Actual source code: aij.c

  1: /*$Id: aij.c,v 1.385 2001/09/07 20:09:22 bsmith Exp $*/
  2: /*
  3:     Defines the basic matrix operations for the AIJ (compressed row)
  4:   matrix storage format.
  5: */

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


 14: EXTERN int MatToSymmetricIJ_SeqAIJ(int,int*,int*,int,int,int**,int**);

 16: #undef __FUNCT__  
 18: int MatGetRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *m,int **ia,int **ja,PetscTruth *done)
 19: {
 20:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 21:   int        ierr,i,ishift;
 22: 
 24:   *m     = A->m;
 25:   if (!ia) return(0);
 26:   ishift = a->indexshift;
 27:   if (symmetric && !A->structurally_symmetric) {
 28:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
 29:   } else if (oshift == 0 && ishift == -1) {
 30:     int nz = a->i[A->m] - 1;
 31:     /* malloc space and  subtract 1 from i and j indices */
 32:     PetscMalloc((A->m+1)*sizeof(int),ia);
 33:     PetscMalloc((nz+1)*sizeof(int),ja);
 34:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] - 1;
 35:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] - 1;
 36:   } else if (oshift == 1 && ishift == 0) {
 37:     int nz = a->i[A->m];
 38:     /* malloc space and  add 1 to i and j indices */
 39:     PetscMalloc((A->m+1)*sizeof(int),ia);
 40:     PetscMalloc((nz+1)*sizeof(int),ja);
 41:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 42:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1;
 43:   } else {
 44:     *ia = a->i; *ja = a->j;
 45:   }
 46:   return(0);
 47: }

 49: #undef __FUNCT__  
 51: int MatRestoreRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int **ia,int **ja,PetscTruth *done)
 52: {
 53:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 54:   int        ishift = a->indexshift,ierr;
 55: 
 57:   if (!ia) return(0);
 58:   if ((symmetric && !A->structurally_symmetric) || (oshift == 0 && ishift == -1) || (oshift == 1 && ishift == 0)) {
 59:     PetscFree(*ia);
 60:     PetscFree(*ja);
 61:   }
 62:   return(0);
 63: }

 65: #undef __FUNCT__  
 67: int MatGetColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *nn,int **ia,int **ja,PetscTruth *done)
 68: {
 69:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 70:   int        ierr,i,ishift = a->indexshift,*collengths,*cia,*cja,n = A->n,m = A->m;
 71:   int        nz = a->i[m]+ishift,row,*jj,mr,col;
 72: 
 74:   *nn     = A->n;
 75:   if (!ia) return(0);
 76:   if (symmetric) {
 77:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
 78:   } else {
 79:     PetscMalloc((n+1)*sizeof(int),&collengths);
 80:     PetscMemzero(collengths,n*sizeof(int));
 81:     PetscMalloc((n+1)*sizeof(int),&cia);
 82:     PetscMalloc((nz+1)*sizeof(int),&cja);
 83:     jj = a->j;
 84:     for (i=0; i<nz; i++) {
 85:       collengths[jj[i] + ishift]++;
 86:     }
 87:     cia[0] = oshift;
 88:     for (i=0; i<n; i++) {
 89:       cia[i+1] = cia[i] + collengths[i];
 90:     }
 91:     PetscMemzero(collengths,n*sizeof(int));
 92:     jj   = a->j;
 93:     for (row=0; row<m; row++) {
 94:       mr = a->i[row+1] - a->i[row];
 95:       for (i=0; i<mr; i++) {
 96:         col = *jj++ + ishift;
 97:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
 98:       }
 99:     }
100:     PetscFree(collengths);
101:     *ia = cia; *ja = cja;
102:   }
103:   return(0);
104: }

106: #undef __FUNCT__  
108: int MatRestoreColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int **ia,int **ja,PetscTruth *done)
109: {

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

115:   PetscFree(*ia);
116:   PetscFree(*ja);
117: 
118:   return(0);
119: }

121: #define CHUNKSIZE   15

123: #undef __FUNCT__  
125: int MatSetValues_SeqAIJ(Mat A,int m,int *im,int n,int *in,PetscScalar *v,InsertMode is)
126: {
127:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
128:   int         *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,sorted = a->sorted;
129:   int         *imax = a->imax,*ai = a->i,*ailen = a->ilen;
130:   int         *aj = a->j,nonew = a->nonew,shift = a->indexshift,ierr;
131:   PetscScalar *ap,value,*aa = a->a;
132:   PetscTruth  ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE);
133:   PetscTruth  roworiented = a->roworiented;

136:   for (k=0; k<m; k++) { /* loop over added rows */
137:     row  = im[k];
138:     if (row < 0) continue;
139: #if defined(PETSC_USE_BOPT_g)  
140:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m);
141: #endif
142:     rp   = aj + ai[row] + shift; ap = aa + ai[row] + shift;
143:     rmax = imax[row]; nrow = ailen[row];
144:     low = 0;
145:     for (l=0; l<n; l++) { /* loop over added columns */
146:       if (in[l] < 0) continue;
147: #if defined(PETSC_USE_BOPT_g)  
148:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n);
149: #endif
150:       col = in[l] - shift;
151:       if (roworiented) {
152:         value = v[l + k*n];
153:       } else {
154:         value = v[k + l*m];
155:       }
156:       if (value == 0.0 && ignorezeroentries) continue;

158:       if (!sorted) low = 0; high = nrow;
159:       while (high-low > 5) {
160:         t = (low+high)/2;
161:         if (rp[t] > col) high = t;
162:         else             low  = t;
163:       }
164:       for (i=low; i<high; i++) {
165:         if (rp[i] > col) break;
166:         if (rp[i] == col) {
167:           if (is == ADD_VALUES) ap[i] += value;
168:           else                  ap[i] = value;
169:           goto noinsert;
170:         }
171:       }
172:       if (nonew == 1) goto noinsert;
173:       else if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix",row,col);
174:       if (nrow >= rmax) {
175:         /* there is no extra room in row, therefore enlarge */
176:         int         new_nz = ai[A->m] + CHUNKSIZE,*new_i,*new_j;
177:         size_t      len;
178:         PetscScalar *new_a;

180:         if (nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix requiring new malloc()",row,col);

182:         /* malloc new storage space */
183:         len     = ((size_t) new_nz)*(sizeof(int)+sizeof(PetscScalar))+(A->m+1)*sizeof(int);
184:         ierr    = PetscMalloc(len,&new_a);
185:         new_j   = (int*)(new_a + new_nz);
186:         new_i   = new_j + new_nz;

188:         /* copy over old data into new slots */
189:         for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];}
190:         for (ii=row+1; ii<A->m+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;}
191:         PetscMemcpy(new_j,aj,(ai[row]+nrow+shift)*sizeof(int));
192:         len  = (((size_t) new_nz) - CHUNKSIZE - ai[row] - nrow - shift);
193:         PetscMemcpy(new_j+ai[row]+shift+nrow+CHUNKSIZE,aj+ai[row]+shift+nrow,len*sizeof(int));
194:         PetscMemcpy(new_a,aa,(((size_t) ai[row])+nrow+shift)*sizeof(PetscScalar));
195:         PetscMemcpy(new_a+ai[row]+shift+nrow+CHUNKSIZE,aa+ai[row]+shift+nrow,len*sizeof(PetscScalar));
196:         /* free up old matrix storage */
197:         PetscFree(a->a);
198:         if (!a->singlemalloc) {
199:           PetscFree(a->i);
200:           PetscFree(a->j);
201:         }
202:         aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;
203:         a->singlemalloc = PETSC_TRUE;

205:         rp   = aj + ai[row] + shift; ap = aa + ai[row] + shift;
206:         rmax = imax[row] = imax[row] + CHUNKSIZE;
207:         PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar)));
208:         a->maxnz += CHUNKSIZE;
209:         a->reallocs++;
210:       }
211:       N = nrow++ - 1; a->nz++;
212:       /* shift up all the later entries in this row */
213:       for (ii=N; ii>=i; ii--) {
214:         rp[ii+1] = rp[ii];
215:         ap[ii+1] = ap[ii];
216:       }
217:       rp[i] = col;
218:       ap[i] = value;
219:       noinsert:;
220:       low = i + 1;
221:     }
222:     ailen[row] = nrow;
223:   }
224:   return(0);
225: }

227: #undef __FUNCT__  
229: int MatGetValues_SeqAIJ(Mat A,int m,int *im,int n,int *in,PetscScalar *v)
230: {
231:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
232:   int          *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
233:   int          *ai = a->i,*ailen = a->ilen,shift = a->indexshift;
234:   PetscScalar  *ap,*aa = a->a,zero = 0.0;

237:   for (k=0; k<m; k++) { /* loop over rows */
238:     row  = im[k];
239:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",row);
240:     if (row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: %d",row);
241:     rp   = aj + ai[row] + shift; ap = aa + ai[row] + shift;
242:     nrow = ailen[row];
243:     for (l=0; l<n; l++) { /* loop over columns */
244:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",in[l]);
245:       if (in[l] >= A->n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: %d",in[l]);
246:       col = in[l] - shift;
247:       high = nrow; low = 0; /* assume unsorted */
248:       while (high-low > 5) {
249:         t = (low+high)/2;
250:         if (rp[t] > col) high = t;
251:         else             low  = t;
252:       }
253:       for (i=low; i<high; i++) {
254:         if (rp[i] > col) break;
255:         if (rp[i] == col) {
256:           *v++ = ap[i];
257:           goto finished;
258:         }
259:       }
260:       *v++ = zero;
261:       finished:;
262:     }
263:   }
264:   return(0);
265: }


268: #undef __FUNCT__  
270: int MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
271: {
272:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
273:   int        i,fd,*col_lens,ierr;

276:   PetscViewerBinaryGetDescriptor(viewer,&fd);
277:   PetscMalloc((4+A->m)*sizeof(int),&col_lens);
278:   col_lens[0] = MAT_FILE_COOKIE;
279:   col_lens[1] = A->m;
280:   col_lens[2] = A->n;
281:   col_lens[3] = a->nz;

283:   /* store lengths of each row and write (including header) to file */
284:   for (i=0; i<A->m; i++) {
285:     col_lens[4+i] = a->i[i+1] - a->i[i];
286:   }
287:   PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,1);
288:   PetscFree(col_lens);

290:   /* store column indices (zero start index) */
291:   if (a->indexshift) {
292:     for (i=0; i<a->nz; i++) a->j[i]--;
293:   }
294:   PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,0);
295:   if (a->indexshift) {
296:     for (i=0; i<a->nz; i++) a->j[i]++;
297:   }

299:   /* store nonzero values */
300:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,0);
301:   return(0);
302: }

304: extern int MatSeqAIJFactorInfo_SuperLU(Mat,PetscViewer);
305: extern int MatMPIAIJFactorInfo_SuperLu(Mat,PetscViewer);
306: extern int MatFactorInfo_Spooles(Mat,PetscViewer);
307: extern int MatSeqAIJFactorInfo_UMFPACK(Mat,PetscViewer);
308: extern int MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

310: #undef __FUNCT__  
312: int MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
313: {
314:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
315:   int               ierr,i,j,m = A->m,shift = a->indexshift;
316:   char              *name;
317:   PetscViewerFormat format;

320:   PetscObjectGetName((PetscObject)A,&name);
321:   PetscViewerGetFormat(viewer,&format);
322:   if (format == PETSC_VIEWER_ASCII_INFO_DETAIL || format == PETSC_VIEWER_ASCII_INFO) {
323:     if (a->inode.size) {
324:       PetscViewerASCIIPrintf(viewer,"using I-node routines: found %d nodes, limit used is %dn",a->inode.node_count,a->inode.limit);
325:     } else {
326:       PetscViewerASCIIPrintf(viewer,"not using I-node routinesn");
327:     }
328:   } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
329:     int nofinalvalue = 0;
330:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) {
331:       nofinalvalue = 1;
332:     }
333:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
334:     PetscViewerASCIIPrintf(viewer,"%% Size = %d %d n",m,A->n);
335:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %d n",a->nz);
336:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%d,3);n",a->nz+nofinalvalue);
337:     PetscViewerASCIIPrintf(viewer,"zzz = [n");

339:     for (i=0; i<m; i++) {
340:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
341: #if defined(PETSC_USE_COMPLEX)
342:         PetscViewerASCIIPrintf(viewer,"%d %d  %18.16e + %18.16ei n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
343: #else
344:         PetscViewerASCIIPrintf(viewer,"%d %d  %18.16en",i+1,a->j[j]+!shift,a->a[j]);
345: #endif
346:       }
347:     }
348:     if (nofinalvalue) {
349:       PetscViewerASCIIPrintf(viewer,"%d %d  %18.16en",m,A->n,0.0);
350:     }
351:     PetscViewerASCIIPrintf(viewer,"];n %s = spconvert(zzz);n",name);
352:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
353:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
354: #if defined(PETSC_HAVE_SUPERLU) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
355:      MatSeqAIJFactorInfo_SuperLU(A,viewer);
356: #endif
357: #if defined(PETSC_HAVE_SUPERLUDIST) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
358:      MatMPIAIJFactorInfo_SuperLu(A,viewer);
359: #endif
360: #if defined(PETSC_HAVE_SPOOLES) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
361:      MatFactorInfo_Spooles(A,viewer);
362: #endif
363: #if defined(PETSC_HAVE_UMFPACK) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
364:      MatSeqAIJFactorInfo_UMFPACK(A,viewer);
365: #endif
366: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
367:      MatSeqAIJFactorInfo_Matlab(A,viewer);
368: #endif

370:      return(0);
371:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
372:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
373:     for (i=0; i<m; i++) {
374:       PetscViewerASCIIPrintf(viewer,"row %d:",i);
375:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
376: #if defined(PETSC_USE_COMPLEX)
377:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
378:           PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
379:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
380:           PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
381:         } else if (PetscRealPart(a->a[j]) != 0.0) {
382:           PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
383:         }
384: #else
385:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);}
386: #endif
387:       }
388:       PetscViewerASCIIPrintf(viewer,"n");
389:     }
390:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
391:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
392:     int nzd=0,fshift=1,*sptr;
393:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
394:     PetscMalloc((m+1)*sizeof(int),&sptr);
395:     for (i=0; i<m; i++) {
396:       sptr[i] = nzd+1;
397:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
398:         if (a->j[j] >= i) {
399: #if defined(PETSC_USE_COMPLEX)
400:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
401: #else
402:           if (a->a[j] != 0.0) nzd++;
403: #endif
404:         }
405:       }
406:     }
407:     sptr[m] = nzd+1;
408:     PetscViewerASCIIPrintf(viewer," %d %dnn",m,nzd);
409:     for (i=0; i<m+1; i+=6) {
410:       if (i+4<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);}
411:       else if (i+3<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);}
412:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
413:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %d %d %dn",sptr[i],sptr[i+1],sptr[i+2]);}
414:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %d %dn",sptr[i],sptr[i+1]);}
415:       else            {PetscViewerASCIIPrintf(viewer," %dn",sptr[i]);}
416:     }
417:     PetscViewerASCIIPrintf(viewer,"n");
418:     PetscFree(sptr);
419:     for (i=0; i<m; i++) {
420:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
421:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %d ",a->j[j]+fshift);}
422:       }
423:       PetscViewerASCIIPrintf(viewer,"n");
424:     }
425:     PetscViewerASCIIPrintf(viewer,"n");
426:     for (i=0; i<m; i++) {
427:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
428:         if (a->j[j] >= i) {
429: #if defined(PETSC_USE_COMPLEX)
430:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
431:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
432:           }
433: #else
434:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
435: #endif
436:         }
437:       }
438:       PetscViewerASCIIPrintf(viewer,"n");
439:     }
440:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
441:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
442:     int         cnt = 0,jcnt;
443:     PetscScalar value;

445:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
446:     for (i=0; i<m; i++) {
447:       jcnt = 0;
448:       for (j=0; j<A->n; j++) {
449:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
450:           value = a->a[cnt++];
451:           jcnt++;
452:         } else {
453:           value = 0.0;
454:         }
455: #if defined(PETSC_USE_COMPLEX)
456:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
457: #else
458:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
459: #endif
460:       }
461:       PetscViewerASCIIPrintf(viewer,"n");
462:     }
463:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
464:   } else {
465:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
466:     for (i=0; i<m; i++) {
467:       PetscViewerASCIIPrintf(viewer,"row %d:",i);
468:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
469: #if defined(PETSC_USE_COMPLEX)
470:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
471:           PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
472:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
473:           PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
474:         } else {
475:           PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
476:         }
477: #else
478:         PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);
479: #endif
480:       }
481:       PetscViewerASCIIPrintf(viewer,"n");
482:     }
483:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
484:   }
485:   PetscViewerFlush(viewer);
486:   return(0);
487: }

489: #undef __FUNCT__  
491: int MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
492: {
493:   Mat               A = (Mat) Aa;
494:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
495:   int               ierr,i,j,m = A->m,shift = a->indexshift,color;
496:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
497:   PetscViewer       viewer;
498:   PetscViewerFormat format;

501:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
502:   PetscViewerGetFormat(viewer,&format);

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

507:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
508:     /* Blue for negative, Cyan for zero and  Red for positive */
509:     color = PETSC_DRAW_BLUE;
510:     for (i=0; i<m; i++) {
511:       y_l = m - i - 1.0; y_r = y_l + 1.0;
512:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
513:         x_l = a->j[j] + shift; x_r = x_l + 1.0;
514: #if defined(PETSC_USE_COMPLEX)
515:         if (PetscRealPart(a->a[j]) >=  0.) continue;
516: #else
517:         if (a->a[j] >=  0.) continue;
518: #endif
519:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
520:       }
521:     }
522:     color = PETSC_DRAW_CYAN;
523:     for (i=0; i<m; i++) {
524:       y_l = m - i - 1.0; y_r = y_l + 1.0;
525:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
526:         x_l = a->j[j] + shift; x_r = x_l + 1.0;
527:         if (a->a[j] !=  0.) continue;
528:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
529:       }
530:     }
531:     color = PETSC_DRAW_RED;
532:     for (i=0; i<m; i++) {
533:       y_l = m - i - 1.0; y_r = y_l + 1.0;
534:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
535:         x_l = a->j[j] + shift; x_r = x_l + 1.0;
536: #if defined(PETSC_USE_COMPLEX)
537:         if (PetscRealPart(a->a[j]) <=  0.) continue;
538: #else
539:         if (a->a[j] <=  0.) continue;
540: #endif
541:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
542:       }
543:     }
544:   } else {
545:     /* use contour shading to indicate magnitude of values */
546:     /* first determine max of all nonzero values */
547:     int    nz = a->nz,count;
548:     PetscDraw   popup;
549:     PetscReal scale;

551:     for (i=0; i<nz; i++) {
552:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
553:     }
554:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
555:     ierr  = PetscDrawGetPopup(draw,&popup);
556:     if (popup) {ierr  = PetscDrawScalePopup(popup,0.0,maxv);}
557:     count = 0;
558:     for (i=0; i<m; i++) {
559:       y_l = m - i - 1.0; y_r = y_l + 1.0;
560:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
561:         x_l = a->j[j] + shift; x_r = x_l + 1.0;
562:         color = PETSC_DRAW_BASIC_COLORS + (int)(scale*PetscAbsScalar(a->a[count]));
563:         ierr  = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
564:         count++;
565:       }
566:     }
567:   }
568:   return(0);
569: }

571: #undef __FUNCT__  
573: int MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
574: {
575:   int        ierr;
576:   PetscDraw  draw;
577:   PetscReal  xr,yr,xl,yl,h,w;
578:   PetscTruth isnull;

581:   PetscViewerDrawGetDraw(viewer,0,&draw);
582:   PetscDrawIsNull(draw,&isnull);
583:   if (isnull) return(0);

585:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
586:   xr  = A->n; yr = A->m; h = yr/10.0; w = xr/10.0;
587:   xr += w;    yr += h;  xl = -w;     yl = -h;
588:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
589:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
590:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
591:   return(0);
592: }

594: #undef __FUNCT__  
596: int MatView_SeqAIJ(Mat A,PetscViewer viewer)
597: {
598:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
599:   int         ierr;
600:   PetscTruth  issocket,isascii,isbinary,isdraw;

603:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
604:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
605:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
606:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
607:   if (issocket) {
608:     if (a->indexshift) {
609:       SETERRQ(1,"Can only socket send sparse matrix with 0 based indexing");
610:     }
611:     PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);
612:   } else if (isascii) {
613:     MatView_SeqAIJ_ASCII(A,viewer);
614:   } else if (isbinary) {
615:     MatView_SeqAIJ_Binary(A,viewer);
616:   } else if (isdraw) {
617:     MatView_SeqAIJ_Draw(A,viewer);
618:   } else {
619:     SETERRQ1(1,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
620:   }
621:   return(0);
622: }

624: EXTERN int Mat_AIJ_CheckInode(Mat,PetscTruth);
625: #undef __FUNCT__  
627: int MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
628: {
629:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
630:   int          fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax,ierr;
631:   int          m = A->m,*ip,N,*ailen = a->ilen,shift = a->indexshift,rmax = 0;
632:   PetscScalar  *aa = a->a,*ap;
633: #if defined(PETSC_HAVE_SUPERLUDIST) || defined(PETSC_HAVE_SPOOLES) || defined(PETSC_HAVE_UMFPACK)
634:   PetscTruth   flag;
635: #endif

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

640:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
641:   for (i=1; i<m; i++) {
642:     /* move each row back by the amount of empty slots (fshift) before it*/
643:     fshift += imax[i-1] - ailen[i-1];
644:     rmax   = PetscMax(rmax,ailen[i]);
645:     if (fshift) {
646:       ip = aj + ai[i] + shift;
647:       ap = aa + ai[i] + shift;
648:       N  = ailen[i];
649:       for (j=0; j<N; j++) {
650:         ip[j-fshift] = ip[j];
651:         ap[j-fshift] = ap[j];
652:       }
653:     }
654:     ai[i] = ai[i-1] + ailen[i-1];
655:   }
656:   if (m) {
657:     fshift += imax[m-1] - ailen[m-1];
658:     ai[m]  = ai[m-1] + ailen[m-1];
659:   }
660:   /* reset ilen and imax for each row */
661:   for (i=0; i<m; i++) {
662:     ailen[i] = imax[i] = ai[i+1] - ai[i];
663:   }
664:   a->nz = ai[m] + shift;

666:   /* diagonals may have moved, so kill the diagonal pointers */
667:   if (fshift && a->diag) {
668:     PetscFree(a->diag);
669:     PetscLogObjectMemory(A,-(m+1)*sizeof(int));
670:     a->diag = 0;
671:   }
672:   PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Matrix size: %d X %d; storage space: %d unneeded,%d usedn",m,A->n,fshift,a->nz);
673:   PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %dn",a->reallocs);
674:   PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Most nonzeros in any row is %dn",rmax);
675:   a->reallocs          = 0;
676:   A->info.nz_unneeded  = (double)fshift;
677:   a->rmax              = rmax;

679:   /* check out for identical nodes. If found, use inode functions */
680:   Mat_AIJ_CheckInode(A,(PetscTruth)(!fshift));

682: #if defined(PETSC_HAVE_SUPERLUDIST) 
683:   PetscOptionsHasName(A->prefix,"-mat_aij_superlu_dist",&flag);
684:   if (flag) { MatUseSuperLU_DIST_MPIAIJ(A); }
685: #endif 

687: #if defined(PETSC_HAVE_SPOOLES) 
688:   PetscOptionsHasName(A->prefix,"-mat_aij_spooles",&flag);
689:   if (flag) { MatUseSpooles_SeqAIJ(A); }
690: #endif 

692: #if defined(PETSC_HAVE_UMFPACK) 
693:   PetscOptionsHasName(A->prefix,"-mat_aij_umfpack",&flag);
694:   if (flag) { MatUseUMFPACK_SeqAIJ(A); }
695: #endif 

697:   return(0);
698: }

700: #undef __FUNCT__  
702: int MatZeroEntries_SeqAIJ(Mat A)
703: {
704:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
705:   int        ierr;

708:   PetscMemzero(a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
709:   return(0);
710: }

712: #undef __FUNCT__  
714: int MatDestroy_SeqAIJ(Mat A)
715: {
716:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
717:   int        ierr;

720: #if defined(PETSC_USE_LOG)
721:   PetscLogObjectState((PetscObject)A,"Rows=%d, Cols=%d, NZ=%d",A->m,A->n,a->nz);
722: #endif
723:   if (a->freedata) {
724:     PetscFree(a->a);
725:     if (!a->singlemalloc) {
726:       PetscFree(a->i);
727:       PetscFree(a->j);
728:     }
729:   }
730:   if (a->row) {
731:     ISDestroy(a->row);
732:   }
733:   if (a->col) {
734:     ISDestroy(a->col);
735:   }
736:   if (a->diag) {PetscFree(a->diag);}
737:   if (a->ilen) {PetscFree(a->ilen);}
738:   if (a->imax) {PetscFree(a->imax);}
739:   if (a->idiag) {PetscFree(a->idiag);}
740:   if (a->solve_work) {PetscFree(a->solve_work);}
741:   if (a->inode.size) {PetscFree(a->inode.size);}
742:   if (a->icol) {ISDestroy(a->icol);}
743:   if (a->saved_values) {PetscFree(a->saved_values);}
744:   if (a->coloring) {ISColoringDestroy(a->coloring);}
745:   PetscFree(a);
746:   return(0);
747: }

749: #undef __FUNCT__  
751: int MatCompress_SeqAIJ(Mat A)
752: {
754:   return(0);
755: }

757: #undef __FUNCT__  
759: int MatSetOption_SeqAIJ(Mat A,MatOption op)
760: {
761:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

764:   switch (op) {
765:     case MAT_ROW_ORIENTED:
766:       a->roworiented       = PETSC_TRUE;
767:       break;
768:     case MAT_KEEP_ZEROED_ROWS:
769:       a->keepzeroedrows    = PETSC_TRUE;
770:       break;
771:     case MAT_COLUMN_ORIENTED:
772:       a->roworiented       = PETSC_FALSE;
773:       break;
774:     case MAT_COLUMNS_SORTED:
775:       a->sorted            = PETSC_TRUE;
776:       break;
777:     case MAT_COLUMNS_UNSORTED:
778:       a->sorted            = PETSC_FALSE;
779:       break;
780:     case MAT_NO_NEW_NONZERO_LOCATIONS:
781:       a->nonew             = 1;
782:       break;
783:     case MAT_NEW_NONZERO_LOCATION_ERR:
784:       a->nonew             = -1;
785:       break;
786:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
787:       a->nonew             = -2;
788:       break;
789:     case MAT_YES_NEW_NONZERO_LOCATIONS:
790:       a->nonew             = 0;
791:       break;
792:     case MAT_IGNORE_ZERO_ENTRIES:
793:       a->ignorezeroentries = PETSC_TRUE;
794:       break;
795:     case MAT_USE_INODES:
796:       a->inode.use         = PETSC_TRUE;
797:       break;
798:     case MAT_DO_NOT_USE_INODES:
799:       a->inode.use         = PETSC_FALSE;
800:       break;
801:     case MAT_ROWS_SORTED:
802:     case MAT_ROWS_UNSORTED:
803:     case MAT_YES_NEW_DIAGONALS:
804:     case MAT_IGNORE_OFF_PROC_ENTRIES:
805:     case MAT_USE_HASH_TABLE:
806:       PetscLogInfo(A,"MatSetOption_SeqAIJ:Option ignoredn");
807:       break;
808:     case MAT_NO_NEW_DIAGONALS:
809:       SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
810:     case MAT_INODE_LIMIT_1:
811:       a->inode.limit  = 1;
812:       break;
813:     case MAT_INODE_LIMIT_2:
814:       a->inode.limit  = 2;
815:       break;
816:     case MAT_INODE_LIMIT_3:
817:       a->inode.limit  = 3;
818:       break;
819:     case MAT_INODE_LIMIT_4:
820:       a->inode.limit  = 4;
821:       break;
822:     case MAT_INODE_LIMIT_5:
823:       a->inode.limit  = 5;
824:       break;
825:     default:
826:       SETERRQ(PETSC_ERR_SUP,"unknown option");
827:   }
828:   return(0);
829: }

831: #undef __FUNCT__  
833: int MatGetDiagonal_SeqAIJ(Mat A,Vec v)
834: {
835:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
836:   int          i,j,n,shift = a->indexshift,ierr;
837:   PetscScalar  *x,zero = 0.0;

840:   VecSet(&zero,v);
841:   VecGetArray(v,&x);
842:   VecGetLocalSize(v,&n);
843:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
844:   for (i=0; i<A->m; i++) {
845:     for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
846:       if (a->j[j]+shift == i) {
847:         x[i] = a->a[j];
848:         break;
849:       }
850:     }
851:   }
852:   VecRestoreArray(v,&x);
853:   return(0);
854: }


857: #undef __FUNCT__  
859: int MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
860: {
861:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
862:   PetscScalar  *x,*y;
863:   int          ierr,m = A->m,shift = a->indexshift;
864: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
865:   PetscScalar  *v,alpha;
866:   int          n,i,*idx;
867: #endif

870:   if (zz != yy) {VecCopy(zz,yy);}
871:   VecGetArray(xx,&x);
872:   VecGetArray(yy,&y);
873:   y = y + shift; /* shift for Fortran start by 1 indexing */

875: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
876:   fortranmulttransposeaddaij_(&m,x,a->i,a->j+shift,a->a+shift,y);
877: #else
878:   for (i=0; i<m; i++) {
879:     idx   = a->j + a->i[i] + shift;
880:     v     = a->a + a->i[i] + shift;
881:     n     = a->i[i+1] - a->i[i];
882:     alpha = x[i];
883:     while (n-->0) {y[*idx++] += alpha * *v++;}
884:   }
885: #endif
886:   PetscLogFlops(2*a->nz);
887:   VecRestoreArray(xx,&x);
888:   VecRestoreArray(yy,&y);
889:   return(0);
890: }

892: #undef __FUNCT__  
894: int MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
895: {
896:   PetscScalar  zero = 0.0;
897:   int          ierr;

900:   VecSet(&zero,yy);
901:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
902:   return(0);
903: }


906: #undef __FUNCT__  
908: int MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
909: {
910:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
911:   PetscScalar  *x,*y,*v;
912:   int          ierr,m = A->m,*idx,shift = a->indexshift,*ii;
913: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
914:   int          n,i,jrow,j;
915:   PetscScalar  sum;
916: #endif

918: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
919: #pragma disjoint(*x,*y,*v)
920: #endif

923:   VecGetArray(xx,&x);
924:   VecGetArray(yy,&y);
925:   x    = x + shift;    /* shift for Fortran start by 1 indexing */
926:   idx  = a->j;
927:   v    = a->a;
928:   ii   = a->i;
929: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
930:   fortranmultaij_(&m,x,ii,idx+shift,v+shift,y);
931: #else
932:   v    += shift; /* shift for Fortran start by 1 indexing */
933:   idx  += shift;
934:   for (i=0; i<m; i++) {
935:     jrow = ii[i];
936:     n    = ii[i+1] - jrow;
937:     sum  = 0.0;
938:     for (j=0; j<n; j++) {
939:       sum += v[jrow]*x[idx[jrow]]; jrow++;
940:      }
941:     y[i] = sum;
942:   }
943: #endif
944:   PetscLogFlops(2*a->nz - m);
945:   VecRestoreArray(xx,&x);
946:   VecRestoreArray(yy,&y);
947:   return(0);
948: }

950: #undef __FUNCT__  
952: int MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
953: {
954:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
955:   PetscScalar  *x,*y,*z,*v;
956:   int          ierr,m = A->m,*idx,shift = a->indexshift,*ii;
957: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
958:   int          n,i,jrow,j;
959: PetscScalar    sum;
960: #endif

963:   VecGetArray(xx,&x);
964:   VecGetArray(yy,&y);
965:   if (zz != yy) {
966:     VecGetArray(zz,&z);
967:   } else {
968:     z = y;
969:   }
970:   x    = x + shift; /* shift for Fortran start by 1 indexing */
971:   idx  = a->j;
972:   v    = a->a;
973:   ii   = a->i;
974: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
975:   fortranmultaddaij_(&m,x,ii,idx+shift,v+shift,y,z);
976: #else
977:   v   += shift; /* shift for Fortran start by 1 indexing */
978:   idx += shift;
979:   for (i=0; i<m; i++) {
980:     jrow = ii[i];
981:     n    = ii[i+1] - jrow;
982:     sum  = y[i];
983:     for (j=0; j<n; j++) {
984:       sum += v[jrow]*x[idx[jrow]]; jrow++;
985:      }
986:     z[i] = sum;
987:   }
988: #endif
989:   PetscLogFlops(2*a->nz);
990:   VecRestoreArray(xx,&x);
991:   VecRestoreArray(yy,&y);
992:   if (zz != yy) {
993:     VecRestoreArray(zz,&z);
994:   }
995:   return(0);
996: }

998: /*
999:      Adds diagonal pointers to sparse matrix structure.
1000: */
1001: #undef __FUNCT__  
1003: int MatMarkDiagonal_SeqAIJ(Mat A)
1004: {
1005:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1006:   int        i,j,*diag,m = A->m,shift = a->indexshift,ierr;

1009:   if (a->diag) return(0);

1011:   PetscMalloc((m+1)*sizeof(int),&diag);
1012:   PetscLogObjectMemory(A,(m+1)*sizeof(int));
1013:   for (i=0; i<A->m; i++) {
1014:     diag[i] = a->i[i+1];
1015:     for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
1016:       if (a->j[j]+shift == i) {
1017:         diag[i] = j - shift;
1018:         break;
1019:       }
1020:     }
1021:   }
1022:   a->diag = diag;
1023:   return(0);
1024: }

1026: /*
1027:      Checks for missing diagonals
1028: */
1029: #undef __FUNCT__  
1031: int MatMissingDiagonal_SeqAIJ(Mat A)
1032: {
1033:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1034:   int        *diag,*jj = a->j,i,shift = a->indexshift,ierr;

1037:   MatMarkDiagonal_SeqAIJ(A);
1038:   diag = a->diag;
1039:   for (i=0; i<A->m; i++) {
1040:     if (jj[diag[i]+shift] != i-shift) {
1041:       SETERRQ1(1,"Matrix is missing diagonal number %d",i);
1042:     }
1043:   }
1044:   return(0);
1045: }

1047: #undef __FUNCT__  
1049: int MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
1050: {
1051:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1052:   PetscScalar  *x,*b,*bs, d,*xs,sum,*v = a->a,*t=0,scale,*ts,*xb,*idiag=0;
1053:   int          ierr,*idx,*diag,n = A->n,m = A->m,i,shift = a->indexshift;

1056:   its = its*lits;
1057:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits);

1059:   VecGetArray(xx,&x);
1060:   if (xx != bb) {
1061:     VecGetArray(bb,&b);
1062:   } else {
1063:     b = x;
1064:   }

1066:   if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
1067:   diag = a->diag;
1068:   xs   = x + shift; /* shifted by one for index start of a or a->j*/
1069:   if (flag == SOR_APPLY_UPPER) {
1070:    /* apply (U + D/omega) to the vector */
1071:     bs = b + shift;
1072:     for (i=0; i<m; i++) {
1073:         d    = fshift + a->a[diag[i] + shift];
1074:         n    = a->i[i+1] - diag[i] - 1;
1075:         PetscLogFlops(2*n-1);
1076:         idx  = a->j + diag[i] + (!shift);
1077:         v    = a->a + diag[i] + (!shift);
1078:         sum  = b[i]*d/omega;
1079:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1080:         x[i] = sum;
1081:     }
1082:     VecRestoreArray(xx,&x);
1083:     if (bb != xx) {VecRestoreArray(bb,&b);}
1084:     return(0);
1085:   }

1087:   /* setup workspace for Eisenstat */
1088:   if (flag & SOR_EISENSTAT) {
1089:     if (!a->idiag) {
1090:       ierr     = PetscMalloc(2*m*sizeof(PetscScalar),&a->idiag);
1091:       a->ssor  = a->idiag + m;
1092:       v        = a->a;
1093:       for (i=0; i<m; i++) { a->idiag[i] = 1.0/v[diag[i]];}
1094:     }
1095:     t     = a->ssor;
1096:     idiag = a->idiag;
1097:   }
1098:     /* Let  A = L + U + D; where L is lower trianglar,
1099:     U is upper triangular, E is diagonal; This routine applies

1101:             (L + E)^{-1} A (U + E)^{-1}

1103:     to a vector efficiently using Eisenstat's trick. This is for
1104:     the case of SSOR preconditioner, so E is D/omega where omega
1105:     is the relaxation factor.
1106:     */

1108:   if (flag == SOR_APPLY_LOWER) {
1109:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1110:   } else if ((flag & SOR_EISENSTAT) && omega == 1.0 && shift == 0 && fshift == 0.0) {
1111:     /* special case for omega = 1.0 saves flops and some integer ops */
1112:     PetscScalar *v2;
1113: 
1114:     v2    = a->a;
1115:     /*  x = (E + U)^{-1} b */
1116:     for (i=m-1; i>=0; i--) {
1117:       n    = a->i[i+1] - diag[i] - 1;
1118:       idx  = a->j + diag[i] + 1;
1119:       v    = a->a + diag[i] + 1;
1120:       sum  = b[i];
1121:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1122:       x[i] = sum*idiag[i];

1124:       /*  t = b - (2*E - D)x */
1125:       t[i] = b[i] - (v2[diag[i]])*x[i];
1126:     }

1128:     /*  t = (E + L)^{-1}t */
1129:     diag = a->diag;
1130:     for (i=0; i<m; i++) {
1131:       n    = diag[i] - a->i[i];
1132:       idx  = a->j + a->i[i];
1133:       v    = a->a + a->i[i];
1134:       sum  = t[i];
1135:       SPARSEDENSEMDOT(sum,t,v,idx,n);
1136:       t[i]  = sum*idiag[i];

1138:       /*  x = x + t */
1139:       x[i] += t[i];
1140:     }

1142:     PetscLogFlops(3*m-1 + 2*a->nz);
1143:     VecRestoreArray(xx,&x);
1144:     if (bb != xx) {VecRestoreArray(bb,&b);}
1145:     return(0);
1146:   } else if (flag & SOR_EISENSTAT) {
1147:     /* Let  A = L + U + D; where L is lower trianglar,
1148:     U is upper triangular, E is diagonal; This routine applies

1150:             (L + E)^{-1} A (U + E)^{-1}

1152:     to a vector efficiently using Eisenstat's trick. This is for
1153:     the case of SSOR preconditioner, so E is D/omega where omega
1154:     is the relaxation factor.
1155:     */
1156:     scale = (2.0/omega) - 1.0;

1158:     /*  x = (E + U)^{-1} b */
1159:     for (i=m-1; i>=0; i--) {
1160:       d    = fshift + a->a[diag[i] + shift];
1161:       n    = a->i[i+1] - diag[i] - 1;
1162:       idx  = a->j + diag[i] + (!shift);
1163:       v    = a->a + diag[i] + (!shift);
1164:       sum  = b[i];
1165:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1166:       x[i] = omega*(sum/d);
1167:     }

1169:     /*  t = b - (2*E - D)x */
1170:     v = a->a;
1171:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++ + shift])*x[i]; }

1173:     /*  t = (E + L)^{-1}t */
1174:     ts = t + shift; /* shifted by one for index start of a or a->j*/
1175:     diag = a->diag;
1176:     for (i=0; i<m; i++) {
1177:       d    = fshift + a->a[diag[i]+shift];
1178:       n    = diag[i] - a->i[i];
1179:       idx  = a->j + a->i[i] + shift;
1180:       v    = a->a + a->i[i] + shift;
1181:       sum  = t[i];
1182:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1183:       t[i] = omega*(sum/d);
1184:       /*  x = x + t */
1185:       x[i] += t[i];
1186:     }

1188:     PetscLogFlops(6*m-1 + 2*a->nz);
1189:     VecRestoreArray(xx,&x);
1190:     if (bb != xx) {VecRestoreArray(bb,&b);}
1191:     return(0);
1192:   }
1193:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1194:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1195: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1196:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1197: #else
1198:       for (i=0; i<m; i++) {
1199:         d    = fshift + a->a[diag[i]+shift];
1200:         n    = diag[i] - a->i[i];
1201:         PetscLogFlops(2*n-1);
1202:         idx  = a->j + a->i[i] + shift;
1203:         v    = a->a + a->i[i] + shift;
1204:         sum  = b[i];
1205:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1206:         x[i] = omega*(sum/d);
1207:       }
1208: #endif
1209:       xb = x;
1210:     } else xb = b;
1211:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1212:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1213:       for (i=0; i<m; i++) {
1214:         x[i] *= a->a[diag[i]+shift];
1215:       }
1216:       PetscLogFlops(m);
1217:     }
1218:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1219: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1220:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,xb);
1221: #else
1222:       for (i=m-1; i>=0; i--) {
1223:         d    = fshift + a->a[diag[i] + shift];
1224:         n    = a->i[i+1] - diag[i] - 1;
1225:         PetscLogFlops(2*n-1);
1226:         idx  = a->j + diag[i] + (!shift);
1227:         v    = a->a + diag[i] + (!shift);
1228:         sum  = xb[i];
1229:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1230:         x[i] = omega*(sum/d);
1231:       }
1232: #endif
1233:     }
1234:     its--;
1235:   }
1236:   while (its--) {
1237:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1238: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1239:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1240: #else
1241:       for (i=0; i<m; i++) {
1242:         d    = fshift + a->a[diag[i]+shift];
1243:         n    = a->i[i+1] - a->i[i];
1244:         PetscLogFlops(2*n-1);
1245:         idx  = a->j + a->i[i] + shift;
1246:         v    = a->a + a->i[i] + shift;
1247:         sum  = b[i];
1248:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1249:         x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1250:       }
1251: #endif
1252:     }
1253:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1254: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1255:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1256: #else
1257:       for (i=m-1; i>=0; i--) {
1258:         d    = fshift + a->a[diag[i] + shift];
1259:         n    = a->i[i+1] - a->i[i];
1260:         PetscLogFlops(2*n-1);
1261:         idx  = a->j + a->i[i] + shift;
1262:         v    = a->a + a->i[i] + shift;
1263:         sum  = b[i];
1264:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1265:         x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1266:       }
1267: #endif
1268:     }
1269:   }
1270:   VecRestoreArray(xx,&x);
1271:   if (bb != xx) {VecRestoreArray(bb,&b);}
1272:   return(0);
1273: }

1275: #undef __FUNCT__  
1277: int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1278: {
1279:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1282:   info->rows_global    = (double)A->m;
1283:   info->columns_global = (double)A->n;
1284:   info->rows_local     = (double)A->m;
1285:   info->columns_local  = (double)A->n;
1286:   info->block_size     = 1.0;
1287:   info->nz_allocated   = (double)a->maxnz;
1288:   info->nz_used        = (double)a->nz;
1289:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1290:   info->assemblies     = (double)A->num_ass;
1291:   info->mallocs        = (double)a->reallocs;
1292:   info->memory         = A->mem;
1293:   if (A->factor) {
1294:     info->fill_ratio_given  = A->info.fill_ratio_given;
1295:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1296:     info->factor_mallocs    = A->info.factor_mallocs;
1297:   } else {
1298:     info->fill_ratio_given  = 0;
1299:     info->fill_ratio_needed = 0;
1300:     info->factor_mallocs    = 0;
1301:   }
1302:   return(0);
1303: }

1305: EXTERN int MatLUFactorSymbolic_SeqAIJ(Mat,IS,IS,MatLUInfo*,Mat*);
1306: EXTERN int MatLUFactorNumeric_SeqAIJ(Mat,Mat*);
1307: EXTERN int MatLUFactor_SeqAIJ(Mat,IS,IS,MatLUInfo*);
1308: EXTERN int MatSolve_SeqAIJ(Mat,Vec,Vec);
1309: EXTERN int MatSolveAdd_SeqAIJ(Mat,Vec,Vec,Vec);
1310: EXTERN int MatSolveTranspose_SeqAIJ(Mat,Vec,Vec);
1311: EXTERN int MatSolveTransposeAdd_SeqAIJ(Mat,Vec,Vec,Vec);

1313: #undef __FUNCT__  
1315: int MatZeroRows_SeqAIJ(Mat A,IS is,PetscScalar *diag)
1316: {
1317:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1318:   int         i,ierr,N,*rows,m = A->m - 1,shift = a->indexshift;

1321:   ISGetLocalSize(is,&N);
1322:   ISGetIndices(is,&rows);
1323:   if (a->keepzeroedrows) {
1324:     for (i=0; i<N; i++) {
1325:       if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1326:       PetscMemzero(&a->a[a->i[rows[i]]+shift],a->ilen[rows[i]]*sizeof(PetscScalar));
1327:     }
1328:     if (diag) {
1329:       MatMissingDiagonal_SeqAIJ(A);
1330:       MatMarkDiagonal_SeqAIJ(A);
1331:       for (i=0; i<N; i++) {
1332:         a->a[a->diag[rows[i]]] = *diag;
1333:       }
1334:     }
1335:   } else {
1336:     if (diag) {
1337:       for (i=0; i<N; i++) {
1338:         if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1339:         if (a->ilen[rows[i]] > 0) {
1340:           a->ilen[rows[i]]          = 1;
1341:           a->a[a->i[rows[i]]+shift] = *diag;
1342:           a->j[a->i[rows[i]]+shift] = rows[i]+shift;
1343:         } else { /* in case row was completely empty */
1344:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);
1345:         }
1346:       }
1347:     } else {
1348:       for (i=0; i<N; i++) {
1349:         if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1350:         a->ilen[rows[i]] = 0;
1351:       }
1352:     }
1353:   }
1354:   ISRestoreIndices(is,&rows);
1355:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1356:   return(0);
1357: }

1359: #undef __FUNCT__  
1361: int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1362: {
1363:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1364:   int        *itmp,i,shift = a->indexshift,ierr;

1367:   if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row);

1369:   *nz = a->i[row+1] - a->i[row];
1370:   if (v) *v = a->a + a->i[row] + shift;
1371:   if (idx) {
1372:     itmp = a->j + a->i[row] + shift;
1373:     if (*nz && shift) {
1374:       PetscMalloc((*nz)*sizeof(int),idx);
1375:       for (i=0; i<(*nz); i++) {(*idx)[i] = itmp[i] + shift;}
1376:     } else if (*nz) {
1377:       *idx = itmp;
1378:     }
1379:     else *idx = 0;
1380:   }
1381:   return(0);
1382: }

1384: #undef __FUNCT__  
1386: int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1387: {
1388:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1392:   if (idx) {if (*idx && a->indexshift) {PetscFree(*idx);}}
1393:   return(0);
1394: }

1396: #undef __FUNCT__  
1398: int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1399: {
1400:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1401:   PetscScalar  *v = a->a;
1402:   PetscReal    sum = 0.0;
1403:   int          i,j,shift = a->indexshift,ierr;

1406:   if (type == NORM_FROBENIUS) {
1407:     for (i=0; i<a->nz; i++) {
1408: #if defined(PETSC_USE_COMPLEX)
1409:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1410: #else
1411:       sum += (*v)*(*v); v++;
1412: #endif
1413:     }
1414:     *nrm = sqrt(sum);
1415:   } else if (type == NORM_1) {
1416:     PetscReal *tmp;
1417:     int    *jj = a->j;
1418:     PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);
1419:     PetscMemzero(tmp,A->n*sizeof(PetscReal));
1420:     *nrm = 0.0;
1421:     for (j=0; j<a->nz; j++) {
1422:         tmp[*jj++ + shift] += PetscAbsScalar(*v);  v++;
1423:     }
1424:     for (j=0; j<A->n; j++) {
1425:       if (tmp[j] > *nrm) *nrm = tmp[j];
1426:     }
1427:     PetscFree(tmp);
1428:   } else if (type == NORM_INFINITY) {
1429:     *nrm = 0.0;
1430:     for (j=0; j<A->m; j++) {
1431:       v = a->a + a->i[j] + shift;
1432:       sum = 0.0;
1433:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1434:         sum += PetscAbsScalar(*v); v++;
1435:       }
1436:       if (sum > *nrm) *nrm = sum;
1437:     }
1438:   } else {
1439:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1440:   }
1441:   return(0);
1442: }

1444: #undef __FUNCT__  
1446: int MatTranspose_SeqAIJ(Mat A,Mat *B)
1447: {
1448:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1449:   Mat          C;
1450:   int          i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col;
1451:   int          shift = a->indexshift;
1452:   PetscScalar  *array = a->a;

1455:   if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1456:   PetscMalloc((1+A->n)*sizeof(int),&col);
1457:   PetscMemzero(col,(1+A->n)*sizeof(int));
1458:   if (shift) {
1459:     for (i=0; i<ai[m]-1; i++) aj[i] -= 1;
1460:   }
1461:   for (i=0; i<ai[m]+shift; i++) col[aj[i]] += 1;
1462:   MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);
1463:   PetscFree(col);
1464:   for (i=0; i<m; i++) {
1465:     len    = ai[i+1]-ai[i];
1466:     ierr   = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);
1467:     array += len;
1468:     aj    += len;
1469:   }
1470:   if (shift) {
1471:     for (i=0; i<ai[m]-1; i++) aj[i] += 1;
1472:   }

1474:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1475:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1477:   if (B) {
1478:     *B = C;
1479:   } else {
1480:     MatHeaderCopy(A,C);
1481:   }
1482:   return(0);
1483: }

1485: #undef __FUNCT__  
1487: int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1488: {
1489:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1490:   PetscScalar  *l,*r,x,*v;
1491:   int          ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj,shift = a->indexshift;

1494:   if (ll) {
1495:     /* The local size is used so that VecMPI can be passed to this routine
1496:        by MatDiagonalScale_MPIAIJ */
1497:     VecGetLocalSize(ll,&m);
1498:     if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1499:     VecGetArray(ll,&l);
1500:     v = a->a;
1501:     for (i=0; i<m; i++) {
1502:       x = l[i];
1503:       M = a->i[i+1] - a->i[i];
1504:       for (j=0; j<M; j++) { (*v++) *= x;}
1505:     }
1506:     VecRestoreArray(ll,&l);
1507:     PetscLogFlops(nz);
1508:   }
1509:   if (rr) {
1510:     VecGetLocalSize(rr,&n);
1511:     if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1512:     VecGetArray(rr,&r);
1513:     v = a->a; jj = a->j;
1514:     for (i=0; i<nz; i++) {
1515:       (*v++) *= r[*jj++ + shift];
1516:     }
1517:     VecRestoreArray(rr,&r);
1518:     PetscLogFlops(nz);
1519:   }
1520:   return(0);
1521: }

1523: #undef __FUNCT__  
1525: int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B)
1526: {
1527:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data,*c;
1528:   int          *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens;
1529:   int          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1530:   int          *irow,*icol,nrows,ncols,shift = a->indexshift,*ssmap;
1531:   int          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1532:   PetscScalar  *a_new,*mat_a;
1533:   Mat          C;
1534:   PetscTruth   stride;

1537:   ISSorted(isrow,(PetscTruth*)&i);
1538:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1539:   ISSorted(iscol,(PetscTruth*)&i);
1540:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1542:   ISGetIndices(isrow,&irow);
1543:   ISGetLocalSize(isrow,&nrows);
1544:   ISGetLocalSize(iscol,&ncols);

1546:   ISStrideGetInfo(iscol,&first,&step);
1547:   ISStride(iscol,&stride);
1548:   if (stride && step == 1) {
1549:     /* special case of contiguous rows */
1550:     ierr   = PetscMalloc((2*nrows+1)*sizeof(int),&lens);
1551:     starts = lens + nrows;
1552:     /* loop over new rows determining lens and starting points */
1553:     for (i=0; i<nrows; i++) {
1554:       kstart  = ai[irow[i]]+shift;
1555:       kend    = kstart + ailen[irow[i]];
1556:       for (k=kstart; k<kend; k++) {
1557:         if (aj[k]+shift >= first) {
1558:           starts[i] = k;
1559:           break;
1560:         }
1561:       }
1562:       sum = 0;
1563:       while (k < kend) {
1564:         if (aj[k++]+shift >= first+ncols) break;
1565:         sum++;
1566:       }
1567:       lens[i] = sum;
1568:     }
1569:     /* create submatrix */
1570:     if (scall == MAT_REUSE_MATRIX) {
1571:       int n_cols,n_rows;
1572:       MatGetSize(*B,&n_rows,&n_cols);
1573:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1574:       MatZeroEntries(*B);
1575:       C = *B;
1576:     } else {
1577:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1578:     }
1579:     c = (Mat_SeqAIJ*)C->data;

1581:     /* loop over rows inserting into submatrix */
1582:     a_new    = c->a;
1583:     j_new    = c->j;
1584:     i_new    = c->i;
1585:     i_new[0] = -shift;
1586:     for (i=0; i<nrows; i++) {
1587:       ii    = starts[i];
1588:       lensi = lens[i];
1589:       for (k=0; k<lensi; k++) {
1590:         *j_new++ = aj[ii+k] - first;
1591:       }
1592:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1593:       a_new      += lensi;
1594:       i_new[i+1]  = i_new[i] + lensi;
1595:       c->ilen[i]  = lensi;
1596:     }
1597:     PetscFree(lens);
1598:   } else {
1599:     ierr  = ISGetIndices(iscol,&icol);
1600:     ierr  = PetscMalloc((1+oldcols)*sizeof(int),&smap);
1601:     ssmap = smap + shift;
1602:     ierr  = PetscMalloc((1+nrows)*sizeof(int),&lens);
1603:     ierr  = PetscMemzero(smap,oldcols*sizeof(int));
1604:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1605:     /* determine lens of each row */
1606:     for (i=0; i<nrows; i++) {
1607:       kstart  = ai[irow[i]]+shift;
1608:       kend    = kstart + a->ilen[irow[i]];
1609:       lens[i] = 0;
1610:       for (k=kstart; k<kend; k++) {
1611:         if (ssmap[aj[k]]) {
1612:           lens[i]++;
1613:         }
1614:       }
1615:     }
1616:     /* Create and fill new matrix */
1617:     if (scall == MAT_REUSE_MATRIX) {
1618:       PetscTruth equal;

1620:       c = (Mat_SeqAIJ *)((*B)->data);
1621:       if ((*B)->m  != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1622:       PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);
1623:       if (!equal) {
1624:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1625:       }
1626:       PetscMemzero(c->ilen,(*B)->m*sizeof(int));
1627:       C = *B;
1628:     } else {
1629:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1630:     }
1631:     c = (Mat_SeqAIJ *)(C->data);
1632:     for (i=0; i<nrows; i++) {
1633:       row    = irow[i];
1634:       kstart = ai[row]+shift;
1635:       kend   = kstart + a->ilen[row];
1636:       mat_i  = c->i[i]+shift;
1637:       mat_j  = c->j + mat_i;
1638:       mat_a  = c->a + mat_i;
1639:       mat_ilen = c->ilen + i;
1640:       for (k=kstart; k<kend; k++) {
1641:         if ((tcol=ssmap[a->j[k]])) {
1642:           *mat_j++ = tcol - (!shift);
1643:           *mat_a++ = a->a[k];
1644:           (*mat_ilen)++;

1646:         }
1647:       }
1648:     }
1649:     /* Free work space */
1650:     ISRestoreIndices(iscol,&icol);
1651:     PetscFree(smap);
1652:     PetscFree(lens);
1653:   }
1654:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1655:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1657:   ISRestoreIndices(isrow,&irow);
1658:   *B = C;
1659:   return(0);
1660: }

1662: /*
1663: */
1664: #undef __FUNCT__  
1666: int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatILUInfo *info)
1667: {
1668:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1669:   int        ierr;
1670:   Mat        outA;
1671:   PetscTruth row_identity,col_identity;

1674:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1675:   ISIdentity(row,&row_identity);
1676:   ISIdentity(col,&col_identity);
1677:   if (!row_identity || !col_identity) {
1678:     SETERRQ(1,"Row and column permutations must be identity for in-place ILU");
1679:   }

1681:   outA          = inA;
1682:   inA->factor   = FACTOR_LU;
1683:   a->row        = row;
1684:   a->col        = col;
1685:   PetscObjectReference((PetscObject)row);
1686:   PetscObjectReference((PetscObject)col);

1688:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1689:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1690:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1691:   PetscLogObjectParent(inA,a->icol);

1693:   if (!a->solve_work) { /* this matrix may have been factored before */
1694:      PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
1695:   }

1697:   if (!a->diag) {
1698:     MatMarkDiagonal_SeqAIJ(inA);
1699:   }
1700:   MatLUFactorNumeric_SeqAIJ(inA,&outA);
1701:   return(0);
1702: }

1704:  #include petscblaslapack.h
1705: #undef __FUNCT__  
1707: int MatScale_SeqAIJ(PetscScalar *alpha,Mat inA)
1708: {
1709:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1710:   int        one = 1;

1713:   BLscal_(&a->nz,alpha,a->a,&one);
1714:   PetscLogFlops(a->nz);
1715:   return(0);
1716: }

1718: #undef __FUNCT__  
1720: int MatGetSubMatrices_SeqAIJ(Mat A,int n,IS *irow,IS *icol,MatReuse scall,Mat **B)
1721: {
1722:   int ierr,i;

1725:   if (scall == MAT_INITIAL_MATRIX) {
1726:     PetscMalloc((n+1)*sizeof(Mat),B);
1727:   }

1729:   for (i=0; i<n; i++) {
1730:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1731:   }
1732:   return(0);
1733: }

1735: #undef __FUNCT__  
1737: int MatGetBlockSize_SeqAIJ(Mat A,int *bs)
1738: {
1740:   *bs = 1;
1741:   return(0);
1742: }

1744: #undef __FUNCT__  
1746: int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS *is,int ov)
1747: {
1748:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1749:   int        shift,row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val;
1750:   int        start,end,*ai,*aj;
1751:   PetscBT    table;

1754:   shift = a->indexshift;
1755:   m     = A->m;
1756:   ai    = a->i;
1757:   aj    = a->j+shift;

1759:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal overlap value used");

1761:   PetscMalloc((m+1)*sizeof(int),&nidx);
1762:   PetscBTCreate(m,table);

1764:   for (i=0; i<is_max; i++) {
1765:     /* Initialize the two local arrays */
1766:     isz  = 0;
1767:     PetscBTMemzero(m,table);
1768: 
1769:     /* Extract the indices, assume there can be duplicate entries */
1770:     ISGetIndices(is[i],&idx);
1771:     ISGetLocalSize(is[i],&n);
1772: 
1773:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1774:     for (j=0; j<n ; ++j){
1775:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1776:     }
1777:     ISRestoreIndices(is[i],&idx);
1778:     ISDestroy(is[i]);
1779: 
1780:     k = 0;
1781:     for (j=0; j<ov; j++){ /* for each overlap */
1782:       n = isz;
1783:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1784:         row   = nidx[k];
1785:         start = ai[row];
1786:         end   = ai[row+1];
1787:         for (l = start; l<end ; l++){
1788:           val = aj[l] + shift;
1789:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1790:         }
1791:       }
1792:     }
1793:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1794:   }
1795:   PetscBTDestroy(table);
1796:   PetscFree(nidx);
1797:   return(0);
1798: }

1800: /* -------------------------------------------------------------- */
1801: #undef __FUNCT__  
1803: int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1804: {
1805:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1806:   PetscScalar  *vwork;
1807:   int          i,ierr,nz,m = A->m,n = A->n,*cwork;
1808:   int          *row,*col,*cnew,j,*lens;
1809:   IS           icolp,irowp;

1812:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1813:   ISGetIndices(irowp,&row);
1814:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1815:   ISGetIndices(icolp,&col);
1816: 
1817:   /* determine lengths of permuted rows */
1818:   PetscMalloc((m+1)*sizeof(int),&lens);
1819:   for (i=0; i<m; i++) {
1820:     lens[row[i]] = a->i[i+1] - a->i[i];
1821:   }
1822:   MatCreateSeqAIJ(A->comm,m,n,0,lens,B);
1823:   PetscFree(lens);

1825:   PetscMalloc(n*sizeof(int),&cnew);
1826:   for (i=0; i<m; i++) {
1827:     MatGetRow(A,i,&nz,&cwork,&vwork);
1828:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1829:     MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1830:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
1831:   }
1832:   PetscFree(cnew);
1833:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1834:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1835:   ISRestoreIndices(irowp,&row);
1836:   ISRestoreIndices(icolp,&col);
1837:   ISDestroy(irowp);
1838:   ISDestroy(icolp);
1839:   return(0);
1840: }

1842: #undef __FUNCT__  
1844: int MatPrintHelp_SeqAIJ(Mat A)
1845: {
1846:   static PetscTruth called = PETSC_FALSE;
1847:   MPI_Comm          comm = A->comm;
1848:   int               ierr;

1851:   if (called) {return(0);} else called = PETSC_TRUE;
1852:   (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):n");
1853:   (*PetscHelpPrintf)(comm,"  -mat_lu_pivotthreshold <threshold>: Set pivoting thresholdn");
1854:   (*PetscHelpPrintf)(comm,"  -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.n");
1855:   (*PetscHelpPrintf)(comm,"  -mat_aij_no_inode: Do not use inodesn");
1856:   (*PetscHelpPrintf)(comm,"  -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)n");
1857: #if defined(PETSC_HAVE_ESSL)
1858:   (*PetscHelpPrintf)(comm,"  -mat_aij_essl: Use IBM sparse LU factorization and solve.n");
1859: #endif
1860: #if defined(PETSC_HAVE_LUSOL)
1861:   (*PetscHelpPrintf)(comm,"  -mat_aij_lusol: Use the Stanford LUSOL sparse factorization and solve.n");
1862: #endif
1863: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
1864:   (*PetscHelpPrintf)(comm,"  -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.n");
1865: #endif
1866:   return(0);
1867: }
1868: EXTERN int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg);
1869: EXTERN int MatFDColoringCreate_SeqAIJ(Mat,ISColoring,MatFDColoring);
1870: EXTERN int MatILUDTFactor_SeqAIJ(Mat,MatILUInfo*,IS,IS,Mat*);
1871: #undef __FUNCT__  
1873: int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1874: {
1875:   int        ierr;
1876:   PetscTruth flg;

1879:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg);
1880:   if (str == SAME_NONZERO_PATTERN && flg) {
1881:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1882:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1884:     if (a->i[A->m]+a->indexshift != b->i[B->m]+a->indexshift) {
1885:       SETERRQ(1,"Number of nonzeros in two matrices are different");
1886:     }
1887:     PetscMemcpy(b->a,a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
1888:   } else {
1889:     MatCopy_Basic(A,B,str);
1890:   }
1891:   return(0);
1892: }

1894: #undef __FUNCT__  
1896: int MatSetUpPreallocation_SeqAIJ(Mat A)
1897: {
1898:   int        ierr;

1901:    MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);
1902:   return(0);
1903: }

1905: #undef __FUNCT__  
1907: int MatGetArray_SeqAIJ(Mat A,PetscScalar **array)
1908: {
1909:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1911:   *array = a->a;
1912:   return(0);
1913: }

1915: #undef __FUNCT__  
1917: int MatRestoreArray_SeqAIJ(Mat A,PetscScalar **array)
1918: {
1920:   return(0);
1921: }

1923: #undef __FUNCT__  
1925: int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1926: {
1927:   int           (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f;
1928:   int           k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1929:   PetscScalar   dx,mone = -1.0,*y,*xx,*w3_array;
1930:   PetscScalar   *vscale_array;
1931:   PetscReal     epsilon = coloring->error_rel,umin = coloring->umin;
1932:   Vec           w1,w2,w3;
1933:   void          *fctx = coloring->fctx;
1934:   PetscTruth    flg;

1937:   if (!coloring->w1) {
1938:     VecDuplicate(x1,&coloring->w1);
1939:     PetscLogObjectParent(coloring,coloring->w1);
1940:     VecDuplicate(x1,&coloring->w2);
1941:     PetscLogObjectParent(coloring,coloring->w2);
1942:     VecDuplicate(x1,&coloring->w3);
1943:     PetscLogObjectParent(coloring,coloring->w3);
1944:   }
1945:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

1947:   MatSetUnfactored(J);
1948:   PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);
1949:   if (flg) {
1950:     PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()n");
1951:   } else {
1952:     MatZeroEntries(J);
1953:   }

1955:   VecGetOwnershipRange(x1,&start,&end);
1956:   VecGetSize(x1,&N);

1958:   /*
1959:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
1960:      coloring->F for the coarser grids from the finest
1961:   */
1962:   if (coloring->F) {
1963:     VecGetLocalSize(coloring->F,&m1);
1964:     VecGetLocalSize(w1,&m2);
1965:     if (m1 != m2) {
1966:       coloring->F = 0;
1967:     }
1968:   }

1970:   if (coloring->F) {
1971:     w1          = coloring->F;
1972:     coloring->F = 0;
1973:   } else {
1974:     (*f)(sctx,x1,w1,fctx);
1975:   }

1977:   /* 
1978:       Compute all the scale factors and share with other processors
1979:   */
1980:   VecGetArray(x1,&xx);xx = xx - start;
1981:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
1982:   for (k=0; k<coloring->ncolors; k++) {
1983:     /*
1984:        Loop over each column associated with color adding the 
1985:        perturbation to the vector w3.
1986:     */
1987:     for (l=0; l<coloring->ncolumns[k]; l++) {
1988:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1989:       dx  = xx[col];
1990:       if (dx == 0.0) dx = 1.0;
1991: #if !defined(PETSC_USE_COMPLEX)
1992:       if (dx < umin && dx >= 0.0)      dx = umin;
1993:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1994: #else
1995:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1996:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1997: #endif
1998:       dx                *= epsilon;
1999:       vscale_array[col] = 1.0/dx;
2000:     }
2001:   }
2002:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2003:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2004:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2006:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2007:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2009:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2010:   else                        vscaleforrow = coloring->columnsforrow;

2012:   VecGetArray(coloring->vscale,&vscale_array);
2013:   /*
2014:       Loop over each color
2015:   */
2016:   for (k=0; k<coloring->ncolors; k++) {
2017:     coloring->currentcolor = k;
2018:     VecCopy(x1,w3);
2019:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2020:     /*
2021:        Loop over each column associated with color adding the 
2022:        perturbation to the vector w3.
2023:     */
2024:     for (l=0; l<coloring->ncolumns[k]; l++) {
2025:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2026:       dx  = xx[col];
2027:       if (dx == 0.0) dx = 1.0;
2028: #if !defined(PETSC_USE_COMPLEX)
2029:       if (dx < umin && dx >= 0.0)      dx = umin;
2030:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2031: #else
2032:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2033:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2034: #endif
2035:       dx            *= epsilon;
2036:       if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter");
2037:       w3_array[col] += dx;
2038:     }
2039:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2041:     /*
2042:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2043:     */

2045:     (*f)(sctx,w3,w2,fctx);
2046:     VecAXPY(&mone,w1,w2);

2048:     /*
2049:        Loop over rows of vector, putting results into Jacobian matrix
2050:     */
2051:     VecGetArray(w2,&y);
2052:     for (l=0; l<coloring->nrows[k]; l++) {
2053:       row    = coloring->rows[k][l];
2054:       col    = coloring->columnsforrow[k][l];
2055:       y[row] *= vscale_array[vscaleforrow[k][l]];
2056:       srow   = row + start;
2057:       ierr   = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2058:     }
2059:     VecRestoreArray(w2,&y);
2060:   }
2061:   coloring->currentcolor = k;
2062:   VecRestoreArray(coloring->vscale,&vscale_array);
2063:   xx = xx + start; ierr  = VecRestoreArray(x1,&xx);
2064:   ierr  = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2065:   ierr  = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2066:   return(0);
2067: }

2069:  #include petscblaslapack.h

2071: #undef __FUNCT__  
2073: int MatAXPY_SeqAIJ(PetscScalar *a,Mat X,Mat Y,MatStructure str)
2074: {
2075:   int        ierr,one=1;
2076:   Mat_SeqAIJ *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;

2079:   if (str == SAME_NONZERO_PATTERN) {
2080:     BLaxpy_(&x->nz,a,x->a,&one,y->a,&one);
2081:   } else {
2082:     MatAXPY_Basic(a,X,Y,str);
2083:   }
2084:   return(0);
2085: }


2088: /* -------------------------------------------------------------------*/
2089: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2090:        MatGetRow_SeqAIJ,
2091:        MatRestoreRow_SeqAIJ,
2092:        MatMult_SeqAIJ,
2093:        MatMultAdd_SeqAIJ,
2094:        MatMultTranspose_SeqAIJ,
2095:        MatMultTransposeAdd_SeqAIJ,
2096:        MatSolve_SeqAIJ,
2097:        MatSolveAdd_SeqAIJ,
2098:        MatSolveTranspose_SeqAIJ,
2099:        MatSolveTransposeAdd_SeqAIJ,
2100:        MatLUFactor_SeqAIJ,
2101:        0,
2102:        MatRelax_SeqAIJ,
2103:        MatTranspose_SeqAIJ,
2104:        MatGetInfo_SeqAIJ,
2105:        MatEqual_SeqAIJ,
2106:        MatGetDiagonal_SeqAIJ,
2107:        MatDiagonalScale_SeqAIJ,
2108:        MatNorm_SeqAIJ,
2109:        0,
2110:        MatAssemblyEnd_SeqAIJ,
2111:        MatCompress_SeqAIJ,
2112:        MatSetOption_SeqAIJ,
2113:        MatZeroEntries_SeqAIJ,
2114:        MatZeroRows_SeqAIJ,
2115:        MatLUFactorSymbolic_SeqAIJ,
2116:        MatLUFactorNumeric_SeqAIJ,
2117:        0,
2118:        MatCholeskyFactorNumeric_SeqAIJ,
2119:        MatSetUpPreallocation_SeqAIJ,
2120:        MatILUFactorSymbolic_SeqAIJ,
2121:        MatICCFactorSymbolic_SeqAIJ,
2122:        MatGetArray_SeqAIJ,
2123:        MatRestoreArray_SeqAIJ,
2124:        MatDuplicate_SeqAIJ,
2125:        0,
2126:        0,
2127:        MatILUFactor_SeqAIJ,
2128:        0,
2129:        MatAXPY_SeqAIJ,
2130:        MatGetSubMatrices_SeqAIJ,
2131:        MatIncreaseOverlap_SeqAIJ,
2132:        MatGetValues_SeqAIJ,
2133:        MatCopy_SeqAIJ,
2134:        MatPrintHelp_SeqAIJ,
2135:        MatScale_SeqAIJ,
2136:        0,
2137:        0,
2138:        MatILUDTFactor_SeqAIJ,
2139:        MatGetBlockSize_SeqAIJ,
2140:        MatGetRowIJ_SeqAIJ,
2141:        MatRestoreRowIJ_SeqAIJ,
2142:        MatGetColumnIJ_SeqAIJ,
2143:        MatRestoreColumnIJ_SeqAIJ,
2144:        MatFDColoringCreate_SeqAIJ,
2145:        0,
2146:        0,
2147:        MatPermute_SeqAIJ,
2148:        0,
2149:        0,
2150:        MatDestroy_SeqAIJ,
2151:        MatView_SeqAIJ,
2152:        MatGetPetscMaps_Petsc,
2153:        0,
2154:        0,
2155:        0,
2156:        0,
2157:        0,
2158:        0,
2159:        0,
2160:        0,
2161:        MatSetColoring_SeqAIJ,
2162:        MatSetValuesAdic_SeqAIJ,
2163:        MatSetValuesAdifor_SeqAIJ,
2164:        MatFDColoringApply_SeqAIJ};

2166: EXTERN_C_BEGIN
2167: #undef __FUNCT__  

2170: int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices)
2171: {
2172:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2173:   int        i,nz,n;


2177:   nz = aij->maxnz;
2178:   n  = mat->n;
2179:   for (i=0; i<nz; i++) {
2180:     aij->j[i] = indices[i];
2181:   }
2182:   aij->nz = nz;
2183:   for (i=0; i<n; i++) {
2184:     aij->ilen[i] = aij->imax[i];
2185:   }

2187:   return(0);
2188: }
2189: EXTERN_C_END

2191: #undef __FUNCT__  
2193: /*@
2194:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2195:        in the matrix.

2197:   Input Parameters:
2198: +  mat - the SeqAIJ matrix
2199: -  indices - the column indices

2201:   Level: advanced

2203:   Notes:
2204:     This can be called if you have precomputed the nonzero structure of the 
2205:   matrix and want to provide it to the matrix object to improve the performance
2206:   of the MatSetValues() operation.

2208:     You MUST have set the correct numbers of nonzeros per row in the call to 
2209:   MatCreateSeqAIJ().

2211:     MUST be called before any calls to MatSetValues();

2213:     The indices should start with zero, not one.

2215: @*/
2216: int MatSeqAIJSetColumnIndices(Mat mat,int *indices)
2217: {
2218:   int ierr,(*f)(Mat,int *);

2222:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2223:   if (f) {
2224:     (*f)(mat,indices);
2225:   } else {
2226:     SETERRQ(1,"Wrong type of matrix to set column indices");
2227:   }
2228:   return(0);
2229: }

2231: /* ----------------------------------------------------------------------------------------*/

2233: EXTERN_C_BEGIN
2234: #undef __FUNCT__  
2236: int MatStoreValues_SeqAIJ(Mat mat)
2237: {
2238:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2239:   size_t       nz = aij->i[mat->m]+aij->indexshift,ierr;

2242:   if (aij->nonew != 1) {
2243:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2244:   }

2246:   /* allocate space for values if not already there */
2247:   if (!aij->saved_values) {
2248:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2249:   }

2251:   /* copy values over */
2252:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2253:   return(0);
2254: }
2255: EXTERN_C_END

2257: #undef __FUNCT__  
2259: /*@
2260:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2261:        example, reuse of the linear part of a Jacobian, while recomputing the 
2262:        nonlinear portion.

2264:    Collect on Mat

2266:   Input Parameters:
2267: .  mat - the matrix (currently on AIJ matrices support this option)

2269:   Level: advanced

2271:   Common Usage, with SNESSolve():
2272: $    Create Jacobian matrix
2273: $    Set linear terms into matrix
2274: $    Apply boundary conditions to matrix, at this time matrix must have 
2275: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2276: $      boundary conditions again will not change the nonzero structure
2277: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2278: $    MatStoreValues(mat);
2279: $    Call SNESSetJacobian() with matrix
2280: $    In your Jacobian routine
2281: $      MatRetrieveValues(mat);
2282: $      Set nonlinear terms in matrix
2283:  
2284:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2285: $    // build linear portion of Jacobian 
2286: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2287: $    MatStoreValues(mat);
2288: $    loop over nonlinear iterations
2289: $       MatRetrieveValues(mat);
2290: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2291: $       // call MatAssemblyBegin/End() on matrix
2292: $       Solve linear system with Jacobian
2293: $    endloop 

2295:   Notes:
2296:     Matrix must already be assemblied before calling this routine
2297:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 
2298:     calling this routine.

2300: .seealso: MatRetrieveValues()

2302: @*/
2303: int MatStoreValues(Mat mat)
2304: {
2305:   int ierr,(*f)(Mat);

2309:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2310:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2312:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2313:   if (f) {
2314:     (*f)(mat);
2315:   } else {
2316:     SETERRQ(1,"Wrong type of matrix to store values");
2317:   }
2318:   return(0);
2319: }

2321: EXTERN_C_BEGIN
2322: #undef __FUNCT__  
2324: int MatRetrieveValues_SeqAIJ(Mat mat)
2325: {
2326:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2327:   int        nz = aij->i[mat->m]+aij->indexshift,ierr;

2330:   if (aij->nonew != 1) {
2331:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2332:   }
2333:   if (!aij->saved_values) {
2334:     SETERRQ(1,"Must call MatStoreValues(A);first");
2335:   }

2337:   /* copy values over */
2338:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2339:   return(0);
2340: }
2341: EXTERN_C_END

2343: #undef __FUNCT__  
2345: /*@
2346:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2347:        example, reuse of the linear part of a Jacobian, while recomputing the 
2348:        nonlinear portion.

2350:    Collect on Mat

2352:   Input Parameters:
2353: .  mat - the matrix (currently on AIJ matrices support this option)

2355:   Level: advanced

2357: .seealso: MatStoreValues()

2359: @*/
2360: int MatRetrieveValues(Mat mat)
2361: {
2362:   int ierr,(*f)(Mat);

2366:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2367:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2369:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2370:   if (f) {
2371:     (*f)(mat);
2372:   } else {
2373:     SETERRQ(1,"Wrong type of matrix to retrieve values");
2374:   }
2375:   return(0);
2376: }

2378: /*
2379:    This allows SeqAIJ matrices to be passed to the matlab engine
2380: */
2381: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2382: #include "engine.h"   /* Matlab include file */
2383: #include "mex.h"      /* Matlab include file */
2384: EXTERN_C_BEGIN
2385: #undef __FUNCT__  
2387: int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *mengine)
2388: {
2389:   int         ierr,i,*ai,*aj;
2390:   Mat         B = (Mat)obj;
2391:   mxArray     *mat;
2392:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)B->data;

2395:   mat  = mxCreateSparse(B->n,B->m,aij->nz,mxREAL);
2396:   PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));
2397:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2398:   PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));
2399:   PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));

2401:   /* Matlab indices start at 0 for sparse (what a surprise) */
2402:   if (aij->indexshift) {
2403:     ai = mxGetJc(mat);
2404:     for (i=0; i<B->m+1; i++) {
2405:       ai[i]--;
2406:     }
2407:     aj = mxGetIr(mat);
2408:     for (i=0; i<aij->nz; i++) {
2409:       aj[i]--;
2410:     }
2411:   }
2412:   PetscObjectName(obj);
2413:   mxSetName(mat,obj->name);
2414:   engPutArray((Engine *)mengine,mat);
2415:   return(0);
2416: }
2417: EXTERN_C_END

2419: EXTERN_C_BEGIN
2420: #undef __FUNCT__  
2422: int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *mengine)
2423: {
2424:   int        ierr,ii;
2425:   Mat        mat = (Mat)obj;
2426:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2427:   mxArray    *mmat;

2430:   PetscFree(aij->a);
2431:   aij->indexshift = 0;

2433:   mmat = engGetArray((Engine *)mengine,obj->name);

2435:   aij->nz           = (mxGetJc(mmat))[mat->m];
2436:   ierr              = PetscMalloc(((size_t) aij->nz)*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);
2437:   aij->j            = (int*)(aij->a + aij->nz);
2438:   aij->i            = aij->j + aij->nz;
2439:   aij->singlemalloc = PETSC_TRUE;
2440:   aij->freedata     = PETSC_TRUE;

2442:   PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));
2443:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2444:   PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));
2445:   PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));

2447:   for (ii=0; ii<mat->m; ii++) {
2448:     aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii];
2449:   }

2451:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
2452:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);

2454:   return(0);
2455: }
2456: EXTERN_C_END
2457: #endif

2459: /* --------------------------------------------------------------------------------*/
2460: #undef __FUNCT__  
2462: /*@C
2463:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2464:    (the default parallel PETSc format).  For good matrix assembly performance
2465:    the user should preallocate the matrix storage by setting the parameter nz
2466:    (or the array nnz).  By setting these parameters accurately, performance
2467:    during matrix assembly can be increased by more than a factor of 50.

2469:    Collective on MPI_Comm

2471:    Input Parameters:
2472: +  comm - MPI communicator, set to PETSC_COMM_SELF
2473: .  m - number of rows
2474: .  n - number of columns
2475: .  nz - number of nonzeros per row (same for all rows)
2476: -  nnz - array containing the number of nonzeros in the various rows 
2477:          (possibly different for each row) or PETSC_NULL

2479:    Output Parameter:
2480: .  A - the matrix 

2482:    Notes:
2483:    The AIJ format (also called the Yale sparse matrix format or
2484:    compressed row storage), is fully compatible with standard Fortran 77
2485:    storage.  That is, the stored row and column indices can begin at
2486:    either one (as in Fortran) or zero.  See the users' manual for details.

2488:    Specify the preallocated storage with either nz or nnz (not both).
2489:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2490:    allocation.  For large problems you MUST preallocate memory or you 
2491:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2493:    By default, this format uses inodes (identical nodes) when possible, to 
2494:    improve numerical efficiency of matrix-vector products and solves. We 
2495:    search for consecutive rows with the same nonzero structure, thereby
2496:    reusing matrix information to achieve increased efficiency.

2498:    Options Database Keys:
2499: +  -mat_aij_no_inode  - Do not use inodes
2500: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2501: -  -mat_aij_oneindex - Internally use indexing starting at 1
2502:         rather than 0.  Note that when calling MatSetValues(),
2503:         the user still MUST index entries starting at 0!

2505:    Level: intermediate

2507: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2509: @*/
2510: int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,int *nnz,Mat *A)
2511: {

2515:   MatCreate(comm,m,n,m,n,A);
2516:   MatSetType(*A,MATSEQAIJ);
2517:   MatSeqAIJSetPreallocation(*A,nz,nnz);
2518:   return(0);
2519: }

2521: #define SKIP_ALLOCATION -4

2523: #undef __FUNCT__  
2525: /*@C
2526:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2527:    the user should preallocate the matrix storage by setting the parameter nz
2528:    (or the array nnz).  By setting these parameters accurately, performance
2529:    during matrix assembly can be increased by more than a factor of 50.

2531:    Collective on MPI_Comm

2533:    Input Parameters:
2534: +  comm - MPI communicator, set to PETSC_COMM_SELF
2535: .  m - number of rows
2536: .  n - number of columns
2537: .  nz - number of nonzeros per row (same for all rows)
2538: -  nnz - array containing the number of nonzeros in the various rows 
2539:          (possibly different for each row) or PETSC_NULL

2541:    Output Parameter:
2542: .  A - the matrix 

2544:    Notes:
2545:    The AIJ format (also called the Yale sparse matrix format or
2546:    compressed row storage), is fully compatible with standard Fortran 77
2547:    storage.  That is, the stored row and column indices can begin at
2548:    either one (as in Fortran) or zero.  See the users' manual for details.

2550:    Specify the preallocated storage with either nz or nnz (not both).
2551:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2552:    allocation.  For large problems you MUST preallocate memory or you 
2553:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2555:    By default, this format uses inodes (identical nodes) when possible, to 
2556:    improve numerical efficiency of matrix-vector products and solves. We 
2557:    search for consecutive rows with the same nonzero structure, thereby
2558:    reusing matrix information to achieve increased efficiency.

2560:    Options Database Keys:
2561: +  -mat_aij_no_inode  - Do not use inodes
2562: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2563: -  -mat_aij_oneindex - Internally use indexing starting at 1
2564:         rather than 0.  Note that when calling MatSetValues(),
2565:         the user still MUST index entries starting at 0!

2567:    Level: intermediate

2569: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2571: @*/
2572: int MatSeqAIJSetPreallocation(Mat B,int nz,int *nnz)
2573: {
2574:   Mat_SeqAIJ *b;
2575:   size_t     len = 0;
2576:   PetscTruth flg2,skipallocation = PETSC_FALSE;
2577:   int        i,ierr;

2580:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg2);
2581:   if (!flg2) return(0);
2582: 
2583:   if (nz == SKIP_ALLOCATION) {
2584:     skipallocation = PETSC_TRUE;
2585:     nz             = 0;
2586:   }

2588:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2589:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2590:   if (nnz) {
2591:     for (i=0; i<B->m; i++) {
2592:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2593:       if (nnz[i] > B->n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->n);
2594:     }
2595:   }

2597:   B->preallocated = PETSC_TRUE;
2598:   b = (Mat_SeqAIJ*)B->data;

2600:   PetscMalloc((B->m+1)*sizeof(int),&b->imax);
2601:   if (!nnz) {
2602:     if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2603:     else if (nz <= 0)        nz = 1;
2604:     for (i=0; i<B->m; i++) b->imax[i] = nz;
2605:     nz = nz*B->m;
2606:   } else {
2607:     nz = 0;
2608:     for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2609:   }

2611:   if (!skipallocation) {
2612:     /* allocate the matrix space */
2613:     len             = ((size_t) nz)*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int);
2614:     ierr            = PetscMalloc(len,&b->a);
2615:     b->j            = (int*)(b->a + nz);
2616:     ierr            = PetscMemzero(b->j,nz*sizeof(int));
2617:     b->i            = b->j + nz;
2618:     b->i[0] = -b->indexshift;
2619:     for (i=1; i<B->m+1; i++) {
2620:       b->i[i] = b->i[i-1] + b->imax[i-1];
2621:     }
2622:     b->singlemalloc = PETSC_TRUE;
2623:     b->freedata     = PETSC_TRUE;
2624:   } else {
2625:     b->freedata     = PETSC_FALSE;
2626:   }

2628:   /* b->ilen will count nonzeros in each row so far. */
2629:   PetscMalloc((B->m+1)*sizeof(int),&b->ilen);
2630:   PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2631:   for (i=0; i<B->m; i++) { b->ilen[i] = 0;}

2633:   b->nz                = 0;
2634:   b->maxnz             = nz;
2635:   B->info.nz_unneeded  = (double)b->maxnz;
2636:   return(0);
2637: }

2639: EXTERN int RegisterApplyPtAPRoutines_Private(Mat);

2641: EXTERN_C_BEGIN
2642: extern int MatConvert_SeqAIJ_SeqSBAIJ(Mat,MatType,Mat*);
2643: EXTERN_C_END

2645: EXTERN_C_BEGIN
2646: #undef __FUNCT__  
2648: int MatCreate_SeqAIJ(Mat B)
2649: {
2650:   Mat_SeqAIJ *b;
2651:   int        ierr,size;
2652:   PetscTruth flg;

2655:   MPI_Comm_size(B->comm,&size);
2656:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

2658:   B->m = B->M = PetscMax(B->m,B->M);
2659:   B->n = B->N = PetscMax(B->n,B->N);

2661:   PetscNew(Mat_SeqAIJ,&b);
2662:   B->data             = (void*)b;
2663:   PetscMemzero(b,sizeof(Mat_SeqAIJ));
2664:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2665:   B->factor           = 0;
2666:   B->lupivotthreshold = 1.0;
2667:   B->mapping          = 0;
2668:   PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);
2669:   PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);
2670:   b->row              = 0;
2671:   b->col              = 0;
2672:   b->icol             = 0;
2673:   b->indexshift       = 0;
2674:   b->reallocs         = 0;
2675:   PetscOptionsHasName(B->prefix,"-mat_aij_oneindex",&flg);
2676:   if (flg) b->indexshift = -1;
2677: 
2678:   PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
2679:   PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);

2681:   b->sorted            = PETSC_FALSE;
2682:   b->ignorezeroentries = PETSC_FALSE;
2683:   b->roworiented       = PETSC_TRUE;
2684:   b->nonew             = 0;
2685:   b->diag              = 0;
2686:   b->solve_work        = 0;
2687:   B->spptr             = 0;
2688:   b->inode.use         = PETSC_TRUE;
2689:   b->inode.node_count  = 0;
2690:   b->inode.size        = 0;
2691:   b->inode.limit       = 5;
2692:   b->inode.max_limit   = 5;
2693:   b->saved_values      = 0;
2694:   b->idiag             = 0;
2695:   b->ssor              = 0;
2696:   b->keepzeroedrows    = PETSC_FALSE;

2698:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

2700: #if defined(PETSC_HAVE_SUPERLU)
2701:   PetscOptionsHasName(B->prefix,"-mat_aij_superlu",&flg);
2702:   if (flg) { MatUseSuperLU_SeqAIJ(B); }
2703: #endif

2705:   PetscOptionsHasName(B->prefix,"-mat_aij_essl",&flg);
2706:   if (flg) { MatUseEssl_SeqAIJ(B); }
2707:   PetscOptionsHasName(B->prefix,"-mat_aij_lusol",&flg);
2708:   if (flg) { MatUseLUSOL_SeqAIJ(B); }
2709:   PetscOptionsHasName(B->prefix,"-mat_aij_matlab",&flg);
2710:   if (flg) {MatUseMatlab_SeqAIJ(B);}
2711:   PetscOptionsHasName(B->prefix,"-mat_aij_dxml",&flg);
2712:   if (flg) {
2713:     if (!b->indexshift) SETERRQ(PETSC_ERR_LIB,"need -mat_aij_oneindex with -mat_aij_dxml");
2714:     MatUseDXML_SeqAIJ(B);
2715:   }
2716:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2717:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
2718:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
2719:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2720:                                      "MatStoreValues_SeqAIJ",
2721:                                      MatStoreValues_SeqAIJ);
2722:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2723:                                      "MatRetrieveValues_SeqAIJ",
2724:                                      MatRetrieveValues_SeqAIJ);
2725:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
2726:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
2727:                                       MatConvert_SeqAIJ_SeqSBAIJ);
2728: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2729:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);
2730:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);
2731: #endif
2732:   RegisterApplyPtAPRoutines_Private(B);
2733:   return(0);
2734: }
2735: EXTERN_C_END

2737: #undef __FUNCT__  
2739: int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2740: {
2741:   Mat        C;
2742:   Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
2743:   int        i,m = A->m,shift = a->indexshift,ierr;
2744:   size_t     len;

2747:   *B = 0;
2748:   MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);
2749:   MatSetType(C,MATSEQAIJ);
2750:   c    = (Mat_SeqAIJ*)C->data;

2752:   C->factor         = A->factor;
2753:   c->row            = 0;
2754:   c->col            = 0;
2755:   c->icol           = 0;
2756:   c->indexshift     = shift;
2757:   c->keepzeroedrows = a->keepzeroedrows;
2758:   C->assembled      = PETSC_TRUE;

2760:   C->M          = A->m;
2761:   C->N          = A->n;

2763:   PetscMalloc((m+1)*sizeof(int),&c->imax);
2764:   PetscMalloc((m+1)*sizeof(int),&c->ilen);
2765:   for (i=0; i<m; i++) {
2766:     c->imax[i] = a->imax[i];
2767:     c->ilen[i] = a->ilen[i];
2768:   }

2770:   /* allocate the matrix space */
2771:   c->singlemalloc = PETSC_TRUE;
2772:   len   = ((size_t) (m+1))*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int));
2773:   ierr  = PetscMalloc(len,&c->a);
2774:   c->j  = (int*)(c->a + a->i[m] + shift);
2775:   c->i  = c->j + a->i[m] + shift;
2776:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));
2777:   if (m > 0) {
2778:     PetscMemcpy(c->j,a->j,(a->i[m]+shift)*sizeof(int));
2779:     if (cpvalues == MAT_COPY_VALUES) {
2780:       PetscMemcpy(c->a,a->a,(a->i[m]+shift)*sizeof(PetscScalar));
2781:     } else {
2782:       PetscMemzero(c->a,(a->i[m]+shift)*sizeof(PetscScalar));
2783:     }
2784:   }

2786:   PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2787:   c->sorted      = a->sorted;
2788:   c->roworiented = a->roworiented;
2789:   c->nonew       = a->nonew;
2790:   c->ilu_preserve_row_sums = a->ilu_preserve_row_sums;
2791:   c->saved_values = 0;
2792:   c->idiag        = 0;
2793:   c->ssor         = 0;
2794:   c->ignorezeroentries = a->ignorezeroentries;
2795:   c->freedata     = PETSC_TRUE;

2797:   if (a->diag) {
2798:     PetscMalloc((m+1)*sizeof(int),&c->diag);
2799:     PetscLogObjectMemory(C,(m+1)*sizeof(int));
2800:     for (i=0; i<m; i++) {
2801:       c->diag[i] = a->diag[i];
2802:     }
2803:   } else c->diag        = 0;
2804:   c->inode.use          = a->inode.use;
2805:   c->inode.limit        = a->inode.limit;
2806:   c->inode.max_limit    = a->inode.max_limit;
2807:   if (a->inode.size){
2808:     ierr                = PetscMalloc((m+1)*sizeof(int),&c->inode.size);
2809:     c->inode.node_count = a->inode.node_count;
2810:     ierr                = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));
2811:   } else {
2812:     c->inode.size       = 0;
2813:     c->inode.node_count = 0;
2814:   }
2815:   c->nz                 = a->nz;
2816:   c->maxnz              = a->maxnz;
2817:   c->solve_work         = 0;
2818:   C->spptr              = 0;      /* Dangerous -I'm throwing away a->spptr */
2819:   C->preallocated       = PETSC_TRUE;

2821:   *B = C;
2822:   PetscFListDuplicate(A->qlist,&C->qlist);
2823:   return(0);
2824: }

2826: EXTERN_C_BEGIN
2827: #undef __FUNCT__  
2829: int MatLoad_SeqAIJ(PetscViewer viewer,MatType type,Mat *A)
2830: {
2831:   Mat_SeqAIJ   *a;
2832:   Mat          B;
2833:   int          i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N,shift;
2834:   MPI_Comm     comm;
2835: 
2837:   PetscObjectGetComm((PetscObject)viewer,&comm);
2838:   MPI_Comm_size(comm,&size);
2839:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
2840:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2841:   PetscBinaryRead(fd,header,4,PETSC_INT);
2842:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
2843:   M = header[1]; N = header[2]; nz = header[3];

2845:   if (nz < 0) {
2846:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
2847:   }

2849:   /* read in row lengths */
2850:   PetscMalloc(M*sizeof(int),&rowlengths);
2851:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

2853:   /* create our matrix */
2854:   MatCreateSeqAIJ(comm,M,N,0,rowlengths,A);
2855:   B = *A;
2856:   a = (Mat_SeqAIJ*)B->data;
2857:   shift = a->indexshift;

2859:   /* read in column indices and adjust for Fortran indexing*/
2860:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);
2861:   if (shift) {
2862:     for (i=0; i<nz; i++) {
2863:       a->j[i] += 1;
2864:     }
2865:   }

2867:   /* read in nonzero values */
2868:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

2870:   /* set matrix "i" values */
2871:   a->i[0] = -shift;
2872:   for (i=1; i<= M; i++) {
2873:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
2874:     a->ilen[i-1] = rowlengths[i-1];
2875:   }
2876:   PetscFree(rowlengths);

2878:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2879:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2880:   return(0);
2881: }
2882: EXTERN_C_END

2884: #undef __FUNCT__  
2886: int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
2887: {
2888:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
2889:   int        ierr;
2890:   PetscTruth flag;

2893:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flag);
2894:   if (!flag) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");

2896:   /* If the  matrix dimensions are not equal,or no of nonzeros or shift */
2897:   if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)|| (a->indexshift != b->indexshift)) {
2898:     *flg = PETSC_FALSE;
2899:     return(0);
2900:   }
2901: 
2902:   /* if the a->i are the same */
2903:   PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);
2904:   if (*flg == PETSC_FALSE) return(0);
2905: 
2906:   /* if a->j are the same */
2907:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);
2908:   if (*flg == PETSC_FALSE) return(0);
2909: 
2910:   /* if a->a are the same */
2911:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

2913:   return(0);
2914: 
2915: }

2917: #undef __FUNCT__  
2919: /*@C
2920:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
2921:               provided by the user.

2923:       Coolective on MPI_Comm

2925:    Input Parameters:
2926: +   comm - must be an MPI communicator of size 1
2927: .   m - number of rows
2928: .   n - number of columns
2929: .   i - row indices
2930: .   j - column indices
2931: -   a - matrix values

2933:    Output Parameter:
2934: .   mat - the matrix

2936:    Level: intermediate

2938:    Notes:
2939:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2940:     once the matrix is destroyed

2942:        You cannot set new nonzero locations into this matrix, that will generate an error.

2944:        The i and j indices can be either 0- or 1 based

2946: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()

2948: @*/
2949: int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat)
2950: {
2951:   int        ierr,ii;
2952:   Mat_SeqAIJ *aij;

2955:   MatCreateSeqAIJ(comm,m,n,SKIP_ALLOCATION,0,mat);
2956:   aij  = (Mat_SeqAIJ*)(*mat)->data;

2958:   if (i[0] == 1) {
2959:     aij->indexshift = -1;
2960:   } else if (i[0]) {
2961:     SETERRQ(1,"i (row indices) do not start with 0 or 1");
2962:   }
2963:   aij->i = i;
2964:   aij->j = j;
2965:   aij->a = a;
2966:   aij->singlemalloc = PETSC_FALSE;
2967:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2968:   aij->freedata     = PETSC_FALSE;

2970:   for (ii=0; ii<m; ii++) {
2971:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
2972: #if defined(PETSC_USE_BOPT_g)
2973:     if (i[ii+1] - i[ii] < 0) SETERRQ2(1,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
2974: #endif    
2975:   }
2976: #if defined(PETSC_USE_BOPT_g)
2977:   for (ii=0; ii<aij->i[m]; ii++) {
2978:     if (j[ii] < -aij->indexshift) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]);
2979:     if (j[ii] > n - 1 -aij->indexshift) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]);
2980:   }
2981: #endif    

2983:   /* changes indices to start at 0 */
2984:   if (i[0]) {
2985:     aij->indexshift = 0;
2986:     for (ii=0; ii<m; ii++) {
2987:       i[ii]--;
2988:     }
2989:     for (ii=0; ii<i[m]; ii++) {
2990:       j[ii]--;
2991:     }
2992:   }

2994:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2995:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2996:   return(0);
2997: }

2999: #undef __FUNCT__  
3001: int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
3002: {
3003:   int        ierr;
3004:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

3007:   if (coloring->ctype == IS_COLORING_LOCAL) {
3008:     ierr        = ISColoringReference(coloring);
3009:     a->coloring = coloring;
3010:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3011:     int        *colors,i,*larray;
3012:     ISColoring ocoloring;

3014:     /* set coloring for diagonal portion */
3015:     PetscMalloc((A->n+1)*sizeof(int),&larray);
3016:     for (i=0; i<A->n; i++) {
3017:       larray[i] = i;
3018:     }
3019:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);
3020:     PetscMalloc((A->n+1)*sizeof(int),&colors);
3021:     for (i=0; i<A->n; i++) {
3022:       colors[i] = coloring->colors[larray[i]];
3023:     }
3024:     PetscFree(larray);
3025:     ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);
3026:     a->coloring = ocoloring;
3027:   }
3028:   return(0);
3029: }

3031: #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
3032: EXTERN_C_BEGIN
3033: #include "adic/ad_utils.h"
3034: EXTERN_C_END

3036: #undef __FUNCT__  
3038: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3039: {
3040:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
3041:   int         m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j,nlen;
3042:   PetscScalar *v = a->a,*values;
3043:   char        *cadvalues = (char *)advalues;

3046:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
3047:   nlen  = PetscADGetDerivTypeSize();
3048:   color = a->coloring->colors;
3049:   /* loop over rows */
3050:   for (i=0; i<m; i++) {
3051:     nz = ii[i+1] - ii[i];
3052:     /* loop over columns putting computed value into matrix */
3053:     values = PetscADGetGradArray(cadvalues);
3054:     for (j=0; j<nz; j++) {
3055:       *v++ = values[color[*jj++]];
3056:     }
3057:     cadvalues += nlen; /* jump to next row of derivatives */
3058:   }
3059:   return(0);
3060: }

3062: #else

3064: #undef __FUNCT__  
3066: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3067: {
3069:   SETERRQ(1,"PETSc installed without ADIC");
3070: }

3072: #endif

3074: #undef __FUNCT__  
3076: int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues)
3077: {
3078:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
3079:   int          m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j;
3080:   PetscScalar  *v = a->a,*values = (PetscScalar *)advalues;

3083:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
3084:   color = a->coloring->colors;
3085:   /* loop over rows */
3086:   for (i=0; i<m; i++) {
3087:     nz = ii[i+1] - ii[i];
3088:     /* loop over columns putting computed value into matrix */
3089:     for (j=0; j<nz; j++) {
3090:       *v++ = values[color[*jj++]];
3091:     }
3092:     values += nl; /* jump to next row of derivatives */
3093:   }
3094:   return(0);
3095: }