Actual source code: aij.c

  1: #define PETSCMAT_DLL


  4: /*
  5:     Defines the basic matrix operations for the AIJ (compressed row)
  6:   matrix storage format.
  7: */


 10:  #include ../src/mat/impls/aij/seq/aij.h
 11:  #include petscblaslapack.h
 12:  #include petscbt.h

 16: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
 17: {
 19:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) Y->data;
 20:   PetscInt       i,*diag, m = Y->rmap->n;
 21:   MatScalar      *aa = aij->a;
 22:   PetscScalar    *v;
 23:   PetscTruth     missing;

 26:   if (Y->assembled) {
 27:     MatMissingDiagonal_SeqAIJ(Y,&missing,PETSC_NULL);
 28:     if (!missing) {
 29:       diag = aij->diag;
 30:       VecGetArray(D,&v);
 31:       if (is == INSERT_VALUES) {
 32:         for (i=0; i<m; i++) {
 33:           aa[diag[i]] = v[i];
 34:         }
 35:       } else {
 36:         for (i=0; i<m; i++) {
 37:           aa[diag[i]] += v[i];
 38:         }
 39:       }
 40:       VecRestoreArray(D,&v);
 41:       return(0);
 42:     }
 43:   }
 44:   MatDiagonalSet_Default(Y,D,is);
 45:   return(0);
 46: }

 50: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth inodecompressed,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 51: {
 52:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 54:   PetscInt       i,ishift;
 55: 
 57:   *m     = A->rmap->n;
 58:   if (!ia) return(0);
 59:   ishift = 0;
 60:   if (symmetric && !A->structurally_symmetric) {
 61:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,ia,ja);
 62:   } else if (oshift == 1) {
 63:     PetscInt nz = a->i[A->rmap->n];
 64:     /* malloc space and  add 1 to i and j indices */
 65:     PetscMalloc((A->rmap->n+1)*sizeof(PetscInt),ia);
 66:     for (i=0; i<A->rmap->n+1; i++) (*ia)[i] = a->i[i] + 1;
 67:     if (ja) {
 68:       PetscMalloc((nz+1)*sizeof(PetscInt),ja);
 69:       for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 70:     }
 71:   } else {
 72:     *ia = a->i;
 73:     if (ja) *ja = a->j;
 74:   }
 75:   return(0);
 76: }

 80: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 81: {
 83: 
 85:   if (!ia) return(0);
 86:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
 87:     PetscFree(*ia);
 88:     if (ja) {PetscFree(*ja);}
 89:   }
 90:   return(0);
 91: }

 95: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth inodecompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 96: {
 97:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 99:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
100:   PetscInt       nz = a->i[m],row,*jj,mr,col;

103:   *nn = n;
104:   if (!ia) return(0);
105:   if (symmetric) {
106:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,ia,ja);
107:   } else {
108:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
109:     PetscMemzero(collengths,n*sizeof(PetscInt));
110:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
111:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
112:     jj = a->j;
113:     for (i=0; i<nz; i++) {
114:       collengths[jj[i]]++;
115:     }
116:     cia[0] = oshift;
117:     for (i=0; i<n; i++) {
118:       cia[i+1] = cia[i] + collengths[i];
119:     }
120:     PetscMemzero(collengths,n*sizeof(PetscInt));
121:     jj   = a->j;
122:     for (row=0; row<m; row++) {
123:       mr = a->i[row+1] - a->i[row];
124:       for (i=0; i<mr; i++) {
125:         col = *jj++;
126:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
127:       }
128:     }
129:     PetscFree(collengths);
130:     *ia = cia; *ja = cja;
131:   }
132:   return(0);
133: }

137: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth inodecompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
138: {

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

144:   PetscFree(*ia);
145:   PetscFree(*ja);
146: 
147:   return(0);
148: }

152: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
153: {
154:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
155:   PetscInt       *ai = a->i;

159:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
160:   return(0);
161: }

163: #define CHUNKSIZE   15

167: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
168: {
169:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
170:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
171:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
173:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
174:   MatScalar      *ap,value,*aa = a->a;
175:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
176:   PetscTruth     roworiented = a->roworiented;

180:   for (k=0; k<m; k++) { /* loop over added rows */
181:     row  = im[k];
182:     if (row < 0) continue;
183: #if defined(PETSC_USE_DEBUG)  
184:     if (row >= A->rmap->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
185: #endif
186:     rp   = aj + ai[row]; ap = aa + ai[row];
187:     rmax = imax[row]; nrow = ailen[row];
188:     low  = 0;
189:     high = nrow;
190:     for (l=0; l<n; l++) { /* loop over added columns */
191:       if (in[l] < 0) continue;
192: #if defined(PETSC_USE_DEBUG)  
193:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
194: #endif
195:       col = in[l];
196:       if (v) {
197:         if (roworiented) {
198:           value = v[l + k*n];
199:         } else {
200:           value = v[k + l*m];
201:         }
202:       } else {
203:         value = 0.;
204:       }
205:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

207:       if (col <= lastcol) low = 0; else high = nrow;
208:       lastcol = col;
209:       while (high-low > 5) {
210:         t = (low+high)/2;
211:         if (rp[t] > col) high = t;
212:         else             low  = t;
213:       }
214:       for (i=low; i<high; i++) {
215:         if (rp[i] > col) break;
216:         if (rp[i] == col) {
217:           if (is == ADD_VALUES) ap[i] += value;
218:           else                  ap[i] = value;
219:           low = i + 1;
220:           goto noinsert;
221:         }
222:       }
223:       if (value == 0.0 && ignorezeroentries) goto noinsert;
224:       if (nonew == 1) goto noinsert;
225:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
226:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
227:       N = nrow++ - 1; a->nz++; high++;
228:       /* shift up all the later entries in this row */
229:       for (ii=N; ii>=i; ii--) {
230:         rp[ii+1] = rp[ii];
231:         ap[ii+1] = ap[ii];
232:       }
233:       rp[i] = col;
234:       ap[i] = value;
235:       low   = i + 1;
236:       noinsert:;
237:     }
238:     ailen[row] = nrow;
239:   }
240:   A->same_nonzero = PETSC_FALSE;
241:   return(0);
242: }


247: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
248: {
249:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
250:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
251:   PetscInt     *ai = a->i,*ailen = a->ilen;
252:   MatScalar    *ap,*aa = a->a;

255:   for (k=0; k<m; k++) { /* loop over rows */
256:     row  = im[k];
257:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
258:     if (row >= A->rmap->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
259:     rp   = aj + ai[row]; ap = aa + ai[row];
260:     nrow = ailen[row];
261:     for (l=0; l<n; l++) { /* loop over columns */
262:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
263:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
264:       col = in[l] ;
265:       high = nrow; low = 0; /* assume unsorted */
266:       while (high-low > 5) {
267:         t = (low+high)/2;
268:         if (rp[t] > col) high = t;
269:         else             low  = t;
270:       }
271:       for (i=low; i<high; i++) {
272:         if (rp[i] > col) break;
273:         if (rp[i] == col) {
274:           *v++ = ap[i];
275:           goto finished;
276:         }
277:       }
278:       *v++ = 0.0;
279:       finished:;
280:     }
281:   }
282:   return(0);
283: }


288: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
289: {
290:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
292:   PetscInt       i,*col_lens;
293:   int            fd;

296:   PetscViewerBinaryGetDescriptor(viewer,&fd);
297:   PetscMalloc((4+A->rmap->n)*sizeof(PetscInt),&col_lens);
298:   col_lens[0] = MAT_FILE_COOKIE;
299:   col_lens[1] = A->rmap->n;
300:   col_lens[2] = A->cmap->n;
301:   col_lens[3] = a->nz;

303:   /* store lengths of each row and write (including header) to file */
304:   for (i=0; i<A->rmap->n; i++) {
305:     col_lens[4+i] = a->i[i+1] - a->i[i];
306:   }
307:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
308:   PetscFree(col_lens);

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

313:   /* store nonzero values */
314:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
315:   return(0);
316: }

318: EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

322: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
323: {
324:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
325:   PetscErrorCode    ierr;
326:   PetscInt          i,j,m = A->rmap->n,shift=0;
327:   const char        *name;
328:   PetscViewerFormat format;

331:   PetscObjectGetName((PetscObject)A,&name);
332:   PetscViewerGetFormat(viewer,&format);
333:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
334:     PetscInt nofinalvalue = 0;
335:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-!shift)) {
336:       nofinalvalue = 1;
337:     }
338:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
339:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
340:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
341:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
342:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

344:     for (i=0; i<m; i++) {
345:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
346: #if defined(PETSC_USE_COMPLEX)
347:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
348: #else
349:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
350: #endif
351:       }
352:     }
353:     if (nofinalvalue) {
354:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
355:     }
356:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
357:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
358:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
359:      return(0);
360:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
361:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
362:     for (i=0; i<m; i++) {
363:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
364:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
365: #if defined(PETSC_USE_COMPLEX)
366:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
367:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
368:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
369:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
370:         } else if (PetscRealPart(a->a[j]) != 0.0) {
371:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
372:         }
373: #else
374:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);}
375: #endif
376:       }
377:       PetscViewerASCIIPrintf(viewer,"\n");
378:     }
379:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
380:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
381:     PetscInt nzd=0,fshift=1,*sptr;
382:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
383:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
384:     for (i=0; i<m; i++) {
385:       sptr[i] = nzd+1;
386:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
387:         if (a->j[j] >= i) {
388: #if defined(PETSC_USE_COMPLEX)
389:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
390: #else
391:           if (a->a[j] != 0.0) nzd++;
392: #endif
393:         }
394:       }
395:     }
396:     sptr[m] = nzd+1;
397:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
398:     for (i=0; i<m+1; i+=6) {
399:       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]);}
400:       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]);}
401:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
402:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
403:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
404:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
405:     }
406:     PetscViewerASCIIPrintf(viewer,"\n");
407:     PetscFree(sptr);
408:     for (i=0; i<m; i++) {
409:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
410:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
411:       }
412:       PetscViewerASCIIPrintf(viewer,"\n");
413:     }
414:     PetscViewerASCIIPrintf(viewer,"\n");
415:     for (i=0; i<m; i++) {
416:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
417:         if (a->j[j] >= i) {
418: #if defined(PETSC_USE_COMPLEX)
419:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
420:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
421:           }
422: #else
423:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
424: #endif
425:         }
426:       }
427:       PetscViewerASCIIPrintf(viewer,"\n");
428:     }
429:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
430:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
431:     PetscInt         cnt = 0,jcnt;
432:     PetscScalar value;

434:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
435:     for (i=0; i<m; i++) {
436:       jcnt = 0;
437:       for (j=0; j<A->cmap->n; j++) {
438:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
439:           value = a->a[cnt++];
440:           jcnt++;
441:         } else {
442:           value = 0.0;
443:         }
444: #if defined(PETSC_USE_COMPLEX)
445:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
446: #else
447:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
448: #endif
449:       }
450:       PetscViewerASCIIPrintf(viewer,"\n");
451:     }
452:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
453:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
454:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
455: #if defined(PETSC_USE_COMPLEX)
456:     PetscViewerASCIIPrintf(viewer,"%%matrix complex general\n");
457: #else
458:     PetscViewerASCIIPrintf(viewer,"%%matrix real general\n");
459: #endif
460:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
461:     for (i=0; i<m; i++) {
462:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
463: #if defined(PETSC_USE_COMPLEX)
464:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
465:           PetscViewerASCIIPrintf(viewer,"%D %D, %G %G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
466:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
467:           PetscViewerASCIIPrintf(viewer,"%D %D, %G -%G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
468:         } else {
469:           PetscViewerASCIIPrintf(viewer,"%D %D, %G\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]));
470:         }
471: #else
472:         PetscViewerASCIIPrintf(viewer,"%D %D %G\n", i+shift, a->j[j]+shift, a->a[j]);
473: #endif
474:       }
475:     }
476:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
477:   } else {
478:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
479:     if (A->factor){
480:       for (i=0; i<m; i++) {
481:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
482:         /* L part */
483:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
484: #if defined(PETSC_USE_COMPLEX)
485:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
486:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
487:           } else if (PetscImaginaryPart(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 {
490:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
491:           }
492: #else
493:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
494: #endif
495:         }
496:         /* diagonal */
497:         j = a->diag[i];
498: #if defined(PETSC_USE_COMPLEX)
499:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
500:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
501:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
502:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
503:           } else {
504:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
505:           }
506: #else
507:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
508: #endif

510:         /* U part */
511:         for (j=a->diag[i+1]+1+shift; j<a->diag[i]+shift; j++) {
512: #if defined(PETSC_USE_COMPLEX)
513:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
514:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
515:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
516:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
517:           } else {
518:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
519:           }
520: #else
521:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
522: #endif
523: }
524:           PetscViewerASCIIPrintf(viewer,"\n");
525:         }
526:     } else {
527:       for (i=0; i<m; i++) {
528:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
529:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
530: #if defined(PETSC_USE_COMPLEX)
531:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
532:             PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
533:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
534:             PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
535:           } else {
536:             PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
537:           }
538: #else
539:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
540: #endif
541:         }
542:         PetscViewerASCIIPrintf(viewer,"\n");
543:       }
544:     }
545:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
546:   }
547:   PetscViewerFlush(viewer);
548:   return(0);
549: }

553: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
554: {
555:   Mat               A = (Mat) Aa;
556:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
557:   PetscErrorCode    ierr;
558:   PetscInt          i,j,m = A->rmap->n,color;
559:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
560:   PetscViewer       viewer;
561:   PetscViewerFormat format;

564:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
565:   PetscViewerGetFormat(viewer,&format);

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

570:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
571:     /* Blue for negative, Cyan for zero and  Red for positive */
572:     color = PETSC_DRAW_BLUE;
573:     for (i=0; i<m; i++) {
574:       y_l = m - i - 1.0; y_r = y_l + 1.0;
575:       for (j=a->i[i]; j<a->i[i+1]; j++) {
576:         x_l = a->j[j] ; x_r = x_l + 1.0;
577: #if defined(PETSC_USE_COMPLEX)
578:         if (PetscRealPart(a->a[j]) >=  0.) continue;
579: #else
580:         if (a->a[j] >=  0.) continue;
581: #endif
582:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
583:       }
584:     }
585:     color = PETSC_DRAW_CYAN;
586:     for (i=0; i<m; i++) {
587:       y_l = m - i - 1.0; y_r = y_l + 1.0;
588:       for (j=a->i[i]; j<a->i[i+1]; j++) {
589:         x_l = a->j[j]; x_r = x_l + 1.0;
590:         if (a->a[j] !=  0.) continue;
591:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
592:       }
593:     }
594:     color = PETSC_DRAW_RED;
595:     for (i=0; i<m; i++) {
596:       y_l = m - i - 1.0; y_r = y_l + 1.0;
597:       for (j=a->i[i]; j<a->i[i+1]; j++) {
598:         x_l = a->j[j]; x_r = x_l + 1.0;
599: #if defined(PETSC_USE_COMPLEX)
600:         if (PetscRealPart(a->a[j]) <=  0.) continue;
601: #else
602:         if (a->a[j] <=  0.) continue;
603: #endif
604:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
605:       }
606:     }
607:   } else {
608:     /* use contour shading to indicate magnitude of values */
609:     /* first determine max of all nonzero values */
610:     PetscInt    nz = a->nz,count;
611:     PetscDraw   popup;
612:     PetscReal scale;

614:     for (i=0; i<nz; i++) {
615:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
616:     }
617:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
618:     PetscDrawGetPopup(draw,&popup);
619:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
620:     count = 0;
621:     for (i=0; i<m; i++) {
622:       y_l = m - i - 1.0; y_r = y_l + 1.0;
623:       for (j=a->i[i]; j<a->i[i+1]; j++) {
624:         x_l = a->j[j]; x_r = x_l + 1.0;
625:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
626:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
627:         count++;
628:       }
629:     }
630:   }
631:   return(0);
632: }

636: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
637: {
639:   PetscDraw      draw;
640:   PetscReal      xr,yr,xl,yl,h,w;
641:   PetscTruth     isnull;

644:   PetscViewerDrawGetDraw(viewer,0,&draw);
645:   PetscDrawIsNull(draw,&isnull);
646:   if (isnull) return(0);

648:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
649:   xr  = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
650:   xr += w;    yr += h;  xl = -w;     yl = -h;
651:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
652:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
653:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
654:   return(0);
655: }

659: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
660: {
662:   PetscTruth     iascii,isbinary,isdraw;

665:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
666:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
667:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
668:   if (iascii) {
669:     MatView_SeqAIJ_ASCII(A,viewer);
670:   } else if (isbinary) {
671:     MatView_SeqAIJ_Binary(A,viewer);
672:   } else if (isdraw) {
673:     MatView_SeqAIJ_Draw(A,viewer);
674:   } else {
675:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
676:   }
677:   MatView_SeqAIJ_Inode(A,viewer);
678:   return(0);
679: }

683: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
684: {
685:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
687:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
688:   PetscInt       m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
689:   MatScalar      *aa = a->a,*ap;
690:   PetscReal      ratio=0.6;

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

695:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
696:   for (i=1; i<m; i++) {
697:     /* move each row back by the amount of empty slots (fshift) before it*/
698:     fshift += imax[i-1] - ailen[i-1];
699:     rmax   = PetscMax(rmax,ailen[i]);
700:     if (fshift) {
701:       ip = aj + ai[i] ;
702:       ap = aa + ai[i] ;
703:       N  = ailen[i];
704:       for (j=0; j<N; j++) {
705:         ip[j-fshift] = ip[j];
706:         ap[j-fshift] = ap[j];
707:       }
708:     }
709:     ai[i] = ai[i-1] + ailen[i-1];
710:   }
711:   if (m) {
712:     fshift += imax[m-1] - ailen[m-1];
713:     ai[m]  = ai[m-1] + ailen[m-1];
714:   }
715:   /* reset ilen and imax for each row */
716:   for (i=0; i<m; i++) {
717:     ailen[i] = imax[i] = ai[i+1] - ai[i];
718:   }
719:   a->nz = ai[m];
720:   if (fshift && a->nounused == -1) {
721:     SETERRQ3(PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);
722:   }

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

729:   a->reallocs          = 0;
730:   A->info.nz_unneeded  = (double)fshift;
731:   a->rmax              = rmax;

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

737:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);

739:   a->idiagvalid = PETSC_FALSE;
740:   return(0);
741: }

745: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
746: {
747:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
748:   PetscInt       i,nz = a->nz;
749:   MatScalar      *aa = a->a;

752:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
753:   return(0);
754: }

758: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
759: {
760:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
761:   PetscInt       i,nz = a->nz;
762:   MatScalar      *aa = a->a;

765:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
766:   return(0);
767: }

771: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
772: {
773:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

777:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
778:   return(0);
779: }

783: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
784: {
785:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

789: #if defined(PETSC_USE_LOG)
790:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
791: #endif
792:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
793:   if (a->row) {
794:     ISDestroy(a->row);
795:   }
796:   if (a->col) {
797:     ISDestroy(a->col);
798:   }
799:   PetscFree(a->diag);
800:   PetscFree2(a->imax,a->ilen);
801:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
802:   PetscFree(a->solve_work);
803:   if (a->icol) {ISDestroy(a->icol);}
804:   PetscFree(a->saved_values);
805:   if (a->coloring) {ISColoringDestroy(a->coloring);}
806:   PetscFree(a->xtoy);
807:   if (a->XtoY) {MatDestroy(a->XtoY);}
808:   if (a->compressedrow.checked && a->compressedrow.use){PetscFree2(a->compressedrow.i,a->compressedrow.rindex);}

810:   MatDestroy_SeqAIJ_Inode(A);

812:   PetscFree(a);

814:   PetscObjectChangeTypeName((PetscObject)A,0);
815:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
816:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
817:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
818:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
819:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
820:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqcsrperm_C","",PETSC_NULL);
821:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
822:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
823:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C","",PETSC_NULL);
824:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
825:   return(0);
826: }

830: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscTruth flg)
831: {
832:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

836:   switch (op) {
837:     case MAT_ROW_ORIENTED:
838:       a->roworiented       = flg;
839:       break;
840:     case MAT_KEEP_NONZERO_PATTERN:
841:       a->keepnonzeropattern    = flg;
842:       break;
843:     case MAT_NEW_NONZERO_LOCATIONS:
844:       a->nonew             = (flg ? 0 : 1);
845:       break;
846:     case MAT_NEW_NONZERO_LOCATION_ERR:
847:       a->nonew             = (flg ? -1 : 0);
848:       break;
849:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
850:       a->nonew             = (flg ? -2 : 0);
851:       break;
852:     case MAT_UNUSED_NONZERO_LOCATION_ERR:
853:       a->nounused          = (flg ? -1 : 0);
854:       break;
855:     case MAT_IGNORE_ZERO_ENTRIES:
856:       a->ignorezeroentries = flg;
857:       break;
858:     case MAT_USE_COMPRESSEDROW:
859:       a->compressedrow.use = flg;
860:       break;
861:     case MAT_SYMMETRIC:
862:     case MAT_STRUCTURALLY_SYMMETRIC:
863:     case MAT_HERMITIAN:
864:     case MAT_SYMMETRY_ETERNAL:
865:     case MAT_NEW_DIAGONALS:
866:     case MAT_IGNORE_OFF_PROC_ENTRIES:
867:     case MAT_USE_HASH_TABLE:
868:       PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
869:       break;
870:     case MAT_USE_INODES:
871:       /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
872:       break;
873:     default:
874:       SETERRQ1(PETSC_ERR_SUP,"unknown option %d",op);
875:   }
876:   MatSetOption_SeqAIJ_Inode(A,op,flg);
877:   return(0);
878: }

882: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
883: {
884:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
886:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
887:   PetscScalar    *aa=a->a,*x,zero=0.0;

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

893:   if (A->factor == MAT_FACTOR_ILU || A->factor == MAT_FACTOR_LU){
894:     PetscInt *diag=a->diag;
895:     VecGetArray(v,&x);
896:     for (i=0; i<n; i++) x[i] = aa[diag[i]];
897:     VecRestoreArray(v,&x);
898:     return(0);
899:   }

901:   VecSet(v,zero);
902:   VecGetArray(v,&x);
903:   for (i=0; i<n; i++) {
904:     nz = ai[i+1] - ai[i];
905:     if (!nz) x[i] = 0.0;
906:     for (j=ai[i]; j<ai[i+1]; j++){
907:       if (aj[j] == i) {
908:         x[i] = aa[j];
909:         break;
910:       }
911:     }
912:   }
913:   VecRestoreArray(v,&x);
914:   return(0);
915: }

917: #include "../src/mat/impls/aij/seq/ftn-kernels/fmult.h"
920: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
921: {
922:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
923:   PetscScalar       *x,*y;
924:   PetscErrorCode    ierr;
925:   PetscInt          m = A->rmap->n;
926: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
927:   MatScalar         *v;
928:   PetscScalar       alpha;
929:   PetscInt          n,i,j,*idx,*ii,*ridx=PETSC_NULL;
930:   Mat_CompressedRow cprow = a->compressedrow;
931:   PetscTruth        usecprow = cprow.use;
932: #endif

935:   if (zz != yy) {VecCopy(zz,yy);}
936:   VecGetArray(xx,&x);
937:   VecGetArray(yy,&y);

939: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
940:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
941: #else
942:   if (usecprow){
943:     m    = cprow.nrows;
944:     ii   = cprow.i;
945:     ridx = cprow.rindex;
946:   } else {
947:     ii = a->i;
948:   }
949:   for (i=0; i<m; i++) {
950:     idx   = a->j + ii[i] ;
951:     v     = a->a + ii[i] ;
952:     n     = ii[i+1] - ii[i];
953:     if (usecprow){
954:       alpha = x[ridx[i]];
955:     } else {
956:       alpha = x[i];
957:     }
958:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
959:   }
960: #endif
961:   PetscLogFlops(2.0*a->nz);
962:   VecRestoreArray(xx,&x);
963:   VecRestoreArray(yy,&y);
964:   return(0);
965: }

969: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
970: {

974:   VecSet(yy,0.0);
975:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
976:   return(0);
977: }

979: #include "../src/mat/impls/aij/seq/ftn-kernels/fmult.h"
982: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
983: {
984:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
985:   PetscScalar       *y;
986:   const PetscScalar *x;
987:   const MatScalar   *aa;
988:   PetscErrorCode    ierr;
989:   PetscInt          m=A->rmap->n;
990:   const PetscInt    *aj,*ii,*ridx=PETSC_NULL;
991:   PetscInt          n,i,nonzerorow=0;
992:   PetscScalar       sum;
993:   PetscTruth        usecprow=a->compressedrow.use;

995: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
996: #pragma disjoint(*x,*y,*aa)
997: #endif

1000:   VecGetArray(xx,(PetscScalar**)&x);
1001:   VecGetArray(yy,&y);
1002:   aj  = a->j;
1003:   aa  = a->a;
1004:   ii  = a->i;
1005:   if (usecprow){ /* use compressed row format */
1006:     m    = a->compressedrow.nrows;
1007:     ii   = a->compressedrow.i;
1008:     ridx = a->compressedrow.rindex;
1009:     for (i=0; i<m; i++){
1010:       n   = ii[i+1] - ii[i];
1011:       aj  = a->j + ii[i];
1012:       aa  = a->a + ii[i];
1013:       sum = 0.0;
1014:       nonzerorow += (n>0);
1015:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1016:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1017:       y[*ridx++] = sum;
1018:     }
1019:   } else { /* do not use compressed row format */
1020: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1021:     fortranmultaij_(&m,x,ii,aj,aa,y);
1022: #else
1023:     for (i=0; i<m; i++) {
1024:       n   = ii[i+1] - ii[i];
1025:       aj  = a->j + ii[i];
1026:       aa  = a->a + ii[i];
1027:       sum  = 0.0;
1028:       nonzerorow += (n>0);
1029:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1030:       y[i] = sum;
1031:     }
1032: #endif
1033:   }
1034:   PetscLogFlops(2.0*a->nz - nonzerorow);
1035:   VecRestoreArray(xx,(PetscScalar**)&x);
1036:   VecRestoreArray(yy,&y);
1037:   return(0);
1038: }

1040: #include "../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h"
1043: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1044: {
1045:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
1046:   PetscScalar     *x,*y,*z;
1047:   const MatScalar *aa;
1048:   PetscErrorCode  ierr;
1049:   PetscInt        m = A->rmap->n,*aj,*ii;
1050: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1051:   PetscInt        n,i,jrow,j,*ridx=PETSC_NULL;
1052:   PetscScalar     sum;
1053:   PetscTruth      usecprow=a->compressedrow.use;
1054: #endif

1057:   VecGetArray(xx,&x);
1058:   VecGetArray(yy,&y);
1059:   if (zz != yy) {
1060:     VecGetArray(zz,&z);
1061:   } else {
1062:     z = y;
1063:   }

1065:   aj  = a->j;
1066:   aa  = a->a;
1067:   ii  = a->i;
1068: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1069:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1070: #else
1071:   if (usecprow){ /* use compressed row format */
1072:     if (zz != yy){
1073:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1074:     }
1075:     m    = a->compressedrow.nrows;
1076:     ii   = a->compressedrow.i;
1077:     ridx = a->compressedrow.rindex;
1078:     for (i=0; i<m; i++){
1079:       n  = ii[i+1] - ii[i];
1080:       aj  = a->j + ii[i];
1081:       aa  = a->a + ii[i];
1082:       sum = y[*ridx];
1083:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
1084:       z[*ridx++] = sum;
1085:     }
1086:   } else { /* do not use compressed row format */
1087:     for (i=0; i<m; i++) {
1088:       jrow = ii[i];
1089:       n    = ii[i+1] - jrow;
1090:       sum  = y[i];
1091:       for (j=0; j<n; j++) {
1092:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
1093:       }
1094:       z[i] = sum;
1095:     }
1096:   }
1097: #endif
1098:   PetscLogFlops(2.0*a->nz);
1099:   VecRestoreArray(xx,&x);
1100:   VecRestoreArray(yy,&y);
1101:   if (zz != yy) {
1102:     VecRestoreArray(zz,&z);
1103:   }
1104:   return(0);
1105: }

1107: /*
1108:      Adds diagonal pointers to sparse matrix structure.
1109: */
1112: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1113: {
1114:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1116:   PetscInt       i,j,m = A->rmap->n;

1119:   if (!a->diag) {
1120:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
1121:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
1122:   }
1123:   for (i=0; i<A->rmap->n; i++) {
1124:     a->diag[i] = a->i[i+1];
1125:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1126:       if (a->j[j] == i) {
1127:         a->diag[i] = j;
1128:         break;
1129:       }
1130:     }
1131:   }
1132:   return(0);
1133: }

1135: /*
1136:      Checks for missing diagonals
1137: */
1140: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscTruth *missing,PetscInt *d)
1141: {
1142:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1143:   PetscInt       *diag,*jj = a->j,i;

1146:   *missing = PETSC_FALSE;
1147:   if (A->rmap->n > 0 && !jj) {
1148:     *missing  = PETSC_TRUE;
1149:     if (d) *d = 0;
1150:     PetscInfo(A,"Matrix has no entries therefor is missing diagonal");
1151:   } else {
1152:     diag = a->diag;
1153:     for (i=0; i<A->rmap->n; i++) {
1154:       if (jj[diag[i]] != i) {
1155:         *missing = PETSC_TRUE;
1156:         if (d) *d = i;
1157:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1158:       }
1159:     }
1160:   }
1161:   return(0);
1162: }

1167: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1168: {
1169:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1171:   PetscInt       i,*diag,m = A->rmap->n;
1172:   MatScalar      *v = a->a;
1173:   PetscScalar    *idiag,*mdiag;

1176:   if (a->idiagvalid) return(0);
1177:   MatMarkDiagonal_SeqAIJ(A);
1178:   diag = a->diag;
1179:   if (!a->idiag) {
1180:     PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
1181:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1182:     v        = a->a;
1183:   }
1184:   mdiag = a->mdiag;
1185:   idiag = a->idiag;
1186: 
1187:   if (omega == 1.0 && !PetscAbsScalar(fshift)) {
1188:     for (i=0; i<m; i++) {
1189:       mdiag[i] = v[diag[i]];
1190:       if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1191:       idiag[i] = 1.0/v[diag[i]];
1192:     }
1193:     PetscLogFlops(m);
1194:   } else {
1195:     for (i=0; i<m; i++) {
1196:       mdiag[i] = v[diag[i]];
1197:       idiag[i] = omega/(fshift + v[diag[i]]);
1198:     }
1199:     PetscLogFlops(2.0*m);
1200:   }
1201:   a->idiagvalid = PETSC_TRUE;
1202:   return(0);
1203: }

1206: #include "../src/mat/impls/aij/seq/ftn-kernels/frelax.h"
1209: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1210: {
1211:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
1212:   PetscScalar        *x,d,sum,*t,scale;
1213:   const MatScalar    *v = a->a,*idiag=0,*mdiag;
1214:   const PetscScalar  *b, *bs,*xb, *ts;
1215:   PetscErrorCode     ierr;
1216:   PetscInt           n = A->cmap->n,m = A->rmap->n,i;
1217:   const PetscInt     *idx,*diag;

1220:   its = its*lits;

1222:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1223:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1224:   a->fshift = fshift;
1225:   a->omega  = omega;

1227:   diag = a->diag;
1228:   t     = a->ssor_work;
1229:   idiag = a->idiag;
1230:   mdiag = a->mdiag;

1232:   VecGetArray(xx,&x);
1233:   if (xx != bb) {
1234:     VecGetArray(bb,(PetscScalar**)&b);
1235:   } else {
1236:     b = x;
1237:   }
1238:   CHKMEMQ;
1239:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1240:   if (flag == SOR_APPLY_UPPER) {
1241:    /* apply (U + D/omega) to the vector */
1242:     bs = b;
1243:     for (i=0; i<m; i++) {
1244:         d    = fshift + mdiag[i];
1245:         n    = a->i[i+1] - diag[i] - 1;
1246:         idx  = a->j + diag[i] + 1;
1247:         v    = a->a + diag[i] + 1;
1248:         sum  = b[i]*d/omega;
1249:         PetscSparseDensePlusDot(sum,bs,v,idx,n);
1250:         x[i] = sum;
1251:     }
1252:     VecRestoreArray(xx,&x);
1253:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1254:     PetscLogFlops(a->nz);
1255:     return(0);
1256:   }

1258:   if (flag == SOR_APPLY_LOWER) {
1259:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1260:   } else if (flag & SOR_EISENSTAT) {
1261:     /* Let  A = L + U + D; where L is lower trianglar,
1262:     U is upper triangular, E = D/omega; This routine applies

1264:             (L + E)^{-1} A (U + E)^{-1}

1266:     to a vector efficiently using Eisenstat's trick. 
1267:     */
1268:     scale = (2.0/omega) - 1.0;

1270:     /*  x = (E + U)^{-1} b */
1271:     for (i=m-1; i>=0; i--) {
1272:       n    = a->i[i+1] - diag[i] - 1;
1273:       idx  = a->j + diag[i] + 1;
1274:       v    = a->a + diag[i] + 1;
1275:       sum  = b[i];
1276:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1277:       x[i] = sum*idiag[i];
1278:     }

1280:     /*  t = b - (2*E - D)x */
1281:     v = a->a;
1282:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1284:     /*  t = (E + L)^{-1}t */
1285:     ts = t;
1286:     diag = a->diag;
1287:     for (i=0; i<m; i++) {
1288:       n    = diag[i] - a->i[i];
1289:       idx  = a->j + a->i[i];
1290:       v    = a->a + a->i[i];
1291:       sum  = t[i];
1292:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1293:       t[i] = sum*idiag[i];
1294:       /*  x = x + t */
1295:       x[i] += t[i];
1296:     }

1298:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1299:     VecRestoreArray(xx,&x);
1300:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1301:     return(0);
1302:   }
1303:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1304:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1305:       for (i=0; i<m; i++) {
1306:         n    = diag[i] - a->i[i];
1307:         idx  = a->j + a->i[i];
1308:         v    = a->a + a->i[i];
1309:         sum  = b[i];
1310:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1311:         t[i] = sum;
1312:         x[i] = sum*idiag[i];
1313:       }
1314:       xb = t;
1315:       PetscLogFlops(a->nz);
1316:     } else xb = b;
1317:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1318:       for (i=m-1; i>=0; i--) {
1319:         n    = a->i[i+1] - diag[i] - 1;
1320:         idx  = a->j + diag[i] + 1;
1321:         v    = a->a + diag[i] + 1;
1322:         sum  = xb[i];
1323:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1324:         if (xb == b) {
1325:           x[i] = sum*idiag[i];
1326:         } else {
1327:           x[i] = (1-omega)*x[i] + sum*idiag[i];
1328:         }
1329:       }
1330:       PetscLogFlops(a->nz);
1331:     }
1332:     its--;
1333:   }
1334:   while (its--) {
1335:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1336:       for (i=0; i<m; i++) {
1337:         n    = a->i[i+1] - a->i[i];
1338:         idx  = a->j + a->i[i];
1339:         v    = a->a + a->i[i];
1340:         sum  = b[i];
1341:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1342:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1343:       }
1344:       PetscLogFlops(2.0*a->nz);
1345:     }
1346:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1347:       for (i=m-1; i>=0; i--) {
1348:         n    = a->i[i+1] - a->i[i];
1349:         idx  = a->j + a->i[i];
1350:         v    = a->a + a->i[i];
1351:         sum  = b[i];
1352:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1353:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1354:       }
1355:       PetscLogFlops(2.0*a->nz);
1356:     }
1357:   }
1358:   VecRestoreArray(xx,&x);
1359:   if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1360:   CHKMEMQ;  return(0);
1361: }


1366: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1367: {
1368:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1371:   info->block_size     = 1.0;
1372:   info->nz_allocated   = (double)a->maxnz;
1373:   info->nz_used        = (double)a->nz;
1374:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1375:   info->assemblies     = (double)A->num_ass;
1376:   info->mallocs        = (double)a->reallocs;
1377:   info->memory         = ((PetscObject)A)->mem;
1378:   if (A->factor) {
1379:     info->fill_ratio_given  = A->info.fill_ratio_given;
1380:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1381:     info->factor_mallocs    = A->info.factor_mallocs;
1382:   } else {
1383:     info->fill_ratio_given  = 0;
1384:     info->fill_ratio_needed = 0;
1385:     info->factor_mallocs    = 0;
1386:   }
1387:   return(0);
1388: }

1392: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1393: {
1394:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1395:   PetscInt       i,m = A->rmap->n - 1,d = 0;
1397:   PetscTruth     missing;

1400:   if (a->keepnonzeropattern) {
1401:     for (i=0; i<N; i++) {
1402:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1403:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1404:     }
1405:     if (diag != 0.0) {
1406:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1407:       if (missing) SETERRQ1(PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1408:       for (i=0; i<N; i++) {
1409:         a->a[a->diag[rows[i]]] = diag;
1410:       }
1411:     }
1412:     A->same_nonzero = PETSC_TRUE;
1413:   } else {
1414:     if (diag != 0.0) {
1415:       for (i=0; i<N; i++) {
1416:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1417:         if (a->ilen[rows[i]] > 0) {
1418:           a->ilen[rows[i]]          = 1;
1419:           a->a[a->i[rows[i]]] = diag;
1420:           a->j[a->i[rows[i]]] = rows[i];
1421:         } else { /* in case row was completely empty */
1422:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1423:         }
1424:       }
1425:     } else {
1426:       for (i=0; i<N; i++) {
1427:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1428:         a->ilen[rows[i]] = 0;
1429:       }
1430:     }
1431:     A->same_nonzero = PETSC_FALSE;
1432:   }
1433:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1434:   return(0);
1435: }

1439: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1440: {
1441:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1442:   PetscInt   *itmp;

1445:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1447:   *nz = a->i[row+1] - a->i[row];
1448:   if (v) *v = a->a + a->i[row];
1449:   if (idx) {
1450:     itmp = a->j + a->i[row];
1451:     if (*nz) {
1452:       *idx = itmp;
1453:     }
1454:     else *idx = 0;
1455:   }
1456:   return(0);
1457: }

1459: /* remove this function? */
1462: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1463: {
1465:   return(0);
1466: }

1470: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1471: {
1472:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1473:   MatScalar      *v = a->a;
1474:   PetscReal      sum = 0.0;
1476:   PetscInt       i,j;

1479:   if (type == NORM_FROBENIUS) {
1480:     for (i=0; i<a->nz; i++) {
1481: #if defined(PETSC_USE_COMPLEX)
1482:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1483: #else
1484:       sum += (*v)*(*v); v++;
1485: #endif
1486:     }
1487:     *nrm = sqrt(sum);
1488:   } else if (type == NORM_1) {
1489:     PetscReal *tmp;
1490:     PetscInt    *jj = a->j;
1491:     PetscMalloc((A->cmap->n+1)*sizeof(PetscReal),&tmp);
1492:     PetscMemzero(tmp,A->cmap->n*sizeof(PetscReal));
1493:     *nrm = 0.0;
1494:     for (j=0; j<a->nz; j++) {
1495:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
1496:     }
1497:     for (j=0; j<A->cmap->n; j++) {
1498:       if (tmp[j] > *nrm) *nrm = tmp[j];
1499:     }
1500:     PetscFree(tmp);
1501:   } else if (type == NORM_INFINITY) {
1502:     *nrm = 0.0;
1503:     for (j=0; j<A->rmap->n; j++) {
1504:       v = a->a + a->i[j];
1505:       sum = 0.0;
1506:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1507:         sum += PetscAbsScalar(*v); v++;
1508:       }
1509:       if (sum > *nrm) *nrm = sum;
1510:     }
1511:   } else {
1512:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1513:   }
1514:   return(0);
1515: }

1519: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
1520: {
1521:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1522:   Mat            C;
1524:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
1525:   MatScalar      *array = a->a;

1528:   if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

1530:   if (reuse == MAT_INITIAL_MATRIX || *B == A) {
1531:     PetscMalloc((1+A->cmap->n)*sizeof(PetscInt),&col);
1532:     PetscMemzero(col,(1+A->cmap->n)*sizeof(PetscInt));
1533: 
1534:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1535:     MatCreate(((PetscObject)A)->comm,&C);
1536:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
1537:     MatSetType(C,((PetscObject)A)->type_name);
1538:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1539:     PetscFree(col);
1540:   } else {
1541:     C = *B;
1542:   }

1544:   for (i=0; i<m; i++) {
1545:     len    = ai[i+1]-ai[i];
1546:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1547:     array += len;
1548:     aj    += len;
1549:   }
1550:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1551:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1553:   if (reuse == MAT_INITIAL_MATRIX || *B != A) {
1554:     *B = C;
1555:   } else {
1556:     MatHeaderCopy(A,C);
1557:   }
1558:   return(0);
1559: }

1564: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1565: {
1566:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1567:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1568:   MatScalar      *va,*vb;
1570:   PetscInt       ma,na,mb,nb, i;

1573:   bij = (Mat_SeqAIJ *) B->data;
1574: 
1575:   MatGetSize(A,&ma,&na);
1576:   MatGetSize(B,&mb,&nb);
1577:   if (ma!=nb || na!=mb){
1578:     *f = PETSC_FALSE;
1579:     return(0);
1580:   }
1581:   aii = aij->i; bii = bij->i;
1582:   adx = aij->j; bdx = bij->j;
1583:   va  = aij->a; vb = bij->a;
1584:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1585:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1586:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1587:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1589:   *f = PETSC_TRUE;
1590:   for (i=0; i<ma; i++) {
1591:     while (aptr[i]<aii[i+1]) {
1592:       PetscInt         idc,idr;
1593:       PetscScalar vc,vr;
1594:       /* column/row index/value */
1595:       idc = adx[aptr[i]];
1596:       idr = bdx[bptr[idc]];
1597:       vc  = va[aptr[i]];
1598:       vr  = vb[bptr[idc]];
1599:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1600:         *f = PETSC_FALSE;
1601:         goto done;
1602:       } else {
1603:         aptr[i]++;
1604:         if (B || i!=idc) bptr[idc]++;
1605:       }
1606:     }
1607:   }
1608:  done:
1609:   PetscFree(aptr);
1610:   if (B) {
1611:     PetscFree(bptr);
1612:   }
1613:   return(0);
1614: }

1620: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1621: {
1622:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1623:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1624:   MatScalar      *va,*vb;
1626:   PetscInt       ma,na,mb,nb, i;

1629:   bij = (Mat_SeqAIJ *) B->data;
1630: 
1631:   MatGetSize(A,&ma,&na);
1632:   MatGetSize(B,&mb,&nb);
1633:   if (ma!=nb || na!=mb){
1634:     *f = PETSC_FALSE;
1635:     return(0);
1636:   }
1637:   aii = aij->i; bii = bij->i;
1638:   adx = aij->j; bdx = bij->j;
1639:   va  = aij->a; vb = bij->a;
1640:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1641:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1642:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1643:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1645:   *f = PETSC_TRUE;
1646:   for (i=0; i<ma; i++) {
1647:     while (aptr[i]<aii[i+1]) {
1648:       PetscInt         idc,idr;
1649:       PetscScalar vc,vr;
1650:       /* column/row index/value */
1651:       idc = adx[aptr[i]];
1652:       idr = bdx[bptr[idc]];
1653:       vc  = va[aptr[i]];
1654:       vr  = vb[bptr[idc]];
1655:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
1656:         *f = PETSC_FALSE;
1657:         goto done;
1658:       } else {
1659:         aptr[i]++;
1660:         if (B || i!=idc) bptr[idc]++;
1661:       }
1662:     }
1663:   }
1664:  done:
1665:   PetscFree(aptr);
1666:   if (B) {
1667:     PetscFree(bptr);
1668:   }
1669:   return(0);
1670: }

1675: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1676: {
1679:   MatIsTranspose_SeqAIJ(A,A,tol,f);
1680:   return(0);
1681: }

1685: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1686: {
1689:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
1690:   return(0);
1691: }

1695: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1696: {
1697:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1698:   PetscScalar    *l,*r,x;
1699:   MatScalar      *v;
1701:   PetscInt       i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj;

1704:   if (ll) {
1705:     /* The local size is used so that VecMPI can be passed to this routine
1706:        by MatDiagonalScale_MPIAIJ */
1707:     VecGetLocalSize(ll,&m);
1708:     if (m != A->rmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1709:     VecGetArray(ll,&l);
1710:     v = a->a;
1711:     for (i=0; i<m; i++) {
1712:       x = l[i];
1713:       M = a->i[i+1] - a->i[i];
1714:       for (j=0; j<M; j++) { (*v++) *= x;}
1715:     }
1716:     VecRestoreArray(ll,&l);
1717:     PetscLogFlops(nz);
1718:   }
1719:   if (rr) {
1720:     VecGetLocalSize(rr,&n);
1721:     if (n != A->cmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1722:     VecGetArray(rr,&r);
1723:     v = a->a; jj = a->j;
1724:     for (i=0; i<nz; i++) {
1725:       (*v++) *= r[*jj++];
1726:     }
1727:     VecRestoreArray(rr,&r);
1728:     PetscLogFlops(nz);
1729:   }
1730:   return(0);
1731: }

1735: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
1736: {
1737:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
1739:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
1740:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1741:   const PetscInt *irow,*icol;
1742:   PetscInt       nrows,ncols;
1743:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1744:   MatScalar      *a_new,*mat_a;
1745:   Mat            C;
1746:   PetscTruth     stride,sorted;

1749:   ISSorted(isrow,&sorted);
1750:   if (!sorted) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1751:   ISSorted(iscol,&sorted);
1752:   if (!sorted) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1754:   ISGetIndices(isrow,&irow);
1755:   ISGetLocalSize(isrow,&nrows);
1756:   ISGetLocalSize(iscol,&ncols);

1758:   ISStrideGetInfo(iscol,&first,&step);
1759:   ISStride(iscol,&stride);
1760:   if (stride && step == 1) {
1761:     /* special case of contiguous rows */
1762:     PetscMalloc2(nrows,PetscInt,&lens,nrows,PetscInt,&starts);
1763:     /* loop over new rows determining lens and starting points */
1764:     for (i=0; i<nrows; i++) {
1765:       kstart  = ai[irow[i]];
1766:       kend    = kstart + ailen[irow[i]];
1767:       for (k=kstart; k<kend; k++) {
1768:         if (aj[k] >= first) {
1769:           starts[i] = k;
1770:           break;
1771:         }
1772:       }
1773:       sum = 0;
1774:       while (k < kend) {
1775:         if (aj[k++] >= first+ncols) break;
1776:         sum++;
1777:       }
1778:       lens[i] = sum;
1779:     }
1780:     /* create submatrix */
1781:     if (scall == MAT_REUSE_MATRIX) {
1782:       PetscInt n_cols,n_rows;
1783:       MatGetSize(*B,&n_rows,&n_cols);
1784:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1785:       MatZeroEntries(*B);
1786:       C = *B;
1787:     } else {
1788:       MatCreate(((PetscObject)A)->comm,&C);
1789:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1790:       MatSetType(C,((PetscObject)A)->type_name);
1791:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1792:     }
1793:     c = (Mat_SeqAIJ*)C->data;

1795:     /* loop over rows inserting into submatrix */
1796:     a_new    = c->a;
1797:     j_new    = c->j;
1798:     i_new    = c->i;

1800:     for (i=0; i<nrows; i++) {
1801:       ii    = starts[i];
1802:       lensi = lens[i];
1803:       for (k=0; k<lensi; k++) {
1804:         *j_new++ = aj[ii+k] - first;
1805:       }
1806:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1807:       a_new      += lensi;
1808:       i_new[i+1]  = i_new[i] + lensi;
1809:       c->ilen[i]  = lensi;
1810:     }
1811:     PetscFree2(lens,starts);
1812:   } else {
1813:     ISGetIndices(iscol,&icol);
1814:     PetscMalloc(oldcols*sizeof(PetscInt),&smap);
1815:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
1816:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
1817:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1818:     /* determine lens of each row */
1819:     for (i=0; i<nrows; i++) {
1820:       kstart  = ai[irow[i]];
1821:       kend    = kstart + a->ilen[irow[i]];
1822:       lens[i] = 0;
1823:       for (k=kstart; k<kend; k++) {
1824:         if (smap[aj[k]]) {
1825:           lens[i]++;
1826:         }
1827:       }
1828:     }
1829:     /* Create and fill new matrix */
1830:     if (scall == MAT_REUSE_MATRIX) {
1831:       PetscTruth equal;

1833:       c = (Mat_SeqAIJ *)((*B)->data);
1834:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1835:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
1836:       if (!equal) {
1837:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1838:       }
1839:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
1840:       C = *B;
1841:     } else {
1842:       MatCreate(((PetscObject)A)->comm,&C);
1843:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1844:       MatSetType(C,((PetscObject)A)->type_name);
1845:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1846:     }
1847:     c = (Mat_SeqAIJ *)(C->data);
1848:     for (i=0; i<nrows; i++) {
1849:       row    = irow[i];
1850:       kstart = ai[row];
1851:       kend   = kstart + a->ilen[row];
1852:       mat_i  = c->i[i];
1853:       mat_j  = c->j + mat_i;
1854:       mat_a  = c->a + mat_i;
1855:       mat_ilen = c->ilen + i;
1856:       for (k=kstart; k<kend; k++) {
1857:         if ((tcol=smap[a->j[k]])) {
1858:           *mat_j++ = tcol - 1;
1859:           *mat_a++ = a->a[k];
1860:           (*mat_ilen)++;

1862:         }
1863:       }
1864:     }
1865:     /* Free work space */
1866:     ISRestoreIndices(iscol,&icol);
1867:     PetscFree(smap);
1868:     PetscFree(lens);
1869:   }
1870:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1871:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1873:   ISRestoreIndices(isrow,&irow);
1874:   *B = C;
1875:   return(0);
1876: }

1880: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
1881: {
1882:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
1884:   Mat            outA;
1885:   PetscTruth     row_identity,col_identity;

1888:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

1890:   ISIdentity(row,&row_identity);
1891:   ISIdentity(col,&col_identity);

1893:   outA          = inA;
1894:   outA->factor  = MAT_FACTOR_LU;
1895:   PetscObjectReference((PetscObject)row);
1896:   if (a->row) { ISDestroy(a->row);}
1897:   a->row = row;
1898:   PetscObjectReference((PetscObject)col);
1899:   if (a->col) { ISDestroy(a->col);}
1900:   a->col = col;

1902:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1903:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1904:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1905:   PetscLogObjectParent(inA,a->icol);

1907:   if (!a->solve_work) { /* this matrix may have been factored before */
1908:      PetscMalloc((inA->rmap->n+1)*sizeof(PetscScalar),&a->solve_work);
1909:      PetscLogObjectMemory(inA, (inA->rmap->n+1)*sizeof(PetscScalar));
1910:   }

1912:   MatMarkDiagonal_SeqAIJ(inA);
1913:   if (row_identity && col_identity) {
1914:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
1915:   } else {
1916:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
1917:   }
1918:   return(0);
1919: }

1923: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
1924: {
1925:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
1926:   PetscScalar    oalpha = alpha;
1928:   PetscBLASInt   one = 1,bnz = PetscBLASIntCast(a->nz);

1931:   BLASscal_(&bnz,&oalpha,a->a,&one);
1932:   PetscLogFlops(a->nz);
1933:   return(0);
1934: }

1938: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1939: {
1941:   PetscInt       i;

1944:   if (scall == MAT_INITIAL_MATRIX) {
1945:     PetscMalloc((n+1)*sizeof(Mat),B);
1946:   }

1948:   for (i=0; i<n; i++) {
1949:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1950:   }
1951:   return(0);
1952: }

1956: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
1957: {
1958:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1960:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
1961:   const PetscInt *idx;
1962:   PetscInt       start,end,*ai,*aj;
1963:   PetscBT        table;

1966:   m     = A->rmap->n;
1967:   ai    = a->i;
1968:   aj    = a->j;

1970:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

1972:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
1973:   PetscBTCreate(m,table);

1975:   for (i=0; i<is_max; i++) {
1976:     /* Initialize the two local arrays */
1977:     isz  = 0;
1978:     PetscBTMemzero(m,table);
1979: 
1980:     /* Extract the indices, assume there can be duplicate entries */
1981:     ISGetIndices(is[i],&idx);
1982:     ISGetLocalSize(is[i],&n);
1983: 
1984:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1985:     for (j=0; j<n ; ++j){
1986:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1987:     }
1988:     ISRestoreIndices(is[i],&idx);
1989:     ISDestroy(is[i]);
1990: 
1991:     k = 0;
1992:     for (j=0; j<ov; j++){ /* for each overlap */
1993:       n = isz;
1994:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1995:         row   = nidx[k];
1996:         start = ai[row];
1997:         end   = ai[row+1];
1998:         for (l = start; l<end ; l++){
1999:           val = aj[l] ;
2000:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
2001:         }
2002:       }
2003:     }
2004:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
2005:   }
2006:   PetscBTDestroy(table);
2007:   PetscFree(nidx);
2008:   return(0);
2009: }

2011: /* -------------------------------------------------------------- */
2014: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2015: {
2016:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2018:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2019:   const PetscInt *row,*col;
2020:   PetscInt       *cnew,j,*lens;
2021:   IS             icolp,irowp;
2022:   PetscInt       *cwork = PETSC_NULL;
2023:   PetscScalar    *vwork = PETSC_NULL;

2026:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2027:   ISGetIndices(irowp,&row);
2028:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2029:   ISGetIndices(icolp,&col);
2030: 
2031:   /* determine lengths of permuted rows */
2032:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
2033:   for (i=0; i<m; i++) {
2034:     lens[row[i]] = a->i[i+1] - a->i[i];
2035:   }
2036:   MatCreate(((PetscObject)A)->comm,B);
2037:   MatSetSizes(*B,m,n,m,n);
2038:   MatSetType(*B,((PetscObject)A)->type_name);
2039:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2040:   PetscFree(lens);

2042:   PetscMalloc(n*sizeof(PetscInt),&cnew);
2043:   for (i=0; i<m; i++) {
2044:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2045:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
2046:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2047:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2048:   }
2049:   PetscFree(cnew);
2050:   (*B)->assembled     = PETSC_FALSE;
2051:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2052:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2053:   ISRestoreIndices(irowp,&row);
2054:   ISRestoreIndices(icolp,&col);
2055:   ISDestroy(irowp);
2056:   ISDestroy(icolp);
2057:   return(0);
2058: }

2062: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2063: {

2067:   /* If the two matrices have the same copy implementation, use fast copy. */
2068:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2069:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2070:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2072:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) {
2073:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
2074:     }
2075:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2076:   } else {
2077:     MatCopy_Basic(A,B,str);
2078:   }
2079:   return(0);
2080: }

2084: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
2085: {

2089:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2090:   return(0);
2091: }

2095: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2096: {
2097:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2099:   *array = a->a;
2100:   return(0);
2101: }

2105: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2106: {
2108:   return(0);
2109: }

2113: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2114: {
2115:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
2117:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
2118:   PetscScalar    dx,*y,*xx,*w3_array;
2119:   PetscScalar    *vscale_array;
2120:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
2121:   Vec            w1,w2,w3;
2122:   void           *fctx = coloring->fctx;
2123:   PetscTruth     flg = PETSC_FALSE;

2126:   if (!coloring->w1) {
2127:     VecDuplicate(x1,&coloring->w1);
2128:     PetscLogObjectParent(coloring,coloring->w1);
2129:     VecDuplicate(x1,&coloring->w2);
2130:     PetscLogObjectParent(coloring,coloring->w2);
2131:     VecDuplicate(x1,&coloring->w3);
2132:     PetscLogObjectParent(coloring,coloring->w3);
2133:   }
2134:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

2136:   MatSetUnfactored(J);
2137:   PetscOptionsGetTruth(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg,PETSC_NULL);
2138:   if (flg) {
2139:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2140:   } else {
2141:     PetscTruth assembled;
2142:     MatAssembled(J,&assembled);
2143:     if (assembled) {
2144:       MatZeroEntries(J);
2145:     }
2146:   }

2148:   VecGetOwnershipRange(x1,&start,&end);
2149:   VecGetSize(x1,&N);

2151:   /*
2152:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
2153:      coloring->F for the coarser grids from the finest
2154:   */
2155:   if (coloring->F) {
2156:     VecGetLocalSize(coloring->F,&m1);
2157:     VecGetLocalSize(w1,&m2);
2158:     if (m1 != m2) {
2159:       coloring->F = 0;
2160:     }
2161:   }

2163:   if (coloring->F) {
2164:     w1          = coloring->F;
2165:     coloring->F = 0;
2166:   } else {
2167:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2168:     (*f)(sctx,x1,w1,fctx);
2169:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2170:   }

2172:   /* 
2173:       Compute all the scale factors and share with other processors
2174:   */
2175:   VecGetArray(x1,&xx);xx = xx - start;
2176:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2177:   for (k=0; k<coloring->ncolors; k++) {
2178:     /*
2179:        Loop over each column associated with color adding the 
2180:        perturbation to the vector w3.
2181:     */
2182:     for (l=0; l<coloring->ncolumns[k]; l++) {
2183:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2184:       dx  = xx[col];
2185:       if (dx == 0.0) dx = 1.0;
2186: #if !defined(PETSC_USE_COMPLEX)
2187:       if (dx < umin && dx >= 0.0)      dx = umin;
2188:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2189: #else
2190:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2191:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2192: #endif
2193:       dx                *= epsilon;
2194:       vscale_array[col] = 1.0/dx;
2195:     }
2196:   }
2197:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2198:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2199:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2201:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2202:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2204:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2205:   else                        vscaleforrow = coloring->columnsforrow;

2207:   VecGetArray(coloring->vscale,&vscale_array);
2208:   /*
2209:       Loop over each color
2210:   */
2211:   for (k=0; k<coloring->ncolors; k++) {
2212:     coloring->currentcolor = k;
2213:     VecCopy(x1,w3);
2214:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2215:     /*
2216:        Loop over each column associated with color adding the 
2217:        perturbation to the vector w3.
2218:     */
2219:     for (l=0; l<coloring->ncolumns[k]; l++) {
2220:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2221:       dx  = xx[col];
2222:       if (dx == 0.0) dx = 1.0;
2223: #if !defined(PETSC_USE_COMPLEX)
2224:       if (dx < umin && dx >= 0.0)      dx = umin;
2225:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2226: #else
2227:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2228:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2229: #endif
2230:       dx            *= epsilon;
2231:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2232:       w3_array[col] += dx;
2233:     }
2234:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2236:     /*
2237:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2238:     */

2240:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2241:     (*f)(sctx,w3,w2,fctx);
2242:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2243:     VecAXPY(w2,-1.0,w1);

2245:     /*
2246:        Loop over rows of vector, putting results into Jacobian matrix
2247:     */
2248:     VecGetArray(w2,&y);
2249:     for (l=0; l<coloring->nrows[k]; l++) {
2250:       row    = coloring->rows[k][l];
2251:       col    = coloring->columnsforrow[k][l];
2252:       y[row] *= vscale_array[vscaleforrow[k][l]];
2253:       srow   = row + start;
2254:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2255:     }
2256:     VecRestoreArray(w2,&y);
2257:   }
2258:   coloring->currentcolor = k;
2259:   VecRestoreArray(coloring->vscale,&vscale_array);
2260:   xx = xx + start; VecRestoreArray(x1,&xx);
2261:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2262:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2263:   return(0);
2264: }

2268: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2269: {
2271:   PetscInt       i;
2272:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2273:   PetscBLASInt   one=1,bnz = PetscBLASIntCast(x->nz);

2276:   if (str == SAME_NONZERO_PATTERN) {
2277:     PetscScalar alpha = a;
2278:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2279:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2280:     if (y->xtoy && y->XtoY != X) {
2281:       PetscFree(y->xtoy);
2282:       MatDestroy(y->XtoY);
2283:     }
2284:     if (!y->xtoy) { /* get xtoy */
2285:       MatAXPYGetxtoy_Private(X->rmap->n,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2286:       y->XtoY = X;
2287:       PetscObjectReference((PetscObject)X);
2288:     }
2289:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2290:     PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %G\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz);
2291:   } else {
2292:     MatAXPY_Basic(Y,a,X,str);
2293:   }
2294:   return(0);
2295: }

2299: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
2300: {

2304:   PetscLayoutSetBlockSize(A->rmap,bs);
2305:   PetscLayoutSetBlockSize(A->cmap,bs);
2306:   return(0);
2307: }

2311: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2312: {
2313: #if defined(PETSC_USE_COMPLEX)
2314:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
2315:   PetscInt    i,nz;
2316:   PetscScalar *a;

2319:   nz = aij->nz;
2320:   a  = aij->a;
2321:   for (i=0; i<nz; i++) {
2322:     a[i] = PetscConj(a[i]);
2323:   }
2324: #else
2326: #endif
2327:   return(0);
2328: }

2332: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2333: {
2334:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2336:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2337:   PetscReal      atmp;
2338:   PetscScalar    *x;
2339:   MatScalar      *aa;

2342:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2343:   aa   = a->a;
2344:   ai   = a->i;
2345:   aj   = a->j;

2347:   VecSet(v,0.0);
2348:   VecGetArray(v,&x);
2349:   VecGetLocalSize(v,&n);
2350:   if (n != A->rmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2351:   for (i=0; i<m; i++) {
2352:     ncols = ai[1] - ai[0]; ai++;
2353:     x[i] = 0.0;
2354:     for (j=0; j<ncols; j++){
2355:       atmp = PetscAbsScalar(*aa);
2356:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2357:       aa++; aj++;
2358:     }
2359:   }
2360:   VecRestoreArray(v,&x);
2361:   return(0);
2362: }

2366: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2367: {
2368:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2370:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2371:   PetscScalar    *x;
2372:   MatScalar      *aa;

2375:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2376:   aa   = a->a;
2377:   ai   = a->i;
2378:   aj   = a->j;

2380:   VecSet(v,0.0);
2381:   VecGetArray(v,&x);
2382:   VecGetLocalSize(v,&n);
2383:   if (n != A->rmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2384:   for (i=0; i<m; i++) {
2385:     ncols = ai[1] - ai[0]; ai++;
2386:     if (ncols == A->cmap->n) { /* row is dense */
2387:       x[i] = *aa; if (idx) idx[i] = 0;
2388:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2389:       x[i] = 0.0;
2390:       if (idx) {
2391:         idx[i] = 0; /* in case ncols is zero */
2392:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2393:           if (aj[j] > j) {
2394:             idx[i] = j;
2395:             break;
2396:           }
2397:         }
2398:       }
2399:     }
2400:     for (j=0; j<ncols; j++){
2401:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2402:       aa++; aj++;
2403:     }
2404:   }
2405:   VecRestoreArray(v,&x);
2406:   return(0);
2407: }

2411: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2412: {
2413:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2415:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2416:   PetscReal      atmp;
2417:   PetscScalar    *x;
2418:   MatScalar      *aa;

2421:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2422:   aa   = a->a;
2423:   ai   = a->i;
2424:   aj   = a->j;

2426:   VecSet(v,0.0);
2427:   VecGetArray(v,&x);
2428:   VecGetLocalSize(v,&n);
2429:   if (n != A->rmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2430:   for (i=0; i<m; i++) {
2431:     ncols = ai[1] - ai[0]; ai++;
2432:     if (ncols) {
2433:       /* Get first nonzero */
2434:       for(j = 0; j < ncols; j++) {
2435:         atmp = PetscAbsScalar(aa[j]);
2436:         if (atmp > 1.0e-12) {x[i] = atmp; if (idx) idx[i] = aj[j]; break;}
2437:       }
2438:       if (j == ncols) {x[i] = *aa; if (idx) idx[i] = *aj;}
2439:     } else {
2440:       x[i] = 0.0; if (idx) idx[i] = 0;
2441:     }
2442:     for(j = 0; j < ncols; j++) {
2443:       atmp = PetscAbsScalar(*aa);
2444:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2445:       aa++; aj++;
2446:     }
2447:   }
2448:   VecRestoreArray(v,&x);
2449:   return(0);
2450: }

2454: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2455: {
2456:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2458:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2459:   PetscScalar    *x;
2460:   MatScalar      *aa;

2463:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2464:   aa   = a->a;
2465:   ai   = a->i;
2466:   aj   = a->j;

2468:   VecSet(v,0.0);
2469:   VecGetArray(v,&x);
2470:   VecGetLocalSize(v,&n);
2471:   if (n != A->rmap->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2472:   for (i=0; i<m; i++) {
2473:     ncols = ai[1] - ai[0]; ai++;
2474:     if (ncols == A->cmap->n) { /* row is dense */
2475:       x[i] = *aa; if (idx) idx[i] = 0;
2476:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2477:       x[i] = 0.0;
2478:       if (idx) {   /* find first implicit 0.0 in the row */
2479:         idx[i] = 0; /* in case ncols is zero */
2480:         for (j=0;j<ncols;j++) {
2481:           if (aj[j] > j) {
2482:             idx[i] = j;
2483:             break;
2484:           }
2485:         }
2486:       }
2487:     }
2488:     for (j=0; j<ncols; j++){
2489:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2490:       aa++; aj++;
2491:     }
2492:   }
2493:   VecRestoreArray(v,&x);
2494:   return(0);
2495: }
2497: /* -------------------------------------------------------------------*/
2498: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2499:        MatGetRow_SeqAIJ,
2500:        MatRestoreRow_SeqAIJ,
2501:        MatMult_SeqAIJ,
2502: /* 4*/ MatMultAdd_SeqAIJ,
2503:        MatMultTranspose_SeqAIJ,
2504:        MatMultTransposeAdd_SeqAIJ,
2505:        0,
2506:        0,
2507:        0,
2508: /*10*/ 0,
2509:        MatLUFactor_SeqAIJ,
2510:        0,
2511:        MatSOR_SeqAIJ,
2512:        MatTranspose_SeqAIJ,
2513: /*15*/ MatGetInfo_SeqAIJ,
2514:        MatEqual_SeqAIJ,
2515:        MatGetDiagonal_SeqAIJ,
2516:        MatDiagonalScale_SeqAIJ,
2517:        MatNorm_SeqAIJ,
2518: /*20*/ 0,
2519:        MatAssemblyEnd_SeqAIJ,
2520:        MatSetOption_SeqAIJ,
2521:        MatZeroEntries_SeqAIJ,
2522: /*24*/ MatZeroRows_SeqAIJ,
2523:        0,
2524:        0,
2525:        0,
2526:        0,
2527: /*29*/ MatSetUpPreallocation_SeqAIJ,
2528:        0,
2529:        0,
2530:        MatGetArray_SeqAIJ,
2531:        MatRestoreArray_SeqAIJ,
2532: /*34*/ MatDuplicate_SeqAIJ,
2533:        0,
2534:        0,
2535:        MatILUFactor_SeqAIJ,
2536:        0,
2537: /*39*/ MatAXPY_SeqAIJ,
2538:        MatGetSubMatrices_SeqAIJ,
2539:        MatIncreaseOverlap_SeqAIJ,
2540:        MatGetValues_SeqAIJ,
2541:        MatCopy_SeqAIJ,
2542: /*44*/ MatGetRowMax_SeqAIJ,
2543:        MatScale_SeqAIJ,
2544:        0,
2545:        MatDiagonalSet_SeqAIJ,
2546:        0,
2547: /*49*/ MatSetBlockSize_SeqAIJ,
2548:        MatGetRowIJ_SeqAIJ,
2549:        MatRestoreRowIJ_SeqAIJ,
2550:        MatGetColumnIJ_SeqAIJ,
2551:        MatRestoreColumnIJ_SeqAIJ,
2552: /*54*/ MatFDColoringCreate_SeqAIJ,
2553:        0,
2554:        0,
2555:        MatPermute_SeqAIJ,
2556:        0,
2557: /*59*/ 0,
2558:        MatDestroy_SeqAIJ,
2559:        MatView_SeqAIJ,
2560:        0,
2561:        0,
2562: /*64*/ 0,
2563:        0,
2564:        0,
2565:        0,
2566:        0,
2567: /*69*/ MatGetRowMaxAbs_SeqAIJ,
2568:        MatGetRowMinAbs_SeqAIJ,
2569:        0,
2570:        MatSetColoring_SeqAIJ,
2571: #if defined(PETSC_HAVE_ADIC)
2572:        MatSetValuesAdic_SeqAIJ,
2573: #else
2574:        0,
2575: #endif
2576: /*74*/ MatSetValuesAdifor_SeqAIJ,
2577:        MatFDColoringApply_AIJ,
2578:        0,
2579:        0,
2580:        0,
2581: /*79*/ 0,
2582:        0,
2583:        0,
2584:        0,
2585:        MatLoad_SeqAIJ,
2586: /*84*/ MatIsSymmetric_SeqAIJ,
2587:        MatIsHermitian_SeqAIJ,
2588:        0,
2589:        0,
2590:        0,
2591: /*89*/ MatMatMult_SeqAIJ_SeqAIJ,
2592:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
2593:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
2594:        MatPtAP_Basic,
2595:        MatPtAPSymbolic_SeqAIJ,
2596: /*94*/ MatPtAPNumeric_SeqAIJ,
2597:        MatMatMultTranspose_SeqAIJ_SeqAIJ,
2598:        MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
2599:        MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
2600:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
2601: /*99*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
2602:        0,
2603:        0,
2604:        MatConjugate_SeqAIJ,
2605:        0,
2606: /*104*/MatSetValuesRow_SeqAIJ,
2607:        MatRealPart_SeqAIJ,
2608:        MatImaginaryPart_SeqAIJ,
2609:        0,
2610:        0,
2611: /*109*/0,
2612:        0,
2613:        MatGetRowMin_SeqAIJ,
2614:        0,
2615:        MatMissingDiagonal_SeqAIJ,
2616: /*114*/0,
2617:        0,
2618:        0,
2619:        0,
2620:        0
2621: };

2626: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
2627: {
2628:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2629:   PetscInt   i,nz,n;


2633:   nz = aij->maxnz;
2634:   n  = mat->rmap->n;
2635:   for (i=0; i<nz; i++) {
2636:     aij->j[i] = indices[i];
2637:   }
2638:   aij->nz = nz;
2639:   for (i=0; i<n; i++) {
2640:     aij->ilen[i] = aij->imax[i];
2641:   }

2643:   return(0);
2644: }

2649: /*@
2650:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2651:        in the matrix.

2653:   Input Parameters:
2654: +  mat - the SeqAIJ matrix
2655: -  indices - the column indices

2657:   Level: advanced

2659:   Notes:
2660:     This can be called if you have precomputed the nonzero structure of the 
2661:   matrix and want to provide it to the matrix object to improve the performance
2662:   of the MatSetValues() operation.

2664:     You MUST have set the correct numbers of nonzeros per row in the call to 
2665:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

2667:     MUST be called before any calls to MatSetValues();

2669:     The indices should start with zero, not one.

2671: @*/
2672: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
2673: {
2674:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

2679:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2680:   if (f) {
2681:     (*f)(mat,indices);
2682:   } else {
2683:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
2684:   }
2685:   return(0);
2686: }

2688: /* ----------------------------------------------------------------------------------------*/

2693: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
2694: {
2695:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2697:   size_t         nz = aij->i[mat->rmap->n];

2700:   if (aij->nonew != 1) {
2701:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2702:   }

2704:   /* allocate space for values if not already there */
2705:   if (!aij->saved_values) {
2706:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2707:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
2708:   }

2710:   /* copy values over */
2711:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2712:   return(0);
2713: }

2718: /*@
2719:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2720:        example, reuse of the linear part of a Jacobian, while recomputing the 
2721:        nonlinear portion.

2723:    Collect on Mat

2725:   Input Parameters:
2726: .  mat - the matrix (currently only AIJ matrices support this option)

2728:   Level: advanced

2730:   Common Usage, with SNESSolve():
2731: $    Create Jacobian matrix
2732: $    Set linear terms into matrix
2733: $    Apply boundary conditions to matrix, at this time matrix must have 
2734: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2735: $      boundary conditions again will not change the nonzero structure
2736: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
2737: $    MatStoreValues(mat);
2738: $    Call SNESSetJacobian() with matrix
2739: $    In your Jacobian routine
2740: $      MatRetrieveValues(mat);
2741: $      Set nonlinear terms in matrix
2742:  
2743:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2744: $    // build linear portion of Jacobian 
2745: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
2746: $    MatStoreValues(mat);
2747: $    loop over nonlinear iterations
2748: $       MatRetrieveValues(mat);
2749: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2750: $       // call MatAssemblyBegin/End() on matrix
2751: $       Solve linear system with Jacobian
2752: $    endloop 

2754:   Notes:
2755:     Matrix must already be assemblied before calling this routine
2756:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before 
2757:     calling this routine.

2759:     When this is called multiple times it overwrites the previous set of stored values
2760:     and does not allocated additional space.

2762: .seealso: MatRetrieveValues()

2764: @*/
2765: PetscErrorCode  MatStoreValues(Mat mat)
2766: {
2767:   PetscErrorCode ierr,(*f)(Mat);

2771:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2772:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2774:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2775:   if (f) {
2776:     (*f)(mat);
2777:   } else {
2778:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values");
2779:   }
2780:   return(0);
2781: }

2786: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
2787: {
2788:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2790:   PetscInt       nz = aij->i[mat->rmap->n];

2793:   if (aij->nonew != 1) {
2794:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2795:   }
2796:   if (!aij->saved_values) {
2797:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2798:   }
2799:   /* copy values over */
2800:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2801:   return(0);
2802: }

2807: /*@
2808:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2809:        example, reuse of the linear part of a Jacobian, while recomputing the 
2810:        nonlinear portion.

2812:    Collect on Mat

2814:   Input Parameters:
2815: .  mat - the matrix (currently on AIJ matrices support this option)

2817:   Level: advanced

2819: .seealso: MatStoreValues()

2821: @*/
2822: PetscErrorCode  MatRetrieveValues(Mat mat)
2823: {
2824:   PetscErrorCode ierr,(*f)(Mat);

2828:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2829:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2831:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2832:   if (f) {
2833:     (*f)(mat);
2834:   } else {
2835:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values");
2836:   }
2837:   return(0);
2838: }


2841: /* --------------------------------------------------------------------------------*/
2844: /*@C
2845:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2846:    (the default parallel PETSc format).  For good matrix assembly performance
2847:    the user should preallocate the matrix storage by setting the parameter nz
2848:    (or the array nnz).  By setting these parameters accurately, performance
2849:    during matrix assembly can be increased by more than a factor of 50.

2851:    Collective on MPI_Comm

2853:    Input Parameters:
2854: +  comm - MPI communicator, set to PETSC_COMM_SELF
2855: .  m - number of rows
2856: .  n - number of columns
2857: .  nz - number of nonzeros per row (same for all rows)
2858: -  nnz - array containing the number of nonzeros in the various rows 
2859:          (possibly different for each row) or PETSC_NULL

2861:    Output Parameter:
2862: .  A - the matrix 

2864:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
2865:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
2866:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

2868:    Notes:
2869:    If nnz is given then nz is ignored

2871:    The AIJ format (also called the Yale sparse matrix format or
2872:    compressed row storage), is fully compatible with standard Fortran 77
2873:    storage.  That is, the stored row and column indices can begin at
2874:    either one (as in Fortran) or zero.  See the users' manual for details.

2876:    Specify the preallocated storage with either nz or nnz (not both).
2877:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2878:    allocation.  For large problems you MUST preallocate memory or you 
2879:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2881:    By default, this format uses inodes (identical nodes) when possible, to 
2882:    improve numerical efficiency of matrix-vector products and solves. We 
2883:    search for consecutive rows with the same nonzero structure, thereby
2884:    reusing matrix information to achieve increased efficiency.

2886:    Options Database Keys:
2887: +  -mat_no_inode  - Do not use inodes
2888: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2889: -  -mat_aij_oneindex - Internally use indexing starting at 1
2890:         rather than 0.  Note that when calling MatSetValues(),
2891:         the user still MUST index entries starting at 0!

2893:    Level: intermediate

2895: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2897: @*/
2898: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2899: {

2903:   MatCreate(comm,A);
2904:   MatSetSizes(*A,m,n,m,n);
2905:   MatSetType(*A,MATSEQAIJ);
2906:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
2907:   return(0);
2908: }

2912: /*@C
2913:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2914:    the user should preallocate the matrix storage by setting the parameter nz
2915:    (or the array nnz).  By setting these parameters accurately, performance
2916:    during matrix assembly can be increased by more than a factor of 50.

2918:    Collective on MPI_Comm

2920:    Input Parameters:
2921: +  B - The matrix-free
2922: .  nz - number of nonzeros per row (same for all rows)
2923: -  nnz - array containing the number of nonzeros in the various rows 
2924:          (possibly different for each row) or PETSC_NULL

2926:    Notes:
2927:      If nnz is given then nz is ignored

2929:     The AIJ format (also called the Yale sparse matrix format or
2930:    compressed row storage), is fully compatible with standard Fortran 77
2931:    storage.  That is, the stored row and column indices can begin at
2932:    either one (as in Fortran) or zero.  See the users' manual for details.

2934:    Specify the preallocated storage with either nz or nnz (not both).
2935:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2936:    allocation.  For large problems you MUST preallocate memory or you 
2937:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2939:    You can call MatGetInfo() to get information on how effective the preallocation was;
2940:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
2941:    You can also run with the option -info and look for messages with the string 
2942:    malloc in them to see if additional memory allocation was needed.

2944:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
2945:    entries or columns indices

2947:    By default, this format uses inodes (identical nodes) when possible, to 
2948:    improve numerical efficiency of matrix-vector products and solves. We 
2949:    search for consecutive rows with the same nonzero structure, thereby
2950:    reusing matrix information to achieve increased efficiency.

2952:    Options Database Keys:
2953: +  -mat_no_inode  - Do not use inodes
2954: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2955: -  -mat_aij_oneindex - Internally use indexing starting at 1
2956:         rather than 0.  Note that when calling MatSetValues(),
2957:         the user still MUST index entries starting at 0!

2959:    Level: intermediate

2961: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

2963: @*/
2964: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
2965: {
2966:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]);

2969:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
2970:   if (f) {
2971:     (*f)(B,nz,nnz);
2972:   }
2973:   return(0);
2974: }

2979: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz)
2980: {
2981:   Mat_SeqAIJ     *b;
2982:   PetscTruth     skipallocation = PETSC_FALSE;
2984:   PetscInt       i;

2987: 
2988:   if (nz == MAT_SKIP_ALLOCATION) {
2989:     skipallocation = PETSC_TRUE;
2990:     nz             = 0;
2991:   }

2993:   PetscLayoutSetBlockSize(B->rmap,1);
2994:   PetscLayoutSetBlockSize(B->cmap,1);
2995:   PetscLayoutSetUp(B->rmap);
2996:   PetscLayoutSetUp(B->cmap);

2998:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2999:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
3000:   if (nnz) {
3001:     for (i=0; i<B->rmap->n; i++) {
3002:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
3003:       if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->cmap->n);
3004:     }
3005:   }

3007:   B->preallocated = PETSC_TRUE;
3008:   b = (Mat_SeqAIJ*)B->data;

3010:   if (!skipallocation) {
3011:     if (!b->imax) {
3012:       PetscMalloc2(B->rmap->n,PetscInt,&b->imax,B->rmap->n,PetscInt,&b->ilen);
3013:       PetscLogObjectMemory(B,2*B->rmap->n*sizeof(PetscInt));
3014:     }
3015:     if (!nnz) {
3016:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3017:       else if (nz <= 0)        nz = 1;
3018:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3019:       nz = nz*B->rmap->n;
3020:     } else {
3021:       nz = 0;
3022:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3023:     }
3024:     /* b->ilen will count nonzeros in each row so far. */
3025:     for (i=0; i<B->rmap->n; i++) { b->ilen[i] = 0; }

3027:     /* allocate the matrix space */
3028:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3029:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap->n+1,PetscInt,&b->i);
3030:     PetscLogObjectMemory(B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3031:     b->i[0] = 0;
3032:     for (i=1; i<B->rmap->n+1; i++) {
3033:       b->i[i] = b->i[i-1] + b->imax[i-1];
3034:     }
3035:     b->singlemalloc = PETSC_TRUE;
3036:     b->free_a       = PETSC_TRUE;
3037:     b->free_ij      = PETSC_TRUE;
3038:   } else {
3039:     b->free_a       = PETSC_FALSE;
3040:     b->free_ij      = PETSC_FALSE;
3041:   }

3043:   b->nz                = 0;
3044:   b->maxnz             = nz;
3045:   B->info.nz_unneeded  = (double)b->maxnz;
3046:   return(0);
3047: }

3050: #undef  __FUNCT__
3052: /*@
3053:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.  

3055:    Input Parameters:
3056: +  B - the matrix 
3057: .  i - the indices into j for the start of each row (starts with zero)
3058: .  j - the column indices for each row (starts with zero) these must be sorted for each row
3059: -  v - optional values in the matrix

3061:    Level: developer

3063:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

3065: .keywords: matrix, aij, compressed row, sparse, sequential

3067: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3068: @*/
3069: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3070: {
3071:   PetscErrorCode (*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);

3076:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",(void (**)(void))&f);
3077:   if (f) {
3078:     (*f)(B,i,j,v);
3079:   }
3080:   return(0);
3081: }

3084: #undef  __FUNCT__
3086: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3087: {
3088:   PetscInt       i;
3089:   PetscInt       m,n;
3090:   PetscInt       nz;
3091:   PetscInt       *nnz, nz_max = 0;
3092:   PetscScalar    *values;

3096:   MatGetSize(B, &m, &n);

3098:   if (Ii[0]) {
3099:     SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
3100:   }
3101:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
3102:   for(i = 0; i < m; i++) {
3103:     nz     = Ii[i+1]- Ii[i];
3104:     nz_max = PetscMax(nz_max, nz);
3105:     if (nz < 0) {
3106:       SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3107:     }
3108:     nnz[i] = nz;
3109:   }
3110:   MatSeqAIJSetPreallocation(B, 0, nnz);
3111:   PetscFree(nnz);

3113:   if (v) {
3114:     values = (PetscScalar*) v;
3115:   } else {
3116:     PetscMalloc(nz_max*sizeof(PetscScalar), &values);
3117:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
3118:   }

3120:   for(i = 0; i < m; i++) {
3121:     nz  = Ii[i+1] - Ii[i];
3122:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3123:   }

3125:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3126:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3128:   if (!v) {
3129:     PetscFree(values);
3130:   }
3131:   return(0);
3132: }

3135:  #include ../src/mat/impls/dense/seq/dense.h
3136:  #include private/petscaxpy.h

3140: /*
3141:     Computes (B'*A')' since computing B*A directly is untenable

3143:                n                       p                          p
3144:         (              )       (              )         (                  )
3145:       m (      A       )  *  n (       B      )   =   m (         C        )
3146:         (              )       (              )         (                  )

3148: */
3149: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3150: {
3151:   PetscErrorCode     ierr;
3152:   Mat_SeqDense       *sub_a = (Mat_SeqDense*)A->data;
3153:   Mat_SeqAIJ         *sub_b = (Mat_SeqAIJ*)B->data;
3154:   Mat_SeqDense       *sub_c = (Mat_SeqDense*)C->data;
3155:   PetscInt           i,n,m,q,p;
3156:   const PetscInt     *ii,*idx;
3157:   const PetscScalar  *b,*a,*a_q;
3158:   PetscScalar        *c,*c_q;

3161:   m = A->rmap->n;
3162:   n = A->cmap->n;
3163:   p = B->cmap->n;
3164:   a = sub_a->v;
3165:   b = sub_b->a;
3166:   c = sub_c->v;
3167:   PetscMemzero(c,m*p*sizeof(PetscScalar));

3169:   ii  = sub_b->i;
3170:   idx = sub_b->j;
3171:   for (i=0; i<n; i++) {
3172:     q = ii[i+1] - ii[i];
3173:     while (q-->0) {
3174:       c_q = c + m*(*idx);
3175:       a_q = a + m*i;
3176:       PetscAXPY(c_q,*b,a_q,m);
3177:       idx++;
3178:       b++;
3179:     }
3180:   }
3181:   return(0);
3182: }

3186: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3187: {
3189:   PetscInt       m=A->rmap->n,n=B->cmap->n;
3190:   Mat            Cmat;

3193:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
3194:   MatCreate(((PetscObject)A)->comm,&Cmat);
3195:   MatSetSizes(Cmat,m,n,m,n);
3196:   MatSetType(Cmat,MATSEQDENSE);
3197:   MatSeqDenseSetPreallocation(Cmat,PETSC_NULL);
3198:   Cmat->assembled = PETSC_TRUE;
3199:   *C = Cmat;
3200:   return(0);
3201: }

3203: /* ----------------------------------------------------------------*/
3206: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3207: {

3211:   if (scall == MAT_INITIAL_MATRIX){
3212:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3213:   }
3214:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3215:   return(0);
3216: }


3219: /*MC
3220:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
3221:    based on compressed sparse row format.

3223:    Options Database Keys:
3224: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3226:   Level: beginner

3228: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3229: M*/

3232: #if defined(PETSC_HAVE_PASTIX)
3234: #endif
3235: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SCALAR_SINGLE) && !defined(PETSC_USE_SCALAR_MAT_SINGLE)
3237: #endif
3242: #if defined(PETSC_HAVE_MUMPS)
3244: #endif
3245: #if defined(PETSC_HAVE_SUPERLU)
3247: #endif
3248: #if defined(PETSC_HAVE_SUPERLU_DIST)
3250: #endif
3251: #if defined(PETSC_HAVE_SPOOLES)
3253: #endif
3254: #if defined(PETSC_HAVE_UMFPACK)
3256: #endif
3257: #if defined(PETSC_HAVE_LUSOL)
3259: #endif
3260: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3264: #endif


3271: PetscErrorCode  MatCreate_SeqAIJ(Mat B)
3272: {
3273:   Mat_SeqAIJ     *b;
3275:   PetscMPIInt    size;

3278:   MPI_Comm_size(((PetscObject)B)->comm,&size);
3279:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3281:   PetscNewLog(B,Mat_SeqAIJ,&b);
3282:   B->data             = (void*)b;
3283:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3284:   B->mapping          = 0;
3285:   b->row              = 0;
3286:   b->col              = 0;
3287:   b->icol             = 0;
3288:   b->reallocs         = 0;
3289:   b->ignorezeroentries = PETSC_FALSE;
3290:   b->roworiented       = PETSC_TRUE;
3291:   b->nonew             = 0;
3292:   b->diag              = 0;
3293:   b->solve_work        = 0;
3294:   B->spptr             = 0;
3295:   b->saved_values      = 0;
3296:   b->idiag             = 0;
3297:   b->mdiag             = 0;
3298:   b->ssor_work         = 0;
3299:   b->omega             = 1.0;
3300:   b->fshift            = 0.0;
3301:   b->idiagvalid        = PETSC_FALSE;
3302:   b->keepnonzeropattern    = PETSC_FALSE;
3303:   b->xtoy              = 0;
3304:   b->XtoY              = 0;
3305:   b->compressedrow.use     = PETSC_FALSE;
3306:   b->compressedrow.nrows   = B->rmap->n;
3307:   b->compressedrow.i       = PETSC_NULL;
3308:   b->compressedrow.rindex  = PETSC_NULL;
3309:   b->compressedrow.checked = PETSC_FALSE;
3310:   B->same_nonzero          = PETSC_FALSE;

3312:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3313: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3314:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_matlab_C",
3315:                                            "MatGetFactor_seqaij_matlab",
3316:                                            MatGetFactor_seqaij_matlab);
3317:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatlabEnginePut_SeqAIJ",MatlabEnginePut_SeqAIJ);
3318:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatlabEngineGet_SeqAIJ",MatlabEngineGet_SeqAIJ);
3319: #endif
3320: #if defined(PETSC_HAVE_PASTIX)
3321:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_pastix_C",
3322:                                            "MatGetFactor_seqaij_pastix",
3323:                                            MatGetFactor_seqaij_pastix);
3324: #endif
3325: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SCALAR_SINGLE) && !defined(PETSC_USE_SCALAR_MAT_SINGLE)
3326:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_essl_C",
3327:                                      "MatGetFactor_seqaij_essl",
3328:                                      MatGetFactor_seqaij_essl);
3329: #endif
3330: #if defined(PETSC_HAVE_SUPERLU)
3331:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_superlu_C",
3332:                                      "MatGetFactor_seqaij_superlu",
3333:                                      MatGetFactor_seqaij_superlu);
3334: #endif
3335: #if defined(PETSC_HAVE_SUPERLU_DIST)
3336:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_superlu_dist_C",
3337:                                      "MatGetFactor_seqaij_superlu_dist",
3338:                                      MatGetFactor_seqaij_superlu_dist);
3339: #endif
3340: #if defined(PETSC_HAVE_SPOOLES)
3341:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_spooles_C",
3342:                                      "MatGetFactor_seqaij_spooles",
3343:                                      MatGetFactor_seqaij_spooles);
3344: #endif
3345: #if defined(PETSC_HAVE_MUMPS)
3346:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_mumps_C",
3347:                                      "MatGetFactor_seqaij_mumps",
3348:                                      MatGetFactor_seqaij_mumps);
3349: #endif
3350: #if defined(PETSC_HAVE_UMFPACK)
3351:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_umfpack_C",
3352:                                      "MatGetFactor_seqaij_umfpack",
3353:                                      MatGetFactor_seqaij_umfpack);
3354: #endif
3355: #if defined(PETSC_HAVE_LUSOL)
3356:     PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_lusol_C",
3357:                                      "MatGetFactor_seqaij_lusol",
3358:                                      MatGetFactor_seqaij_lusol);
3359: #endif
3360:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_petsc_C",
3361:                                      "MatGetFactor_seqaij_petsc",
3362:                                      MatGetFactor_seqaij_petsc);
3363:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactorAvailable_petsc_C",
3364:                                      "MatGetFactorAvailable_seqaij_petsc",
3365:                                      MatGetFactorAvailable_seqaij_petsc);
3366:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatGetFactor_bas_C",
3367:                                      "MatGetFactor_seqaij_bas",
3368:                                      MatGetFactor_seqaij_bas);
3369:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
3370:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
3371:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
3372:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
3373:                                      "MatStoreValues_SeqAIJ",
3374:                                      MatStoreValues_SeqAIJ);
3375:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
3376:                                      "MatRetrieveValues_SeqAIJ",
3377:                                      MatRetrieveValues_SeqAIJ);
3378:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
3379:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
3380:                                       MatConvert_SeqAIJ_SeqSBAIJ);
3381:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
3382:                                      "MatConvert_SeqAIJ_SeqBAIJ",
3383:                                       MatConvert_SeqAIJ_SeqBAIJ);
3384:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcsrperm_C",
3385:                                      "MatConvert_SeqAIJ_SeqCSRPERM",
3386:                                       MatConvert_SeqAIJ_SeqCSRPERM);
3387:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcrl_C",
3388:                                      "MatConvert_SeqAIJ_SeqCRL",
3389:                                       MatConvert_SeqAIJ_SeqCRL);
3390:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
3391:                                      "MatIsTranspose_SeqAIJ",
3392:                                       MatIsTranspose_SeqAIJ);
3393:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsHermitianTranspose_C",
3394:                                      "MatIsHermitianTranspose_SeqAIJ",
3395:                                       MatIsTranspose_SeqAIJ);
3396:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
3397:                                      "MatSeqAIJSetPreallocation_SeqAIJ",
3398:                                       MatSeqAIJSetPreallocation_SeqAIJ);
3399:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",
3400:                                      "MatSeqAIJSetPreallocationCSR_SeqAIJ",
3401:                                       MatSeqAIJSetPreallocationCSR_SeqAIJ);
3402:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
3403:                                      "MatReorderForNonzeroDiagonal_SeqAIJ",
3404:                                       MatReorderForNonzeroDiagonal_SeqAIJ);
3405:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMult_seqdense_seqaij_C",
3406:                                      "MatMatMult_SeqDense_SeqAIJ",
3407:                                       MatMatMult_SeqDense_SeqAIJ);
3408:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",
3409:                                      "MatMatMultSymbolic_SeqDense_SeqAIJ",
3410:                                       MatMatMultSymbolic_SeqDense_SeqAIJ);
3411:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",
3412:                                      "MatMatMultNumeric_SeqDense_SeqAIJ",
3413:                                       MatMatMultNumeric_SeqDense_SeqAIJ);
3414:   MatCreate_SeqAIJ_Inode(B);
3415:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3416:   return(0);
3417: }

3422: /*
3423:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
3424: */
3425: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscTruth mallocmatspace)
3426: {
3427:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3429:   PetscInt       i,m = A->rmap->n;

3432:   c = (Mat_SeqAIJ*)C->data;

3434:   C->factor           = A->factor;

3436:   c->row            = 0;
3437:   c->col            = 0;
3438:   c->icol           = 0;
3439:   c->reallocs       = 0;

3441:   C->assembled      = PETSC_TRUE;
3442: 
3443:   PetscLayoutSetBlockSize(C->rmap,1);
3444:   PetscLayoutSetBlockSize(C->cmap,1);
3445:   PetscLayoutSetUp(C->rmap);
3446:   PetscLayoutSetUp(C->cmap);

3448:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
3449:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
3450:   for (i=0; i<m; i++) {
3451:     c->imax[i] = a->imax[i];
3452:     c->ilen[i] = a->ilen[i];
3453:   }

3455:   /* allocate the matrix space */
3456:   if (mallocmatspace){
3457:     PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
3458:     PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
3459:     c->singlemalloc = PETSC_TRUE;
3460:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
3461:     if (m > 0) {
3462:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
3463:       if (cpvalues == MAT_COPY_VALUES) {
3464:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
3465:       } else {
3466:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
3467:       }
3468:     }
3469:   }

3471:   c->ignorezeroentries = a->ignorezeroentries;
3472:   c->roworiented       = a->roworiented;
3473:   c->nonew             = a->nonew;
3474:   if (a->diag) {
3475:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
3476:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
3477:     for (i=0; i<m; i++) {
3478:       c->diag[i] = a->diag[i];
3479:     }
3480:   } else c->diag           = 0;
3481:   c->solve_work            = 0;
3482:   c->saved_values          = 0;
3483:   c->idiag                 = 0;
3484:   c->ssor_work             = 0;
3485:   c->keepnonzeropattern    = a->keepnonzeropattern;
3486:   c->free_a                = PETSC_TRUE;
3487:   c->free_ij               = PETSC_TRUE;
3488:   c->xtoy                  = 0;
3489:   c->XtoY                  = 0;

3491:   c->nz                 = a->nz;
3492:   c->maxnz              = a->maxnz;
3493:   C->preallocated       = PETSC_TRUE;

3495:   c->compressedrow.use     = a->compressedrow.use;
3496:   c->compressedrow.nrows   = a->compressedrow.nrows;
3497:   c->compressedrow.checked = a->compressedrow.checked;
3498:   if (a->compressedrow.checked && a->compressedrow.use){
3499:     i = a->compressedrow.nrows;
3500:     PetscMalloc2(i+1,PetscInt,&c->compressedrow.i,i,PetscInt,&c->compressedrow.rindex);
3501:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3502:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3503:   } else {
3504:     c->compressedrow.use    = PETSC_FALSE;
3505:     c->compressedrow.i      = PETSC_NULL;
3506:     c->compressedrow.rindex = PETSC_NULL;
3507:   }
3508:   C->same_nonzero = A->same_nonzero;
3509:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);

3511:   PetscFListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3512:   return(0);
3513: }

3517: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3518: {

3522:   MatCreate(((PetscObject)A)->comm,B);
3523:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
3524:   MatSetType(*B,MATSEQAIJ);
3525:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
3526:   return(0);
3527: }

3531: PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer, const MatType type,Mat *A)
3532: {
3533:   Mat_SeqAIJ     *a;
3534:   Mat            B;
3536:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N;
3537:   int            fd;
3538:   PetscMPIInt    size;
3539:   MPI_Comm       comm;
3540: 
3542:   PetscObjectGetComm((PetscObject)viewer,&comm);
3543:   MPI_Comm_size(comm,&size);
3544:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
3545:   PetscViewerBinaryGetDescriptor(viewer,&fd);
3546:   PetscBinaryRead(fd,header,4,PETSC_INT);
3547:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
3548:   M = header[1]; N = header[2]; nz = header[3];

3550:   if (nz < 0) {
3551:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
3552:   }

3554:   /* read in row lengths */
3555:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
3556:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

3558:   /* check if sum of rowlengths is same as nz */
3559:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
3560:   if (sum != nz) SETERRQ2(PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

3562:   /* create our matrix */
3563:   MatCreate(comm,&B);
3564:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M,N);
3565:   MatSetType(B,type);
3566:   MatSeqAIJSetPreallocation_SeqAIJ(B,0,rowlengths);
3567:   a = (Mat_SeqAIJ*)B->data;

3569:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

3571:   /* read in nonzero values */
3572:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

3574:   /* set matrix "i" values */
3575:   a->i[0] = 0;
3576:   for (i=1; i<= M; i++) {
3577:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
3578:     a->ilen[i-1] = rowlengths[i-1];
3579:   }
3580:   PetscFree(rowlengths);

3582:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3583:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3584:   *A = B;
3585:   return(0);
3586: }

3590: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
3591: {
3592:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
3594: #if defined(PETSC_USE_COMPLEX)
3595:   PetscInt k;
3596: #endif

3599:   /* If the  matrix dimensions are not equal,or no of nonzeros */
3600:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
3601:     *flg = PETSC_FALSE;
3602:     return(0);
3603:   }
3604: 
3605:   /* if the a->i are the same */
3606:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
3607:   if (!*flg) return(0);
3608: 
3609:   /* if a->j are the same */
3610:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
3611:   if (!*flg) return(0);
3612: 
3613:   /* if a->a are the same */
3614: #if defined(PETSC_USE_COMPLEX)
3615:   for (k=0; k<a->nz; k++){
3616:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])){
3617:       *flg = PETSC_FALSE;
3618:       return(0);
3619:     }
3620:   }
3621: #else
3622:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
3623: #endif
3624:   return(0);
3625: }

3629: /*@
3630:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
3631:               provided by the user.

3633:       Collective on MPI_Comm

3635:    Input Parameters:
3636: +   comm - must be an MPI communicator of size 1
3637: .   m - number of rows
3638: .   n - number of columns
3639: .   i - row indices
3640: .   j - column indices
3641: -   a - matrix values

3643:    Output Parameter:
3644: .   mat - the matrix

3646:    Level: intermediate

3648:    Notes:
3649:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
3650:     once the matrix is destroyed

3652:        You cannot set new nonzero locations into this matrix, that will generate an error.

3654:        The i and j indices are 0 based

3656:        The format which is used for the sparse matrix input, is equivalent to a
3657:     row-major ordering.. i.e for the following matrix, the input data expected is
3658:     as shown:

3660:         1 0 0
3661:         2 0 3
3662:         4 5 6

3664:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
3665:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
3666:         v =  {1,2,3,4,5,6}  [size = nz = 6]

3668:         
3669: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

3671: @*/
3672: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
3673: {
3675:   PetscInt       ii;
3676:   Mat_SeqAIJ     *aij;
3677: #if defined(PETSC_USE_DEBUG)
3678:   PetscInt       jj;
3679: #endif

3682:   if (i[0]) {
3683:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3684:   }
3685:   MatCreate(comm,mat);
3686:   MatSetSizes(*mat,m,n,m,n);
3687:   MatSetType(*mat,MATSEQAIJ);
3688:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
3689:   aij  = (Mat_SeqAIJ*)(*mat)->data;
3690:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

3692:   aij->i = i;
3693:   aij->j = j;
3694:   aij->a = a;
3695:   aij->singlemalloc = PETSC_FALSE;
3696:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3697:   aij->free_a       = PETSC_FALSE;
3698:   aij->free_ij      = PETSC_FALSE;

3700:   for (ii=0; ii<m; ii++) {
3701:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
3702: #if defined(PETSC_USE_DEBUG)
3703:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
3704:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
3705:       if (j[jj] < j[jj-1]) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
3706:       if (j[jj] == j[jj]-1) SETERRQ3(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);
3707:     }
3708: #endif    
3709:   }
3710: #if defined(PETSC_USE_DEBUG)
3711:   for (ii=0; ii<aij->i[m]; ii++) {
3712:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3713:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
3714:   }
3715: #endif    

3717:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3718:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3719:   return(0);
3720: }

3724: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
3725: {
3727:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

3730:   if (coloring->ctype == IS_COLORING_GLOBAL) {
3731:     ISColoringReference(coloring);
3732:     a->coloring = coloring;
3733:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3734:     PetscInt             i,*larray;
3735:     ISColoring      ocoloring;
3736:     ISColoringValue *colors;

3738:     /* set coloring for diagonal portion */
3739:     PetscMalloc(A->cmap->n*sizeof(PetscInt),&larray);
3740:     for (i=0; i<A->cmap->n; i++) {
3741:       larray[i] = i;
3742:     }
3743:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->cmap->n,larray,PETSC_NULL,larray);
3744:     PetscMalloc(A->cmap->n*sizeof(ISColoringValue),&colors);
3745:     for (i=0; i<A->cmap->n; i++) {
3746:       colors[i] = coloring->colors[larray[i]];
3747:     }
3748:     PetscFree(larray);
3749:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,&ocoloring);
3750:     a->coloring = ocoloring;
3751:   }
3752:   return(0);
3753: }

3755: #if defined(PETSC_HAVE_ADIC)
3757: #include "adic/ad_utils.h"

3762: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3763: {
3764:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3765:   PetscInt        m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j,nlen;
3766:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
3767:   ISColoringValue *color;

3770:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3771:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
3772:   color = a->coloring->colors;
3773:   /* loop over rows */
3774:   for (i=0; i<m; i++) {
3775:     nz = ii[i+1] - ii[i];
3776:     /* loop over columns putting computed value into matrix */
3777:     for (j=0; j<nz; j++) {
3778:       *v++ = values[color[*jj++]];
3779:     }
3780:     values += nlen; /* jump to next row of derivatives */
3781:   }
3782:   return(0);
3783: }
3784: #endif

3788: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
3789: {
3790:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3791:   PetscInt         m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
3792:   MatScalar       *v = a->a;
3793:   PetscScalar     *values = (PetscScalar *)advalues;
3794:   ISColoringValue *color;

3797:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3798:   color = a->coloring->colors;
3799:   /* loop over rows */
3800:   for (i=0; i<m; i++) {
3801:     nz = ii[i+1] - ii[i];
3802:     /* loop over columns putting computed value into matrix */
3803:     for (j=0; j<nz; j++) {
3804:       *v++ = values[color[*jj++]];
3805:     }
3806:     values += nl; /* jump to next row of derivatives */
3807:   }
3808:   return(0);
3809: }

3811: /*
3812:     Special version for direct calls from Fortran 
3813: */
3814: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3815: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
3816: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3817: #define matsetvaluesseqaij_ matsetvaluesseqaij
3818: #endif

3820: /* Change these macros so can be used in void function */
3821: #undef CHKERRQ
3822: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)A)->comm,ierr) 
3823: #undef SETERRQ2
3824: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(((PetscObject)A)->comm,ierr) 

3829: void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
3830: {
3831:   Mat            A = *AA;
3832:   PetscInt       m = *mm, n = *nn;
3833:   InsertMode     is = *isis;
3834:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3835:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
3836:   PetscInt       *imax,*ai,*ailen;
3838:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
3839:   MatScalar      *ap,value,*aa;
3840:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
3841:   PetscTruth     roworiented = a->roworiented;

3844:   MatPreallocated(A);
3845:   imax = a->imax;
3846:   ai = a->i;
3847:   ailen = a->ilen;
3848:   aj = a->j;
3849:   aa = a->a;

3851:   for (k=0; k<m; k++) { /* loop over added rows */
3852:     row  = im[k];
3853:     if (row < 0) continue;
3854: #if defined(PETSC_USE_DEBUG)  
3855:     if (row >= A->rmap->n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
3856: #endif
3857:     rp   = aj + ai[row]; ap = aa + ai[row];
3858:     rmax = imax[row]; nrow = ailen[row];
3859:     low  = 0;
3860:     high = nrow;
3861:     for (l=0; l<n; l++) { /* loop over added columns */
3862:       if (in[l] < 0) continue;
3863: #if defined(PETSC_USE_DEBUG)  
3864:       if (in[l] >= A->cmap->n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
3865: #endif
3866:       col = in[l];
3867:       if (roworiented) {
3868:         value = v[l + k*n];
3869:       } else {
3870:         value = v[k + l*m];
3871:       }
3872:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

3874:       if (col <= lastcol) low = 0; else high = nrow;
3875:       lastcol = col;
3876:       while (high-low > 5) {
3877:         t = (low+high)/2;
3878:         if (rp[t] > col) high = t;
3879:         else             low  = t;
3880:       }
3881:       for (i=low; i<high; i++) {
3882:         if (rp[i] > col) break;
3883:         if (rp[i] == col) {
3884:           if (is == ADD_VALUES) ap[i] += value;
3885:           else                  ap[i] = value;
3886:           goto noinsert;
3887:         }
3888:       }
3889:       if (value == 0.0 && ignorezeroentries) goto noinsert;
3890:       if (nonew == 1) goto noinsert;
3891:       if (nonew == -1) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
3892:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
3893:       N = nrow++ - 1; a->nz++; high++;
3894:       /* shift up all the later entries in this row */
3895:       for (ii=N; ii>=i; ii--) {
3896:         rp[ii+1] = rp[ii];
3897:         ap[ii+1] = ap[ii];
3898:       }
3899:       rp[i] = col;
3900:       ap[i] = value;
3901:       noinsert:;
3902:       low = i + 1;
3903:     }
3904:     ailen[row] = nrow;
3905:   }
3906:   A->same_nonzero = PETSC_FALSE;
3907:   PetscFunctionReturnVoid();
3908: }