numrows  = S?Q+1:Q;
1318:       kerneldatap[i].specidx  = PETSC_NULL;
1319:       kerneldatap[i].nzr      = iindex; /* serves as the 'base' row (needed to access correctly into output vector y) */
1320:       pdata[i] = &kerneldatap[i];
1321:       iindex += kerneldatap[i].numrows;
1322:     }
1323:     MainJob(MatMult_Kernel,(void**)pdata,iNumThreads);
1324:     /* collect results */
1325:     for(i=0; i<iNumThreads; i++) {
1326:       nonzerorow += kerneldatap[i].nzr;
1327:     }
1328:     free(kerneldatap);
1329:     free(pdata);
1330: #endif
1331:   }

1333:   PetscLogFlops(2.0*a->nz - nonzerorow);
1334:   VecRestoreArrayRead(xx,&x);
1335:   VecRestoreArray(yy,&y);
1336:   return(0);
1337: }
1338: /* ******************* */
1339: #endif

1341: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1344: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1345: {
1346:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1347:   PetscScalar       *y,*z;
1348:   const PetscScalar *x;
1349:   const MatScalar   *aa;
1350:   PetscErrorCode    ierr;
1351:   PetscInt          m = A->rmap->n,*aj,*ii;
1352:   PetscInt          n,i,*ridx=PETSC_NULL;
1353:   PetscScalar       sum;
1354:   PetscBool         usecprow=a->compressedrow.use;

1357:   VecGetArrayRead(xx,&x);
1358:   VecGetArray(yy,&y);
1359:   if (zz != yy) {
1360:     VecGetArray(zz,&z);
1361:   } else {
1362:     z = y;
1363:   }

1365:   aj  = a->j;
1366:   aa  = a->a;
1367:   ii  = a->i;
1368:   if (usecprow){ /* use compressed row format */
1369:     if (zz != yy){
1370:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1371:     }
1372:     m    = a->compressedrow.nrows;
1373:     ii   = a->compressedrow.i;
1374:     ridx = a->compressedrow.rindex;
1375:     for (i=0; i<m; i++){
1376:       n  = ii[i+1] - ii[i];
1377:       aj  = a->j + ii[i];
1378:       aa  = a->a + ii[i];
1379:       sum = y[*ridx];
1380:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1381:       z[*ridx++] = sum;
1382:     }
1383:   } else { /* do not use compressed row format */
1384: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1385:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1386: #else
1387:     for (i=0; i<m; i++) {
1388:       n    = ii[i+1] - ii[i];
1389:       aj  = a->j + ii[i];
1390:       aa  = a->a + ii[i];
1391:       sum  = y[i];
1392:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1393:       z[i] = sum;
1394:     }
1395: #endif
1396:   }
1397:   PetscLogFlops(2.0*a->nz);
1398:   VecRestoreArrayRead(xx,&x);
1399:   VecRestoreArray(yy,&y);
1400:   if (zz != yy) {
1401:     VecRestoreArray(zz,&z);
1402:   }
1403: #if defined(PETSC_HAVE_CUSP)
1404:   /*
1405:   VecView(xx,0);
1406:   VecView(zz,0);
1407:   MatView(A,0);
1408:   */
1409: #endif
1410:   return(0);
1411: }

1413: /*
1414:      Adds diagonal pointers to sparse matrix structure.
1415: */
1418: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1419: {
1420:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1422:   PetscInt       i,j,m = A->rmap->n;

1425:   if (!a->diag) {
1426:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
1427:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
1428:   }
1429:   for (i=0; i<A->rmap->n; i++) {
1430:     a->diag[i] = a->i[i+1];
1431:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1432:       if (a->j[j] == i) {
1433:         a->diag[i] = j;
1434:         break;
1435:       }
1436:     }
1437:   }
1438:   return(0);
1439: }

1441: /*
1442:      Checks for missing diagonals
1443: */
1446: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1447: {
1448:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1449:   PetscInt       *diag,*jj = a->j,i;

1452:   *missing = PETSC_FALSE;
1453:   if (A->rmap->n > 0 && !jj) {
1454:     *missing  = PETSC_TRUE;
1455:     if (d) *d = 0;
1456:     PetscInfo(A,"Matrix has no entries therefor is missing diagonal");
1457:   } else {
1458:     diag = a->diag;
1459:     for (i=0; i<A->rmap->n; i++) {
1460:       if (jj[diag[i]] != i) {
1461:         *missing = PETSC_TRUE;
1462:         if (d) *d = i;
1463:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1464:       }
1465:     }
1466:   }
1467:   return(0);
1468: }

1473: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1474: {
1475:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1477:   PetscInt       i,*diag,m = A->rmap->n;
1478:   MatScalar      *v = a->a;
1479:   PetscScalar    *idiag,*mdiag;

1482:   if (a->idiagvalid) return(0);
1483:   MatMarkDiagonal_SeqAIJ(A);
1484:   diag = a->diag;
1485:   if (!a->idiag) {
1486:     PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
1487:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1488:     v        = a->a;
1489:   }
1490:   mdiag = a->mdiag;
1491:   idiag = a->idiag;
1492: 
1493:   if (omega == 1.0 && !PetscAbsScalar(fshift)) {
1494:     for (i=0; i<m; i++) {
1495:       mdiag[i] = v[diag[i]];
1496:       if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1497:       idiag[i] = 1.0/v[diag[i]];
1498:     }
1499:     PetscLogFlops(m);
1500:   } else {
1501:     for (i=0; i<m; i++) {
1502:       mdiag[i] = v[diag[i]];
1503:       idiag[i] = omega/(fshift + v[diag[i]]);
1504:     }
1505:     PetscLogFlops(2.0*m);
1506:   }
1507:   a->idiagvalid = PETSC_TRUE;
1508:   return(0);
1509: }

1512: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1515: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1516: {
1517:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
1518:   PetscScalar        *x,d,sum,*t,scale;
1519:   const MatScalar    *v = a->a,*idiag=0,*mdiag;
1520:   const PetscScalar  *b, *bs,*xb, *ts;
1521:   PetscErrorCode     ierr;
1522:   PetscInt           n = A->cmap->n,m = A->rmap->n,i;
1523:   const PetscInt     *idx,*diag;

1526:   its = its*lits;

1528:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1529:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1530:   a->fshift = fshift;
1531:   a->omega  = omega;

1533:   diag = a->diag;
1534:   t     = a->ssor_work;
1535:   idiag = a->idiag;
1536:   mdiag = a->mdiag;

1538:   VecGetArray(xx,&x);
1539:   VecGetArrayRead(bb,&b);
1540:   CHKMEMQ;
1541:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1542:   if (flag == SOR_APPLY_UPPER) {
1543:    /* apply (U + D/omega) to the vector */
1544:     bs = b;
1545:     for (i=0; i<m; i++) {
1546:         d    = fshift + mdiag[i];
1547:         n    = a->i[i+1] - diag[i] - 1;
1548:         idx  = a->j + diag[i] + 1;
1549:         v    = a->a + diag[i] + 1;
1550:         sum  = b[i]*d/omega;
1551:         PetscSparseDensePlusDot(sum,bs,v,idx,n);
1552:         x[i] = sum;
1553:     }
1554:     VecRestoreArray(xx,&x);
1555:     VecRestoreArrayRead(bb,&b);
1556:     PetscLogFlops(a->nz);
1557:     return(0);
1558:   }

1560:   if (flag == SOR_APPLY_LOWER) {
1561:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1562:   } else if (flag & SOR_EISENSTAT) {
1563:     /* Let  A = L + U + D; where L is lower trianglar,
1564:     U is upper triangular, E = D/omega; This routine applies

1566:             (L + E)^{-1} A (U + E)^{-1}

1568:     to a vector efficiently using Eisenstat's trick. 
1569:     */
1570:     scale = (2.0/omega) - 1.0;

1572:     /*  x = (E + U)^{-1} b */
1573:     for (i=m-1; i>=0; i--) {
1574:       n    = a->i[i+1] - diag[i] - 1;
1575:       idx  = a->j + diag[i] + 1;
1576:       v    = a->a + diag[i] + 1;
1577:       sum  = b[i];
1578:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1579:       x[i] = sum*idiag[i];
1580:     }

1582:     /*  t = b - (2*E - D)x */
1583:     v = a->a;
1584:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1586:     /*  t = (E + L)^{-1}t */
1587:     ts = t;
1588:     diag = a->diag;
1589:     for (i=0; i<m; i++) {
1590:       n    = diag[i] - a->i[i];
1591:       idx  = a->j + a->i[i];
1592:       v    = a->a + a->i[i];
1593:       sum  = t[i];
1594:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1595:       t[i] = sum*idiag[i];
1596:       /*  x = x + t */
1597:       x[i] += t[i];
1598:     }

1600:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1601:     VecRestoreArray(xx,&x);
1602:     VecRestoreArrayRead(bb,&b);
1603:     return(0);
1604:   }
1605:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1606:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1607:       for (i=0; i<m; i++) {
1608:         n    = diag[i] - a->i[i];
1609:         idx  = a->j + a->i[i];
1610:         v    = a->a + a->i[i];
1611:         sum  = b[i];
1612:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1613:         t[i] = sum;
1614:         x[i] = sum*idiag[i];
1615:       }
1616:       xb = t;
1617:       PetscLogFlops(a->nz);
1618:     } else xb = b;
1619:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1620:       for (i=m-1; i>=0; i--) {
1621:         n    = a->i[i+1] - diag[i] - 1;
1622:         idx  = a->j + diag[i] + 1;
1623:         v    = a->a + diag[i] + 1;
1624:         sum  = xb[i];
1625:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1626:         if (xb == b) {
1627:           x[i] = sum*idiag[i];
1628:         } else {
1629:           x[i] = (1-omega)*x[i] + sum*idiag[i];
1630:         }
1631:       }
1632:       PetscLogFlops(a->nz);
1633:     }
1634:     its--;
1635:   }
1636:   while (its--) {
1637:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1638:       for (i=0; i<m; i++) {
1639:         n    = a->i[i+1] - a->i[i];
1640:         idx  = a->j + a->i[i];
1641:         v    = a->a + a->i[i];
1642:         sum  = b[i];
1643:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1644:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1645:       }
1646:       PetscLogFlops(2.0*a->nz);
1647:     }
1648:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1649:       for (i=m-1; i>=0; i--) {
1650:         n    = a->i[i+1] - a->i[i];
1651:         idx  = a->j + a->i[i];
1652:         v    = a->a + a->i[i];
1653:         sum  = b[i];
1654:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1655:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1656:       }
1657:       PetscLogFlops(2.0*a->nz);
1658:     }
1659:   }
1660:   VecRestoreArray(xx,&x);
1661:   VecRestoreArrayRead(bb,&b);
1662:   CHKMEMQ;  return(0);
1663: }


1668: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1669: {
1670:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1673:   info->block_size     = 1.0;
1674:   info->nz_allocated   = (double)a->maxnz;
1675:   info->nz_used        = (double)a->nz;
1676:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1677:   info->assemblies     = (double)A->num_ass;
1678:   info->mallocs        = (double)A->info.mallocs;
1679:   info->memory         = ((PetscObject)A)->mem;
1680:   if (A->factortype) {
1681:     info->fill_ratio_given  = A->info.fill_ratio_given;
1682:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1683:     info->factor_mallocs    = A->info.factor_mallocs;
1684:   } else {
1685:     info->fill_ratio_given  = 0;
1686:     info->fill_ratio_needed = 0;
1687:     info->factor_mallocs    = 0;
1688:   }
1689:   return(0);
1690: }

1694: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1695: {
1696:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1697:   PetscInt          i,m = A->rmap->n - 1,d = 0;
1698:   PetscErrorCode    ierr;
1699:   const PetscScalar *xx;
1700:   PetscScalar       *bb;
1701:   PetscBool         missing;

1704:   if (x && b) {
1705:     VecGetArrayRead(x,&xx);
1706:     VecGetArray(b,&bb);
1707:     for (i=0; i<N; i++) {
1708:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1709:       bb[rows[i]] = diag*xx[rows[i]];
1710:     }
1711:     VecRestoreArrayRead(x,&xx);
1712:     VecRestoreArray(b,&bb);
1713:   }

1715:   if (a->keepnonzeropattern) {
1716:     for (i=0; i<N; i++) {
1717:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1718:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1719:     }
1720:     if (diag != 0.0) {
1721:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1722:       if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1723:       for (i=0; i<N; i++) {
1724:         a->a[a->diag[rows[i]]] = diag;
1725:       }
1726:     }
1727:     A->same_nonzero = PETSC_TRUE;
1728:   } else {
1729:     if (diag != 0.0) {
1730:       for (i=0; i<N; i++) {
1731:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1732:         if (a->ilen[rows[i]] > 0) {
1733:           a->ilen[rows[i]]          = 1;
1734:           a->a[a->i[rows[i]]] = diag;
1735:           a->j[a->i[rows[i]]] = rows[i];
1736:         } else { /* in case row was completely empty */
1737:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1738:         }
1739:       }
1740:     } else {
1741:       for (i=0; i<N; i++) {
1742:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1743:         a->ilen[rows[i]] = 0;
1744:       }
1745:     }
1746:     A->same_nonzero = PETSC_FALSE;
1747:   }
1748:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1749:   return(0);
1750: }

1754: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1755: {
1756:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1757:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
1758:   PetscErrorCode    ierr;
1759:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
1760:   const PetscScalar *xx;
1761:   PetscScalar       *bb;

1764:   if (x && b) {
1765:     VecGetArrayRead(x,&xx);
1766:     VecGetArray(b,&bb);
1767:     vecs = PETSC_TRUE;
1768:   }
1769:   PetscMalloc(A->rmap->n*sizeof(PetscBool),&zeroed);
1770:   PetscMemzero(zeroed,A->rmap->n*sizeof(PetscBool));
1771:   for (i=0; i<N; i++) {
1772:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1773:     PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1774:     zeroed[rows[i]] = PETSC_TRUE;
1775:   }
1776:   for (i=0; i<A->rmap->n; i++) {
1777:     if (!zeroed[i]) {
1778:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1779:         if (zeroed[a->j[j]]) {
1780:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1781:           a->a[j] = 0.0;
1782:         }
1783:       }
1784:     } else if (vecs) bb[i] = diag*xx[i];
1785:   }
1786:   if (x && b) {
1787:     VecRestoreArrayRead(x,&xx);
1788:     VecRestoreArray(b,&bb);
1789:   }
1790:   PetscFree(zeroed);
1791:   if (diag != 0.0) {
1792:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1793:     if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1794:     for (i=0; i<N; i++) {
1795:       a->a[a->diag[rows[i]]] = diag;
1796:     }
1797:   }
1798:   A->same_nonzero = PETSC_TRUE;
1799:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1800:   return(0);
1801: }

1805: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1806: {
1807:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1808:   PetscInt   *itmp;

1811:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1813:   *nz = a->i[row+1] - a->i[row];
1814:   if (v) *v = a->a + a->i[row];
1815:   if (idx) {
1816:     itmp = a->j + a->i[row];
1817:     if (*nz) {
1818:       *idx = itmp;
1819:     }
1820:     else *idx = 0;
1821:   }
1822:   return(0);
1823: }

1825: /* remove this function? */
1828: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1829: {
1831:   return(0);
1832: }

1836: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1837: {
1838:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1839:   MatScalar      *v = a->a;
1840:   PetscReal      sum = 0.0;
1842:   PetscInt       i,j;

1845:   if (type == NORM_FROBENIUS) {
1846:     for (i=0; i<a->nz; i++) {
1847: #if defined(PETSC_USE_COMPLEX)
1848:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1849: #else
1850:       sum += (*v)*(*v); v++;
1851: #endif
1852:     }
1853:     *nrm = PetscSqrtReal(sum);
1854:   } else if (type == NORM_1) {
1855:     PetscReal *tmp;
1856:     PetscInt    *jj = a->j;
1857:     PetscMalloc((A->cmap->n+1)*sizeof(PetscReal),&tmp);
1858:     PetscMemzero(tmp,A->cmap->n*sizeof(PetscReal));
1859:     *nrm = 0.0;
1860:     for (j=0; j<a->nz; j++) {
1861:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
1862:     }
1863:     for (j=0; j<A->cmap->n; j++) {
1864:       if (tmp[j] > *nrm) *nrm = tmp[j];
1865:     }
1866:     PetscFree(tmp);
1867:   } else if (type == NORM_INFINITY) {
1868:     *nrm = 0.0;
1869:     for (j=0; j<A->rmap->n; j++) {
1870:       v = a->a + a->i[j];
1871:       sum = 0.0;
1872:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1873:         sum += PetscAbsScalar(*v); v++;
1874:       }
1875:       if (sum > *nrm) *nrm = sum;
1876:     }
1877:   } else {
1878:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1879:   }
1880:   return(0);
1881: }

1885: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
1886: {
1887:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1888:   Mat            C;
1890:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
1891:   MatScalar      *array = a->a;

1894:   if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

1896:   if (reuse == MAT_INITIAL_MATRIX || *B == A) {
1897:     PetscMalloc((1+A->cmap->n)*sizeof(PetscInt),&col);
1898:     PetscMemzero(col,(1+A->cmap->n)*sizeof(PetscInt));
1899: 
1900:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1901:     MatCreate(((PetscObject)A)->comm,&C);
1902:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
1903:     MatSetType(C,((PetscObject)A)->type_name);
1904:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1905:     PetscFree(col);
1906:   } else {
1907:     C = *B;
1908:   }

1910:   for (i=0; i<m; i++) {
1911:     len    = ai[i+1]-ai[i];
1912:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1913:     array += len;
1914:     aj    += len;
1915:   }
1916:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1917:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1919:   if (reuse == MAT_INITIAL_MATRIX || *B != A) {
1920:     *B = C;
1921:   } else {
1922:     MatHeaderMerge(A,C);
1923:   }
1924:   return(0);
1925: }

1930: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
1931: {
1932:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1933:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1934:   MatScalar      *va,*vb;
1936:   PetscInt       ma,na,mb,nb, i;

1939:   bij = (Mat_SeqAIJ *) B->data;

1941:   MatGetSize(A,&ma,&na);
1942:   MatGetSize(B,&mb,&nb);
1943:   if (ma!=nb || na!=mb){
1944:     *f = PETSC_FALSE;
1945:     return(0);
1946:   }
1947:   aii = aij->i; bii = bij->i;
1948:   adx = aij->j; bdx = bij->j;
1949:   va  = aij->a; vb = bij->a;
1950:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1951:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1952:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1953:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1955:   *f = PETSC_TRUE;
1956:   for (i=0; i<ma; i++) {
1957:     while (aptr[i]<aii[i+1]) {
1958:       PetscInt         idc,idr;
1959:       PetscScalar vc,vr;
1960:       /* column/row index/value */
1961:       idc = adx[aptr[i]];
1962:       idr = bdx[bptr[idc]];
1963:       vc  = va[aptr[i]];
1964:       vr  = vb[bptr[idc]];
1965:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1966:         *f = PETSC_FALSE;
1967:         goto done;
1968:       } else {
1969:         aptr[i]++;
1970:         if (B || i!=idc) bptr[idc]++;
1971:       }
1972:     }
1973:   }
1974:  done:
1975:   PetscFree(aptr);
1976:   PetscFree(bptr);
1977:   return(0);
1978: }

1984: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
1985: {
1986:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1987:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1988:   MatScalar      *va,*vb;
1990:   PetscInt       ma,na,mb,nb, i;

1993:   bij = (Mat_SeqAIJ *) B->data;

1995:   MatGetSize(A,&ma,&na);
1996:   MatGetSize(B,&mb,&nb);
1997:   if (ma!=nb || na!=mb){
1998:     *f = PETSC_FALSE;
1999:     return(0);
2000:   }
2001:   aii = aij->i; bii = bij->i;
2002:   adx = aij->j; bdx = bij->j;
2003:   va  = aij->a; vb = bij->a;
2004:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
2005:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
2006:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2007:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2009:   *f = PETSC_TRUE;
2010:   for (i=0; i<ma; i++) {
2011:     while (aptr[i]<aii[i+1]) {
2012:       PetscInt         idc,idr;
2013:       PetscScalar vc,vr;
2014:       /* column/row index/value */
2015:       idc = adx[aptr[i]];
2016:       idr = bdx[bptr[idc]];
2017:       vc  = va[aptr[i]];
2018:       vr  = vb[bptr[idc]];
2019:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2020:         *f = PETSC_FALSE;
2021:         goto done;
2022:       } else {
2023:         aptr[i]++;
2024:         if (B || i!=idc) bptr[idc]++;
2025:       }
2026:     }
2027:   }
2028:  done:
2029:   PetscFree(aptr);
2030:   PetscFree(bptr);
2031:   return(0);
2032: }

2037: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2038: {
2041:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2042:   return(0);
2043: }

2047: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2048: {
2051:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2052:   return(0);
2053: }

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

2066:   if (ll) {
2067:     /* The local size is used so that VecMPI can be passed to this routine
2068:        by MatDiagonalScale_MPIAIJ */
2069:     VecGetLocalSize(ll,&m);
2070:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2071:     VecGetArray(ll,&l);
2072:     v = a->a;
2073:     for (i=0; i<m; i++) {
2074:       x = l[i];
2075:       M = a->i[i+1] - a->i[i];
2076:       for (j=0; j<M; j++) { (*v++) *= x;}
2077:     }
2078:     VecRestoreArray(ll,&l);
2079:     PetscLogFlops(nz);
2080:   }
2081:   if (rr) {
2082:     VecGetLocalSize(rr,&n);
2083:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2084:     VecGetArray(rr,&r);
2085:     v = a->a; jj = a->j;
2086:     for (i=0; i<nz; i++) {
2087:       (*v++) *= r[*jj++];
2088:     }
2089:     VecRestoreArray(rr,&r);
2090:     PetscLogFlops(nz);
2091:   }
2092:   a->idiagvalid  = PETSC_FALSE;
2093:   a->ibdiagvalid = PETSC_FALSE;
2094:   return(0);
2095: }

2099: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2100: {
2101:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
2103:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2104:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2105:   const PetscInt *irow,*icol;
2106:   PetscInt       nrows,ncols;
2107:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2108:   MatScalar      *a_new,*mat_a;
2109:   Mat            C;
2110:   PetscBool      stride,sorted;

2113:   ISSorted(isrow,&sorted);
2114:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
2115:   ISSorted(iscol,&sorted);
2116:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

2118:   ISGetIndices(isrow,&irow);
2119:   ISGetLocalSize(isrow,&nrows);
2120:   ISGetLocalSize(iscol,&ncols);

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

2159:     /* loop over rows inserting into submatrix */
2160:     a_new    = c->a;
2161:     j_new    = c->j;
2162:     i_new    = c->i;

2164:     for (i=0; i<nrows; i++) {
2165:       ii    = starts[i];
2166:       lensi = lens[i];
2167:       for (k=0; k<lensi; k++) {
2168:         *j_new++ = aj[ii+k] - first;
2169:       }
2170:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2171:       a_new      += lensi;
2172:       i_new[i+1]  = i_new[i] + lensi;
2173:       c->ilen[i]  = lensi;
2174:     }
2175:     PetscFree2(lens,starts);
2176:   } else {
2177:     ISGetIndices(iscol,&icol);
2178:     PetscMalloc(oldcols*sizeof(PetscInt),&smap);
2179:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
2180:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
2181:     for (i=0; i<ncols; i++) {
2182: #if defined(PETSC_USE_DEBUG)
2183:       if (icol[i] >= oldcols) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D <= A->cmap->n %D",i,icol[i],oldcols);
2184: #endif
2185:       smap[icol[i]] = i+1;
2186:     }

2188:     /* determine lens of each row */
2189:     for (i=0; i<nrows; i++) {
2190:       kstart  = ai[irow[i]];
2191:       kend    = kstart + a->ilen[irow[i]];
2192:       lens[i] = 0;
2193:       for (k=kstart; k<kend; k++) {
2194:         if (smap[aj[k]]) {
2195:           lens[i]++;
2196:         }
2197:       }
2198:     }
2199:     /* Create and fill new matrix */
2200:     if (scall == MAT_REUSE_MATRIX) {
2201:       PetscBool  equal;

2203:       c = (Mat_SeqAIJ *)((*B)->data);
2204:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2205:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2206:       if (!equal) {
2207:         SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2208:       }
2209:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2210:       C = *B;
2211:     } else {
2212:       MatCreate(((PetscObject)A)->comm,&C);
2213:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2214:       MatSetType(C,((PetscObject)A)->type_name);
2215:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2216:     }
2217:     c = (Mat_SeqAIJ *)(C->data);
2218:     for (i=0; i<nrows; i++) {
2219:       row    = irow[i];
2220:       kstart = ai[row];
2221:       kend   = kstart + a->ilen[row];
2222:       mat_i  = c->i[i];
2223:       mat_j  = c->j + mat_i;
2224:       mat_a  = c->a + mat_i;
2225:       mat_ilen = c->ilen + i;
2226:       for (k=kstart; k<kend; k++) {
2227:         if ((tcol=smap[a->j[k]])) {
2228:           *mat_j++ = tcol - 1;
2229:           *mat_a++ = a->a[k];
2230:           (*mat_ilen)++;

2232:         }
2233:       }
2234:     }
2235:     /* Free work space */
2236:     ISRestoreIndices(iscol,&icol);
2237:     PetscFree(smap);
2238:     PetscFree(lens);
2239:   }
2240:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2241:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2243:   ISRestoreIndices(isrow,&irow);
2244:   *B = C;
2245:   return(0);
2246: }

2250: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,Mat* subMat)
2251: {
2253:   Mat            B;

2256:   MatCreate(subComm,&B);
2257:   MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2258:   MatSetType(B,MATSEQAIJ);
2259:   MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2260:   *subMat = B;
2261:   return(0);
2262: }

2266: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2267: {
2268:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2270:   Mat            outA;
2271:   PetscBool      row_identity,col_identity;

2274:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2276:   ISIdentity(row,&row_identity);
2277:   ISIdentity(col,&col_identity);

2279:   outA              = inA;
2280:   outA->factortype  = MAT_FACTOR_LU;
2281:   PetscObjectReference((PetscObject)row);
2282:   ISDestroy(&a->row);
2283:   a->row = row;
2284:   PetscObjectReference((PetscObject)col);
2285:   ISDestroy(&a->col);
2286:   a->col = col;

2288:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2289:   ISDestroy(&a->icol);
2290:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2291:   PetscLogObjectParent(inA,a->icol);

2293:   if (!a->solve_work) { /* this matrix may have been factored before */
2294:      PetscMalloc((inA->rmap->n+1)*sizeof(PetscScalar),&a->solve_work);
2295:      PetscLogObjectMemory(inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2296:   }

2298:   MatMarkDiagonal_SeqAIJ(inA);
2299:   if (row_identity && col_identity) {
2300:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2301:   } else {
2302:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2303:   }
2304:   return(0);
2305: }

2309: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2310: {
2311:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2312:   PetscScalar    oalpha = alpha;
2314:   PetscBLASInt   one = 1,bnz = PetscBLASIntCast(a->nz);

2317:   BLASscal_(&bnz,&oalpha,a->a,&one);
2318:   PetscLogFlops(a->nz);
2319:   a->idiagvalid  = PETSC_FALSE;
2320:   a->ibdiagvalid = PETSC_FALSE;
2321:   return(0);
2322: }

2326: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2327: {
2329:   PetscInt       i;

2332:   if (scall == MAT_INITIAL_MATRIX) {
2333:     PetscMalloc((n+1)*sizeof(Mat),B);
2334:   }

2336:   for (i=0; i<n; i++) {
2337:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2338:   }
2339:   return(0);
2340: }

2344: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2345: {
2346:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2348:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2349:   const PetscInt *idx;
2350:   PetscInt       start,end,*ai,*aj;
2351:   PetscBT        table;

2354:   m     = A->rmap->n;
2355:   ai    = a->i;
2356:   aj    = a->j;

2358:   if (ov < 0)  SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2360:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
2361:   PetscBTCreate(m,table);

2363:   for (i=0; i<is_max; i++) {
2364:     /* Initialize the two local arrays */
2365:     isz  = 0;
2366:     PetscBTMemzero(m,table);
2367: 
2368:     /* Extract the indices, assume there can be duplicate entries */
2369:     ISGetIndices(is[i],&idx);
2370:     ISGetLocalSize(is[i],&n);
2371: 
2372:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2373:     for (j=0; j<n ; ++j){
2374:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
2375:     }
2376:     ISRestoreIndices(is[i],&idx);
2377:     ISDestroy(&is[i]);
2378: 
2379:     k = 0;
2380:     for (j=0; j<ov; j++){ /* for each overlap */
2381:       n = isz;
2382:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
2383:         row   = nidx[k];
2384:         start = ai[row];
2385:         end   = ai[row+1];
2386:         for (l = start; l<end ; l++){
2387:           val = aj[l] ;
2388:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
2389:         }
2390:       }
2391:     }
2392:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2393:   }
2394:   PetscBTDestroy(table);
2395:   PetscFree(nidx);
2396:   return(0);
2397: }

2399: /* -------------------------------------------------------------- */
2402: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2403: {
2404:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2406:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2407:   const PetscInt *row,*col;
2408:   PetscInt       *cnew,j,*lens;
2409:   IS             icolp,irowp;
2410:   PetscInt       *cwork = PETSC_NULL;
2411:   PetscScalar    *vwork = PETSC_NULL;

2414:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2415:   ISGetIndices(irowp,&row);
2416:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2417:   ISGetIndices(icolp,&col);
2418: 
2419:   /* determine lengths of permuted rows */
2420:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
2421:   for (i=0; i<m; i++) {
2422:     lens[row[i]] = a->i[i+1] - a->i[i];
2423:   }
2424:   MatCreate(((PetscObject)A)->comm,B);
2425:   MatSetSizes(*B,m,n,m,n);
2426:   MatSetType(*B,((PetscObject)A)->type_name);
2427:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2428:   PetscFree(lens);

2430:   PetscMalloc(n*sizeof(PetscInt),&cnew);
2431:   for (i=0; i<m; i++) {
2432:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2433:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
2434:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2435:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2436:   }
2437:   PetscFree(cnew);
2438:   (*B)->assembled     = PETSC_FALSE;
2439:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2440:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2441:   ISRestoreIndices(irowp,&row);
2442:   ISRestoreIndices(icolp,&col);
2443:   ISDestroy(&irowp);
2444:   ISDestroy(&icolp);
2445:   return(0);
2446: }

2450: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2451: {

2455:   /* If the two matrices have the same copy implementation, use fast copy. */
2456:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2457:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2458:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2460:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
2461:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2462:   } else {
2463:     MatCopy_Basic(A,B,str);
2464:   }
2465:   return(0);
2466: }

2470: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
2471: {

2475:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2476:   return(0);
2477: }

2481: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2482: {
2483:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2485:   *array = a->a;
2486:   return(0);
2487: }

2491: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2492: {
2494:   return(0);
2495: }

2499: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2500: {
2501:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
2503:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
2504:   PetscScalar    dx,*y,*xx,*w3_array;
2505:   PetscScalar    *vscale_array;
2506:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
2507:   Vec            w1,w2,w3;
2508:   void           *fctx = coloring->fctx;
2509:   PetscBool      flg = PETSC_FALSE;

2512:   if (!coloring->w1) {
2513:     VecDuplicate(x1,&coloring->w1);
2514:     PetscLogObjectParent(coloring,coloring->w1);
2515:     VecDuplicate(x1,&coloring->w2);
2516:     PetscLogObjectParent(coloring,coloring->w2);
2517:     VecDuplicate(x1,&coloring->w3);
2518:     PetscLogObjectParent(coloring,coloring->w3);
2519:   }
2520:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

2522:   MatSetUnfactored(J);
2523:   PetscOptionsGetBool(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg,PETSC_NULL);
2524:   if (flg) {
2525:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2526:   } else {
2527:     PetscBool  assembled;
2528:     MatAssembled(J,&assembled);
2529:     if (assembled) {
2530:       MatZeroEntries(J);
2531:     }
2532:   }

2534:   VecGetOwnershipRange(x1,&start,&end);
2535:   VecGetSize(x1,&N);

2537:   /*
2538:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
2539:      coloring->F for the coarser grids from the finest
2540:   */
2541:   if (coloring->F) {
2542:     VecGetLocalSize(coloring->F,&m1);
2543:     VecGetLocalSize(w1,&m2);
2544:     if (m1 != m2) {
2545:       coloring->F = 0;
2546:     }
2547:   }

2549:   if (coloring->F) {
2550:     w1          = coloring->F;
2551:     coloring->F = 0;
2552:   } else {
2553:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2554:     (*f)(sctx,x1,w1,fctx);
2555:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2556:   }

2558:   /* 
2559:       Compute all the scale factors and share with other processors
2560:   */
2561:   VecGetArray(x1,&xx);xx = xx - start;
2562:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2563:   for (k=0; k<coloring->ncolors; k++) {
2564:     /*
2565:        Loop over each column associated with color adding the 
2566:        perturbation to the vector w3.
2567:     */
2568:     for (l=0; l<coloring->ncolumns[k]; l++) {
2569:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2570:       dx  = xx[col];
2571:       if (dx == 0.0) dx = 1.0;
2572: #if !defined(PETSC_USE_COMPLEX)
2573:       if (dx < umin && dx >= 0.0)      dx = umin;
2574:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2575: #else
2576:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2577:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2578: #endif
2579:       dx                *= epsilon;
2580:       vscale_array[col] = 1.0/dx;
2581:     }
2582:   }
2583:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2584:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2585:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2587:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2588:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2590:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2591:   else                        vscaleforrow = coloring->columnsforrow;

2593:   VecGetArray(coloring->vscale,&vscale_array);
2594:   /*
2595:       Loop over each color
2596:   */
2597:   for (k=0; k<coloring->ncolors; k++) {
2598:     coloring->currentcolor = k;
2599:     VecCopy(x1,w3);
2600:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2601:     /*
2602:        Loop over each column associated with color adding the 
2603:        perturbation to the vector w3.
2604:     */
2605:     for (l=0; l<coloring->ncolumns[k]; l++) {
2606:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2607:       dx  = xx[col];
2608:       if (dx == 0.0) dx = 1.0;
2609: #if !defined(PETSC_USE_COMPLEX)
2610:       if (dx < umin && dx >= 0.0)      dx = umin;
2611:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2612: #else
2613:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2614:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2615: #endif
2616:       dx            *= epsilon;
2617:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2618:       w3_array[col] += dx;
2619:     }
2620:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2622:     /*
2623:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2624:     */

2626:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2627:     (*f)(sctx,w3,w2,fctx);
2628:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2629:     VecAXPY(w2,-1.0,w1);

2631:     /*
2632:        Loop over rows of vector, putting results into Jacobian matrix
2633:     */
2634:     VecGetArray(w2,&y);
2635:     for (l=0; l<coloring->nrows[k]; l++) {
2636:       row    = coloring->rows[k][l];
2637:       col    = coloring->columnsforrow[k][l];
2638:       y[row] *= vscale_array[vscaleforrow[k][l]];
2639:       srow   = row + start;
2640:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2641:     }
2642:     VecRestoreArray(w2,&y);
2643:   }
2644:   coloring->currentcolor = k;
2645:   VecRestoreArray(coloring->vscale,&vscale_array);
2646:   xx = xx + start; VecRestoreArray(x1,&xx);
2647:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2648:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2649:   return(0);
2650: }

2652: /* 
2653:    Computes the number of nonzeros per row needed for preallocation when X and Y 
2654:    have different nonzero structure. 
2655: */
2658: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt* nnz)
2659: {
2660:   PetscInt          i,m=Y->rmap->N;
2661:   Mat_SeqAIJ        *x = (Mat_SeqAIJ*)X->data;
2662:   Mat_SeqAIJ        *y = (Mat_SeqAIJ*)Y->data;
2663:   const PetscInt    *xi = x->i,*yi = y->i;

2666:   /* Set the number of nonzeros in the new matrix */
2667:   for(i=0; i<m; i++) {
2668:     PetscInt j,k,nzx = xi[i+1] - xi[i],nzy = yi[i+1] - yi[i];
2669:     const PetscInt *xj = x->j+xi[i],*yj = y->j+yi[i];
2670:     nnz[i] = 0;
2671:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
2672:       for (; k<nzy && yj[k]<xj[j]; k++) nnz[i]++; /* Catch up to X */
2673:       if (k<nzy && yj[k]==xj[j]) k++;             /* Skip duplicate */
2674:       nnz[i]++;
2675:     }
2676:     for (; k<nzy; k++) nnz[i]++;
2677:   }
2678:   return(0);
2679: }

2683: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2684: {
2686:   PetscInt       i;
2687:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2688:   PetscBLASInt   one=1,bnz = PetscBLASIntCast(x->nz);

2691:   if (str == SAME_NONZERO_PATTERN) {
2692:     PetscScalar alpha = a;
2693:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2694:     y->idiagvalid  = PETSC_FALSE;
2695:     y->ibdiagvalid = PETSC_FALSE;
2696:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2697:     if (y->xtoy && y->XtoY != X) {
2698:       PetscFree(y->xtoy);
2699:       MatDestroy(&y->XtoY);
2700:     }
2701:     if (!y->xtoy) { /* get xtoy */
2702:       MatAXPYGetxtoy_Private(X->rmap->n,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2703:       y->XtoY = X;
2704:       PetscObjectReference((PetscObject)X);
2705:     }
2706:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2707:     PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %G\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz);
2708:   } else {
2709:     Mat      B;
2710:     PetscInt *nnz;
2711:     PetscMalloc(Y->rmap->N*sizeof(PetscInt),&nnz);
2712:     MatCreate(((PetscObject)Y)->comm,&B);
2713:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2714:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2715:     MatSetType(B,MATSEQAIJ);
2716:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2717:     MatSeqAIJSetPreallocation(B,PETSC_NULL,nnz);
2718:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2719:     MatHeaderReplace(Y,B);
2720:     PetscFree(nnz);
2721:   }
2722:   return(0);
2723: }

2727: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
2728: {

2732:   PetscLayoutSetBlockSize(A->rmap,bs);
2733:   PetscLayoutSetBlockSize(A->cmap,bs);
2734:   return(0);
2735: }

2739: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2740: {
2741: #if defined(PETSC_USE_COMPLEX)
2742:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
2743:   PetscInt    i,nz;
2744:   PetscScalar *a;

2747:   nz = aij->nz;
2748:   a  = aij->a;
2749:   for (i=0; i<nz; i++) {
2750:     a[i] = PetscConj(a[i]);
2751:   }
2752: #else
2754: #endif
2755:   return(0);
2756: }

2760: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2761: {
2762:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2764:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2765:   PetscReal      atmp;
2766:   PetscScalar    *x;
2767:   MatScalar      *aa;

2770:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2771:   aa   = a->a;
2772:   ai   = a->i;
2773:   aj   = a->j;

2775:   VecSet(v,0.0);
2776:   VecGetArray(v,&x);
2777:   VecGetLocalSize(v,&n);
2778:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2779:   for (i=0; i<m; i++) {
2780:     ncols = ai[1] - ai[0]; ai++;
2781:     x[i] = 0.0;
2782:     for (j=0; j<ncols; j++){
2783:       atmp = PetscAbsScalar(*aa);
2784:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2785:       aa++; aj++;
2786:     }
2787:   }
2788:   VecRestoreArray(v,&x);
2789:   return(0);
2790: }

2794: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2795: {
2796:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2798:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2799:   PetscScalar    *x;
2800:   MatScalar      *aa;

2803:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2804:   aa   = a->a;
2805:   ai   = a->i;
2806:   aj   = a->j;

2808:   VecSet(v,0.0);
2809:   VecGetArray(v,&x);
2810:   VecGetLocalSize(v,&n);
2811:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2812:   for (i=0; i<m; i++) {
2813:     ncols = ai[1] - ai[0]; ai++;
2814:     if (ncols == A->cmap->n) { /* row is dense */
2815:       x[i] = *aa; if (idx) idx[i] = 0;
2816:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2817:       x[i] = 0.0;
2818:       if (idx) {
2819:         idx[i] = 0; /* in case ncols is zero */
2820:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2821:           if (aj[j] > j) {
2822:             idx[i] = j;
2823:             break;
2824:           }
2825:         }
2826:       }
2827:     }
2828:     for (j=0; j<ncols; j++){
2829:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2830:       aa++; aj++;
2831:     }
2832:   }
2833:   VecRestoreArray(v,&x);
2834:   return(0);
2835: }

2839: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2840: {
2841:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2843:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2844:   PetscReal      atmp;
2845:   PetscScalar    *x;
2846:   MatScalar      *aa;

2849:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2850:   aa   = a->a;
2851:   ai   = a->i;
2852:   aj   = a->j;

2854:   VecSet(v,0.0);
2855:   VecGetArray(v,&x);
2856:   VecGetLocalSize(v,&n);
2857:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2858:   for (i=0; i<m; i++) {
2859:     ncols = ai[1] - ai[0]; ai++;
2860:     if (ncols) {
2861:       /* Get first nonzero */
2862:       for(j = 0; j < ncols; j++) {
2863:         atmp = PetscAbsScalar(aa[j]);
2864:         if (atmp > 1.0e-12) {x[i] = atmp; if (idx) idx[i] = aj[j]; break;}
2865:       }
2866:       if (j == ncols) {x[i] = *aa; if (idx) idx[i] = *aj;}
2867:     } else {
2868:       x[i] = 0.0; if (idx) idx[i] = 0;
2869:     }
2870:     for(j = 0; j < ncols; j++) {
2871:       atmp = PetscAbsScalar(*aa);
2872:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2873:       aa++; aj++;
2874:     }
2875:   }
2876:   VecRestoreArray(v,&x);
2877:   return(0);
2878: }

2882: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2883: {
2884:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2886:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2887:   PetscScalar    *x;
2888:   MatScalar      *aa;

2891:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2892:   aa   = a->a;
2893:   ai   = a->i;
2894:   aj   = a->j;

2896:   VecSet(v,0.0);
2897:   VecGetArray(v,&x);
2898:   VecGetLocalSize(v,&n);
2899:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2900:   for (i=0; i<m; i++) {
2901:     ncols = ai[1] - ai[0]; ai++;
2902:     if (ncols == A->cmap->n) { /* row is dense */
2903:       x[i] = *aa; if (idx) idx[i] = 0;
2904:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2905:       x[i] = 0.0;
2906:       if (idx) {   /* find first implicit 0.0 in the row */
2907:         idx[i] = 0; /* in case ncols is zero */
2908:         for (j=0;j<ncols;j++) {
2909:           if (aj[j] > j) {
2910:             idx[i] = j;
2911:             break;
2912:           }
2913:         }
2914:       }
2915:     }
2916:     for (j=0; j<ncols; j++){
2917:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2918:       aa++; aj++;
2919:     }
2920:   }
2921:   VecRestoreArray(v,&x);
2922:   return(0);
2923: }

2925: #include <petscblaslapack.h>
2926: #include <../src/mat/blockinvert.h>

2930: PetscErrorCode  MatInvertBlockDiagonal_SeqAIJ(Mat A,PetscScalar **values)
2931: {
2932:   Mat_SeqAIJ    *a = (Mat_SeqAIJ*) A->data;
2934:   PetscInt       i,bs = A->rmap->bs,mbs = A->rmap->n/A->rmap->bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2935:   MatScalar      *diag,work[25],*v_work;
2936:   PetscReal      shift = 0.0;

2939:   if (a->ibdiagvalid) {
2940:     if (values) *values = a->ibdiag;
2941:     return(0);
2942:   }
2943:   MatMarkDiagonal_SeqAIJ(A);
2944:   if (!a->ibdiag) {
2945:     PetscMalloc(bs2*mbs*sizeof(PetscScalar),&a->ibdiag);
2946:     PetscLogObjectMemory(A,bs2*mbs*sizeof(PetscScalar));
2947:   }
2948:   diag    = a->ibdiag;
2949:   if (values) *values = a->ibdiag;
2950:   /* factor and invert each block */
2951:   switch (bs){
2952:     case 1:
2953:       for (i=0; i<mbs; i++) {
2954:         MatGetValues(A,1,&i,1,&i,diag+i);
2955:         diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
2956:       }
2957:       break;
2958:     case 2:
2959:       for (i=0; i<mbs; i++) {
2960:         ij[0] = 2*i; ij[1] = 2*i + 1;
2961:         MatGetValues(A,2,ij,2,ij,diag);
2962:         Kernel_A_gets_inverse_A_2(diag,shift);
2963:         Kernel_A_gets_transpose_A_2(diag);
2964:         diag  += 4;
2965:       }
2966:       break;
2967:     case 3:
2968:       for (i=0; i<mbs; i++) {
2969:         ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
2970:         MatGetValues(A,3,ij,3,ij,diag);
2971:         Kernel_A_gets_inverse_A_3(diag,shift);
2972:         Kernel_A_gets_transpose_A_3(diag);
2973:         diag    += 9;
2974:       }
2975:       break;
2976:     case 4:
2977:       for (i=0; i<mbs; i++) {
2978:         ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
2979:         MatGetValues(A,4,ij,4,ij,diag);
2980:         Kernel_A_gets_inverse_A_4(diag,shift);
2981:         Kernel_A_gets_transpose_A_4(diag);
2982:         diag  += 16;
2983:       }
2984:       break;
2985:     case 5:
2986:       for (i=0; i<mbs; i++) {
2987:         ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
2988:         MatGetValues(A,5,ij,5,ij,diag);
2989:         Kernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
2990:         Kernel_A_gets_transpose_A_5(diag);
2991:         diag  += 25;
2992:       }
2993:       break;
2994:     case 6:
2995:       for (i=0; i<mbs; i++) {
2996:         ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
2997:         MatGetValues(A,6,ij,6,ij,diag);
2998:         Kernel_A_gets_inverse_A_6(diag,shift);
2999:         Kernel_A_gets_transpose_A_6(diag);
3000:         diag  += 36;
3001:       }
3002:       break;
3003:     case 7:
3004:       for (i=0; i<mbs; i++) {
3005:         ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
3006:         MatGetValues(A,7,ij,7,ij,diag);
3007:         Kernel_A_gets_inverse_A_7(diag,shift);
3008:         Kernel_A_gets_transpose_A_7(diag);
3009:         diag  += 49;
3010:       }
3011:       break;
3012:     default:
3013:       PetscMalloc3(bs,MatScalar,&v_work,bs,PetscInt,&v_pivots,bs,PetscInt,&IJ);
3014:       for (i=0; i<mbs; i++) {
3015:         for (j=0; j<bs; j++) {
3016:           IJ[j] = bs*i + j;
3017:         }
3018:         MatGetValues(A,bs,IJ,bs,IJ,diag);
3019:         Kernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
3020:         Kernel_A_gets_transpose_A_N(diag,bs);
3021:         diag  += bs2;
3022:       }
3023:       PetscFree3(v_work,v_pivots,IJ);
3024:   }
3025:   a->ibdiagvalid = PETSC_TRUE;
3026:   return(0);
3027: }

3030: /* -------------------------------------------------------------------*/
3031: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3032:        MatGetRow_SeqAIJ,
3033:        MatRestoreRow_SeqAIJ,
3034:        MatMult_SeqAIJ,
3035: /* 4*/ MatMultAdd_SeqAIJ,
3036:        MatMultTranspose_SeqAIJ,
3037:        MatMultTransposeAdd_SeqAIJ,
3038:        0,
3039:        0,
3040:        0,
3041: /*10*/ 0,
3042:        MatLUFactor_SeqAIJ,
3043:        0,
3044:        MatSOR_SeqAIJ,
3045:        MatTranspose_SeqAIJ,
3046: /*15*/ MatGetInfo_SeqAIJ,
3047:        MatEqual_SeqAIJ,
3048:        MatGetDiagonal_SeqAIJ,
3049:        MatDiagonalScale_SeqAIJ,
3050:        MatNorm_SeqAIJ,
3051: /*20*/ 0,
3052:        MatAssemblyEnd_SeqAIJ,
3053:        MatSetOption_SeqAIJ,
3054:        MatZeroEntries_SeqAIJ,
3055: /*24*/ MatZeroRows_SeqAIJ,
3056:        0,
3057:        0,
3058:        0,
3059:        0,
3060: /*29*/ MatSetUpPreallocation_SeqAIJ,
3061:        0,
3062:        0,
3063:        MatGetArray_SeqAIJ,
3064:        MatRestoreArray_SeqAIJ,
3065: /*34*/ MatDuplicate_SeqAIJ,
3066:        0,
3067:        0,
3068:        MatILUFactor_SeqAIJ,
3069:        0,
3070: /*39*/ MatAXPY_SeqAIJ,
3071:        MatGetSubMatrices_SeqAIJ,
3072:        MatIncreaseOverlap_SeqAIJ,
3073:        MatGetValues_SeqAIJ,
3074:        MatCopy_SeqAIJ,
3075: /*44*/ MatGetRowMax_SeqAIJ,
3076:        MatScale_SeqAIJ,
3077:        0,
3078:        MatDiagonalSet_SeqAIJ,
3079:        MatZeroRowsColumns_SeqAIJ,
3080: /*49*/ MatSetBlockSize_SeqAIJ,
3081:        MatGetRowIJ_SeqAIJ,
3082:        MatRestoreRowIJ_SeqAIJ,
3083:        MatGetColumnIJ_SeqAIJ,
3084:        MatRestoreColumnIJ_SeqAIJ,
3085: /*54*/ MatFDColoringCreate_SeqAIJ,
3086:        0,
3087:        0,
3088:        MatPermute_SeqAIJ,
3089:        0,
3090: /*59*/ 0,
3091:        MatDestroy_SeqAIJ,
3092:        MatView_SeqAIJ,
3093:        0,
3094:        0,
3095: /*64*/ 0,
3096:        0,
3097:        0,
3098:        0,
3099:        0,
3100: /*69*/ MatGetRowMaxAbs_SeqAIJ,
3101:        MatGetRowMinAbs_SeqAIJ,
3102:        0,
3103:        MatSetColoring_SeqAIJ,
3104: #if defined(PETSC_HAVE_ADIC)
3105:        MatSetValuesAdic_SeqAIJ,
3106: #else
3107:        0,
3108: #endif
3109: /*74*/ MatSetValuesAdifor_SeqAIJ,
3110:        MatFDColoringApply_AIJ,
3111:        0,
3112:        0,
3113:        0,
3114: /*79*/ MatFindZeroDiagonals_SeqAIJ,
3115:        0,
3116:        0,
3117:        0,
3118:        MatLoad_SeqAIJ,
3119: /*84*/ MatIsSymmetric_SeqAIJ,
3120:        MatIsHermitian_SeqAIJ,
3121:        0,
3122:        0,
3123:        0,
3124: /*89*/ MatMatMult_SeqAIJ_SeqAIJ,
3125:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3126:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
3127:        MatPtAP_Basic,
3128:        MatPtAPSymbolic_SeqAIJ,
3129: /*94*/ MatPtAPNumeric_SeqAIJ,
3130:        MatMatMultTranspose_SeqAIJ_SeqAIJ,
3131:        MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
3132:        MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
3133:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
3134: /*99*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
3135:        0,
3136:        0,
3137:        MatConjugate_SeqAIJ,
3138:        0,
3139: /*104*/MatSetValuesRow_SeqAIJ,
3140:        MatRealPart_SeqAIJ,
3141:        MatImaginaryPart_SeqAIJ,
3142:        0,
3143:        0,
3144: /*109*/MatMatSolve_SeqAIJ,
3145:        0,
3146:        MatGetRowMin_SeqAIJ,
3147:        0,
3148:        MatMissingDiagonal_SeqAIJ,
3149: /*114*/0,
3150:        0,
3151:        0,
3152:        0,
3153:        0,
3154: /*119*/0,
3155:        0,
3156:        0,
3157:        0,
3158:        MatGetMultiProcBlock_SeqAIJ,
3159: /*124*/MatFindNonzeroRows_SeqAIJ,
3160:        MatGetColumnNorms_SeqAIJ,
3161:        MatInvertBlockDiagonal_SeqAIJ,
3162:        0,
3163:        0,
3164: /*129*/0
3165: };

3170: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3171: {
3172:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3173:   PetscInt   i,nz,n;


3177:   nz = aij->maxnz;
3178:   n  = mat->rmap->n;
3179:   for (i=0; i<nz; i++) {
3180:     aij->j[i] = indices[i];
3181:   }
3182:   aij->nz = nz;
3183:   for (i=0; i<n; i++) {
3184:     aij->ilen[i] = aij->imax[i];
3185:   }

3187:   return(0);
3188: }

3193: /*@
3194:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3195:        in the matrix.

3197:   Input Parameters:
3198: +  mat - the SeqAIJ matrix
3199: -  indices - the column indices

3201:   Level: advanced

3203:   Notes:
3204:     This can be called if you have precomputed the nonzero structure of the 
3205:   matrix and want to provide it to the matrix object to improve the performance
3206:   of the MatSetValues() operation.

3208:     You MUST have set the correct numbers of nonzeros per row in the call to 
3209:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3211:     MUST be called before any calls to MatSetValues();

3213:     The indices should start with zero, not one.

3215: @*/
3216: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3217: {

3223:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt *),(mat,indices));
3224:   return(0);
3225: }

3227: /* ----------------------------------------------------------------------------------------*/

3232: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3233: {
3234:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
3236:   size_t         nz = aij->i[mat->rmap->n];

3239:   if (aij->nonew != 1) {
3240:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3241:   }

3243:   /* allocate space for values if not already there */
3244:   if (!aij->saved_values) {
3245:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
3246:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
3247:   }

3249:   /* copy values over */
3250:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3251:   return(0);
3252: }

3257: /*@
3258:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
3259:        example, reuse of the linear part of a Jacobian, while recomputing the 
3260:        nonlinear portion.

3262:    Collect on Mat

3264:   Input Parameters:
3265: .  mat - the matrix (currently only AIJ matrices support this option)

3267:   Level: advanced

3269:   Common Usage, with SNESSolve():
3270: $    Create Jacobian matrix
3271: $    Set linear terms into matrix
3272: $    Apply boundary conditions to matrix, at this time matrix must have 
3273: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
3274: $      boundary conditions again will not change the nonzero structure
3275: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3276: $    MatStoreValues(mat);
3277: $    Call SNESSetJacobian() with matrix
3278: $    In your Jacobian routine
3279: $      MatRetrieveValues(mat);
3280: $      Set nonlinear terms in matrix
3281:  
3282:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3283: $    // build linear portion of Jacobian
3284: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3285: $    MatStoreValues(mat);
3286: $    loop over nonlinear iterations
3287: $       MatRetrieveValues(mat);
3288: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3289: $       // call MatAssemblyBegin/End() on matrix
3290: $       Solve linear system with Jacobian
3291: $    endloop 

3293:   Notes:
3294:     Matrix must already be assemblied before calling this routine
3295:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before 
3296:     calling this routine.

3298:     When this is called multiple times it overwrites the previous set of stored values
3299:     and does not allocated additional space.

3301: .seealso: MatRetrieveValues()

3303: @*/
3304: PetscErrorCode  MatStoreValues(Mat mat)
3305: {

3310:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3311:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3312:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3313:   return(0);
3314: }

3319: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3320: {
3321:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
3323:   PetscInt       nz = aij->i[mat->rmap->n];

3326:   if (aij->nonew != 1) {
3327:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3328:   }
3329:   if (!aij->saved_values) {
3330:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3331:   }
3332:   /* copy values over */
3333:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3334:   return(0);
3335: }

3340: /*@
3341:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
3342:        example, reuse of the linear part of a Jacobian, while recomputing the 
3343:        nonlinear portion.

3345:    Collect on Mat

3347:   Input Parameters:
3348: .  mat - the matrix (currently on AIJ matrices support this option)

3350:   Level: advanced

3352: .seealso: MatStoreValues()

3354: @*/
3355: PetscErrorCode  MatRetrieveValues(Mat mat)
3356: {

3361:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3362:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3363:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3364:   return(0);
3365: }


3368: /* --------------------------------------------------------------------------------*/
3371: /*@C
3372:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3373:    (the default parallel PETSc format).  For good matrix assembly performance
3374:    the user should preallocate the matrix storage by setting the parameter nz
3375:    (or the array nnz).  By setting these parameters accurately, performance
3376:    during matrix assembly can be increased by more than a factor of 50.

3378:    Collective on MPI_Comm

3380:    Input Parameters:
3381: +  comm - MPI communicator, set to PETSC_COMM_SELF
3382: .  m - number of rows
3383: .  n - number of columns
3384: .  nz - number of nonzeros per row (same for all rows)
3385: -  nnz - array containing the number of nonzeros in the various rows 
3386:          (possibly different for each row) or PETSC_NULL

3388:    Output Parameter:
3389: .  A - the matrix 

3391:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3392:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
3393:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3395:    Notes:
3396:    If nnz is given then nz is ignored

3398:    The AIJ format (also called the Yale sparse matrix format or
3399:    compressed row storage), is fully compatible with standard Fortran 77
3400:    storage.  That is, the stored row and column indices can begin at
3401:    either one (as in Fortran) or zero.  See the users' manual for details.

3403:    Specify the preallocated storage with either nz or nnz (not both).
3404:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
3405:    allocation.  For large problems you MUST preallocate memory or you 
3406:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3408:    By default, this format uses inodes (identical nodes) when possible, to 
3409:    improve numerical efficiency of matrix-vector products and solves. We 
3410:    search for consecutive rows with the same nonzero structure, thereby
3411:    reusing matrix information to achieve increased efficiency.

3413:    Options Database Keys:
3414: +  -mat_no_inode  - Do not use inodes
3415: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3417:    Level: intermediate

3419: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3421: @*/
3422: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3423: {

3427:   MatCreate(comm,A);
3428:   MatSetSizes(*A,m,n,m,n);
3429:   MatSetType(*A,MATSEQAIJ);
3430:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3431:   return(0);
3432: }

3436: /*@C
3437:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3438:    the user should preallocate the matrix storage by setting the parameter nz
3439:    (or the array nnz).  By setting these parameters accurately, performance
3440:    during matrix assembly can be increased by more than a factor of 50.

3442:    Collective on MPI_Comm

3444:    Input Parameters:
3445: +  B - The matrix-free
3446: .  nz - number of nonzeros per row (same for all rows)
3447: -  nnz - array containing the number of nonzeros in the various rows 
3448:          (possibly different for each row) or PETSC_NULL

3450:    Notes:
3451:      If nnz is given then nz is ignored

3453:     The AIJ format (also called the Yale sparse matrix format or
3454:    compressed row storage), is fully compatible with standard Fortran 77
3455:    storage.  That is, the stored row and column indices can begin at
3456:    either one (as in Fortran) or zero.  See the users' manual for details.

3458:    Specify the preallocated storage with either nz or nnz (not both).
3459:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
3460:    allocation.  For large problems you MUST preallocate memory or you 
3461:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3463:    You can call MatGetInfo() to get information on how effective the preallocation was;
3464:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3465:    You can also run with the option -info and look for messages with the string 
3466:    malloc in them to see if additional memory allocation was needed.

3468:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3469:    entries or columns indices

3471:    By default, this format uses inodes (identical nodes) when possible, to 
3472:    improve numerical efficiency of matrix-vector products and solves. We 
3473:    search for consecutive rows with the same nonzero structure, thereby
3474:    reusing matrix information to achieve increased efficiency.

3476:    Options Database Keys:
3477: +  -mat_no_inode  - Do not use inodes
3478: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3479: -  -mat_aij_oneindex - Internally use indexing starting at 1
3480:         rather than 0.  Note that when calling MatSetValues(),
3481:         the user still MUST index entries starting at 0!

3483:    Level: intermediate

3485: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

3487: @*/
3488: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3489: {

3493:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3494:   return(0);
3495: }

3500: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3501: {
3502:   Mat_SeqAIJ     *b;
3503:   PetscBool      skipallocation = PETSC_FALSE;
3505:   PetscInt       i;

3508: 
3509:   if (nz == MAT_SKIP_ALLOCATION) {
3510:     skipallocation = PETSC_TRUE;
3511:     nz             = 0;
3512:   }

3514:   PetscLayoutSetBlockSize(B->rmap,1);
3515:   PetscLayoutSetBlockSize(B->cmap,1);
3516:   PetscLayoutSetUp(B->rmap);
3517:   PetscLayoutSetUp(B->cmap);

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

3528:   B->preallocated = PETSC_TRUE;
3529:   b = (Mat_SeqAIJ*)B->data;

3531:   if (!skipallocation) {
3532:     if (!b->imax) {
3533:       PetscMalloc2(B->rmap->n,PetscInt,&b->imax,B->rmap->n,PetscInt,&b->ilen);
3534:       PetscLogObjectMemory(B,2*B->rmap->n*sizeof(PetscInt));
3535:     }
3536:     if (!nnz) {
3537:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3538:       else if (nz < 0) nz = 1;
3539:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3540:       nz = nz*B->rmap->n;
3541:     } else {
3542:       nz = 0;
3543:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3544:     }
3545:     /* b->ilen will count nonzeros in each row so far. */
3546:     for (i=0; i<B->rmap->n; i++) { b->ilen[i] = 0; }

3548:     /* allocate the matrix space */
3549:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3550:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap->n+1,PetscInt,&b->i);
3551:     PetscLogObjectMemory(B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3552:     b->i[0] = 0;
3553:     for (i=1; i<B->rmap->n+1; i++) {
3554:       b->i[i] = b->i[i-1] + b->imax[i-1];
3555:     }
3556:     b->singlemalloc = PETSC_TRUE;
3557:     b->free_a       = PETSC_TRUE;
3558:     b->free_ij      = PETSC_TRUE;
3559:   } else {
3560:     b->free_a       = PETSC_FALSE;
3561:     b->free_ij      = PETSC_FALSE;
3562:   }

3564:   b->nz                = 0;
3565:   b->maxnz             = nz;
3566:   B->info.nz_unneeded  = (double)b->maxnz;
3567:   return(0);
3568: }

3571: #undef  __FUNCT__
3573: /*@
3574:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.  

3576:    Input Parameters:
3577: +  B - the matrix 
3578: .  i - the indices into j for the start of each row (starts with zero)
3579: .  j - the column indices for each row (starts with zero) these must be sorted for each row
3580: -  v - optional values in the matrix

3582:    Level: developer

3584:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

3586: .keywords: matrix, aij, compressed row, sparse, sequential

3588: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3589: @*/
3590: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3591: {

3596:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3597:   return(0);
3598: }

3601: #undef  __FUNCT__
3603: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3604: {
3605:   PetscInt       i;
3606:   PetscInt       m,n;
3607:   PetscInt       nz;
3608:   PetscInt       *nnz, nz_max = 0;
3609:   PetscScalar    *values;

3613:   MatGetSize(B, &m, &n);

3615:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
3616:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
3617:   for(i = 0; i < m; i++) {
3618:     nz     = Ii[i+1]- Ii[i];
3619:     nz_max = PetscMax(nz_max, nz);
3620:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3621:     nnz[i] = nz;
3622:   }
3623:   MatSeqAIJSetPreallocation(B, 0, nnz);
3624:   PetscFree(nnz);

3626:   if (v) {
3627:     values = (PetscScalar*) v;
3628:   } else {
3629:     PetscMalloc(nz_max*sizeof(PetscScalar), &values);
3630:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
3631:   }

3633:   for(i = 0; i < m; i++) {
3634:     nz  = Ii[i+1] - Ii[i];
3635:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3636:   }

3638:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3639:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3641:   if (!v) {
3642:     PetscFree(values);
3643:   }
3644:   return(0);
3645: }

3648: #include <../src/mat/impls/dense/seq/dense.h>
3649: #include <private/petscaxpy.h>

3653: /*
3654:     Computes (B'*A')' since computing B*A directly is untenable

3656:                n                       p                          p
3657:         (              )       (              )         (                  )
3658:       m (      A       )  *  n (       B      )   =   m (         C        )
3659:         (              )       (              )         (                  )

3661: */
3662: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3663: {
3664:   PetscErrorCode     ierr;
3665:   Mat_SeqDense       *sub_a = (Mat_SeqDense*)A->data;
3666:   Mat_SeqAIJ         *sub_b = (Mat_SeqAIJ*)B->data;
3667:   Mat_SeqDense       *sub_c = (Mat_SeqDense*)C->data;
3668:   PetscInt           i,n,m,q,p;
3669:   const PetscInt     *ii,*idx;
3670:   const PetscScalar  *b,*a,*a_q;
3671:   PetscScalar        *c,*c_q;

3674:   m = A->rmap->n;
3675:   n = A->cmap->n;
3676:   p = B->cmap->n;
3677:   a = sub_a->v;
3678:   b = sub_b->a;
3679:   c = sub_c->v;
3680:   PetscMemzero(c,m*p*sizeof(PetscScalar));

3682:   ii  = sub_b->i;
3683:   idx = sub_b->j;
3684:   for (i=0; i<n; i++) {
3685:     q = ii[i+1] - ii[i];
3686:     while (q-->0) {
3687:       c_q = c + m*(*idx);
3688:       a_q = a + m*i;
3689:       PetscAXPY(c_q,*b,a_q,m);
3690:       idx++;
3691:       b++;
3692:     }
3693:   }
3694:   return(0);
3695: }

3699: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3700: {
3702:   PetscInt       m=A->rmap->n,n=B->cmap->n;
3703:   Mat            Cmat;

3706:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
3707:   MatCreate(((PetscObject)A)->comm,&Cmat);
3708:   MatSetSizes(Cmat,m,n,m,n);
3709:   MatSetType(Cmat,MATSEQDENSE);
3710:   MatSeqDenseSetPreallocation(Cmat,PETSC_NULL);
3711:   Cmat->assembled = PETSC_TRUE;
3712:   *C = Cmat;
3713:   return(0);
3714: }

3716: /* ----------------------------------------------------------------*/
3719: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3720: {

3724:   if (scall == MAT_INITIAL_MATRIX){
3725:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3726:   }
3727:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3728:   return(0);
3729: }


3732: /*MC
3733:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
3734:    based on compressed sparse row format.

3736:    Options Database Keys:
3737: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3739:   Level: beginner

3741: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3742: M*/

3745: #if defined(PETSC_HAVE_PASTIX)
3747: #endif
3748: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
3750: #endif
3755: #if defined(PETSC_HAVE_MUMPS)
3757: #endif
3758: #if defined(PETSC_HAVE_SUPERLU)
3760: #endif
3761: #if defined(PETSC_HAVE_SUPERLU_DIST)
3763: #endif
3764: #if defined(PETSC_HAVE_SPOOLES)
3766: #endif
3767: #if defined(PETSC_HAVE_UMFPACK)
3769: #endif
3770: #if defined(PETSC_HAVE_CHOLMOD)
3772: #endif
3773: #if defined(PETSC_HAVE_LUSOL)
3775: #endif
3776: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3780: #endif

3786: PetscErrorCode  MatCreate_SeqAIJ(Mat B)
3787: {
3788:   Mat_SeqAIJ     *b;
3790:   PetscMPIInt    size;

3793:   MPI_Comm_size(((PetscObject)B)->comm,&size);
3794:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3796:   PetscNewLog(B,Mat_SeqAIJ,&b);
3797:   B->data             = (void*)b;
3798:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3799:   b->row              = 0;
3800:   b->col              = 0;
3801:   b->icol             = 0;
3802:   b->reallocs         = 0;
3803:   b->ignorezeroentries = PETSC_FALSE;
3804:   b->roworiented       = PETSC_TRUE;
3805:   b->nonew             = 0;
3806:   b->diag              = 0;
3807:   b->solve_work        = 0;
3808:   B->spptr             = 0;
3809:   b->saved_values      = 0;
3810:   b->idiag             = 0;
3811:   b->mdiag             = 0;
3812:   b->ssor_work         = 0;
3813:   b->omega             = 1.0;
3814:   b->fshift            = 0.0;
3815:   b->idiagvalid        = PETSC_FALSE;
3816:   b->ibdiagvalid       = PETSC_FALSE;
3817:   b->keepnonzeropattern    = PETSC_FALSE;
3818:   b->xtoy              = 0;
3819:   b->XtoY              = 0;
3820:   B->same_nonzero          = PETSC_FALSE;

3822:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3823: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3824:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_matlab_C","MatGetFactor_seqaij_matlab",MatGetFactor_seqaij_matlab);
3825:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatlabEnginePut_SeqAIJ",MatlabEnginePut_SeqAIJ);
3826:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatlabEngineGet_SeqAIJ",MatlabEngineGet_SeqAIJ);
3827: #endif
3828: #if defined(PETSC_HAVE_PASTIX)
3829:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_pastix_C","MatGetFactor_seqaij_pastix",MatGetFactor_seqaij_pastix);
3830: #endif
3831: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
3832:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_essl_C","MatGetFactor_seqaij_essl",MatGetFactor_seqaij_essl);
3833: #endif
3834: #if defined(PETSC_HAVE_SUPERLU)
3835:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_superlu_C","MatGetFactor_seqaij_superlu",MatGetFactor_seqaij_superlu);
3836: #endif
3837: #if defined(PETSC_HAVE_SUPERLU_DIST)
3838:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_superlu_dist_C","MatGetFactor_seqaij_superlu_dist",MatGetFactor_seqaij_superlu_dist);
3839: #endif
3840: #if defined(PETSC_HAVE_SPOOLES)
3841:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_spooles_C","MatGetFactor_seqaij_spooles",MatGetFactor_seqaij_spooles);
3842: #endif
3843: #if defined(PETSC_HAVE_MUMPS)
3844:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mumps_C","MatGetFactor_aij_mumps",MatGetFactor_aij_mumps);
3845: #endif
3846: #if defined(PETSC_HAVE_UMFPACK)
3847:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_umfpack_C","MatGetFactor_seqaij_umfpack",MatGetFactor_seqaij_umfpack);
3848: #endif
3849: #if defined(PETSC_HAVE_CHOLMOD)
3850:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_cholmod_C","MatGetFactor_seqaij_cholmod",MatGetFactor_seqaij_cholmod);
3851: #endif
3852: #if defined(PETSC_HAVE_LUSOL)
3853:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_lusol_C","MatGetFactor_seqaij_lusol",MatGetFactor_seqaij_lusol);
3854: #endif
3855:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_petsc_C","MatGetFactor_seqaij_petsc",MatGetFactor_seqaij_petsc);
3856:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactorAvailable_petsc_C","MatGetFactorAvailable_seqaij_petsc",MatGetFactorAvailable_seqaij_petsc);
3857:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_bas_C","MatGetFactor_seqaij_bas",MatGetFactor_seqaij_bas);
3858:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C","MatSeqAIJSetColumnIndices_SeqAIJ",MatSeqAIJSetColumnIndices_SeqAIJ);
3859:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C","MatStoreValues_SeqAIJ",MatStoreValues_SeqAIJ);
3860:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C","MatRetrieveValues_SeqAIJ",MatRetrieveValues_SeqAIJ);
3861:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C","MatConvert_SeqAIJ_SeqSBAIJ",MatConvert_SeqAIJ_SeqSBAIJ);
3862:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C","MatConvert_SeqAIJ_SeqBAIJ",MatConvert_SeqAIJ_SeqBAIJ);
3863:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijperm_C","MatConvert_SeqAIJ_SeqAIJPERM",MatConvert_SeqAIJ_SeqAIJPERM);
3864:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C","MatConvert_SeqAIJ_SeqAIJCRL",MatConvert_SeqAIJ_SeqAIJCRL);
3865:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C","MatIsTranspose_SeqAIJ",MatIsTranspose_SeqAIJ);
3866:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsHermitianTranspose_C","MatIsHermitianTranspose_SeqAIJ",MatIsTranspose_SeqAIJ);
3867:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C","MatSeqAIJSetPreallocation_SeqAIJ",MatSeqAIJSetPreallocation_SeqAIJ);
3868:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C","MatSeqAIJSetPreallocationCSR_SeqAIJ",MatSeqAIJSetPreallocationCSR_SeqAIJ);
3869:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C","MatReorderForNonzeroDiagonal_SeqAIJ",MatReorderForNonzeroDiagonal_SeqAIJ);
3870:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMult_seqdense_seqaij_C","MatMatMult_SeqDense_SeqAIJ",MatMatMult_SeqDense_SeqAIJ);
3871:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C","MatMatMultSymbolic_SeqDense_SeqAIJ",MatMatMultSymbolic_SeqDense_SeqAIJ);
3872:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C","MatMatMultNumeric_SeqDense_SeqAIJ",MatMatMultNumeric_SeqDense_SeqAIJ);
3873:   MatCreate_SeqAIJ_Inode(B);
3874:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3875:   return(0);
3876: }

3879: #if defined(PETSC_HAVE_PTHREADCLASSES)
3883: PetscErrorCode  MatCreate_SeqAIJPThread(Mat B)
3884: {

3888:   MatCreate_SeqAIJ(B);
3889:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3890:   B->ops->mult = MatMult_SeqAIJPThread;
3891:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJPTHREAD);
3892:   return(0);
3893: }
3895: #endif

3899: /*
3900:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
3901: */
3902: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool  mallocmatspace)
3903: {
3904:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3906:   PetscInt       i,m = A->rmap->n;

3909:   c = (Mat_SeqAIJ*)C->data;

3911:   C->factortype     = A->factortype;
3912:   c->row            = 0;
3913:   c->col            = 0;
3914:   c->icol           = 0;
3915:   c->reallocs       = 0;

3917:   C->assembled      = PETSC_TRUE;
3918: 
3919:   PetscLayoutReference(A->rmap,&C->rmap);
3920:   PetscLayoutReference(A->cmap,&C->cmap);

3922:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
3923:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
3924:   for (i=0; i<m; i++) {
3925:     c->imax[i] = a->imax[i];
3926:     c->ilen[i] = a->ilen[i];
3927:   }

3929:   /* allocate the matrix space */
3930:   if (mallocmatspace){
3931:     PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
3932:     PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
3933:     c->singlemalloc = PETSC_TRUE;
3934:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
3935:     if (m > 0) {
3936:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
3937:       if (cpvalues == MAT_COPY_VALUES) {
3938:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
3939:       } else {
3940:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
3941:       }
3942:     }
3943:   }

3945:   c->ignorezeroentries = a->ignorezeroentries;
3946:   c->roworiented       = a->roworiented;
3947:   c->nonew             = a->nonew;
3948:   if (a->diag) {
3949:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
3950:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
3951:     for (i=0; i<m; i++) {
3952:       c->diag[i] = a->diag[i];
3953:     }
3954:   } else c->diag           = 0;
3955:   c->solve_work            = 0;
3956:   c->saved_values          = 0;
3957:   c->idiag                 = 0;
3958:   c->ssor_work             = 0;
3959:   c->keepnonzeropattern    = a->keepnonzeropattern;
3960:   c->free_a                = PETSC_TRUE;
3961:   c->free_ij               = PETSC_TRUE;
3962:   c->xtoy                  = 0;
3963:   c->XtoY                  = 0;

3965:   c->nz                 = a->nz;
3966:   c->maxnz              = a->nz; /* Since we allocate exactly the right amount */
3967:   C->preallocated       = PETSC_TRUE;

3969:   c->compressedrow.use     = a->compressedrow.use;
3970:   c->compressedrow.nrows   = a->compressedrow.nrows;
3971:   c->compressedrow.check   = a->compressedrow.check;
3972:   if (a->compressedrow.use){
3973:     i = a->compressedrow.nrows;
3974:     PetscMalloc2(i+1,PetscInt,&c->compressedrow.i,i,PetscInt,&c->compressedrow.rindex);
3975:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3976:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3977:   } else {
3978:     c->compressedrow.use    = PETSC_FALSE;
3979:     c->compressedrow.i      = PETSC_NULL;
3980:     c->compressedrow.rindex = PETSC_NULL;
3981:   }
3982:   C->same_nonzero = A->same_nonzero;
3983:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);

3985:   PetscFListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3986:   return(0);
3987: }

3991: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3992: {

3996:   MatCreate(((PetscObject)A)->comm,B);
3997:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
3998:   MatSetType(*B,MATSEQAIJ);
3999:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4000:   return(0);
4001: }

4005: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4006: {
4007:   Mat_SeqAIJ     *a;
4009:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4010:   int            fd;
4011:   PetscMPIInt    size;
4012:   MPI_Comm       comm;
4013:   PetscInt       bs = 1;
4014: 
4016:   PetscObjectGetComm((PetscObject)viewer,&comm);
4017:   MPI_Comm_size(comm,&size);
4018:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");

4020:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading SEQAIJ matrix","Mat");
4021:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
4022:   PetscOptionsEnd();

4024:   PetscViewerBinaryGetDescriptor(viewer,&fd);
4025:   PetscBinaryRead(fd,header,4,PETSC_INT);
4026:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4027:   M = header[1]; N = header[2]; nz = header[3];

4029:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");

4031:   /* read in row lengths */
4032:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
4033:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

4035:   /* check if sum of rowlengths is same as nz */
4036:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4037:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

4039:   /* set global size if not set already*/
4040:   if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4041:     MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4042:   } else {
4043:     /* if sizes and type are already set, check if the vector global sizes are correct */
4044:     MatGetSize(newMat,&rows,&cols);
4045:     if (rows < 0 && cols < 0){ /* user might provide local size instead of global size */
4046:       MatGetLocalSize(newMat,&rows,&cols);
4047:     }
4048:     if (M != rows ||  N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%d, %d) than the input matrix (%d, %d)",M,N,rows,cols);
4049:   }
4050:   MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4051:   a = (Mat_SeqAIJ*)newMat->data;

4053:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

4055:   /* read in nonzero values */
4056:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

4058:   /* set matrix "i" values */
4059:   a->i[0] = 0;
4060:   for (i=1; i<= M; i++) {
4061:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
4062:     a->ilen[i-1] = rowlengths[i-1];
4063:   }
4064:   PetscFree(rowlengths);

4066:   MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4067:   MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4068:   if (bs > 1) {MatSetBlockSize(newMat,bs);}
4069:   return(0);
4070: }

4074: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4075: {
4076:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
4078: #if defined(PETSC_USE_COMPLEX)
4079:   PetscInt k;
4080: #endif

4083:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4084:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4085:     *flg = PETSC_FALSE;
4086:     return(0);
4087:   }
4088: 
4089:   /* if the a->i are the same */
4090:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4091:   if (!*flg) return(0);
4092: 
4093:   /* if a->j are the same */
4094:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4095:   if (!*flg) return(0);
4096: 
4097:   /* if a->a are the same */
4098: #if defined(PETSC_USE_COMPLEX)
4099:   for (k=0; k<a->nz; k++){
4100:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])){
4101:       *flg = PETSC_FALSE;
4102:       return(0);
4103:     }
4104:   }
4105: #else
4106:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4107: #endif
4108:   return(0);
4109: }

4113: /*@
4114:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4115:               provided by the user.

4117:       Collective on MPI_Comm

4119:    Input Parameters:
4120: +   comm - must be an MPI communicator of size 1
4121: .   m - number of rows
4122: .   n - number of columns
4123: .   i - row indices
4124: .   j - column indices
4125: -   a - matrix values

4127:    Output Parameter:
4128: .   mat - the matrix

4130:    Level: intermediate

4132:    Notes:
4133:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4134:     once the matrix is destroyed and not before

4136:        You cannot set new nonzero locations into this matrix, that will generate an error.

4138:        The i and j indices are 0 based

4140:        The format which is used for the sparse matrix input, is equivalent to a
4141:     row-major ordering.. i.e for the following matrix, the input data expected is
4142:     as shown:

4144:         1 0 0
4145:         2 0 3
4146:         4 5 6

4148:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4149:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
4150:         v =  {1,2,3,4,5,6}  [size = nz = 6]

4152:         
4153: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4155: @*/
4156: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
4157: {
4159:   PetscInt       ii;
4160:   Mat_SeqAIJ     *aij;
4161: #if defined(PETSC_USE_DEBUG)
4162:   PetscInt       jj;
4163: #endif

4166:   if (i[0]) {
4167:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4168:   }
4169:   MatCreate(comm,mat);
4170:   MatSetSizes(*mat,m,n,m,n);
4171:   MatSetType(*mat,MATSEQAIJ);
4172:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4173:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4174:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

4176:   aij->i = i;
4177:   aij->j = j;
4178:   aij->a = a;
4179:   aij->singlemalloc = PETSC_FALSE;
4180:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4181:   aij->free_a       = PETSC_FALSE;
4182:   aij->free_ij      = PETSC_FALSE;

4184:   for (ii=0; ii<m; ii++) {
4185:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4186: #if defined(PETSC_USE_DEBUG)
4187:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
4188:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4189:       if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4190:       if (j[jj] == j[jj]-1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4191:     }
4192: #endif    
4193:   }
4194: #if defined(PETSC_USE_DEBUG)
4195:   for (ii=0; ii<aij->i[m]; ii++) {
4196:     if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
4197:     if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
4198:   }
4199: #endif    

4201:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4202:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4203:   return(0);
4204: }

4208: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
4209: {
4211:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

4214:   if (coloring->ctype == IS_COLORING_GLOBAL) {
4215:     ISColoringReference(coloring);
4216:     a->coloring = coloring;
4217:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
4218:     PetscInt             i,*larray;
4219:     ISColoring      ocoloring;
4220:     ISColoringValue *colors;

4222:     /* set coloring for diagonal portion */
4223:     PetscMalloc(A->cmap->n*sizeof(PetscInt),&larray);
4224:     for (i=0; i<A->cmap->n; i++) {
4225:       larray[i] = i;
4226:     }
4227:     ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,PETSC_NULL,larray);
4228:     PetscMalloc(A->cmap->n*sizeof(ISColoringValue),&colors);
4229:     for (i=0; i<A->cmap->n; i++) {
4230:       colors[i] = coloring->colors[larray[i]];
4231:     }
4232:     PetscFree(larray);
4233:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,&ocoloring);
4234:     a->coloring = ocoloring;
4235:   }
4236:   return(0);
4237: }

4239: #if defined(PETSC_HAVE_ADIC)
4241: #include <adic/ad_utils.h>

4246: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
4247: {
4248:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
4249:   PetscInt        m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j,nlen;
4250:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
4251:   ISColoringValue *color;

4254:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4255:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
4256:   color = a->coloring->colors;
4257:   /* loop over rows */
4258:   for (i=0; i<m; i++) {
4259:     nz = ii[i+1] - ii[i];
4260:     /* loop over columns putting computed value into matrix */
4261:     for (j=0; j<nz; j++) {
4262:       *v++ = values[color[*jj++]];
4263:     }
4264:     values += nlen; /* jump to next row of derivatives */
4265:   }
4266:   return(0);
4267: }
4268: #endif

4272: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
4273: {
4274:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
4275:   PetscInt         m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
4276:   MatScalar       *v = a->a;
4277:   PetscScalar     *values = (PetscScalar *)advalues;
4278:   ISColoringValue *color;

4281:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4282:   color = a->coloring->colors;
4283:   /* loop over rows */
4284:   for (i=0; i<m; i++) {
4285:     nz = ii[i+1] - ii[i];
4286:     /* loop over columns putting computed value into matrix */
4287:     for (j=0; j<nz; j++) {
4288:       *v++ = values[color[*jj++]];
4289:     }
4290:     values += nl; /* jump to next row of derivatives */
4291:   }
4292:   return(0);
4293: }

4295: /*
4296:     Special version for direct calls from Fortran 
4297: */
4298: #include <private/fortranimpl.h>
4299: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4300: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4301: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4302: #define matsetvaluesseqaij_ matsetvaluesseqaij
4303: #endif

4305: /* Change these macros so can be used in void function */
4306: #undef CHKERRQ
4307: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)A)->comm,ierr) 
4308: #undef SETERRQ2
4309: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr) 

4314: void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
4315: {
4316:   Mat            A = *AA;
4317:   PetscInt       m = *mm, n = *nn;
4318:   InsertMode     is = *isis;
4319:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4320:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4321:   PetscInt       *imax,*ai,*ailen;
4323:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
4324:   MatScalar      *ap,value,*aa;
4325:   PetscBool      ignorezeroentries = a->ignorezeroentries;
4326:   PetscBool      roworiented = a->roworiented;

4329:   MatPreallocated(A);
4330:   imax = a->imax;
4331:   ai = a->i;
4332:   ailen = a->ilen;
4333:   aj = a->j;
4334:   aa = a->a;

4336:   for (k=0; k<m; k++) { /* loop over added rows */
4337:     row  = im[k];
4338:     if (row < 0) continue;
4339: #if defined(PETSC_USE_DEBUG)  
4340:     if (row >= A->rmap->n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4341: #endif
4342:     rp   = aj + ai[row]; ap = aa + ai[row];
4343:     rmax = imax[row]; nrow = ailen[row];
4344:     low  = 0;
4345:     high = nrow;
4346:     for (l=0; l<n; l++) { /* loop over added columns */
4347:       if (in[l] < 0) continue;
4348: #if defined(PETSC_USE_DEBUG)  
4349:       if (in[l] >= A->cmap->n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4350: #endif
4351:       col = in[l];
4352:       if (roworiented) {
4353:         value = v[l + k*n];
4354:       } else {
4355:         value = v[k + l*m];
4356:       }
4357:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

4359:       if (col <= lastcol) low = 0; else high = nrow;
4360:       lastcol = col;
4361:       while (high-low > 5) {
4362:         t = (low+high)/2;
4363:         if (rp[t] > col) high = t;
4364:         else             low  = t;
4365:       }
4366:       for (i=low; i<high; i++) {
4367:         if (rp[i] > col) break;
4368:         if (rp[i] == col) {
4369:           if (is == ADD_VALUES) ap[i] += value;
4370:           else                  ap[i] = value;
4371:           goto noinsert;
4372:         }
4373:       }
4374:       if (value == 0.0 && ignorezeroentries) goto noinsert;
4375:       if (nonew == 1) goto noinsert;
4376:       if (nonew == -1) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4377:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4378:       N = nrow++ - 1; a->nz++; high++;
4379:       /* shift up all the later entries in this row */
4380:       for (ii=N; ii>=i; ii--) {
4381:         rp[ii+1] = rp[ii];
4382:         ap[ii+1] = ap[ii];
4383:       }
4384:       rp[i] = col;
4385:       ap[i] = value;
4386:       noinsert:;
4387:       low = i + 1;
4388:     }
4389:     ailen[row] = nrow;
4390:   }
4391:   A->same_nonzero = PETSC_FALSE;
4392:   PetscFunctionReturnVoid();
4393: }