Actual source code: aij.c

petsc-3.4.4 2014-03-13
  2: /*
  3:     Defines the basic matrix operations for the AIJ (compressed row)
  4:   matrix storage format.
  5: */


  8: #include <../src/mat/impls/aij/seq/aij.h>          /*I "petscmat.h" I*/
  9: #include <petscblaslapack.h>
 10: #include <petscbt.h>
 11: #include <petsc-private/kernels/blocktranspose.h>
 12: #if defined(PETSC_THREADCOMM_ACTIVE)
 13: #include <petscthreadcomm.h>
 14: #endif

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

 25:   MatGetSize(A,&m,&n);
 26:   PetscMemzero(norms,n*sizeof(PetscReal));
 27:   if (type == NORM_2) {
 28:     for (i=0; i<aij->i[m]; i++) {
 29:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
 30:     }
 31:   } else if (type == NORM_1) {
 32:     for (i=0; i<aij->i[m]; i++) {
 33:       norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
 34:     }
 35:   } else if (type == NORM_INFINITY) {
 36:     for (i=0; i<aij->i[m]; i++) {
 37:       norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
 38:     }
 39:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");

 41:   if (type == NORM_2) {
 42:     for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
 43:   }
 44:   return(0);
 45: }

 49: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
 50: {
 51:   Mat_SeqAIJ      *a  = (Mat_SeqAIJ*)A->data;
 52:   const MatScalar *aa = a->a;
 53:   PetscInt        i,m=A->rmap->n,cnt = 0;
 54:   const PetscInt  *jj = a->j,*diag;
 55:   PetscInt        *rows;
 56:   PetscErrorCode  ierr;

 59:   MatMarkDiagonal_SeqAIJ(A);
 60:   diag = a->diag;
 61:   for (i=0; i<m; i++) {
 62:     if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 63:       cnt++;
 64:     }
 65:   }
 66:   PetscMalloc(cnt*sizeof(PetscInt),&rows);
 67:   cnt  = 0;
 68:   for (i=0; i<m; i++) {
 69:     if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
 70:       rows[cnt++] = i;
 71:     }
 72:   }
 73:   *nrows = cnt;
 74:   *zrows = rows;
 75:   return(0);
 76: }

 80: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
 81: {
 82:   PetscInt       nrows,*rows;

 86:   *zrows = NULL;
 87:   MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
 88:   ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
 89:   return(0);
 90: }

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

104:   *keptrows = 0;
105:   ii        = a->i;
106:   for (i=0; i<m; i++) {
107:     n = ii[i+1] - ii[i];
108:     if (!n) {
109:       cnt++;
110:       goto ok1;
111:     }
112:     aa = a->a + ii[i];
113:     for (j=0; j<n; j++) {
114:       if (aa[j] != 0.0) goto ok1;
115:     }
116:     cnt++;
117: ok1:;
118:   }
119:   if (!cnt) return(0);
120:   PetscMalloc((A->rmap->n-cnt)*sizeof(PetscInt),&rows);
121:   cnt  = 0;
122:   for (i=0; i<m; i++) {
123:     n = ii[i+1] - ii[i];
124:     if (!n) continue;
125:     aa = a->a + ii[i];
126:     for (j=0; j<n; j++) {
127:       if (aa[j] != 0.0) {
128:         rows[cnt++] = i;
129:         break;
130:       }
131:     }
132:   }
133:   ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
134:   return(0);
135: }

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

149:   if (Y->assembled) {
150:     MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
151:     if (!missing) {
152:       diag = aij->diag;
153:       VecGetArray(D,&v);
154:       if (is == INSERT_VALUES) {
155:         for (i=0; i<m; i++) {
156:           aa[diag[i]] = v[i];
157:         }
158:       } else {
159:         for (i=0; i<m; i++) {
160:           aa[diag[i]] += v[i];
161:         }
162:       }
163:       VecRestoreArray(D,&v);
164:       return(0);
165:     }
166:     MatSeqAIJInvalidateDiagonal(Y);
167:   }
168:   MatDiagonalSet_Default(Y,D,is);
169:   return(0);
170: }

174: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
175: {
176:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
178:   PetscInt       i,ishift;

181:   *m = A->rmap->n;
182:   if (!ia) return(0);
183:   ishift = 0;
184:   if (symmetric && !A->structurally_symmetric) {
185:     MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
186:   } else if (oshift == 1) {
187:     PetscInt *tia;
188:     PetscInt nz = a->i[A->rmap->n];
189:     /* malloc space and  add 1 to i and j indices */
190:     PetscMalloc((A->rmap->n+1)*sizeof(PetscInt),&tia);
191:     for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
192:     *ia = tia;
193:     if (ja) {
194:       PetscInt *tja;
195:       PetscMalloc((nz+1)*sizeof(PetscInt),&tja);
196:       for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
197:       *ja = tja;
198:     }
199:   } else {
200:     *ia = a->i;
201:     if (ja) *ja = a->j;
202:   }
203:   return(0);
204: }

208: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
209: {

213:   if (!ia) return(0);
214:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
215:     PetscFree(*ia);
216:     if (ja) {PetscFree(*ja);}
217:   }
218:   return(0);
219: }

223: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
224: {
225:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
227:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
228:   PetscInt       nz = a->i[m],row,*jj,mr,col;

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

255:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
256:       }
257:     }
258:     PetscFree(collengths);
259:     *ia  = cia; *ja = cja;
260:   }
261:   return(0);
262: }

266: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool  *done)
267: {

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

273:   PetscFree(*ia);
274:   PetscFree(*ja);
275:   return(0);
276: }

280: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
281: {
282:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
283:   PetscInt       *ai = a->i;

287:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
288:   return(0);
289: }

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

306:   for (k=0; k<m; k++) { /* loop over added rows */
307:     row = im[k];
308:     if (row < 0) continue;
309: #if defined(PETSC_USE_DEBUG)
310:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
311: #endif
312:     rp   = aj + ai[row]; ap = aa + ai[row];
313:     rmax = imax[row]; nrow = ailen[row];
314:     low  = 0;
315:     high = nrow;
316:     for (l=0; l<n; l++) { /* loop over added columns */
317:       if (in[l] < 0) continue;
318: #if defined(PETSC_USE_DEBUG)
319:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
320: #endif
321:       col = in[l];
322:       if (v) {
323:         if (roworiented) {
324:           value = v[l + k*n];
325:         } else {
326:           value = v[k + l*m];
327:         }
328:       } else {
329:         value = 0.;
330:       }
331:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

333:       if (col <= lastcol) low = 0;
334:       else high = nrow;
335:       lastcol = col;
336:       while (high-low > 5) {
337:         t = (low+high)/2;
338:         if (rp[t] > col) high = t;
339:         else low = t;
340:       }
341:       for (i=low; i<high; i++) {
342:         if (rp[i] > col) break;
343:         if (rp[i] == col) {
344:           if (is == ADD_VALUES) ap[i] += value;
345:           else ap[i] = value;
346:           low = i + 1;
347:           goto noinsert;
348:         }
349:       }
350:       if (value == 0.0 && ignorezeroentries) goto noinsert;
351:       if (nonew == 1) goto noinsert;
352:       if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
353:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
354:       N = nrow++ - 1; a->nz++; high++;
355:       /* shift up all the later entries in this row */
356:       for (ii=N; ii>=i; ii--) {
357:         rp[ii+1] = rp[ii];
358:         ap[ii+1] = ap[ii];
359:       }
360:       rp[i] = col;
361:       ap[i] = value;
362:       low   = i + 1;
363: noinsert:;
364:     }
365:     ailen[row] = nrow;
366:   }
367:   A->same_nonzero = PETSC_FALSE;
368:   return(0);
369: }


374: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
375: {
376:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
377:   PetscInt   *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
378:   PetscInt   *ai = a->i,*ailen = a->ilen;
379:   MatScalar  *ap,*aa = a->a;

382:   for (k=0; k<m; k++) { /* loop over rows */
383:     row = im[k];
384:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
385:     if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
386:     rp   = aj + ai[row]; ap = aa + ai[row];
387:     nrow = ailen[row];
388:     for (l=0; l<n; l++) { /* loop over columns */
389:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
390:       if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
391:       col  = in[l];
392:       high = nrow; low = 0; /* assume unsorted */
393:       while (high-low > 5) {
394:         t = (low+high)/2;
395:         if (rp[t] > col) high = t;
396:         else low = t;
397:       }
398:       for (i=low; i<high; i++) {
399:         if (rp[i] > col) break;
400:         if (rp[i] == col) {
401:           *v++ = ap[i];
402:           goto finished;
403:         }
404:       }
405:       *v++ = 0.0;
406: finished:;
407:     }
408:   }
409:   return(0);
410: }


415: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
416: {
417:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
419:   PetscInt       i,*col_lens;
420:   int            fd;
421:   FILE           *file;

424:   PetscViewerBinaryGetDescriptor(viewer,&fd);
425:   PetscMalloc((4+A->rmap->n)*sizeof(PetscInt),&col_lens);

427:   col_lens[0] = MAT_FILE_CLASSID;
428:   col_lens[1] = A->rmap->n;
429:   col_lens[2] = A->cmap->n;
430:   col_lens[3] = a->nz;

432:   /* store lengths of each row and write (including header) to file */
433:   for (i=0; i<A->rmap->n; i++) {
434:     col_lens[4+i] = a->i[i+1] - a->i[i];
435:   }
436:   PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
437:   PetscFree(col_lens);

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

442:   /* store nonzero values */
443:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);

445:   PetscViewerBinaryGetInfoPointer(viewer,&file);
446:   if (file) {
447:     fprintf(file,"-matload_block_size %d\n",(int)A->rmap->bs);
448:   }
449:   return(0);
450: }

452: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

456: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
457: {
458:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
459:   PetscErrorCode    ierr;
460:   PetscInt          i,j,m = A->rmap->n,shift=0;
461:   const char        *name;
462:   PetscViewerFormat format;

465:   PetscViewerGetFormat(viewer,&format);
466:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
467:     PetscInt nofinalvalue = 0;
468:     if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-!shift))) {
469:       nofinalvalue = 1;
470:     }
471:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
472:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
473:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
474:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
475:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

477:     for (i=0; i<m; i++) {
478:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
479: #if defined(PETSC_USE_COMPLEX)
480:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
481: #else
482:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
483: #endif
484:       }
485:     }
486:     if (nofinalvalue) {
487:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap->n,0.0);
488:     }
489:     PetscObjectGetName((PetscObject)A,&name);
490:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
491:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
492:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
493:     return(0);
494:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
495:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
496:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
497:     for (i=0; i<m; i++) {
498:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
499:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
500: #if defined(PETSC_USE_COMPLEX)
501:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
502:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
503:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
504:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
505:         } else if (PetscRealPart(a->a[j]) != 0.0) {
506:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
507:         }
508: #else
509:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);}
510: #endif
511:       }
512:       PetscViewerASCIIPrintf(viewer,"\n");
513:     }
514:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
515:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
516:     PetscInt nzd=0,fshift=1,*sptr;
517:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
518:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
519:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
520:     for (i=0; i<m; i++) {
521:       sptr[i] = nzd+1;
522:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
523:         if (a->j[j] >= i) {
524: #if defined(PETSC_USE_COMPLEX)
525:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
526: #else
527:           if (a->a[j] != 0.0) nzd++;
528: #endif
529:         }
530:       }
531:     }
532:     sptr[m] = nzd+1;
533:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
534:     for (i=0; i<m+1; i+=6) {
535:       if (i+4<m) {
536:         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]);
537:       } else if (i+3<m) {
538:         PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
539:       } else if (i+2<m) {
540:         PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
541:       } else if (i+1<m) {
542:         PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
543:       } else if (i<m) {
544:         PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
545:       } else {
546:         PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
547:       }
548:     }
549:     PetscViewerASCIIPrintf(viewer,"\n");
550:     PetscFree(sptr);
551:     for (i=0; i<m; i++) {
552:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
553:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
554:       }
555:       PetscViewerASCIIPrintf(viewer,"\n");
556:     }
557:     PetscViewerASCIIPrintf(viewer,"\n");
558:     for (i=0; i<m; i++) {
559:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
560:         if (a->j[j] >= i) {
561: #if defined(PETSC_USE_COMPLEX)
562:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
563:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
564:           }
565: #else
566:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
567: #endif
568:         }
569:       }
570:       PetscViewerASCIIPrintf(viewer,"\n");
571:     }
572:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
573:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
574:     PetscInt    cnt = 0,jcnt;
575:     PetscScalar value;

577:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
578:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
579:     for (i=0; i<m; i++) {
580:       jcnt = 0;
581:       for (j=0; j<A->cmap->n; j++) {
582:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
583:           value = a->a[cnt++];
584:           jcnt++;
585:         } else {
586:           value = 0.0;
587:         }
588: #if defined(PETSC_USE_COMPLEX)
589:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
590: #else
591:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
592: #endif
593:       }
594:       PetscViewerASCIIPrintf(viewer,"\n");
595:     }
596:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
597:   } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
598:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
599:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
600: #if defined(PETSC_USE_COMPLEX)
601:     PetscViewerASCIIPrintf(viewer,"%%matrix complex general\n");
602: #else
603:     PetscViewerASCIIPrintf(viewer,"%%matrix real general\n");
604: #endif
605:     PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
606:     for (i=0; i<m; i++) {
607:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
608: #if defined(PETSC_USE_COMPLEX)
609:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
610:           PetscViewerASCIIPrintf(viewer,"%D %D, %g %g\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
611:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
612:           PetscViewerASCIIPrintf(viewer,"%D %D, %g -%g\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
613:         } else {
614:           PetscViewerASCIIPrintf(viewer,"%D %D, %g\n", i+shift,a->j[j]+shift,PetscRealPart(a->a[j]));
615:         }
616: #else
617:         PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+shift, a->j[j]+shift, (double)a->a[j]);
618: #endif
619:       }
620:     }
621:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
622:   } else {
623:     PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
624:     PetscObjectPrintClassNamePrefixType((PetscObject)A,viewer,"Matrix Object");
625:     if (A->factortype) {
626:       for (i=0; i<m; i++) {
627:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
628:         /* L part */
629:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
630: #if defined(PETSC_USE_COMPLEX)
631:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
632:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
633:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
634:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
635:           } else {
636:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
637:           }
638: #else
639:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);
640: #endif
641:         }
642:         /* diagonal */
643:         j = a->diag[i];
644: #if defined(PETSC_USE_COMPLEX)
645:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
646:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),PetscImaginaryPart(1.0/a->a[j]));
647:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
648:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(1.0/a->a[j]),-PetscImaginaryPart(1.0/a->a[j]));
649:         } else {
650:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(1.0/a->a[j]));
651:         }
652: #else
653:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)1.0/a->a[j]);
654: #endif

656:         /* U part */
657:         for (j=a->diag[i+1]+1+shift; j<a->diag[i]+shift; j++) {
658: #if defined(PETSC_USE_COMPLEX)
659:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
660:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
661:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
662:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
663:           } else {
664:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
665:           }
666: #else
667:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);
668: #endif
669:         }
670:         PetscViewerASCIIPrintf(viewer,"\n");
671:       }
672:     } else {
673:       for (i=0; i<m; i++) {
674:         PetscViewerASCIIPrintf(viewer,"row %D:",i);
675:         for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
676: #if defined(PETSC_USE_COMPLEX)
677:           if (PetscImaginaryPart(a->a[j]) > 0.0) {
678:             PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
679:           } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
680:             PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
681:           } else {
682:             PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
683:           }
684: #else
685:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,(double)a->a[j]);
686: #endif
687:         }
688:         PetscViewerASCIIPrintf(viewer,"\n");
689:       }
690:     }
691:     PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
692:   }
693:   PetscViewerFlush(viewer);
694:   return(0);
695: }

697: #include <petscdraw.h>
700: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
701: {
702:   Mat               A  = (Mat) Aa;
703:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
704:   PetscErrorCode    ierr;
705:   PetscInt          i,j,m = A->rmap->n,color;
706:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
707:   PetscViewer       viewer;
708:   PetscViewerFormat format;

711:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
712:   PetscViewerGetFormat(viewer,&format);

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

717:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
718:     /* Blue for negative, Cyan for zero and  Red for positive */
719:     color = PETSC_DRAW_BLUE;
720:     for (i=0; i<m; i++) {
721:       y_l = m - i - 1.0; y_r = y_l + 1.0;
722:       for (j=a->i[i]; j<a->i[i+1]; j++) {
723:         x_l = a->j[j]; x_r = x_l + 1.0;
724:         if (PetscRealPart(a->a[j]) >=  0.) continue;
725:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
726:       }
727:     }
728:     color = PETSC_DRAW_CYAN;
729:     for (i=0; i<m; i++) {
730:       y_l = m - i - 1.0; y_r = y_l + 1.0;
731:       for (j=a->i[i]; j<a->i[i+1]; j++) {
732:         x_l = a->j[j]; x_r = x_l + 1.0;
733:         if (a->a[j] !=  0.) continue;
734:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
735:       }
736:     }
737:     color = PETSC_DRAW_RED;
738:     for (i=0; i<m; i++) {
739:       y_l = m - i - 1.0; y_r = y_l + 1.0;
740:       for (j=a->i[i]; j<a->i[i+1]; j++) {
741:         x_l = a->j[j]; x_r = x_l + 1.0;
742:         if (PetscRealPart(a->a[j]) <=  0.) continue;
743:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
744:       }
745:     }
746:   } else {
747:     /* use contour shading to indicate magnitude of values */
748:     /* first determine max of all nonzero values */
749:     PetscInt  nz = a->nz,count;
750:     PetscDraw popup;
751:     PetscReal scale;

753:     for (i=0; i<nz; i++) {
754:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
755:     }
756:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
757:     PetscDrawGetPopup(draw,&popup);
758:     if (popup) {
759:       PetscDrawScalePopup(popup,0.0,maxv);
760:     }
761:     count = 0;
762:     for (i=0; i<m; i++) {
763:       y_l = m - i - 1.0; y_r = y_l + 1.0;
764:       for (j=a->i[i]; j<a->i[i+1]; j++) {
765:         x_l   = a->j[j]; x_r = x_l + 1.0;
766:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
767:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
768:         count++;
769:       }
770:     }
771:   }
772:   return(0);
773: }

775: #include <petscdraw.h>
778: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
779: {
781:   PetscDraw      draw;
782:   PetscReal      xr,yr,xl,yl,h,w;
783:   PetscBool      isnull;

786:   PetscViewerDrawGetDraw(viewer,0,&draw);
787:   PetscDrawIsNull(draw,&isnull);
788:   if (isnull) return(0);

790:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
791:   xr   = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
792:   xr  += w;    yr += h;  xl = -w;     yl = -h;
793:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
794:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
795:   PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
796:   return(0);
797: }

801: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
802: {
804:   PetscBool      iascii,isbinary,isdraw;

807:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
808:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
809:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
810:   if (iascii) {
811:     MatView_SeqAIJ_ASCII(A,viewer);
812:   } else if (isbinary) {
813:     MatView_SeqAIJ_Binary(A,viewer);
814:   } else if (isdraw) {
815:     MatView_SeqAIJ_Draw(A,viewer);
816:   }
817:   MatView_SeqAIJ_Inode(A,viewer);
818:   return(0);
819: }

823: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
824: {
825:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
827:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
828:   PetscInt       m      = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
829:   MatScalar      *aa    = a->a,*ap;
830:   PetscReal      ratio  = 0.6;

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

835:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
836:   for (i=1; i<m; i++) {
837:     /* move each row back by the amount of empty slots (fshift) before it*/
838:     fshift += imax[i-1] - ailen[i-1];
839:     rmax    = PetscMax(rmax,ailen[i]);
840:     if (fshift) {
841:       ip = aj + ai[i];
842:       ap = aa + ai[i];
843:       N  = ailen[i];
844:       for (j=0; j<N; j++) {
845:         ip[j-fshift] = ip[j];
846:         ap[j-fshift] = ap[j];
847:       }
848:     }
849:     ai[i] = ai[i-1] + ailen[i-1];
850:   }
851:   if (m) {
852:     fshift += imax[m-1] - ailen[m-1];
853:     ai[m]   = ai[m-1] + ailen[m-1];
854:   }
855:   /* reset ilen and imax for each row */
856:   for (i=0; i<m; i++) {
857:     ailen[i] = imax[i] = ai[i+1] - ai[i];
858:   }
859:   a->nz = ai[m];
860:   if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);

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

867:   A->info.mallocs    += a->reallocs;
868:   a->reallocs         = 0;
869:   A->info.nz_unneeded = (double)fshift;
870:   a->rmax             = rmax;

872:   MatCheckCompressedRow(A,&a->compressedrow,a->i,m,ratio);

874:   A->same_nonzero = PETSC_TRUE;

876:   MatAssemblyEnd_SeqAIJ_Inode(A,mode);

878:   MatSeqAIJInvalidateDiagonal(A);
879:   return(0);
880: }

884: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
885: {
886:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
887:   PetscInt       i,nz = a->nz;
888:   MatScalar      *aa = a->a;

892:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
893:   MatSeqAIJInvalidateDiagonal(A);
894:   return(0);
895: }

899: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
900: {
901:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
902:   PetscInt       i,nz = a->nz;
903:   MatScalar      *aa = a->a;

907:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
908:   MatSeqAIJInvalidateDiagonal(A);
909:   return(0);
910: }

912: #if defined(PETSC_THREADCOMM_ACTIVE)
913: PetscErrorCode MatZeroEntries_SeqAIJ_Kernel(PetscInt thread_id,Mat A)
914: {
916:   PetscInt       *trstarts=A->rmap->trstarts;
917:   PetscInt       n,start,end;
918:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

920:   start = trstarts[thread_id];
921:   end   = trstarts[thread_id+1];
922:   n     = a->i[end] - a->i[start];
923:   PetscMemzero(a->a+a->i[start],n*sizeof(PetscScalar));
924:   return 0;
925: }

929: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
930: {

934:   PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatZeroEntries_SeqAIJ_Kernel,1,A);
935:   MatSeqAIJInvalidateDiagonal(A);
936:   return(0);
937: }
938: #else
941: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
942: {
943:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

947:   PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
948:   MatSeqAIJInvalidateDiagonal(A);
949:   return(0);
950: }
951: #endif

955: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
956: {
957:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

961: #if defined(PETSC_USE_LOG)
962:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
963: #endif
964:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
965:   ISDestroy(&a->row);
966:   ISDestroy(&a->col);
967:   PetscFree(a->diag);
968:   PetscFree(a->ibdiag);
969:   PetscFree2(a->imax,a->ilen);
970:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
971:   PetscFree(a->solve_work);
972:   ISDestroy(&a->icol);
973:   PetscFree(a->saved_values);
974:   ISColoringDestroy(&a->coloring);
975:   PetscFree(a->xtoy);
976:   MatDestroy(&a->XtoY);
977:   PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
978:   PetscFree(a->matmult_abdense);

980:   MatDestroy_SeqAIJ_Inode(A);
981:   PetscFree(A->data);

983:   PetscObjectChangeTypeName((PetscObject)A,0);
984:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
985:   PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
986:   PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
987:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
988:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
989:   PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
990:   PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
991:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
992:   PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
993:   PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
994:   return(0);
995: }

999: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1000: {
1001:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

1005:   switch (op) {
1006:   case MAT_ROW_ORIENTED:
1007:     a->roworiented = flg;
1008:     break;
1009:   case MAT_KEEP_NONZERO_PATTERN:
1010:     a->keepnonzeropattern = flg;
1011:     break;
1012:   case MAT_NEW_NONZERO_LOCATIONS:
1013:     a->nonew = (flg ? 0 : 1);
1014:     break;
1015:   case MAT_NEW_NONZERO_LOCATION_ERR:
1016:     a->nonew = (flg ? -1 : 0);
1017:     break;
1018:   case MAT_NEW_NONZERO_ALLOCATION_ERR:
1019:     a->nonew = (flg ? -2 : 0);
1020:     break;
1021:   case MAT_UNUSED_NONZERO_LOCATION_ERR:
1022:     a->nounused = (flg ? -1 : 0);
1023:     break;
1024:   case MAT_IGNORE_ZERO_ENTRIES:
1025:     a->ignorezeroentries = flg;
1026:     break;
1027:   case MAT_CHECK_COMPRESSED_ROW:
1028:     a->compressedrow.check = flg;
1029:     break;
1030:   case MAT_SPD:
1031:   case MAT_SYMMETRIC:
1032:   case MAT_STRUCTURALLY_SYMMETRIC:
1033:   case MAT_HERMITIAN:
1034:   case MAT_SYMMETRY_ETERNAL:
1035:     /* These options are handled directly by MatSetOption() */
1036:     break;
1037:   case MAT_NEW_DIAGONALS:
1038:   case MAT_IGNORE_OFF_PROC_ENTRIES:
1039:   case MAT_USE_HASH_TABLE:
1040:     PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1041:     break;
1042:   case MAT_USE_INODES:
1043:     /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1044:     break;
1045:   default:
1046:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1047:   }
1048:   MatSetOption_SeqAIJ_Inode(A,op,flg);
1049:   return(0);
1050: }

1054: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1055: {
1056:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1058:   PetscInt       i,j,n,*ai=a->i,*aj=a->j,nz;
1059:   PetscScalar    *aa=a->a,*x,zero=0.0;

1062:   VecGetLocalSize(v,&n);
1063:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");

1065:   if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1066:     PetscInt *diag=a->diag;
1067:     VecGetArray(v,&x);
1068:     for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1069:     VecRestoreArray(v,&x);
1070:     return(0);
1071:   }

1073:   VecSet(v,zero);
1074:   VecGetArray(v,&x);
1075:   for (i=0; i<n; i++) {
1076:     nz = ai[i+1] - ai[i];
1077:     if (!nz) x[i] = 0.0;
1078:     for (j=ai[i]; j<ai[i+1]; j++) {
1079:       if (aj[j] == i) {
1080:         x[i] = aa[j];
1081:         break;
1082:       }
1083:     }
1084:   }
1085:   VecRestoreArray(v,&x);
1086:   return(0);
1087: }

1089: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1092: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1093: {
1094:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1095:   PetscScalar    *x,*y;
1097:   PetscInt       m = A->rmap->n;
1098: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1099:   MatScalar         *v;
1100:   PetscScalar       alpha;
1101:   PetscInt          n,i,j,*idx,*ii,*ridx=NULL;
1102:   Mat_CompressedRow cprow    = a->compressedrow;
1103:   PetscBool         usecprow = cprow.use;
1104: #endif

1107:   if (zz != yy) {VecCopy(zz,yy);}
1108:   VecGetArray(xx,&x);
1109:   VecGetArray(yy,&y);

1111: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1112:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1113: #else
1114:   if (usecprow) {
1115:     m    = cprow.nrows;
1116:     ii   = cprow.i;
1117:     ridx = cprow.rindex;
1118:   } else {
1119:     ii = a->i;
1120:   }
1121:   for (i=0; i<m; i++) {
1122:     idx = a->j + ii[i];
1123:     v   = a->a + ii[i];
1124:     n   = ii[i+1] - ii[i];
1125:     if (usecprow) {
1126:       alpha = x[ridx[i]];
1127:     } else {
1128:       alpha = x[i];
1129:     }
1130:     for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1131:   }
1132: #endif
1133:   PetscLogFlops(2.0*a->nz);
1134:   VecRestoreArray(xx,&x);
1135:   VecRestoreArray(yy,&y);
1136:   return(0);
1137: }

1141: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1142: {

1146:   VecSet(yy,0.0);
1147:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1148:   return(0);
1149: }

1151: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1152: #if defined(PETSC_THREADCOMM_ACTIVE)
1153: PetscErrorCode MatMult_SeqAIJ_Kernel(PetscInt thread_id,Mat A,Vec xx,Vec yy)
1154: {
1155:   PetscErrorCode    ierr;
1156:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1157:   PetscScalar       *y;
1158:   const PetscScalar *x;
1159:   const MatScalar   *aa;
1160:   PetscInt          *trstarts=A->rmap->trstarts;
1161:   PetscInt          n,start,end,i;
1162:   const PetscInt    *aj,*ai;
1163:   PetscScalar       sum;

1165:   VecGetArrayRead(xx,&x);
1166:   VecGetArray(yy,&y);
1167:   start = trstarts[thread_id];
1168:   end   = trstarts[thread_id+1];
1169:   aj    = a->j;
1170:   aa    = a->a;
1171:   ai    = a->i;
1172:   for (i=start; i<end; i++) {
1173:     n   = ai[i+1] - ai[i];
1174:     aj  = a->j + ai[i];
1175:     aa  = a->a + ai[i];
1176:     sum = 0.0;
1177:     PetscSparseDensePlusDot(sum,x,aa,aj,n);
1178:     y[i] = sum;
1179:   }
1180:   VecRestoreArrayRead(xx,&x);
1181:   VecRestoreArray(yy,&y);
1182:   return 0;
1183: }

1187: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1188: {
1189:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1190:   PetscScalar       *y;
1191:   const PetscScalar *x;
1192:   const MatScalar   *aa;
1193:   PetscErrorCode    ierr;
1194:   PetscInt          m=A->rmap->n;
1195:   const PetscInt    *aj,*ii,*ridx=NULL;
1196:   PetscInt          n,i,nonzerorow=0;
1197:   PetscScalar       sum;
1198:   PetscBool         usecprow=a->compressedrow.use;

1200: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1201: #pragma disjoint(*x,*y,*aa)
1202: #endif

1205:   aj = a->j;
1206:   aa = a->a;
1207:   ii = a->i;
1208:   if (usecprow) { /* use compressed row format */
1209:     VecGetArrayRead(xx,&x);
1210:     VecGetArray(yy,&y);
1211:     m    = a->compressedrow.nrows;
1212:     ii   = a->compressedrow.i;
1213:     ridx = a->compressedrow.rindex;
1214:     for (i=0; i<m; i++) {
1215:       n           = ii[i+1] - ii[i];
1216:       aj          = a->j + ii[i];
1217:       aa          = a->a + ii[i];
1218:       sum         = 0.0;
1219:       nonzerorow += (n>0);
1220:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1221:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1222:       y[*ridx++] = sum;
1223:     }
1224:     VecRestoreArrayRead(xx,&x);
1225:     VecRestoreArray(yy,&y);
1226:   } else { /* do not use compressed row format */
1227: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1228:     fortranmultaij_(&m,x,ii,aj,aa,y);
1229: #else
1230:     PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatMult_SeqAIJ_Kernel,3,A,xx,yy);
1231: #endif
1232:   }
1233:   PetscLogFlops(2.0*a->nz - nonzerorow);
1234:   return(0);
1235: }
1236: #else
1239: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1240: {
1241:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1242:   PetscScalar       *y;
1243:   const PetscScalar *x;
1244:   const MatScalar   *aa;
1245:   PetscErrorCode    ierr;
1246:   PetscInt          m=A->rmap->n;
1247:   const PetscInt    *aj,*ii,*ridx=NULL;
1248:   PetscInt          n,i,nonzerorow=0;
1249:   PetscScalar       sum;
1250:   PetscBool         usecprow=a->compressedrow.use;

1252: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1253: #pragma disjoint(*x,*y,*aa)
1254: #endif

1257:   VecGetArrayRead(xx,&x);
1258:   VecGetArray(yy,&y);
1259:   aj   = a->j;
1260:   aa   = a->a;
1261:   ii   = a->i;
1262:   if (usecprow) { /* use compressed row format */
1263:     m    = a->compressedrow.nrows;
1264:     ii   = a->compressedrow.i;
1265:     ridx = a->compressedrow.rindex;
1266:     for (i=0; i<m; i++) {
1267:       n           = ii[i+1] - ii[i];
1268:       aj          = a->j + ii[i];
1269:       aa          = a->a + ii[i];
1270:       sum         = 0.0;
1271:       nonzerorow += (n>0);
1272:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1273:       /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1274:       y[*ridx++] = sum;
1275:     }
1276:   } else { /* do not use compressed row format */
1277: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1278:     fortranmultaij_(&m,x,ii,aj,aa,y);
1279: #else
1280: #if defined(PETSC_THREADCOMM_ACTIVE)
1281:     PetscThreadCommRunKernel(PetscObjectComm((PetscObject)A),(PetscThreadKernel)MatMult_SeqAIJ_Kernel,3,A,xx,yy);
1282: #else
1283:     for (i=0; i<m; i++) {
1284:       n           = ii[i+1] - ii[i];
1285:       aj          = a->j + ii[i];
1286:       aa          = a->a + ii[i];
1287:       sum         = 0.0;
1288:       nonzerorow += (n>0);
1289:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1290:       y[i] = sum;
1291:     }
1292: #endif
1293: #endif
1294:   }
1295:   PetscLogFlops(2.0*a->nz - nonzerorow);
1296:   VecRestoreArrayRead(xx,&x);
1297:   VecRestoreArray(yy,&y);
1298:   return(0);
1299: }
1300: #endif

1302: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1305: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1306: {
1307:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1308:   PetscScalar       *y,*z;
1309:   const PetscScalar *x;
1310:   const MatScalar   *aa;
1311:   PetscErrorCode    ierr;
1312:   PetscInt          m = A->rmap->n,*aj,*ii;
1313:   PetscInt          n,i,*ridx=NULL;
1314:   PetscScalar       sum;
1315:   PetscBool         usecprow=a->compressedrow.use;

1318:   VecGetArrayRead(xx,&x);
1319:   VecGetArray(yy,&y);
1320:   if (zz != yy) {
1321:     VecGetArray(zz,&z);
1322:   } else {
1323:     z = y;
1324:   }

1326:   aj = a->j;
1327:   aa = a->a;
1328:   ii = a->i;
1329:   if (usecprow) { /* use compressed row format */
1330:     if (zz != yy) {
1331:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
1332:     }
1333:     m    = a->compressedrow.nrows;
1334:     ii   = a->compressedrow.i;
1335:     ridx = a->compressedrow.rindex;
1336:     for (i=0; i<m; i++) {
1337:       n   = ii[i+1] - ii[i];
1338:       aj  = a->j + ii[i];
1339:       aa  = a->a + ii[i];
1340:       sum = y[*ridx];
1341:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1342:       z[*ridx++] = sum;
1343:     }
1344:   } else { /* do not use compressed row format */
1345: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1346:     fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1347: #else
1348:     for (i=0; i<m; i++) {
1349:       n   = ii[i+1] - ii[i];
1350:       aj  = a->j + ii[i];
1351:       aa  = a->a + ii[i];
1352:       sum = y[i];
1353:       PetscSparseDensePlusDot(sum,x,aa,aj,n);
1354:       z[i] = sum;
1355:     }
1356: #endif
1357:   }
1358:   PetscLogFlops(2.0*a->nz);
1359:   VecRestoreArrayRead(xx,&x);
1360:   VecRestoreArray(yy,&y);
1361:   if (zz != yy) {
1362:     VecRestoreArray(zz,&z);
1363:   }
1364: #if defined(PETSC_HAVE_CUSP)
1365:   /*
1366:   VecView(xx,0);
1367:   VecView(zz,0);
1368:   MatView(A,0);
1369:   */
1370: #endif
1371:   return(0);
1372: }

1374: /*
1375:      Adds diagonal pointers to sparse matrix structure.
1376: */
1379: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1380: {
1381:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1383:   PetscInt       i,j,m = A->rmap->n;

1386:   if (!a->diag) {
1387:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
1388:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
1389:   }
1390:   for (i=0; i<A->rmap->n; i++) {
1391:     a->diag[i] = a->i[i+1];
1392:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1393:       if (a->j[j] == i) {
1394:         a->diag[i] = j;
1395:         break;
1396:       }
1397:     }
1398:   }
1399:   return(0);
1400: }

1402: /*
1403:      Checks for missing diagonals
1404: */
1407: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool  *missing,PetscInt *d)
1408: {
1409:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1410:   PetscInt   *diag,*jj = a->j,i;

1413:   *missing = PETSC_FALSE;
1414:   if (A->rmap->n > 0 && !jj) {
1415:     *missing = PETSC_TRUE;
1416:     if (d) *d = 0;
1417:     PetscInfo(A,"Matrix has no entries therefore is missing diagonal");
1418:   } else {
1419:     diag = a->diag;
1420:     for (i=0; i<A->rmap->n; i++) {
1421:       if (jj[diag[i]] != i) {
1422:         *missing = PETSC_TRUE;
1423:         if (d) *d = i;
1424:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1425:         break;
1426:       }
1427:     }
1428:   }
1429:   return(0);
1430: }

1434: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1435: {
1436:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1438:   PetscInt       i,*diag,m = A->rmap->n;
1439:   MatScalar      *v = a->a;
1440:   PetscScalar    *idiag,*mdiag;

1443:   if (a->idiagvalid) return(0);
1444:   MatMarkDiagonal_SeqAIJ(A);
1445:   diag = a->diag;
1446:   if (!a->idiag) {
1447:     PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
1448:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1449:     v    = a->a;
1450:   }
1451:   mdiag = a->mdiag;
1452:   idiag = a->idiag;

1454:   if (omega == 1.0 && !PetscAbsScalar(fshift)) {
1455:     for (i=0; i<m; i++) {
1456:       mdiag[i] = v[diag[i]];
1457:       if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1458:       idiag[i] = 1.0/v[diag[i]];
1459:     }
1460:     PetscLogFlops(m);
1461:   } else {
1462:     for (i=0; i<m; i++) {
1463:       mdiag[i] = v[diag[i]];
1464:       idiag[i] = omega/(fshift + v[diag[i]]);
1465:     }
1466:     PetscLogFlops(2.0*m);
1467:   }
1468:   a->idiagvalid = PETSC_TRUE;
1469:   return(0);
1470: }

1472: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1475: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1476: {
1477:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1478:   PetscScalar       *x,d,sum,*t,scale;
1479:   const MatScalar   *v = a->a,*idiag=0,*mdiag;
1480:   const PetscScalar *b, *bs,*xb, *ts;
1481:   PetscErrorCode    ierr;
1482:   PetscInt          n = A->cmap->n,m = A->rmap->n,i;
1483:   const PetscInt    *idx,*diag;

1486:   its = its*lits;

1488:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1489:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1490:   a->fshift = fshift;
1491:   a->omega  = omega;

1493:   diag  = a->diag;
1494:   t     = a->ssor_work;
1495:   idiag = a->idiag;
1496:   mdiag = a->mdiag;

1498:   VecGetArray(xx,&x);
1499:   VecGetArrayRead(bb,&b);
1500:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1501:   if (flag == SOR_APPLY_UPPER) {
1502:     /* apply (U + D/omega) to the vector */
1503:     bs = b;
1504:     for (i=0; i<m; i++) {
1505:       d   = fshift + mdiag[i];
1506:       n   = a->i[i+1] - diag[i] - 1;
1507:       idx = a->j + diag[i] + 1;
1508:       v   = a->a + diag[i] + 1;
1509:       sum = b[i]*d/omega;
1510:       PetscSparseDensePlusDot(sum,bs,v,idx,n);
1511:       x[i] = sum;
1512:     }
1513:     VecRestoreArray(xx,&x);
1514:     VecRestoreArrayRead(bb,&b);
1515:     PetscLogFlops(a->nz);
1516:     return(0);
1517:   }

1519:   if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1520:   else if (flag & SOR_EISENSTAT) {
1521:     /* Let  A = L + U + D; where L is lower trianglar,
1522:     U is upper triangular, E = D/omega; This routine applies

1524:             (L + E)^{-1} A (U + E)^{-1}

1526:     to a vector efficiently using Eisenstat's trick.
1527:     */
1528:     scale = (2.0/omega) - 1.0;

1530:     /*  x = (E + U)^{-1} b */
1531:     for (i=m-1; i>=0; i--) {
1532:       n   = a->i[i+1] - diag[i] - 1;
1533:       idx = a->j + diag[i] + 1;
1534:       v   = a->a + diag[i] + 1;
1535:       sum = b[i];
1536:       PetscSparseDenseMinusDot(sum,x,v,idx,n);
1537:       x[i] = sum*idiag[i];
1538:     }

1540:     /*  t = b - (2*E - D)x */
1541:     v = a->a;
1542:     for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];

1544:     /*  t = (E + L)^{-1}t */
1545:     ts   = t;
1546:     diag = a->diag;
1547:     for (i=0; i<m; i++) {
1548:       n   = diag[i] - a->i[i];
1549:       idx = a->j + a->i[i];
1550:       v   = a->a + a->i[i];
1551:       sum = t[i];
1552:       PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1553:       t[i] = sum*idiag[i];
1554:       /*  x = x + t */
1555:       x[i] += t[i];
1556:     }

1558:     PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1559:     VecRestoreArray(xx,&x);
1560:     VecRestoreArrayRead(bb,&b);
1561:     return(0);
1562:   }
1563:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1564:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1565:       for (i=0; i<m; i++) {
1566:         n   = diag[i] - a->i[i];
1567:         idx = a->j + a->i[i];
1568:         v   = a->a + a->i[i];
1569:         sum = b[i];
1570:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1571:         t[i] = sum;
1572:         x[i] = sum*idiag[i];
1573:       }
1574:       xb   = t;
1575:       PetscLogFlops(a->nz);
1576:     } else xb = b;
1577:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1578:       for (i=m-1; i>=0; i--) {
1579:         n   = a->i[i+1] - diag[i] - 1;
1580:         idx = a->j + diag[i] + 1;
1581:         v   = a->a + diag[i] + 1;
1582:         sum = xb[i];
1583:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1584:         if (xb == b) {
1585:           x[i] = sum*idiag[i];
1586:         } else {
1587:           x[i] = (1-omega)*x[i] + sum*idiag[i];
1588:         }
1589:       }
1590:       PetscLogFlops(a->nz);
1591:     }
1592:     its--;
1593:   }
1594:   while (its--) {
1595:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1596:       for (i=0; i<m; i++) {
1597:         n   = a->i[i+1] - a->i[i];
1598:         idx = a->j + a->i[i];
1599:         v   = a->a + a->i[i];
1600:         sum = b[i];
1601:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1602:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1603:       }
1604:       PetscLogFlops(2.0*a->nz);
1605:     }
1606:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1607:       for (i=m-1; i>=0; i--) {
1608:         n   = a->i[i+1] - a->i[i];
1609:         idx = a->j + a->i[i];
1610:         v   = a->a + a->i[i];
1611:         sum = b[i];
1612:         PetscSparseDenseMinusDot(sum,x,v,idx,n);
1613:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1614:       }
1615:       PetscLogFlops(2.0*a->nz);
1616:     }
1617:   }
1618:   VecRestoreArray(xx,&x);
1619:   VecRestoreArrayRead(bb,&b);
1620:   return(0);
1621: }


1626: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1627: {
1628:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1631:   info->block_size   = 1.0;
1632:   info->nz_allocated = (double)a->maxnz;
1633:   info->nz_used      = (double)a->nz;
1634:   info->nz_unneeded  = (double)(a->maxnz - a->nz);
1635:   info->assemblies   = (double)A->num_ass;
1636:   info->mallocs      = (double)A->info.mallocs;
1637:   info->memory       = ((PetscObject)A)->mem;
1638:   if (A->factortype) {
1639:     info->fill_ratio_given  = A->info.fill_ratio_given;
1640:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1641:     info->factor_mallocs    = A->info.factor_mallocs;
1642:   } else {
1643:     info->fill_ratio_given  = 0;
1644:     info->fill_ratio_needed = 0;
1645:     info->factor_mallocs    = 0;
1646:   }
1647:   return(0);
1648: }

1652: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1653: {
1654:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1655:   PetscInt          i,m = A->rmap->n - 1,d = 0;
1656:   PetscErrorCode    ierr;
1657:   const PetscScalar *xx;
1658:   PetscScalar       *bb;
1659:   PetscBool         missing;

1662:   if (x && b) {
1663:     VecGetArrayRead(x,&xx);
1664:     VecGetArray(b,&bb);
1665:     for (i=0; i<N; i++) {
1666:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1667:       bb[rows[i]] = diag*xx[rows[i]];
1668:     }
1669:     VecRestoreArrayRead(x,&xx);
1670:     VecRestoreArray(b,&bb);
1671:   }

1673:   if (a->keepnonzeropattern) {
1674:     for (i=0; i<N; i++) {
1675:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1676:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1677:     }
1678:     if (diag != 0.0) {
1679:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1680:       if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1681:       for (i=0; i<N; i++) {
1682:         a->a[a->diag[rows[i]]] = diag;
1683:       }
1684:     }
1685:     A->same_nonzero = PETSC_TRUE;
1686:   } else {
1687:     if (diag != 0.0) {
1688:       for (i=0; i<N; i++) {
1689:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1690:         if (a->ilen[rows[i]] > 0) {
1691:           a->ilen[rows[i]]    = 1;
1692:           a->a[a->i[rows[i]]] = diag;
1693:           a->j[a->i[rows[i]]] = rows[i];
1694:         } else { /* in case row was completely empty */
1695:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1696:         }
1697:       }
1698:     } else {
1699:       for (i=0; i<N; i++) {
1700:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1701:         a->ilen[rows[i]] = 0;
1702:       }
1703:     }
1704:     A->same_nonzero = PETSC_FALSE;
1705:   }
1706:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1707:   return(0);
1708: }

1712: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1713: {
1714:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
1715:   PetscInt          i,j,m = A->rmap->n - 1,d = 0;
1716:   PetscErrorCode    ierr;
1717:   PetscBool         missing,*zeroed,vecs = PETSC_FALSE;
1718:   const PetscScalar *xx;
1719:   PetscScalar       *bb;

1722:   if (x && b) {
1723:     VecGetArrayRead(x,&xx);
1724:     VecGetArray(b,&bb);
1725:     vecs = PETSC_TRUE;
1726:   }
1727:   PetscMalloc(A->rmap->n*sizeof(PetscBool),&zeroed);
1728:   PetscMemzero(zeroed,A->rmap->n*sizeof(PetscBool));
1729:   for (i=0; i<N; i++) {
1730:     if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1731:     PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));

1733:     zeroed[rows[i]] = PETSC_TRUE;
1734:   }
1735:   for (i=0; i<A->rmap->n; i++) {
1736:     if (!zeroed[i]) {
1737:       for (j=a->i[i]; j<a->i[i+1]; j++) {
1738:         if (zeroed[a->j[j]]) {
1739:           if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1740:           a->a[j] = 0.0;
1741:         }
1742:       }
1743:     } else if (vecs) bb[i] = diag*xx[i];
1744:   }
1745:   if (x && b) {
1746:     VecRestoreArrayRead(x,&xx);
1747:     VecRestoreArray(b,&bb);
1748:   }
1749:   PetscFree(zeroed);
1750:   if (diag != 0.0) {
1751:     MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1752:     if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1753:     for (i=0; i<N; i++) {
1754:       a->a[a->diag[rows[i]]] = diag;
1755:     }
1756:   }
1757:   A->same_nonzero = PETSC_TRUE;
1758:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1759:   return(0);
1760: }

1764: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1765: {
1766:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1767:   PetscInt   *itmp;

1770:   if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1772:   *nz = a->i[row+1] - a->i[row];
1773:   if (v) *v = a->a + a->i[row];
1774:   if (idx) {
1775:     itmp = a->j + a->i[row];
1776:     if (*nz) *idx = itmp;
1777:     else *idx = 0;
1778:   }
1779:   return(0);
1780: }

1782: /* remove this function? */
1785: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1786: {
1788:   return(0);
1789: }

1793: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1794: {
1795:   Mat_SeqAIJ     *a  = (Mat_SeqAIJ*)A->data;
1796:   MatScalar      *v  = a->a;
1797:   PetscReal      sum = 0.0;
1799:   PetscInt       i,j;

1802:   if (type == NORM_FROBENIUS) {
1803:     for (i=0; i<a->nz; i++) {
1804:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1805:     }
1806:     *nrm = PetscSqrtReal(sum);
1807:   } else if (type == NORM_1) {
1808:     PetscReal *tmp;
1809:     PetscInt  *jj = a->j;
1810:     PetscMalloc((A->cmap->n+1)*sizeof(PetscReal),&tmp);
1811:     PetscMemzero(tmp,A->cmap->n*sizeof(PetscReal));
1812:     *nrm = 0.0;
1813:     for (j=0; j<a->nz; j++) {
1814:       tmp[*jj++] += PetscAbsScalar(*v);  v++;
1815:     }
1816:     for (j=0; j<A->cmap->n; j++) {
1817:       if (tmp[j] > *nrm) *nrm = tmp[j];
1818:     }
1819:     PetscFree(tmp);
1820:   } else if (type == NORM_INFINITY) {
1821:     *nrm = 0.0;
1822:     for (j=0; j<A->rmap->n; j++) {
1823:       v   = a->a + a->i[j];
1824:       sum = 0.0;
1825:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1826:         sum += PetscAbsScalar(*v); v++;
1827:       }
1828:       if (sum > *nrm) *nrm = sum;
1829:     }
1830:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1831:   return(0);
1832: }

1834: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
1837: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
1838: {
1840:   PetscInt       i,j,anzj;
1841:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data,*b;
1842:   PetscInt       an=A->cmap->N,am=A->rmap->N;
1843:   PetscInt       *ati,*atj,*atfill,*ai=a->i,*aj=a->j;

1846:   /* Allocate space for symbolic transpose info and work array */
1847:   PetscMalloc((an+1)*sizeof(PetscInt),&ati);
1848:   PetscMalloc(ai[am]*sizeof(PetscInt),&atj);
1849:   PetscMalloc(an*sizeof(PetscInt),&atfill);
1850:   PetscMemzero(ati,(an+1)*sizeof(PetscInt));

1852:   /* Walk through aj and count ## of non-zeros in each row of A^T. */
1853:   /* Note: offset by 1 for fast conversion into csr format. */
1854:   for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
1855:   /* Form ati for csr format of A^T. */
1856:   for (i=0;i<an;i++) ati[i+1] += ati[i];

1858:   /* Copy ati into atfill so we have locations of the next free space in atj */
1859:   PetscMemcpy(atfill,ati,an*sizeof(PetscInt));

1861:   /* Walk through A row-wise and mark nonzero entries of A^T. */
1862:   for (i=0;i<am;i++) {
1863:     anzj = ai[i+1] - ai[i];
1864:     for (j=0;j<anzj;j++) {
1865:       atj[atfill[*aj]] = i;
1866:       atfill[*aj++]   += 1;
1867:     }
1868:   }

1870:   /* Clean up temporary space and complete requests. */
1871:   PetscFree(atfill);
1872:   MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);

1874:   (*B)->rmap->bs = A->cmap->bs;
1875:   (*B)->cmap->bs = A->rmap->bs;

1877:   b          = (Mat_SeqAIJ*)((*B)->data);
1878:   b->free_a  = PETSC_FALSE;
1879:   b->free_ij = PETSC_TRUE;
1880:   b->nonew   = 0;
1881:   return(0);
1882: }

1886: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
1887: {
1888:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1889:   Mat            C;
1891:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
1892:   MatScalar      *array = a->a;

1895:   if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");

1897:   if (reuse == MAT_INITIAL_MATRIX || *B == A) {
1898:     PetscMalloc((1+A->cmap->n)*sizeof(PetscInt),&col);
1899:     PetscMemzero(col,(1+A->cmap->n)*sizeof(PetscInt));

1901:     for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1902:     MatCreate(PetscObjectComm((PetscObject)A),&C);
1903:     MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
1904:     MatSetBlockSizes(C,A->cmap->bs,A->rmap->bs);
1905:     MatSetType(C,((PetscObject)A)->type_name);
1906:     MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1907:     PetscFree(col);
1908:   } else {
1909:     C = *B;
1910:   }

1912:   for (i=0; i<m; i++) {
1913:     len    = ai[i+1]-ai[i];
1914:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1915:     array += len;
1916:     aj    += len;
1917:   }
1918:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1919:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1921:   if (reuse == MAT_INITIAL_MATRIX || *B != A) {
1922:     *B = C;
1923:   } else {
1924:     MatHeaderMerge(A,C);
1925:   }
1926:   return(0);
1927: }

1931: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
1932: {
1933:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
1934:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1935:   MatScalar      *va,*vb;
1937:   PetscInt       ma,na,mb,nb, i;

1940:   bij = (Mat_SeqAIJ*) B->data;

1942:   MatGetSize(A,&ma,&na);
1943:   MatGetSize(B,&mb,&nb);
1944:   if (ma!=nb || na!=mb) {
1945:     *f = PETSC_FALSE;
1946:     return(0);
1947:   }
1948:   aii  = aij->i; bii = bij->i;
1949:   adx  = aij->j; bdx = bij->j;
1950:   va   = aij->a; vb = bij->a;
1951:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1952:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1953:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1954:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1956:   *f = PETSC_TRUE;
1957:   for (i=0; i<ma; i++) {
1958:     while (aptr[i]<aii[i+1]) {
1959:       PetscInt    idc,idr;
1960:       PetscScalar vc,vr;
1961:       /* column/row index/value */
1962:       idc = adx[aptr[i]];
1963:       idr = bdx[bptr[idc]];
1964:       vc  = va[aptr[i]];
1965:       vr  = vb[bptr[idc]];
1966:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1967:         *f = PETSC_FALSE;
1968:         goto done;
1969:       } else {
1970:         aptr[i]++;
1971:         if (B || i!=idc) bptr[idc]++;
1972:       }
1973:     }
1974:   }
1975: done:
1976:   PetscFree(aptr);
1977:   PetscFree(bptr);
1978:   return(0);
1979: }

1983: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool  *f)
1984: {
1985:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
1986:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr;
1987:   MatScalar      *va,*vb;
1989:   PetscInt       ma,na,mb,nb, i;

1992:   bij = (Mat_SeqAIJ*) B->data;

1994:   MatGetSize(A,&ma,&na);
1995:   MatGetSize(B,&mb,&nb);
1996:   if (ma!=nb || na!=mb) {
1997:     *f = PETSC_FALSE;
1998:     return(0);
1999:   }
2000:   aii  = aij->i; bii = bij->i;
2001:   adx  = aij->j; bdx = bij->j;
2002:   va   = aij->a; vb = bij->a;
2003:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
2004:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
2005:   for (i=0; i<ma; i++) aptr[i] = aii[i];
2006:   for (i=0; i<mb; i++) bptr[i] = bii[i];

2008:   *f = PETSC_TRUE;
2009:   for (i=0; i<ma; i++) {
2010:     while (aptr[i]<aii[i+1]) {
2011:       PetscInt    idc,idr;
2012:       PetscScalar vc,vr;
2013:       /* column/row index/value */
2014:       idc = adx[aptr[i]];
2015:       idr = bdx[bptr[idc]];
2016:       vc  = va[aptr[i]];
2017:       vr  = vb[bptr[idc]];
2018:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2019:         *f = PETSC_FALSE;
2020:         goto done;
2021:       } else {
2022:         aptr[i]++;
2023:         if (B || i!=idc) bptr[idc]++;
2024:       }
2025:     }
2026:   }
2027: done:
2028:   PetscFree(aptr);
2029:   PetscFree(bptr);
2030:   return(0);
2031: }

2035: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2036: {

2040:   MatIsTranspose_SeqAIJ(A,A,tol,f);
2041:   return(0);
2042: }

2046: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool  *f)
2047: {

2051:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2052:   return(0);
2053: }

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

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

2096: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2097: {
2098:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
2100:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2101:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2102:   const PetscInt *irow,*icol;
2103:   PetscInt       nrows,ncols;
2104:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2105:   MatScalar      *a_new,*mat_a;
2106:   Mat            C;
2107:   PetscBool      stride,sorted;

2110:   ISSorted(isrow,&sorted);
2111:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
2112:   ISSorted(iscol,&sorted);
2113:   if (!sorted) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

2115:   ISGetIndices(isrow,&irow);
2116:   ISGetLocalSize(isrow,&nrows);
2117:   ISGetLocalSize(iscol,&ncols);

2119:   ISStrideGetInfo(iscol,&first,&step);
2120:   PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2121:   if (stride && step == 1) {
2122:     /* special case of contiguous rows */
2123:     PetscMalloc2(nrows,PetscInt,&lens,nrows,PetscInt,&starts);
2124:     /* loop over new rows determining lens and starting points */
2125:     for (i=0; i<nrows; i++) {
2126:       kstart = ai[irow[i]];
2127:       kend   = kstart + ailen[irow[i]];
2128:       for (k=kstart; k<kend; k++) {
2129:         if (aj[k] >= first) {
2130:           starts[i] = k;
2131:           break;
2132:         }
2133:       }
2134:       sum = 0;
2135:       while (k < kend) {
2136:         if (aj[k++] >= first+ncols) break;
2137:         sum++;
2138:       }
2139:       lens[i] = sum;
2140:     }
2141:     /* create submatrix */
2142:     if (scall == MAT_REUSE_MATRIX) {
2143:       PetscInt n_cols,n_rows;
2144:       MatGetSize(*B,&n_rows,&n_cols);
2145:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2146:       MatZeroEntries(*B);
2147:       C    = *B;
2148:     } else {
2149:       PetscInt rbs,cbs;
2150:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2151:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2152:       ISGetBlockSize(isrow,&rbs);
2153:       ISGetBlockSize(iscol,&cbs);
2154:       MatSetBlockSizes(C,rbs,cbs);
2155:       MatSetType(C,((PetscObject)A)->type_name);
2156:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2157:     }
2158:     c = (Mat_SeqAIJ*)C->data;

2160:     /* loop over rows inserting into submatrix */
2161:     a_new = c->a;
2162:     j_new = c->j;
2163:     i_new = c->i;

2165:     for (i=0; i<nrows; i++) {
2166:       ii    = starts[i];
2167:       lensi = lens[i];
2168:       for (k=0; k<lensi; k++) {
2169:         *j_new++ = aj[ii+k] - first;
2170:       }
2171:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2172:       a_new     += lensi;
2173:       i_new[i+1] = i_new[i] + lensi;
2174:       c->ilen[i] = lensi;
2175:     }
2176:     PetscFree2(lens,starts);
2177:   } else {
2178:     ISGetIndices(iscol,&icol);
2179:     PetscMalloc(oldcols*sizeof(PetscInt),&smap);
2180:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
2181:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
2182:     for (i=0; i<ncols; i++) {
2183: #if defined(PETSC_USE_DEBUG)
2184:       if (icol[i] >= oldcols) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D <= A->cmap->n %D",i,icol[i],oldcols);
2185: #endif
2186:       smap[icol[i]] = i+1;
2187:     }

2189:     /* determine lens of each row */
2190:     for (i=0; i<nrows; i++) {
2191:       kstart  = ai[irow[i]];
2192:       kend    = kstart + a->ilen[irow[i]];
2193:       lens[i] = 0;
2194:       for (k=kstart; k<kend; k++) {
2195:         if (smap[aj[k]]) {
2196:           lens[i]++;
2197:         }
2198:       }
2199:     }
2200:     /* Create and fill new matrix */
2201:     if (scall == MAT_REUSE_MATRIX) {
2202:       PetscBool equal;

2204:       c = (Mat_SeqAIJ*)((*B)->data);
2205:       if ((*B)->rmap->n  != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2206:       PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2207:       if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2208:       PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2209:       C    = *B;
2210:     } else {
2211:       PetscInt rbs,cbs;
2212:       MatCreate(PetscObjectComm((PetscObject)A),&C);
2213:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2214:       ISGetBlockSize(isrow,&rbs);
2215:       ISGetBlockSize(iscol,&cbs);
2216:       MatSetBlockSizes(C,rbs,cbs);
2217:       MatSetType(C,((PetscObject)A)->type_name);
2218:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2219:     }
2220:     c = (Mat_SeqAIJ*)(C->data);
2221:     for (i=0; i<nrows; i++) {
2222:       row      = irow[i];
2223:       kstart   = ai[row];
2224:       kend     = kstart + a->ilen[row];
2225:       mat_i    = c->i[i];
2226:       mat_j    = c->j + mat_i;
2227:       mat_a    = c->a + mat_i;
2228:       mat_ilen = c->ilen + i;
2229:       for (k=kstart; k<kend; k++) {
2230:         if ((tcol=smap[a->j[k]])) {
2231:           *mat_j++ = tcol - 1;
2232:           *mat_a++ = a->a[k];
2233:           (*mat_ilen)++;

2235:         }
2236:       }
2237:     }
2238:     /* Free work space */
2239:     ISRestoreIndices(iscol,&icol);
2240:     PetscFree(smap);
2241:     PetscFree(lens);
2242:   }
2243:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2244:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

2246:   ISRestoreIndices(isrow,&irow);
2247:   *B   = C;
2248:   return(0);
2249: }

2253: PetscErrorCode  MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2254: {
2256:   Mat            B;

2259:   MatCreate(subComm,&B);
2260:   MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2261:   MatSetBlockSizes(B,mat->rmap->bs,mat->cmap->bs);
2262:   MatSetType(B,MATSEQAIJ);
2263:   MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2264:   *subMat = B;
2265:   return(0);
2266: }

2270: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2271: {
2272:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
2274:   Mat            outA;
2275:   PetscBool      row_identity,col_identity;

2278:   if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");

2280:   ISIdentity(row,&row_identity);
2281:   ISIdentity(col,&col_identity);

2283:   outA             = inA;
2284:   outA->factortype = MAT_FACTOR_LU;

2286:   PetscObjectReference((PetscObject)row);
2287:   ISDestroy(&a->row);

2289:   a->row = row;

2291:   PetscObjectReference((PetscObject)col);
2292:   ISDestroy(&a->col);

2294:   a->col = col;

2296:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2297:   ISDestroy(&a->icol);
2298:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2299:   PetscLogObjectParent(inA,a->icol);

2301:   if (!a->solve_work) { /* this matrix may have been factored before */
2302:     PetscMalloc((inA->rmap->n+1)*sizeof(PetscScalar),&a->solve_work);
2303:     PetscLogObjectMemory(inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2304:   }

2306:   MatMarkDiagonal_SeqAIJ(inA);
2307:   if (row_identity && col_identity) {
2308:     MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2309:   } else {
2310:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2311:   }
2312:   return(0);
2313: }

2317: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2318: {
2319:   Mat_SeqAIJ     *a     = (Mat_SeqAIJ*)inA->data;
2320:   PetscScalar    oalpha = alpha;
2322:   PetscBLASInt   one = 1,bnz;

2325:   PetscBLASIntCast(a->nz,&bnz);
2326:   PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2327:   PetscLogFlops(a->nz);
2328:   MatSeqAIJInvalidateDiagonal(inA);
2329:   return(0);
2330: }

2334: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2335: {
2337:   PetscInt       i;

2340:   if (scall == MAT_INITIAL_MATRIX) {
2341:     PetscMalloc((n+1)*sizeof(Mat),B);
2342:   }

2344:   for (i=0; i<n; i++) {
2345:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2346:   }
2347:   return(0);
2348: }

2352: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2353: {
2354:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2356:   PetscInt       row,i,j,k,l,m,n,*nidx,isz,val;
2357:   const PetscInt *idx;
2358:   PetscInt       start,end,*ai,*aj;
2359:   PetscBT        table;

2362:   m  = A->rmap->n;
2363:   ai = a->i;
2364:   aj = a->j;

2366:   if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

2368:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
2369:   PetscBTCreate(m,&table);

2371:   for (i=0; i<is_max; i++) {
2372:     /* Initialize the two local arrays */
2373:     isz  = 0;
2374:     PetscBTMemzero(m,table);

2376:     /* Extract the indices, assume there can be duplicate entries */
2377:     ISGetIndices(is[i],&idx);
2378:     ISGetLocalSize(is[i],&n);

2380:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2381:     for (j=0; j<n; ++j) {
2382:       if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2383:     }
2384:     ISRestoreIndices(is[i],&idx);
2385:     ISDestroy(&is[i]);

2387:     k = 0;
2388:     for (j=0; j<ov; j++) { /* for each overlap */
2389:       n = isz;
2390:       for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2391:         row   = nidx[k];
2392:         start = ai[row];
2393:         end   = ai[row+1];
2394:         for (l = start; l<end; l++) {
2395:           val = aj[l];
2396:           if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2397:         }
2398:       }
2399:     }
2400:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2401:   }
2402:   PetscBTDestroy(&table);
2403:   PetscFree(nidx);
2404:   return(0);
2405: }

2407: /* -------------------------------------------------------------- */
2410: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2411: {
2412:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2414:   PetscInt       i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2415:   const PetscInt *row,*col;
2416:   PetscInt       *cnew,j,*lens;
2417:   IS             icolp,irowp;
2418:   PetscInt       *cwork = NULL;
2419:   PetscScalar    *vwork = NULL;

2422:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2423:   ISGetIndices(irowp,&row);
2424:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2425:   ISGetIndices(icolp,&col);

2427:   /* determine lengths of permuted rows */
2428:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
2429:   for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2430:   MatCreate(PetscObjectComm((PetscObject)A),B);
2431:   MatSetSizes(*B,m,n,m,n);
2432:   MatSetBlockSizes(*B,A->rmap->bs,A->cmap->bs);
2433:   MatSetType(*B,((PetscObject)A)->type_name);
2434:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2435:   PetscFree(lens);

2437:   PetscMalloc(n*sizeof(PetscInt),&cnew);
2438:   for (i=0; i<m; i++) {
2439:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2440:     for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2441:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2442:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2443:   }
2444:   PetscFree(cnew);

2446:   (*B)->assembled = PETSC_FALSE;

2448:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2449:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2450:   ISRestoreIndices(irowp,&row);
2451:   ISRestoreIndices(icolp,&col);
2452:   ISDestroy(&irowp);
2453:   ISDestroy(&icolp);
2454:   return(0);
2455: }

2459: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2460: {

2464:   /* If the two matrices have the same copy implementation, use fast copy. */
2465:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2466:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2467:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

2469:     if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
2470:     PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2471:   } else {
2472:     MatCopy_Basic(A,B,str);
2473:   }
2474:   return(0);
2475: }

2479: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2480: {

2484:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2485:   return(0);
2486: }

2490: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2491: {
2492:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2495:   *array = a->a;
2496:   return(0);
2497: }

2501: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2502: {
2504:   return(0);
2505: }

2509: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2510: {
2511:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void*))coloring->f;
2513:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow;
2514:   PetscScalar    dx,*y,*xx,*w3_array;
2515:   PetscScalar    *vscale_array;
2516:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
2517:   Vec            w1,w2,w3;
2518:   void           *fctx = coloring->fctx;
2519:   PetscBool      flg   = PETSC_FALSE;

2522:   if (!coloring->w1) {
2523:     VecDuplicate(x1,&coloring->w1);
2524:     PetscLogObjectParent(coloring,coloring->w1);
2525:     VecDuplicate(x1,&coloring->w2);
2526:     PetscLogObjectParent(coloring,coloring->w2);
2527:     VecDuplicate(x1,&coloring->w3);
2528:     PetscLogObjectParent(coloring,coloring->w3);
2529:   }
2530:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

2532:   MatSetUnfactored(J);
2533:   PetscOptionsGetBool(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg,NULL);
2534:   if (flg) {
2535:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2536:   } else {
2537:     PetscBool assembled;
2538:     MatAssembled(J,&assembled);
2539:     if (assembled) {
2540:       MatZeroEntries(J);
2541:     }
2542:   }

2544:   VecGetOwnershipRange(x1,&start,&end);
2545:   VecGetSize(x1,&N);

2547:   if (!coloring->fset) {
2548:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2549:     (*f)(sctx,x1,w1,fctx);
2550:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2551:   } else {
2552:     coloring->fset = PETSC_FALSE;
2553:   }

2555:   /*
2556:       Compute all the scale factors and share with other processors
2557:   */
2558:   VecGetArray(x1,&xx);xx = xx - start;
2559:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2560:   for (k=0; k<coloring->ncolors; k++) {
2561:     /*
2562:        Loop over each column associated with color adding the
2563:        perturbation to the vector w3.
2564:     */
2565:     for (l=0; l<coloring->ncolumns[k]; l++) {
2566:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2567:       dx  = xx[col];
2568:       if (dx == 0.0) dx = 1.0;
2569:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2570:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2571:       dx               *= epsilon;
2572:       vscale_array[col] = 1.0/dx;
2573:     }
2574:   }
2575:   vscale_array = vscale_array + start;

2577:   VecRestoreArray(coloring->vscale,&vscale_array);
2578:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2579:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2581:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2582:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2584:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2585:   else                        vscaleforrow = coloring->columnsforrow;

2587:   VecGetArray(coloring->vscale,&vscale_array);
2588:   /*
2589:       Loop over each color
2590:   */
2591:   for (k=0; k<coloring->ncolors; k++) {
2592:     coloring->currentcolor = k;

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

2612:     /*
2613:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2614:     */

2616:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2617:     (*f)(sctx,w3,w2,fctx);
2618:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2619:     VecAXPY(w2,-1.0,w1);

2621:     /*
2622:        Loop over rows of vector, putting results into Jacobian matrix
2623:     */
2624:     VecGetArray(w2,&y);
2625:     for (l=0; l<coloring->nrows[k]; l++) {
2626:       row     = coloring->rows[k][l];
2627:       col     = coloring->columnsforrow[k][l];
2628:       y[row] *= vscale_array[vscaleforrow[k][l]];
2629:       srow    = row + start;
2630:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2631:     }
2632:     VecRestoreArray(w2,&y);
2633:   }
2634:   coloring->currentcolor = k;

2636:   VecRestoreArray(coloring->vscale,&vscale_array);
2637:   xx   = xx + start;
2638:   VecRestoreArray(x1,&xx);
2639:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2640:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2641:   return(0);
2642: }

2644: /*
2645:    Computes the number of nonzeros per row needed for preallocation when X and Y
2646:    have different nonzero structure.
2647: */
2650: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2651: {
2652:   PetscInt       i,m=Y->rmap->N;
2653:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ*)X->data;
2654:   Mat_SeqAIJ     *y  = (Mat_SeqAIJ*)Y->data;
2655:   const PetscInt *xi = x->i,*yi = y->i;

2658:   /* Set the number of nonzeros in the new matrix */
2659:   for (i=0; i<m; i++) {
2660:     PetscInt       j,k,nzx = xi[i+1] - xi[i],nzy = yi[i+1] - yi[i];
2661:     const PetscInt *xj = x->j+xi[i],*yj = y->j+yi[i];
2662:     nnz[i] = 0;
2663:     for (j=0,k=0; j<nzx; j++) {                   /* Point in X */
2664:       for (; k<nzy && yj[k]<xj[j]; k++) nnz[i]++; /* Catch up to X */
2665:       if (k<nzy && yj[k]==xj[j]) k++;             /* Skip duplicate */
2666:       nnz[i]++;
2667:     }
2668:     for (; k<nzy; k++) nnz[i]++;
2669:   }
2670:   return(0);
2671: }

2675: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2676: {
2678:   PetscInt       i;
2679:   Mat_SeqAIJ     *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2680:   PetscBLASInt   one=1,bnz;

2683:   PetscBLASIntCast(x->nz,&bnz);
2684:   if (str == SAME_NONZERO_PATTERN) {
2685:     PetscScalar alpha = a;
2686:     PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2687:     MatSeqAIJInvalidateDiagonal(Y);
2688:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2689:     if (y->xtoy && y->XtoY != X) {
2690:       PetscFree(y->xtoy);
2691:       MatDestroy(&y->XtoY);
2692:     }
2693:     if (!y->xtoy) { /* get xtoy */
2694:       MatAXPYGetxtoy_Private(X->rmap->n,x->i,x->j,NULL, y->i,y->j,NULL, &y->xtoy);
2695:       y->XtoY = X;
2696:       PetscObjectReference((PetscObject)X);
2697:     }
2698:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2699:     PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(double)(PetscReal)(x->nz)/(y->nz+1));
2700:   } else {
2701:     Mat      B;
2702:     PetscInt *nnz;
2703:     PetscMalloc(Y->rmap->N*sizeof(PetscInt),&nnz);
2704:     MatCreate(PetscObjectComm((PetscObject)Y),&B);
2705:     PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2706:     MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2707:     MatSetBlockSizes(B,Y->rmap->bs,Y->cmap->bs);
2708:     MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2709:     MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2710:     MatSeqAIJSetPreallocation(B,0,nnz);
2711:     MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2712:     MatHeaderReplace(Y,B);
2713:     PetscFree(nnz);
2714:   }
2715:   return(0);
2716: }

2720: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2721: {
2722: #if defined(PETSC_USE_COMPLEX)
2723:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)mat->data;
2724:   PetscInt    i,nz;
2725:   PetscScalar *a;

2728:   nz = aij->nz;
2729:   a  = aij->a;
2730:   for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2731: #else
2733: #endif
2734:   return(0);
2735: }

2739: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2740: {
2741:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2743:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2744:   PetscReal      atmp;
2745:   PetscScalar    *x;
2746:   MatScalar      *aa;

2749:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2750:   aa = a->a;
2751:   ai = a->i;
2752:   aj = a->j;

2754:   VecSet(v,0.0);
2755:   VecGetArray(v,&x);
2756:   VecGetLocalSize(v,&n);
2757:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2758:   for (i=0; i<m; i++) {
2759:     ncols = ai[1] - ai[0]; ai++;
2760:     x[i]  = 0.0;
2761:     for (j=0; j<ncols; j++) {
2762:       atmp = PetscAbsScalar(*aa);
2763:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2764:       aa++; aj++;
2765:     }
2766:   }
2767:   VecRestoreArray(v,&x);
2768:   return(0);
2769: }

2773: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2774: {
2775:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2777:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2778:   PetscScalar    *x;
2779:   MatScalar      *aa;

2782:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2783:   aa = a->a;
2784:   ai = a->i;
2785:   aj = a->j;

2787:   VecSet(v,0.0);
2788:   VecGetArray(v,&x);
2789:   VecGetLocalSize(v,&n);
2790:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2791:   for (i=0; i<m; i++) {
2792:     ncols = ai[1] - ai[0]; ai++;
2793:     if (ncols == A->cmap->n) { /* row is dense */
2794:       x[i] = *aa; if (idx) idx[i] = 0;
2795:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2796:       x[i] = 0.0;
2797:       if (idx) {
2798:         idx[i] = 0; /* in case ncols is zero */
2799:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2800:           if (aj[j] > j) {
2801:             idx[i] = j;
2802:             break;
2803:           }
2804:         }
2805:       }
2806:     }
2807:     for (j=0; j<ncols; j++) {
2808:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2809:       aa++; aj++;
2810:     }
2811:   }
2812:   VecRestoreArray(v,&x);
2813:   return(0);
2814: }

2818: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2819: {
2820:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2822:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2823:   PetscReal      atmp;
2824:   PetscScalar    *x;
2825:   MatScalar      *aa;

2828:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2829:   aa = a->a;
2830:   ai = a->i;
2831:   aj = a->j;

2833:   VecSet(v,0.0);
2834:   VecGetArray(v,&x);
2835:   VecGetLocalSize(v,&n);
2836:   if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %d vs. %d rows", A->rmap->n, n);
2837:   for (i=0; i<m; i++) {
2838:     ncols = ai[1] - ai[0]; ai++;
2839:     if (ncols) {
2840:       /* Get first nonzero */
2841:       for (j = 0; j < ncols; j++) {
2842:         atmp = PetscAbsScalar(aa[j]);
2843:         if (atmp > 1.0e-12) {
2844:           x[i] = atmp;
2845:           if (idx) idx[i] = aj[j];
2846:           break;
2847:         }
2848:       }
2849:       if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
2850:     } else {
2851:       x[i] = 0.0; if (idx) idx[i] = 0;
2852:     }
2853:     for (j = 0; j < ncols; j++) {
2854:       atmp = PetscAbsScalar(*aa);
2855:       if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2856:       aa++; aj++;
2857:     }
2858:   }
2859:   VecRestoreArray(v,&x);
2860:   return(0);
2861: }

2865: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2866: {
2867:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2869:   PetscInt       i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2870:   PetscScalar    *x;
2871:   MatScalar      *aa;

2874:   if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2875:   aa = a->a;
2876:   ai = a->i;
2877:   aj = a->j;

2879:   VecSet(v,0.0);
2880:   VecGetArray(v,&x);
2881:   VecGetLocalSize(v,&n);
2882:   if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2883:   for (i=0; i<m; i++) {
2884:     ncols = ai[1] - ai[0]; ai++;
2885:     if (ncols == A->cmap->n) { /* row is dense */
2886:       x[i] = *aa; if (idx) idx[i] = 0;
2887:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2888:       x[i] = 0.0;
2889:       if (idx) {   /* find first implicit 0.0 in the row */
2890:         idx[i] = 0; /* in case ncols is zero */
2891:         for (j=0; j<ncols; j++) {
2892:           if (aj[j] > j) {
2893:             idx[i] = j;
2894:             break;
2895:           }
2896:         }
2897:       }
2898:     }
2899:     for (j=0; j<ncols; j++) {
2900:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2901:       aa++; aj++;
2902:     }
2903:   }
2904:   VecRestoreArray(v,&x);
2905:   return(0);
2906: }

2908: #include <petscblaslapack.h>
2909: #include <petsc-private/kernels/blockinvert.h>

2913: PetscErrorCode  MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
2914: {
2915:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
2917:   PetscInt       i,bs = A->rmap->bs,mbs = A->rmap->n/A->rmap->bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2918:   MatScalar      *diag,work[25],*v_work;
2919:   PetscReal      shift = 0.0;

2922:   if (a->ibdiagvalid) {
2923:     if (values) *values = a->ibdiag;
2924:     return(0);
2925:   }
2926:   MatMarkDiagonal_SeqAIJ(A);
2927:   if (!a->ibdiag) {
2928:     PetscMalloc(bs2*mbs*sizeof(PetscScalar),&a->ibdiag);
2929:     PetscLogObjectMemory(A,bs2*mbs*sizeof(PetscScalar));
2930:   }
2931:   diag = a->ibdiag;
2932:   if (values) *values = a->ibdiag;
2933:   /* factor and invert each block */
2934:   switch (bs) {
2935:   case 1:
2936:     for (i=0; i<mbs; i++) {
2937:       MatGetValues(A,1,&i,1,&i,diag+i);
2938:       diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
2939:     }
2940:     break;
2941:   case 2:
2942:     for (i=0; i<mbs; i++) {
2943:       ij[0] = 2*i; ij[1] = 2*i + 1;
2944:       MatGetValues(A,2,ij,2,ij,diag);
2945:       PetscKernel_A_gets_inverse_A_2(diag,shift);
2946:       PetscKernel_A_gets_transpose_A_2(diag);
2947:       diag += 4;
2948:     }
2949:     break;
2950:   case 3:
2951:     for (i=0; i<mbs; i++) {
2952:       ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
2953:       MatGetValues(A,3,ij,3,ij,diag);
2954:       PetscKernel_A_gets_inverse_A_3(diag,shift);
2955:       PetscKernel_A_gets_transpose_A_3(diag);
2956:       diag += 9;
2957:     }
2958:     break;
2959:   case 4:
2960:     for (i=0; i<mbs; i++) {
2961:       ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
2962:       MatGetValues(A,4,ij,4,ij,diag);
2963:       PetscKernel_A_gets_inverse_A_4(diag,shift);
2964:       PetscKernel_A_gets_transpose_A_4(diag);
2965:       diag += 16;
2966:     }
2967:     break;
2968:   case 5:
2969:     for (i=0; i<mbs; i++) {
2970:       ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
2971:       MatGetValues(A,5,ij,5,ij,diag);
2972:       PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
2973:       PetscKernel_A_gets_transpose_A_5(diag);
2974:       diag += 25;
2975:     }
2976:     break;
2977:   case 6:
2978:     for (i=0; i<mbs; i++) {
2979:       ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
2980:       MatGetValues(A,6,ij,6,ij,diag);
2981:       PetscKernel_A_gets_inverse_A_6(diag,shift);
2982:       PetscKernel_A_gets_transpose_A_6(diag);
2983:       diag += 36;
2984:     }
2985:     break;
2986:   case 7:
2987:     for (i=0; i<mbs; i++) {
2988:       ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
2989:       MatGetValues(A,7,ij,7,ij,diag);
2990:       PetscKernel_A_gets_inverse_A_7(diag,shift);
2991:       PetscKernel_A_gets_transpose_A_7(diag);
2992:       diag += 49;
2993:     }
2994:     break;
2995:   default:
2996:     PetscMalloc3(bs,MatScalar,&v_work,bs,PetscInt,&v_pivots,bs,PetscInt,&IJ);
2997:     for (i=0; i<mbs; i++) {
2998:       for (j=0; j<bs; j++) {
2999:         IJ[j] = bs*i + j;
3000:       }
3001:       MatGetValues(A,bs,IJ,bs,IJ,diag);
3002:       PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
3003:       PetscKernel_A_gets_transpose_A_N(diag,bs);
3004:       diag += bs2;
3005:     }
3006:     PetscFree3(v_work,v_pivots,IJ);
3007:   }
3008:   a->ibdiagvalid = PETSC_TRUE;
3009:   return(0);
3010: }

3014: static PetscErrorCode  MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3015: {
3017:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)x->data;
3018:   PetscScalar    a;
3019:   PetscInt       m,n,i,j,col;

3022:   if (!x->assembled) {
3023:     MatGetSize(x,&m,&n);
3024:     for (i=0; i<m; i++) {
3025:       for (j=0; j<aij->imax[i]; j++) {
3026:         PetscRandomGetValue(rctx,&a);
3027:         col  = (PetscInt)(n*PetscRealPart(a));
3028:         MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3029:       }
3030:     }
3031:   } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded");
3032:   MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3033:   MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3034:   return(0);
3035: }

3037: extern PetscErrorCode  MatFDColoringApply_AIJ(Mat,MatFDColoring,Vec,MatStructure*,void*);
3038: /* -------------------------------------------------------------------*/
3039: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3040:                                         MatGetRow_SeqAIJ,
3041:                                         MatRestoreRow_SeqAIJ,
3042:                                         MatMult_SeqAIJ,
3043:                                 /*  4*/ MatMultAdd_SeqAIJ,
3044:                                         MatMultTranspose_SeqAIJ,
3045:                                         MatMultTransposeAdd_SeqAIJ,
3046:                                         0,
3047:                                         0,
3048:                                         0,
3049:                                 /* 10*/ 0,
3050:                                         MatLUFactor_SeqAIJ,
3051:                                         0,
3052:                                         MatSOR_SeqAIJ,
3053:                                         MatTranspose_SeqAIJ,
3054:                                 /*1 5*/ MatGetInfo_SeqAIJ,
3055:                                         MatEqual_SeqAIJ,
3056:                                         MatGetDiagonal_SeqAIJ,
3057:                                         MatDiagonalScale_SeqAIJ,
3058:                                         MatNorm_SeqAIJ,
3059:                                 /* 20*/ 0,
3060:                                         MatAssemblyEnd_SeqAIJ,
3061:                                         MatSetOption_SeqAIJ,
3062:                                         MatZeroEntries_SeqAIJ,
3063:                                 /* 24*/ MatZeroRows_SeqAIJ,
3064:                                         0,
3065:                                         0,
3066:                                         0,
3067:                                         0,
3068:                                 /* 29*/ MatSetUp_SeqAIJ,
3069:                                         0,
3070:                                         0,
3071:                                         0,
3072:                                         0,
3073:                                 /* 34*/ MatDuplicate_SeqAIJ,
3074:                                         0,
3075:                                         0,
3076:                                         MatILUFactor_SeqAIJ,
3077:                                         0,
3078:                                 /* 39*/ MatAXPY_SeqAIJ,
3079:                                         MatGetSubMatrices_SeqAIJ,
3080:                                         MatIncreaseOverlap_SeqAIJ,
3081:                                         MatGetValues_SeqAIJ,
3082:                                         MatCopy_SeqAIJ,
3083:                                 /* 44*/ MatGetRowMax_SeqAIJ,
3084:                                         MatScale_SeqAIJ,
3085:                                         0,
3086:                                         MatDiagonalSet_SeqAIJ,
3087:                                         MatZeroRowsColumns_SeqAIJ,
3088:                                 /* 49*/ MatSetRandom_SeqAIJ,
3089:                                         MatGetRowIJ_SeqAIJ,
3090:                                         MatRestoreRowIJ_SeqAIJ,
3091:                                         MatGetColumnIJ_SeqAIJ,
3092:                                         MatRestoreColumnIJ_SeqAIJ,
3093:                                 /* 54*/ MatFDColoringCreate_SeqAIJ,
3094:                                         0,
3095:                                         0,
3096:                                         MatPermute_SeqAIJ,
3097:                                         0,
3098:                                 /* 59*/ 0,
3099:                                         MatDestroy_SeqAIJ,
3100:                                         MatView_SeqAIJ,
3101:                                         0,
3102:                                         MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3103:                                 /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3104:                                         MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3105:                                         0,
3106:                                         0,
3107:                                         0,
3108:                                 /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3109:                                         MatGetRowMinAbs_SeqAIJ,
3110:                                         0,
3111:                                         MatSetColoring_SeqAIJ,
3112:                                         0,
3113:                                 /* 74*/ MatSetValuesAdifor_SeqAIJ,
3114:                                         MatFDColoringApply_AIJ,
3115:                                         0,
3116:                                         0,
3117:                                         0,
3118:                                 /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3119:                                         0,
3120:                                         0,
3121:                                         0,
3122:                                         MatLoad_SeqAIJ,
3123:                                 /* 84*/ MatIsSymmetric_SeqAIJ,
3124:                                         MatIsHermitian_SeqAIJ,
3125:                                         0,
3126:                                         0,
3127:                                         0,
3128:                                 /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3129:                                         MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3130:                                         MatMatMultNumeric_SeqAIJ_SeqAIJ,
3131:                                         MatPtAP_SeqAIJ_SeqAIJ,
3132:                                         MatPtAPSymbolic_SeqAIJ_SeqAIJ,
3133:                                 /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
3134:                                         MatMatTransposeMult_SeqAIJ_SeqAIJ,
3135:                                         MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3136:                                         MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3137:                                         0,
3138:                                 /* 99*/ 0,
3139:                                         0,
3140:                                         0,
3141:                                         MatConjugate_SeqAIJ,
3142:                                         0,
3143:                                 /*104*/ MatSetValuesRow_SeqAIJ,
3144:                                         MatRealPart_SeqAIJ,
3145:                                         MatImaginaryPart_SeqAIJ,
3146:                                         0,
3147:                                         0,
3148:                                 /*109*/ MatMatSolve_SeqAIJ,
3149:                                         0,
3150:                                         MatGetRowMin_SeqAIJ,
3151:                                         0,
3152:                                         MatMissingDiagonal_SeqAIJ,
3153:                                 /*114*/ 0,
3154:                                         0,
3155:                                         0,
3156:                                         0,
3157:                                         0,
3158:                                 /*119*/ 0,
3159:                                         0,
3160:                                         0,
3161:                                         0,
3162:                                         MatGetMultiProcBlock_SeqAIJ,
3163:                                 /*124*/ MatFindNonzeroRows_SeqAIJ,
3164:                                         MatGetColumnNorms_SeqAIJ,
3165:                                         MatInvertBlockDiagonal_SeqAIJ,
3166:                                         0,
3167:                                         0,
3168:                                 /*129*/ 0,
3169:                                         MatTransposeMatMult_SeqAIJ_SeqAIJ,
3170:                                         MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3171:                                         MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3172:                                         MatTransposeColoringCreate_SeqAIJ,
3173:                                 /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3174:                                         MatTransColoringApplyDenToSp_SeqAIJ,
3175:                                         MatRARt_SeqAIJ_SeqAIJ,
3176:                                         MatRARtSymbolic_SeqAIJ_SeqAIJ,
3177:                                         MatRARtNumeric_SeqAIJ_SeqAIJ,
3178:                                  /*139*/0,
3179:                                         0
3180: };

3184: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3185: {
3186:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3187:   PetscInt   i,nz,n;

3190:   nz = aij->maxnz;
3191:   n  = mat->rmap->n;
3192:   for (i=0; i<nz; i++) {
3193:     aij->j[i] = indices[i];
3194:   }
3195:   aij->nz = nz;
3196:   for (i=0; i<n; i++) {
3197:     aij->ilen[i] = aij->imax[i];
3198:   }
3199:   return(0);
3200: }

3204: /*@
3205:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3206:        in the matrix.

3208:   Input Parameters:
3209: +  mat - the SeqAIJ matrix
3210: -  indices - the column indices

3212:   Level: advanced

3214:   Notes:
3215:     This can be called if you have precomputed the nonzero structure of the
3216:   matrix and want to provide it to the matrix object to improve the performance
3217:   of the MatSetValues() operation.

3219:     You MUST have set the correct numbers of nonzeros per row in the call to
3220:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

3222:     MUST be called before any calls to MatSetValues();

3224:     The indices should start with zero, not one.

3226: @*/
3227: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3228: {

3234:   PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3235:   return(0);
3236: }

3238: /* ----------------------------------------------------------------------------------------*/

3242: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
3243: {
3244:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3246:   size_t         nz = aij->i[mat->rmap->n];

3249:   if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");

3251:   /* allocate space for values if not already there */
3252:   if (!aij->saved_values) {
3253:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
3254:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
3255:   }

3257:   /* copy values over */
3258:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3259:   return(0);
3260: }

3264: /*@
3265:     MatStoreValues - Stashes a copy of the matrix values; this allows, for
3266:        example, reuse of the linear part of a Jacobian, while recomputing the
3267:        nonlinear portion.

3269:    Collect on Mat

3271:   Input Parameters:
3272: .  mat - the matrix (currently only AIJ matrices support this option)

3274:   Level: advanced

3276:   Common Usage, with SNESSolve():
3277: $    Create Jacobian matrix
3278: $    Set linear terms into matrix
3279: $    Apply boundary conditions to matrix, at this time matrix must have
3280: $      final nonzero structure (i.e. setting the nonlinear terms and applying
3281: $      boundary conditions again will not change the nonzero structure
3282: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3283: $    MatStoreValues(mat);
3284: $    Call SNESSetJacobian() with matrix
3285: $    In your Jacobian routine
3286: $      MatRetrieveValues(mat);
3287: $      Set nonlinear terms in matrix

3289:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3290: $    // build linear portion of Jacobian
3291: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3292: $    MatStoreValues(mat);
3293: $    loop over nonlinear iterations
3294: $       MatRetrieveValues(mat);
3295: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3296: $       // call MatAssemblyBegin/End() on matrix
3297: $       Solve linear system with Jacobian
3298: $    endloop

3300:   Notes:
3301:     Matrix must already be assemblied before calling this routine
3302:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3303:     calling this routine.

3305:     When this is called multiple times it overwrites the previous set of stored values
3306:     and does not allocated additional space.

3308: .seealso: MatRetrieveValues()

3310: @*/
3311: PetscErrorCode  MatStoreValues(Mat mat)
3312: {

3317:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3318:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3319:   PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3320:   return(0);
3321: }

3325: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
3326: {
3327:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*)mat->data;
3329:   PetscInt       nz = aij->i[mat->rmap->n];

3332:   if (aij->nonew != 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3333:   if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3334:   /* copy values over */
3335:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3336:   return(0);
3337: }

3341: /*@
3342:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3343:        example, reuse of the linear part of a Jacobian, while recomputing the
3344:        nonlinear portion.

3346:    Collect on Mat

3348:   Input Parameters:
3349: .  mat - the matrix (currently on AIJ matrices support this option)

3351:   Level: advanced

3353: .seealso: MatStoreValues()

3355: @*/
3356: PetscErrorCode  MatRetrieveValues(Mat mat)
3357: {

3362:   if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3363:   if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3364:   PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3365:   return(0);
3366: }


3369: /* --------------------------------------------------------------------------------*/
3372: /*@C
3373:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3374:    (the default parallel PETSc format).  For good matrix assembly performance
3375:    the user should preallocate the matrix storage by setting the parameter nz
3376:    (or the array nnz).  By setting these parameters accurately, performance
3377:    during matrix assembly can be increased by more than a factor of 50.

3379:    Collective on MPI_Comm

3381:    Input Parameters:
3382: +  comm - MPI communicator, set to PETSC_COMM_SELF
3383: .  m - number of rows
3384: .  n - number of columns
3385: .  nz - number of nonzeros per row (same for all rows)
3386: -  nnz - array containing the number of nonzeros in the various rows
3387:          (possibly different for each row) or NULL

3389:    Output Parameter:
3390: .  A - the matrix

3392:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3393:    MatXXXXSetPreallocation() paradgm instead of this routine directly.
3394:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

3396:    Notes:
3397:    If nnz is given then nz is ignored

3399:    The AIJ format (also called the Yale sparse matrix format or
3400:    compressed row storage), is fully compatible with standard Fortran 77
3401:    storage.  That is, the stored row and column indices can begin at
3402:    either one (as in Fortran) or zero.  See the users' manual for details.

3404:    Specify the preallocated storage with either nz or nnz (not both).
3405:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3406:    allocation.  For large problems you MUST preallocate memory or you
3407:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3409:    By default, this format uses inodes (identical nodes) when possible, to
3410:    improve numerical efficiency of matrix-vector products and solves. We
3411:    search for consecutive rows with the same nonzero structure, thereby
3412:    reusing matrix information to achieve increased efficiency.

3414:    Options Database Keys:
3415: +  -mat_no_inode  - Do not use inodes
3416: -  -mat_inode_limit <limit> - Sets inode limit (max limit=5)

3418:    Level: intermediate

3420: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

3422: @*/
3423: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3424: {

3428:   MatCreate(comm,A);
3429:   MatSetSizes(*A,m,n,m,n);
3430:   MatSetType(*A,MATSEQAIJ);
3431:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3432:   return(0);
3433: }

3437: /*@C
3438:    MatSeqAIJSetPreallocation - For good matrix assembly performance
3439:    the user should preallocate the matrix storage by setting the parameter nz
3440:    (or the array nnz).  By setting these parameters accurately, performance
3441:    during matrix assembly can be increased by more than a factor of 50.

3443:    Collective on MPI_Comm

3445:    Input Parameters:
3446: +  B - The matrix-free
3447: .  nz - number of nonzeros per row (same for all rows)
3448: -  nnz - array containing the number of nonzeros in the various rows
3449:          (possibly different for each row) or NULL

3451:    Notes:
3452:      If nnz is given then nz is ignored

3454:     The AIJ format (also called the Yale sparse matrix format or
3455:    compressed row storage), is fully compatible with standard Fortran 77
3456:    storage.  That is, the stored row and column indices can begin at
3457:    either one (as in Fortran) or zero.  See the users' manual for details.

3459:    Specify the preallocated storage with either nz or nnz (not both).
3460:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3461:    allocation.  For large problems you MUST preallocate memory or you
3462:    will get TERRIBLE performance, see the users' manual chapter on matrices.

3464:    You can call MatGetInfo() to get information on how effective the preallocation was;
3465:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3466:    You can also run with the option -info and look for messages with the string
3467:    malloc in them to see if additional memory allocation was needed.

3469:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3470:    entries or columns indices

3472:    By default, this format uses inodes (identical nodes) when possible, to
3473:    improve numerical efficiency of matrix-vector products and solves. We
3474:    search for consecutive rows with the same nonzero structure, thereby
3475:    reusing matrix information to achieve increased efficiency.

3477:    Options Database Keys:
3478: +  -mat_no_inode  - Do not use inodes
3479: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3480: -  -mat_aij_oneindex - Internally use indexing starting at 1
3481:         rather than 0.  Note that when calling MatSetValues(),
3482:         the user still MUST index entries starting at 0!

3484:    Level: intermediate

3486: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

3488: @*/
3489: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3490: {

3496:   PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3497:   return(0);
3498: }

3502: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3503: {
3504:   Mat_SeqAIJ     *b;
3505:   PetscBool      skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3507:   PetscInt       i;

3510:   if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3511:   if (nz == MAT_SKIP_ALLOCATION) {
3512:     skipallocation = PETSC_TRUE;
3513:     nz             = 0;
3514:   }

3516:   PetscLayoutSetUp(B->rmap);
3517:   PetscLayoutSetUp(B->cmap);

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

3528:   B->preallocated = PETSC_TRUE;

3530:   b = (Mat_SeqAIJ*)B->data;

3532:   if (!skipallocation) {
3533:     if (!b->imax) {
3534:       PetscMalloc2(B->rmap->n,PetscInt,&b->imax,B->rmap->n,PetscInt,&b->ilen);
3535:       PetscLogObjectMemory(B,2*B->rmap->n*sizeof(PetscInt));
3536:     }
3537:     if (!nnz) {
3538:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3539:       else if (nz < 0) nz = 1;
3540:       for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3541:       nz = nz*B->rmap->n;
3542:     } else {
3543:       nz = 0;
3544:       for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3545:     }
3546:     /* b->ilen will count nonzeros in each row so far. */
3547:     for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;

3549:     /* allocate the matrix space */
3550:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3551:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap->n+1,PetscInt,&b->i);
3552:     PetscLogObjectMemory(B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3553:     b->i[0] = 0;
3554:     for (i=1; i<B->rmap->n+1; i++) {
3555:       b->i[i] = b->i[i-1] + b->imax[i-1];
3556:     }
3557:     b->singlemalloc = PETSC_TRUE;
3558:     b->free_a       = PETSC_TRUE;
3559:     b->free_ij      = PETSC_TRUE;
3560: #if defined(PETSC_THREADCOMM_ACTIVE)
3561:     MatZeroEntries_SeqAIJ(B);
3562: #endif
3563:   } else {
3564:     b->free_a  = PETSC_FALSE;
3565:     b->free_ij = PETSC_FALSE;
3566:   }

3568:   b->nz               = 0;
3569:   b->maxnz            = nz;
3570:   B->info.nz_unneeded = (double)b->maxnz;
3571:   if (realalloc) {
3572:     MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3573:   }
3574:   return(0);
3575: }

3577: #undef  __FUNCT__
3579: /*@
3580:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.

3582:    Input Parameters:
3583: +  B - the matrix
3584: .  i - the indices into j for the start of each row (starts with zero)
3585: .  j - the column indices for each row (starts with zero) these must be sorted for each row
3586: -  v - optional values in the matrix

3588:    Level: developer

3590:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

3592: .keywords: matrix, aij, compressed row, sparse, sequential

3594: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3595: @*/
3596: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3597: {

3603:   PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3604:   return(0);
3605: }

3607: #undef  __FUNCT__
3609: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3610: {
3611:   PetscInt       i;
3612:   PetscInt       m,n;
3613:   PetscInt       nz;
3614:   PetscInt       *nnz, nz_max = 0;
3615:   PetscScalar    *values;

3619:   if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);

3621:   PetscLayoutSetUp(B->rmap);
3622:   PetscLayoutSetUp(B->cmap);

3624:   MatGetSize(B, &m, &n);
3625:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
3626:   for (i = 0; i < m; i++) {
3627:     nz     = Ii[i+1]- Ii[i];
3628:     nz_max = PetscMax(nz_max, nz);
3629:     if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3630:     nnz[i] = nz;
3631:   }
3632:   MatSeqAIJSetPreallocation(B, 0, nnz);
3633:   PetscFree(nnz);

3635:   if (v) {
3636:     values = (PetscScalar*) v;
3637:   } else {
3638:     PetscMalloc(nz_max*sizeof(PetscScalar), &values);
3639:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
3640:   }

3642:   for (i = 0; i < m; i++) {
3643:     nz   = Ii[i+1] - Ii[i];
3644:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3645:   }

3647:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3648:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

3650:   if (!v) {
3651:     PetscFree(values);
3652:   }
3653:   MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3654:   return(0);
3655: }

3657: #include <../src/mat/impls/dense/seq/dense.h>
3658: #include <petsc-private/kernels/petscaxpy.h>

3662: /*
3663:     Computes (B'*A')' since computing B*A directly is untenable

3665:                n                       p                          p
3666:         (              )       (              )         (                  )
3667:       m (      A       )  *  n (       B      )   =   m (         C        )
3668:         (              )       (              )         (                  )

3670: */
3671: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3672: {
3673:   PetscErrorCode    ierr;
3674:   Mat_SeqDense      *sub_a = (Mat_SeqDense*)A->data;
3675:   Mat_SeqAIJ        *sub_b = (Mat_SeqAIJ*)B->data;
3676:   Mat_SeqDense      *sub_c = (Mat_SeqDense*)C->data;
3677:   PetscInt          i,n,m,q,p;
3678:   const PetscInt    *ii,*idx;
3679:   const PetscScalar *b,*a,*a_q;
3680:   PetscScalar       *c,*c_q;

3683:   m    = A->rmap->n;
3684:   n    = A->cmap->n;
3685:   p    = B->cmap->n;
3686:   a    = sub_a->v;
3687:   b    = sub_b->a;
3688:   c    = sub_c->v;
3689:   PetscMemzero(c,m*p*sizeof(PetscScalar));

3691:   ii  = sub_b->i;
3692:   idx = sub_b->j;
3693:   for (i=0; i<n; i++) {
3694:     q = ii[i+1] - ii[i];
3695:     while (q-->0) {
3696:       c_q = c + m*(*idx);
3697:       a_q = a + m*i;
3698:       PetscKernelAXPY(c_q,*b,a_q,m);
3699:       idx++;
3700:       b++;
3701:     }
3702:   }
3703:   return(0);
3704: }

3708: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3709: {
3711:   PetscInt       m=A->rmap->n,n=B->cmap->n;
3712:   Mat            Cmat;

3715:   if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %d != B->rmap->n %d\n",A->cmap->n,B->rmap->n);
3716:   MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
3717:   MatSetSizes(Cmat,m,n,m,n);
3718:   MatSetBlockSizes(Cmat,A->rmap->bs,B->cmap->bs);
3719:   MatSetType(Cmat,MATSEQDENSE);
3720:   MatSeqDenseSetPreallocation(Cmat,NULL);

3722:   Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;

3724:   *C = Cmat;
3725:   return(0);
3726: }

3728: /* ----------------------------------------------------------------*/
3731: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3732: {

3736:   if (scall == MAT_INITIAL_MATRIX) {
3737:     PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
3738:     MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3739:     PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
3740:   }
3741:   PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
3742:   MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3743:   PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
3744:   return(0);
3745: }


3748: /*MC
3749:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
3750:    based on compressed sparse row format.

3752:    Options Database Keys:
3753: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3755:   Level: beginner

3757: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3758: M*/

3760: /*MC
3761:    MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.

3763:    This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
3764:    and MATMPIAIJ otherwise.  As a result, for single process communicators,
3765:   MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
3766:   for communicators controlling multiple processes.  It is recommended that you call both of
3767:   the above preallocation routines for simplicity.

3769:    Options Database Keys:
3770: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()

3772:   Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
3773:    enough exist.

3775:   Level: beginner

3777: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
3778: M*/

3780: /*MC
3781:    MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.

3783:    This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
3784:    and MATMPIAIJCRL otherwise.  As a result, for single process communicators,
3785:    MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
3786:   for communicators controlling multiple processes.  It is recommended that you call both of
3787:   the above preallocation routines for simplicity.

3789:    Options Database Keys:
3790: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()

3792:   Level: beginner

3794: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
3795: M*/

3797: #if defined(PETSC_HAVE_PASTIX)
3798: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_pastix(Mat,MatFactorType,Mat*);
3799: #endif
3800: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
3801: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_essl(Mat,MatFactorType,Mat*);
3802: #endif
3803: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
3804: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_petsc(Mat,MatFactorType,Mat*);
3805: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_bas(Mat,MatFactorType,Mat*);
3806: extern PetscErrorCode  MatGetFactorAvailable_seqaij_petsc(Mat,MatFactorType,PetscBool*);
3807: #if defined(PETSC_HAVE_MUMPS)
3808: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_mumps(Mat,MatFactorType,Mat*);
3809: #endif
3810: #if defined(PETSC_HAVE_SUPERLU)
3811: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_superlu(Mat,MatFactorType,Mat*);
3812: #endif
3813: #if defined(PETSC_HAVE_SUPERLU_DIST)
3814: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_superlu_dist(Mat,MatFactorType,Mat*);
3815: #endif
3816: #if defined(PETSC_HAVE_UMFPACK)
3817: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_umfpack(Mat,MatFactorType,Mat*);
3818: #endif
3819: #if defined(PETSC_HAVE_CHOLMOD)
3820: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_cholmod(Mat,MatFactorType,Mat*);
3821: #endif
3822: #if defined(PETSC_HAVE_LUSOL)
3823: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_lusol(Mat,MatFactorType,Mat*);
3824: #endif
3825: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3826: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_matlab(Mat,MatFactorType,Mat*);
3827: extern PetscErrorCode  MatlabEnginePut_SeqAIJ(PetscObject,void*);
3828: extern PetscErrorCode  MatlabEngineGet_SeqAIJ(PetscObject,void*);
3829: #endif
3830: #if defined(PETSC_HAVE_CLIQUE)
3831: PETSC_EXTERN PetscErrorCode MatGetFactor_aij_clique(Mat,MatFactorType,Mat*);
3832: #endif


3837: /*@C
3838:    MatSeqAIJGetArray - gives access to the array where the data for a SeqSeqAIJ matrix is stored

3840:    Not Collective

3842:    Input Parameter:
3843: .  mat - a MATSEQDENSE matrix

3845:    Output Parameter:
3846: .   array - pointer to the data

3848:    Level: intermediate

3850: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3851: @*/
3852: PetscErrorCode  MatSeqAIJGetArray(Mat A,PetscScalar **array)
3853: {

3857:   PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
3858:   return(0);
3859: }

3863: /*@C
3864:    MatSeqAIJRestoreArray - returns access to the array where the data for a SeqSeqAIJ matrix is stored obtained by MatSeqAIJGetArray()

3866:    Not Collective

3868:    Input Parameters:
3869: .  mat - a MATSEQDENSE matrix
3870: .  array - pointer to the data

3872:    Level: intermediate

3874: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
3875: @*/
3876: PetscErrorCode  MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
3877: {

3881:   PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
3882:   return(0);
3883: }

3887: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
3888: {
3889:   Mat_SeqAIJ     *b;
3891:   PetscMPIInt    size;

3894:   MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
3895:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3897:   PetscNewLog(B,Mat_SeqAIJ,&b);

3899:   B->data = (void*)b;

3901:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));

3903:   b->row                = 0;
3904:   b->col                = 0;
3905:   b->icol               = 0;
3906:   b->reallocs           = 0;
3907:   b->ignorezeroentries  = PETSC_FALSE;
3908:   b->roworiented        = PETSC_TRUE;
3909:   b->nonew              = 0;
3910:   b->diag               = 0;
3911:   b->solve_work         = 0;
3912:   B->spptr              = 0;
3913:   b->saved_values       = 0;
3914:   b->idiag              = 0;
3915:   b->mdiag              = 0;
3916:   b->ssor_work          = 0;
3917:   b->omega              = 1.0;
3918:   b->fshift             = 0.0;
3919:   b->idiagvalid         = PETSC_FALSE;
3920:   b->ibdiagvalid        = PETSC_FALSE;
3921:   b->keepnonzeropattern = PETSC_FALSE;
3922:   b->xtoy               = 0;
3923:   b->XtoY               = 0;
3924:   B->same_nonzero       = PETSC_FALSE;

3926:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3927:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
3928:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);

3930: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3931:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_matlab_C",MatGetFactor_seqaij_matlab);
3932:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
3933:   PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
3934: #endif
3935: #if defined(PETSC_HAVE_PASTIX)
3936:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_pastix_C",MatGetFactor_seqaij_pastix);
3937: #endif
3938: #if defined(PETSC_HAVE_ESSL) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_REAL_SINGLE) && !defined(PETSC_USE_REAL___FLOAT128)
3939:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_essl_C",MatGetFactor_seqaij_essl);
3940: #endif
3941: #if defined(PETSC_HAVE_SUPERLU)
3942:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_C",MatGetFactor_seqaij_superlu);
3943: #endif
3944: #if defined(PETSC_HAVE_SUPERLU_DIST)
3945:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_superlu_dist_C",MatGetFactor_seqaij_superlu_dist);
3946: #endif
3947: #if defined(PETSC_HAVE_MUMPS)
3948:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_mumps_C",MatGetFactor_aij_mumps);
3949: #endif
3950: #if defined(PETSC_HAVE_UMFPACK)
3951:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_umfpack_C",MatGetFactor_seqaij_umfpack);
3952: #endif
3953: #if defined(PETSC_HAVE_CHOLMOD)
3954:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_cholmod_C",MatGetFactor_seqaij_cholmod);
3955: #endif
3956: #if defined(PETSC_HAVE_LUSOL)
3957:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_lusol_C",MatGetFactor_seqaij_lusol);
3958: #endif
3959: #if defined(PETSC_HAVE_CLIQUE)
3960:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_clique_C",MatGetFactor_aij_clique);
3961: #endif

3963:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_petsc_C",MatGetFactor_seqaij_petsc);
3964:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactorAvailable_petsc_C",MatGetFactorAvailable_seqaij_petsc);
3965:   PetscObjectComposeFunction((PetscObject)B,"MatGetFactor_bas_C",MatGetFactor_seqaij_bas);
3966:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
3967:   PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
3968:   PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
3969:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
3970:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
3971:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
3972:   PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
3973:   PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
3974:   PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
3975:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
3976:   PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
3977:   PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
3978:   PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
3979:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
3980:   PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
3981:   MatCreate_SeqAIJ_Inode(B);
3982:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3983:   return(0);
3984: }

3988: /*
3989:     Given a matrix generated with MatGetFactor() duplicates all the information in A into B
3990: */
3991: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
3992: {
3993:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3995:   PetscInt       i,m = A->rmap->n;

3998:   c = (Mat_SeqAIJ*)C->data;

4000:   C->factortype = A->factortype;
4001:   c->row        = 0;
4002:   c->col        = 0;
4003:   c->icol       = 0;
4004:   c->reallocs   = 0;

4006:   C->assembled = PETSC_TRUE;

4008:   PetscLayoutReference(A->rmap,&C->rmap);
4009:   PetscLayoutReference(A->cmap,&C->cmap);

4011:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
4012:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
4013:   for (i=0; i<m; i++) {
4014:     c->imax[i] = a->imax[i];
4015:     c->ilen[i] = a->ilen[i];
4016:   }

4018:   /* allocate the matrix space */
4019:   if (mallocmatspace) {
4020:     PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
4021:     PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));

4023:     c->singlemalloc = PETSC_TRUE;

4025:     PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4026:     if (m > 0) {
4027:       PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4028:       if (cpvalues == MAT_COPY_VALUES) {
4029:         PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4030:       } else {
4031:         PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4032:       }
4033:     }
4034:   }

4036:   c->ignorezeroentries = a->ignorezeroentries;
4037:   c->roworiented       = a->roworiented;
4038:   c->nonew             = a->nonew;
4039:   if (a->diag) {
4040:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
4041:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
4042:     for (i=0; i<m; i++) {
4043:       c->diag[i] = a->diag[i];
4044:     }
4045:   } else c->diag = 0;

4047:   c->solve_work         = 0;
4048:   c->saved_values       = 0;
4049:   c->idiag              = 0;
4050:   c->ssor_work          = 0;
4051:   c->keepnonzeropattern = a->keepnonzeropattern;
4052:   c->free_a             = PETSC_TRUE;
4053:   c->free_ij            = PETSC_TRUE;
4054:   c->xtoy               = 0;
4055:   c->XtoY               = 0;

4057:   c->rmax         = a->rmax;
4058:   c->nz           = a->nz;
4059:   c->maxnz        = a->nz;       /* Since we allocate exactly the right amount */
4060:   C->preallocated = PETSC_TRUE;

4062:   c->compressedrow.use   = a->compressedrow.use;
4063:   c->compressedrow.nrows = a->compressedrow.nrows;
4064:   c->compressedrow.check = a->compressedrow.check;
4065:   if (a->compressedrow.use) {
4066:     i    = a->compressedrow.nrows;
4067:     PetscMalloc2(i+1,PetscInt,&c->compressedrow.i,i,PetscInt,&c->compressedrow.rindex);
4068:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4069:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4070:   } else {
4071:     c->compressedrow.use    = PETSC_FALSE;
4072:     c->compressedrow.i      = NULL;
4073:     c->compressedrow.rindex = NULL;
4074:   }
4075:   C->same_nonzero = A->same_nonzero;

4077:   MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4078:   PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4079:   return(0);
4080: }

4084: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4085: {

4089:   MatCreate(PetscObjectComm((PetscObject)A),B);
4090:   MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4091:   MatSetBlockSizes(*B,A->rmap->bs,A->cmap->bs);
4092:   MatSetType(*B,((PetscObject)A)->type_name);
4093:   MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4094:   return(0);
4095: }

4099: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4100: {
4101:   Mat_SeqAIJ     *a;
4103:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4104:   int            fd;
4105:   PetscMPIInt    size;
4106:   MPI_Comm       comm;
4107:   PetscInt       bs = 1;

4110:   PetscObjectGetComm((PetscObject)viewer,&comm);
4111:   MPI_Comm_size(comm,&size);
4112:   if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");

4114:   PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4115:   PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4116:   PetscOptionsEnd();
4117:   if (bs > 1) {MatSetBlockSize(newMat,bs);}

4119:   PetscViewerBinaryGetDescriptor(viewer,&fd);
4120:   PetscBinaryRead(fd,header,4,PETSC_INT);
4121:   if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4122:   M = header[1]; N = header[2]; nz = header[3];

4124:   if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");

4126:   /* read in row lengths */
4127:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
4128:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

4130:   /* check if sum of rowlengths is same as nz */
4131:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4132:   if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

4134:   /* set global size if not set already*/
4135:   if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4136:     MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4137:   } else {
4138:     /* if sizes and type are already set, check if the vector global sizes are correct */
4139:     MatGetSize(newMat,&rows,&cols);
4140:     if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4141:       MatGetLocalSize(newMat,&rows,&cols);
4142:     }
4143:     if (M != rows ||  N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%d, %d) than the input matrix (%d, %d)",M,N,rows,cols);
4144:   }
4145:   MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4146:   a    = (Mat_SeqAIJ*)newMat->data;

4148:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

4150:   /* read in nonzero values */
4151:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

4153:   /* set matrix "i" values */
4154:   a->i[0] = 0;
4155:   for (i=1; i<= M; i++) {
4156:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
4157:     a->ilen[i-1] = rowlengths[i-1];
4158:   }
4159:   PetscFree(rowlengths);

4161:   MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4162:   MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4163:   return(0);
4164: }

4168: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4169: {
4170:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4172: #if defined(PETSC_USE_COMPLEX)
4173:   PetscInt k;
4174: #endif

4177:   /* If the  matrix dimensions are not equal,or no of nonzeros */
4178:   if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4179:     *flg = PETSC_FALSE;
4180:     return(0);
4181:   }

4183:   /* if the a->i are the same */
4184:   PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4185:   if (!*flg) return(0);

4187:   /* if a->j are the same */
4188:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4189:   if (!*flg) return(0);

4191:   /* if a->a are the same */
4192: #if defined(PETSC_USE_COMPLEX)
4193:   for (k=0; k<a->nz; k++) {
4194:     if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4195:       *flg = PETSC_FALSE;
4196:       return(0);
4197:     }
4198:   }
4199: #else
4200:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4201: #endif
4202:   return(0);
4203: }

4207: /*@
4208:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4209:               provided by the user.

4211:       Collective on MPI_Comm

4213:    Input Parameters:
4214: +   comm - must be an MPI communicator of size 1
4215: .   m - number of rows
4216: .   n - number of columns
4217: .   i - row indices
4218: .   j - column indices
4219: -   a - matrix values

4221:    Output Parameter:
4222: .   mat - the matrix

4224:    Level: intermediate

4226:    Notes:
4227:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
4228:     once the matrix is destroyed and not before

4230:        You cannot set new nonzero locations into this matrix, that will generate an error.

4232:        The i and j indices are 0 based

4234:        The format which is used for the sparse matrix input, is equivalent to a
4235:     row-major ordering.. i.e for the following matrix, the input data expected is
4236:     as shown:

4238:         1 0 0
4239:         2 0 3
4240:         4 5 6

4242:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
4243:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
4244:         v =  {1,2,3,4,5,6}  [size = nz = 6]


4247: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4249: @*/
4250: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat)
4251: {
4253:   PetscInt       ii;
4254:   Mat_SeqAIJ     *aij;
4255: #if defined(PETSC_USE_DEBUG)
4256:   PetscInt jj;
4257: #endif

4260:   if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4261:   MatCreate(comm,mat);
4262:   MatSetSizes(*mat,m,n,m,n);
4263:   /* MatSetBlockSizes(*mat,,); */
4264:   MatSetType(*mat,MATSEQAIJ);
4265:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4266:   aij  = (Mat_SeqAIJ*)(*mat)->data;
4267:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

4269:   aij->i            = i;
4270:   aij->j            = j;
4271:   aij->a            = a;
4272:   aij->singlemalloc = PETSC_FALSE;
4273:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4274:   aij->free_a       = PETSC_FALSE;
4275:   aij->free_ij      = PETSC_FALSE;

4277:   for (ii=0; ii<m; ii++) {
4278:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4279: #if defined(PETSC_USE_DEBUG)
4280:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
4281:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4282:       if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4283:       if (j[jj] == j[jj]-1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
4284:     }
4285: #endif
4286:   }
4287: #if defined(PETSC_USE_DEBUG)
4288:   for (ii=0; ii<aij->i[m]; ii++) {
4289:     if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
4290:     if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
4291:   }
4292: #endif

4294:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4295:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4296:   return(0);
4297: }
4300: /*@C
4301:      MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4302:               provided by the user.

4304:       Collective on MPI_Comm

4306:    Input Parameters:
4307: +   comm - must be an MPI communicator of size 1
4308: .   m   - number of rows
4309: .   n   - number of columns
4310: .   i   - row indices
4311: .   j   - column indices
4312: .   a   - matrix values
4313: .   nz  - number of nonzeros
4314: -   idx - 0 or 1 based

4316:    Output Parameter:
4317: .   mat - the matrix

4319:    Level: intermediate

4321:    Notes:
4322:        The i and j indices are 0 based

4324:        The format which is used for the sparse matrix input, is equivalent to a
4325:     row-major ordering.. i.e for the following matrix, the input data expected is
4326:     as shown:

4328:         1 0 0
4329:         2 0 3
4330:         4 5 6

4332:         i =  {0,1,1,2,2,2}
4333:         j =  {0,0,2,0,1,2}
4334:         v =  {1,2,3,4,5,6}


4337: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

4339: @*/
4340: PetscErrorCode  MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx)
4341: {
4343:   PetscInt       ii, *nnz, one = 1,row,col;


4347:   PetscMalloc(m*sizeof(PetscInt),&nnz);
4348:   PetscMemzero(nnz,m*sizeof(PetscInt));
4349:   for (ii = 0; ii < nz; ii++) {
4350:     nnz[i[ii] - !!idx] += 1;
4351:   }
4352:   MatCreate(comm,mat);
4353:   MatSetSizes(*mat,m,n,m,n);
4354:   MatSetType(*mat,MATSEQAIJ);
4355:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4356:   for (ii = 0; ii < nz; ii++) {
4357:     if (idx) {
4358:       row = i[ii] - 1;
4359:       col = j[ii] - 1;
4360:     } else {
4361:       row = i[ii];
4362:       col = j[ii];
4363:     }
4364:     MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4365:   }
4366:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4367:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4368:   PetscFree(nnz);
4369:   return(0);
4370: }

4374: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
4375: {
4377:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

4380:   if (coloring->ctype == IS_COLORING_GLOBAL) {
4381:     ISColoringReference(coloring);
4382:     a->coloring = coloring;
4383:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
4384:     PetscInt        i,*larray;
4385:     ISColoring      ocoloring;
4386:     ISColoringValue *colors;

4388:     /* set coloring for diagonal portion */
4389:     PetscMalloc(A->cmap->n*sizeof(PetscInt),&larray);
4390:     for (i=0; i<A->cmap->n; i++) larray[i] = i;
4391:     ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,NULL,larray);
4392:     PetscMalloc(A->cmap->n*sizeof(ISColoringValue),&colors);
4393:     for (i=0; i<A->cmap->n; i++) colors[i] = coloring->colors[larray[i]];
4394:     PetscFree(larray);
4395:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,&ocoloring);
4396:     a->coloring = ocoloring;
4397:   }
4398:   return(0);
4399: }

4403: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
4404: {
4405:   Mat_SeqAIJ      *a      = (Mat_SeqAIJ*)A->data;
4406:   PetscInt        m       = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
4407:   MatScalar       *v      = a->a;
4408:   PetscScalar     *values = (PetscScalar*)advalues;
4409:   ISColoringValue *color;

4412:   if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4413:   color = a->coloring->colors;
4414:   /* loop over rows */
4415:   for (i=0; i<m; i++) {
4416:     nz = ii[i+1] - ii[i];
4417:     /* loop over columns putting computed value into matrix */
4418:     for (j=0; j<nz; j++) *v++ = values[color[*jj++]];
4419:     values += nl; /* jump to next row of derivatives */
4420:   }
4421:   return(0);
4422: }

4426: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4427: {
4428:   Mat_SeqAIJ     *a=(Mat_SeqAIJ*)A->data;

4432:   a->idiagvalid  = PETSC_FALSE;
4433:   a->ibdiagvalid = PETSC_FALSE;

4435:   MatSeqAIJInvalidateDiagonal_Inode(A);
4436:   return(0);
4437: }

4439: /*
4440:     Special version for direct calls from Fortran
4441: */
4442: #include <petsc-private/fortranimpl.h>
4443: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4444: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4445: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4446: #define matsetvaluesseqaij_ matsetvaluesseqaij
4447: #endif

4449: /* Change these macros so can be used in void function */
4450: #undef CHKERRQ
4451: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4452: #undef SETERRQ2
4453: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4454: #undef SETERRQ3
4455: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)

4459: PETSC_EXTERN void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
4460: {
4461:   Mat            A  = *AA;
4462:   PetscInt       m  = *mm, n = *nn;
4463:   InsertMode     is = *isis;
4464:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
4465:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4466:   PetscInt       *imax,*ai,*ailen;
4468:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
4469:   MatScalar      *ap,value,*aa;
4470:   PetscBool      ignorezeroentries = a->ignorezeroentries;
4471:   PetscBool      roworiented       = a->roworiented;

4474:   MatCheckPreallocated(A,1);
4475:   imax  = a->imax;
4476:   ai    = a->i;
4477:   ailen = a->ilen;
4478:   aj    = a->j;
4479:   aa    = a->a;

4481:   for (k=0; k<m; k++) { /* loop over added rows */
4482:     row = im[k];
4483:     if (row < 0) continue;
4484: #if defined(PETSC_USE_DEBUG)
4485:     if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4486: #endif
4487:     rp   = aj + ai[row]; ap = aa + ai[row];
4488:     rmax = imax[row]; nrow = ailen[row];
4489:     low  = 0;
4490:     high = nrow;
4491:     for (l=0; l<n; l++) { /* loop over added columns */
4492:       if (in[l] < 0) continue;
4493: #if defined(PETSC_USE_DEBUG)
4494:       if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4495: #endif
4496:       col = in[l];
4497:       if (roworiented) value = v[l + k*n];
4498:       else value = v[k + l*m];

4500:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

4502:       if (col <= lastcol) low = 0;
4503:       else high = nrow;
4504:       lastcol = col;
4505:       while (high-low > 5) {
4506:         t = (low+high)/2;
4507:         if (rp[t] > col) high = t;
4508:         else             low  = t;
4509:       }
4510:       for (i=low; i<high; i++) {
4511:         if (rp[i] > col) break;
4512:         if (rp[i] == col) {
4513:           if (is == ADD_VALUES) ap[i] += value;
4514:           else                  ap[i] = value;
4515:           goto noinsert;
4516:         }
4517:       }
4518:       if (value == 0.0 && ignorezeroentries) goto noinsert;
4519:       if (nonew == 1) goto noinsert;
4520:       if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4521:       MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4522:       N = nrow++ - 1; a->nz++; high++;
4523:       /* shift up all the later entries in this row */
4524:       for (ii=N; ii>=i; ii--) {
4525:         rp[ii+1] = rp[ii];
4526:         ap[ii+1] = ap[ii];
4527:       }
4528:       rp[i] = col;
4529:       ap[i] = value;
4530: noinsert:;
4531:       low = i + 1;
4532:     }
4533:     ailen[row] = nrow;
4534:   }
4535:   A->same_nonzero = PETSC_FALSE;
4536:   PetscFunctionReturnVoid();
4537: }