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: int MatGetRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *m,int **ia,int **ja,PetscTruth *done)
 17: {
 18:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 19:   int        ierr,i,ishift;
 20: 
 22:   *m     = A->m;
 23:   if (!ia) return(0);
 24:   ishift = a->indexshift;
 25:   if (symmetric && !A->structurally_symmetric) {
 26:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
 27:   } else if (oshift == 0 && ishift == -1) {
 28:     int nz = a->i[A->m] - 1;
 29:     /* malloc space and  subtract 1 from i and j indices */
 30:     PetscMalloc((A->m+1)*sizeof(int),ia);
 31:     PetscMalloc((nz+1)*sizeof(int),ja);
 32:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] - 1;
 33:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] - 1;
 34:   } else if (oshift == 1 && ishift == 0) {
 35:     int nz = a->i[A->m];
 36:     /* malloc space and  add 1 to i and j indices */
 37:     PetscMalloc((A->m+1)*sizeof(int),ia);
 38:     PetscMalloc((nz+1)*sizeof(int),ja);
 39:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 40:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1;
 41:   } else {
 42:     *ia = a->i; *ja = a->j;
 43:   }
 44:   return(0);
 45: }

 47: int MatRestoreRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int **ia,int **ja,PetscTruth *done)
 48: {
 49:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 50:   int        ishift = a->indexshift,ierr;
 51: 
 53:   if (!ia) return(0);
 54:   if ((symmetric && !A->structurally_symmetric) || (oshift == 0 && ishift == -1) || (oshift == 1 && ishift == 0)) {
 55:     PetscFree(*ia);
 56:     PetscFree(*ja);
 57:   }
 58:   return(0);
 59: }

 61: int MatGetColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *nn,int **ia,int **ja,PetscTruth *done)
 62: {
 63:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 64:   int        ierr,i,ishift = a->indexshift,*collengths,*cia,*cja,n = A->n,m = A->m;
 65:   int        nz = a->i[m]+ishift,row,*jj,mr,col;
 66: 
 68:   *nn     = A->n;
 69:   if (!ia) return(0);
 70:   if (symmetric) {
 71:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
 72:   } else {
 73:     PetscMalloc((n+1)*sizeof(int),&collengths);
 74:     PetscMemzero(collengths,n*sizeof(int));
 75:     PetscMalloc((n+1)*sizeof(int),&cia);
 76:     PetscMalloc((nz+1)*sizeof(int),&cja);
 77:     jj = a->j;
 78:     for (i=0; i<nz; i++) {
 79:       collengths[jj[i] + ishift]++;
 80:     }
 81:     cia[0] = oshift;
 82:     for (i=0; i<n; i++) {
 83:       cia[i+1] = cia[i] + collengths[i];
 84:     }
 85:     PetscMemzero(collengths,n*sizeof(int));
 86:     jj   = a->j;
 87:     for (row=0; row<m; row++) {
 88:       mr = a->i[row+1] - a->i[row];
 89:       for (i=0; i<mr; i++) {
 90:         col = *jj++ + ishift;
 91:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
 92:       }
 93:     }
 94:     PetscFree(collengths);
 95:     *ia = cia; *ja = cja;
 96:   }
 97:   return(0);
 98: }

100: int MatRestoreColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int **ia,int **ja,PetscTruth *done)
101: {

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

107:   PetscFree(*ia);
108:   PetscFree(*ja);
109: 
110:   return(0);
111: }

113: #define CHUNKSIZE   15

115: int MatSetValues_SeqAIJ(Mat A,int m,int *im,int n,int *in,PetscScalar *v,InsertMode is)
116: {
117:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
118:   int         *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,sorted = a->sorted;
119:   int         *imax = a->imax,*ai = a->i,*ailen = a->ilen;
120:   int         *aj = a->j,nonew = a->nonew,shift = a->indexshift,ierr;
121:   PetscScalar *ap,value,*aa = a->a;
122:   PetscTruth  ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE);
123:   PetscTruth  roworiented = a->roworiented;

126:   for (k=0; k<m; k++) { /* loop over added rows */
127:     row  = im[k];
128:     if (row < 0) continue;
129: #if defined(PETSC_USE_BOPT_g)  
130:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m);
131: #endif
132:     rp   = aj + ai[row] + shift; ap = aa + ai[row] + shift;
133:     rmax = imax[row]; nrow = ailen[row];
134:     low = 0;
135:     for (l=0; l<n; l++) { /* loop over added columns */
136:       if (in[l] < 0) continue;
137: #if defined(PETSC_USE_BOPT_g)  
138:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n);
139: #endif
140:       col = in[l] - shift;
141:       if (roworiented) {
142:         value = v[l + k*n];
143:       } else {
144:         value = v[k + l*m];
145:       }
146:       if (value == 0.0 && ignorezeroentries) continue;

148:       if (!sorted) low = 0; high = nrow;
149:       while (high-low > 5) {
150:         t = (low+high)/2;
151:         if (rp[t] > col) high = t;
152:         else             low  = t;
153:       }
154:       for (i=low; i<high; i++) {
155:         if (rp[i] > col) break;
156:         if (rp[i] == col) {
157:           if (is == ADD_VALUES) ap[i] += value;
158:           else                  ap[i] = value;
159:           goto noinsert;
160:         }
161:       }
162:       if (nonew == 1) goto noinsert;
163:       else if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix",row,col);
164:       if (nrow >= rmax) {
165:         /* there is no extra room in row, therefore enlarge */
166:         int         new_nz = ai[A->m] + CHUNKSIZE,len,*new_i,*new_j;
167:         PetscScalar *new_a;

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

171:         /* malloc new storage space */
172:         len     = new_nz*(sizeof(int)+sizeof(PetscScalar))+(A->m+1)*sizeof(int);
173:         ierr    = PetscMalloc(len,&new_a);
174:         new_j   = (int*)(new_a + new_nz);
175:         new_i   = new_j + new_nz;

177:         /* copy over old data into new slots */
178:         for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];}
179:         for (ii=row+1; ii<A->m+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;}
180:         PetscMemcpy(new_j,aj,(ai[row]+nrow+shift)*sizeof(int));
181:         len  = (new_nz - CHUNKSIZE - ai[row] - nrow - shift);
182:         PetscMemcpy(new_j+ai[row]+shift+nrow+CHUNKSIZE,aj+ai[row]+shift+nrow,len*sizeof(int));
183:         PetscMemcpy(new_a,aa,(ai[row]+nrow+shift)*sizeof(PetscScalar));
184:         PetscMemcpy(new_a+ai[row]+shift+nrow+CHUNKSIZE,aa+ai[row]+shift+nrow,len*sizeof(PetscScalar));
185:         /* free up old matrix storage */
186:         PetscFree(a->a);
187:         if (!a->singlemalloc) {
188:           PetscFree(a->i);
189:           PetscFree(a->j);
190:         }
191:         aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;
192:         a->singlemalloc = PETSC_TRUE;

194:         rp   = aj + ai[row] + shift; ap = aa + ai[row] + shift;
195:         rmax = imax[row] = imax[row] + CHUNKSIZE;
196:         PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar)));
197:         a->maxnz += CHUNKSIZE;
198:         a->reallocs++;
199:       }
200:       N = nrow++ - 1; a->nz++;
201:       /* shift up all the later entries in this row */
202:       for (ii=N; ii>=i; ii--) {
203:         rp[ii+1] = rp[ii];
204:         ap[ii+1] = ap[ii];
205:       }
206:       rp[i] = col;
207:       ap[i] = value;
208:       noinsert:;
209:       low = i + 1;
210:     }
211:     ailen[row] = nrow;
212:   }
213:   return(0);
214: }

216: int MatGetValues_SeqAIJ(Mat A,int m,int *im,int n,int *in,PetscScalar *v)
217: {
218:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
219:   int          *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
220:   int          *ai = a->i,*ailen = a->ilen,shift = a->indexshift;
221:   PetscScalar  *ap,*aa = a->a,zero = 0.0;

224:   for (k=0; k<m; k++) { /* loop over rows */
225:     row  = im[k];
226:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",row);
227:     if (row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: %d",row);
228:     rp   = aj + ai[row] + shift; ap = aa + ai[row] + shift;
229:     nrow = ailen[row];
230:     for (l=0; l<n; l++) { /* loop over columns */
231:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",in[l]);
232:       if (in[l] >= A->n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: %d",in[l]);
233:       col = in[l] - shift;
234:       high = nrow; low = 0; /* assume unsorted */
235:       while (high-low > 5) {
236:         t = (low+high)/2;
237:         if (rp[t] > col) high = t;
238:         else             low  = t;
239:       }
240:       for (i=low; i<high; i++) {
241:         if (rp[i] > col) break;
242:         if (rp[i] == col) {
243:           *v++ = ap[i];
244:           goto finished;
245:         }
246:       }
247:       *v++ = zero;
248:       finished:;
249:     }
250:   }
251:   return(0);
252: }


255: int MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
256: {
257:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
258:   int        i,fd,*col_lens,ierr;

261:   PetscViewerBinaryGetDescriptor(viewer,&fd);
262:   PetscMalloc((4+A->m)*sizeof(int),&col_lens);
263:   col_lens[0] = MAT_FILE_COOKIE;
264:   col_lens[1] = A->m;
265:   col_lens[2] = A->n;
266:   col_lens[3] = a->nz;

268:   /* store lengths of each row and write (including header) to file */
269:   for (i=0; i<A->m; i++) {
270:     col_lens[4+i] = a->i[i+1] - a->i[i];
271:   }
272:   PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,1);
273:   PetscFree(col_lens);

275:   /* store column indices (zero start index) */
276:   if (a->indexshift) {
277:     for (i=0; i<a->nz; i++) a->j[i]--;
278:   }
279:   PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,0);
280:   if (a->indexshift) {
281:     for (i=0; i<a->nz; i++) a->j[i]++;
282:   }

284:   /* store nonzero values */
285:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,0);
286:   return(0);
287: }

289: extern int MatMPIAIJFactorInfo_SuperLu(Mat,PetscViewer);
290: extern int MatFactorInfo_Spooles(Mat,PetscViewer);

292: int MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
293: {
294:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
295:   int               ierr,i,j,m = A->m,shift = a->indexshift;
296:   char              *name;
297:   PetscViewerFormat format;

300:   PetscObjectGetName((PetscObject)A,&name);
301:   PetscViewerGetFormat(viewer,&format);
302:   if (format == PETSC_VIEWER_ASCII_INFO_LONG || format == PETSC_VIEWER_ASCII_INFO) {
303:     if (a->inode.size) {
304:       PetscViewerASCIIPrintf(viewer,"using I-node routines: found %d nodes, limit used is %dn",a->inode.node_count,a->inode.limit);
305:     } else {
306:       PetscViewerASCIIPrintf(viewer,"not using I-node routinesn");
307:     }
308:   } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
309:     int nofinalvalue = 0;
310:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) {
311:       nofinalvalue = 1;
312:     }
313:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
314:     PetscViewerASCIIPrintf(viewer,"%% Size = %d %d n",m,A->n);
315:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %d n",a->nz);
316:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%d,3);n",a->nz+nofinalvalue);
317:     PetscViewerASCIIPrintf(viewer,"zzz = [n");

319:     for (i=0; i<m; i++) {
320:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
321: #if defined(PETSC_USE_COMPLEX)
322:         PetscViewerASCIIPrintf(viewer,"%d %d  %18.16e + %18.16ei n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
323: #else
324:         PetscViewerASCIIPrintf(viewer,"%d %d  %18.16en",i+1,a->j[j]+!shift,a->a[j]);
325: #endif
326:       }
327:     }
328:     if (nofinalvalue) {
329:       PetscViewerASCIIPrintf(viewer,"%d %d  %18.16en",m,A->n,0.0);
330:     }
331:     PetscViewerASCIIPrintf(viewer,"];n %s = spconvert(zzz);n",name);
332:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
333:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
334: #if defined(PETSC_HAVE_SUPERLUDIST) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
335:      MatMPIAIJFactorInfo_SuperLu(A,viewer);
336: #endif
337: #if defined(PETSC_HAVE_SPOOLES) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
338:      MatFactorInfo_Spooles(A,viewer);
339: #endif
340:      return(0);
341:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
342:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
343:     for (i=0; i<m; i++) {
344:       PetscViewerASCIIPrintf(viewer,"row %d:",i);
345:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
346: #if defined(PETSC_USE_COMPLEX)
347:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
348:           PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
349:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
350:           PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
351:         } else if (PetscRealPart(a->a[j]) != 0.0) {
352:           PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
353:         }
354: #else
355:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);}
356: #endif
357:       }
358:       PetscViewerASCIIPrintf(viewer,"n");
359:     }
360:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
361:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
362:     int nzd=0,fshift=1,*sptr;
363:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
364:     PetscMalloc((m+1)*sizeof(int),&sptr);
365:     for (i=0; i<m; i++) {
366:       sptr[i] = nzd+1;
367:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
368:         if (a->j[j] >= i) {
369: #if defined(PETSC_USE_COMPLEX)
370:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
371: #else
372:           if (a->a[j] != 0.0) nzd++;
373: #endif
374:         }
375:       }
376:     }
377:     sptr[m] = nzd+1;
378:     PetscViewerASCIIPrintf(viewer," %d %dnn",m,nzd);
379:     for (i=0; i<m+1; i+=6) {
380:       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]);}
381:       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]);}
382:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
383:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %d %d %dn",sptr[i],sptr[i+1],sptr[i+2]);}
384:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %d %dn",sptr[i],sptr[i+1]);}
385:       else            {PetscViewerASCIIPrintf(viewer," %dn",sptr[i]);}
386:     }
387:     PetscViewerASCIIPrintf(viewer,"n");
388:     PetscFree(sptr);
389:     for (i=0; i<m; i++) {
390:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
391:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %d ",a->j[j]+fshift);}
392:       }
393:       PetscViewerASCIIPrintf(viewer,"n");
394:     }
395:     PetscViewerASCIIPrintf(viewer,"n");
396:     for (i=0; i<m; i++) {
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) {
401:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
402:           }
403: #else
404:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
405: #endif
406:         }
407:       }
408:       PetscViewerASCIIPrintf(viewer,"n");
409:     }
410:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
411:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
412:     int         cnt = 0,jcnt;
413:     PetscScalar value;

415:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
416:     for (i=0; i<m; i++) {
417:       jcnt = 0;
418:       for (j=0; j<A->n; j++) {
419:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
420:           value = a->a[cnt++];
421:           jcnt++;
422:         } else {
423:           value = 0.0;
424:         }
425: #if defined(PETSC_USE_COMPLEX)
426:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
427: #else
428:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
429: #endif
430:       }
431:       PetscViewerASCIIPrintf(viewer,"n");
432:     }
433:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
434:   } else {
435:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
436:     for (i=0; i<m; i++) {
437:       PetscViewerASCIIPrintf(viewer,"row %d:",i);
438:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
439: #if defined(PETSC_USE_COMPLEX)
440:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
441:           PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
442:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
443:           PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
444:         } else {
445:           PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
446:         }
447: #else
448:         PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);
449: #endif
450:       }
451:       PetscViewerASCIIPrintf(viewer,"n");
452:     }
453:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
454:   }
455:   PetscViewerFlush(viewer);
456:   return(0);
457: }

459: int MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
460: {
461:   Mat               A = (Mat) Aa;
462:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
463:   int               ierr,i,j,m = A->m,shift = a->indexshift,color;
464:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
465:   PetscViewer       viewer;
466:   PetscViewerFormat format;

469:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
470:   PetscViewerGetFormat(viewer,&format);

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

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

519:     for (i=0; i<nz; i++) {
520:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
521:     }
522:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
523:     ierr  = PetscDrawGetPopup(draw,&popup);
524:     if (popup) {ierr  = PetscDrawScalePopup(popup,0.0,maxv);}
525:     count = 0;
526:     for (i=0; i<m; i++) {
527:       y_l = m - i - 1.0; y_r = y_l + 1.0;
528:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
529:         x_l = a->j[j] + shift; x_r = x_l + 1.0;
530:         color = PETSC_DRAW_BASIC_COLORS + (int)(scale*PetscAbsScalar(a->a[count]));
531:         ierr  = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
532:         count++;
533:       }
534:     }
535:   }
536:   return(0);
537: }

539: int MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
540: {
541:   int        ierr;
542:   PetscDraw  draw;
543:   PetscReal  xr,yr,xl,yl,h,w;
544:   PetscTruth isnull;

547:   PetscViewerDrawGetDraw(viewer,0,&draw);
548:   PetscDrawIsNull(draw,&isnull);
549:   if (isnull) return(0);

551:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
552:   xr  = A->n; yr = A->m; h = yr/10.0; w = xr/10.0;
553:   xr += w;    yr += h;  xl = -w;     yl = -h;
554:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
555:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
556:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
557:   return(0);
558: }

560: int MatView_SeqAIJ(Mat A,PetscViewer viewer)
561: {
562:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
563:   int         ierr;
564:   PetscTruth  issocket,isascii,isbinary,isdraw;

567:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
568:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
569:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
570:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
571:   if (issocket) {
572:     if (a->indexshift) {
573:       SETERRQ(1,"Can only socket send sparse matrix with 0 based indexing");
574:     }
575:     PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);
576:   } else if (isascii) {
577:     MatView_SeqAIJ_ASCII(A,viewer);
578:   } else if (isbinary) {
579:     MatView_SeqAIJ_Binary(A,viewer);
580:   } else if (isdraw) {
581:     MatView_SeqAIJ_Draw(A,viewer);
582:   } else {
583:     SETERRQ1(1,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
584:   }
585:   return(0);
586: }

588: EXTERN int Mat_AIJ_CheckInode(Mat,PetscTruth);
589: EXTERN int MatUseSuperLU_DIST_MPIAIJ(Mat);
590: EXTERN int MatUseSpooles_SeqAIJ(Mat);
591: int MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
592: {
593:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
594:   int          fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax,ierr;
595:   int          m = A->m,*ip,N,*ailen = a->ilen,shift = a->indexshift,rmax = 0;
596:   PetscScalar  *aa = a->a,*ap;
597: #if defined(PETSC_HAVE_SUPERLUDIST) || defined(PETSC_HAVE_SPOOLES) 
598:   PetscTruth   flag;
599: #endif

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

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

630:   /* diagonals may have moved, so kill the diagonal pointers */
631:   if (fshift && a->diag) {
632:     PetscFree(a->diag);
633:     PetscLogObjectMemory(A,-(m+1)*sizeof(int));
634:     a->diag = 0;
635:   }
636:   PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Matrix size: %d X %d; storage space: %d unneeded,%d usedn",m,A->n,fshift,a->nz);
637:   PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %dn",a->reallocs);
638:   PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Most nonzeros in any row is %dn",rmax);
639:   a->reallocs          = 0;
640:   A->info.nz_unneeded  = (double)fshift;
641:   a->rmax              = rmax;

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

646: #if defined(PETSC_HAVE_SUPERLUDIST) 
647:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_superlu_dist",&flag);
648:   if (flag) { MatUseSuperLU_DIST_MPIAIJ(A); }
649: #endif 

651: #if defined(PETSC_HAVE_SPOOLES) 
652:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_spooles",&flag);
653:   if (flag) { MatUseSpooles_SeqAIJ(A); }
654: #endif 

656:   return(0);
657: }

659: int MatZeroEntries_SeqAIJ(Mat A)
660: {
661:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
662:   int        ierr;

665:   PetscMemzero(a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
666:   return(0);
667: }

669: int MatDestroy_SeqAIJ(Mat A)
670: {
671:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
672:   int        ierr;

675: #if defined(PETSC_USE_LOG)
676:   PetscLogObjectState((PetscObject)A,"Rows=%d, Cols=%d, NZ=%d",A->m,A->n,a->nz);
677: #endif
678:   if (a->freedata) {
679:     PetscFree(a->a);
680:     if (!a->singlemalloc) {
681:       PetscFree(a->i);
682:       PetscFree(a->j);
683:     }
684:   }
685:   if (a->row) {
686:     ISDestroy(a->row);
687:   }
688:   if (a->col) {
689:     ISDestroy(a->col);
690:   }
691:   if (a->diag) {PetscFree(a->diag);}
692:   if (a->ilen) {PetscFree(a->ilen);}
693:   if (a->imax) {PetscFree(a->imax);}
694:   if (a->idiag) {PetscFree(a->idiag);}
695:   if (a->solve_work) {PetscFree(a->solve_work);}
696:   if (a->inode.size) {PetscFree(a->inode.size);}
697:   if (a->icol) {ISDestroy(a->icol);}
698:   if (a->saved_values) {PetscFree(a->saved_values);}
699:   if (a->coloring) {ISColoringDestroy(a->coloring);}
700:   PetscFree(a);
701:   return(0);
702: }

704: int MatCompress_SeqAIJ(Mat A)
705: {
707:   return(0);
708: }

710: int MatSetOption_SeqAIJ(Mat A,MatOption op)
711: {
712:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

715:   switch (op) {
716:     case MAT_ROW_ORIENTED:
717:       a->roworiented       = PETSC_TRUE;
718:       break;
719:     case MAT_KEEP_ZEROED_ROWS:
720:       a->keepzeroedrows    = PETSC_TRUE;
721:       break;
722:     case MAT_COLUMN_ORIENTED:
723:       a->roworiented       = PETSC_FALSE;
724:       break;
725:     case MAT_COLUMNS_SORTED:
726:       a->sorted            = PETSC_TRUE;
727:       break;
728:     case MAT_COLUMNS_UNSORTED:
729:       a->sorted            = PETSC_FALSE;
730:       break;
731:     case MAT_NO_NEW_NONZERO_LOCATIONS:
732:       a->nonew             = 1;
733:       break;
734:     case MAT_NEW_NONZERO_LOCATION_ERR:
735:       a->nonew             = -1;
736:       break;
737:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
738:       a->nonew             = -2;
739:       break;
740:     case MAT_YES_NEW_NONZERO_LOCATIONS:
741:       a->nonew             = 0;
742:       break;
743:     case MAT_IGNORE_ZERO_ENTRIES:
744:       a->ignorezeroentries = PETSC_TRUE;
745:       break;
746:     case MAT_USE_INODES:
747:       a->inode.use         = PETSC_TRUE;
748:       break;
749:     case MAT_DO_NOT_USE_INODES:
750:       a->inode.use         = PETSC_FALSE;
751:       break;
752:     case MAT_ROWS_SORTED:
753:     case MAT_ROWS_UNSORTED:
754:     case MAT_YES_NEW_DIAGONALS:
755:     case MAT_IGNORE_OFF_PROC_ENTRIES:
756:     case MAT_USE_HASH_TABLE:
757:     case MAT_USE_SINGLE_PRECISION_SOLVES:
758:       PetscLogInfo(A,"MatSetOption_SeqAIJ:Option ignoredn");
759:       break;
760:     case MAT_NO_NEW_DIAGONALS:
761:       SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
762:     case MAT_INODE_LIMIT_1:
763:       a->inode.limit  = 1;
764:       break;
765:     case MAT_INODE_LIMIT_2:
766:       a->inode.limit  = 2;
767:       break;
768:     case MAT_INODE_LIMIT_3:
769:       a->inode.limit  = 3;
770:       break;
771:     case MAT_INODE_LIMIT_4:
772:       a->inode.limit  = 4;
773:       break;
774:     case MAT_INODE_LIMIT_5:
775:       a->inode.limit  = 5;
776:       break;
777:     default:
778:       SETERRQ(PETSC_ERR_SUP,"unknown option");
779:   }
780:   return(0);
781: }

783: int MatGetDiagonal_SeqAIJ(Mat A,Vec v)
784: {
785:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
786:   int          i,j,n,shift = a->indexshift,ierr;
787:   PetscScalar  *x,zero = 0.0;

790:   VecSet(&zero,v);
791:   VecGetArray(v,&x);
792:   VecGetLocalSize(v,&n);
793:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
794:   for (i=0; i<A->m; i++) {
795:     for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
796:       if (a->j[j]+shift == i) {
797:         x[i] = a->a[j];
798:         break;
799:       }
800:     }
801:   }
802:   VecRestoreArray(v,&x);
803:   return(0);
804: }


807: int MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
808: {
809:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
810:   PetscScalar  *x,*y;
811:   int          ierr,m = A->m,shift = a->indexshift;
812: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
813:   PetscScalar  *v,alpha;
814:   int          n,i,*idx;
815: #endif

818:   if (zz != yy) {VecCopy(zz,yy);}
819:   VecGetArray(xx,&x);
820:   VecGetArray(yy,&y);
821:   y = y + shift; /* shift for Fortran start by 1 indexing */

823: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
824:   fortranmulttransposeaddaij_(&m,x,a->i,a->j+shift,a->a+shift,y);
825: #else
826:   for (i=0; i<m; i++) {
827:     idx   = a->j + a->i[i] + shift;
828:     v     = a->a + a->i[i] + shift;
829:     n     = a->i[i+1] - a->i[i];
830:     alpha = x[i];
831:     while (n-->0) {y[*idx++] += alpha * *v++;}
832:   }
833: #endif
834:   PetscLogFlops(2*a->nz);
835:   VecRestoreArray(xx,&x);
836:   VecRestoreArray(yy,&y);
837:   return(0);
838: }

840: int MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
841: {
842:   PetscScalar  zero = 0.0;
843:   int          ierr;

846:   VecSet(&zero,yy);
847:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
848:   return(0);
849: }


852: int MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
853: {
854:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
855:   PetscScalar  *x,*y,*v,sum;
856:   int          ierr,m = A->m,*idx,shift = a->indexshift,*ii;
857: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
858:   int          n,i,jrow,j;
859: #endif

861: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
862: #pragma disjoint(*x,*y,*v)
863: #endif

866:   VecGetArray(xx,&x);
867:   VecGetArray(yy,&y);
868:   x    = x + shift;    /* shift for Fortran start by 1 indexing */
869:   idx  = a->j;
870:   v    = a->a;
871:   ii   = a->i;
872: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
873:   fortranmultaij_(&m,x,ii,idx+shift,v+shift,y);
874: #else
875:   v    += shift; /* shift for Fortran start by 1 indexing */
876:   idx  += shift;
877:   for (i=0; i<m; i++) {
878:     jrow = ii[i];
879:     n    = ii[i+1] - jrow;
880:     sum  = 0.0;
881:     for (j=0; j<n; j++) {
882:       sum += v[jrow]*x[idx[jrow]]; jrow++;
883:      }
884:     y[i] = sum;
885:   }
886: #endif
887:   PetscLogFlops(2*a->nz - m);
888:   VecRestoreArray(xx,&x);
889:   VecRestoreArray(yy,&y);
890:   return(0);
891: }

893: int MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
894: {
895:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
896:   PetscScalar  *x,*y,*z,*v,sum;
897:   int          ierr,m = A->m,*idx,shift = a->indexshift,*ii;
898: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
899:   int          n,i,jrow,j;
900: #endif

903:   VecGetArray(xx,&x);
904:   VecGetArray(yy,&y);
905:   if (zz != yy) {
906:     VecGetArray(zz,&z);
907:   } else {
908:     z = y;
909:   }
910:   x    = x + shift; /* shift for Fortran start by 1 indexing */
911:   idx  = a->j;
912:   v    = a->a;
913:   ii   = a->i;
914: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
915:   fortranmultaddaij_(&m,x,ii,idx+shift,v+shift,y,z);
916: #else
917:   v   += shift; /* shift for Fortran start by 1 indexing */
918:   idx += shift;
919:   for (i=0; i<m; i++) {
920:     jrow = ii[i];
921:     n    = ii[i+1] - jrow;
922:     sum  = y[i];
923:     for (j=0; j<n; j++) {
924:       sum += v[jrow]*x[idx[jrow]]; jrow++;
925:      }
926:     z[i] = sum;
927:   }
928: #endif
929:   PetscLogFlops(2*a->nz);
930:   VecRestoreArray(xx,&x);
931:   VecRestoreArray(yy,&y);
932:   if (zz != yy) {
933:     VecRestoreArray(zz,&z);
934:   }
935:   return(0);
936: }

938: /*
939:      Adds diagonal pointers to sparse matrix structure.
940: */
941: int MatMarkDiagonal_SeqAIJ(Mat A)
942: {
943:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
944:   int        i,j,*diag,m = A->m,shift = a->indexshift,ierr;

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

949:   PetscMalloc((m+1)*sizeof(int),&diag);
950:   PetscLogObjectMemory(A,(m+1)*sizeof(int));
951:   for (i=0; i<A->m; i++) {
952:     diag[i] = a->i[i+1];
953:     for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
954:       if (a->j[j]+shift == i) {
955:         diag[i] = j - shift;
956:         break;
957:       }
958:     }
959:   }
960:   a->diag = diag;
961:   return(0);
962: }

964: /*
965:      Checks for missing diagonals
966: */
967: int MatMissingDiagonal_SeqAIJ(Mat A)
968: {
969:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
970:   int        *diag,*jj = a->j,i,shift = a->indexshift,ierr;

973:   MatMarkDiagonal_SeqAIJ(A);
974:   diag = a->diag;
975:   for (i=0; i<A->m; i++) {
976:     if (jj[diag[i]+shift] != i-shift) {
977:       SETERRQ1(1,"Matrix is missing diagonal number %d",i);
978:     }
979:   }
980:   return(0);
981: }

983: int MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
984: {
985:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
986:   PetscScalar  *x,*b,*bs, d,*xs,sum,*v = a->a,*t=0,scale,*ts,*xb,*idiag=0;
987:   int          ierr,*idx,*diag,n = A->n,m = A->m,i,shift = a->indexshift;

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

993:   VecGetArray(xx,&x);
994:   if (xx != bb) {
995:     VecGetArray(bb,&b);
996:   } else {
997:     b = x;
998:   }

1000:   if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
1001:   diag = a->diag;
1002:   xs   = x + shift; /* shifted by one for index start of a or a->j*/
1003:   if (flag == SOR_APPLY_UPPER) {
1004:    /* apply (U + D/omega) to the vector */
1005:     bs = b + shift;
1006:     for (i=0; i<m; i++) {
1007:         d    = fshift + a->a[diag[i] + shift];
1008:         n    = a->i[i+1] - diag[i] - 1;
1009:         PetscLogFlops(2*n-1);
1010:         idx  = a->j + diag[i] + (!shift);
1011:         v    = a->a + diag[i] + (!shift);
1012:         sum  = b[i]*d/omega;
1013:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1014:         x[i] = sum;
1015:     }
1016:     VecRestoreArray(xx,&x);
1017:     if (bb != xx) {VecRestoreArray(bb,&b);}
1018:     return(0);
1019:   }

1021:   /* setup workspace for Eisenstat */
1022:   if (flag & SOR_EISENSTAT) {
1023:     if (!a->idiag) {
1024:       ierr     = PetscMalloc(2*m*sizeof(PetscScalar),&a->idiag);
1025:       a->ssor  = a->idiag + m;
1026:       v        = a->a;
1027:       for (i=0; i<m; i++) { a->idiag[i] = 1.0/v[diag[i]];}
1028:     }
1029:     t     = a->ssor;
1030:     idiag = a->idiag;
1031:   }
1032:     /* Let  A = L + U + D; where L is lower trianglar,
1033:     U is upper triangular, E is diagonal; This routine applies

1035:             (L + E)^{-1} A (U + E)^{-1}

1037:     to a vector efficiently using Eisenstat's trick. This is for
1038:     the case of SSOR preconditioner, so E is D/omega where omega
1039:     is the relaxation factor.
1040:     */

1042:   if (flag == SOR_APPLY_LOWER) {
1043:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1044:   } else if ((flag & SOR_EISENSTAT) && omega == 1.0 && shift == 0 && fshift == 0.0) {
1045:     /* special case for omega = 1.0 saves flops and some integer ops */
1046:     PetscScalar *v2;
1047: 
1048:     v2    = a->a;
1049:     /*  x = (E + U)^{-1} b */
1050:     for (i=m-1; i>=0; i--) {
1051:       n    = a->i[i+1] - diag[i] - 1;
1052:       idx  = a->j + diag[i] + 1;
1053:       v    = a->a + diag[i] + 1;
1054:       sum  = b[i];
1055:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1056:       x[i] = sum*idiag[i];

1058:       /*  t = b - (2*E - D)x */
1059:       t[i] = b[i] - (v2[diag[i]])*x[i];
1060:     }

1062:     /*  t = (E + L)^{-1}t */
1063:     diag = a->diag;
1064:     for (i=0; i<m; i++) {
1065:       n    = diag[i] - a->i[i];
1066:       idx  = a->j + a->i[i];
1067:       v    = a->a + a->i[i];
1068:       sum  = t[i];
1069:       SPARSEDENSEMDOT(sum,t,v,idx,n);
1070:       t[i]  = sum*idiag[i];

1072:       /*  x = x + t */
1073:       x[i] += t[i];
1074:     }

1076:     PetscLogFlops(3*m-1 + 2*a->nz);
1077:     VecRestoreArray(xx,&x);
1078:     if (bb != xx) {VecRestoreArray(bb,&b);}
1079:     return(0);
1080:   } else if (flag & SOR_EISENSTAT) {
1081:     /* Let  A = L + U + D; where L is lower trianglar,
1082:     U is upper triangular, E is diagonal; This routine applies

1084:             (L + E)^{-1} A (U + E)^{-1}

1086:     to a vector efficiently using Eisenstat's trick. This is for
1087:     the case of SSOR preconditioner, so E is D/omega where omega
1088:     is the relaxation factor.
1089:     */
1090:     scale = (2.0/omega) - 1.0;

1092:     /*  x = (E + U)^{-1} b */
1093:     for (i=m-1; i>=0; i--) {
1094:       d    = fshift + a->a[diag[i] + shift];
1095:       n    = a->i[i+1] - diag[i] - 1;
1096:       idx  = a->j + diag[i] + (!shift);
1097:       v    = a->a + diag[i] + (!shift);
1098:       sum  = b[i];
1099:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1100:       x[i] = omega*(sum/d);
1101:     }

1103:     /*  t = b - (2*E - D)x */
1104:     v = a->a;
1105:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++ + shift])*x[i]; }

1107:     /*  t = (E + L)^{-1}t */
1108:     ts = t + shift; /* shifted by one for index start of a or a->j*/
1109:     diag = a->diag;
1110:     for (i=0; i<m; i++) {
1111:       d    = fshift + a->a[diag[i]+shift];
1112:       n    = diag[i] - a->i[i];
1113:       idx  = a->j + a->i[i] + shift;
1114:       v    = a->a + a->i[i] + shift;
1115:       sum  = t[i];
1116:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1117:       t[i] = omega*(sum/d);
1118:       /*  x = x + t */
1119:       x[i] += t[i];
1120:     }

1122:     PetscLogFlops(6*m-1 + 2*a->nz);
1123:     VecRestoreArray(xx,&x);
1124:     if (bb != xx) {VecRestoreArray(bb,&b);}
1125:     return(0);
1126:   }
1127:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1128:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1129: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1130:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1131: #else
1132:       for (i=0; i<m; i++) {
1133:         d    = fshift + a->a[diag[i]+shift];
1134:         n    = diag[i] - a->i[i];
1135:         PetscLogFlops(2*n-1);
1136:         idx  = a->j + a->i[i] + shift;
1137:         v    = a->a + a->i[i] + shift;
1138:         sum  = b[i];
1139:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1140:         x[i] = omega*(sum/d);
1141:       }
1142: #endif
1143:       xb = x;
1144:     } else xb = b;
1145:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1146:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1147:       for (i=0; i<m; i++) {
1148:         x[i] *= a->a[diag[i]+shift];
1149:       }
1150:       PetscLogFlops(m);
1151:     }
1152:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1153: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1154:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,xb);
1155: #else
1156:       for (i=m-1; i>=0; i--) {
1157:         d    = fshift + a->a[diag[i] + shift];
1158:         n    = a->i[i+1] - diag[i] - 1;
1159:         PetscLogFlops(2*n-1);
1160:         idx  = a->j + diag[i] + (!shift);
1161:         v    = a->a + diag[i] + (!shift);
1162:         sum  = xb[i];
1163:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1164:         x[i] = omega*(sum/d);
1165:       }
1166: #endif
1167:     }
1168:     its--;
1169:   }
1170:   while (its--) {
1171:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1172: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1173:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1174: #else
1175:       for (i=0; i<m; i++) {
1176:         d    = fshift + a->a[diag[i]+shift];
1177:         n    = a->i[i+1] - a->i[i];
1178:         PetscLogFlops(2*n-1);
1179:         idx  = a->j + a->i[i] + shift;
1180:         v    = a->a + a->i[i] + shift;
1181:         sum  = b[i];
1182:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1183:         x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1184:       }
1185: #endif
1186:     }
1187:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1188: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1189:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1190: #else
1191:       for (i=m-1; i>=0; i--) {
1192:         d    = fshift + a->a[diag[i] + shift];
1193:         n    = a->i[i+1] - a->i[i];
1194:         PetscLogFlops(2*n-1);
1195:         idx  = a->j + a->i[i] + shift;
1196:         v    = a->a + a->i[i] + shift;
1197:         sum  = b[i];
1198:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1199:         x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1200:       }
1201: #endif
1202:     }
1203:   }
1204:   VecRestoreArray(xx,&x);
1205:   if (bb != xx) {VecRestoreArray(bb,&b);}
1206:   return(0);
1207: }

1209: int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1210: {
1211:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1214:   info->rows_global    = (double)A->m;
1215:   info->columns_global = (double)A->n;
1216:   info->rows_local     = (double)A->m;
1217:   info->columns_local  = (double)A->n;
1218:   info->block_size     = 1.0;
1219:   info->nz_allocated   = (double)a->maxnz;
1220:   info->nz_used        = (double)a->nz;
1221:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1222:   info->assemblies     = (double)A->num_ass;
1223:   info->mallocs        = (double)a->reallocs;
1224:   info->memory         = A->mem;
1225:   if (A->factor) {
1226:     info->fill_ratio_given  = A->info.fill_ratio_given;
1227:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1228:     info->factor_mallocs    = A->info.factor_mallocs;
1229:   } else {
1230:     info->fill_ratio_given  = 0;
1231:     info->fill_ratio_needed = 0;
1232:     info->factor_mallocs    = 0;
1233:   }
1234:   return(0);
1235: }

1237: EXTERN int MatLUFactorSymbolic_SeqAIJ(Mat,IS,IS,MatLUInfo*,Mat*);
1238: EXTERN int MatLUFactorNumeric_SeqAIJ(Mat,Mat*);
1239: EXTERN int MatLUFactor_SeqAIJ(Mat,IS,IS,MatLUInfo*);
1240: EXTERN int MatSolve_SeqAIJ(Mat,Vec,Vec);
1241: EXTERN int MatSolveAdd_SeqAIJ(Mat,Vec,Vec,Vec);
1242: EXTERN int MatSolveTranspose_SeqAIJ(Mat,Vec,Vec);
1243: EXTERN int MatSolveTransposeAdd_SeqAIJ(Mat,Vec,Vec,Vec);

1245: int MatZeroRows_SeqAIJ(Mat A,IS is,PetscScalar *diag)
1246: {
1247:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1248:   int         i,ierr,N,*rows,m = A->m - 1,shift = a->indexshift;

1251:   ISGetLocalSize(is,&N);
1252:   ISGetIndices(is,&rows);
1253:   if (a->keepzeroedrows) {
1254:     for (i=0; i<N; i++) {
1255:       if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1256:       PetscMemzero(&a->a[a->i[rows[i]]+shift],a->ilen[rows[i]]*sizeof(PetscScalar));
1257:     }
1258:     if (diag) {
1259:       MatMissingDiagonal_SeqAIJ(A);
1260:       MatMarkDiagonal_SeqAIJ(A);
1261:       for (i=0; i<N; i++) {
1262:         a->a[a->diag[rows[i]]] = *diag;
1263:       }
1264:     }
1265:   } else {
1266:     if (diag) {
1267:       for (i=0; i<N; i++) {
1268:         if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1269:         if (a->ilen[rows[i]] > 0) {
1270:           a->ilen[rows[i]]          = 1;
1271:           a->a[a->i[rows[i]]+shift] = *diag;
1272:           a->j[a->i[rows[i]]+shift] = rows[i]+shift;
1273:         } else { /* in case row was completely empty */
1274:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);
1275:         }
1276:       }
1277:     } else {
1278:       for (i=0; i<N; i++) {
1279:         if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1280:         a->ilen[rows[i]] = 0;
1281:       }
1282:     }
1283:   }
1284:   ISRestoreIndices(is,&rows);
1285:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1286:   return(0);
1287: }

1289: int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1290: {
1291:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1292:   int        *itmp,i,shift = a->indexshift,ierr;

1295:   if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row);

1297:   *nz = a->i[row+1] - a->i[row];
1298:   if (v) *v = a->a + a->i[row] + shift;
1299:   if (idx) {
1300:     itmp = a->j + a->i[row] + shift;
1301:     if (*nz && shift) {
1302:       PetscMalloc((*nz)*sizeof(int),idx);
1303:       for (i=0; i<(*nz); i++) {(*idx)[i] = itmp[i] + shift;}
1304:     } else if (*nz) {
1305:       *idx = itmp;
1306:     }
1307:     else *idx = 0;
1308:   }
1309:   return(0);
1310: }

1312: int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1313: {
1314:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1318:   if (idx) {if (*idx && a->indexshift) {PetscFree(*idx);}}
1319:   return(0);
1320: }

1322: int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1323: {
1324:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1325:   PetscScalar  *v = a->a;
1326:   PetscReal    sum = 0.0;
1327:   int          i,j,shift = a->indexshift,ierr;

1330:   if (type == NORM_FROBENIUS) {
1331:     for (i=0; i<a->nz; i++) {
1332: #if defined(PETSC_USE_COMPLEX)
1333:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1334: #else
1335:       sum += (*v)*(*v); v++;
1336: #endif
1337:     }
1338:     *nrm = sqrt(sum);
1339:   } else if (type == NORM_1) {
1340:     PetscReal *tmp;
1341:     int    *jj = a->j;
1342:     PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);
1343:     PetscMemzero(tmp,A->n*sizeof(PetscReal));
1344:     *nrm = 0.0;
1345:     for (j=0; j<a->nz; j++) {
1346:         tmp[*jj++ + shift] += PetscAbsScalar(*v);  v++;
1347:     }
1348:     for (j=0; j<A->n; j++) {
1349:       if (tmp[j] > *nrm) *nrm = tmp[j];
1350:     }
1351:     PetscFree(tmp);
1352:   } else if (type == NORM_INFINITY) {
1353:     *nrm = 0.0;
1354:     for (j=0; j<A->m; j++) {
1355:       v = a->a + a->i[j] + shift;
1356:       sum = 0.0;
1357:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1358:         sum += PetscAbsScalar(*v); v++;
1359:       }
1360:       if (sum > *nrm) *nrm = sum;
1361:     }
1362:   } else {
1363:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1364:   }
1365:   return(0);
1366: }

1368: int MatTranspose_SeqAIJ(Mat A,Mat *B)
1369: {
1370:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1371:   Mat          C;
1372:   int          i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col;
1373:   int          shift = a->indexshift;
1374:   PetscScalar  *array = a->a;

1377:   if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1378:   PetscMalloc((1+A->n)*sizeof(int),&col);
1379:   PetscMemzero(col,(1+A->n)*sizeof(int));
1380:   if (shift) {
1381:     for (i=0; i<ai[m]-1; i++) aj[i] -= 1;
1382:   }
1383:   for (i=0; i<ai[m]+shift; i++) col[aj[i]] += 1;
1384:   MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);
1385:   PetscFree(col);
1386:   for (i=0; i<m; i++) {
1387:     len    = ai[i+1]-ai[i];
1388:     ierr   = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);
1389:     array += len;
1390:     aj    += len;
1391:   }
1392:   if (shift) {
1393:     for (i=0; i<ai[m]-1; i++) aj[i] += 1;
1394:   }

1396:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1397:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1399:   if (B) {
1400:     *B = C;
1401:   } else {
1402:     MatHeaderCopy(A,C);
1403:   }
1404:   return(0);
1405: }

1407: int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1408: {
1409:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1410:   PetscScalar  *l,*r,x,*v;
1411:   int          ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj,shift = a->indexshift;

1414:   if (ll) {
1415:     /* The local size is used so that VecMPI can be passed to this routine
1416:        by MatDiagonalScale_MPIAIJ */
1417:     VecGetLocalSize(ll,&m);
1418:     if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1419:     VecGetArray(ll,&l);
1420:     v = a->a;
1421:     for (i=0; i<m; i++) {
1422:       x = l[i];
1423:       M = a->i[i+1] - a->i[i];
1424:       for (j=0; j<M; j++) { (*v++) *= x;}
1425:     }
1426:     VecRestoreArray(ll,&l);
1427:     PetscLogFlops(nz);
1428:   }
1429:   if (rr) {
1430:     VecGetLocalSize(rr,&n);
1431:     if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1432:     VecGetArray(rr,&r);
1433:     v = a->a; jj = a->j;
1434:     for (i=0; i<nz; i++) {
1435:       (*v++) *= r[*jj++ + shift];
1436:     }
1437:     VecRestoreArray(rr,&r);
1438:     PetscLogFlops(nz);
1439:   }
1440:   return(0);
1441: }

1443: int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B)
1444: {
1445:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data,*c;
1446:   int          *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens;
1447:   int          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1448:   int          *irow,*icol,nrows,ncols,shift = a->indexshift,*ssmap;
1449:   int          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1450:   PetscScalar  *a_new,*mat_a;
1451:   Mat          C;
1452:   PetscTruth   stride;

1455:   ISSorted(isrow,(PetscTruth*)&i);
1456:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1457:   ISSorted(iscol,(PetscTruth*)&i);
1458:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1460:   ISGetIndices(isrow,&irow);
1461:   ISGetLocalSize(isrow,&nrows);
1462:   ISGetLocalSize(iscol,&ncols);

1464:   ISStrideGetInfo(iscol,&first,&step);
1465:   ISStride(iscol,&stride);
1466:   if (stride && step == 1) {
1467:     /* special case of contiguous rows */
1468:     ierr   = PetscMalloc((2*nrows+1)*sizeof(int),&lens);
1469:     starts = lens + nrows;
1470:     /* loop over new rows determining lens and starting points */
1471:     for (i=0; i<nrows; i++) {
1472:       kstart  = ai[irow[i]]+shift;
1473:       kend    = kstart + ailen[irow[i]];
1474:       for (k=kstart; k<kend; k++) {
1475:         if (aj[k]+shift >= first) {
1476:           starts[i] = k;
1477:           break;
1478:         }
1479:       }
1480:       sum = 0;
1481:       while (k < kend) {
1482:         if (aj[k++]+shift >= first+ncols) break;
1483:         sum++;
1484:       }
1485:       lens[i] = sum;
1486:     }
1487:     /* create submatrix */
1488:     if (scall == MAT_REUSE_MATRIX) {
1489:       int n_cols,n_rows;
1490:       MatGetSize(*B,&n_rows,&n_cols);
1491:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1492:       MatZeroEntries(*B);
1493:       C = *B;
1494:     } else {
1495:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1496:     }
1497:     c = (Mat_SeqAIJ*)C->data;

1499:     /* loop over rows inserting into submatrix */
1500:     a_new    = c->a;
1501:     j_new    = c->j;
1502:     i_new    = c->i;
1503:     i_new[0] = -shift;
1504:     for (i=0; i<nrows; i++) {
1505:       ii    = starts[i];
1506:       lensi = lens[i];
1507:       for (k=0; k<lensi; k++) {
1508:         *j_new++ = aj[ii+k] - first;
1509:       }
1510:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1511:       a_new      += lensi;
1512:       i_new[i+1]  = i_new[i] + lensi;
1513:       c->ilen[i]  = lensi;
1514:     }
1515:     PetscFree(lens);
1516:   } else {
1517:     ierr  = ISGetIndices(iscol,&icol);
1518:     ierr  = PetscMalloc((1+oldcols)*sizeof(int),&smap);
1519:     ssmap = smap + shift;
1520:     ierr  = PetscMalloc((1+nrows)*sizeof(int),&lens);
1521:     ierr  = PetscMemzero(smap,oldcols*sizeof(int));
1522:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1523:     /* determine lens of each row */
1524:     for (i=0; i<nrows; i++) {
1525:       kstart  = ai[irow[i]]+shift;
1526:       kend    = kstart + a->ilen[irow[i]];
1527:       lens[i] = 0;
1528:       for (k=kstart; k<kend; k++) {
1529:         if (ssmap[aj[k]]) {
1530:           lens[i]++;
1531:         }
1532:       }
1533:     }
1534:     /* Create and fill new matrix */
1535:     if (scall == MAT_REUSE_MATRIX) {
1536:       PetscTruth equal;

1538:       c = (Mat_SeqAIJ *)((*B)->data);
1539:       if ((*B)->m  != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1540:       PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);
1541:       if (!equal) {
1542:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1543:       }
1544:       PetscMemzero(c->ilen,(*B)->m*sizeof(int));
1545:       C = *B;
1546:     } else {
1547:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1548:     }
1549:     c = (Mat_SeqAIJ *)(C->data);
1550:     for (i=0; i<nrows; i++) {
1551:       row    = irow[i];
1552:       kstart = ai[row]+shift;
1553:       kend   = kstart + a->ilen[row];
1554:       mat_i  = c->i[i]+shift;
1555:       mat_j  = c->j + mat_i;
1556:       mat_a  = c->a + mat_i;
1557:       mat_ilen = c->ilen + i;
1558:       for (k=kstart; k<kend; k++) {
1559:         if ((tcol=ssmap[a->j[k]])) {
1560:           *mat_j++ = tcol - (!shift);
1561:           *mat_a++ = a->a[k];
1562:           (*mat_ilen)++;

1564:         }
1565:       }
1566:     }
1567:     /* Free work space */
1568:     ISRestoreIndices(iscol,&icol);
1569:     PetscFree(smap);
1570:     PetscFree(lens);
1571:   }
1572:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1573:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1575:   ISRestoreIndices(isrow,&irow);
1576:   *B = C;
1577:   return(0);
1578: }

1580: /*
1581: */
1582: int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatILUInfo *info)
1583: {
1584:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1585:   int        ierr;
1586:   Mat        outA;
1587:   PetscTruth row_identity,col_identity;

1590:   if (info && info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1591:   ISIdentity(row,&row_identity);
1592:   ISIdentity(col,&col_identity);
1593:   if (!row_identity || !col_identity) {
1594:     SETERRQ(1,"Row and column permutations must be identity for in-place ILU");
1595:   }

1597:   outA          = inA;
1598:   inA->factor   = FACTOR_LU;
1599:   a->row        = row;
1600:   a->col        = col;
1601:   PetscObjectReference((PetscObject)row);
1602:   PetscObjectReference((PetscObject)col);

1604:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1605:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1606:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1607:   PetscLogObjectParent(inA,a->icol);

1609:   if (!a->solve_work) { /* this matrix may have been factored before */
1610:      PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
1611:   }

1613:   if (!a->diag) {
1614:     MatMarkDiagonal_SeqAIJ(inA);
1615:   }
1616:   MatLUFactorNumeric_SeqAIJ(inA,&outA);
1617:   return(0);
1618: }

1620:  #include petscblaslapack.h
1621: int MatScale_SeqAIJ(PetscScalar *alpha,Mat inA)
1622: {
1623:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1624:   int        one = 1;

1627:   BLscal_(&a->nz,alpha,a->a,&one);
1628:   PetscLogFlops(a->nz);
1629:   return(0);
1630: }

1632: int MatGetSubMatrices_SeqAIJ(Mat A,int n,IS *irow,IS *icol,MatReuse scall,Mat **B)
1633: {
1634:   int ierr,i;

1637:   if (scall == MAT_INITIAL_MATRIX) {
1638:     PetscMalloc((n+1)*sizeof(Mat),B);
1639:   }

1641:   for (i=0; i<n; i++) {
1642:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1643:   }
1644:   return(0);
1645: }

1647: int MatGetBlockSize_SeqAIJ(Mat A,int *bs)
1648: {
1650:   *bs = 1;
1651:   return(0);
1652: }

1654: int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS *is,int ov)
1655: {
1656:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1657:   int        shift,row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val;
1658:   int        start,end,*ai,*aj;
1659:   PetscBT    table;

1662:   shift = a->indexshift;
1663:   m     = A->m;
1664:   ai    = a->i;
1665:   aj    = a->j+shift;

1667:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal overlap value used");

1669:   PetscMalloc((m+1)*sizeof(int),&nidx);
1670:   PetscBTCreate(m,table);

1672:   for (i=0; i<is_max; i++) {
1673:     /* Initialize the two local arrays */
1674:     isz  = 0;
1675:     PetscBTMemzero(m,table);
1676: 
1677:     /* Extract the indices, assume there can be duplicate entries */
1678:     ISGetIndices(is[i],&idx);
1679:     ISGetLocalSize(is[i],&n);
1680: 
1681:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1682:     for (j=0; j<n ; ++j){
1683:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1684:     }
1685:     ISRestoreIndices(is[i],&idx);
1686:     ISDestroy(is[i]);
1687: 
1688:     k = 0;
1689:     for (j=0; j<ov; j++){ /* for each overlap */
1690:       n = isz;
1691:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1692:         row   = nidx[k];
1693:         start = ai[row];
1694:         end   = ai[row+1];
1695:         for (l = start; l<end ; l++){
1696:           val = aj[l] + shift;
1697:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1698:         }
1699:       }
1700:     }
1701:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1702:   }
1703:   PetscBTDestroy(table);
1704:   PetscFree(nidx);
1705:   return(0);
1706: }

1708: /* -------------------------------------------------------------- */
1709: int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1710: {
1711:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1712:   PetscScalar  *vwork;
1713:   int          i,ierr,nz,m = A->m,n = A->n,*cwork;
1714:   int          *row,*col,*cnew,j,*lens;
1715:   IS           icolp,irowp;

1718:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1719:   ISGetIndices(irowp,&row);
1720:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1721:   ISGetIndices(icolp,&col);
1722: 
1723:   /* determine lengths of permuted rows */
1724:   PetscMalloc((m+1)*sizeof(int),&lens);
1725:   for (i=0; i<m; i++) {
1726:     lens[row[i]] = a->i[i+1] - a->i[i];
1727:   }
1728:   MatCreateSeqAIJ(A->comm,m,n,0,lens,B);
1729:   PetscFree(lens);

1731:   PetscMalloc(n*sizeof(int),&cnew);
1732:   for (i=0; i<m; i++) {
1733:     MatGetRow(A,i,&nz,&cwork,&vwork);
1734:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1735:     MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1736:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
1737:   }
1738:   PetscFree(cnew);
1739:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1740:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1741:   ISRestoreIndices(irowp,&row);
1742:   ISRestoreIndices(icolp,&col);
1743:   ISDestroy(irowp);
1744:   ISDestroy(icolp);
1745:   return(0);
1746: }

1748: int MatPrintHelp_SeqAIJ(Mat A)
1749: {
1750:   static PetscTruth called = PETSC_FALSE;
1751:   MPI_Comm          comm = A->comm;
1752:   int               ierr;

1755:   if (called) {return(0);} else called = PETSC_TRUE;
1756:   (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):n");
1757:   (*PetscHelpPrintf)(comm,"  -mat_lu_pivotthreshold <threshold>: Set pivoting thresholdn");
1758:   (*PetscHelpPrintf)(comm,"  -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.n");
1759:   (*PetscHelpPrintf)(comm,"  -mat_aij_no_inode: Do not use inodesn");
1760:   (*PetscHelpPrintf)(comm,"  -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)n");
1761: #if defined(PETSC_HAVE_ESSL)
1762:   (*PetscHelpPrintf)(comm,"  -mat_aij_essl: Use IBM sparse LU factorization and solve.n");
1763: #endif
1764: #if defined(PETSC_HAVE_LUSOL)
1765:   (*PetscHelpPrintf)(comm,"  -mat_aij_lusol: Use the Stanford LUSOL sparse factorization and solve.n");
1766: #endif
1767: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
1768:   (*PetscHelpPrintf)(comm,"  -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.n");
1769: #endif
1770:   return(0);
1771: }
1772: EXTERN int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg);
1773: EXTERN int MatFDColoringCreate_SeqAIJ(Mat,ISColoring,MatFDColoring);
1774: EXTERN int MatILUDTFactor_SeqAIJ(Mat,MatILUInfo*,IS,IS,Mat*);
1775: int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1776: {
1777:   int        ierr;
1778:   PetscTruth flg;

1781:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg);
1782:   if (str == SAME_NONZERO_PATTERN && flg) {
1783:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1784:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1786:     if (a->i[A->m]+a->indexshift != b->i[B->m]+a->indexshift) {
1787:       SETERRQ(1,"Number of nonzeros in two matrices are different");
1788:     }
1789:     PetscMemcpy(b->a,a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
1790:   } else {
1791:     MatCopy_Basic(A,B,str);
1792:   }
1793:   return(0);
1794: }

1796: int MatSetUpPreallocation_SeqAIJ(Mat A)
1797: {
1798:   int        ierr;

1801:    MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);
1802:   return(0);
1803: }

1805: int MatGetArray_SeqAIJ(Mat A,PetscScalar **array)
1806: {
1807:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1809:   *array = a->a;
1810:   return(0);
1811: }

1813: int MatRestoreArray_SeqAIJ(Mat A,PetscScalar **array)
1814: {
1816:   return(0);
1817: }

1819: int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1820: {
1821:   int           (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f;
1822:   int           k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1823:   PetscScalar   dx,mone = -1.0,*y,*xx,*w3_array;
1824:   PetscScalar   *vscale_array;
1825:   PetscReal     epsilon = coloring->error_rel,umin = coloring->umin;
1826:   Vec           w1,w2,w3;
1827:   void          *fctx = coloring->fctx;
1828:   PetscTruth    flg;

1831:   if (!coloring->w1) {
1832:     VecDuplicate(x1,&coloring->w1);
1833:     PetscLogObjectParent(coloring,coloring->w1);
1834:     VecDuplicate(x1,&coloring->w2);
1835:     PetscLogObjectParent(coloring,coloring->w2);
1836:     VecDuplicate(x1,&coloring->w3);
1837:     PetscLogObjectParent(coloring,coloring->w3);
1838:   }
1839:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

1841:   MatSetUnfactored(J);
1842:   PetscOptionsHasName(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg);
1843:   if (flg) {
1844:     PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()n");
1845:   } else {
1846:     MatZeroEntries(J);
1847:   }

1849:   VecGetOwnershipRange(x1,&start,&end);
1850:   VecGetSize(x1,&N);

1852:   /*
1853:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
1854:      coloring->F for the coarser grids from the finest
1855:   */
1856:   if (coloring->F) {
1857:     VecGetLocalSize(coloring->F,&m1);
1858:     VecGetLocalSize(w1,&m2);
1859:     if (m1 != m2) {
1860:       coloring->F = 0;
1861:     }
1862:   }

1864:   if (coloring->F) {
1865:     w1          = coloring->F;
1866:     coloring->F = 0;
1867:   } else {
1868:     (*f)(sctx,x1,w1,fctx);
1869:   }

1871:   /* 
1872:       Compute all the scale factors and share with other processors
1873:   */
1874:   VecGetArray(x1,&xx);xx = xx - start;
1875:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
1876:   for (k=0; k<coloring->ncolors; k++) {
1877:     /*
1878:        Loop over each column associated with color adding the 
1879:        perturbation to the vector w3.
1880:     */
1881:     for (l=0; l<coloring->ncolumns[k]; l++) {
1882:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1883:       dx  = xx[col];
1884:       if (dx == 0.0) dx = 1.0;
1885: #if !defined(PETSC_USE_COMPLEX)
1886:       if (dx < umin && dx >= 0.0)      dx = umin;
1887:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1888: #else
1889:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1890:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1891: #endif
1892:       dx                *= epsilon;
1893:       vscale_array[col] = 1.0/dx;
1894:     }
1895:   }
1896:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
1897:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
1898:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

1900:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
1901:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

1903:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
1904:   else                        vscaleforrow = coloring->columnsforrow;

1906:   VecGetArray(coloring->vscale,&vscale_array);
1907:   /*
1908:       Loop over each color
1909:   */
1910:   for (k=0; k<coloring->ncolors; k++) {
1911:     VecCopy(x1,w3);
1912:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
1913:     /*
1914:        Loop over each column associated with color adding the 
1915:        perturbation to the vector w3.
1916:     */
1917:     for (l=0; l<coloring->ncolumns[k]; l++) {
1918:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1919:       dx  = xx[col];
1920:       if (dx == 0.0) dx = 1.0;
1921: #if !defined(PETSC_USE_COMPLEX)
1922:       if (dx < umin && dx >= 0.0)      dx = umin;
1923:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1924: #else
1925:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1926:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1927: #endif
1928:       dx            *= epsilon;
1929:       if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter");
1930:       w3_array[col] += dx;
1931:     }
1932:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

1934:     /*
1935:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
1936:     */

1938:     (*f)(sctx,w3,w2,fctx);
1939:     VecAXPY(&mone,w1,w2);

1941:     /*
1942:        Loop over rows of vector, putting results into Jacobian matrix
1943:     */
1944:     VecGetArray(w2,&y);
1945:     for (l=0; l<coloring->nrows[k]; l++) {
1946:       row    = coloring->rows[k][l];
1947:       col    = coloring->columnsforrow[k][l];
1948:       y[row] *= vscale_array[vscaleforrow[k][l]];
1949:       srow   = row + start;
1950:       ierr   = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
1951:     }
1952:     VecRestoreArray(w2,&y);
1953:   }
1954:   VecRestoreArray(coloring->vscale,&vscale_array);
1955:   xx = xx + start; ierr  = VecRestoreArray(x1,&xx);
1956:   ierr  = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
1957:   ierr  = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
1958:   return(0);
1959: }

1961:  #include petscblaslapack.h

1963: int MatAXPY_SeqAIJ(PetscScalar *a,Mat X,Mat Y,MatStructure str)
1964: {
1965:   int        ierr,one=1;
1966:   Mat_SeqAIJ *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;

1969:   if (str == SAME_NONZERO_PATTERN) {
1970:     BLaxpy_(&x->nz,a,x->a,&one,y->a,&one);
1971:   } else {
1972:     MatAXPY_Basic(a,X,Y,str);
1973:   }
1974:   return(0);
1975: }


1978: /* -------------------------------------------------------------------*/
1979: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
1980:        MatGetRow_SeqAIJ,
1981:        MatRestoreRow_SeqAIJ,
1982:        MatMult_SeqAIJ,
1983:        MatMultAdd_SeqAIJ,
1984:        MatMultTranspose_SeqAIJ,
1985:        MatMultTransposeAdd_SeqAIJ,
1986:        MatSolve_SeqAIJ,
1987:        MatSolveAdd_SeqAIJ,
1988:        MatSolveTranspose_SeqAIJ,
1989:        MatSolveTransposeAdd_SeqAIJ,
1990:        MatLUFactor_SeqAIJ,
1991:        0,
1992:        MatRelax_SeqAIJ,
1993:        MatTranspose_SeqAIJ,
1994:        MatGetInfo_SeqAIJ,
1995:        MatEqual_SeqAIJ,
1996:        MatGetDiagonal_SeqAIJ,
1997:        MatDiagonalScale_SeqAIJ,
1998:        MatNorm_SeqAIJ,
1999:        0,
2000:        MatAssemblyEnd_SeqAIJ,
2001:        MatCompress_SeqAIJ,
2002:        MatSetOption_SeqAIJ,
2003:        MatZeroEntries_SeqAIJ,
2004:        MatZeroRows_SeqAIJ,
2005:        MatLUFactorSymbolic_SeqAIJ,
2006:        MatLUFactorNumeric_SeqAIJ,
2007:        0,
2008:        0,
2009:        MatSetUpPreallocation_SeqAIJ,
2010:        MatILUFactorSymbolic_SeqAIJ,
2011:        0,
2012:        MatGetArray_SeqAIJ,
2013:        MatRestoreArray_SeqAIJ,
2014:        MatDuplicate_SeqAIJ,
2015:        0,
2016:        0,
2017:        MatILUFactor_SeqAIJ,
2018:        0,
2019:        MatAXPY_SeqAIJ,
2020:        MatGetSubMatrices_SeqAIJ,
2021:        MatIncreaseOverlap_SeqAIJ,
2022:        MatGetValues_SeqAIJ,
2023:        MatCopy_SeqAIJ,
2024:        MatPrintHelp_SeqAIJ,
2025:        MatScale_SeqAIJ,
2026:        0,
2027:        0,
2028:        MatILUDTFactor_SeqAIJ,
2029:        MatGetBlockSize_SeqAIJ,
2030:        MatGetRowIJ_SeqAIJ,
2031:        MatRestoreRowIJ_SeqAIJ,
2032:        MatGetColumnIJ_SeqAIJ,
2033:        MatRestoreColumnIJ_SeqAIJ,
2034:        MatFDColoringCreate_SeqAIJ,
2035:        0,
2036:        0,
2037:        MatPermute_SeqAIJ,
2038:        0,
2039:        0,
2040:        MatDestroy_SeqAIJ,
2041:        MatView_SeqAIJ,
2042:        MatGetPetscMaps_Petsc,
2043:        0,
2044:        0,
2045:        0,
2046:        0,
2047:        0,
2048:        0,
2049:        0,
2050:        0,
2051:        MatSetColoring_SeqAIJ,
2052:        MatSetValuesAdic_SeqAIJ,
2053:        MatSetValuesAdifor_SeqAIJ,
2054:        MatFDColoringApply_SeqAIJ};

2056: EXTERN int MatUseSuperLU_SeqAIJ(Mat);
2057: EXTERN int MatUseEssl_SeqAIJ(Mat);
2058: EXTERN int MatUseLUSOL_SeqAIJ(Mat);
2059: EXTERN int MatUseMatlab_SeqAIJ(Mat);
2060: EXTERN int MatUseDXML_SeqAIJ(Mat);

2062: EXTERN_C_BEGIN

2064: int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices)
2065: {
2066:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2067:   int        i,nz,n;


2071:   nz = aij->maxnz;
2072:   n  = mat->n;
2073:   for (i=0; i<nz; i++) {
2074:     aij->j[i] = indices[i];
2075:   }
2076:   aij->nz = nz;
2077:   for (i=0; i<n; i++) {
2078:     aij->ilen[i] = aij->imax[i];
2079:   }

2081:   return(0);
2082: }
2083: EXTERN_C_END

2085: /*@
2086:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2087:        in the matrix.

2089:   Input Parameters:
2090: +  mat - the SeqAIJ matrix
2091: -  indices - the column indices

2093:   Level: advanced

2095:   Notes:
2096:     This can be called if you have precomputed the nonzero structure of the 
2097:   matrix and want to provide it to the matrix object to improve the performance
2098:   of the MatSetValues() operation.

2100:     You MUST have set the correct numbers of nonzeros per row in the call to 
2101:   MatCreateSeqAIJ().

2103:     MUST be called before any calls to MatSetValues();

2105:     The indices should start with zero, not one.

2107: @*/
2108: int MatSeqAIJSetColumnIndices(Mat mat,int *indices)
2109: {
2110:   int ierr,(*f)(Mat,int *);

2114:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2115:   if (f) {
2116:     (*f)(mat,indices);
2117:   } else {
2118:     SETERRQ(1,"Wrong type of matrix to set column indices");
2119:   }
2120:   return(0);
2121: }

2123: /* ----------------------------------------------------------------------------------------*/

2125: EXTERN_C_BEGIN
2126: int MatStoreValues_SeqAIJ(Mat mat)
2127: {
2128:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2129:   int        nz = aij->i[mat->m]+aij->indexshift,ierr;

2132:   if (aij->nonew != 1) {
2133:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2134:   }

2136:   /* allocate space for values if not already there */
2137:   if (!aij->saved_values) {
2138:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2139:   }

2141:   /* copy values over */
2142:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2143:   return(0);
2144: }
2145: EXTERN_C_END

2147: /*@
2148:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2149:        example, reuse of the linear part of a Jacobian, while recomputing the 
2150:        nonlinear portion.

2152:    Collect on Mat

2154:   Input Parameters:
2155: .  mat - the matrix (currently on AIJ matrices support this option)

2157:   Level: advanced

2159:   Common Usage, with SNESSolve():
2160: $    Create Jacobian matrix
2161: $    Set linear terms into matrix
2162: $    Apply boundary conditions to matrix, at this time matrix must have 
2163: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2164: $      boundary conditions again will not change the nonzero structure
2165: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2166: $    MatStoreValues(mat);
2167: $    Call SNESSetJacobian() with matrix
2168: $    In your Jacobian routine
2169: $      MatRetrieveValues(mat);
2170: $      Set nonlinear terms in matrix
2171:  
2172:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2173: $    // build linear portion of Jacobian 
2174: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2175: $    MatStoreValues(mat);
2176: $    loop over nonlinear iterations
2177: $       MatRetrieveValues(mat);
2178: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2179: $       // call MatAssemblyBegin/End() on matrix
2180: $       Solve linear system with Jacobian
2181: $    endloop 

2183:   Notes:
2184:     Matrix must already be assemblied before calling this routine
2185:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 
2186:     calling this routine.

2188: .seealso: MatRetrieveValues()

2190: @*/
2191: int MatStoreValues(Mat mat)
2192: {
2193:   int ierr,(*f)(Mat);

2197:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2198:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2200:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2201:   if (f) {
2202:     (*f)(mat);
2203:   } else {
2204:     SETERRQ(1,"Wrong type of matrix to store values");
2205:   }
2206:   return(0);
2207: }

2209: EXTERN_C_BEGIN
2210: int MatRetrieveValues_SeqAIJ(Mat mat)
2211: {
2212:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2213:   int        nz = aij->i[mat->m]+aij->indexshift,ierr;

2216:   if (aij->nonew != 1) {
2217:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2218:   }
2219:   if (!aij->saved_values) {
2220:     SETERRQ(1,"Must call MatStoreValues(A);first");
2221:   }

2223:   /* copy values over */
2224:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2225:   return(0);
2226: }
2227: EXTERN_C_END

2229: /*@
2230:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2231:        example, reuse of the linear part of a Jacobian, while recomputing the 
2232:        nonlinear portion.

2234:    Collect on Mat

2236:   Input Parameters:
2237: .  mat - the matrix (currently on AIJ matrices support this option)

2239:   Level: advanced

2241: .seealso: MatStoreValues()

2243: @*/
2244: int MatRetrieveValues(Mat mat)
2245: {
2246:   int ierr,(*f)(Mat);

2250:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2251:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2253:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2254:   if (f) {
2255:     (*f)(mat);
2256:   } else {
2257:     SETERRQ(1,"Wrong type of matrix to retrieve values");
2258:   }
2259:   return(0);
2260: }

2262: /*
2263:    This allows SeqAIJ matrices to be passed to the matlab engine
2264: */
2265: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2266: #include "engine.h"   /* Matlab include file */
2267: #include "mex.h"      /* Matlab include file */
2268: EXTERN_C_BEGIN
2269: int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *engine)
2270: {
2271:   int         ierr,i,*ai,*aj;
2272:   Mat         B = (Mat)obj;
2273:   PetscScalar *array;
2274:   mxArray     *mat;
2275:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)B->data;

2278:   mat  = mxCreateSparse(B->n,B->m,aij->nz,mxREAL);
2279:   PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));
2280:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2281:   PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));
2282:   PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));

2284:   /* Matlab indices start at 0 for sparse (what a surprise) */
2285:   if (aij->indexshift) {
2286:     for (i=0; i<B->m+1; i++) {
2287:       ai[i]--;
2288:     }
2289:     for (i=0; i<aij->nz; i++) {
2290:       aj[i]--;
2291:     }
2292:   }
2293:   PetscObjectName(obj);
2294:   mxSetName(mat,obj->name);
2295:   engPutArray((Engine *)engine,mat);
2296:   return(0);
2297: }
2298: EXTERN_C_END

2300: EXTERN_C_BEGIN
2301: int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *engine)
2302: {
2303:   int        ierr,ii;
2304:   Mat        mat = (Mat)obj;
2305:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2306:   mxArray    *mmat;

2309:   PetscFree(aij->a);
2310:   aij->indexshift = 0;

2312:   mmat = engGetArray((Engine *)engine,obj->name);

2314:   aij->nz           = (mxGetJc(mmat))[mat->m];
2315:   ierr              = PetscMalloc(aij->nz*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);
2316:   aij->j            = (int*)(aij->a + aij->nz);
2317:   aij->i            = aij->j + aij->nz;
2318:   aij->singlemalloc = PETSC_TRUE;
2319:   aij->freedata     = PETSC_TRUE;

2321:   PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));
2322:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2323:   PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));
2324:   PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));

2326:   for (ii=0; ii<mat->m; ii++) {
2327:     aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii];
2328:   }

2330:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
2331:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);

2333:   return(0);
2334: }
2335: EXTERN_C_END
2336: #endif

2338: /* --------------------------------------------------------------------------------*/
2339: /*@C
2340:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2341:    (the default parallel PETSc format).  For good matrix assembly performance
2342:    the user should preallocate the matrix storage by setting the parameter nz
2343:    (or the array nnz).  By setting these parameters accurately, performance
2344:    during matrix assembly can be increased by more than a factor of 50.

2346:    Collective on MPI_Comm

2348:    Input Parameters:
2349: +  comm - MPI communicator, set to PETSC_COMM_SELF
2350: .  m - number of rows
2351: .  n - number of columns
2352: .  nz - number of nonzeros per row (same for all rows)
2353: -  nnz - array containing the number of nonzeros in the various rows 
2354:          (possibly different for each row) or PETSC_NULL

2356:    Output Parameter:
2357: .  A - the matrix 

2359:    Notes:
2360:    The AIJ format (also called the Yale sparse matrix format or
2361:    compressed row storage), is fully compatible with standard Fortran 77
2362:    storage.  That is, the stored row and column indices can begin at
2363:    either one (as in Fortran) or zero.  See the users' manual for details.

2365:    Specify the preallocated storage with either nz or nnz (not both).
2366:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2367:    allocation.  For large problems you MUST preallocate memory or you 
2368:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2370:    By default, this format uses inodes (identical nodes) when possible, to 
2371:    improve numerical efficiency of matrix-vector products and solves. We 
2372:    search for consecutive rows with the same nonzero structure, thereby
2373:    reusing matrix information to achieve increased efficiency.

2375:    Options Database Keys:
2376: +  -mat_aij_no_inode  - Do not use inodes
2377: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2378: -  -mat_aij_oneindex - Internally use indexing starting at 1
2379:         rather than 0.  Note that when calling MatSetValues(),
2380:         the user still MUST index entries starting at 0!

2382:    Level: intermediate

2384: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2386: @*/
2387: int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,int *nnz,Mat *A)
2388: {

2392:   MatCreate(comm,m,n,m,n,A);
2393:   MatSetType(*A,MATSEQAIJ);
2394:   MatSeqAIJSetPreallocation(*A,nz,nnz);
2395:   return(0);
2396: }

2398: #define SKIP_ALLOCATION -4

2400: /*@C
2401:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2402:    the user should preallocate the matrix storage by setting the parameter nz
2403:    (or the array nnz).  By setting these parameters accurately, performance
2404:    during matrix assembly can be increased by more than a factor of 50.

2406:    Collective on MPI_Comm

2408:    Input Parameters:
2409: +  comm - MPI communicator, set to PETSC_COMM_SELF
2410: .  m - number of rows
2411: .  n - number of columns
2412: .  nz - number of nonzeros per row (same for all rows)
2413: -  nnz - array containing the number of nonzeros in the various rows 
2414:          (possibly different for each row) or PETSC_NULL

2416:    Output Parameter:
2417: .  A - the matrix 

2419:    Notes:
2420:    The AIJ format (also called the Yale sparse matrix format or
2421:    compressed row storage), is fully compatible with standard Fortran 77
2422:    storage.  That is, the stored row and column indices can begin at
2423:    either one (as in Fortran) or zero.  See the users' manual for details.

2425:    Specify the preallocated storage with either nz or nnz (not both).
2426:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2427:    allocation.  For large problems you MUST preallocate memory or you 
2428:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2430:    By default, this format uses inodes (identical nodes) when possible, to 
2431:    improve numerical efficiency of matrix-vector products and solves. We 
2432:    search for consecutive rows with the same nonzero structure, thereby
2433:    reusing matrix information to achieve increased efficiency.

2435:    Options Database Keys:
2436: +  -mat_aij_no_inode  - Do not use inodes
2437: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2438: -  -mat_aij_oneindex - Internally use indexing starting at 1
2439:         rather than 0.  Note that when calling MatSetValues(),
2440:         the user still MUST index entries starting at 0!

2442:    Level: intermediate

2444: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2446: @*/
2447: int MatSeqAIJSetPreallocation(Mat B,int nz,int *nnz)
2448: {
2449:   Mat_SeqAIJ *b;
2450:   int        i,len=0,ierr;
2451:   PetscTruth flg2,skipallocation = PETSC_FALSE;

2454:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg2);
2455:   if (!flg2) return(0);
2456: 
2457:   if (nz == SKIP_ALLOCATION) {
2458:     skipallocation = PETSC_TRUE;
2459:     nz             = 0;
2460:   }

2462:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2463:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2464:   if (nnz) {
2465:     for (i=0; i<B->m; i++) {
2466:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2467:       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);
2468:     }
2469:   }

2471:   B->preallocated = PETSC_TRUE;
2472:   b = (Mat_SeqAIJ*)B->data;

2474:   PetscMalloc((B->m+1)*sizeof(int),&b->imax);
2475:   if (!nnz) {
2476:     if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2477:     else if (nz <= 0)        nz = 1;
2478:     for (i=0; i<B->m; i++) b->imax[i] = nz;
2479:     nz = nz*B->m;
2480:   } else {
2481:     nz = 0;
2482:     for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2483:   }

2485:   if (!skipallocation) {
2486:     /* allocate the matrix space */
2487:     len             = nz*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int);
2488:     ierr            = PetscMalloc(len,&b->a);
2489:     b->j            = (int*)(b->a + nz);
2490:     ierr            = PetscMemzero(b->j,nz*sizeof(int));
2491:     b->i            = b->j + nz;
2492:     b->i[0] = -b->indexshift;
2493:     for (i=1; i<B->m+1; i++) {
2494:       b->i[i] = b->i[i-1] + b->imax[i-1];
2495:     }
2496:     b->singlemalloc = PETSC_TRUE;
2497:     b->freedata     = PETSC_TRUE;
2498:   } else {
2499:     b->freedata     = PETSC_FALSE;
2500:   }

2502:   /* b->ilen will count nonzeros in each row so far. */
2503:   PetscMalloc((B->m+1)*sizeof(int),&b->ilen);
2504:   PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2505:   for (i=0; i<B->m; i++) { b->ilen[i] = 0;}

2507:   b->nz                = 0;
2508:   b->maxnz             = nz;
2509:   B->info.nz_unneeded  = (double)b->maxnz;
2510:   return(0);
2511: }

2513: EXTERN int RegisterApplyPtAPRoutines_Private(Mat);

2515: EXTERN_C_BEGIN
2516: int MatCreate_SeqAIJ(Mat B)
2517: {
2518:   Mat_SeqAIJ *b;
2519:   int        ierr,size;
2520:   PetscTruth flg;

2523:   MPI_Comm_size(B->comm,&size);
2524:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

2526:   B->m = B->M = PetscMax(B->m,B->M);
2527:   B->n = B->N = PetscMax(B->n,B->N);

2529:   PetscNew(Mat_SeqAIJ,&b);
2530:   B->data             = (void*)b;
2531:   PetscMemzero(b,sizeof(Mat_SeqAIJ));
2532:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2533:   B->factor           = 0;
2534:   B->lupivotthreshold = 1.0;
2535:   B->mapping          = 0;
2536:   PetscOptionsGetReal(PETSC_NULL,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);
2537:   PetscOptionsHasName(PETSC_NULL,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);
2538:   b->row              = 0;
2539:   b->col              = 0;
2540:   b->icol             = 0;
2541:   b->indexshift       = 0;
2542:   b->reallocs         = 0;
2543:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_oneindex",&flg);
2544:   if (flg) b->indexshift = -1;
2545: 
2546:   PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
2547:   PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);

2549:   b->sorted            = PETSC_FALSE;
2550:   b->ignorezeroentries = PETSC_FALSE;
2551:   b->roworiented       = PETSC_TRUE;
2552:   b->nonew             = 0;
2553:   b->diag              = 0;
2554:   b->solve_work        = 0;
2555:   B->spptr             = 0;
2556:   b->inode.use         = PETSC_TRUE;
2557:   b->inode.node_count  = 0;
2558:   b->inode.size        = 0;
2559:   b->inode.limit       = 5;
2560:   b->inode.max_limit   = 5;
2561:   b->saved_values      = 0;
2562:   b->idiag             = 0;
2563:   b->ssor              = 0;
2564:   b->keepzeroedrows    = PETSC_FALSE;

2566:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

2568: #if defined(PETSC_HAVE_SUPERLU)
2569:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_superlu",&flg);
2570:   if (flg) { MatUseSuperLU_SeqAIJ(B); }
2571: #endif
2572:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_essl",&flg);
2573:   if (flg) { MatUseEssl_SeqAIJ(B); }
2574:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_lusol",&flg);
2575:   if (flg) { MatUseLUSOL_SeqAIJ(B); }
2576:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_matlab",&flg);
2577:   if (flg) {MatUseMatlab_SeqAIJ(B);}
2578:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_dxml",&flg);
2579:   if (flg) {
2580:     if (!b->indexshift) SETERRQ(PETSC_ERR_LIB,"need -mat_aij_oneindex with -mat_aij_dxml");
2581:     MatUseDXML_SeqAIJ(B);
2582:   }
2583:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2584:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
2585:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
2586:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2587:                                      "MatStoreValues_SeqAIJ",
2588:                                      MatStoreValues_SeqAIJ);
2589:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2590:                                      "MatRetrieveValues_SeqAIJ",
2591:                                      MatRetrieveValues_SeqAIJ);
2592: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2593:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);
2594:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);
2595: #endif
2596:   RegisterApplyPtAPRoutines_Private(B);
2597:   return(0);
2598: }
2599: EXTERN_C_END

2601: int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2602: {
2603:   Mat        C;
2604:   Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
2605:   int        i,len,m = A->m,shift = a->indexshift,ierr;

2608:   *B = 0;
2609:   MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);
2610:   MatSetType(C,MATSEQAIJ);
2611:   c    = (Mat_SeqAIJ*)C->data;

2613:   C->factor         = A->factor;
2614:   c->row            = 0;
2615:   c->col            = 0;
2616:   c->icol           = 0;
2617:   c->indexshift     = shift;
2618:   c->keepzeroedrows = a->keepzeroedrows;
2619:   C->assembled      = PETSC_TRUE;

2621:   C->M          = A->m;
2622:   C->N          = A->n;

2624:   PetscMalloc((m+1)*sizeof(int),&c->imax);
2625:   PetscMalloc((m+1)*sizeof(int),&c->ilen);
2626:   for (i=0; i<m; i++) {
2627:     c->imax[i] = a->imax[i];
2628:     c->ilen[i] = a->ilen[i];
2629:   }

2631:   /* allocate the matrix space */
2632:   c->singlemalloc = PETSC_TRUE;
2633:   len   = (m+1)*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int));
2634:   ierr  = PetscMalloc(len,&c->a);
2635:   c->j  = (int*)(c->a + a->i[m] + shift);
2636:   c->i  = c->j + a->i[m] + shift;
2637:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));
2638:   if (m > 0) {
2639:     PetscMemcpy(c->j,a->j,(a->i[m]+shift)*sizeof(int));
2640:     if (cpvalues == MAT_COPY_VALUES) {
2641:       PetscMemcpy(c->a,a->a,(a->i[m]+shift)*sizeof(PetscScalar));
2642:     } else {
2643:       PetscMemzero(c->a,(a->i[m]+shift)*sizeof(PetscScalar));
2644:     }
2645:   }

2647:   PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2648:   c->sorted      = a->sorted;
2649:   c->roworiented = a->roworiented;
2650:   c->nonew       = a->nonew;
2651:   c->ilu_preserve_row_sums = a->ilu_preserve_row_sums;
2652:   c->saved_values = 0;
2653:   c->idiag        = 0;
2654:   c->ssor         = 0;
2655:   c->ignorezeroentries = a->ignorezeroentries;
2656:   c->freedata     = PETSC_TRUE;

2658:   if (a->diag) {
2659:     PetscMalloc((m+1)*sizeof(int),&c->diag);
2660:     PetscLogObjectMemory(C,(m+1)*sizeof(int));
2661:     for (i=0; i<m; i++) {
2662:       c->diag[i] = a->diag[i];
2663:     }
2664:   } else c->diag        = 0;
2665:   c->inode.use          = a->inode.use;
2666:   c->inode.limit        = a->inode.limit;
2667:   c->inode.max_limit    = a->inode.max_limit;
2668:   if (a->inode.size){
2669:     ierr                = PetscMalloc((m+1)*sizeof(int),&c->inode.size);
2670:     c->inode.node_count = a->inode.node_count;
2671:     ierr                = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));
2672:   } else {
2673:     c->inode.size       = 0;
2674:     c->inode.node_count = 0;
2675:   }
2676:   c->nz                 = a->nz;
2677:   c->maxnz              = a->maxnz;
2678:   c->solve_work         = 0;
2679:   C->spptr              = 0;      /* Dangerous -I'm throwing away a->spptr */
2680:   C->preallocated       = PETSC_TRUE;

2682:   *B = C;
2683:   PetscFListDuplicate(A->qlist,&C->qlist);
2684:   return(0);
2685: }

2687: EXTERN_C_BEGIN
2688: int MatLoad_SeqAIJ(PetscViewer viewer,MatType type,Mat *A)
2689: {
2690:   Mat_SeqAIJ   *a;
2691:   Mat          B;
2692:   int          i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N,shift;
2693:   MPI_Comm     comm;
2694: 
2696:   PetscObjectGetComm((PetscObject)viewer,&comm);
2697:   MPI_Comm_size(comm,&size);
2698:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
2699:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2700:   PetscBinaryRead(fd,header,4,PETSC_INT);
2701:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
2702:   M = header[1]; N = header[2]; nz = header[3];

2704:   if (nz < 0) {
2705:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
2706:   }

2708:   /* read in row lengths */
2709:   PetscMalloc(M*sizeof(int),&rowlengths);
2710:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

2712:   /* create our matrix */
2713:   MatCreateSeqAIJ(comm,M,N,0,rowlengths,A);
2714:   B = *A;
2715:   a = (Mat_SeqAIJ*)B->data;
2716:   shift = a->indexshift;

2718:   /* read in column indices and adjust for Fortran indexing*/
2719:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);
2720:   if (shift) {
2721:     for (i=0; i<nz; i++) {
2722:       a->j[i] += 1;
2723:     }
2724:   }

2726:   /* read in nonzero values */
2727:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

2729:   /* set matrix "i" values */
2730:   a->i[0] = -shift;
2731:   for (i=1; i<= M; i++) {
2732:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
2733:     a->ilen[i-1] = rowlengths[i-1];
2734:   }
2735:   PetscFree(rowlengths);

2737:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2738:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2739:   return(0);
2740: }
2741: EXTERN_C_END

2743: int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
2744: {
2745:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
2746:   int        ierr;
2747:   PetscTruth flag;

2750:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flag);
2751:   if (!flag) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");

2753:   /* If the  matrix dimensions are not equal,or no of nonzeros or shift */
2754:   if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)|| (a->indexshift != b->indexshift)) {
2755:     *flg = PETSC_FALSE;
2756:     return(0);
2757:   }
2758: 
2759:   /* if the a->i are the same */
2760:   PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);
2761:   if (*flg == PETSC_FALSE) return(0);
2762: 
2763:   /* if a->j are the same */
2764:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);
2765:   if (*flg == PETSC_FALSE) return(0);
2766: 
2767:   /* if a->a are the same */
2768:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

2770:   return(0);
2771: 
2772: }

2774: /*@C
2775:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
2776:               provided by the user.

2778:       Coolective on MPI_Comm

2780:    Input Parameters:
2781: +   comm - must be an MPI communicator of size 1
2782: .   m - number of rows
2783: .   n - number of columns
2784: .   i - row indices
2785: .   j - column indices
2786: -   a - matrix values

2788:    Output Parameter:
2789: .   mat - the matrix

2791:    Level: intermediate

2793:    Notes:
2794:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2795:     once the matrix is destroyed

2797:        You cannot set new nonzero locations into this matrix, that will generate an error.

2799:        The i and j indices can be either 0- or 1 based

2801: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()

2803: @*/
2804: int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat)
2805: {
2806:   int        ierr,ii;
2807:   Mat_SeqAIJ *aij;

2810:   MatCreateSeqAIJ(comm,m,n,SKIP_ALLOCATION,0,mat);
2811:   aij  = (Mat_SeqAIJ*)(*mat)->data;

2813:   if (i[0] == 1) {
2814:     aij->indexshift = -1;
2815:   } else if (i[0]) {
2816:     SETERRQ(1,"i (row indices) do not start with 0 or 1");
2817:   }
2818:   aij->i = i;
2819:   aij->j = j;
2820:   aij->a = a;
2821:   aij->singlemalloc = PETSC_FALSE;
2822:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2823:   aij->freedata     = PETSC_FALSE;

2825:   for (ii=0; ii<m; ii++) {
2826:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
2827: #if defined(PETSC_USE_BOPT_g)
2828:     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]);
2829: #endif    
2830:   }
2831: #if defined(PETSC_USE_BOPT_g)
2832:   for (ii=0; ii<aij->i[m]; ii++) {
2833:     if (j[ii] < -aij->indexshift) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]);
2834:     if (j[ii] > n - 1 -aij->indexshift) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]);
2835:   }
2836: #endif    

2838:   /* changes indices to start at 0 */
2839:   if (i[0]) {
2840:     aij->indexshift = 0;
2841:     for (ii=0; ii<m; ii++) {
2842:       i[ii]--;
2843:     }
2844:     for (ii=0; ii<i[m]; ii++) {
2845:       j[ii]--;
2846:     }
2847:   }

2849:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2850:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2851:   return(0);
2852: }

2854: int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
2855: {
2856:   int        ierr;
2857:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2860:   if (coloring->ctype == IS_COLORING_LOCAL) {
2861:     ierr        = ISColoringReference(coloring);
2862:     a->coloring = coloring;
2863:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
2864:     int        *colors,i,*larray;
2865:     ISColoring ocoloring;

2867:     /* set coloring for diagonal portion */
2868:     PetscMalloc((A->n+1)*sizeof(int),&larray);
2869:     for (i=0; i<A->n; i++) {
2870:       larray[i] = i;
2871:     }
2872:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);
2873:     PetscMalloc((A->n+1)*sizeof(int),&colors);
2874:     for (i=0; i<A->n; i++) {
2875:       colors[i] = coloring->colors[larray[i]];
2876:     }
2877:     PetscFree(larray);
2878:     ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);
2879:     a->coloring = ocoloring;
2880:   }
2881:   return(0);
2882: }

2884: #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2885: EXTERN_C_BEGIN
2886: #include "adic/ad_utils.h"
2887: EXTERN_C_END

2889: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2890: {
2891:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
2892:   int         m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j,nlen;
2893:   PetscScalar *v = a->a,*values;
2894:   char        *cadvalues = (char *)advalues;

2897:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
2898:   nlen  = PetscADGetDerivTypeSize();
2899:   color = a->coloring->colors;
2900:   /* loop over rows */
2901:   for (i=0; i<m; i++) {
2902:     nz = ii[i+1] - ii[i];
2903:     /* loop over columns putting computed value into matrix */
2904:     values = PetscADGetGradArray(cadvalues);
2905:     for (j=0; j<nz; j++) {
2906:       *v++ = values[color[*jj++]];
2907:     }
2908:     cadvalues += nlen; /* jump to next row of derivatives */
2909:   }
2910:   return(0);
2911: }

2913: #else

2915: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2916: {
2918:   SETERRQ(1,"PETSc installed without ADIC");
2919: }

2921: #endif

2923: int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues)
2924: {
2925:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
2926:   int          m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j;
2927:   PetscScalar  *v = a->a,*values = (PetscScalar *)advalues;

2930:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
2931:   color = a->coloring->colors;
2932:   /* loop over rows */
2933:   for (i=0; i<m; i++) {
2934:     nz = ii[i+1] - ii[i];
2935:     /* loop over columns putting computed value into matrix */
2936:     for (j=0; j<nz; j++) {
2937:       *v++ = values[color[*jj++]];
2938:     }
2939:     values += nl; /* jump to next row of derivatives */
2940:   }
2941:   return(0);
2942: }