Actual source code: aij.c

petsc-3.3-p5 2012-12-01
  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_Private(Mat A,PetscInt *nrows,PetscInt **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:   *nrows = cnt;
 71:   *zrows = rows;
 72:   return(0);
 73: }

 77: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
 78: {
 79:   PetscInt       nrows,*rows;

 83:   *zrows = PETSC_NULL;
 84:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
 85:   ISCreateGeneral(((PetscObject)A)->comm,nrows,rows,PETSC_OWN_POINTER,zrows);
 86:   return(0);
 87: }

 91: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
 92: {
 93:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
 94:   const MatScalar   *aa;
 95:   PetscInt          m=A->rmap->n,cnt = 0;
 96:   const PetscInt    *ii;
 97:   PetscInt          n,i,j,*rows;
 98:   PetscErrorCode    ierr;

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

136: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
137: {
139:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) Y->data;
140:   PetscInt       i,*diag, m = Y->rmap->n;
141:   MatScalar      *aa = aij->a;
142:   PetscScalar    *v;
143:   PetscBool      missing;

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

172: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
173: {
174:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
176:   PetscInt       i,ishift;
177: 
179:   *m     = A->rmap->n;
180:   if (!ia) return(0);
181:   ishift = 0;
182:   if (symmetric && !A->structurally_symmetric) {
183:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,ia,ja);
184:   } else if (oshift == 1) {
185:     PetscInt nz = a->i[A->rmap->n];
186:     /* malloc space and  add 1 to i and j indices */
187:     PetscMalloc((A->rmap->n+1)*sizeof(PetscInt),ia);
188:     for (i=0; i<A->rmap->n+1; i++) (*ia)[i] = a->i[i] + 1;
189:     if (ja) {
190:       PetscMalloc((nz+1)*sizeof(PetscInt),ja);
191:       for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
192:     }
193:   } else {
194:     *ia = a->i;
195:     if (ja) *ja = a->j;
196:   }
197:   return(0);
198: }

202: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
203: {
205: 
207:   if (!ia) return(0);
208:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
209:     PetscFree(*ia);
210:     if (ja) {PetscFree(*ja);}
211:   }
212:   return(0);
213: }

217: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
218: {
219:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
221:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
222:   PetscInt       nz = a->i[m],row,*jj,mr,col;

225:   *nn = n;
226:   if (!ia) return(0);
227:   if (symmetric) {
228:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,ia,ja);
229:   } else {
230:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
231:     PetscMemzero(collengths,n*sizeof(PetscInt));
232:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
233:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
234:     jj = a->j;
235:     for (i=0; i<nz; i++) {
236:       collengths[jj[i]]++;
237:     }
238:     cia[0] = oshift;
239:     for (i=0; i<n; i++) {
240:       cia[i+1] = cia[i] + collengths[i];
241:     }
242:     PetscMemzero(collengths,n*sizeof(PetscInt));
243:     jj   = a->j;
244:     for (row=0; row<m; row++) {
245:       mr = a->i[row+1] - a->i[row];
246:       for (i=0; i<mr; i++) {
247:         col = *jj++;
248:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
249:       }
250:     }
251:     PetscFree(collengths);
252:     *ia = cia; *ja = cja;
253:   }
254:   return(0);
255: }

259: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool  symmetric,PetscBool  inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscBool  *done)
260: {

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

266:   PetscFree(*ia);
267:   PetscFree(*ja);
268: 
269:   return(0);
270: }

274: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
275: {
276:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
277:   PetscInt       *ai = a->i;

281:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
282:   return(0);
283: }

287: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
288: {
289:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
290:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
291:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
293:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
294:   MatScalar      *ap,value,*aa = a->a;
295:   PetscBool      ignorezeroentries = a->ignorezeroentries;
296:   PetscBool      roworiented = a->roworiented;

300:   for (k=0; k<m; k++) { /* loop over added rows */
301:     row  = im[k];
302:     if (row < 0) continue;
303: #if defined(PETSC_USE_DEBUG)  
304:     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);
305: #endif
306:     rp   = aj + ai[row]; ap = aa + ai[row];
307:     rmax = imax[row]; nrow = ailen[row];
308:     low  = 0;
309:     high = nrow;
310:     for (l=0; l<n; l++) { /* loop over added columns */
311:       if (in[l] < 0) continue;
312: #if defined(PETSC_USE_DEBUG)  
313:       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);
314: #endif
315:       col = in[l];
316:       if (v) {
317:         if (roworiented) {
318:           value = v[l + k*n];
319:         } else {
320:           value = v[k + l*m];
321:         }
322:       } else {
323:         value = 0.;
324:       }
325:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

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


367: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
368: {
369:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
370:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
371:   PetscInt     *ai = a->i,*ailen = a->ilen;
372:   MatScalar    *ap,*aa = a->a;

375:   for (k=0; k<m; k++) { /* loop over rows */
376:     row  = im[k];
377:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
378:     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);
379:     rp   = aj + ai[row]; ap = aa + ai[row];
380:     nrow = ailen[row];
381:     for (l=0; l<n; l++) { /* loop over columns */
382:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
383:       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);
384:       col = in[l] ;
385:       high = nrow; low = 0; /* assume unsorted */
386:       while (high-low > 5) {
387:         t = (low+high)/2;
388:         if (rp[t] > col) high = t;
389:         else             low  = t;
390:       }
391:       for (i=low; i<high; i++) {
392:         if (rp[i] > col) break;
393:         if (rp[i] == col) {
394:           *v++ = ap[i];
395:           goto finished;
396:         }
397:       }
398:       *v++ = 0.0;
399:       finished:;
400:     }
401:   }
402:   return(0);
403: }


408: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
409: {
410:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
412:   PetscInt       i,*col_lens;
413:   int            fd;

416:   PetscViewerBinaryGetDescriptor(viewer,&fd);
417:   PetscMalloc((4+A->rmap->n)*sizeof(PetscInt),&col_lens);
418:   col_lens[0] = MAT_FILE_CLASSID;
419:   col_lens[1] = A->rmap->n;
420:   col_lens[2] = A->cmap->n;
421:   col_lens[3] = a->nz;

423:   /* store lengths of each row and write (including header) to file */
424:   for (i=0; i<A->rmap->n; i++) {
425:     col_lens[4+i] = a->i[i+1] - a->i[i];
426:   }
427:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
428:   PetscFree(col_lens);

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

433:   /* store nonzero values */
434:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
435:   return(0);
436: }

438: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

442: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
443: {
444:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
445:   PetscErrorCode    ierr;
446:   PetscInt          i,j,m = A->rmap->n,shift=0;
447:   const char        *name;
448:   PetscViewerFormat format;

451:   PetscViewerGetFormat(viewer,&format);
452:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
453:     PetscInt nofinalvalue = 0;
454:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-!shift)) {
455:       nofinalvalue = 1;
456:     }
457:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
458:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
459:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
460:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
461:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

463:     for (i=0; i<m; i++) {
464:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
465: #if defined(PETSC_USE_COMPLEX)
466:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
467: #else
468:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
469: #endif
470:       }
471:     }
472:     if (nofinalvalue) {
473:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
474:     }
475:     PetscObjectGetName((PetscObject)A,&name);
476:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
477:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
478:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
479:      return(0);
480:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
481:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
482:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
483:     for (i=0; i<m; i++) {
484:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
485:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
486: #if defined(PETSC_USE_COMPLEX)
487:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
488:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
489:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
490:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
491:         } else if (PetscRealPart(a->a[j]) != 0.0) {
492:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
493:         }
494: #else
495:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);}
496: #endif
497:       }
498:       PetscViewerASCIIPrintf(viewer,"\n");
499:     }
500:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
501:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
502:     PetscInt nzd=0,fshift=1,*sptr;
503:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
504:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
505:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
506:     for (i=0; i<m; i++) {
507:       sptr[i] = nzd+1;
508:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
509:         if (a->j[j] >= i) {
510: #if defined(PETSC_USE_COMPLEX)
511:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
512: #else
513:           if (a->a[j] != 0.0) nzd++;
514: #endif
515:         }
516:       }
517:     }
518:     sptr[m] = nzd+1;
519:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
520:     for (i=0; i<m+1; i+=6) {
521:       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]);}
522:       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]);}
523:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
524:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
525:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
526:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
527:     }
528:     PetscViewerASCIIPrintf(viewer,"\n");
529:     PetscFree(sptr);
530:     for (i=0; i<m; i++) {
531:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
532:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
533:       }
534:       PetscViewerASCIIPrintf(viewer,"\n");
535:     }
536:     PetscViewerASCIIPrintf(viewer,"\n");
537:     for (i=0; i<m; i++) {
538:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
539:         if (a->j[j] >= i) {
540: #if defined(PETSC_USE_COMPLEX)
541:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
542:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
543:           }
544: #else
545:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
546: #endif
547:         }
548:       }
549:       PetscViewerASCIIPrintf(viewer,"\n");
550:     }
551:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
552:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
553:     PetscInt         cnt = 0,jcnt;
554:     PetscScalar value;

556:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
557:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
558:     for (i=0; i<m; i++) {
559:       jcnt = 0;
560:       for (j=0; j<A->cmap->n; j++) {
561:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
562:           value = a->a[cnt++];
563:           jcnt++;
564:         } else {
565:           value = 0.0;
566:         }
567: #if defined(PETSC_USE_COMPLEX)
568:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
569: #else
570:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
571: #endif
572:       }
573:       PetscViewerASCIIPrintf(viewer,"\n");
574:     }
575:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
576:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
577:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
578:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
579: #if defined(PETSC_USE_COMPLEX)
580:     PetscViewerASCIIPrintf(viewer,"%%matrix complex general\n");
581: #else
582:     PetscViewerASCIIPrintf(viewer,"%%matrix real general\n");
583: #endif
584:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
585:     for (i=0; i<m; i++) {
586:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
587: #if defined(PETSC_USE_COMPLEX)
588:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
589:           PetscViewerASCIIPrintf(viewer,"%D %D, %G %G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
590:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
591:           PetscViewerASCIIPrintf(viewer,"%D %D, %G -%G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
592:         } else {
593:           PetscViewerASCIIPrintf(viewer,"%D %D, %G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]));
594:         }
595: #else
596:         PetscViewerASCIIPrintf(viewer,"%D %D %G\n", i+shift, a->j[j]+shift, a->a[j]);
597: #endif
598:       }
599:     }
600:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
601:   } else {
602:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
603:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
604:     if (A->factortype){
605:       for (i=0; i<m; i++) {
606:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
607:         /* L part */
608:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
609: #if defined(PETSC_USE_COMPLEX)
610:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
611:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
612:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
613:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
614:           } else {
615:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
616:           }
617: #else
618:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
619: #endif
620:         }
621:         /* diagonal */
622:         j = a->diag[i];
623: #if defined(PETSC_USE_COMPLEX)
624:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
625:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),PetscImaginaryPart(1.0/a->a[j]));
626:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
627:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),-PetscImaginaryPart(1.0/a->a[j]));
628:           } else {
629:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(1.0/a->a[j]));
630:           }
631: #else
632:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,1.0/a->a[j]);
633: #endif

635:         /* U part */
636:         for (j=a->diag[i+1]+1+shift; j<a->diag[i]+shift; j++) {
637: #if defined(PETSC_USE_COMPLEX)
638:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
639:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
640:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
641:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
642:           } else {
643:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
644:           }
645: #else
646:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
647: #endif
648: }
649:           PetscViewerASCIIPrintf(viewer,"\n");
650:         }
651:     } else {
652:       for (i=0; i<m; i++) {
653:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
654:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
655: #if defined(PETSC_USE_COMPLEX)
656:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
657:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
658:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
659:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
660:           } else {
661:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
662:           }
663: #else
664:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
665: #endif
666:         }
667:         PetscViewerASCIIPrintf(viewer,"\n");
668:       }
669:     }
670:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
671:   }
672:   PetscViewerFlush(viewer);
673:   return(0);
674: }

678: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
679: {
680:   Mat               A = (Mat) Aa;
681:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
682:   PetscErrorCode    ierr;
683:   PetscInt          i,j,m = A->rmap->n,color;
684:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
685:   PetscViewer       viewer;
686:   PetscViewerFormat format;

689:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
690:   PetscViewerGetFormat(viewer,&format);

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

695:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
696:     /* Blue for negative, Cyan for zero and  Red for positive */
697:     color = PETSC_DRAW_BLUE;
698:     for (i=0; i<m; i++) {
699:       y_l = m - i - 1.0; y_r = y_l + 1.0;
700:       for (j=a->i[i]; j<a->i[i+1]; j++) {
701:         x_l = a->j[j] ; x_r = x_l + 1.0;
702: #if defined(PETSC_USE_COMPLEX)
703:         if (PetscRealPart(a->a[j]) >=  0.) continue;
704: #else
705:         if (a->a[j] >=  0.) continue;
706: #endif
707:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
708:       }
709:     }
710:     color = PETSC_DRAW_CYAN;
711:     for (i=0; i<m; i++) {
712:       y_l = m - i - 1.0; y_r = y_l + 1.0;
713:       for (j=a->i[i]; j<a->i[i+1]; j++) {
714:         x_l = a->j[j]; x_r = x_l + 1.0;
715:         if (a->a[j] !=  0.) continue;
716:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
717:       }
718:     }
719:     color = PETSC_DRAW_RED;
720:     for (i=0; i<m; i++) {
721:       y_l = m - i - 1.0; y_r = y_l + 1.0;
722:       for (j=a->i[i]; j<a->i[i+1]; j++) {
723:         x_l = a->j[j]; x_r = x_l + 1.0;
724: #if defined(PETSC_USE_COMPLEX)
725:         if (PetscRealPart(a->a[j]) <=  0.) continue;
726: #else
727:         if (a->a[j] <=  0.) continue;
728: #endif
729:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
730:       }
731:     }
732:   } else {
733:     /* use contour shading to indicate magnitude of values */
734:     /* first determine max of all nonzero values */
735:     PetscInt    nz = a->nz,count;
736:     PetscDraw   popup;
737:     PetscReal scale;

739:     for (i=0; i<nz; i++) {
740:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
741:     }
742:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
743:     PetscDrawGetPopup(draw,&popup);
744:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
745:     count = 0;
746:     for (i=0; i<m; i++) {
747:       y_l = m - i - 1.0; y_r = y_l + 1.0;
748:       for (j=a->i[i]; j<a->i[i+1]; j++) {
749:         x_l = a->j[j]; x_r = x_l + 1.0;
750:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
751:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
752:         count++;
753:       }
754:     }
755:   }
756:   return(0);
757: }

761: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
762: {
764:   PetscDraw      draw;
765:   PetscReal      xr,yr,xl,yl,h,w;
766:   PetscBool      isnull;

769:   PetscViewerDrawGetDraw(viewer,0,&draw);
770:   PetscDrawIsNull(draw,&isnull);
771:   if (isnull) return(0);

773:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
774:   xr  = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
775:   xr += w;    yr += h;  xl = -w;     yl = -h;
776:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
777:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
778:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
779:   return(0);
780: }

784: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
785: {
787:   PetscBool      iascii,isbinary,isdraw;

790:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
791:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
792:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
793:   if (iascii) {
794:     MatView_SeqAIJ_ASCII(A,viewer);
795:   } else if (isbinary) {
796:     MatView_SeqAIJ_Binary(A,viewer);
797:   } else if (isdraw) {
798:     MatView_SeqAIJ_Draw(A,viewer);
799:   } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
800:   MatView_SeqAIJ_Inode(A,viewer);
801:   return(0);
802: }

806: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
807: {
808:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
810:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
811:   PetscInt       m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
812:   MatScalar      *aa = a->a,*ap;
813:   PetscReal      ratio=0.6;

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

818:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
819:   for (i=1; i<m; i++) {
820:     /* move each row back by the amount of empty slots (fshift) before it*/
821:     fshift += imax[i-1] - ailen[i-1];
822:     rmax   = PetscMax(rmax,ailen[i]);
823:     if (fshift) {
824:       ip = aj + ai[i] ;
825:       ap = aa + ai[i] ;
826:       N  = ailen[i];
827:       for (j=0; j<N; j++) {
828:         ip[j-fshift] = ip[j];
829:         ap[j-fshift] = ap[j];
830:       }
831:     }
832:     ai[i] = ai[i-1] + ailen[i-1];
833:   }
834:   if (m) {
835:     fshift += imax[m-1] - ailen[m-1];
836:     ai[m]  = ai[m-1] + ailen[m-1];
837:   }
838:   /* reset ilen and imax for each row */
839:   for (i=0; i<m; i++) {
840:     ailen[i] = imax[i] = ai[i+1] - ai[i];
841:   }
842:   a->nz = ai[m];
843:   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);

845:   MatMarkDiagonal_SeqAIJ(A);
846:   PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
847:   PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
848:   PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);
849:   A->info.mallocs     += a->reallocs;
850:   a->reallocs          = 0;
851:   A->info.nz_unneeded  = (double)fshift;
852:   a->rmax              = rmax;

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

857:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);

859:   a->idiagvalid  = PETSC_FALSE;
860:   a->ibdiagvalid = PETSC_FALSE;
861:   return(0);
862: }

866: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
867: {
868:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
869:   PetscInt       i,nz = a->nz;
870:   MatScalar      *aa = a->a;

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

881: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
882: {
883:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
884:   PetscInt       i,nz = a->nz;
885:   MatScalar      *aa = a->a;

888:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
889:   a->idiagvalid  = PETSC_FALSE;
890:   a->ibdiagvalid = PETSC_FALSE;
891:   return(0);
892: }

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

902:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
903:   a->idiagvalid  = PETSC_FALSE;
904:   a->ibdiagvalid = PETSC_FALSE;
905:   return(0);
906: }

910: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
911: {
912:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

916: #if defined(PETSC_USE_LOG)
917:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
918: #endif
919:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
920:   ISDestroy(&a->row);
921:   ISDestroy(&a->col);
922:   PetscFree(a->diag);
923:   PetscFree(a->ibdiag);
924:   PetscFree2(a->imax,a->ilen);
925:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
926:   PetscFree(a->solve_work);
927:   ISDestroy(&a->icol);
928:   PetscFree(a->saved_values);
929:   ISColoringDestroy(&a->coloring);
930:   PetscFree(a->xtoy);
931:   MatDestroy(&a->XtoY);
932:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
933:   PetscFree(a->matmult_abdense);

935:   MatDestroy_SeqAIJ_Inode(A);
936:   PetscFree(A->data);

938:   PetscObjectChangeTypeName((PetscObject)A,0);
939:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
940:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
941:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
942:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
943:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
944:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqaijperm_C","",PETSC_NULL);
945:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
946:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
947:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C","",PETSC_NULL);
948:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
949:   return(0);
950: }

954: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool  flg)
955: {
956:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

960:   switch (op) {
961:     case MAT_ROW_ORIENTED:
962:       a->roworiented       = flg;
963:       break;
964:     case MAT_KEEP_NONZERO_PATTERN:
965:       a->keepnonzeropattern    = flg;
966:       break;
967:     case MAT_NEW_NONZERO_LOCATIONS:
968:       a->nonew             = (flg ? 0 : 1);
969:       break;
970:     case MAT_NEW_NONZERO_LOCATION_ERR:
971:       a->nonew             = (flg ? -1 : 0);
972:       break;
973:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
974:       a->nonew             = (flg ? -2 : 0);
975:       break;
976:     case MAT_UNUSED_NONZERO_LOCATION_ERR:
977:       a->nounused          = (flg ? -1 : 0);
978:       break;
979:     case MAT_IGNORE_ZERO_ENTRIES:
980:       a->ignorezeroentries = flg;
981:       break;
982:     case MAT_CHECK_COMPRESSED_ROW:
983:       a->compressedrow.check = flg;
984:       break;
985:     case MAT_SPD:
986:       A->spd_set                         = PETSC_TRUE;
987:       A->spd                             = flg;
988:       if (flg) {
989:         A->symmetric                     = PETSC_TRUE;
990:         A->structurally_symmetric        = PETSC_TRUE;
991:         A->symmetric_set                 = PETSC_TRUE;
992:         A->structurally_symmetric_set    = PETSC_TRUE;
993:       }
994:       break;
995:     case MAT_SYMMETRIC:
996:     case MAT_STRUCTURALLY_SYMMETRIC:
997:     case MAT_HERMITIAN:
998:     case MAT_SYMMETRY_ETERNAL:
999:     case MAT_NEW_DIAGONALS:
1000:     case MAT_IGNORE_OFF_PROC_ENTRIES:
1001:     case MAT_USE_HASH_TABLE:
1002:       PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1003:       break;
1004:     case MAT_USE_INODES:
1005:       /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1006:       break;
1007:     default:
1008:       SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1009:   }
1010:   MatSetOption_SeqAIJ_Inode(A,op,flg);
1011:   return(0);
1012: }

1016: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1017: {
1018:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1020:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
1021:   PetscScalar    *aa=a->a,*x,zero=0.0;

1024:   VecGetLocalSize(v,&n);
1025:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1027:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU){
1028:     PetscInt *diag=a->diag;
1029:     VecGetArray(v,&x);
1030:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1031:     VecRestoreArray(v,&x);
1032:     return(0);
1033:   }

1035:   VecSet(v,zero);
1036:   VecGetArray(v,&x);
1037:   for (i=0; i<n; i++) {
1038:     nz = ai[i+1] - ai[i];
1039:     if (!nz) x[i] = 0.0;
1040:     for (j=ai[i]; j<ai[i+1]; j++){
1041:       if (aj[j] == i) {
1042:         x[i] = aa[j];
1043:         break;
1044:       }
1045:     }
1046:   }
1047:   VecRestoreArray(v,&x);
1048:   return(0);
1049: }

1051: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1054: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1055: {
1056:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1057:   PetscScalar       *x,*y;
1058:   PetscErrorCode    ierr;
1059:   PetscInt          m = A->rmap->n;
1060: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1061:   MatScalar         *v;
1062:   PetscScalar       alpha;
1063:   PetscInt          n,i,j,*idx,*ii,*ridx=PETSC_NULL;
1064:   Mat_CompressedRow cprow = a->compressedrow;
1065:   PetscBool         usecprow = cprow.use;
1066: #endif

1069:   if (zz != yy) {VecCopy(zz,yy);}
1070:   VecGetArray(xx,&x);
1071:   VecGetArray(yy,&y);

1073: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1074:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1075: #else
1076:   if (usecprow){
1077:     m    = cprow.nrows;
1078:     ii   = cprow.i;
1079:     ridx = cprow.rindex;
1080:   } else {
1081:     ii = a->i;
1082:   }
1083:   for (i=0; i<m; i++) {
1084:     idx   = a->j + ii[i] ;
1085:     v     = a->a + ii[i] ;
1086:     n     = ii[i+1] - ii[i];
1087:     if (usecprow){
1088:       alpha = x[ridx[i]];
1089:     } else {
1090:       alpha = x[i];
1091:     }
1092:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1093:   }
1094: #endif
1095:   PetscLogFlops(2.0*a->nz);
1096:   VecRestoreArray(xx,&x);
1097:   VecRestoreArray(yy,&y);
1098:   return(0);
1099: }

1103: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1104: {

1108:   VecSet(yy,0.0);
1109:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1110:   return(0);
1111: }

1113: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1116: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1117: {
1118:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1119:   PetscScalar       *y;
1120:   const PetscScalar *x;
1121:   const MatScalar   *aa;
1122:   PetscErrorCode    ierr;
1123:   PetscInt          m=A->rmap->n;
1124:   const PetscInt    *aj,*ii,*ridx=PETSC_NULL;
1125:   PetscInt          n,i,nonzerorow=0;
1126:   PetscScalar       sum;
1127:   PetscBool         usecprow=a->compressedrow.use;

1129: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1130: #pragma disjoint(*x,*y,*aa)
1131: #endif

1134:   VecGetArrayRead(xx,&x);
1135:   VecGetArray(yy,&y);
1136:   aj  = a->j;
1137:   aa  = a->a;
1138:   ii  = a->i;
1139:   if (usecprow){ /* use compressed row format */
1140:     m    = a->compressedrow.nrows;
1141:     ii   = a->compressedrow.i;
1142:     ridx = a->compressedrow.rindex;
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:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1151:       y[*ridx++] = sum;
1152:     }
1153:   } else { /* do not use compressed row format */
1154: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1155:     fortranmultaij_(&m,x,ii,aj,aa,y);
1156: #else
1157:     for (i=0; i<m; i++) {
1158:       n   = ii[i+1] - ii[i];
1159:       aj  = a->j + ii[i];
1160:       aa  = a->a + ii[i];
1161:       sum  = 0.0;
1162:       nonzerorow += (n>0);
1163:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1164:       y[i] = sum;
1165:     }
1166: #endif
1167:   }
1168:   PetscLogFlops(2.0*a->nz - nonzerorow);
1169:   VecRestoreArrayRead(xx,&x);
1170:   VecRestoreArray(yy,&y);
1171:   return(0);
1172: }

1174: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1177: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1178: {
1179:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1180:   PetscScalar       *y,*z;
1181:   const PetscScalar *x;
1182:   const MatScalar   *aa;
1183:   PetscErrorCode    ierr;
1184:   PetscInt          m = A->rmap->n,*aj,*ii;
1185:   PetscInt          n,i,*ridx=PETSC_NULL;
1186:   PetscScalar       sum;
1187:   PetscBool         usecprow=a->compressedrow.use;

1190:   VecGetArrayRead(xx,&x);
1191:   VecGetArray(yy,&y);
1192:   if (zz != yy) {
1193:     VecGetArray(zz,&z);
1194:   } else {
1195:     z = y;
1196:   }

1198:   aj  = a->j;
1199:   aa  = a->a;
1200:   ii  = a->i;
1201:   if (usecprow){ /* use compressed row format */
1202:     if (zz != yy){
1203:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1204:     }
1205:     m    = a->compressedrow.nrows;
1206:     ii   = a->compressedrow.i;
1207:     ridx = a->compressedrow.rindex;
1208:     for (i=0; i<m; i++){
1209:       n  = ii[i+1] - ii[i];
1210:       aj  = a->j + ii[i];
1211:       aa  = a->a + ii[i];
1212:       sum = y[*ridx];
1213:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1214:       z[*ridx++] = sum;
1215:     }
1216:   } else { /* do not use compressed row format */
1217: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1218:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1219: #else
1220:     for (i=0; i<m; i++) {
1221:       n    = ii[i+1] - ii[i];
1222:       aj  = a->j + ii[i];
1223:       aa  = a->a + ii[i];
1224:       sum  = y[i];
1225:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1226:       z[i] = sum;
1227:     }
1228: #endif
1229:   }
1230:   PetscLogFlops(2.0*a->nz);
1231:   VecRestoreArrayRead(xx,&x);
1232:   VecRestoreArray(yy,&y);
1233:   if (zz != yy) {
1234:     VecRestoreArray(zz,&z);
1235:   }
1236: #if defined(PETSC_HAVE_CUSP)
1237:   /*
1238:   VecView(xx,0);
1239:   VecView(zz,0);
1240:   MatView(A,0);
1241:   */
1242: #endif
1243:   return(0);
1244: }

1246: /*
1247:      Adds diagonal pointers to sparse matrix structure.
1248: */
1251: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1252: {
1253:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1255:   PetscInt       i,j,m = A->rmap->n;

1258:   if (!a->diag) {
1259:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
1260:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
1261:   }
1262:   for (i=0; i<A->rmap->n; i++) {
1263:     a->diag[i] = a->i[i+1];
1264:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1265:       if (a->j[j] == i) {
1266:         a->diag[i] = j;
1267:         break;
1268:       }
1269:     }
1270:   }
1271:   return(0);
1272: }

1274: /*
1275:      Checks for missing diagonals
1276: */
1279: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1280: {
1281:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1282:   PetscInt       *diag,*jj = a->j,i;

1285:   *missing = PETSC_FALSE;
1286:   if (A->rmap->n > 0 && !jj) {
1287:     *missing  = PETSC_TRUE;
1288:     if (d) *d = 0;
1289:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal");
1290:   } else {
1291:     diag = a->diag;
1292:     for (i=0; i<A->rmap->n; i++) {
1293:       if (jj[diag[i]] != i) {
1294:         *missing = PETSC_TRUE;
1295:         if (d) *d = i;
1296:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1297:         break;
1298:       }
1299:     }
1300:   }
1301:   return(0);
1302: }

1304: EXTERN_C_BEGIN
1307: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1308: {
1309:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1311:   PetscInt       i,*diag,m = A->rmap->n;
1312:   MatScalar      *v = a->a;
1313:   PetscScalar    *idiag,*mdiag;

1316:   if (a->idiagvalid) return(0);
1317:   MatMarkDiagonal_SeqAIJ(A);
1318:   diag = a->diag;
1319:   if (!a->idiag) {
1320:     PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
1321:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1322:     v        = a->a;
1323:   }
1324:   mdiag = a->mdiag;
1325:   idiag = a->idiag;
1326: 
1327:   if (omega == 1.0 && !PetscAbsScalar(fshift)) {
1328:     for (i=0; i<m; i++) {
1329:       mdiag[i] = v[diag[i]];
1330:       if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1331:       idiag[i] = 1.0/v[diag[i]];
1332:     }
1333:     PetscLogFlops(m);
1334:   } else {
1335:     for (i=0; i<m; i++) {
1336:       mdiag[i] = v[diag[i]];
1337:       idiag[i] = omega/(fshift + v[diag[i]]);
1338:     }
1339:     PetscLogFlops(2.0*m);
1340:   }
1341:   a->idiagvalid = PETSC_TRUE;
1342:   return(0);
1343: }
1344: EXTERN_C_END

1346: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1349: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1350: {
1351:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
1352:   PetscScalar        *x,d,sum,*t,scale;
1353:   const MatScalar    *v = a->a,*idiag=0,*mdiag;
1354:   const PetscScalar  *b, *bs,*xb, *ts;
1355:   PetscErrorCode     ierr;
1356:   PetscInt           n = A->cmap->n,m = A->rmap->n,i;
1357:   const PetscInt     *idx,*diag;

1360:   its = its*lits;

1362:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1363:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1364:   a->fshift = fshift;
1365:   a->omega  = omega;

1367:   diag = a->diag;
1368:   t     = a->ssor_work;
1369:   idiag = a->idiag;
1370:   mdiag = a->mdiag;

1372:   VecGetArray(xx,&x);
1373:   VecGetArrayRead(bb,&b);
1374:   CHKMEMQ;
1375:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1376:   if (flag == SOR_APPLY_UPPER) {
1377:    /* apply (U + D/omega) to the vector */
1378:     bs = b;
1379:     for (i=0; i<m; i++) {
1380:         d    = fshift + mdiag[i];
1381:         n    = a->i[i+1] - diag[i] - 1;
1382:         idx  = a->j + diag[i] + 1;
1383:         v    = a->a + diag[i] + 1;
1384:         sum  = b[i]*d/omega;
1385:         PetscSparseDensePlusDot(sum,bs,v,idx,n);
1386:         x[i] = sum;
1387:     }
1388:     VecRestoreArray(xx,&x);
1389:     VecRestoreArrayRead(bb,&b);
1390:     PetscLogFlops(a->nz);
1391:     return(0);
1392:   }

1394:   if (flag == SOR_APPLY_LOWER) {
1395:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1396:   } else if (flag & SOR_EISENSTAT) {
1397:     /* Let  A = L + U + D; where L is lower trianglar,
1398:     U is upper triangular, E = D/omega; This routine applies

1400:             (L + E)^{-1} A (U + E)^{-1}

1402:     to a vector efficiently using Eisenstat's trick. 
1403:     */
1404:     scale = (2.0/omega) - 1.0;

1406:     /*  x = (E + U)^{-1} b */
1407:     for (i=m-1; i>=0; i--) {
1408:       n    = a->i[i+1] - diag[i] - 1;
1409:       idx  = a->j + diag[i] + 1;
1410:       v    = a->a + diag[i] + 1;
1411:       sum  = b[i];
1412:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1413:       x[i] = sum*idiag[i];
1414:     }

1416:     /*  t = b - (2*E - D)x */
1417:     v = a->a;
1418:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1420:     /*  t = (E + L)^{-1}t */
1421:     ts = t;
1422:     diag = a->diag;
1423:     for (i=0; i<m; i++) {
1424:       n    = diag[i] - a->i[i];
1425:       idx  = a->j + a->i[i];
1426:       v    = a->a + a->i[i];
1427:       sum  = t[i];
1428:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1429:       t[i] = sum*idiag[i];
1430:       /*  x = x + t */
1431:       x[i] += t[i];
1432:     }

1434:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1435:     VecRestoreArray(xx,&x);
1436:     VecRestoreArrayRead(bb,&b);
1437:     return(0);
1438:   }
1439:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1440:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1441:       for (i=0; i<m; i++) {
1442:         n    = diag[i] - a->i[i];
1443:         idx  = a->j + a->i[i];
1444:         v    = a->a + a->i[i];
1445:         sum  = b[i];
1446:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1447:         t[i] = sum;
1448:         x[i] = sum*idiag[i];
1449:       }
1450:       xb = t;
1451:       PetscLogFlops(a->nz);
1452:     } else xb = b;
1453:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1454:       for (i=m-1; i>=0; i--) {
1455:         n    = a->i[i+1] - diag[i] - 1;
1456:         idx  = a->j + diag[i] + 1;
1457:         v    = a->a + diag[i] + 1;
1458:         sum  = xb[i];
1459:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1460:         if (xb == b) {
1461:           x[i] = sum*idiag[i];
1462:         } else {
1463:           x[i] = (1-omega)*x[i] + sum*idiag[i];
1464:         }
1465:       }
1466:       PetscLogFlops(a->nz);
1467:     }
1468:     its--;
1469:   }
1470:   while (its--) {
1471:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1472:       for (i=0; i<m; i++) {
1473:         n    = a->i[i+1] - a->i[i];
1474:         idx  = a->j + a->i[i];
1475:         v    = a->a + a->i[i];
1476:         sum  = b[i];
1477:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1478:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1479:       }
1480:       PetscLogFlops(2.0*a->nz);
1481:     }
1482:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1483:       for (i=m-1; i>=0; i--) {
1484:         n    = a->i[i+1] - a->i[i];
1485:         idx  = a->j + a->i[i];
1486:         v    = a->a + a->i[i];
1487:         sum  = b[i];
1488:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1489:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1490:       }
1491:       PetscLogFlops(2.0*a->nz);
1492:     }
1493:   }
1494:   VecRestoreArray(xx,&x);
1495:   VecRestoreArrayRead(bb,&b);
1496:   CHKMEMQ;  return(0);
1497: }


1502: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1503: {
1504:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1507:   info->block_size     = 1.0;
1508:   info->nz_allocated   = (double)a->maxnz;
1509:   info->nz_used        = (double)a->nz;
1510:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1511:   info->assemblies     = (double)A->num_ass;
1512:   info->mallocs        = (double)A->info.mallocs;
1513:   info->memory         = ((PetscObject)A)->mem;
1514:   if (A->factortype) {
1515:     info->fill_ratio_given  = A->info.fill_ratio_given;
1516:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1517:     info->factor_mallocs    = A->info.factor_mallocs;
1518:   } else {
1519:     info->fill_ratio_given  = 0;
1520:     info->fill_ratio_needed = 0;
1521:     info->factor_mallocs    = 0;
1522:   }
1523:   return(0);
1524: }

1528: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1529: {
1530:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1531:   PetscInt          i,m = A->rmap->n - 1,d = 0;
1532:   PetscErrorCode    ierr;
1533:   const PetscScalar *xx;
1534:   PetscScalar       *bb;
1535:   PetscBool         missing;

1538:   if (x && b) {
1539:     VecGetArrayRead(x,&xx);
1540:     VecGetArray(b,&bb);
1541:     for (i=0; i<N; i++) {
1542:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1543:       bb[rows[i]] = diag*xx[rows[i]];
1544:     }
1545:     VecRestoreArrayRead(x,&xx);
1546:     VecRestoreArray(b,&bb);
1547:   }

1549:   if (a->keepnonzeropattern) {
1550:     for (i=0; i<N; i++) {
1551:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1552:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1553:     }
1554:     if (diag != 0.0) {
1555:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1556:       if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1557:       for (i=0; i<N; i++) {
1558:         a->a[a->diag[rows[i]]] = diag;
1559:       }
1560:     }
1561:     A->same_nonzero = PETSC_TRUE;
1562:   } else {
1563:     if (diag != 0.0) {
1564:       for (i=0; i<N; i++) {
1565:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1566:         if (a->ilen[rows[i]] > 0) {
1567:           a->ilen[rows[i]]          = 1;
1568:           a->a[a->i[rows[i]]] = diag;
1569:           a->j[a->i[rows[i]]] = rows[i];
1570:         } else { /* in case row was completely empty */
1571:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1572:         }
1573:       }
1574:     } else {
1575:       for (i=0; i<N; i++) {
1576:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1577:         a->ilen[rows[i]] = 0;
1578:       }
1579:     }
1580:     A->same_nonzero = PETSC_FALSE;
1581:   }
1582:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1583:   return(0);
1584: }

1588: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1589: {
1590:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1591:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
1592:   PetscErrorCode    ierr;
1593:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
1594:   const PetscScalar *xx;
1595:   PetscScalar       *bb;

1598:   if (x && b) {
1599:     VecGetArrayRead(x,&xx);
1600:     VecGetArray(b,&bb);
1601:     vecs = PETSC_TRUE;
1602:   }
1603:   PetscMalloc(A->rmap->n*sizeof(PetscBool),&zeroed);
1604:   PetscMemzero(zeroed,A->rmap->n*sizeof(PetscBool));
1605:   for (i=0; i<N; i++) {
1606:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1607:     PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1608:     zeroed[rows[i]] = PETSC_TRUE;
1609:   }
1610:   for (i=0; i<A->rmap->n; i++) {
1611:     if (!zeroed[i]) {
1612:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1613:         if (zeroed[a->j[j]]) {
1614:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1615:           a->a[j] = 0.0;
1616:         }
1617:       }
1618:     } else if (vecs) bb[i] = diag*xx[i];
1619:   }
1620:   if (x && b) {
1621:     VecRestoreArrayRead(x,&xx);
1622:     VecRestoreArray(b,&bb);
1623:   }
1624:   PetscFree(zeroed);
1625:   if (diag != 0.0) {
1626:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1627:     if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1628:     for (i=0; i<N; i++) {
1629:       a->a[a->diag[rows[i]]] = diag;
1630:     }
1631:   }
1632:   A->same_nonzero = PETSC_TRUE;
1633:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1634:   return(0);
1635: }

1639: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1640: {
1641:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1642:   PetscInt   *itmp;

1645:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1647:   *nz = a->i[row+1] - a->i[row];
1648:   if (v) *v = a->a + a->i[row];
1649:   if (idx) {
1650:     itmp = a->j + a->i[row];
1651:     if (*nz) {
1652:       *idx = itmp;
1653:     }
1654:     else *idx = 0;
1655:   }
1656:   return(0);
1657: }

1659: /* remove this function? */
1662: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1663: {
1665:   return(0);
1666: }

1670: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1671: {
1672:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1673:   MatScalar      *v = a->a;
1674:   PetscReal      sum = 0.0;
1676:   PetscInt       i,j;

1679:   if (type == NORM_FROBENIUS) {
1680:     for (i=0; i<a->nz; i++) {
1681: #if defined(PETSC_USE_COMPLEX)
1682:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1683: #else
1684:       sum += (*v)*(*v); v++;
1685: #endif
1686:     }
1687:     *nrm = PetscSqrtReal(sum);
1688:   } else if (type == NORM_1) {
1689:     PetscReal *tmp;
1690:     PetscInt    *jj = a->j;
1691:     PetscMalloc((A->cmap->n+1)*sizeof(PetscReal),&tmp);
1692:     PetscMemzero(tmp,A->cmap->n*sizeof(PetscReal));
1693:     *nrm = 0.0;
1694:     for (j=0; j<a->nz; j++) {
1695:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
1696:     }
1697:     for (j=0; j<A->cmap->n; j++) {
1698:       if (tmp[j] > *nrm) *nrm = tmp[j];
1699:     }
1700:     PetscFree(tmp);
1701:   } else if (type == NORM_INFINITY) {
1702:     *nrm = 0.0;
1703:     for (j=0; j<A->rmap->n; j++) {
1704:       v = a->a + a->i[j];
1705:       sum = 0.0;
1706:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1707:         sum += PetscAbsScalar(*v); v++;
1708:       }
1709:       if (sum > *nrm) *nrm = sum;
1710:     }
1711:   } else {
1712:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1713:   }
1714:   return(0);
1715: }

1717: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
1720: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
1721: {
1723:   PetscInt       i,j,anzj;
1724:   Mat_SeqAIJ     *a=(Mat_SeqAIJ *)A->data,*b;
1725:   PetscInt       an=A->cmap->N,am=A->rmap->N;
1726:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

1729:   /* Allocate space for symbolic transpose info and work array */
1730:   PetscMalloc((an+1)*sizeof(PetscInt),&ati);
1731:   PetscMalloc(ai[am]*sizeof(PetscInt),&atj);
1732:   PetscMalloc(an*sizeof(PetscInt),&atfill);
1733:   PetscMemzero(ati,(an+1)*sizeof(PetscInt));

1735:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
1736:   /* Note: offset by 1 for fast conversion into csr format. */
1737:   for (i=0;i<ai[am];i++) {
1738:     ati[aj[i]+1] += 1;
1739:   }
1740:   /* Form ati for csr format of A^T. */
1741:   for (i=0;i<an;i++) {
1742:     ati[i+1] += ati[i];
1743:   }

1745:   /* Copy ati into atfill so we have locations of the next free space in atj */
1746:   PetscMemcpy(atfill,ati,an*sizeof(PetscInt));

1748:   /* Walk through A row-wise and mark nonzero entries of A^T. */
1749:   for (i=0;i<am;i++) {
1750:     anzj = ai[i+1] - ai[i];
1751:     for (j=0;j<anzj;j++) {
1752:       atj[atfill[*aj]] = i;
1753:       atfill[*aj++]   += 1;
1754:     }
1755:   }

1757:   /* Clean up temporary space and complete requests. */
1758:   PetscFree(atfill);
1759:   MatCreateSeqAIJWithArrays(((PetscObject)A)->comm,an,am,ati,atj,PETSC_NULL,B);
1760:   (*B)->rmap->bs = A->cmap->bs;
1761:   (*B)->cmap->bs = A->rmap->bs;

1763:   b = (Mat_SeqAIJ *)((*B)->data);
1764:   b->free_a   = PETSC_FALSE;
1765:   b->free_ij  = PETSC_TRUE;
1766:   b->nonew    = 0;
1767:   return(0);
1768: }

1772: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
1773: {
1774:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1775:   Mat            C;
1777:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
1778:   MatScalar      *array = a->a;

1781:   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");

1783:   if (reuse == MAT_INITIAL_MATRIX || *B == A) {
1784:     PetscMalloc((1+A->cmap->n)*sizeof(PetscInt),&col);
1785:     PetscMemzero(col,(1+A->cmap->n)*sizeof(PetscInt));
1786: 
1787:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1788:     MatCreate(((PetscObject)A)->comm,&C);
1789:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
1790:     MatSetBlockSizes(C,A->cmap->bs,A->rmap->bs);
1791:     MatSetType(C,((PetscObject)A)->type_name);
1792:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1793:     PetscFree(col);
1794:   } else {
1795:     C = *B;
1796:   }

1798:   for (i=0; i<m; i++) {
1799:     len    = ai[i+1]-ai[i];
1800:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1801:     array += len;
1802:     aj    += len;
1803:   }
1804:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1805:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1807:   if (reuse == MAT_INITIAL_MATRIX || *B != A) {
1808:     *B = C;
1809:   } else {
1810:     MatHeaderMerge(A,C);
1811:   }
1812:   return(0);
1813: }

1815: EXTERN_C_BEGIN
1818: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
1819: {
1820:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1821:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1822:   MatScalar      *va,*vb;
1824:   PetscInt       ma,na,mb,nb, i;

1827:   bij = (Mat_SeqAIJ *) B->data;

1829:   MatGetSize(A,&ma,&na);
1830:   MatGetSize(B,&mb,&nb);
1831:   if (ma!=nb || na!=mb){
1832:     *f = PETSC_FALSE;
1833:     return(0);
1834:   }
1835:   aii = aij->i; bii = bij->i;
1836:   adx = aij->j; bdx = bij->j;
1837:   va  = aij->a; vb = bij->a;
1838:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1839:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1840:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1841:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1843:   *f = PETSC_TRUE;
1844:   for (i=0; i<ma; i++) {
1845:     while (aptr[i]<aii[i+1]) {
1846:       PetscInt         idc,idr;
1847:       PetscScalar vc,vr;
1848:       /* column/row index/value */
1849:       idc = adx[aptr[i]];
1850:       idr = bdx[bptr[idc]];
1851:       vc  = va[aptr[i]];
1852:       vr  = vb[bptr[idc]];
1853:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1854:         *f = PETSC_FALSE;
1855:         goto done;
1856:       } else {
1857:         aptr[i]++;
1858:         if (B || i!=idc) bptr[idc]++;
1859:       }
1860:     }
1861:   }
1862:  done:
1863:   PetscFree(aptr);
1864:   PetscFree(bptr);
1865:   return(0);
1866: }
1867: EXTERN_C_END

1869: EXTERN_C_BEGIN
1872: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
1873: {
1874:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1875:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1876:   MatScalar      *va,*vb;
1878:   PetscInt       ma,na,mb,nb, i;

1881:   bij = (Mat_SeqAIJ *) B->data;

1883:   MatGetSize(A,&ma,&na);
1884:   MatGetSize(B,&mb,&nb);
1885:   if (ma!=nb || na!=mb){
1886:     *f = PETSC_FALSE;
1887:     return(0);
1888:   }
1889:   aii = aij->i; bii = bij->i;
1890:   adx = aij->j; bdx = bij->j;
1891:   va  = aij->a; vb = bij->a;
1892:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1893:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1894:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1895:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1897:   *f = PETSC_TRUE;
1898:   for (i=0; i<ma; i++) {
1899:     while (aptr[i]<aii[i+1]) {
1900:       PetscInt         idc,idr;
1901:       PetscScalar vc,vr;
1902:       /* column/row index/value */
1903:       idc = adx[aptr[i]];
1904:       idr = bdx[bptr[idc]];
1905:       vc  = va[aptr[i]];
1906:       vr  = vb[bptr[idc]];
1907:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
1908:         *f = PETSC_FALSE;
1909:         goto done;
1910:       } else {
1911:         aptr[i]++;
1912:         if (B || i!=idc) bptr[idc]++;
1913:       }
1914:     }
1915:   }
1916:  done:
1917:   PetscFree(aptr);
1918:   PetscFree(bptr);
1919:   return(0);
1920: }
1921: EXTERN_C_END

1925: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
1926: {
1929:   MatIsTranspose_SeqAIJ(A,A,tol,f);
1930:   return(0);
1931: }

1935: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
1936: {
1939:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
1940:   return(0);
1941: }

1945: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1946: {
1947:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1948:   PetscScalar    *l,*r,x;
1949:   MatScalar      *v;
1951:   PetscInt       i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj;

1954:   if (ll) {
1955:     /* The local size is used so that VecMPI can be passed to this routine
1956:        by MatDiagonalScale_MPIAIJ */
1957:     VecGetLocalSize(ll,&m);
1958:     if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1959:     VecGetArray(ll,&l);
1960:     v = a->a;
1961:     for (i=0; i<m; i++) {
1962:       x = l[i];
1963:       M = a->i[i+1] - a->i[i];
1964:       for (j=0; j<M; j++) { (*v++) *= x;}
1965:     }
1966:     VecRestoreArray(ll,&l);
1967:     PetscLogFlops(nz);
1968:   }
1969:   if (rr) {
1970:     VecGetLocalSize(rr,&n);
1971:     if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1972:     VecGetArray(rr,&r);
1973:     v = a->a; jj = a->j;
1974:     for (i=0; i<nz; i++) {
1975:       (*v++) *= r[*jj++];
1976:     }
1977:     VecRestoreArray(rr,&r);
1978:     PetscLogFlops(nz);
1979:   }
1980:   a->idiagvalid  = PETSC_FALSE;
1981:   a->ibdiagvalid = PETSC_FALSE;
1982:   return(0);
1983: }

1987: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
1988: {
1989:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
1991:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
1992:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1993:   const PetscInt *irow,*icol;
1994:   PetscInt       nrows,ncols;
1995:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1996:   MatScalar      *a_new,*mat_a;
1997:   Mat            C;
1998:   PetscBool      stride,sorted;

2001:   ISSorted(isrow,&sorted);
2002:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
2003:   ISSorted(iscol,&sorted);
2004:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

2006:   ISGetIndices(isrow,&irow);
2007:   ISGetLocalSize(isrow,&nrows);
2008:   ISGetLocalSize(iscol,&ncols);

2010:   ISStrideGetInfo(iscol,&first,&step);
2011:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2012:   if (stride && step == 1) {
2013:     /* special case of contiguous rows */
2014:     PetscMalloc2(nrows,PetscInt,&lens,nrows,PetscInt,&starts);
2015:     /* loop over new rows determining lens and starting points */
2016:     for (i=0; i<nrows; i++) {
2017:       kstart  = ai[irow[i]];
2018:       kend    = kstart + ailen[irow[i]];
2019:       for (k=kstart; k<kend; k++) {
2020:         if (aj[k] >= first) {
2021:           starts[i] = k;
2022:           break;
2023:         }
2024:       }
2025:       sum = 0;
2026:       while (k < kend) {
2027:         if (aj[k++] >= first+ncols) break;
2028:         sum++;
2029:       }
2030:       lens[i] = sum;
2031:     }
2032:     /* create submatrix */
2033:     if (scall == MAT_REUSE_MATRIX) {
2034:       PetscInt n_cols,n_rows;
2035:       MatGetSize(*B,&n_rows,&n_cols);
2036:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2037:       MatZeroEntries(*B);
2038:       C = *B;
2039:     } else {
2040:       PetscInt rbs,cbs;
2041:       MatCreate(((PetscObject)A)->comm,&C);
2042:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2043:       ISGetBlockSize(isrow,&rbs);
2044:       ISGetBlockSize(iscol,&cbs);
2045:       MatSetBlockSizes(C,rbs,cbs);
2046:       MatSetType(C,((PetscObject)A)->type_name);
2047:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2048:     }
2049:     c = (Mat_SeqAIJ*)C->data;

2051:     /* loop over rows inserting into submatrix */
2052:     a_new    = c->a;
2053:     j_new    = c->j;
2054:     i_new    = c->i;

2056:     for (i=0; i<nrows; i++) {
2057:       ii    = starts[i];
2058:       lensi = lens[i];
2059:       for (k=0; k<lensi; k++) {
2060:         *j_new++ = aj[ii+k] - first;
2061:       }
2062:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2063:       a_new      += lensi;
2064:       i_new[i+1]  = i_new[i] + lensi;
2065:       c->ilen[i]  = lensi;
2066:     }
2067:     PetscFree2(lens,starts);
2068:   } else {
2069:     ISGetIndices(iscol,&icol);
2070:     PetscMalloc(oldcols*sizeof(PetscInt),&smap);
2071:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
2072:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
2073:     for (i=0; i<ncols; i++) {
2074: #if defined(PETSC_USE_DEBUG)
2075:       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);
2076: #endif
2077:       smap[icol[i]] = i+1;
2078:     }

2080:     /* determine lens of each row */
2081:     for (i=0; i<nrows; i++) {
2082:       kstart  = ai[irow[i]];
2083:       kend    = kstart + a->ilen[irow[i]];
2084:       lens[i] = 0;
2085:       for (k=kstart; k<kend; k++) {
2086:         if (smap[aj[k]]) {
2087:           lens[i]++;
2088:         }
2089:       }
2090:     }
2091:     /* Create and fill new matrix */
2092:     if (scall == MAT_REUSE_MATRIX) {
2093:       PetscBool  equal;

2095:       c = (Mat_SeqAIJ *)((*B)->data);
2096:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2097:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2098:       if (!equal) {
2099:         SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2100:       }
2101:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2102:       C = *B;
2103:     } else {
2104:       PetscInt rbs,cbs;
2105:       MatCreate(((PetscObject)A)->comm,&C);
2106:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2107:       ISGetBlockSize(isrow,&rbs);
2108:       ISGetBlockSize(iscol,&cbs);
2109:       MatSetBlockSizes(C,rbs,cbs);
2110:       MatSetType(C,((PetscObject)A)->type_name);
2111:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2112:     }
2113:     c = (Mat_SeqAIJ *)(C->data);
2114:     for (i=0; i<nrows; i++) {
2115:       row    = irow[i];
2116:       kstart = ai[row];
2117:       kend   = kstart + a->ilen[row];
2118:       mat_i  = c->i[i];
2119:       mat_j  = c->j + mat_i;
2120:       mat_a  = c->a + mat_i;
2121:       mat_ilen = c->ilen + i;
2122:       for (k=kstart; k<kend; k++) {
2123:         if ((tcol=smap[a->j[k]])) {
2124:           *mat_j++ = tcol - 1;
2125:           *mat_a++ = a->a[k];
2126:           (*mat_ilen)++;

2128:         }
2129:       }
2130:     }
2131:     /* Free work space */
2132:     ISRestoreIndices(iscol,&icol);
2133:     PetscFree(smap);
2134:     PetscFree(lens);
2135:   }
2136:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2137:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2139:   ISRestoreIndices(isrow,&irow);
2140:   *B = C;
2141:   return(0);
2142: }

2146: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat* subMat)
2147: {
2149:   Mat            B;

2152:   MatCreate(subComm,&B);
2153:   MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2154:   MatSetBlockSizes(B,mat->rmap->bs,mat->cmap->bs);
2155:   MatSetType(B,MATSEQAIJ);
2156:   MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2157:   *subMat = B;
2158:   return(0);
2159: }

2163: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2164: {
2165:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2167:   Mat            outA;
2168:   PetscBool      row_identity,col_identity;

2171:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2173:   ISIdentity(row,&row_identity);
2174:   ISIdentity(col,&col_identity);

2176:   outA              = inA;
2177:   outA->factortype  = MAT_FACTOR_LU;
2178:   PetscObjectReference((PetscObject)row);
2179:   ISDestroy(&a->row);
2180:   a->row = row;
2181:   PetscObjectReference((PetscObject)col);
2182:   ISDestroy(&a->col);
2183:   a->col = col;

2185:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2186:   ISDestroy(&a->icol);
2187:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2188:   PetscLogObjectParent(inA,a->icol);

2190:   if (!a->solve_work) { /* this matrix may have been factored before */
2191:      PetscMalloc((inA->rmap->n+1)*sizeof(PetscScalar),&a->solve_work);
2192:      PetscLogObjectMemory(inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2193:   }

2195:   MatMarkDiagonal_SeqAIJ(inA);
2196:   if (row_identity && col_identity) {
2197:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2198:   } else {
2199:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2200:   }
2201:   return(0);
2202: }

2206: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2207: {
2208:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2209:   PetscScalar    oalpha = alpha;
2211:   PetscBLASInt   one = 1,bnz = PetscBLASIntCast(a->nz);

2214:   BLASscal_(&bnz,&oalpha,a->a,&one);
2215:   PetscLogFlops(a->nz);
2216:   a->idiagvalid  = PETSC_FALSE;
2217:   a->ibdiagvalid = PETSC_FALSE;
2218:   return(0);
2219: }

2223: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2224: {
2226:   PetscInt       i;

2229:   if (scall == MAT_INITIAL_MATRIX) {
2230:     PetscMalloc((n+1)*sizeof(Mat),B);
2231:   }

2233:   for (i=0; i<n; i++) {
2234:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2235:   }
2236:   return(0);
2237: }

2241: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2242: {
2243:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2245:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2246:   const PetscInt *idx;
2247:   PetscInt       start,end,*ai,*aj;
2248:   PetscBT        table;

2251:   m     = A->rmap->n;
2252:   ai    = a->i;
2253:   aj    = a->j;

2255:   if (ov < 0)  SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2257:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
2258:   PetscBTCreate(m,&table);

2260:   for (i=0; i<is_max; i++) {
2261:     /* Initialize the two local arrays */
2262:     isz  = 0;
2263:     PetscBTMemzero(m,table);
2264: 
2265:     /* Extract the indices, assume there can be duplicate entries */
2266:     ISGetIndices(is[i],&idx);
2267:     ISGetLocalSize(is[i],&n);
2268: 
2269:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2270:     for (j=0; j<n ; ++j){
2271:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
2272:     }
2273:     ISRestoreIndices(is[i],&idx);
2274:     ISDestroy(&is[i]);
2275: 
2276:     k = 0;
2277:     for (j=0; j<ov; j++){ /* for each overlap */
2278:       n = isz;
2279:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
2280:         row   = nidx[k];
2281:         start = ai[row];
2282:         end   = ai[row+1];
2283:         for (l = start; l<end ; l++){
2284:           val = aj[l] ;
2285:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
2286:         }
2287:       }
2288:     }
2289:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2290:   }
2291:   PetscBTDestroy(&table);
2292:   PetscFree(nidx);
2293:   return(0);
2294: }

2296: /* -------------------------------------------------------------- */
2299: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2300: {
2301:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2303:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2304:   const PetscInt *row,*col;
2305:   PetscInt       *cnew,j,*lens;
2306:   IS             icolp,irowp;
2307:   PetscInt       *cwork = PETSC_NULL;
2308:   PetscScalar    *vwork = PETSC_NULL;

2311:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2312:   ISGetIndices(irowp,&row);
2313:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2314:   ISGetIndices(icolp,&col);
2315: 
2316:   /* determine lengths of permuted rows */
2317:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
2318:   for (i=0; i<m; i++) {
2319:     lens[row[i]] = a->i[i+1] - a->i[i];
2320:   }
2321:   MatCreate(((PetscObject)A)->comm,B);
2322:   MatSetSizes(*B,m,n,m,n);
2323:   MatSetBlockSizes(*B,A->rmap->bs,A->cmap->bs);
2324:   MatSetType(*B,((PetscObject)A)->type_name);
2325:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2326:   PetscFree(lens);

2328:   PetscMalloc(n*sizeof(PetscInt),&cnew);
2329:   for (i=0; i<m; i++) {
2330:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2331:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
2332:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2333:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2334:   }
2335:   PetscFree(cnew);
2336:   (*B)->assembled     = PETSC_FALSE;
2337:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2338:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2339:   ISRestoreIndices(irowp,&row);
2340:   ISRestoreIndices(icolp,&col);
2341:   ISDestroy(&irowp);
2342:   ISDestroy(&icolp);
2343:   return(0);
2344: }

2348: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2349: {

2353:   /* If the two matrices have the same copy implementation, use fast copy. */
2354:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2355:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2356:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2358:     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");
2359:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2360:   } else {
2361:     MatCopy_Basic(A,B,str);
2362:   }
2363:   return(0);
2364: }

2368: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2369: {

2373:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2374:   return(0);
2375: }

2379: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2380: {
2381:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2383:   *array = a->a;
2384:   return(0);
2385: }

2389: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2390: {
2392:   return(0);
2393: }

2397: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2398: {
2399:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
2401:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
2402:   PetscScalar    dx,*y,*xx,*w3_array;
2403:   PetscScalar    *vscale_array;
2404:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
2405:   Vec            w1,w2,w3;
2406:   void           *fctx = coloring->fctx;
2407:   PetscBool      flg = PETSC_FALSE;

2410:   if (!coloring->w1) {
2411:     VecDuplicate(x1,&coloring->w1);
2412:     PetscLogObjectParent(coloring,coloring->w1);
2413:     VecDuplicate(x1,&coloring->w2);
2414:     PetscLogObjectParent(coloring,coloring->w2);
2415:     VecDuplicate(x1,&coloring->w3);
2416:     PetscLogObjectParent(coloring,coloring->w3);
2417:   }
2418:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

2420:   MatSetUnfactored(J);
2421:   PetscOptionsGetBool(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg,PETSC_NULL);
2422:   if (flg) {
2423:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2424:   } else {
2425:     PetscBool  assembled;
2426:     MatAssembled(J,&assembled);
2427:     if (assembled) {
2428:       MatZeroEntries(J);
2429:     }
2430:   }

2432:   VecGetOwnershipRange(x1,&start,&end);
2433:   VecGetSize(x1,&N);

2435:   /*
2436:        This is a horrible, horrible, hack. 
2437:   */
2438:   if (coloring->F) {
2439:     VecGetLocalSize(coloring->F,&m1);
2440:     VecGetLocalSize(w1,&m2);
2441:     if (m1 != m2) {
2442:       coloring->F = 0;
2443:     }
2444:   }

2446:   if (coloring->F) {
2447:     w1          = coloring->F;
2448:     coloring->F = 0;
2449:   } else {
2450:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2451:     (*f)(sctx,x1,w1,fctx);
2452:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2453:   }

2455:   /* 
2456:       Compute all the scale factors and share with other processors
2457:   */
2458:   VecGetArray(x1,&xx);xx = xx - start;
2459:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2460:   for (k=0; k<coloring->ncolors; k++) {
2461:     /*
2462:        Loop over each column associated with color adding the 
2463:        perturbation to the vector w3.
2464:     */
2465:     for (l=0; l<coloring->ncolumns[k]; l++) {
2466:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2467:       dx  = xx[col];
2468:       if (dx == 0.0) dx = 1.0;
2469: #if !defined(PETSC_USE_COMPLEX)
2470:       if (dx < umin && dx >= 0.0)      dx = umin;
2471:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2472: #else
2473:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2474:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2475: #endif
2476:       dx                *= epsilon;
2477:       vscale_array[col] = 1.0/dx;
2478:     }
2479:   }
2480:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2481:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2482:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2484:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2485:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2487:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2488:   else                        vscaleforrow = coloring->columnsforrow;

2490:   VecGetArray(coloring->vscale,&vscale_array);
2491:   /*
2492:       Loop over each color
2493:   */
2494:   for (k=0; k<coloring->ncolors; k++) {
2495:     coloring->currentcolor = k;
2496:     VecCopy(x1,w3);
2497:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2498:     /*
2499:        Loop over each column associated with color adding the 
2500:        perturbation to the vector w3.
2501:     */
2502:     for (l=0; l<coloring->ncolumns[k]; l++) {
2503:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2504:       dx  = xx[col];
2505:       if (dx == 0.0) dx = 1.0;
2506: #if !defined(PETSC_USE_COMPLEX)
2507:       if (dx < umin && dx >= 0.0)      dx = umin;
2508:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2509: #else
2510:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2511:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2512: #endif
2513:       dx            *= epsilon;
2514:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2515:       w3_array[col] += dx;
2516:     }
2517:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2519:     /*
2520:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2521:     */

2523:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2524:     (*f)(sctx,w3,w2,fctx);
2525:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2526:     VecAXPY(w2,-1.0,w1);

2528:     /*
2529:        Loop over rows of vector, putting results into Jacobian matrix
2530:     */
2531:     VecGetArray(w2,&y);
2532:     for (l=0; l<coloring->nrows[k]; l++) {
2533:       row    = coloring->rows[k][l];
2534:       col    = coloring->columnsforrow[k][l];
2535:       y[row] *= vscale_array[vscaleforrow[k][l]];
2536:       srow   = row + start;
2537:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2538:     }
2539:     VecRestoreArray(w2,&y);
2540:   }
2541:   coloring->currentcolor = k;
2542:   VecRestoreArray(coloring->vscale,&vscale_array);
2543:   xx = xx + start; VecRestoreArray(x1,&xx);
2544:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2545:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2546:   return(0);
2547: }

2549: /* 
2550:    Computes the number of nonzeros per row needed for preallocation when X and Y 
2551:    have different nonzero structure. 
2552: */
2555: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt* nnz)
2556: {
2557:   PetscInt          i,m=Y->rmap->N;
2558:   Mat_SeqAIJ        *x = (Mat_SeqAIJ*)X->data;
2559:   Mat_SeqAIJ        *y = (Mat_SeqAIJ*)Y->data;
2560:   const PetscInt    *xi = x->i,*yi = y->i;

2563:   /* Set the number of nonzeros in the new matrix */
2564:   for(i=0; i<m; i++) {
2565:     PetscInt j,k,nzx = xi[i+1] - xi[i],nzy = yi[i+1] - yi[i];
2566:     const PetscInt *xj = x->j+xi[i],*yj = y->j+yi[i];
2567:     nnz[i] = 0;
2568:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
2569:       for (; k<nzy && yj[k]<xj[j]; k++) nnz[i]++; /* Catch up to X */
2570:       if (k<nzy && yj[k]==xj[j]) k++;             /* Skip duplicate */
2571:       nnz[i]++;
2572:     }
2573:     for (; k<nzy; k++) nnz[i]++;
2574:   }
2575:   return(0);
2576: }

2580: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2581: {
2583:   PetscInt       i;
2584:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2585:   PetscBLASInt   one=1,bnz = PetscBLASIntCast(x->nz);

2588:   if (str == SAME_NONZERO_PATTERN) {
2589:     PetscScalar alpha = a;
2590:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2591:     y->idiagvalid  = PETSC_FALSE;
2592:     y->ibdiagvalid = PETSC_FALSE;
2593:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2594:     if (y->xtoy && y->XtoY != X) {
2595:       PetscFree(y->xtoy);
2596:       MatDestroy(&y->XtoY);
2597:     }
2598:     if (!y->xtoy) { /* get xtoy */
2599:       MatAXPYGetxtoy_Private(X->rmap->n,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2600:       y->XtoY = X;
2601:       PetscObjectReference((PetscObject)X);
2602:     }
2603:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2604:     PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %G\n",x->nz,y->nz,(PetscReal)(x->nz)/(y->nz+1));
2605:   } else {
2606:     Mat      B;
2607:     PetscInt *nnz;
2608:     PetscMalloc(Y->rmap->N*sizeof(PetscInt),&nnz);
2609:     MatCreate(((PetscObject)Y)->comm,&B);
2610:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2611:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2612:     MatSetBlockSizes(B,Y->rmap->bs,Y->cmap->bs);
2613:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2614:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2615:     MatSeqAIJSetPreallocation(B,0,nnz);
2616:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2617:     MatHeaderReplace(Y,B);
2618:     PetscFree(nnz);
2619:   }
2620:   return(0);
2621: }

2625: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2626: {
2627: #if defined(PETSC_USE_COMPLEX)
2628:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
2629:   PetscInt    i,nz;
2630:   PetscScalar *a;

2633:   nz = aij->nz;
2634:   a  = aij->a;
2635:   for (i=0; i<nz; i++) {
2636:     a[i] = PetscConj(a[i]);
2637:   }
2638: #else
2640: #endif
2641:   return(0);
2642: }

2646: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2647: {
2648:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2650:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2651:   PetscReal      atmp;
2652:   PetscScalar    *x;
2653:   MatScalar      *aa;

2656:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2657:   aa   = a->a;
2658:   ai   = a->i;
2659:   aj   = a->j;

2661:   VecSet(v,0.0);
2662:   VecGetArray(v,&x);
2663:   VecGetLocalSize(v,&n);
2664:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2665:   for (i=0; i<m; i++) {
2666:     ncols = ai[1] - ai[0]; ai++;
2667:     x[i] = 0.0;
2668:     for (j=0; j<ncols; j++){
2669:       atmp = PetscAbsScalar(*aa);
2670:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2671:       aa++; aj++;
2672:     }
2673:   }
2674:   VecRestoreArray(v,&x);
2675:   return(0);
2676: }

2680: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2681: {
2682:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2684:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2685:   PetscScalar    *x;
2686:   MatScalar      *aa;

2689:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2690:   aa   = a->a;
2691:   ai   = a->i;
2692:   aj   = a->j;

2694:   VecSet(v,0.0);
2695:   VecGetArray(v,&x);
2696:   VecGetLocalSize(v,&n);
2697:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2698:   for (i=0; i<m; i++) {
2699:     ncols = ai[1] - ai[0]; ai++;
2700:     if (ncols == A->cmap->n) { /* row is dense */
2701:       x[i] = *aa; if (idx) idx[i] = 0;
2702:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2703:       x[i] = 0.0;
2704:       if (idx) {
2705:         idx[i] = 0; /* in case ncols is zero */
2706:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2707:           if (aj[j] > j) {
2708:             idx[i] = j;
2709:             break;
2710:           }
2711:         }
2712:       }
2713:     }
2714:     for (j=0; j<ncols; j++){
2715:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2716:       aa++; aj++;
2717:     }
2718:   }
2719:   VecRestoreArray(v,&x);
2720:   return(0);
2721: }

2725: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2726: {
2727:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2729:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2730:   PetscReal      atmp;
2731:   PetscScalar    *x;
2732:   MatScalar      *aa;

2735:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2736:   aa   = a->a;
2737:   ai   = a->i;
2738:   aj   = a->j;

2740:   VecSet(v,0.0);
2741:   VecGetArray(v,&x);
2742:   VecGetLocalSize(v,&n);
2743:   if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %d vs. %d rows", A->rmap->n, n);
2744:   for (i=0; i<m; i++) {
2745:     ncols = ai[1] - ai[0]; ai++;
2746:     if (ncols) {
2747:       /* Get first nonzero */
2748:       for(j = 0; j < ncols; j++) {
2749:         atmp = PetscAbsScalar(aa[j]);
2750:         if (atmp > 1.0e-12) {x[i] = atmp; if (idx) idx[i] = aj[j]; break;}
2751:       }
2752:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
2753:     } else {
2754:       x[i] = 0.0; if (idx) idx[i] = 0;
2755:     }
2756:     for(j = 0; j < ncols; j++) {
2757:       atmp = PetscAbsScalar(*aa);
2758:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2759:       aa++; aj++;
2760:     }
2761:   }
2762:   VecRestoreArray(v,&x);
2763:   return(0);
2764: }

2768: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2769: {
2770:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2772:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2773:   PetscScalar    *x;
2774:   MatScalar      *aa;

2777:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2778:   aa   = a->a;
2779:   ai   = a->i;
2780:   aj   = a->j;

2782:   VecSet(v,0.0);
2783:   VecGetArray(v,&x);
2784:   VecGetLocalSize(v,&n);
2785:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2786:   for (i=0; i<m; i++) {
2787:     ncols = ai[1] - ai[0]; ai++;
2788:     if (ncols == A->cmap->n) { /* row is dense */
2789:       x[i] = *aa; if (idx) idx[i] = 0;
2790:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2791:       x[i] = 0.0;
2792:       if (idx) {   /* find first implicit 0.0 in the row */
2793:         idx[i] = 0; /* in case ncols is zero */
2794:         for (j=0;j<ncols;j++) {
2795:           if (aj[j] > j) {
2796:             idx[i] = j;
2797:             break;
2798:           }
2799:         }
2800:       }
2801:     }
2802:     for (j=0; j<ncols; j++){
2803:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2804:       aa++; aj++;
2805:     }
2806:   }
2807:   VecRestoreArray(v,&x);
2808:   return(0);
2809: }

2811: #include <petscblaslapack.h>
2812: #include <../src/mat/blockinvert.h>

2816: PetscErrorCode  MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
2817: {
2818:   Mat_SeqAIJ    *a = (Mat_SeqAIJ*) A->data;
2820:   PetscInt       i,bs = A->rmap->bs,mbs = A->rmap->n/A->rmap->bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2821:   MatScalar      *diag,work[25],*v_work;
2822:   PetscReal      shift = 0.0;

2825:   if (a->ibdiagvalid) {
2826:     if (values) *values = a->ibdiag;
2827:     return(0);
2828:   }
2829:   MatMarkDiagonal_SeqAIJ(A);
2830:   if (!a->ibdiag) {
2831:     PetscMalloc(bs2*mbs*sizeof(PetscScalar),&a->ibdiag);
2832:     PetscLogObjectMemory(A,bs2*mbs*sizeof(PetscScalar));
2833:   }
2834:   diag    = a->ibdiag;
2835:   if (values) *values = a->ibdiag;
2836:   /* factor and invert each block */
2837:   switch (bs){
2838:     case 1:
2839:       for (i=0; i<mbs; i++) {
2840:         MatGetValues(A,1,&i,1,&i,diag+i);
2841:         diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
2842:       }
2843:       break;
2844:     case 2:
2845:       for (i=0; i<mbs; i++) {
2846:         ij[0] = 2*i; ij[1] = 2*i + 1;
2847:         MatGetValues(A,2,ij,2,ij,diag);
2848:         PetscKernel_A_gets_inverse_A_2(diag,shift);
2849:         PetscKernel_A_gets_transpose_A_2(diag);
2850:         diag  += 4;
2851:       }
2852:       break;
2853:     case 3:
2854:       for (i=0; i<mbs; i++) {
2855:         ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
2856:         MatGetValues(A,3,ij,3,ij,diag);
2857:         PetscKernel_A_gets_inverse_A_3(diag,shift);
2858:         PetscKernel_A_gets_transpose_A_3(diag);
2859:         diag    += 9;
2860:       }
2861:       break;
2862:     case 4:
2863:       for (i=0; i<mbs; i++) {
2864:         ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
2865:         MatGetValues(A,4,ij,4,ij,diag);
2866:         PetscKernel_A_gets_inverse_A_4(diag,shift);
2867:         PetscKernel_A_gets_transpose_A_4(diag);
2868:         diag  += 16;
2869:       }
2870:       break;
2871:     case 5:
2872:       for (i=0; i<mbs; i++) {
2873:         ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
2874:         MatGetValues(A,5,ij,5,ij,diag);
2875:         PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
2876:         PetscKernel_A_gets_transpose_A_5(diag);
2877:         diag  += 25;
2878:       }
2879:       break;
2880:     case 6:
2881:       for (i=0; i<mbs; i++) {
2882:         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;
2883:         MatGetValues(A,6,ij,6,ij,diag);
2884:         PetscKernel_A_gets_inverse_A_6(diag,shift);
2885:         PetscKernel_A_gets_transpose_A_6(diag);
2886:         diag  += 36;
2887:       }
2888:       break;
2889:     case 7:
2890:       for (i=0; i<mbs; i++) {
2891:         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;
2892:         MatGetValues(A,7,ij,7,ij,diag);
2893:         PetscKernel_A_gets_inverse_A_7(diag,shift);
2894:         PetscKernel_A_gets_transpose_A_7(diag);
2895:         diag  += 49;
2896:       }
2897:       break;
2898:     default:
2899:       PetscMalloc3(bs,MatScalar,&v_work,bs,PetscInt,&v_pivots,bs,PetscInt,&IJ);
2900:       for (i=0; i<mbs; i++) {
2901:         for (j=0; j<bs; j++) {
2902:           IJ[j] = bs*i + j;
2903:         }
2904:         MatGetValues(A,bs,IJ,bs,IJ,diag);
2905:         PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
2906:         PetscKernel_A_gets_transpose_A_N(diag,bs);
2907:         diag  += bs2;
2908:       }
2909:       PetscFree3(v_work,v_pivots,IJ);
2910:   }
2911:   a->ibdiagvalid = PETSC_TRUE;
2912:   return(0);
2913: }

2915: extern PetscErrorCode  MatFDColoringApply_AIJ(Mat,MatFDColoring,Vec,MatStructure*,void*);
2916: /* -------------------------------------------------------------------*/
2917: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2918:        MatGetRow_SeqAIJ,
2919:        MatRestoreRow_SeqAIJ,
2920:        MatMult_SeqAIJ,
2921: /* 4*/ MatMultAdd_SeqAIJ,
2922:        MatMultTranspose_SeqAIJ,
2923:        MatMultTransposeAdd_SeqAIJ,
2924:        0,
2925:        0,
2926:        0,
2927: /*10*/ 0,
2928:        MatLUFactor_SeqAIJ,
2929:        0,
2930:        MatSOR_SeqAIJ,
2931:        MatTranspose_SeqAIJ,
2932: /*15*/ MatGetInfo_SeqAIJ,
2933:        MatEqual_SeqAIJ,
2934:        MatGetDiagonal_SeqAIJ,
2935:        MatDiagonalScale_SeqAIJ,
2936:        MatNorm_SeqAIJ,
2937: /*20*/ 0,
2938:        MatAssemblyEnd_SeqAIJ,
2939:        MatSetOption_SeqAIJ,
2940:        MatZeroEntries_SeqAIJ,
2941: /*24*/ MatZeroRows_SeqAIJ,
2942:        0,
2943:        0,
2944:        0,
2945:        0,
2946: /*29*/ MatSetUp_SeqAIJ,
2947:        0,
2948:        0,
2949:        MatGetArray_SeqAIJ,
2950:        MatRestoreArray_SeqAIJ,
2951: /*34*/ MatDuplicate_SeqAIJ,
2952:        0,
2953:        0,
2954:        MatILUFactor_SeqAIJ,
2955:        0,
2956: /*39*/ MatAXPY_SeqAIJ,
2957:        MatGetSubMatrices_SeqAIJ,
2958:        MatIncreaseOverlap_SeqAIJ,
2959:        MatGetValues_SeqAIJ,
2960:        MatCopy_SeqAIJ,
2961: /*44*/ MatGetRowMax_SeqAIJ,
2962:        MatScale_SeqAIJ,
2963:        0,
2964:        MatDiagonalSet_SeqAIJ,
2965:        MatZeroRowsColumns_SeqAIJ,
2966: /*49*/ 0,
2967:        MatGetRowIJ_SeqAIJ,
2968:        MatRestoreRowIJ_SeqAIJ,
2969:        MatGetColumnIJ_SeqAIJ,
2970:        MatRestoreColumnIJ_SeqAIJ,
2971: /*54*/ MatFDColoringCreate_SeqAIJ,
2972:        0,
2973:        0,
2974:        MatPermute_SeqAIJ,
2975:        0,
2976: /*59*/ 0,
2977:        MatDestroy_SeqAIJ,
2978:        MatView_SeqAIJ,
2979:        0,
2980:        0,
2981: /*64*/ 0,
2982:        0,
2983:        0,
2984:        0,
2985:        0,
2986: /*69*/ MatGetRowMaxAbs_SeqAIJ,
2987:        MatGetRowMinAbs_SeqAIJ,
2988:        0,
2989:        MatSetColoring_SeqAIJ,
2990: #if defined(PETSC_HAVE_ADIC)
2991:        MatSetValuesAdic_SeqAIJ,
2992: #else
2993:        0,
2994: #endif
2995: /*74*/ MatSetValuesAdifor_SeqAIJ,
2996:        MatFDColoringApply_AIJ,
2997:        0,
2998:        0,
2999:        0,
3000: /*79*/ MatFindZeroDiagonals_SeqAIJ,
3001:        0,
3002:        0,
3003:        0,
3004:        MatLoad_SeqAIJ,
3005: /*84*/ MatIsSymmetric_SeqAIJ,
3006:        MatIsHermitian_SeqAIJ,
3007:        0,
3008:        0,
3009:        0,
3010: /*89*/ MatMatMult_SeqAIJ_SeqAIJ,
3011:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3012:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
3013:        MatPtAP_Basic,
3014:        MatPtAPSymbolic_SeqAIJ,
3015: /*94*/ MatPtAPNumeric_SeqAIJ,
3016:        MatMatTransposeMult_SeqAIJ_SeqAIJ,
3017:        MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3018:        MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3019:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
3020: /*99*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
3021:        0,
3022:        0,
3023:        MatConjugate_SeqAIJ,
3024:        0,
3025: /*104*/MatSetValuesRow_SeqAIJ,
3026:        MatRealPart_SeqAIJ,
3027:        MatImaginaryPart_SeqAIJ,
3028:        0,
3029:        0,
3030: /*109*/MatMatSolve_SeqAIJ,
3031:        0,
3032:        MatGetRowMin_SeqAIJ,
3033:        0,
3034:        MatMissingDiagonal_SeqAIJ,
3035: /*114*/0,
3036:        0,
3037:        0,
3038:        0,
3039:        0,
3040: /*119*/0,
3041:        0,
3042:        0,
3043:        0,
3044:        MatGetMultiProcBlock_SeqAIJ,
3045: /*124*/MatFindNonzeroRows_SeqAIJ,
3046:        MatGetColumnNorms_SeqAIJ,
3047:        MatInvertBlockDiagonal_SeqAIJ,
3048:        0,
3049:        0,
3050: /*129*/0,
3051:        MatTransposeMatMult_SeqAIJ_SeqAIJ,
3052:        MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3053:        MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3054:        MatTransposeColoringCreate_SeqAIJ,
3055: /*134*/MatTransColoringApplySpToDen_SeqAIJ,
3056:        MatTransColoringApplyDenToSp_SeqAIJ,
3057:        MatRARt_SeqAIJ_SeqAIJ,
3058:        MatRARtSymbolic_SeqAIJ_SeqAIJ,
3059:        MatRARtNumeric_SeqAIJ_SeqAIJ
3060: };

3062: EXTERN_C_BEGIN
3065: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3066: {
3067:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3068:   PetscInt   i,nz,n;


3072:   nz = aij->maxnz;
3073:   n  = mat->rmap->n;
3074:   for (i=0; i<nz; i++) {
3075:     aij->j[i] = indices[i];
3076:   }
3077:   aij->nz = nz;
3078:   for (i=0; i<n; i++) {
3079:     aij->ilen[i] = aij->imax[i];
3080:   }

3082:   return(0);
3083: }
3084: EXTERN_C_END

3088: /*@
3089:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3090:        in the matrix.

3092:   Input Parameters:
3093: +  mat - the SeqAIJ matrix
3094: -  indices - the column indices

3096:   Level: advanced

3098:   Notes:
3099:     This can be called if you have precomputed the nonzero structure of the 
3100:   matrix and want to provide it to the matrix object to improve the performance
3101:   of the MatSetValues() operation.

3103:     You MUST have set the correct numbers of nonzeros per row in the call to 
3104:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3106:     MUST be called before any calls to MatSetValues();

3108:     The indices should start with zero, not one.

3110: @*/
3111: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3112: {

3118:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt *),(mat,indices));
3119:   return(0);
3120: }

3122: /* ----------------------------------------------------------------------------------------*/

3124: EXTERN_C_BEGIN
3127: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3128: {
3129:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
3131:   size_t         nz = aij->i[mat->rmap->n];

3134:   if (aij->nonew != 1) {
3135:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3136:   }

3138:   /* allocate space for values if not already there */
3139:   if (!aij->saved_values) {
3140:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
3141:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
3142:   }

3144:   /* copy values over */
3145:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3146:   return(0);
3147: }
3148: EXTERN_C_END

3152: /*@
3153:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
3154:        example, reuse of the linear part of a Jacobian, while recomputing the 
3155:        nonlinear portion.

3157:    Collect on Mat

3159:   Input Parameters:
3160: .  mat - the matrix (currently only AIJ matrices support this option)

3162:   Level: advanced

3164:   Common Usage, with SNESSolve():
3165: $    Create Jacobian matrix
3166: $    Set linear terms into matrix
3167: $    Apply boundary conditions to matrix, at this time matrix must have 
3168: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
3169: $      boundary conditions again will not change the nonzero structure
3170: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3171: $    MatStoreValues(mat);
3172: $    Call SNESSetJacobian() with matrix
3173: $    In your Jacobian routine
3174: $      MatRetrieveValues(mat);
3175: $      Set nonlinear terms in matrix
3176:  
3177:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3178: $    // build linear portion of Jacobian
3179: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3180: $    MatStoreValues(mat);
3181: $    loop over nonlinear iterations
3182: $       MatRetrieveValues(mat);
3183: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3184: $       // call MatAssemblyBegin/End() on matrix
3185: $       Solve linear system with Jacobian
3186: $    endloop 

3188:   Notes:
3189:     Matrix must already be assemblied before calling this routine
3190:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before 
3191:     calling this routine.

3193:     When this is called multiple times it overwrites the previous set of stored values
3194:     and does not allocated additional space.

3196: .seealso: MatRetrieveValues()

3198: @*/
3199: PetscErrorCode  MatStoreValues(Mat mat)
3200: {

3205:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3206:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3207:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3208:   return(0);
3209: }

3211: EXTERN_C_BEGIN
3214: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3215: {
3216:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
3218:   PetscInt       nz = aij->i[mat->rmap->n];

3221:   if (aij->nonew != 1) {
3222:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3223:   }
3224:   if (!aij->saved_values) {
3225:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3226:   }
3227:   /* copy values over */
3228:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3229:   return(0);
3230: }
3231: EXTERN_C_END

3235: /*@
3236:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
3237:        example, reuse of the linear part of a Jacobian, while recomputing the 
3238:        nonlinear portion.

3240:    Collect on Mat

3242:   Input Parameters:
3243: .  mat - the matrix (currently on AIJ matrices support this option)

3245:   Level: advanced

3247: .seealso: MatStoreValues()

3249: @*/
3250: PetscErrorCode  MatRetrieveValues(Mat mat)
3251: {

3256:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3257:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3258:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3259:   return(0);
3260: }


3263: /* --------------------------------------------------------------------------------*/
3266: /*@C
3267:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3268:    (the default parallel PETSc format).  For good matrix assembly performance
3269:    the user should preallocate the matrix storage by setting the parameter nz
3270:    (or the array nnz).  By setting these parameters accurately, performance
3271:    during matrix assembly can be increased by more than a factor of 50.

3273:    Collective on MPI_Comm

3275:    Input Parameters:
3276: +  comm - MPI communicator, set to PETSC_COMM_SELF
3277: .  m - number of rows
3278: .  n - number of columns
3279: .  nz - number of nonzeros per row (same for all rows)
3280: -  nnz - array containing the number of nonzeros in the various rows 
3281:          (possibly different for each row) or PETSC_NULL

3283:    Output Parameter:
3284: .  A - the matrix 

3286:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3287:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
3288:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3290:    Notes:
3291:    If nnz is given then nz is ignored

3293:    The AIJ format (also called the Yale sparse matrix format or
3294:    compressed row storage), is fully compatible with standard Fortran 77
3295:    storage.  That is, the stored row and column indices can begin at
3296:    either one (as in Fortran) or zero.  See the users' manual for details.

3298:    Specify the preallocated storage with either nz or nnz (not both).
3299:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
3300:    allocation.  For large problems you MUST preallocate memory or you 
3301:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3303:    By default, this format uses inodes (identical nodes) when possible, to 
3304:    improve numerical efficiency of matrix-vector products and solves. We 
3305:    search for consecutive rows with the same nonzero structure, thereby
3306:    reusing matrix information to achieve increased efficiency.

3308:    Options Database Keys:
3309: +  -mat_no_inode  - Do not use inodes
3310: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3312:    Level: intermediate

3314: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3316: @*/
3317: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3318: {

3322:   MatCreate(comm,A);
3323:   MatSetSizes(*A,m,n,m,n);
3324:   MatSetType(*A,MATSEQAIJ);
3325:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3326:   return(0);
3327: }

3331: /*@C
3332:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3333:    the user should preallocate the matrix storage by setting the parameter nz
3334:    (or the array nnz).  By setting these parameters accurately, performance
3335:    during matrix assembly can be increased by more than a factor of 50.

3337:    Collective on MPI_Comm

3339:    Input Parameters:
3340: +  B - The matrix-free
3341: .  nz - number of nonzeros per row (same for all rows)
3342: -  nnz - array containing the number of nonzeros in the various rows 
3343:          (possibly different for each row) or PETSC_NULL

3345:    Notes:
3346:      If nnz is given then nz is ignored

3348:     The AIJ format (also called the Yale sparse matrix format or
3349:    compressed row storage), is fully compatible with standard Fortran 77
3350:    storage.  That is, the stored row and column indices can begin at
3351:    either one (as in Fortran) or zero.  See the users' manual for details.

3353:    Specify the preallocated storage with either nz or nnz (not both).
3354:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
3355:    allocation.  For large problems you MUST preallocate memory or you 
3356:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3358:    You can call MatGetInfo() to get information on how effective the preallocation was;
3359:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3360:    You can also run with the option -info and look for messages with the string 
3361:    malloc in them to see if additional memory allocation was needed.

3363:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3364:    entries or columns indices

3366:    By default, this format uses inodes (identical nodes) when possible, to 
3367:    improve numerical efficiency of matrix-vector products and solves. We 
3368:    search for consecutive rows with the same nonzero structure, thereby
3369:    reusing matrix information to achieve increased efficiency.

3371:    Options Database Keys:
3372: +  -mat_no_inode  - Do not use inodes
3373: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3374: -  -mat_aij_oneindex - Internally use indexing starting at 1
3375:         rather than 0.  Note that when calling MatSetValues(),
3376:         the user still MUST index entries starting at 0!

3378:    Level: intermediate

3380: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

3382: @*/
3383: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3384: {

3390:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3391:   return(0);
3392: }

3394: EXTERN_C_BEGIN
3397: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3398: {
3399:   Mat_SeqAIJ     *b;
3400:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3402:   PetscInt       i;

3405:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3406:   if (nz == MAT_SKIP_ALLOCATION) {
3407:     skipallocation = PETSC_TRUE;
3408:     nz             = 0;
3409:   }

3411:   PetscLayoutSetUp(B->rmap);
3412:   PetscLayoutSetUp(B->cmap);

3414:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3415:   if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
3416:   if (nnz) {
3417:     for (i=0; i<B->rmap->n; i++) {
3418:       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]);
3419:       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);
3420:     }
3421:   }

3423:   B->preallocated = PETSC_TRUE;
3424:   b = (Mat_SeqAIJ*)B->data;

3426:   if (!skipallocation) {
3427:     if (!b->imax) {
3428:       PetscMalloc2(B->rmap->n,PetscInt,&b->imax,B->rmap->n,PetscInt,&b->ilen);
3429:       PetscLogObjectMemory(B,2*B->rmap->n*sizeof(PetscInt));
3430:     }
3431:     if (!nnz) {
3432:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3433:       else if (nz < 0) nz = 1;
3434:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3435:       nz = nz*B->rmap->n;
3436:     } else {
3437:       nz = 0;
3438:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3439:     }
3440:     /* b->ilen will count nonzeros in each row so far. */
3441:     for (i=0; i<B->rmap->n; i++) { b->ilen[i] = 0; }

3443:     /* allocate the matrix space */
3444:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3445:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap->n+1,PetscInt,&b->i);
3446:     PetscLogObjectMemory(B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3447:     b->i[0] = 0;
3448:     for (i=1; i<B->rmap->n+1; i++) {
3449:       b->i[i] = b->i[i-1] + b->imax[i-1];
3450:     }
3451:     b->singlemalloc = PETSC_TRUE;
3452:     b->free_a       = PETSC_TRUE;
3453:     b->free_ij      = PETSC_TRUE;
3454:   } else {
3455:     b->free_a       = PETSC_FALSE;
3456:     b->free_ij      = PETSC_FALSE;
3457:   }

3459:   b->nz                = 0;
3460:   b->maxnz             = nz;
3461:   B->info.nz_unneeded  = (double)b->maxnz;
3462:   if (realalloc) {MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);}
3463:   return(0);
3464: }
3465: EXTERN_C_END

3467: #undef  __FUNCT__
3469: /*@
3470:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.  

3472:    Input Parameters:
3473: +  B - the matrix 
3474: .  i - the indices into j for the start of each row (starts with zero)
3475: .  j - the column indices for each row (starts with zero) these must be sorted for each row
3476: -  v - optional values in the matrix

3478:    Level: developer

3480:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

3482: .keywords: matrix, aij, compressed row, sparse, sequential

3484: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3485: @*/
3486: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3487: {

3493:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3494:   return(0);
3495: }

3497: EXTERN_C_BEGIN
3498: #undef  __FUNCT__
3500: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3501: {
3502:   PetscInt       i;
3503:   PetscInt       m,n;
3504:   PetscInt       nz;
3505:   PetscInt       *nnz, nz_max = 0;
3506:   PetscScalar    *values;

3510:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

3512:   PetscLayoutSetUp(B->rmap);
3513:   PetscLayoutSetUp(B->cmap);

3515:   MatGetSize(B, &m, &n);
3516:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
3517:   for(i = 0; i < m; i++) {
3518:     nz     = Ii[i+1]- Ii[i];
3519:     nz_max = PetscMax(nz_max, nz);
3520:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3521:     nnz[i] = nz;
3522:   }
3523:   MatSeqAIJSetPreallocation(B, 0, nnz);
3524:   PetscFree(nnz);

3526:   if (v) {
3527:     values = (PetscScalar*) v;
3528:   } else {
3529:     PetscMalloc(nz_max*sizeof(PetscScalar), &values);
3530:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
3531:   }

3533:   for(i = 0; i < m; i++) {
3534:     nz  = Ii[i+1] - Ii[i];
3535:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3536:   }

3538:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3539:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3541:   if (!v) {
3542:     PetscFree(values);
3543:   }
3544:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3545:   return(0);
3546: }
3547: EXTERN_C_END

3549: #include <../src/mat/impls/dense/seq/dense.h>
3550: #include <petsc-private/petscaxpy.h>

3554: /*
3555:     Computes (B'*A')' since computing B*A directly is untenable

3557:                n                       p                          p
3558:         (              )       (              )         (                  )
3559:       m (      A       )  *  n (       B      )   =   m (         C        )
3560:         (              )       (              )         (                  )

3562: */
3563: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3564: {
3565:   PetscErrorCode     ierr;
3566:   Mat_SeqDense       *sub_a = (Mat_SeqDense*)A->data;
3567:   Mat_SeqAIJ         *sub_b = (Mat_SeqAIJ*)B->data;
3568:   Mat_SeqDense       *sub_c = (Mat_SeqDense*)C->data;
3569:   PetscInt           i,n,m,q,p;
3570:   const PetscInt     *ii,*idx;
3571:   const PetscScalar  *b,*a,*a_q;
3572:   PetscScalar        *c,*c_q;

3575:   m = A->rmap->n;
3576:   n = A->cmap->n;
3577:   p = B->cmap->n;
3578:   a = sub_a->v;
3579:   b = sub_b->a;
3580:   c = sub_c->v;
3581:   PetscMemzero(c,m*p*sizeof(PetscScalar));

3583:   ii  = sub_b->i;
3584:   idx = sub_b->j;
3585:   for (i=0; i<n; i++) {
3586:     q = ii[i+1] - ii[i];
3587:     while (q-->0) {
3588:       c_q = c + m*(*idx);
3589:       a_q = a + m*i;
3590:       PetscAXPY(c_q,*b,a_q,m);
3591:       idx++;
3592:       b++;
3593:     }
3594:   }
3595:   return(0);
3596: }

3600: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3601: {
3603:   PetscInt       m=A->rmap->n,n=B->cmap->n;
3604:   Mat            Cmat;

3607:   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);
3608:   MatCreate(((PetscObject)A)->comm,&Cmat);
3609:   MatSetSizes(Cmat,m,n,m,n);
3610:   MatSetBlockSizes(Cmat,A->rmap->bs,B->cmap->bs);
3611:   MatSetType(Cmat,MATSEQDENSE);
3612:   MatSeqDenseSetPreallocation(Cmat,PETSC_NULL);
3613:   Cmat->assembled    = PETSC_TRUE;
3614:   Cmat->ops->matmult = MatMatMult_SeqDense_SeqAIJ;
3615:   *C = Cmat;
3616:   return(0);
3617: }

3619: /* ----------------------------------------------------------------*/
3622: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3623: {

3627:   if (scall == MAT_INITIAL_MATRIX){
3628:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3629:   }
3630:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3631:   return(0);
3632: }


3635: /*MC
3636:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
3637:    based on compressed sparse row format.

3639:    Options Database Keys:
3640: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3642:   Level: beginner

3644: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3645: M*/

3647: EXTERN_C_BEGIN
3648: #if defined(PETSC_HAVE_PASTIX)
3649: extern PetscErrorCode MatGetFactor_seqaij_pastix(Mat,MatFactorType,Mat*);
3650: #endif
3651: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
3652: extern PetscErrorCode MatGetFactor_seqaij_essl(Mat,MatFactorType,Mat *);
3653: #endif
3654: extern PetscErrorCode  MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
3655: extern PetscErrorCode  MatGetFactor_seqaij_petsc(Mat,MatFactorType,Mat*);
3656: extern PetscErrorCode  MatGetFactor_seqaij_bas(Mat,MatFactorType,Mat*);
3657: extern PetscErrorCode  MatGetFactorAvailable_seqaij_petsc(Mat,MatFactorType,PetscBool  *);
3658: #if defined(PETSC_HAVE_MUMPS)
3659: extern PetscErrorCode  MatGetFactor_aij_mumps(Mat,MatFactorType,Mat*);
3660: #endif
3661: #if defined(PETSC_HAVE_SUPERLU)
3662: extern PetscErrorCode  MatGetFactor_seqaij_superlu(Mat,MatFactorType,Mat*);
3663: #endif
3664: #if defined(PETSC_HAVE_SUPERLU_DIST)
3665: extern PetscErrorCode MatGetFactor_seqaij_superlu_dist(Mat,MatFactorType,Mat*);
3666: #endif
3667: #if defined(PETSC_HAVE_SPOOLES)
3668: extern PetscErrorCode  MatGetFactor_seqaij_spooles(Mat,MatFactorType,Mat*);
3669: #endif
3670: #if defined(PETSC_HAVE_UMFPACK)
3671: extern PetscErrorCode  MatGetFactor_seqaij_umfpack(Mat,MatFactorType,Mat*);
3672: #endif
3673: #if defined(PETSC_HAVE_CHOLMOD)
3674: extern PetscErrorCode  MatGetFactor_seqaij_cholmod(Mat,MatFactorType,Mat*);
3675: #endif
3676: #if defined(PETSC_HAVE_LUSOL)
3677: extern PetscErrorCode  MatGetFactor_seqaij_lusol(Mat,MatFactorType,Mat*);
3678: #endif
3679: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3680: extern PetscErrorCode  MatGetFactor_seqaij_matlab(Mat,MatFactorType,Mat*);
3681: extern PetscErrorCode  MatlabEnginePut_SeqAIJ(PetscObject,void*);
3682: extern PetscErrorCode  MatlabEngineGet_SeqAIJ(PetscObject,void*);
3683: #endif
3684: EXTERN_C_END


3687: EXTERN_C_BEGIN
3690: PetscErrorCode  MatCreate_SeqAIJ(Mat B)
3691: {
3692:   Mat_SeqAIJ     *b;
3694:   PetscMPIInt    size;

3697:   MPI_Comm_size(((PetscObject)B)->comm,&size);
3698:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3700:   PetscNewLog(B,Mat_SeqAIJ,&b);
3701:   B->data             = (void*)b;
3702:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3703:   b->row              = 0;
3704:   b->col              = 0;
3705:   b->icol             = 0;
3706:   b->reallocs         = 0;
3707:   b->ignorezeroentries = PETSC_FALSE;
3708:   b->roworiented       = PETSC_TRUE;
3709:   b->nonew             = 0;
3710:   b->diag              = 0;
3711:   b->solve_work        = 0;
3712:   B->spptr             = 0;
3713:   b->saved_values      = 0;
3714:   b->idiag             = 0;
3715:   b->mdiag             = 0;
3716:   b->ssor_work         = 0;
3717:   b->omega             = 1.0;
3718:   b->fshift            = 0.0;
3719:   b->idiagvalid        = PETSC_FALSE;
3720:   b->ibdiagvalid       = PETSC_FALSE;
3721:   b->keepnonzeropattern    = PETSC_FALSE;
3722:   b->xtoy              = 0;
3723:   b->XtoY              = 0;
3724:   B->same_nonzero          = PETSC_FALSE;

3726:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3727: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3728:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_matlab_C","MatGetFactor_seqaij_matlab",MatGetFactor_seqaij_matlab);
3729:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatlabEnginePut_SeqAIJ",MatlabEnginePut_SeqAIJ);
3730:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatlabEngineGet_SeqAIJ",MatlabEngineGet_SeqAIJ);
3731: #endif
3732: #if defined(PETSC_HAVE_PASTIX)
3733:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_pastix_C","MatGetFactor_seqaij_pastix",MatGetFactor_seqaij_pastix);
3734: #endif
3735: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
3736:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_essl_C","MatGetFactor_seqaij_essl",MatGetFactor_seqaij_essl);
3737: #endif
3738: #if defined(PETSC_HAVE_SUPERLU)
3739:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_superlu_C","MatGetFactor_seqaij_superlu",MatGetFactor_seqaij_superlu);
3740: #endif
3741: #if defined(PETSC_HAVE_SUPERLU_DIST)
3742:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_superlu_dist_C","MatGetFactor_seqaij_superlu_dist",MatGetFactor_seqaij_superlu_dist);
3743: #endif
3744: #if defined(PETSC_HAVE_SPOOLES)
3745:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_spooles_C","MatGetFactor_seqaij_spooles",MatGetFactor_seqaij_spooles);
3746: #endif
3747: #if defined(PETSC_HAVE_MUMPS)
3748:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mumps_C","MatGetFactor_aij_mumps",MatGetFactor_aij_mumps);
3749: #endif
3750: #if defined(PETSC_HAVE_UMFPACK)
3751:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_umfpack_C","MatGetFactor_seqaij_umfpack",MatGetFactor_seqaij_umfpack);
3752: #endif
3753: #if defined(PETSC_HAVE_CHOLMOD)
3754:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_cholmod_C","MatGetFactor_seqaij_cholmod",MatGetFactor_seqaij_cholmod);
3755: #endif
3756: #if defined(PETSC_HAVE_LUSOL)
3757:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_lusol_C","MatGetFactor_seqaij_lusol",MatGetFactor_seqaij_lusol);
3758: #endif
3759:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_petsc_C","MatGetFactor_seqaij_petsc",MatGetFactor_seqaij_petsc);
3760:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactorAvailable_petsc_C","MatGetFactorAvailable_seqaij_petsc",MatGetFactorAvailable_seqaij_petsc);
3761:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_bas_C","MatGetFactor_seqaij_bas",MatGetFactor_seqaij_bas);
3762:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C","MatSeqAIJSetColumnIndices_SeqAIJ",MatSeqAIJSetColumnIndices_SeqAIJ);
3763:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C","MatStoreValues_SeqAIJ",MatStoreValues_SeqAIJ);
3764:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C","MatRetrieveValues_SeqAIJ",MatRetrieveValues_SeqAIJ);
3765:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C","MatConvert_SeqAIJ_SeqSBAIJ",MatConvert_SeqAIJ_SeqSBAIJ);
3766:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C","MatConvert_SeqAIJ_SeqBAIJ",MatConvert_SeqAIJ_SeqBAIJ);
3767:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijperm_C","MatConvert_SeqAIJ_SeqAIJPERM",MatConvert_SeqAIJ_SeqAIJPERM);
3768:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C","MatConvert_SeqAIJ_SeqAIJCRL",MatConvert_SeqAIJ_SeqAIJCRL);
3769:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C","MatIsTranspose_SeqAIJ",MatIsTranspose_SeqAIJ);
3770:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsHermitianTranspose_C","MatIsHermitianTranspose_SeqAIJ",MatIsTranspose_SeqAIJ);
3771:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C","MatSeqAIJSetPreallocation_SeqAIJ",MatSeqAIJSetPreallocation_SeqAIJ);
3772:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C","MatSeqAIJSetPreallocationCSR_SeqAIJ",MatSeqAIJSetPreallocationCSR_SeqAIJ);
3773:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C","MatReorderForNonzeroDiagonal_SeqAIJ",MatReorderForNonzeroDiagonal_SeqAIJ);
3774:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMult_seqdense_seqaij_C","MatMatMult_SeqDense_SeqAIJ",MatMatMult_SeqDense_SeqAIJ);
3775:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C","MatMatMultSymbolic_SeqDense_SeqAIJ",MatMatMultSymbolic_SeqDense_SeqAIJ);
3776:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C","MatMatMultNumeric_SeqDense_SeqAIJ",MatMatMultNumeric_SeqDense_SeqAIJ);
3777:   MatCreate_SeqAIJ_Inode(B);
3778:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3779:   return(0);
3780: }
3781: EXTERN_C_END

3785: /*
3786:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
3787: */
3788: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool  mallocmatspace)
3789: {
3790:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3792:   PetscInt       i,m = A->rmap->n;

3795:   c = (Mat_SeqAIJ*)C->data;

3797:   C->factortype     = A->factortype;
3798:   c->row            = 0;
3799:   c->col            = 0;
3800:   c->icol           = 0;
3801:   c->reallocs       = 0;

3803:   C->assembled      = PETSC_TRUE;
3804: 
3805:   PetscLayoutReference(A->rmap,&C->rmap);
3806:   PetscLayoutReference(A->cmap,&C->cmap);

3808:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
3809:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
3810:   for (i=0; i<m; i++) {
3811:     c->imax[i] = a->imax[i];
3812:     c->ilen[i] = a->ilen[i];
3813:   }

3815:   /* allocate the matrix space */
3816:   if (mallocmatspace){
3817:     PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
3818:     PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
3819:     c->singlemalloc = PETSC_TRUE;
3820:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
3821:     if (m > 0) {
3822:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
3823:       if (cpvalues == MAT_COPY_VALUES) {
3824:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
3825:       } else {
3826:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
3827:       }
3828:     }
3829:   }

3831:   c->ignorezeroentries = a->ignorezeroentries;
3832:   c->roworiented       = a->roworiented;
3833:   c->nonew             = a->nonew;
3834:   if (a->diag) {
3835:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
3836:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
3837:     for (i=0; i<m; i++) {
3838:       c->diag[i] = a->diag[i];
3839:     }
3840:   } else c->diag           = 0;
3841:   c->solve_work            = 0;
3842:   c->saved_values          = 0;
3843:   c->idiag                 = 0;
3844:   c->ssor_work             = 0;
3845:   c->keepnonzeropattern    = a->keepnonzeropattern;
3846:   c->free_a                = PETSC_TRUE;
3847:   c->free_ij               = PETSC_TRUE;
3848:   c->xtoy                  = 0;
3849:   c->XtoY                  = 0;

3851:   c->rmax               = a->rmax;
3852:   c->nz                 = a->nz;
3853:   c->maxnz              = a->nz; /* Since we allocate exactly the right amount */
3854:   C->preallocated       = PETSC_TRUE;

3856:   c->compressedrow.use     = a->compressedrow.use;
3857:   c->compressedrow.nrows   = a->compressedrow.nrows;
3858:   c->compressedrow.check   = a->compressedrow.check;
3859:   if (a->compressedrow.use){
3860:     i = a->compressedrow.nrows;
3861:     PetscMalloc2(i+1,PetscInt,&c->compressedrow.i,i,PetscInt,&c->compressedrow.rindex);
3862:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3863:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3864:   } else {
3865:     c->compressedrow.use    = PETSC_FALSE;
3866:     c->compressedrow.i      = PETSC_NULL;
3867:     c->compressedrow.rindex = PETSC_NULL;
3868:   }
3869:   C->same_nonzero = A->same_nonzero;
3870:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);

3872:   PetscFListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3873:   return(0);
3874: }

3878: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3879: {

3883:   MatCreate(((PetscObject)A)->comm,B);
3884:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
3885:   MatSetBlockSizes(*B,A->rmap->bs,A->cmap->bs);
3886:   MatSetType(*B,((PetscObject)A)->type_name);
3887:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
3888:   return(0);
3889: }

3893: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
3894: {
3895:   Mat_SeqAIJ     *a;
3897:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
3898:   int            fd;
3899:   PetscMPIInt    size;
3900:   MPI_Comm       comm;
3901:   PetscInt       bs = 1;
3902: 
3904:   PetscObjectGetComm((PetscObject)viewer,&comm);
3905:   MPI_Comm_size(comm,&size);
3906:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");

3908:   PetscOptionsBegin(comm,PETSC_NULL,"Options for loading SEQAIJ matrix","Mat");
3909:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,PETSC_NULL);
3910:   PetscOptionsEnd();

3912:   PetscViewerBinaryGetDescriptor(viewer,&fd);
3913:   PetscBinaryRead(fd,header,4,PETSC_INT);
3914:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
3915:   M = header[1]; N = header[2]; nz = header[3];

3917:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");

3919:   /* read in row lengths */
3920:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
3921:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

3923:   /* check if sum of rowlengths is same as nz */
3924:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
3925:   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);

3927:   /* set global size if not set already*/
3928:   if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
3929:     MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
3930:   } else {
3931:     /* if sizes and type are already set, check if the vector global sizes are correct */
3932:     MatGetSize(newMat,&rows,&cols);
3933:     if (rows < 0 && cols < 0){ /* user might provide local size instead of global size */
3934:       MatGetLocalSize(newMat,&rows,&cols);
3935:     }
3936:     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);
3937:   }
3938:   MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
3939:   a = (Mat_SeqAIJ*)newMat->data;

3941:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

3943:   /* read in nonzero values */
3944:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

3946:   /* set matrix "i" values */
3947:   a->i[0] = 0;
3948:   for (i=1; i<= M; i++) {
3949:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
3950:     a->ilen[i-1] = rowlengths[i-1];
3951:   }
3952:   PetscFree(rowlengths);

3954:   MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
3955:   MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
3956:   if (bs > 1) {MatSetBlockSize(newMat,bs);}
3957:   return(0);
3958: }

3962: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
3963: {
3964:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
3966: #if defined(PETSC_USE_COMPLEX)
3967:   PetscInt k;
3968: #endif

3971:   /* If the  matrix dimensions are not equal,or no of nonzeros */
3972:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
3973:     *flg = PETSC_FALSE;
3974:     return(0);
3975:   }
3976: 
3977:   /* if the a->i are the same */
3978:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
3979:   if (!*flg) return(0);
3980: 
3981:   /* if a->j are the same */
3982:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
3983:   if (!*flg) return(0);
3984: 
3985:   /* if a->a are the same */
3986: #if defined(PETSC_USE_COMPLEX)
3987:   for (k=0; k<a->nz; k++){
3988:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])){
3989:       *flg = PETSC_FALSE;
3990:       return(0);
3991:     }
3992:   }
3993: #else
3994:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
3995: #endif
3996:   return(0);
3997: }

4001: /*@
4002:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4003:               provided by the user.

4005:       Collective on MPI_Comm

4007:    Input Parameters:
4008: +   comm - must be an MPI communicator of size 1
4009: .   m - number of rows
4010: .   n - number of columns
4011: .   i - row indices
4012: .   j - column indices
4013: -   a - matrix values

4015:    Output Parameter:
4016: .   mat - the matrix

4018:    Level: intermediate

4020:    Notes:
4021:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4022:     once the matrix is destroyed and not before

4024:        You cannot set new nonzero locations into this matrix, that will generate an error.

4026:        The i and j indices are 0 based

4028:        The format which is used for the sparse matrix input, is equivalent to a
4029:     row-major ordering.. i.e for the following matrix, the input data expected is
4030:     as shown:

4032:         1 0 0
4033:         2 0 3
4034:         4 5 6

4036:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4037:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
4038:         v =  {1,2,3,4,5,6}  [size = nz = 6]

4040:         
4041: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4043: @*/
4044: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
4045: {
4047:   PetscInt       ii;
4048:   Mat_SeqAIJ     *aij;
4049: #if defined(PETSC_USE_DEBUG)
4050:   PetscInt       jj;
4051: #endif

4054:   if (i[0]) {
4055:     SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4056:   }
4057:   MatCreate(comm,mat);
4058:   MatSetSizes(*mat,m,n,m,n);
4059:   /* MatSetBlockSizes(*mat,,); */
4060:   MatSetType(*mat,MATSEQAIJ);
4061:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4062:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4063:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

4065:   aij->i = i;
4066:   aij->j = j;
4067:   aij->a = a;
4068:   aij->singlemalloc = PETSC_FALSE;
4069:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4070:   aij->free_a       = PETSC_FALSE;
4071:   aij->free_ij      = PETSC_FALSE;

4073:   for (ii=0; ii<m; ii++) {
4074:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4075: #if defined(PETSC_USE_DEBUG)
4076:     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]);
4077:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4078:       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);
4079:       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);
4080:     }
4081: #endif    
4082:   }
4083: #if defined(PETSC_USE_DEBUG)
4084:   for (ii=0; ii<aij->i[m]; ii++) {
4085:     if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
4086:     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]);
4087:   }
4088: #endif    

4090:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4091:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4092:   return(0);
4093: }
4096: /*@C
4097:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4098:               provided by the user.

4100:       Collective on MPI_Comm

4102:    Input Parameters:
4103: +   comm - must be an MPI communicator of size 1
4104: .   m   - number of rows
4105: .   n   - number of columns
4106: .   i   - row indices
4107: .   j   - column indices
4108: .   a   - matrix values
4109: .   nz  - number of nonzeros
4110: -   idx - 0 or 1 based

4112:    Output Parameter:
4113: .   mat - the matrix

4115:    Level: intermediate

4117:    Notes:
4118:        The i and j indices are 0 based

4120:        The format which is used for the sparse matrix input, is equivalent to a
4121:     row-major ordering.. i.e for the following matrix, the input data expected is
4122:     as shown:

4124:         1 0 0
4125:         2 0 3
4126:         4 5 6

4128:         i =  {0,1,1,2,2,2}  
4129:         j =  {0,0,2,0,1,2}
4130:         v =  {1,2,3,4,5,6}

4132:         
4133: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4135: @*/
4136: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx)
4137: {
4139:   PetscInt       ii, *nnz, one = 1,row,col;


4143:   PetscMalloc(m*sizeof(PetscInt),&nnz);
4144:   PetscMemzero(nnz,m*sizeof(PetscInt));
4145:   for (ii = 0; ii < nz; ii++){
4146:     nnz[i[ii]] += 1;
4147:   }
4148:   MatCreate(comm,mat);
4149:   MatSetSizes(*mat,m,n,m,n);
4150:   /* MatSetBlockSizes(*mat,,); */
4151:   MatSetType(*mat,MATSEQAIJ);
4152:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4153:   for (ii = 0; ii < nz; ii++){
4154:     if (idx){
4155:       row = i[ii] - 1;
4156:       col = j[ii] - 1;
4157:     } else {
4158:       row = i[ii];
4159:       col = j[ii];
4160:     }
4161:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4162:   }
4163:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4164:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4165:   PetscFree(nnz);
4166:   return(0);
4167: }

4171: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
4172: {
4174:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

4177:   if (coloring->ctype == IS_COLORING_GLOBAL) {
4178:     ISColoringReference(coloring);
4179:     a->coloring = coloring;
4180:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
4181:     PetscInt             i,*larray;
4182:     ISColoring      ocoloring;
4183:     ISColoringValue *colors;

4185:     /* set coloring for diagonal portion */
4186:     PetscMalloc(A->cmap->n*sizeof(PetscInt),&larray);
4187:     for (i=0; i<A->cmap->n; i++) {
4188:       larray[i] = i;
4189:     }
4190:     ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,PETSC_NULL,larray);
4191:     PetscMalloc(A->cmap->n*sizeof(ISColoringValue),&colors);
4192:     for (i=0; i<A->cmap->n; i++) {
4193:       colors[i] = coloring->colors[larray[i]];
4194:     }
4195:     PetscFree(larray);
4196:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,&ocoloring);
4197:     a->coloring = ocoloring;
4198:   }
4199:   return(0);
4200: }

4202: #if defined(PETSC_HAVE_ADIC)
4203: EXTERN_C_BEGIN
4204: #include <adic/ad_utils.h>
4205: EXTERN_C_END

4209: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
4210: {
4211:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
4212:   PetscInt        m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j,nlen;
4213:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
4214:   ISColoringValue *color;

4217:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4218:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
4219:   color = a->coloring->colors;
4220:   /* loop over rows */
4221:   for (i=0; i<m; i++) {
4222:     nz = ii[i+1] - ii[i];
4223:     /* loop over columns putting computed value into matrix */
4224:     for (j=0; j<nz; j++) {
4225:       *v++ = values[color[*jj++]];
4226:     }
4227:     values += nlen; /* jump to next row of derivatives */
4228:   }
4229:   return(0);
4230: }
4231: #endif

4235: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
4236: {
4237:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
4238:   PetscInt         m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
4239:   MatScalar       *v = a->a;
4240:   PetscScalar     *values = (PetscScalar *)advalues;
4241:   ISColoringValue *color;

4244:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4245:   color = a->coloring->colors;
4246:   /* loop over rows */
4247:   for (i=0; i<m; i++) {
4248:     nz = ii[i+1] - ii[i];
4249:     /* loop over columns putting computed value into matrix */
4250:     for (j=0; j<nz; j++) {
4251:       *v++ = values[color[*jj++]];
4252:     }
4253:     values += nl; /* jump to next row of derivatives */
4254:   }
4255:   return(0);
4256: }

4258: /*
4259:     Special version for direct calls from Fortran 
4260: */
4261: #include <petsc-private/fortranimpl.h>
4262: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4263: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4264: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4265: #define matsetvaluesseqaij_ matsetvaluesseqaij
4266: #endif

4268: /* Change these macros so can be used in void function */
4269: #undef CHKERRQ
4270: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)A)->comm,ierr) 
4271: #undef SETERRQ2
4272: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr) 
4273: #undef SETERRQ3
4274: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr) 

4276: EXTERN_C_BEGIN
4279: void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
4280: {
4281:   Mat            A = *AA;
4282:   PetscInt       m = *mm, n = *nn;
4283:   InsertMode     is = *isis;
4284:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4285:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4286:   PetscInt       *imax,*ai,*ailen;
4288:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
4289:   MatScalar      *ap,value,*aa;
4290:   PetscBool      ignorezeroentries = a->ignorezeroentries;
4291:   PetscBool      roworiented = a->roworiented;

4294:   MatCheckPreallocated(A,1);
4295:   imax = a->imax;
4296:   ai = a->i;
4297:   ailen = a->ilen;
4298:   aj = a->j;
4299:   aa = a->a;

4301:   for (k=0; k<m; k++) { /* loop over added rows */
4302:     row  = im[k];
4303:     if (row < 0) continue;
4304: #if defined(PETSC_USE_DEBUG)  
4305:     if (row >= A->rmap->n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4306: #endif
4307:     rp   = aj + ai[row]; ap = aa + ai[row];
4308:     rmax = imax[row]; nrow = ailen[row];
4309:     low  = 0;
4310:     high = nrow;
4311:     for (l=0; l<n; l++) { /* loop over added columns */
4312:       if (in[l] < 0) continue;
4313: #if defined(PETSC_USE_DEBUG)  
4314:       if (in[l] >= A->cmap->n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4315: #endif
4316:       col = in[l];
4317:       if (roworiented) {
4318:         value = v[l + k*n];
4319:       } else {
4320:         value = v[k + l*m];
4321:       }
4322:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

4324:       if (col <= lastcol) low = 0; else high = nrow;
4325:       lastcol = col;
4326:       while (high-low > 5) {
4327:         t = (low+high)/2;
4328:         if (rp[t] > col) high = t;
4329:         else             low  = t;
4330:       }
4331:       for (i=low; i<high; i++) {
4332:         if (rp[i] > col) break;
4333:         if (rp[i] == col) {
4334:           if (is == ADD_VALUES) ap[i] += value;
4335:           else                  ap[i] = value;
4336:           goto noinsert;
4337:         }
4338:       }
4339:       if (value == 0.0 && ignorezeroentries) goto noinsert;
4340:       if (nonew == 1) goto noinsert;
4341:       if (nonew == -1) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4342:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4343:       N = nrow++ - 1; a->nz++; high++;
4344:       /* shift up all the later entries in this row */
4345:       for (ii=N; ii>=i; ii--) {
4346:         rp[ii+1] = rp[ii];
4347:         ap[ii+1] = ap[ii];
4348:       }
4349:       rp[i] = col;
4350:       ap[i] = value;
4351:       noinsert:;
4352:       low = i + 1;
4353:     }
4354:     ailen[row] = nrow;
4355:   }
4356:   A->same_nonzero = PETSC_FALSE;
4357:   PetscFunctionReturnVoid();
4358: }
4359: EXTERN_C_END