Actual source code: aij.c

  1: #define PETSCMAT_DLL

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

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

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

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

 48: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 49: {
 50:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 52:   PetscInt       i,ishift;
 53: 
 55:   *m     = A->rmap.n;
 56:   if (!ia) return(0);
 57:   ishift = 0;
 58:   if (symmetric && !A->structurally_symmetric) {
 59:     MatToSymmetricIJ_SeqAIJ(A->rmap.n,a->i,a->j,ishift,oshift,ia,ja);
 60:   } else if (oshift == 1) {
 61:     PetscInt nz = a->i[A->rmap.n];
 62:     /* malloc space and  add 1 to i and j indices */
 63:     PetscMalloc((A->rmap.n+1)*sizeof(PetscInt),ia);
 64:     PetscMalloc((nz+1)*sizeof(PetscInt),ja);
 65:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 66:     for (i=0; i<A->rmap.n+1; i++) (*ia)[i] = a->i[i] + 1;
 67:   } else {
 68:     *ia = a->i; *ja = a->j;
 69:   }
 70:   return(0);
 71: }

 75: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 76: {
 78: 
 80:   if (!ia) return(0);
 81:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
 82:     PetscFree(*ia);
 83:     PetscFree(*ja);
 84:   }
 85:   return(0);
 86: }

 90: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 91: {
 92:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 94:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap.n,m = A->rmap.n;
 95:   PetscInt       nz = a->i[m],row,*jj,mr,col;
 96: 
 98:   *nn = n;
 99:   if (!ia) return(0);
100:   if (symmetric) {
101:     MatToSymmetricIJ_SeqAIJ(A->rmap.n,a->i,a->j,0,oshift,ia,ja);
102:   } else {
103:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
104:     PetscMemzero(collengths,n*sizeof(PetscInt));
105:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
106:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
107:     jj = a->j;
108:     for (i=0; i<nz; i++) {
109:       collengths[jj[i]]++;
110:     }
111:     cia[0] = oshift;
112:     for (i=0; i<n; i++) {
113:       cia[i+1] = cia[i] + collengths[i];
114:     }
115:     PetscMemzero(collengths,n*sizeof(PetscInt));
116:     jj   = a->j;
117:     for (row=0; row<m; row++) {
118:       mr = a->i[row+1] - a->i[row];
119:       for (i=0; i<mr; i++) {
120:         col = *jj++;
121:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
122:       }
123:     }
124:     PetscFree(collengths);
125:     *ia = cia; *ja = cja;
126:   }
127:   return(0);
128: }

132: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
133: {

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

139:   PetscFree(*ia);
140:   PetscFree(*ja);
141: 
142:   return(0);
143: }

147: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
148: {
149:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
150:   PetscInt       *ai = a->i;

154:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
155:   return(0);
156: }

158: #define CHUNKSIZE   15

162: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
163: {
164:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
165:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
166:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
168:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
169:   PetscScalar    *ap,value,*aa = a->a;
170:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
171:   PetscTruth     roworiented = a->roworiented;

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

197:       if (col <= lastcol) low = 0; else high = nrow;
198:       lastcol = col;
199:       while (high-low > 5) {
200:         t = (low+high)/2;
201:         if (rp[t] > col) high = t;
202:         else             low  = t;
203:       }
204:       for (i=low; i<high; i++) {
205:         if (rp[i] > col) break;
206:         if (rp[i] == col) {
207:           if (is == ADD_VALUES) ap[i] += value;
208:           else                  ap[i] = value;
209:           goto noinsert;
210:         }
211:       }
212:       if (value == 0.0 && ignorezeroentries) goto noinsert;
213:       if (nonew == 1) goto noinsert;
214:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
215:       MatSeqXAIJReallocateAIJ(A,A->rmap.n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
216:       N = nrow++ - 1; a->nz++; high++;
217:       /* shift up all the later entries in this row */
218:       for (ii=N; ii>=i; ii--) {
219:         rp[ii+1] = rp[ii];
220:         ap[ii+1] = ap[ii];
221:       }
222:       rp[i] = col;
223:       ap[i] = value;
224:       noinsert:;
225:       low = i + 1;
226:     }
227:     ailen[row] = nrow;
228:   }
229:   A->same_nonzero = PETSC_FALSE;
230:   return(0);
231: }


236: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
237: {
238:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
239:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
240:   PetscInt     *ai = a->i,*ailen = a->ilen;
241:   PetscScalar  *ap,*aa = a->a,zero = 0.0;

244:   for (k=0; k<m; k++) { /* loop over rows */
245:     row  = im[k];
246:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row);
247:     if (row >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.n-1);
248:     rp   = aj + ai[row]; ap = aa + ai[row];
249:     nrow = ailen[row];
250:     for (l=0; l<n; l++) { /* loop over columns */
251:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]);
252:       if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
253:       col = in[l] ;
254:       high = nrow; low = 0; /* assume unsorted */
255:       while (high-low > 5) {
256:         t = (low+high)/2;
257:         if (rp[t] > col) high = t;
258:         else             low  = t;
259:       }
260:       for (i=low; i<high; i++) {
261:         if (rp[i] > col) break;
262:         if (rp[i] == col) {
263:           *v++ = ap[i];
264:           goto finished;
265:         }
266:       }
267:       *v++ = zero;
268:       finished:;
269:     }
270:   }
271:   return(0);
272: }


277: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
278: {
279:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
281:   PetscInt       i,*col_lens;
282:   int            fd;

285:   PetscViewerBinaryGetDescriptor(viewer,&fd);
286:   PetscMalloc((4+A->rmap.n)*sizeof(PetscInt),&col_lens);
287:   col_lens[0] = MAT_FILE_COOKIE;
288:   col_lens[1] = A->rmap.n;
289:   col_lens[2] = A->cmap.n;
290:   col_lens[3] = a->nz;

292:   /* store lengths of each row and write (including header) to file */
293:   for (i=0; i<A->rmap.n; i++) {
294:     col_lens[4+i] = a->i[i+1] - a->i[i];
295:   }
296:   PetscBinaryWrite(fd,col_lens,4+A->rmap.n,PETSC_INT,PETSC_TRUE);
297:   PetscFree(col_lens);

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

302:   /* store nonzero values */
303:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
304:   return(0);
305: }

307: EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

311: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
312: {
313:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
314:   PetscErrorCode    ierr;
315:   PetscInt          i,j,m = A->rmap.n,shift=0;
316:   const char        *name;
317:   PetscViewerFormat format;

320:   PetscObjectGetName((PetscObject)A,&name);
321:   PetscViewerGetFormat(viewer,&format);
322:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
323:     PetscInt nofinalvalue = 0;
324:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap.n-!shift)) {
325:       nofinalvalue = 1;
326:     }
327:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
328:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap.n);
329:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
330:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
331:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

333:     for (i=0; i<m; i++) {
334:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
335: #if defined(PETSC_USE_COMPLEX)
336:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
337: #else
338:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
339: #endif
340:       }
341:     }
342:     if (nofinalvalue) {
343:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap.n,0.0);
344:     }
345:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
346:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
347:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
348:      return(0);
349:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
350:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
351:     for (i=0; i<m; i++) {
352:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
353:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
354: #if defined(PETSC_USE_COMPLEX)
355:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
356:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
357:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
358:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
359:         } else if (PetscRealPart(a->a[j]) != 0.0) {
360:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
361:         }
362: #else
363:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);}
364: #endif
365:       }
366:       PetscViewerASCIIPrintf(viewer,"\n");
367:     }
368:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
369:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
370:     PetscInt nzd=0,fshift=1,*sptr;
371:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
372:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
373:     for (i=0; i<m; i++) {
374:       sptr[i] = nzd+1;
375:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
376:         if (a->j[j] >= i) {
377: #if defined(PETSC_USE_COMPLEX)
378:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
379: #else
380:           if (a->a[j] != 0.0) nzd++;
381: #endif
382:         }
383:       }
384:     }
385:     sptr[m] = nzd+1;
386:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
387:     for (i=0; i<m+1; i+=6) {
388:       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]);}
389:       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]);}
390:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
391:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
392:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
393:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
394:     }
395:     PetscViewerASCIIPrintf(viewer,"\n");
396:     PetscFree(sptr);
397:     for (i=0; i<m; i++) {
398:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
399:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
400:       }
401:       PetscViewerASCIIPrintf(viewer,"\n");
402:     }
403:     PetscViewerASCIIPrintf(viewer,"\n");
404:     for (i=0; i<m; i++) {
405:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
406:         if (a->j[j] >= i) {
407: #if defined(PETSC_USE_COMPLEX)
408:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
409:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
410:           }
411: #else
412:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
413: #endif
414:         }
415:       }
416:       PetscViewerASCIIPrintf(viewer,"\n");
417:     }
418:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
419:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
420:     PetscInt         cnt = 0,jcnt;
421:     PetscScalar value;

423:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
424:     for (i=0; i<m; i++) {
425:       jcnt = 0;
426:       for (j=0; j<A->cmap.n; j++) {
427:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
428:           value = a->a[cnt++];
429:           jcnt++;
430:         } else {
431:           value = 0.0;
432:         }
433: #if defined(PETSC_USE_COMPLEX)
434:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
435: #else
436:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
437: #endif
438:       }
439:       PetscViewerASCIIPrintf(viewer,"\n");
440:     }
441:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
442:   } else {
443:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
444:     for (i=0; i<m; i++) {
445:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
446:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
447: #if defined(PETSC_USE_COMPLEX)
448:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
449:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
450:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
451:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
452:         } else {
453:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
454:         }
455: #else
456:         PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
457: #endif
458:       }
459:       PetscViewerASCIIPrintf(viewer,"\n");
460:     }
461:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
462:   }
463:   PetscViewerFlush(viewer);
464:   return(0);
465: }

469: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
470: {
471:   Mat               A = (Mat) Aa;
472:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
473:   PetscErrorCode    ierr;
474:   PetscInt          i,j,m = A->rmap.n,color;
475:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
476:   PetscViewer       viewer;
477:   PetscViewerFormat format;

480:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
481:   PetscViewerGetFormat(viewer,&format);

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

486:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
487:     /* Blue for negative, Cyan for zero and  Red for positive */
488:     color = PETSC_DRAW_BLUE;
489:     for (i=0; i<m; i++) {
490:       y_l = m - i - 1.0; y_r = y_l + 1.0;
491:       for (j=a->i[i]; j<a->i[i+1]; j++) {
492:         x_l = a->j[j] ; x_r = x_l + 1.0;
493: #if defined(PETSC_USE_COMPLEX)
494:         if (PetscRealPart(a->a[j]) >=  0.) continue;
495: #else
496:         if (a->a[j] >=  0.) continue;
497: #endif
498:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
499:       }
500:     }
501:     color = PETSC_DRAW_CYAN;
502:     for (i=0; i<m; i++) {
503:       y_l = m - i - 1.0; y_r = y_l + 1.0;
504:       for (j=a->i[i]; j<a->i[i+1]; j++) {
505:         x_l = a->j[j]; x_r = x_l + 1.0;
506:         if (a->a[j] !=  0.) continue;
507:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
508:       }
509:     }
510:     color = PETSC_DRAW_RED;
511:     for (i=0; i<m; i++) {
512:       y_l = m - i - 1.0; y_r = y_l + 1.0;
513:       for (j=a->i[i]; j<a->i[i+1]; j++) {
514:         x_l = a->j[j]; x_r = x_l + 1.0;
515: #if defined(PETSC_USE_COMPLEX)
516:         if (PetscRealPart(a->a[j]) <=  0.) continue;
517: #else
518:         if (a->a[j] <=  0.) continue;
519: #endif
520:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
521:       }
522:     }
523:   } else {
524:     /* use contour shading to indicate magnitude of values */
525:     /* first determine max of all nonzero values */
526:     PetscInt    nz = a->nz,count;
527:     PetscDraw   popup;
528:     PetscReal scale;

530:     for (i=0; i<nz; i++) {
531:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
532:     }
533:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
534:     PetscDrawGetPopup(draw,&popup);
535:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
536:     count = 0;
537:     for (i=0; i<m; i++) {
538:       y_l = m - i - 1.0; y_r = y_l + 1.0;
539:       for (j=a->i[i]; j<a->i[i+1]; j++) {
540:         x_l = a->j[j]; x_r = x_l + 1.0;
541:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
542:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
543:         count++;
544:       }
545:     }
546:   }
547:   return(0);
548: }

552: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
553: {
555:   PetscDraw      draw;
556:   PetscReal      xr,yr,xl,yl,h,w;
557:   PetscTruth     isnull;

560:   PetscViewerDrawGetDraw(viewer,0,&draw);
561:   PetscDrawIsNull(draw,&isnull);
562:   if (isnull) return(0);

564:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
565:   xr  = A->cmap.n; yr = A->rmap.n; h = yr/10.0; w = xr/10.0;
566:   xr += w;    yr += h;  xl = -w;     yl = -h;
567:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
568:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
569:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
570:   return(0);
571: }

575: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
576: {
578:   PetscTruth     iascii,isbinary,isdraw;

581:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
582:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
583:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
584:   if (iascii) {
585:     MatView_SeqAIJ_ASCII(A,viewer);
586:   } else if (isbinary) {
587:     MatView_SeqAIJ_Binary(A,viewer);
588:   } else if (isdraw) {
589:     MatView_SeqAIJ_Draw(A,viewer);
590:   } else {
591:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
592:   }
593:   MatView_Inode(A,viewer);
594:   return(0);
595: }

599: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
600: {
601:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
603:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
604:   PetscInt       m = A->rmap.n,*ip,N,*ailen = a->ilen,rmax = 0;
605:   PetscScalar    *aa = a->a,*ap;
606:   PetscReal      ratio=0.6;

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

611:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
612:   for (i=1; i<m; i++) {
613:     /* move each row back by the amount of empty slots (fshift) before it*/
614:     fshift += imax[i-1] - ailen[i-1];
615:     rmax   = PetscMax(rmax,ailen[i]);
616:     if (fshift) {
617:       ip = aj + ai[i] ;
618:       ap = aa + ai[i] ;
619:       N  = ailen[i];
620:       for (j=0; j<N; j++) {
621:         ip[j-fshift] = ip[j];
622:         ap[j-fshift] = ap[j];
623:       }
624:     }
625:     ai[i] = ai[i-1] + ailen[i-1];
626:   }
627:   if (m) {
628:     fshift += imax[m-1] - ailen[m-1];
629:     ai[m]  = ai[m-1] + ailen[m-1];
630:   }
631:   /* reset ilen and imax for each row */
632:   for (i=0; i<m; i++) {
633:     ailen[i] = imax[i] = ai[i+1] - ai[i];
634:   }
635:   a->nz = ai[m];

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

642:   a->reallocs          = 0;
643:   A->info.nz_unneeded  = (double)fshift;
644:   a->rmax              = rmax;

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

650:   MatAssemblyEnd_Inode(A,mode);
651:   return(0);
652: }

656: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
657: {
658:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
659:   PetscInt       i,nz = a->nz;
660:   PetscScalar    *aa = a->a;

663:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
664:   return(0);
665: }

669: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
670: {
671:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
672:   PetscInt       i,nz = a->nz;
673:   PetscScalar    *aa = a->a;

676:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
677:   return(0);
678: }

682: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
683: {
684:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

688:   PetscMemzero(a->a,(a->i[A->rmap.n])*sizeof(PetscScalar));
689:   return(0);
690: }

694: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
695: {
696:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

700: #if defined(PETSC_USE_LOG)
701:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.n,A->cmap.n,a->nz);
702: #endif
703:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
704:   if (a->row) {
705:     ISDestroy(a->row);
706:   }
707:   if (a->col) {
708:     ISDestroy(a->col);
709:   }
710:   PetscFree(a->diag);
711:   PetscFree2(a->imax,a->ilen);
712:   PetscFree(a->idiag);
713:   PetscFree(a->solve_work);
714:   if (a->icol) {ISDestroy(a->icol);}
715:   PetscFree(a->saved_values);
716:   if (a->coloring) {ISColoringDestroy(a->coloring);}
717:   PetscFree(a->xtoy);
718:   if (a->XtoY) {MatDestroy(a->XtoY);}
719:   if (a->compressedrow.use){PetscFree(a->compressedrow.i);}

721:   MatDestroy_Inode(A);

723:   PetscFree(a);

725:   PetscObjectChangeTypeName((PetscObject)A,0);
726:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
727:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
728:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
729:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
730:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
731:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqcsrperm_C","",PETSC_NULL);
732:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
733:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
734:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C","",PETSC_NULL);
735:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
736:   return(0);
737: }

741: PetscErrorCode MatCompress_SeqAIJ(Mat A)
742: {
744:   return(0);
745: }

749: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op)
750: {
751:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

755:   switch (op) {
756:     case MAT_ROW_ORIENTED:
757:       a->roworiented       = PETSC_TRUE;
758:       break;
759:     case MAT_KEEP_ZEROED_ROWS:
760:       a->keepzeroedrows    = PETSC_TRUE;
761:       break;
762:     case MAT_COLUMN_ORIENTED:
763:       a->roworiented       = PETSC_FALSE;
764:       break;
765:     case MAT_COLUMNS_SORTED:
766:       a->sorted            = PETSC_TRUE;
767:       break;
768:     case MAT_COLUMNS_UNSORTED:
769:       a->sorted            = PETSC_FALSE;
770:       break;
771:     case MAT_NO_NEW_NONZERO_LOCATIONS:
772:       a->nonew             = 1;
773:       break;
774:     case MAT_NEW_NONZERO_LOCATION_ERR:
775:       a->nonew             = -1;
776:       break;
777:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
778:       a->nonew             = -2;
779:       break;
780:     case MAT_YES_NEW_NONZERO_LOCATIONS:
781:       a->nonew             = 0;
782:       break;
783:     case MAT_IGNORE_ZERO_ENTRIES:
784:       a->ignorezeroentries = PETSC_TRUE;
785:       break;
786:     case MAT_USE_COMPRESSEDROW:
787:       a->compressedrow.use = PETSC_TRUE;
788:       break;
789:     case MAT_DO_NOT_USE_COMPRESSEDROW:
790:       a->compressedrow.use = PETSC_FALSE;
791:       break;
792:     case MAT_ROWS_SORTED:
793:     case MAT_ROWS_UNSORTED:
794:     case MAT_YES_NEW_DIAGONALS:
795:     case MAT_IGNORE_OFF_PROC_ENTRIES:
796:     case MAT_USE_HASH_TABLE:
797:       PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
798:       break;
799:     case MAT_NO_NEW_DIAGONALS:
800:       SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
801:     default:
802:       break;
803:   }
804:   MatSetOption_Inode(A,op);
805:   return(0);
806: }

810: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
811: {
812:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
814:   PetscInt       i,j,n;
815:   PetscScalar    *x,zero = 0.0;

818:   VecSet(v,zero);
819:   VecGetArray(v,&x);
820:   VecGetLocalSize(v,&n);
821:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
822:   for (i=0; i<A->rmap.n; i++) {
823:     for (j=a->i[i]; j<a->i[i+1]; j++) {
824:       if (a->j[j] == i) {
825:         x[i] = a->a[j];
826:         break;
827:       }
828:     }
829:   }
830:   VecRestoreArray(v,&x);
831:   return(0);
832: }

836: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
837: {
838:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
839:   PetscScalar       *x,*y;
840:   PetscErrorCode    ierr;
841:   PetscInt          m = A->rmap.n;
842: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
843:   PetscScalar       *v,alpha;
844:   PetscInt          n,i,*idx,*ii,*ridx=PETSC_NULL;
845:   Mat_CompressedRow cprow = a->compressedrow;
846:   PetscTruth        usecprow = cprow.use;
847: #endif

850:   if (zz != yy) {VecCopy(zz,yy);}
851:   VecGetArray(xx,&x);
852:   VecGetArray(yy,&y);

854: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
855:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
856: #else
857:   if (usecprow){
858:     m    = cprow.nrows;
859:     ii   = cprow.i;
860:     ridx = cprow.rindex;
861:   } else {
862:     ii = a->i;
863:   }
864:   for (i=0; i<m; i++) {
865:     idx   = a->j + ii[i] ;
866:     v     = a->a + ii[i] ;
867:     n     = ii[i+1] - ii[i];
868:     if (usecprow){
869:       alpha = x[ridx[i]];
870:     } else {
871:       alpha = x[i];
872:     }
873:     while (n-->0) {y[*idx++] += alpha * *v++;}
874:   }
875: #endif
876:   PetscLogFlops(2*a->nz);
877:   VecRestoreArray(xx,&x);
878:   VecRestoreArray(yy,&y);
879:   return(0);
880: }

884: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
885: {
886:   PetscScalar    zero = 0.0;

890:   VecSet(yy,zero);
891:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
892:   return(0);
893: }


898: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
899: {
900:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
901:   PetscScalar    *x,*y,*aa;
903:   PetscInt       m=A->rmap.n,*aj,*ii;
904:   PetscInt       n,i,j,nonzerorow=0,*ridx=PETSC_NULL;
905:   PetscScalar    sum;
906:   PetscTruth     usecprow=a->compressedrow.use;
907: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
908:   PetscInt       jrow;
909: #endif

911: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
912: #pragma disjoint(*x,*y,*aa)
913: #endif

916:   VecGetArray(xx,&x);
917:   VecGetArray(yy,&y);
918:   aj  = a->j;
919:   aa  = a->a;
920:   ii  = a->i;
921:   if (usecprow){ /* use compressed row format */
922:     m    = a->compressedrow.nrows;
923:     ii   = a->compressedrow.i;
924:     ridx = a->compressedrow.rindex;
925:     for (i=0; i<m; i++){
926:       n   = ii[i+1] - ii[i];
927:       aj  = a->j + ii[i];
928:       aa  = a->a + ii[i];
929:       sum = 0.0;
930:       nonzerorow += (n>0);
931:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
932:       y[*ridx++] = sum;
933:     }
934:   } else { /* do not use compressed row format */
935: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
936:     fortranmultaij_(&m,x,ii,aj,aa,y);
937: #else
938:     for (i=0; i<m; i++) {
939:       jrow = ii[i];
940:       n    = ii[i+1] - jrow;
941:       sum  = 0.0;
942:       nonzerorow += (n>0);
943:       for (j=0; j<n; j++) {
944:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
945:       }
946:       y[i] = sum;
947:     }
948: #endif
949:   }
950:   PetscLogFlops(2*a->nz - nonzerorow);
951:   VecRestoreArray(xx,&x);
952:   VecRestoreArray(yy,&y);
953:   return(0);
954: }

958: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
959: {
960:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
961:   PetscScalar    *x,*y,*z,*aa;
963:   PetscInt       m = A->rmap.n,*aj,*ii;
964: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
965:   PetscInt       n,i,jrow,j,*ridx=PETSC_NULL;
966:   PetscScalar    sum;
967:   PetscTruth     usecprow=a->compressedrow.use;
968: #endif

971:   VecGetArray(xx,&x);
972:   VecGetArray(yy,&y);
973:   if (zz != yy) {
974:     VecGetArray(zz,&z);
975:   } else {
976:     z = y;
977:   }

979:   aj  = a->j;
980:   aa  = a->a;
981:   ii  = a->i;
982: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
983:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
984: #else
985:   if (usecprow){ /* use compressed row format */
986:     if (zz != yy){
987:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
988:     }
989:     m    = a->compressedrow.nrows;
990:     ii   = a->compressedrow.i;
991:     ridx = a->compressedrow.rindex;
992:     for (i=0; i<m; i++){
993:       n  = ii[i+1] - ii[i];
994:       aj  = a->j + ii[i];
995:       aa  = a->a + ii[i];
996:       sum = y[*ridx];
997:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
998:       z[*ridx++] = sum;
999:     }
1000:   } else { /* do not use compressed row format */
1001:     for (i=0; i<m; i++) {
1002:       jrow = ii[i];
1003:       n    = ii[i+1] - jrow;
1004:       sum  = y[i];
1005:       for (j=0; j<n; j++) {
1006:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
1007:       }
1008:       z[i] = sum;
1009:     }
1010:   }
1011: #endif
1012:   PetscLogFlops(2*a->nz);
1013:   VecRestoreArray(xx,&x);
1014:   VecRestoreArray(yy,&y);
1015:   if (zz != yy) {
1016:     VecRestoreArray(zz,&z);
1017:   }
1018:   return(0);
1019: }

1021: /*
1022:      Adds diagonal pointers to sparse matrix structure.
1023: */
1026: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1027: {
1028:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1030:   PetscInt       i,j,m = A->rmap.n;

1033:   if (!a->diag) {
1034:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
1035:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
1036:   }
1037:   for (i=0; i<A->rmap.n; i++) {
1038:     a->diag[i] = a->i[i+1];
1039:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1040:       if (a->j[j] == i) {
1041:         a->diag[i] = j;
1042:         break;
1043:       }
1044:     }
1045:   }
1046:   return(0);
1047: }

1049: /*
1050:      Checks for missing diagonals
1051: */
1054: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscTruth *missing,PetscInt *d)
1055: {
1056:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1057:   PetscInt       *diag,*jj = a->j,i;

1060:   *missing = PETSC_FALSE;
1061:   if (A->rmap.n > 0 && !jj) {
1062:     *missing  = PETSC_TRUE;
1063:     if (d) *d = 0;
1064:     PetscInfo(A,"Matrix has no entries therefor is missing diagonal");
1065:   } else {
1066:     diag = a->diag;
1067:     for (i=0; i<A->rmap.n; i++) {
1068:       if (jj[diag[i]] != i) {
1069:         *missing = PETSC_TRUE;
1070:         if (d) *d = i;
1071:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1072:       }
1073:     }
1074:   }
1075:   return(0);
1076: }

1080: PetscErrorCode MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1081: {
1082:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
1083:   PetscScalar        *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag;
1084:   const PetscScalar  *v = a->a, *b, *bs,*xb, *ts;
1085:   PetscErrorCode     ierr;
1086:   PetscInt           n = A->cmap.n,m = A->rmap.n,i;
1087:   const PetscInt     *idx,*diag;

1090:   its = its*lits;
1091:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);

1093:   diag = a->diag;
1094:   if (!a->idiag) {
1095:     PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);
1096:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1097:     a->ssor  = a->idiag + m;
1098:     mdiag    = a->ssor + m;
1099:     v        = a->a;

1101:     /* this is wrong when fshift omega changes each iteration */
1102:     if (omega == 1.0 && !fshift) {
1103:       for (i=0; i<m; i++) {
1104:         mdiag[i]    = v[diag[i]];
1105:         if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1106:         a->idiag[i] = 1.0/v[diag[i]];
1107:       }
1108:       PetscLogFlops(m);
1109:     } else {
1110:       for (i=0; i<m; i++) {
1111:         mdiag[i]    = v[diag[i]];
1112:         a->idiag[i] = omega/(fshift + v[diag[i]]);
1113:       }
1114:       PetscLogFlops(2*m);
1115:     }
1116:   }
1117:   t     = a->ssor;
1118:   idiag = a->idiag;
1119:   mdiag = a->idiag + 2*m;

1121:   VecGetArray(xx,&x);
1122:   if (xx != bb) {
1123:     VecGetArray(bb,(PetscScalar**)&b);
1124:   } else {
1125:     b = x;
1126:   }

1128:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1129:   xs   = x;
1130:   if (flag == SOR_APPLY_UPPER) {
1131:    /* apply (U + D/omega) to the vector */
1132:     bs = b;
1133:     for (i=0; i<m; i++) {
1134:         d    = fshift + a->a[diag[i]];
1135:         n    = a->i[i+1] - diag[i] - 1;
1136:         idx  = a->j + diag[i] + 1;
1137:         v    = a->a + diag[i] + 1;
1138:         sum  = b[i]*d/omega;
1139:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1140:         x[i] = sum;
1141:     }
1142:     VecRestoreArray(xx,&x);
1143:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1144:     PetscLogFlops(a->nz);
1145:     return(0);
1146:   }


1149:     /* Let  A = L + U + D; where L is lower trianglar,
1150:     U is upper triangular, E is diagonal; This routine applies

1152:             (L + E)^{-1} A (U + E)^{-1}

1154:     to a vector efficiently using Eisenstat's trick. This is for
1155:     the case of SSOR preconditioner, so E is D/omega where omega
1156:     is the relaxation factor.
1157:     */

1159:   if (flag == SOR_APPLY_LOWER) {
1160:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1161:   } else if (flag & SOR_EISENSTAT) {
1162:     /* Let  A = L + U + D; where L is lower trianglar,
1163:     U is upper triangular, E is diagonal; This routine applies

1165:             (L + E)^{-1} A (U + E)^{-1}

1167:     to a vector efficiently using Eisenstat's trick. This is for
1168:     the case of SSOR preconditioner, so E is D/omega where omega
1169:     is the relaxation factor.
1170:     */
1171:     scale = (2.0/omega) - 1.0;

1173:     /*  x = (E + U)^{-1} b */
1174:     for (i=m-1; i>=0; i--) {
1175:       n    = a->i[i+1] - diag[i] - 1;
1176:       idx  = a->j + diag[i] + 1;
1177:       v    = a->a + diag[i] + 1;
1178:       sum  = b[i];
1179:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1180:       x[i] = sum*idiag[i];
1181:     }

1183:     /*  t = b - (2*E - D)x */
1184:     v = a->a;
1185:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1187:     /*  t = (E + L)^{-1}t */
1188:     ts = t;
1189:     diag = a->diag;
1190:     for (i=0; i<m; i++) {
1191:       n    = diag[i] - a->i[i];
1192:       idx  = a->j + a->i[i];
1193:       v    = a->a + a->i[i];
1194:       sum  = t[i];
1195:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1196:       t[i] = sum*idiag[i];
1197:       /*  x = x + t */
1198:       x[i] += t[i];
1199:     }

1201:     PetscLogFlops(6*m-1 + 2*a->nz);
1202:     VecRestoreArray(xx,&x);
1203:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1204:     return(0);
1205:   }
1206:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1207:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1208: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1209:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)b);
1210: #else
1211:       for (i=0; i<m; i++) {
1212:         n    = diag[i] - a->i[i];
1213:         idx  = a->j + a->i[i];
1214:         v    = a->a + a->i[i];
1215:         sum  = b[i];
1216:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1217:         x[i] = sum*idiag[i];
1218:       }
1219: #endif
1220:       xb = x;
1221:       PetscLogFlops(a->nz);
1222:     } else xb = b;
1223:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1224:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1225:       for (i=0; i<m; i++) {
1226:         x[i] *= mdiag[i];
1227:       }
1228:       PetscLogFlops(m);
1229:     }
1230:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1231: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1232:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)xb);
1233: #else
1234:       for (i=m-1; i>=0; i--) {
1235:         n    = a->i[i+1] - diag[i] - 1;
1236:         idx  = a->j + diag[i] + 1;
1237:         v    = a->a + diag[i] + 1;
1238:         sum  = xb[i];
1239:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1240:         x[i] = sum*idiag[i];
1241:       }
1242: #endif
1243:       PetscLogFlops(a->nz);
1244:     }
1245:     its--;
1246:   }
1247:   while (its--) {
1248:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1249: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1250:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1251: #else
1252:       for (i=0; i<m; i++) {
1253:         n    = a->i[i+1] - a->i[i];
1254:         idx  = a->j + a->i[i];
1255:         v    = a->a + a->i[i];
1256:         sum  = b[i];
1257:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1258:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1259:       }
1260: #endif 
1261:       PetscLogFlops(a->nz);
1262:     }
1263:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1264: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1265:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1266: #else
1267:       for (i=m-1; i>=0; i--) {
1268:         n    = a->i[i+1] - a->i[i];
1269:         idx  = a->j + a->i[i];
1270:         v    = a->a + a->i[i];
1271:         sum  = b[i];
1272:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1273:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1274:       }
1275: #endif
1276:       PetscLogFlops(a->nz);
1277:     }
1278:   }
1279:   VecRestoreArray(xx,&x);
1280:   if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1281:   return(0);
1282: }

1286: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1287: {
1288:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1291:   info->rows_global    = (double)A->rmap.n;
1292:   info->columns_global = (double)A->cmap.n;
1293:   info->rows_local     = (double)A->rmap.n;
1294:   info->columns_local  = (double)A->cmap.n;
1295:   info->block_size     = 1.0;
1296:   info->nz_allocated   = (double)a->maxnz;
1297:   info->nz_used        = (double)a->nz;
1298:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1299:   info->assemblies     = (double)A->num_ass;
1300:   info->mallocs        = (double)a->reallocs;
1301:   info->memory         = A->mem;
1302:   if (A->factor) {
1303:     info->fill_ratio_given  = A->info.fill_ratio_given;
1304:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1305:     info->factor_mallocs    = A->info.factor_mallocs;
1306:   } else {
1307:     info->fill_ratio_given  = 0;
1308:     info->fill_ratio_needed = 0;
1309:     info->factor_mallocs    = 0;
1310:   }
1311:   return(0);
1312: }

1316: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1317: {
1318:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1319:   PetscInt       i,m = A->rmap.n - 1,d;
1321:   PetscTruth     missing;

1324:   if (a->keepzeroedrows) {
1325:     for (i=0; i<N; i++) {
1326:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1327:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1328:     }
1329:     if (diag != 0.0) {
1330:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1331:       if (missing) SETERRQ1(PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1332:       for (i=0; i<N; i++) {
1333:         a->a[a->diag[rows[i]]] = diag;
1334:       }
1335:     }
1336:     A->same_nonzero = PETSC_TRUE;
1337:   } else {
1338:     if (diag != 0.0) {
1339:       for (i=0; i<N; i++) {
1340:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1341:         if (a->ilen[rows[i]] > 0) {
1342:           a->ilen[rows[i]]          = 1;
1343:           a->a[a->i[rows[i]]] = diag;
1344:           a->j[a->i[rows[i]]] = rows[i];
1345:         } else { /* in case row was completely empty */
1346:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1347:         }
1348:       }
1349:     } else {
1350:       for (i=0; i<N; i++) {
1351:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1352:         a->ilen[rows[i]] = 0;
1353:       }
1354:     }
1355:     A->same_nonzero = PETSC_FALSE;
1356:   }
1357:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1358:   return(0);
1359: }

1363: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1364: {
1365:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1366:   PetscInt   *itmp;

1369:   if (row < 0 || row >= A->rmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1371:   *nz = a->i[row+1] - a->i[row];
1372:   if (v) *v = a->a + a->i[row];
1373:   if (idx) {
1374:     itmp = a->j + a->i[row];
1375:     if (*nz) {
1376:       *idx = itmp;
1377:     }
1378:     else *idx = 0;
1379:   }
1380:   return(0);
1381: }

1383: /* remove this function? */
1386: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1387: {
1389:   return(0);
1390: }

1394: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1395: {
1396:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1397:   PetscScalar    *v = a->a;
1398:   PetscReal      sum = 0.0;
1400:   PetscInt       i,j;

1403:   if (type == NORM_FROBENIUS) {
1404:     for (i=0; i<a->nz; i++) {
1405: #if defined(PETSC_USE_COMPLEX)
1406:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1407: #else
1408:       sum += (*v)*(*v); v++;
1409: #endif
1410:     }
1411:     *nrm = sqrt(sum);
1412:   } else if (type == NORM_1) {
1413:     PetscReal *tmp;
1414:     PetscInt    *jj = a->j;
1415:     PetscMalloc((A->cmap.n+1)*sizeof(PetscReal),&tmp);
1416:     PetscMemzero(tmp,A->cmap.n*sizeof(PetscReal));
1417:     *nrm = 0.0;
1418:     for (j=0; j<a->nz; j++) {
1419:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
1420:     }
1421:     for (j=0; j<A->cmap.n; j++) {
1422:       if (tmp[j] > *nrm) *nrm = tmp[j];
1423:     }
1424:     PetscFree(tmp);
1425:   } else if (type == NORM_INFINITY) {
1426:     *nrm = 0.0;
1427:     for (j=0; j<A->rmap.n; j++) {
1428:       v = a->a + a->i[j];
1429:       sum = 0.0;
1430:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1431:         sum += PetscAbsScalar(*v); v++;
1432:       }
1433:       if (sum > *nrm) *nrm = sum;
1434:     }
1435:   } else {
1436:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1437:   }
1438:   return(0);
1439: }

1443: PetscErrorCode MatTranspose_SeqAIJ(Mat A,Mat *B)
1444: {
1445:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1446:   Mat            C;
1448:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap.n,len,*col;
1449:   PetscScalar    *array = a->a;

1452:   if (!B && m != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1453:   PetscMalloc((1+A->cmap.n)*sizeof(PetscInt),&col);
1454:   PetscMemzero(col,(1+A->cmap.n)*sizeof(PetscInt));
1455: 
1456:   for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1457:   MatCreate(A->comm,&C);
1458:   MatSetSizes(C,A->cmap.n,m,A->cmap.n,m);
1459:   MatSetType(C,A->type_name);
1460:   MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1461:   PetscFree(col);
1462:   for (i=0; i<m; i++) {
1463:     len    = ai[i+1]-ai[i];
1464:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1465:     array += len;
1466:     aj    += len;
1467:   }

1469:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1470:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1472:   if (B) {
1473:     *B = C;
1474:   } else {
1475:     MatHeaderCopy(A,C);
1476:   }
1477:   return(0);
1478: }

1483: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1484: {
1485:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1486:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1488:   PetscInt       ma,na,mb,nb, i;

1491:   bij = (Mat_SeqAIJ *) B->data;
1492: 
1493:   MatGetSize(A,&ma,&na);
1494:   MatGetSize(B,&mb,&nb);
1495:   if (ma!=nb || na!=mb){
1496:     *f = PETSC_FALSE;
1497:     return(0);
1498:   }
1499:   aii = aij->i; bii = bij->i;
1500:   adx = aij->j; bdx = bij->j;
1501:   va  = aij->a; vb = bij->a;
1502:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1503:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1504:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1505:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1507:   *f = PETSC_TRUE;
1508:   for (i=0; i<ma; i++) {
1509:     while (aptr[i]<aii[i+1]) {
1510:       PetscInt         idc,idr;
1511:       PetscScalar vc,vr;
1512:       /* column/row index/value */
1513:       idc = adx[aptr[i]];
1514:       idr = bdx[bptr[idc]];
1515:       vc  = va[aptr[i]];
1516:       vr  = vb[bptr[idc]];
1517:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1518:         *f = PETSC_FALSE;
1519:         goto done;
1520:       } else {
1521:         aptr[i]++;
1522:         if (B || i!=idc) bptr[idc]++;
1523:       }
1524:     }
1525:   }
1526:  done:
1527:   PetscFree(aptr);
1528:   if (B) {
1529:     PetscFree(bptr);
1530:   }
1531:   return(0);
1532: }

1537: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1538: {
1541:   MatIsTranspose_SeqAIJ(A,A,tol,f);
1542:   return(0);
1543: }

1547: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1548: {
1549:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1550:   PetscScalar    *l,*r,x,*v;
1552:   PetscInt       i,j,m = A->rmap.n,n = A->cmap.n,M,nz = a->nz,*jj;

1555:   if (ll) {
1556:     /* The local size is used so that VecMPI can be passed to this routine
1557:        by MatDiagonalScale_MPIAIJ */
1558:     VecGetLocalSize(ll,&m);
1559:     if (m != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1560:     VecGetArray(ll,&l);
1561:     v = a->a;
1562:     for (i=0; i<m; i++) {
1563:       x = l[i];
1564:       M = a->i[i+1] - a->i[i];
1565:       for (j=0; j<M; j++) { (*v++) *= x;}
1566:     }
1567:     VecRestoreArray(ll,&l);
1568:     PetscLogFlops(nz);
1569:   }
1570:   if (rr) {
1571:     VecGetLocalSize(rr,&n);
1572:     if (n != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1573:     VecGetArray(rr,&r);
1574:     v = a->a; jj = a->j;
1575:     for (i=0; i<nz; i++) {
1576:       (*v++) *= r[*jj++];
1577:     }
1578:     VecRestoreArray(rr,&r);
1579:     PetscLogFlops(nz);
1580:   }
1581:   return(0);
1582: }

1586: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
1587: {
1588:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
1590:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap.n,*lens;
1591:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1592:   PetscInt       *irow,*icol,nrows,ncols;
1593:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1594:   PetscScalar    *a_new,*mat_a;
1595:   Mat            C;
1596:   PetscTruth     stride;

1599:   ISSorted(isrow,(PetscTruth*)&i);
1600:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1601:   ISSorted(iscol,(PetscTruth*)&i);
1602:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1604:   ISGetIndices(isrow,&irow);
1605:   ISGetLocalSize(isrow,&nrows);
1606:   ISGetLocalSize(iscol,&ncols);

1608:   ISStrideGetInfo(iscol,&first,&step);
1609:   ISStride(iscol,&stride);
1610:   if (stride && step == 1) {
1611:     /* special case of contiguous rows */
1612:     PetscMalloc((2*nrows+1)*sizeof(PetscInt),&lens);
1613:     starts = lens + nrows;
1614:     /* loop over new rows determining lens and starting points */
1615:     for (i=0; i<nrows; i++) {
1616:       kstart  = ai[irow[i]];
1617:       kend    = kstart + ailen[irow[i]];
1618:       for (k=kstart; k<kend; k++) {
1619:         if (aj[k] >= first) {
1620:           starts[i] = k;
1621:           break;
1622:         }
1623:       }
1624:       sum = 0;
1625:       while (k < kend) {
1626:         if (aj[k++] >= first+ncols) break;
1627:         sum++;
1628:       }
1629:       lens[i] = sum;
1630:     }
1631:     /* create submatrix */
1632:     if (scall == MAT_REUSE_MATRIX) {
1633:       PetscInt n_cols,n_rows;
1634:       MatGetSize(*B,&n_rows,&n_cols);
1635:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1636:       MatZeroEntries(*B);
1637:       C = *B;
1638:     } else {
1639:       MatCreate(A->comm,&C);
1640:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1641:       MatSetType(C,A->type_name);
1642:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1643:     }
1644:     c = (Mat_SeqAIJ*)C->data;

1646:     /* loop over rows inserting into submatrix */
1647:     a_new    = c->a;
1648:     j_new    = c->j;
1649:     i_new    = c->i;

1651:     for (i=0; i<nrows; i++) {
1652:       ii    = starts[i];
1653:       lensi = lens[i];
1654:       for (k=0; k<lensi; k++) {
1655:         *j_new++ = aj[ii+k] - first;
1656:       }
1657:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1658:       a_new      += lensi;
1659:       i_new[i+1]  = i_new[i] + lensi;
1660:       c->ilen[i]  = lensi;
1661:     }
1662:     PetscFree(lens);
1663:   } else {
1664:     ISGetIndices(iscol,&icol);
1665:     PetscMalloc((1+oldcols)*sizeof(PetscInt),&smap);
1666: 
1667:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
1668:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
1669:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1670:     /* determine lens of each row */
1671:     for (i=0; i<nrows; i++) {
1672:       kstart  = ai[irow[i]];
1673:       kend    = kstart + a->ilen[irow[i]];
1674:       lens[i] = 0;
1675:       for (k=kstart; k<kend; k++) {
1676:         if (smap[aj[k]]) {
1677:           lens[i]++;
1678:         }
1679:       }
1680:     }
1681:     /* Create and fill new matrix */
1682:     if (scall == MAT_REUSE_MATRIX) {
1683:       PetscTruth equal;

1685:       c = (Mat_SeqAIJ *)((*B)->data);
1686:       if ((*B)->rmap.n  != nrows || (*B)->cmap.n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1687:       PetscMemcmp(c->ilen,lens,(*B)->rmap.n*sizeof(PetscInt),&equal);
1688:       if (!equal) {
1689:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1690:       }
1691:       PetscMemzero(c->ilen,(*B)->rmap.n*sizeof(PetscInt));
1692:       C = *B;
1693:     } else {
1694:       MatCreate(A->comm,&C);
1695:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1696:       MatSetType(C,A->type_name);
1697:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1698:     }
1699:     c = (Mat_SeqAIJ *)(C->data);
1700:     for (i=0; i<nrows; i++) {
1701:       row    = irow[i];
1702:       kstart = ai[row];
1703:       kend   = kstart + a->ilen[row];
1704:       mat_i  = c->i[i];
1705:       mat_j  = c->j + mat_i;
1706:       mat_a  = c->a + mat_i;
1707:       mat_ilen = c->ilen + i;
1708:       for (k=kstart; k<kend; k++) {
1709:         if ((tcol=smap[a->j[k]])) {
1710:           *mat_j++ = tcol - 1;
1711:           *mat_a++ = a->a[k];
1712:           (*mat_ilen)++;

1714:         }
1715:       }
1716:     }
1717:     /* Free work space */
1718:     ISRestoreIndices(iscol,&icol);
1719:     PetscFree(smap);
1720:     PetscFree(lens);
1721:   }
1722:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1723:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1725:   ISRestoreIndices(isrow,&irow);
1726:   *B = C;
1727:   return(0);
1728: }

1730: /*
1731: */
1734: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1735: {
1736:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
1738:   Mat            outA;
1739:   PetscTruth     row_identity,col_identity;

1742:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1743:   ISIdentity(row,&row_identity);
1744:   ISIdentity(col,&col_identity);

1746:   outA          = inA;
1747:   inA->factor   = FACTOR_LU;
1748:   PetscObjectReference((PetscObject)row);
1749:   if (a->row) { ISDestroy(a->row); }
1750:   a->row = row;
1751:   PetscObjectReference((PetscObject)col);
1752:   if (a->col) { ISDestroy(a->col); }
1753:   a->col = col;

1755:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1756:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1757:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1758:   PetscLogObjectParent(inA,a->icol);

1760:   if (!a->solve_work) { /* this matrix may have been factored before */
1761:      PetscMalloc((inA->rmap.n+1)*sizeof(PetscScalar),&a->solve_work);
1762:      PetscLogObjectMemory(inA, (inA->rmap.n+1)*sizeof(PetscScalar));
1763:   }

1765:   MatMarkDiagonal_SeqAIJ(inA);
1766:   if (row_identity && col_identity) {
1767:     MatLUFactorNumeric_SeqAIJ(inA,info,&outA);
1768:   } else {
1769:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(inA,info,&outA);
1770:   }
1771:   return(0);
1772: }

1774:  #include petscblaslapack.h
1777: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
1778: {
1779:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)inA->data;
1780:   PetscBLASInt bnz = (PetscBLASInt)a->nz,one = 1;
1781:   PetscScalar oalpha = alpha;


1786:   BLASscal_(&bnz,&oalpha,a->a,&one);
1787:   PetscLogFlops(a->nz);
1788:   return(0);
1789: }

1793: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1794: {
1796:   PetscInt       i;

1799:   if (scall == MAT_INITIAL_MATRIX) {
1800:     PetscMalloc((n+1)*sizeof(Mat),B);
1801:   }

1803:   for (i=0; i<n; i++) {
1804:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1805:   }
1806:   return(0);
1807: }

1811: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
1812: {
1813:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1815:   PetscInt       row,i,j,k,l,m,n,*idx,*nidx,isz,val;
1816:   PetscInt       start,end,*ai,*aj;
1817:   PetscBT        table;

1820:   m     = A->rmap.n;
1821:   ai    = a->i;
1822:   aj    = a->j;

1824:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

1826:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
1827:   PetscBTCreate(m,table);

1829:   for (i=0; i<is_max; i++) {
1830:     /* Initialize the two local arrays */
1831:     isz  = 0;
1832:     PetscBTMemzero(m,table);
1833: 
1834:     /* Extract the indices, assume there can be duplicate entries */
1835:     ISGetIndices(is[i],&idx);
1836:     ISGetLocalSize(is[i],&n);
1837: 
1838:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1839:     for (j=0; j<n ; ++j){
1840:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1841:     }
1842:     ISRestoreIndices(is[i],&idx);
1843:     ISDestroy(is[i]);
1844: 
1845:     k = 0;
1846:     for (j=0; j<ov; j++){ /* for each overlap */
1847:       n = isz;
1848:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1849:         row   = nidx[k];
1850:         start = ai[row];
1851:         end   = ai[row+1];
1852:         for (l = start; l<end ; l++){
1853:           val = aj[l] ;
1854:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1855:         }
1856:       }
1857:     }
1858:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1859:   }
1860:   PetscBTDestroy(table);
1861:   PetscFree(nidx);
1862:   return(0);
1863: }

1865: /* -------------------------------------------------------------- */
1868: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1869: {
1870:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1872:   PetscInt       i,nz,m = A->rmap.n,n = A->cmap.n,*col;
1873:   PetscInt       *row,*cnew,j,*lens;
1874:   IS             icolp,irowp;
1875:   PetscInt       *cwork;
1876:   PetscScalar    *vwork;

1879:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1880:   ISGetIndices(irowp,&row);
1881:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1882:   ISGetIndices(icolp,&col);
1883: 
1884:   /* determine lengths of permuted rows */
1885:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
1886:   for (i=0; i<m; i++) {
1887:     lens[row[i]] = a->i[i+1] - a->i[i];
1888:   }
1889:   MatCreate(A->comm,B);
1890:   MatSetSizes(*B,m,n,m,n);
1891:   MatSetType(*B,A->type_name);
1892:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
1893:   PetscFree(lens);

1895:   PetscMalloc(n*sizeof(PetscInt),&cnew);
1896:   for (i=0; i<m; i++) {
1897:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1898:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1899:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1900:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1901:   }
1902:   PetscFree(cnew);
1903:   (*B)->assembled     = PETSC_FALSE;
1904:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1905:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1906:   ISRestoreIndices(irowp,&row);
1907:   ISRestoreIndices(icolp,&col);
1908:   ISDestroy(irowp);
1909:   ISDestroy(icolp);
1910:   return(0);
1911: }

1915: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1916: {

1920:   /* If the two matrices have the same copy implementation, use fast copy. */
1921:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1922:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1923:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1925:     if (a->i[A->rmap.n] != b->i[B->rmap.n]) {
1926:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1927:     }
1928:     PetscMemcpy(b->a,a->a,(a->i[A->rmap.n])*sizeof(PetscScalar));
1929:   } else {
1930:     MatCopy_Basic(A,B,str);
1931:   }
1932:   return(0);
1933: }

1937: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
1938: {

1942:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
1943:   return(0);
1944: }

1948: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
1949: {
1950:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1952:   *array = a->a;
1953:   return(0);
1954: }

1958: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
1959: {
1961:   return(0);
1962: }

1966: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1967: {
1968:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
1970:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1971:   PetscScalar    dx,*y,*xx,*w3_array;
1972:   PetscScalar    *vscale_array;
1973:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
1974:   Vec            w1,w2,w3;
1975:   void           *fctx = coloring->fctx;
1976:   PetscTruth     flg;

1979:   if (!coloring->w1) {
1980:     VecDuplicate(x1,&coloring->w1);
1981:     PetscLogObjectParent(coloring,coloring->w1);
1982:     VecDuplicate(x1,&coloring->w2);
1983:     PetscLogObjectParent(coloring,coloring->w2);
1984:     VecDuplicate(x1,&coloring->w3);
1985:     PetscLogObjectParent(coloring,coloring->w3);
1986:   }
1987:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

1989:   MatSetUnfactored(J);
1990:   PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);
1991:   if (flg) {
1992:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
1993:   } else {
1994:     PetscTruth assembled;
1995:     MatAssembled(J,&assembled);
1996:     if (assembled) {
1997:       MatZeroEntries(J);
1998:     }
1999:   }

2001:   VecGetOwnershipRange(x1,&start,&end);
2002:   VecGetSize(x1,&N);

2004:   /*
2005:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
2006:      coloring->F for the coarser grids from the finest
2007:   */
2008:   if (coloring->F) {
2009:     VecGetLocalSize(coloring->F,&m1);
2010:     VecGetLocalSize(w1,&m2);
2011:     if (m1 != m2) {
2012:       coloring->F = 0;
2013:     }
2014:   }

2016:   if (coloring->F) {
2017:     w1          = coloring->F;
2018:     coloring->F = 0;
2019:   } else {
2021:     (*f)(sctx,x1,w1,fctx);
2023:   }

2025:   /* 
2026:       Compute all the scale factors and share with other processors
2027:   */
2028:   VecGetArray(x1,&xx);xx = xx - start;
2029:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2030:   for (k=0; k<coloring->ncolors; k++) {
2031:     /*
2032:        Loop over each column associated with color adding the 
2033:        perturbation to the vector w3.
2034:     */
2035:     for (l=0; l<coloring->ncolumns[k]; l++) {
2036:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2037:       dx  = xx[col];
2038:       if (dx == 0.0) dx = 1.0;
2039: #if !defined(PETSC_USE_COMPLEX)
2040:       if (dx < umin && dx >= 0.0)      dx = umin;
2041:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2042: #else
2043:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2044:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2045: #endif
2046:       dx                *= epsilon;
2047:       vscale_array[col] = 1.0/dx;
2048:     }
2049:   }
2050:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2051:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2052:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2054:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2055:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2057:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2058:   else                        vscaleforrow = coloring->columnsforrow;

2060:   VecGetArray(coloring->vscale,&vscale_array);
2061:   /*
2062:       Loop over each color
2063:   */
2064:   for (k=0; k<coloring->ncolors; k++) {
2065:     coloring->currentcolor = k;
2066:     VecCopy(x1,w3);
2067:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2068:     /*
2069:        Loop over each column associated with color adding the 
2070:        perturbation to the vector w3.
2071:     */
2072:     for (l=0; l<coloring->ncolumns[k]; l++) {
2073:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2074:       dx  = xx[col];
2075:       if (dx == 0.0) dx = 1.0;
2076: #if !defined(PETSC_USE_COMPLEX)
2077:       if (dx < umin && dx >= 0.0)      dx = umin;
2078:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2079: #else
2080:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2081:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2082: #endif
2083:       dx            *= epsilon;
2084:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2085:       w3_array[col] += dx;
2086:     }
2087:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2089:     /*
2090:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2091:     */

2094:     (*f)(sctx,w3,w2,fctx);
2096:     VecAXPY(w2,-1.0,w1);

2098:     /*
2099:        Loop over rows of vector, putting results into Jacobian matrix
2100:     */
2101:     VecGetArray(w2,&y);
2102:     for (l=0; l<coloring->nrows[k]; l++) {
2103:       row    = coloring->rows[k][l];
2104:       col    = coloring->columnsforrow[k][l];
2105:       y[row] *= vscale_array[vscaleforrow[k][l]];
2106:       srow   = row + start;
2107:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2108:     }
2109:     VecRestoreArray(w2,&y);
2110:   }
2111:   coloring->currentcolor = k;
2112:   VecRestoreArray(coloring->vscale,&vscale_array);
2113:   xx = xx + start; VecRestoreArray(x1,&xx);
2114:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2115:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2116:   return(0);
2117: }

2119:  #include petscblaslapack.h
2122: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2123: {
2125:   PetscInt       i;
2126:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2127:   PetscBLASInt   one=1,bnz = (PetscBLASInt)x->nz;

2130:   if (str == SAME_NONZERO_PATTERN) {
2131:     PetscScalar alpha = a;
2132:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2133:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2134:     if (y->xtoy && y->XtoY != X) {
2135:       PetscFree(y->xtoy);
2136:       MatDestroy(y->XtoY);
2137:     }
2138:     if (!y->xtoy) { /* get xtoy */
2139:       MatAXPYGetxtoy_Private(X->rmap.n,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2140:       y->XtoY = X;
2141:       PetscObjectReference((PetscObject)X);
2142:     }
2143:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2144:     PetscInfo3(0,"ratio of nnz(X)/nnz(Y): %d/%d = %G\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz);
2145:   } else {
2146:     MatAXPY_Basic(Y,a,X,str);
2147:   }
2148:   return(0);
2149: }

2153: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
2154: {
2156:   return(0);
2157: }

2161: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2162: {
2163: #if defined(PETSC_USE_COMPLEX)
2164:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
2165:   PetscInt    i,nz;
2166:   PetscScalar *a;

2169:   nz = aij->nz;
2170:   a  = aij->a;
2171:   for (i=0; i<nz; i++) {
2172:     a[i] = PetscConj(a[i]);
2173:   }
2174: #else
2176: #endif
2177:   return(0);
2178: }

2182: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2183: {
2184:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2186:   PetscInt       i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2187:   PetscReal      atmp;
2188:   PetscScalar    *x;
2189:   MatScalar      *aa;

2192:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2193:   aa   = a->a;
2194:   ai   = a->i;
2195:   aj   = a->j;

2197:   VecSet(v,0.0);
2198:   VecGetArray(v,&x);
2199:   VecGetLocalSize(v,&n);
2200:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2201:   for (i=0; i<m; i++) {
2202:     ncols = ai[1] - ai[0]; ai++;
2203:     x[i] = 0.0; if (idx) idx[i] = 0;
2204:     for (j=0; j<ncols; j++){
2205:       atmp = PetscAbsScalar(*aa);
2206:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2207:       aa++; aj++;
2208:     }
2209:   }
2210:   VecRestoreArray(v,&x);
2211:   return(0);
2212: }

2216: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2217: {
2218:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2220:   PetscInt       i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2221:   PetscScalar    *x;
2222:   MatScalar      *aa;

2225:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2226:   aa   = a->a;
2227:   ai   = a->i;
2228:   aj   = a->j;

2230:   VecSet(v,0.0);
2231:   VecGetArray(v,&x);
2232:   VecGetLocalSize(v,&n);
2233:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2234:   for (i=0; i<m; i++) {
2235:     ncols = ai[1] - ai[0]; ai++;
2236:     if (ncols == A->cmap.n) { /* row is dense */
2237:       x[i] = *aa; if (idx) idx[i] = 0;
2238:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2239:       x[i] = 0.0;
2240:       if (idx) {
2241:         idx[i] = 0; /* in case ncols is zero */
2242:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2243:           if (aj[j] > j) {
2244:             idx[i] = j;
2245:             break;
2246:           }
2247:         }
2248:       }
2249:     }
2250:     for (j=0; j<ncols; j++){
2251:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2252:       aa++; aj++;
2253:     }
2254:   }
2255:   VecRestoreArray(v,&x);
2256:   return(0);
2257: }

2261: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2262: {
2263:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2265:   PetscInt       i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2266:   PetscScalar    *x;
2267:   MatScalar      *aa;

2270:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2271:   aa   = a->a;
2272:   ai   = a->i;
2273:   aj   = a->j;

2275:   VecSet(v,0.0);
2276:   VecGetArray(v,&x);
2277:   VecGetLocalSize(v,&n);
2278:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2279:   for (i=0; i<m; i++) {
2280:     ncols = ai[1] - ai[0]; ai++;
2281:     if (ncols == A->cmap.n) { /* row is dense */
2282:       x[i] = *aa; if (idx) idx[i] = 0;
2283:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2284:       x[i] = 0.0;
2285:       if (idx) {   /* find first implicit 0.0 in the row */
2286:         idx[i] = 0; /* in case ncols is zero */
2287:         for (j=0;j<ncols;j++) {
2288:           if (aj[j] > j) {
2289:             idx[i] = j;
2290:             break;
2291:           }
2292:         }
2293:       }
2294:     }
2295:     for (j=0; j<ncols; j++){
2296:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2297:       aa++; aj++;
2298:     }
2299:   }
2300:   VecRestoreArray(v,&x);
2301:   return(0);
2302: }

2304: /* -------------------------------------------------------------------*/
2305: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2306:        MatGetRow_SeqAIJ,
2307:        MatRestoreRow_SeqAIJ,
2308:        MatMult_SeqAIJ,
2309: /* 4*/ MatMultAdd_SeqAIJ,
2310:        MatMultTranspose_SeqAIJ,
2311:        MatMultTransposeAdd_SeqAIJ,
2312:        MatSolve_SeqAIJ,
2313:        MatSolveAdd_SeqAIJ,
2314:        MatSolveTranspose_SeqAIJ,
2315: /*10*/ MatSolveTransposeAdd_SeqAIJ,
2316:        MatLUFactor_SeqAIJ,
2317:        0,
2318:        MatRelax_SeqAIJ,
2319:        MatTranspose_SeqAIJ,
2320: /*15*/ MatGetInfo_SeqAIJ,
2321:        MatEqual_SeqAIJ,
2322:        MatGetDiagonal_SeqAIJ,
2323:        MatDiagonalScale_SeqAIJ,
2324:        MatNorm_SeqAIJ,
2325: /*20*/ 0,
2326:        MatAssemblyEnd_SeqAIJ,
2327:        MatCompress_SeqAIJ,
2328:        MatSetOption_SeqAIJ,
2329:        MatZeroEntries_SeqAIJ,
2330: /*25*/ MatZeroRows_SeqAIJ,
2331:        MatLUFactorSymbolic_SeqAIJ,
2332:        MatLUFactorNumeric_SeqAIJ,
2333:        MatCholeskyFactorSymbolic_SeqAIJ,
2334:        MatCholeskyFactorNumeric_SeqAIJ,
2335: /*30*/ MatSetUpPreallocation_SeqAIJ,
2336:        MatILUFactorSymbolic_SeqAIJ,
2337:        MatICCFactorSymbolic_SeqAIJ,
2338:        MatGetArray_SeqAIJ,
2339:        MatRestoreArray_SeqAIJ,
2340: /*35*/ MatDuplicate_SeqAIJ,
2341:        0,
2342:        0,
2343:        MatILUFactor_SeqAIJ,
2344:        0,
2345: /*40*/ MatAXPY_SeqAIJ,
2346:        MatGetSubMatrices_SeqAIJ,
2347:        MatIncreaseOverlap_SeqAIJ,
2348:        MatGetValues_SeqAIJ,
2349:        MatCopy_SeqAIJ,
2350: /*45*/ MatGetRowMax_SeqAIJ,
2351:        MatScale_SeqAIJ,
2352:        0,
2353:        MatDiagonalSet_SeqAIJ,
2354:        MatILUDTFactor_SeqAIJ,
2355: /*50*/ MatSetBlockSize_SeqAIJ,
2356:        MatGetRowIJ_SeqAIJ,
2357:        MatRestoreRowIJ_SeqAIJ,
2358:        MatGetColumnIJ_SeqAIJ,
2359:        MatRestoreColumnIJ_SeqAIJ,
2360: /*55*/ MatFDColoringCreate_SeqAIJ,
2361:        0,
2362:        0,
2363:        MatPermute_SeqAIJ,
2364:        0,
2365: /*60*/ 0,
2366:        MatDestroy_SeqAIJ,
2367:        MatView_SeqAIJ,
2368:        0,
2369:        0,
2370: /*65*/ 0,
2371:        0,
2372:        0,
2373:        0,
2374:        0,
2375: /*70*/ MatGetRowMaxAbs_SeqAIJ,
2376:        0,
2377:        MatSetColoring_SeqAIJ,
2378: #if defined(PETSC_HAVE_ADIC)
2379:        MatSetValuesAdic_SeqAIJ,
2380: #else
2381:        0,
2382: #endif
2383:        MatSetValuesAdifor_SeqAIJ,
2384: /*75*/ 0,
2385:        0,
2386:        0,
2387:        0,
2388:        0,
2389: /*80*/ 0,
2390:        0,
2391:        0,
2392:        0,
2393:        MatLoad_SeqAIJ,
2394: /*85*/ MatIsSymmetric_SeqAIJ,
2395:        0,
2396:        0,
2397:        0,
2398:        0,
2399: /*90*/ MatMatMult_SeqAIJ_SeqAIJ,
2400:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
2401:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
2402:        MatPtAP_Basic,
2403:        MatPtAPSymbolic_SeqAIJ,
2404: /*95*/ MatPtAPNumeric_SeqAIJ,
2405:        MatMatMultTranspose_SeqAIJ_SeqAIJ,
2406:        MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
2407:        MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
2408:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
2409: /*100*/MatPtAPNumeric_SeqAIJ_SeqAIJ,
2410:        0,
2411:        0,
2412:        MatConjugate_SeqAIJ,
2413:        0,
2414: /*105*/MatSetValuesRow_SeqAIJ,
2415:        MatRealPart_SeqAIJ,
2416:        MatImaginaryPart_SeqAIJ,
2417:        0,
2418:        0,
2419: /*110*/MatMatSolve_SeqAIJ,
2420:        0,
2421:        MatGetRowMin_SeqAIJ
2422: };

2427: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
2428: {
2429:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2430:   PetscInt   i,nz,n;


2434:   nz = aij->maxnz;
2435:   n  = mat->rmap.n;
2436:   for (i=0; i<nz; i++) {
2437:     aij->j[i] = indices[i];
2438:   }
2439:   aij->nz = nz;
2440:   for (i=0; i<n; i++) {
2441:     aij->ilen[i] = aij->imax[i];
2442:   }

2444:   return(0);
2445: }

2450: /*@
2451:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2452:        in the matrix.

2454:   Input Parameters:
2455: +  mat - the SeqAIJ matrix
2456: -  indices - the column indices

2458:   Level: advanced

2460:   Notes:
2461:     This can be called if you have precomputed the nonzero structure of the 
2462:   matrix and want to provide it to the matrix object to improve the performance
2463:   of the MatSetValues() operation.

2465:     You MUST have set the correct numbers of nonzeros per row in the call to 
2466:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

2468:     MUST be called before any calls to MatSetValues();

2470:     The indices should start with zero, not one.

2472: @*/
2473: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
2474: {
2475:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

2480:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2481:   if (f) {
2482:     (*f)(mat,indices);
2483:   } else {
2484:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
2485:   }
2486:   return(0);
2487: }

2489: /* ----------------------------------------------------------------------------------------*/

2494: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
2495: {
2496:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2498:   size_t         nz = aij->i[mat->rmap.n];

2501:   if (aij->nonew != 1) {
2502:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2503:   }

2505:   /* allocate space for values if not already there */
2506:   if (!aij->saved_values) {
2507:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2508:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
2509:   }

2511:   /* copy values over */
2512:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2513:   return(0);
2514: }

2519: /*@
2520:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2521:        example, reuse of the linear part of a Jacobian, while recomputing the 
2522:        nonlinear portion.

2524:    Collect on Mat

2526:   Input Parameters:
2527: .  mat - the matrix (currently only AIJ matrices support this option)

2529:   Level: advanced

2531:   Common Usage, with SNESSolve():
2532: $    Create Jacobian matrix
2533: $    Set linear terms into matrix
2534: $    Apply boundary conditions to matrix, at this time matrix must have 
2535: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2536: $      boundary conditions again will not change the nonzero structure
2537: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2538: $    MatStoreValues(mat);
2539: $    Call SNESSetJacobian() with matrix
2540: $    In your Jacobian routine
2541: $      MatRetrieveValues(mat);
2542: $      Set nonlinear terms in matrix
2543:  
2544:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2545: $    // build linear portion of Jacobian 
2546: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2547: $    MatStoreValues(mat);
2548: $    loop over nonlinear iterations
2549: $       MatRetrieveValues(mat);
2550: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2551: $       // call MatAssemblyBegin/End() on matrix
2552: $       Solve linear system with Jacobian
2553: $    endloop 

2555:   Notes:
2556:     Matrix must already be assemblied before calling this routine
2557:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 
2558:     calling this routine.

2560:     When this is called multiple times it overwrites the previous set of stored values
2561:     and does not allocated additional space.

2563: .seealso: MatRetrieveValues()

2565: @*/
2566: PetscErrorCode  MatStoreValues(Mat mat)
2567: {
2568:   PetscErrorCode ierr,(*f)(Mat);

2572:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2573:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2575:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2576:   if (f) {
2577:     (*f)(mat);
2578:   } else {
2579:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values");
2580:   }
2581:   return(0);
2582: }

2587: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
2588: {
2589:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2591:   PetscInt       nz = aij->i[mat->rmap.n];

2594:   if (aij->nonew != 1) {
2595:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2596:   }
2597:   if (!aij->saved_values) {
2598:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2599:   }
2600:   /* copy values over */
2601:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2602:   return(0);
2603: }

2608: /*@
2609:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2610:        example, reuse of the linear part of a Jacobian, while recomputing the 
2611:        nonlinear portion.

2613:    Collect on Mat

2615:   Input Parameters:
2616: .  mat - the matrix (currently on AIJ matrices support this option)

2618:   Level: advanced

2620: .seealso: MatStoreValues()

2622: @*/
2623: PetscErrorCode  MatRetrieveValues(Mat mat)
2624: {
2625:   PetscErrorCode ierr,(*f)(Mat);

2629:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2630:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2632:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2633:   if (f) {
2634:     (*f)(mat);
2635:   } else {
2636:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values");
2637:   }
2638:   return(0);
2639: }


2642: /* --------------------------------------------------------------------------------*/
2645: /*@C
2646:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2647:    (the default parallel PETSc format).  For good matrix assembly performance
2648:    the user should preallocate the matrix storage by setting the parameter nz
2649:    (or the array nnz).  By setting these parameters accurately, performance
2650:    during matrix assembly can be increased by more than a factor of 50.

2652:    Collective on MPI_Comm

2654:    Input Parameters:
2655: +  comm - MPI communicator, set to PETSC_COMM_SELF
2656: .  m - number of rows
2657: .  n - number of columns
2658: .  nz - number of nonzeros per row (same for all rows)
2659: -  nnz - array containing the number of nonzeros in the various rows 
2660:          (possibly different for each row) or PETSC_NULL

2662:    Output Parameter:
2663: .  A - the matrix 

2665:    Notes:
2666:    If nnz is given then nz is ignored

2668:    The AIJ format (also called the Yale sparse matrix format or
2669:    compressed row storage), is fully compatible with standard Fortran 77
2670:    storage.  That is, the stored row and column indices can begin at
2671:    either one (as in Fortran) or zero.  See the users' manual for details.

2673:    Specify the preallocated storage with either nz or nnz (not both).
2674:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2675:    allocation.  For large problems you MUST preallocate memory or you 
2676:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2678:    By default, this format uses inodes (identical nodes) when possible, to 
2679:    improve numerical efficiency of matrix-vector products and solves. We 
2680:    search for consecutive rows with the same nonzero structure, thereby
2681:    reusing matrix information to achieve increased efficiency.

2683:    Options Database Keys:
2684: +  -mat_no_inode  - Do not use inodes
2685: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2686: -  -mat_aij_oneindex - Internally use indexing starting at 1
2687:         rather than 0.  Note that when calling MatSetValues(),
2688:         the user still MUST index entries starting at 0!

2690:    Level: intermediate

2692: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2694: @*/
2695: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2696: {

2700:   MatCreate(comm,A);
2701:   MatSetSizes(*A,m,n,m,n);
2702:   MatSetType(*A,MATSEQAIJ);
2703:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
2704:   return(0);
2705: }

2709: /*@C
2710:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2711:    the user should preallocate the matrix storage by setting the parameter nz
2712:    (or the array nnz).  By setting these parameters accurately, performance
2713:    during matrix assembly can be increased by more than a factor of 50.

2715:    Collective on MPI_Comm

2717:    Input Parameters:
2718: +  B - The matrix
2719: .  nz - number of nonzeros per row (same for all rows)
2720: -  nnz - array containing the number of nonzeros in the various rows 
2721:          (possibly different for each row) or PETSC_NULL

2723:    Notes:
2724:      If nnz is given then nz is ignored

2726:     The AIJ format (also called the Yale sparse matrix format or
2727:    compressed row storage), is fully compatible with standard Fortran 77
2728:    storage.  That is, the stored row and column indices can begin at
2729:    either one (as in Fortran) or zero.  See the users' manual for details.

2731:    Specify the preallocated storage with either nz or nnz (not both).
2732:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2733:    allocation.  For large problems you MUST preallocate memory or you 
2734:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2736:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
2737:    entries or columns indices

2739:    By default, this format uses inodes (identical nodes) when possible, to 
2740:    improve numerical efficiency of matrix-vector products and solves. We 
2741:    search for consecutive rows with the same nonzero structure, thereby
2742:    reusing matrix information to achieve increased efficiency.

2744:    Options Database Keys:
2745: +  -mat_no_inode  - Do not use inodes
2746: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2747: -  -mat_aij_oneindex - Internally use indexing starting at 1
2748:         rather than 0.  Note that when calling MatSetValues(),
2749:         the user still MUST index entries starting at 0!

2751:    Level: intermediate

2753: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2755: @*/
2756: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
2757: {
2758:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]);

2761:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
2762:   if (f) {
2763:     (*f)(B,nz,nnz);
2764:   }
2765:   return(0);
2766: }

2771: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz)
2772: {
2773:   Mat_SeqAIJ     *b;
2774:   PetscTruth     skipallocation = PETSC_FALSE;
2776:   PetscInt       i;

2779: 
2780:   if (nz == MAT_SKIP_ALLOCATION) {
2781:     skipallocation = PETSC_TRUE;
2782:     nz             = 0;
2783:   }

2785:   B->rmap.bs = B->cmap.bs = 1;
2786:   PetscMapSetUp(&B->rmap);
2787:   PetscMapSetUp(&B->cmap);

2789:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2790:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2791:   if (nnz) {
2792:     for (i=0; i<B->rmap.n; i++) {
2793:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2794:       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);
2795:     }
2796:   }

2798:   B->preallocated = PETSC_TRUE;
2799:   b = (Mat_SeqAIJ*)B->data;

2801:   if (!skipallocation) {
2802:     PetscMalloc2(B->rmap.n,PetscInt,&b->imax,B->rmap.n,PetscInt,&b->ilen);
2803:     PetscLogObjectMemory(B,2*B->rmap.n*sizeof(PetscInt));
2804:     if (!nnz) {
2805:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2806:       else if (nz <= 0)        nz = 1;
2807:       for (i=0; i<B->rmap.n; i++) b->imax[i] = nz;
2808:       nz = nz*B->rmap.n;
2809:     } else {
2810:       nz = 0;
2811:       for (i=0; i<B->rmap.n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2812:     }

2814:     /* b->ilen will count nonzeros in each row so far. */
2815:     for (i=0; i<B->rmap.n; i++) { b->ilen[i] = 0;}

2817:     /* allocate the matrix space */
2818:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.n+1,PetscInt,&b->i);
2819:     PetscLogObjectMemory(B,(B->rmap.n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
2820:     b->i[0] = 0;
2821:     for (i=1; i<B->rmap.n+1; i++) {
2822:       b->i[i] = b->i[i-1] + b->imax[i-1];
2823:     }
2824:     b->singlemalloc = PETSC_TRUE;
2825:     b->free_a       = PETSC_TRUE;
2826:     b->free_ij      = PETSC_TRUE;
2827:   } else {
2828:     b->free_a       = PETSC_FALSE;
2829:     b->free_ij      = PETSC_FALSE;
2830:   }

2832:   b->nz                = 0;
2833:   b->maxnz             = nz;
2834:   B->info.nz_unneeded  = (double)b->maxnz;
2835:   return(0);
2836: }

2839: #undef  __FUNCT__
2841: /*@C
2842:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.  

2844:    Input Parameters:
2845: +  B - the matrix 
2846: .  i - the indices into j for the start of each row (starts with zero)
2847: .  j - the column indices for each row (starts with zero) these must be sorted for each row
2848: -  v - optional values in the matrix

2850:    Contributed by: Lisandro Dalchin

2852:    Level: developer

2854: .keywords: matrix, aij, compressed row, sparse, sequential

2856: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
2857: @*/
2858: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
2859: {
2860:   PetscErrorCode (*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);

2865:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",(void (**)(void))&f);
2866:   if (f) {
2867:     (*f)(B,i,j,v);
2868:   }
2869:   return(0);
2870: }

2873: #undef  __FUNCT__
2875: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2876: {
2877:   PetscInt       i;
2878:   PetscInt       m,n;
2879:   PetscInt       nz;
2880:   PetscInt       *nnz, nz_max = 0;
2881:   PetscScalar    *values;

2885:   MatGetSize(B, &m, &n);

2887:   if (Ii[0]) {
2888:     SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
2889:   }
2890:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
2891:   for(i = 0; i < m; i++) {
2892:     nz     = Ii[i+1]- Ii[i];
2893:     nz_max = PetscMax(nz_max, nz);
2894:     if (nz < 0) {
2895:       SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
2896:     }
2897:     nnz[i] = nz;
2898:   }
2899:   MatSeqAIJSetPreallocation(B, 0, nnz);
2900:   PetscFree(nnz);

2902:   if (v) {
2903:     values = (PetscScalar*) v;
2904:   } else {
2905:     PetscMalloc((nz_max+1)*sizeof(PetscScalar), &values);
2906:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
2907:   }

2909:   MatSetOption(B,MAT_COLUMNS_SORTED);

2911:   for(i = 0; i < m; i++) {
2912:     nz  = Ii[i+1] - Ii[i];
2913:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
2914:   }

2916:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2917:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2918:   MatSetOption(B,MAT_COLUMNS_UNSORTED);

2920:   if (!v) {
2921:     PetscFree(values);
2922:   }
2923:   return(0);
2924: }

2927: /*MC
2928:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
2929:    based on compressed sparse row format.

2931:    Options Database Keys:
2932: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

2934:   Level: beginner

2936: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
2937: M*/


2946: PetscErrorCode  MatCreate_SeqAIJ(Mat B)
2947: {
2948:   Mat_SeqAIJ     *b;
2950:   PetscMPIInt    size;

2953:   MPI_Comm_size(B->comm,&size);
2954:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

2956:   PetscNew(Mat_SeqAIJ,&b);
2957:   B->data             = (void*)b;
2958:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2959:   B->factor           = 0;
2960:   B->mapping          = 0;
2961:   b->row              = 0;
2962:   b->col              = 0;
2963:   b->icol             = 0;
2964:   b->reallocs         = 0;
2965:   b->sorted            = PETSC_FALSE;
2966:   b->ignorezeroentries = PETSC_FALSE;
2967:   b->roworiented       = PETSC_TRUE;
2968:   b->nonew             = 0;
2969:   b->diag              = 0;
2970:   b->solve_work        = 0;
2971:   B->spptr             = 0;
2972:   b->saved_values      = 0;
2973:   b->idiag             = 0;
2974:   b->ssor              = 0;
2975:   b->keepzeroedrows    = PETSC_FALSE;
2976:   b->xtoy              = 0;
2977:   b->XtoY              = 0;
2978:   b->compressedrow.use     = PETSC_FALSE;
2979:   b->compressedrow.nrows   = B->rmap.n;
2980:   b->compressedrow.i       = PETSC_NULL;
2981:   b->compressedrow.rindex  = PETSC_NULL;
2982:   b->compressedrow.checked = PETSC_FALSE;
2983:   B->same_nonzero          = PETSC_FALSE;

2985:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
2986:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2987:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
2988:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
2989:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2990:                                      "MatStoreValues_SeqAIJ",
2991:                                      MatStoreValues_SeqAIJ);
2992:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2993:                                      "MatRetrieveValues_SeqAIJ",
2994:                                      MatRetrieveValues_SeqAIJ);
2995:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
2996:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
2997:                                       MatConvert_SeqAIJ_SeqSBAIJ);
2998:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
2999:                                      "MatConvert_SeqAIJ_SeqBAIJ",
3000:                                       MatConvert_SeqAIJ_SeqBAIJ);
3001:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcsrperm_C",
3002:                                      "MatConvert_SeqAIJ_SeqCSRPERM",
3003:                                       MatConvert_SeqAIJ_SeqCSRPERM);
3004:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcrl_C",
3005:                                      "MatConvert_SeqAIJ_SeqCRL",
3006:                                       MatConvert_SeqAIJ_SeqCRL);
3007:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
3008:                                      "MatIsTranspose_SeqAIJ",
3009:                                       MatIsTranspose_SeqAIJ);
3010:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
3011:                                      "MatSeqAIJSetPreallocation_SeqAIJ",
3012:                                       MatSeqAIJSetPreallocation_SeqAIJ);
3013:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",
3014:                                      "MatSeqAIJSetPreallocationCSR_SeqAIJ",
3015:                                       MatSeqAIJSetPreallocationCSR_SeqAIJ);
3016:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
3017:                                      "MatReorderForNonzeroDiagonal_SeqAIJ",
3018:                                       MatReorderForNonzeroDiagonal_SeqAIJ);
3019:   MatCreate_Inode(B);
3020:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3021:   return(0);
3022: }

3027: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3028: {
3029:   Mat            C;
3030:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3032:   PetscInt       i,m = A->rmap.n;

3035:   *B = 0;
3036:   MatCreate(A->comm,&C);
3037:   MatSetSizes(C,A->rmap.n,A->cmap.n,A->rmap.n,A->cmap.n);
3038:   MatSetType(C,A->type_name);
3039:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
3040: 
3041:   PetscMapCopy(A->comm,&A->rmap,&C->rmap);
3042:   PetscMapCopy(A->comm,&A->cmap,&C->cmap);

3044:   c = (Mat_SeqAIJ*)C->data;

3046:   C->factor           = A->factor;

3048:   c->row            = 0;
3049:   c->col            = 0;
3050:   c->icol           = 0;
3051:   c->reallocs       = 0;

3053:   C->assembled      = PETSC_TRUE;
3054: 
3055:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
3056:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
3057:   for (i=0; i<m; i++) {
3058:     c->imax[i] = a->imax[i];
3059:     c->ilen[i] = a->ilen[i];
3060:   }

3062:   /* allocate the matrix space */
3063:   PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
3064:   PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
3065:   c->singlemalloc = PETSC_TRUE;
3066:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
3067:   if (m > 0) {
3068:     PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
3069:     if (cpvalues == MAT_COPY_VALUES) {
3070:       PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
3071:     } else {
3072:       PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
3073:     }
3074:   }

3076:   c->sorted            = a->sorted;
3077:   c->ignorezeroentries = a->ignorezeroentries;
3078:   c->roworiented       = a->roworiented;
3079:   c->nonew             = a->nonew;
3080:   if (a->diag) {
3081:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
3082:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
3083:     for (i=0; i<m; i++) {
3084:       c->diag[i] = a->diag[i];
3085:     }
3086:   } else c->diag        = 0;
3087:   c->solve_work         = 0;
3088:   c->saved_values          = 0;
3089:   c->idiag                 = 0;
3090:   c->ssor                  = 0;
3091:   c->keepzeroedrows        = a->keepzeroedrows;
3092:   c->free_a                = PETSC_TRUE;
3093:   c->free_ij               = PETSC_TRUE;
3094:   c->xtoy                  = 0;
3095:   c->XtoY                  = 0;

3097:   c->nz                 = a->nz;
3098:   c->maxnz              = a->maxnz;
3099:   C->preallocated       = PETSC_TRUE;

3101:   c->compressedrow.use     = a->compressedrow.use;
3102:   c->compressedrow.nrows   = a->compressedrow.nrows;
3103:   c->compressedrow.checked = a->compressedrow.checked;
3104:   if ( a->compressedrow.checked && a->compressedrow.use){
3105:     i = a->compressedrow.nrows;
3106:     PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
3107:     c->compressedrow.rindex = c->compressedrow.i + i + 1;
3108:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3109:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3110:   } else {
3111:     c->compressedrow.use    = PETSC_FALSE;
3112:     c->compressedrow.i      = PETSC_NULL;
3113:     c->compressedrow.rindex = PETSC_NULL;
3114:   }
3115:   C->same_nonzero = A->same_nonzero;
3116:   MatDuplicate_Inode(A,cpvalues,&C);

3118:   *B = C;
3119:   PetscFListDuplicate(A->qlist,&C->qlist);
3120:   return(0);
3121: }

3125: PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer, MatType type,Mat *A)
3126: {
3127:   Mat_SeqAIJ     *a;
3128:   Mat            B;
3130:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N;
3131:   int            fd;
3132:   PetscMPIInt    size;
3133:   MPI_Comm       comm;
3134: 
3136:   PetscObjectGetComm((PetscObject)viewer,&comm);
3137:   MPI_Comm_size(comm,&size);
3138:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
3139:   PetscViewerBinaryGetDescriptor(viewer,&fd);
3140:   PetscBinaryRead(fd,header,4,PETSC_INT);
3141:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
3142:   M = header[1]; N = header[2]; nz = header[3];

3144:   if (nz < 0) {
3145:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
3146:   }

3148:   /* read in row lengths */
3149:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
3150:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

3152:   /* check if sum of rowlengths is same as nz */
3153:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
3154:   if (sum != nz) SETERRQ2(PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

3156:   /* create our matrix */
3157:   MatCreate(comm,&B);
3158:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M,N);
3159:   MatSetType(B,type);
3160:   MatSeqAIJSetPreallocation_SeqAIJ(B,0,rowlengths);
3161:   a = (Mat_SeqAIJ*)B->data;

3163:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

3165:   /* read in nonzero values */
3166:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

3168:   /* set matrix "i" values */
3169:   a->i[0] = 0;
3170:   for (i=1; i<= M; i++) {
3171:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
3172:     a->ilen[i-1] = rowlengths[i-1];
3173:   }
3174:   PetscFree(rowlengths);

3176:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3177:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3178:   *A = B;
3179:   return(0);
3180: }

3184: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
3185: {
3186:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;

3190:   /* If the  matrix dimensions are not equal,or no of nonzeros */
3191:   if ((A->rmap.n != B->rmap.n) || (A->cmap.n != B->cmap.n) ||(a->nz != b->nz)) {
3192:     *flg = PETSC_FALSE;
3193:     return(0);
3194:   }
3195: 
3196:   /* if the a->i are the same */
3197:   PetscMemcmp(a->i,b->i,(A->rmap.n+1)*sizeof(PetscInt),flg);
3198:   if (!*flg) return(0);
3199: 
3200:   /* if a->j are the same */
3201:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
3202:   if (!*flg) return(0);
3203: 
3204:   /* if a->a are the same */
3205:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

3207:   return(0);
3208: 
3209: }

3213: /*@
3214:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
3215:               provided by the user.

3217:       Collective on MPI_Comm

3219:    Input Parameters:
3220: +   comm - must be an MPI communicator of size 1
3221: .   m - number of rows
3222: .   n - number of columns
3223: .   i - row indices
3224: .   j - column indices
3225: -   a - matrix values

3227:    Output Parameter:
3228: .   mat - the matrix

3230:    Level: intermediate

3232:    Notes:
3233:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
3234:     once the matrix is destroyed

3236:        You cannot set new nonzero locations into this matrix, that will generate an error.

3238:        The i and j indices are 0 based

3240:        The format which is used for the sparse matrix input, is equivalent to a
3241:     row-major ordering.. i.e for the following matrix, the input data expected is
3242:     as shown:

3244:         1 0 0
3245:         2 0 3
3246:         4 5 6

3248:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
3249:         j =  {0,0,2,0,1,2}  [size = nz = 6]
3250:         v =  {1,2,3,4,5,6}  [size = nz = 6]

3252: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

3254: @*/
3255: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
3256: {
3258:   PetscInt       ii;
3259:   Mat_SeqAIJ     *aij;

3262:   if (i[0]) {
3263:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3264:   }
3265:   MatCreate(comm,mat);
3266:   MatSetSizes(*mat,m,n,m,n);
3267:   MatSetType(*mat,MATSEQAIJ);
3268:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
3269:   aij  = (Mat_SeqAIJ*)(*mat)->data;
3270:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

3272:   aij->i = i;
3273:   aij->j = j;
3274:   aij->a = a;
3275:   aij->singlemalloc = PETSC_FALSE;
3276:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3277:   aij->free_a       = PETSC_FALSE;
3278:   aij->free_ij      = PETSC_FALSE;

3280:   for (ii=0; ii<m; ii++) {
3281:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
3282: #if defined(PETSC_USE_DEBUG)
3283:     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]);
3284: #endif    
3285:   }
3286: #if defined(PETSC_USE_DEBUG)
3287:   for (ii=0; ii<aij->i[m]; ii++) {
3288:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3289:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
3290:   }
3291: #endif    

3293:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3294:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3295:   return(0);
3296: }

3300: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
3301: {
3303:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

3306:   if (coloring->ctype == IS_COLORING_GLOBAL) {
3307:     ISColoringReference(coloring);
3308:     a->coloring = coloring;
3309:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3310:     PetscInt             i,*larray;
3311:     ISColoring      ocoloring;
3312:     ISColoringValue *colors;

3314:     /* set coloring for diagonal portion */
3315:     PetscMalloc((A->cmap.n+1)*sizeof(PetscInt),&larray);
3316:     for (i=0; i<A->cmap.n; i++) {
3317:       larray[i] = i;
3318:     }
3319:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->cmap.n,larray,PETSC_NULL,larray);
3320:     PetscMalloc((A->cmap.n+1)*sizeof(ISColoringValue),&colors);
3321:     for (i=0; i<A->cmap.n; i++) {
3322:       colors[i] = coloring->colors[larray[i]];
3323:     }
3324:     PetscFree(larray);
3325:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap.n,colors,&ocoloring);
3326:     a->coloring = ocoloring;
3327:   }
3328:   return(0);
3329: }

3331: #if defined(PETSC_HAVE_ADIC)
3333: #include "adic/ad_utils.h"

3338: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3339: {
3340:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3341:   PetscInt        m = A->rmap.n,*ii = a->i,*jj = a->j,nz,i,j,nlen;
3342:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
3343:   ISColoringValue *color;

3346:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3347:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
3348:   color = a->coloring->colors;
3349:   /* loop over rows */
3350:   for (i=0; i<m; i++) {
3351:     nz = ii[i+1] - ii[i];
3352:     /* loop over columns putting computed value into matrix */
3353:     for (j=0; j<nz; j++) {
3354:       *v++ = values[color[*jj++]];
3355:     }
3356:     values += nlen; /* jump to next row of derivatives */
3357:   }
3358:   return(0);
3359: }
3360: #endif

3364: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
3365: {
3366:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3367:   PetscInt             m = A->rmap.n,*ii = a->i,*jj = a->j,nz,i,j;
3368:   PetscScalar     *v = a->a,*values = (PetscScalar *)advalues;
3369:   ISColoringValue *color;

3372:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3373:   color = a->coloring->colors;
3374:   /* loop over rows */
3375:   for (i=0; i<m; i++) {
3376:     nz = ii[i+1] - ii[i];
3377:     /* loop over columns putting computed value into matrix */
3378:     for (j=0; j<nz; j++) {
3379:       *v++ = values[color[*jj++]];
3380:     }
3381:     values += nl; /* jump to next row of derivatives */
3382:   }
3383:   return(0);
3384: }

3386: /*
3387:     Special version for direct calls from Fortran 
3388: */
3389: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3390: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
3391: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3392: #define matsetvaluesseqaij_ matsetvaluesseqaij
3393: #endif

3395: /* Change these macros so can be used in void function */
3396: #undef CHKERRQ
3397: #define CHKERRQ(ierr) CHKERRABORT(A->comm,ierr) 
3398: #undef SETERRQ2
3399: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(A->comm,ierr) 

3404: void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
3405: {
3406:   Mat            A = *AA;
3407:   PetscInt       m = *mm, n = *nn;
3408:   InsertMode     is = *isis;
3409:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3410:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
3411:   PetscInt       *imax,*ai,*ailen;
3413:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
3414:   PetscScalar    *ap,value,*aa;
3415:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
3416:   PetscTruth     roworiented = a->roworiented;

3419:   MatPreallocated(A);
3420:   imax = a->imax;
3421:   ai = a->i;
3422:   ailen = a->ilen;
3423:   aj = a->j;
3424:   aa = a->a;

3426:   for (k=0; k<m; k++) { /* loop over added rows */
3427:     row  = im[k];
3428:     if (row < 0) continue;
3429: #if defined(PETSC_USE_DEBUG)  
3430:     if (row >= A->rmap.n) SETERRABORT(A->comm,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
3431: #endif
3432:     rp   = aj + ai[row]; ap = aa + ai[row];
3433:     rmax = imax[row]; nrow = ailen[row];
3434:     low  = 0;
3435:     high = nrow;
3436:     for (l=0; l<n; l++) { /* loop over added columns */
3437:       if (in[l] < 0) continue;
3438: #if defined(PETSC_USE_DEBUG)  
3439:       if (in[l] >= A->cmap.n) SETERRABORT(A->comm,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
3440: #endif
3441:       col = in[l];
3442:       if (roworiented) {
3443:         value = v[l + k*n];
3444:       } else {
3445:         value = v[k + l*m];
3446:       }
3447:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

3449:       if (col <= lastcol) low = 0; else high = nrow;
3450:       lastcol = col;
3451:       while (high-low > 5) {
3452:         t = (low+high)/2;
3453:         if (rp[t] > col) high = t;
3454:         else             low  = t;
3455:       }
3456:       for (i=low; i<high; i++) {
3457:         if (rp[i] > col) break;
3458:         if (rp[i] == col) {
3459:           if (is == ADD_VALUES) ap[i] += value;
3460:           else                  ap[i] = value;
3461:           goto noinsert;
3462:         }
3463:       }
3464:       if (value == 0.0 && ignorezeroentries) goto noinsert;
3465:       if (nonew == 1) goto noinsert;
3466:       if (nonew == -1) SETERRABORT(A->comm,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
3467:       MatSeqXAIJReallocateAIJ(A,A->rmap.n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
3468:       N = nrow++ - 1; a->nz++; high++;
3469:       /* shift up all the later entries in this row */
3470:       for (ii=N; ii>=i; ii--) {
3471:         rp[ii+1] = rp[ii];
3472:         ap[ii+1] = ap[ii];
3473:       }
3474:       rp[i] = col;
3475:       ap[i] = value;
3476:       noinsert:;
3477:       low = i + 1;
3478:     }
3479:     ailen[row] = nrow;
3480:   }
3481:   A->same_nonzero = PETSC_FALSE;
3482:   PetscFunctionReturnVoid();
3483: }