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;
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:   PetscScalar  sum;
860: #endif

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

867:   VecGetArray(xx,&x);
868:   VecGetArray(yy,&y);
869:   x    = x + shift;    /* shift for Fortran start by 1 indexing */
870:   idx  = a->j;
871:   v    = a->a;
872:   ii   = a->i;
873: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
874:   fortranmultaij_(&m,x,ii,idx+shift,v+shift,y);
875: #else
876:   v    += shift; /* shift for Fortran start by 1 indexing */
877:   idx  += shift;
878:   for (i=0; i<m; i++) {
879:     jrow = ii[i];
880:     n    = ii[i+1] - jrow;
881:     sum  = 0.0;
882:     for (j=0; j<n; j++) {
883:       sum += v[jrow]*x[idx[jrow]]; jrow++;
884:      }
885:     y[i] = sum;
886:   }
887: #endif
888:   PetscLogFlops(2*a->nz - m);
889:   VecRestoreArray(xx,&x);
890:   VecRestoreArray(yy,&y);
891:   return(0);
892: }

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

905:   VecGetArray(xx,&x);
906:   VecGetArray(yy,&y);
907:   if (zz != yy) {
908:     VecGetArray(zz,&z);
909:   } else {
910:     z = y;
911:   }
912:   x    = x + shift; /* shift for Fortran start by 1 indexing */
913:   idx  = a->j;
914:   v    = a->a;
915:   ii   = a->i;
916: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
917:   fortranmultaddaij_(&m,x,ii,idx+shift,v+shift,y,z);
918: #else
919:   v   += shift; /* shift for Fortran start by 1 indexing */
920:   idx += shift;
921:   for (i=0; i<m; i++) {
922:     jrow = ii[i];
923:     n    = ii[i+1] - jrow;
924:     sum  = y[i];
925:     for (j=0; j<n; j++) {
926:       sum += v[jrow]*x[idx[jrow]]; jrow++;
927:      }
928:     z[i] = sum;
929:   }
930: #endif
931:   PetscLogFlops(2*a->nz);
932:   VecRestoreArray(xx,&x);
933:   VecRestoreArray(yy,&y);
934:   if (zz != yy) {
935:     VecRestoreArray(zz,&z);
936:   }
937:   return(0);
938: }

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

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

951:   PetscMalloc((m+1)*sizeof(int),&diag);
952:   PetscLogObjectMemory(A,(m+1)*sizeof(int));
953:   for (i=0; i<A->m; i++) {
954:     diag[i] = a->i[i+1];
955:     for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
956:       if (a->j[j]+shift == i) {
957:         diag[i] = j - shift;
958:         break;
959:       }
960:     }
961:   }
962:   a->diag = diag;
963:   return(0);
964: }

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

975:   MatMarkDiagonal_SeqAIJ(A);
976:   diag = a->diag;
977:   for (i=0; i<A->m; i++) {
978:     if (jj[diag[i]+shift] != i-shift) {
979:       SETERRQ1(1,"Matrix is missing diagonal number %d",i);
980:     }
981:   }
982:   return(0);
983: }

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

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

995:   VecGetArray(xx,&x);
996:   if (xx != bb) {
997:     VecGetArray(bb,&b);
998:   } else {
999:     b = x;
1000:   }

1002:   if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
1003:   diag = a->diag;
1004:   xs   = x + shift; /* shifted by one for index start of a or a->j*/
1005:   if (flag == SOR_APPLY_UPPER) {
1006:    /* apply (U + D/omega) to the vector */
1007:     bs = b + shift;
1008:     for (i=0; i<m; i++) {
1009:         d    = fshift + a->a[diag[i] + shift];
1010:         n    = a->i[i+1] - diag[i] - 1;
1011:         PetscLogFlops(2*n-1);
1012:         idx  = a->j + diag[i] + (!shift);
1013:         v    = a->a + diag[i] + (!shift);
1014:         sum  = b[i]*d/omega;
1015:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1016:         x[i] = sum;
1017:     }
1018:     VecRestoreArray(xx,&x);
1019:     if (bb != xx) {VecRestoreArray(bb,&b);}
1020:     return(0);
1021:   }

1023:   /* setup workspace for Eisenstat */
1024:   if (flag & SOR_EISENSTAT) {
1025:     if (!a->idiag) {
1026:       ierr     = PetscMalloc(2*m*sizeof(PetscScalar),&a->idiag);
1027:       a->ssor  = a->idiag + m;
1028:       v        = a->a;
1029:       for (i=0; i<m; i++) { a->idiag[i] = 1.0/v[diag[i]];}
1030:     }
1031:     t     = a->ssor;
1032:     idiag = a->idiag;
1033:   }
1034:     /* Let  A = L + U + D; where L is lower trianglar,
1035:     U is upper triangular, E is diagonal; This routine applies

1037:             (L + E)^{-1} A (U + E)^{-1}

1039:     to a vector efficiently using Eisenstat's trick. This is for
1040:     the case of SSOR preconditioner, so E is D/omega where omega
1041:     is the relaxation factor.
1042:     */

1044:   if (flag == SOR_APPLY_LOWER) {
1045:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1046:   } else if ((flag & SOR_EISENSTAT) && omega == 1.0 && shift == 0 && fshift == 0.0) {
1047:     /* special case for omega = 1.0 saves flops and some integer ops */
1048:     PetscScalar *v2;
1049: 
1050:     v2    = a->a;
1051:     /*  x = (E + U)^{-1} b */
1052:     for (i=m-1; i>=0; i--) {
1053:       n    = a->i[i+1] - diag[i] - 1;
1054:       idx  = a->j + diag[i] + 1;
1055:       v    = a->a + diag[i] + 1;
1056:       sum  = b[i];
1057:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1058:       x[i] = sum*idiag[i];

1060:       /*  t = b - (2*E - D)x */
1061:       t[i] = b[i] - (v2[diag[i]])*x[i];
1062:     }

1064:     /*  t = (E + L)^{-1}t */
1065:     diag = a->diag;
1066:     for (i=0; i<m; i++) {
1067:       n    = diag[i] - a->i[i];
1068:       idx  = a->j + a->i[i];
1069:       v    = a->a + a->i[i];
1070:       sum  = t[i];
1071:       SPARSEDENSEMDOT(sum,t,v,idx,n);
1072:       t[i]  = sum*idiag[i];

1074:       /*  x = x + t */
1075:       x[i] += t[i];
1076:     }

1078:     PetscLogFlops(3*m-1 + 2*a->nz);
1079:     VecRestoreArray(xx,&x);
1080:     if (bb != xx) {VecRestoreArray(bb,&b);}
1081:     return(0);
1082:   } else if (flag & SOR_EISENSTAT) {
1083:     /* Let  A = L + U + D; where L is lower trianglar,
1084:     U is upper triangular, E is diagonal; This routine applies

1086:             (L + E)^{-1} A (U + E)^{-1}

1088:     to a vector efficiently using Eisenstat's trick. This is for
1089:     the case of SSOR preconditioner, so E is D/omega where omega
1090:     is the relaxation factor.
1091:     */
1092:     scale = (2.0/omega) - 1.0;

1094:     /*  x = (E + U)^{-1} b */
1095:     for (i=m-1; i>=0; i--) {
1096:       d    = fshift + a->a[diag[i] + shift];
1097:       n    = a->i[i+1] - diag[i] - 1;
1098:       idx  = a->j + diag[i] + (!shift);
1099:       v    = a->a + diag[i] + (!shift);
1100:       sum  = b[i];
1101:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1102:       x[i] = omega*(sum/d);
1103:     }

1105:     /*  t = b - (2*E - D)x */
1106:     v = a->a;
1107:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++ + shift])*x[i]; }

1109:     /*  t = (E + L)^{-1}t */
1110:     ts = t + shift; /* shifted by one for index start of a or a->j*/
1111:     diag = a->diag;
1112:     for (i=0; i<m; i++) {
1113:       d    = fshift + a->a[diag[i]+shift];
1114:       n    = diag[i] - a->i[i];
1115:       idx  = a->j + a->i[i] + shift;
1116:       v    = a->a + a->i[i] + shift;
1117:       sum  = t[i];
1118:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1119:       t[i] = omega*(sum/d);
1120:       /*  x = x + t */
1121:       x[i] += t[i];
1122:     }

1124:     PetscLogFlops(6*m-1 + 2*a->nz);
1125:     VecRestoreArray(xx,&x);
1126:     if (bb != xx) {VecRestoreArray(bb,&b);}
1127:     return(0);
1128:   }
1129:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1130:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1131: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1132:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1133: #else
1134:       for (i=0; i<m; i++) {
1135:         d    = fshift + a->a[diag[i]+shift];
1136:         n    = diag[i] - a->i[i];
1137:         PetscLogFlops(2*n-1);
1138:         idx  = a->j + a->i[i] + shift;
1139:         v    = a->a + a->i[i] + shift;
1140:         sum  = b[i];
1141:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1142:         x[i] = omega*(sum/d);
1143:       }
1144: #endif
1145:       xb = x;
1146:     } else xb = b;
1147:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1148:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1149:       for (i=0; i<m; i++) {
1150:         x[i] *= a->a[diag[i]+shift];
1151:       }
1152:       PetscLogFlops(m);
1153:     }
1154:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1155: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1156:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,xb);
1157: #else
1158:       for (i=m-1; i>=0; i--) {
1159:         d    = fshift + a->a[diag[i] + shift];
1160:         n    = a->i[i+1] - diag[i] - 1;
1161:         PetscLogFlops(2*n-1);
1162:         idx  = a->j + diag[i] + (!shift);
1163:         v    = a->a + diag[i] + (!shift);
1164:         sum  = xb[i];
1165:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1166:         x[i] = omega*(sum/d);
1167:       }
1168: #endif
1169:     }
1170:     its--;
1171:   }
1172:   while (its--) {
1173:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1174: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1175:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1176: #else
1177:       for (i=0; i<m; i++) {
1178:         d    = fshift + a->a[diag[i]+shift];
1179:         n    = a->i[i+1] - a->i[i];
1180:         PetscLogFlops(2*n-1);
1181:         idx  = a->j + a->i[i] + shift;
1182:         v    = a->a + a->i[i] + shift;
1183:         sum  = b[i];
1184:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1185:         x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1186:       }
1187: #endif
1188:     }
1189:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1190: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1191:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1192: #else
1193:       for (i=m-1; i>=0; i--) {
1194:         d    = fshift + a->a[diag[i] + shift];
1195:         n    = a->i[i+1] - a->i[i];
1196:         PetscLogFlops(2*n-1);
1197:         idx  = a->j + a->i[i] + shift;
1198:         v    = a->a + a->i[i] + shift;
1199:         sum  = b[i];
1200:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1201:         x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1202:       }
1203: #endif
1204:     }
1205:   }
1206:   VecRestoreArray(xx,&x);
1207:   if (bb != xx) {VecRestoreArray(bb,&b);}
1208:   return(0);
1209: }

1211: int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1212: {
1213:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1216:   info->rows_global    = (double)A->m;
1217:   info->columns_global = (double)A->n;
1218:   info->rows_local     = (double)A->m;
1219:   info->columns_local  = (double)A->n;
1220:   info->block_size     = 1.0;
1221:   info->nz_allocated   = (double)a->maxnz;
1222:   info->nz_used        = (double)a->nz;
1223:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1224:   info->assemblies     = (double)A->num_ass;
1225:   info->mallocs        = (double)a->reallocs;
1226:   info->memory         = A->mem;
1227:   if (A->factor) {
1228:     info->fill_ratio_given  = A->info.fill_ratio_given;
1229:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1230:     info->factor_mallocs    = A->info.factor_mallocs;
1231:   } else {
1232:     info->fill_ratio_given  = 0;
1233:     info->fill_ratio_needed = 0;
1234:     info->factor_mallocs    = 0;
1235:   }
1236:   return(0);
1237: }

1239: EXTERN int MatLUFactorSymbolic_SeqAIJ(Mat,IS,IS,MatLUInfo*,Mat*);
1240: EXTERN int MatLUFactorNumeric_SeqAIJ(Mat,Mat*);
1241: EXTERN int MatLUFactor_SeqAIJ(Mat,IS,IS,MatLUInfo*);
1242: EXTERN int MatSolve_SeqAIJ(Mat,Vec,Vec);
1243: EXTERN int MatSolveAdd_SeqAIJ(Mat,Vec,Vec,Vec);
1244: EXTERN int MatSolveTranspose_SeqAIJ(Mat,Vec,Vec);
1245: EXTERN int MatSolveTransposeAdd_SeqAIJ(Mat,Vec,Vec,Vec);

1247: int MatZeroRows_SeqAIJ(Mat A,IS is,PetscScalar *diag)
1248: {
1249:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1250:   int         i,ierr,N,*rows,m = A->m - 1,shift = a->indexshift;

1253:   ISGetLocalSize(is,&N);
1254:   ISGetIndices(is,&rows);
1255:   if (a->keepzeroedrows) {
1256:     for (i=0; i<N; i++) {
1257:       if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1258:       PetscMemzero(&a->a[a->i[rows[i]]+shift],a->ilen[rows[i]]*sizeof(PetscScalar));
1259:     }
1260:     if (diag) {
1261:       MatMissingDiagonal_SeqAIJ(A);
1262:       MatMarkDiagonal_SeqAIJ(A);
1263:       for (i=0; i<N; i++) {
1264:         a->a[a->diag[rows[i]]] = *diag;
1265:       }
1266:     }
1267:   } else {
1268:     if (diag) {
1269:       for (i=0; i<N; i++) {
1270:         if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1271:         if (a->ilen[rows[i]] > 0) {
1272:           a->ilen[rows[i]]          = 1;
1273:           a->a[a->i[rows[i]]+shift] = *diag;
1274:           a->j[a->i[rows[i]]+shift] = rows[i]+shift;
1275:         } else { /* in case row was completely empty */
1276:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);
1277:         }
1278:       }
1279:     } else {
1280:       for (i=0; i<N; i++) {
1281:         if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1282:         a->ilen[rows[i]] = 0;
1283:       }
1284:     }
1285:   }
1286:   ISRestoreIndices(is,&rows);
1287:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1288:   return(0);
1289: }

1291: int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1292: {
1293:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1294:   int        *itmp,i,shift = a->indexshift,ierr;

1297:   if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row);

1299:   *nz = a->i[row+1] - a->i[row];
1300:   if (v) *v = a->a + a->i[row] + shift;
1301:   if (idx) {
1302:     itmp = a->j + a->i[row] + shift;
1303:     if (*nz && shift) {
1304:       PetscMalloc((*nz)*sizeof(int),idx);
1305:       for (i=0; i<(*nz); i++) {(*idx)[i] = itmp[i] + shift;}
1306:     } else if (*nz) {
1307:       *idx = itmp;
1308:     }
1309:     else *idx = 0;
1310:   }
1311:   return(0);
1312: }

1314: int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1315: {
1316:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1320:   if (idx) {if (*idx && a->indexshift) {PetscFree(*idx);}}
1321:   return(0);
1322: }

1324: int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1325: {
1326:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1327:   PetscScalar  *v = a->a;
1328:   PetscReal    sum = 0.0;
1329:   int          i,j,shift = a->indexshift,ierr;

1332:   if (type == NORM_FROBENIUS) {
1333:     for (i=0; i<a->nz; i++) {
1334: #if defined(PETSC_USE_COMPLEX)
1335:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1336: #else
1337:       sum += (*v)*(*v); v++;
1338: #endif
1339:     }
1340:     *nrm = sqrt(sum);
1341:   } else if (type == NORM_1) {
1342:     PetscReal *tmp;
1343:     int    *jj = a->j;
1344:     PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);
1345:     PetscMemzero(tmp,A->n*sizeof(PetscReal));
1346:     *nrm = 0.0;
1347:     for (j=0; j<a->nz; j++) {
1348:         tmp[*jj++ + shift] += PetscAbsScalar(*v);  v++;
1349:     }
1350:     for (j=0; j<A->n; j++) {
1351:       if (tmp[j] > *nrm) *nrm = tmp[j];
1352:     }
1353:     PetscFree(tmp);
1354:   } else if (type == NORM_INFINITY) {
1355:     *nrm = 0.0;
1356:     for (j=0; j<A->m; j++) {
1357:       v = a->a + a->i[j] + shift;
1358:       sum = 0.0;
1359:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1360:         sum += PetscAbsScalar(*v); v++;
1361:       }
1362:       if (sum > *nrm) *nrm = sum;
1363:     }
1364:   } else {
1365:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1366:   }
1367:   return(0);
1368: }

1370: int MatTranspose_SeqAIJ(Mat A,Mat *B)
1371: {
1372:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1373:   Mat          C;
1374:   int          i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col;
1375:   int          shift = a->indexshift;
1376:   PetscScalar  *array = a->a;

1379:   if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1380:   PetscMalloc((1+A->n)*sizeof(int),&col);
1381:   PetscMemzero(col,(1+A->n)*sizeof(int));
1382:   if (shift) {
1383:     for (i=0; i<ai[m]-1; i++) aj[i] -= 1;
1384:   }
1385:   for (i=0; i<ai[m]+shift; i++) col[aj[i]] += 1;
1386:   MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);
1387:   PetscFree(col);
1388:   for (i=0; i<m; i++) {
1389:     len    = ai[i+1]-ai[i];
1390:     ierr   = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);
1391:     array += len;
1392:     aj    += len;
1393:   }
1394:   if (shift) {
1395:     for (i=0; i<ai[m]-1; i++) aj[i] += 1;
1396:   }

1398:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1399:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1401:   if (B) {
1402:     *B = C;
1403:   } else {
1404:     MatHeaderCopy(A,C);
1405:   }
1406:   return(0);
1407: }

1409: int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1410: {
1411:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1412:   PetscScalar  *l,*r,x,*v;
1413:   int          ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj,shift = a->indexshift;

1416:   if (ll) {
1417:     /* The local size is used so that VecMPI can be passed to this routine
1418:        by MatDiagonalScale_MPIAIJ */
1419:     VecGetLocalSize(ll,&m);
1420:     if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1421:     VecGetArray(ll,&l);
1422:     v = a->a;
1423:     for (i=0; i<m; i++) {
1424:       x = l[i];
1425:       M = a->i[i+1] - a->i[i];
1426:       for (j=0; j<M; j++) { (*v++) *= x;}
1427:     }
1428:     VecRestoreArray(ll,&l);
1429:     PetscLogFlops(nz);
1430:   }
1431:   if (rr) {
1432:     VecGetLocalSize(rr,&n);
1433:     if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1434:     VecGetArray(rr,&r);
1435:     v = a->a; jj = a->j;
1436:     for (i=0; i<nz; i++) {
1437:       (*v++) *= r[*jj++ + shift];
1438:     }
1439:     VecRestoreArray(rr,&r);
1440:     PetscLogFlops(nz);
1441:   }
1442:   return(0);
1443: }

1445: int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B)
1446: {
1447:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data,*c;
1448:   int          *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens;
1449:   int          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1450:   int          *irow,*icol,nrows,ncols,shift = a->indexshift,*ssmap;
1451:   int          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1452:   PetscScalar  *a_new,*mat_a;
1453:   Mat          C;
1454:   PetscTruth   stride;

1457:   ISSorted(isrow,(PetscTruth*)&i);
1458:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1459:   ISSorted(iscol,(PetscTruth*)&i);
1460:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1462:   ISGetIndices(isrow,&irow);
1463:   ISGetLocalSize(isrow,&nrows);
1464:   ISGetLocalSize(iscol,&ncols);

1466:   ISStrideGetInfo(iscol,&first,&step);
1467:   ISStride(iscol,&stride);
1468:   if (stride && step == 1) {
1469:     /* special case of contiguous rows */
1470:     ierr   = PetscMalloc((2*nrows+1)*sizeof(int),&lens);
1471:     starts = lens + nrows;
1472:     /* loop over new rows determining lens and starting points */
1473:     for (i=0; i<nrows; i++) {
1474:       kstart  = ai[irow[i]]+shift;
1475:       kend    = kstart + ailen[irow[i]];
1476:       for (k=kstart; k<kend; k++) {
1477:         if (aj[k]+shift >= first) {
1478:           starts[i] = k;
1479:           break;
1480:         }
1481:       }
1482:       sum = 0;
1483:       while (k < kend) {
1484:         if (aj[k++]+shift >= first+ncols) break;
1485:         sum++;
1486:       }
1487:       lens[i] = sum;
1488:     }
1489:     /* create submatrix */
1490:     if (scall == MAT_REUSE_MATRIX) {
1491:       int n_cols,n_rows;
1492:       MatGetSize(*B,&n_rows,&n_cols);
1493:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1494:       MatZeroEntries(*B);
1495:       C = *B;
1496:     } else {
1497:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1498:     }
1499:     c = (Mat_SeqAIJ*)C->data;

1501:     /* loop over rows inserting into submatrix */
1502:     a_new    = c->a;
1503:     j_new    = c->j;
1504:     i_new    = c->i;
1505:     i_new[0] = -shift;
1506:     for (i=0; i<nrows; i++) {
1507:       ii    = starts[i];
1508:       lensi = lens[i];
1509:       for (k=0; k<lensi; k++) {
1510:         *j_new++ = aj[ii+k] - first;
1511:       }
1512:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1513:       a_new      += lensi;
1514:       i_new[i+1]  = i_new[i] + lensi;
1515:       c->ilen[i]  = lensi;
1516:     }
1517:     PetscFree(lens);
1518:   } else {
1519:     ierr  = ISGetIndices(iscol,&icol);
1520:     ierr  = PetscMalloc((1+oldcols)*sizeof(int),&smap);
1521:     ssmap = smap + shift;
1522:     ierr  = PetscMalloc((1+nrows)*sizeof(int),&lens);
1523:     ierr  = PetscMemzero(smap,oldcols*sizeof(int));
1524:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1525:     /* determine lens of each row */
1526:     for (i=0; i<nrows; i++) {
1527:       kstart  = ai[irow[i]]+shift;
1528:       kend    = kstart + a->ilen[irow[i]];
1529:       lens[i] = 0;
1530:       for (k=kstart; k<kend; k++) {
1531:         if (ssmap[aj[k]]) {
1532:           lens[i]++;
1533:         }
1534:       }
1535:     }
1536:     /* Create and fill new matrix */
1537:     if (scall == MAT_REUSE_MATRIX) {
1538:       PetscTruth equal;

1540:       c = (Mat_SeqAIJ *)((*B)->data);
1541:       if ((*B)->m  != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1542:       PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);
1543:       if (!equal) {
1544:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1545:       }
1546:       PetscMemzero(c->ilen,(*B)->m*sizeof(int));
1547:       C = *B;
1548:     } else {
1549:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1550:     }
1551:     c = (Mat_SeqAIJ *)(C->data);
1552:     for (i=0; i<nrows; i++) {
1553:       row    = irow[i];
1554:       kstart = ai[row]+shift;
1555:       kend   = kstart + a->ilen[row];
1556:       mat_i  = c->i[i]+shift;
1557:       mat_j  = c->j + mat_i;
1558:       mat_a  = c->a + mat_i;
1559:       mat_ilen = c->ilen + i;
1560:       for (k=kstart; k<kend; k++) {
1561:         if ((tcol=ssmap[a->j[k]])) {
1562:           *mat_j++ = tcol - (!shift);
1563:           *mat_a++ = a->a[k];
1564:           (*mat_ilen)++;

1566:         }
1567:       }
1568:     }
1569:     /* Free work space */
1570:     ISRestoreIndices(iscol,&icol);
1571:     PetscFree(smap);
1572:     PetscFree(lens);
1573:   }
1574:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1575:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1577:   ISRestoreIndices(isrow,&irow);
1578:   *B = C;
1579:   return(0);
1580: }

1582: /*
1583: */
1584: int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatILUInfo *info)
1585: {
1586:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1587:   int        ierr;
1588:   Mat        outA;
1589:   PetscTruth row_identity,col_identity;

1592:   if (info && info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1593:   ISIdentity(row,&row_identity);
1594:   ISIdentity(col,&col_identity);
1595:   if (!row_identity || !col_identity) {
1596:     SETERRQ(1,"Row and column permutations must be identity for in-place ILU");
1597:   }

1599:   outA          = inA;
1600:   inA->factor   = FACTOR_LU;
1601:   a->row        = row;
1602:   a->col        = col;
1603:   PetscObjectReference((PetscObject)row);
1604:   PetscObjectReference((PetscObject)col);

1606:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1607:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1608:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1609:   PetscLogObjectParent(inA,a->icol);

1611:   if (!a->solve_work) { /* this matrix may have been factored before */
1612:      PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
1613:   }

1615:   if (!a->diag) {
1616:     MatMarkDiagonal_SeqAIJ(inA);
1617:   }
1618:   MatLUFactorNumeric_SeqAIJ(inA,&outA);
1619:   return(0);
1620: }

1622:  #include petscblaslapack.h
1623: int MatScale_SeqAIJ(PetscScalar *alpha,Mat inA)
1624: {
1625:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1626:   int        one = 1;

1629:   BLscal_(&a->nz,alpha,a->a,&one);
1630:   PetscLogFlops(a->nz);
1631:   return(0);
1632: }

1634: int MatGetSubMatrices_SeqAIJ(Mat A,int n,IS *irow,IS *icol,MatReuse scall,Mat **B)
1635: {
1636:   int ierr,i;

1639:   if (scall == MAT_INITIAL_MATRIX) {
1640:     PetscMalloc((n+1)*sizeof(Mat),B);
1641:   }

1643:   for (i=0; i<n; i++) {
1644:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1645:   }
1646:   return(0);
1647: }

1649: int MatGetBlockSize_SeqAIJ(Mat A,int *bs)
1650: {
1652:   *bs = 1;
1653:   return(0);
1654: }

1656: int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS *is,int ov)
1657: {
1658:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1659:   int        shift,row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val;
1660:   int        start,end,*ai,*aj;
1661:   PetscBT    table;

1664:   shift = a->indexshift;
1665:   m     = A->m;
1666:   ai    = a->i;
1667:   aj    = a->j+shift;

1669:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal overlap value used");

1671:   PetscMalloc((m+1)*sizeof(int),&nidx);
1672:   PetscBTCreate(m,table);

1674:   for (i=0; i<is_max; i++) {
1675:     /* Initialize the two local arrays */
1676:     isz  = 0;
1677:     PetscBTMemzero(m,table);
1678: 
1679:     /* Extract the indices, assume there can be duplicate entries */
1680:     ISGetIndices(is[i],&idx);
1681:     ISGetLocalSize(is[i],&n);
1682: 
1683:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1684:     for (j=0; j<n ; ++j){
1685:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1686:     }
1687:     ISRestoreIndices(is[i],&idx);
1688:     ISDestroy(is[i]);
1689: 
1690:     k = 0;
1691:     for (j=0; j<ov; j++){ /* for each overlap */
1692:       n = isz;
1693:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1694:         row   = nidx[k];
1695:         start = ai[row];
1696:         end   = ai[row+1];
1697:         for (l = start; l<end ; l++){
1698:           val = aj[l] + shift;
1699:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1700:         }
1701:       }
1702:     }
1703:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1704:   }
1705:   PetscBTDestroy(table);
1706:   PetscFree(nidx);
1707:   return(0);
1708: }

1710: /* -------------------------------------------------------------- */
1711: int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1712: {
1713:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1714:   PetscScalar  *vwork;
1715:   int          i,ierr,nz,m = A->m,n = A->n,*cwork;
1716:   int          *row,*col,*cnew,j,*lens;
1717:   IS           icolp,irowp;

1720:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1721:   ISGetIndices(irowp,&row);
1722:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1723:   ISGetIndices(icolp,&col);
1724: 
1725:   /* determine lengths of permuted rows */
1726:   PetscMalloc((m+1)*sizeof(int),&lens);
1727:   for (i=0; i<m; i++) {
1728:     lens[row[i]] = a->i[i+1] - a->i[i];
1729:   }
1730:   MatCreateSeqAIJ(A->comm,m,n,0,lens,B);
1731:   PetscFree(lens);

1733:   PetscMalloc(n*sizeof(int),&cnew);
1734:   for (i=0; i<m; i++) {
1735:     MatGetRow(A,i,&nz,&cwork,&vwork);
1736:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1737:     MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1738:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
1739:   }
1740:   PetscFree(cnew);
1741:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1742:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1743:   ISRestoreIndices(irowp,&row);
1744:   ISRestoreIndices(icolp,&col);
1745:   ISDestroy(irowp);
1746:   ISDestroy(icolp);
1747:   return(0);
1748: }

1750: int MatPrintHelp_SeqAIJ(Mat A)
1751: {
1752:   static PetscTruth called = PETSC_FALSE;
1753:   MPI_Comm          comm = A->comm;
1754:   int               ierr;

1757:   if (called) {return(0);} else called = PETSC_TRUE;
1758:   (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):n");
1759:   (*PetscHelpPrintf)(comm,"  -mat_lu_pivotthreshold <threshold>: Set pivoting thresholdn");
1760:   (*PetscHelpPrintf)(comm,"  -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.n");
1761:   (*PetscHelpPrintf)(comm,"  -mat_aij_no_inode: Do not use inodesn");
1762:   (*PetscHelpPrintf)(comm,"  -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)n");
1763: #if defined(PETSC_HAVE_ESSL)
1764:   (*PetscHelpPrintf)(comm,"  -mat_aij_essl: Use IBM sparse LU factorization and solve.n");
1765: #endif
1766: #if defined(PETSC_HAVE_LUSOL)
1767:   (*PetscHelpPrintf)(comm,"  -mat_aij_lusol: Use the Stanford LUSOL sparse factorization and solve.n");
1768: #endif
1769: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
1770:   (*PetscHelpPrintf)(comm,"  -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.n");
1771: #endif
1772:   return(0);
1773: }
1774: EXTERN int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg);
1775: EXTERN int MatFDColoringCreate_SeqAIJ(Mat,ISColoring,MatFDColoring);
1776: EXTERN int MatILUDTFactor_SeqAIJ(Mat,MatILUInfo*,IS,IS,Mat*);
1777: int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1778: {
1779:   int        ierr;
1780:   PetscTruth flg;

1783:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg);
1784:   if (str == SAME_NONZERO_PATTERN && flg) {
1785:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1786:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1788:     if (a->i[A->m]+a->indexshift != b->i[B->m]+a->indexshift) {
1789:       SETERRQ(1,"Number of nonzeros in two matrices are different");
1790:     }
1791:     PetscMemcpy(b->a,a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
1792:   } else {
1793:     MatCopy_Basic(A,B,str);
1794:   }
1795:   return(0);
1796: }

1798: int MatSetUpPreallocation_SeqAIJ(Mat A)
1799: {
1800:   int        ierr;

1803:    MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);
1804:   return(0);
1805: }

1807: int MatGetArray_SeqAIJ(Mat A,PetscScalar **array)
1808: {
1809:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1811:   *array = a->a;
1812:   return(0);
1813: }

1815: int MatRestoreArray_SeqAIJ(Mat A,PetscScalar **array)
1816: {
1818:   return(0);
1819: }

1821: int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1822: {
1823:   int           (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f;
1824:   int           k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1825:   PetscScalar   dx,mone = -1.0,*y,*xx,*w3_array;
1826:   PetscScalar   *vscale_array;
1827:   PetscReal     epsilon = coloring->error_rel,umin = coloring->umin;
1828:   Vec           w1,w2,w3;
1829:   void          *fctx = coloring->fctx;
1830:   PetscTruth    flg;

1833:   if (!coloring->w1) {
1834:     VecDuplicate(x1,&coloring->w1);
1835:     PetscLogObjectParent(coloring,coloring->w1);
1836:     VecDuplicate(x1,&coloring->w2);
1837:     PetscLogObjectParent(coloring,coloring->w2);
1838:     VecDuplicate(x1,&coloring->w3);
1839:     PetscLogObjectParent(coloring,coloring->w3);
1840:   }
1841:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

1843:   MatSetUnfactored(J);
1844:   PetscOptionsHasName(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg);
1845:   if (flg) {
1846:     PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()n");
1847:   } else {
1848:     MatZeroEntries(J);
1849:   }

1851:   VecGetOwnershipRange(x1,&start,&end);
1852:   VecGetSize(x1,&N);

1854:   /*
1855:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
1856:      coloring->F for the coarser grids from the finest
1857:   */
1858:   if (coloring->F) {
1859:     VecGetLocalSize(coloring->F,&m1);
1860:     VecGetLocalSize(w1,&m2);
1861:     if (m1 != m2) {
1862:       coloring->F = 0;
1863:     }
1864:   }

1866:   if (coloring->F) {
1867:     w1          = coloring->F;
1868:     coloring->F = 0;
1869:   } else {
1870:     (*f)(sctx,x1,w1,fctx);
1871:   }

1873:   /* 
1874:       Compute all the scale factors and share with other processors
1875:   */
1876:   VecGetArray(x1,&xx);xx = xx - start;
1877:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
1878:   for (k=0; k<coloring->ncolors; k++) {
1879:     /*
1880:        Loop over each column associated with color adding the 
1881:        perturbation to the vector w3.
1882:     */
1883:     for (l=0; l<coloring->ncolumns[k]; l++) {
1884:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1885:       dx  = xx[col];
1886:       if (dx == 0.0) dx = 1.0;
1887: #if !defined(PETSC_USE_COMPLEX)
1888:       if (dx < umin && dx >= 0.0)      dx = umin;
1889:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1890: #else
1891:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1892:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1893: #endif
1894:       dx                *= epsilon;
1895:       vscale_array[col] = 1.0/dx;
1896:     }
1897:   }
1898:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
1899:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
1900:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

1902:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
1903:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

1905:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
1906:   else                        vscaleforrow = coloring->columnsforrow;

1908:   VecGetArray(coloring->vscale,&vscale_array);
1909:   /*
1910:       Loop over each color
1911:   */
1912:   for (k=0; k<coloring->ncolors; k++) {
1913:     VecCopy(x1,w3);
1914:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
1915:     /*
1916:        Loop over each column associated with color adding the 
1917:        perturbation to the vector w3.
1918:     */
1919:     for (l=0; l<coloring->ncolumns[k]; l++) {
1920:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1921:       dx  = xx[col];
1922:       if (dx == 0.0) dx = 1.0;
1923: #if !defined(PETSC_USE_COMPLEX)
1924:       if (dx < umin && dx >= 0.0)      dx = umin;
1925:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1926: #else
1927:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1928:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1929: #endif
1930:       dx            *= epsilon;
1931:       if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter");
1932:       w3_array[col] += dx;
1933:     }
1934:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

1936:     /*
1937:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
1938:     */

1940:     (*f)(sctx,w3,w2,fctx);
1941:     VecAXPY(&mone,w1,w2);

1943:     /*
1944:        Loop over rows of vector, putting results into Jacobian matrix
1945:     */
1946:     VecGetArray(w2,&y);
1947:     for (l=0; l<coloring->nrows[k]; l++) {
1948:       row    = coloring->rows[k][l];
1949:       col    = coloring->columnsforrow[k][l];
1950:       y[row] *= vscale_array[vscaleforrow[k][l]];
1951:       srow   = row + start;
1952:       ierr   = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
1953:     }
1954:     VecRestoreArray(w2,&y);
1955:   }
1956:   VecRestoreArray(coloring->vscale,&vscale_array);
1957:   xx = xx + start; ierr  = VecRestoreArray(x1,&xx);
1958:   ierr  = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
1959:   ierr  = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
1960:   return(0);
1961: }

1963:  #include petscblaslapack.h

1965: int MatAXPY_SeqAIJ(PetscScalar *a,Mat X,Mat Y,MatStructure str)
1966: {
1967:   int        ierr,one=1;
1968:   Mat_SeqAIJ *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;

1971:   if (str == SAME_NONZERO_PATTERN) {
1972:     BLaxpy_(&x->nz,a,x->a,&one,y->a,&one);
1973:   } else {
1974:     MatAXPY_Basic(a,X,Y,str);
1975:   }
1976:   return(0);
1977: }


1980: /* -------------------------------------------------------------------*/
1981: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
1982:        MatGetRow_SeqAIJ,
1983:        MatRestoreRow_SeqAIJ,
1984:        MatMult_SeqAIJ,
1985:        MatMultAdd_SeqAIJ,
1986:        MatMultTranspose_SeqAIJ,
1987:        MatMultTransposeAdd_SeqAIJ,
1988:        MatSolve_SeqAIJ,
1989:        MatSolveAdd_SeqAIJ,
1990:        MatSolveTranspose_SeqAIJ,
1991:        MatSolveTransposeAdd_SeqAIJ,
1992:        MatLUFactor_SeqAIJ,
1993:        0,
1994:        MatRelax_SeqAIJ,
1995:        MatTranspose_SeqAIJ,
1996:        MatGetInfo_SeqAIJ,
1997:        MatEqual_SeqAIJ,
1998:        MatGetDiagonal_SeqAIJ,
1999:        MatDiagonalScale_SeqAIJ,
2000:        MatNorm_SeqAIJ,
2001:        0,
2002:        MatAssemblyEnd_SeqAIJ,
2003:        MatCompress_SeqAIJ,
2004:        MatSetOption_SeqAIJ,
2005:        MatZeroEntries_SeqAIJ,
2006:        MatZeroRows_SeqAIJ,
2007:        MatLUFactorSymbolic_SeqAIJ,
2008:        MatLUFactorNumeric_SeqAIJ,
2009:        0,
2010:        0,
2011:        MatSetUpPreallocation_SeqAIJ,
2012:        MatILUFactorSymbolic_SeqAIJ,
2013:        0,
2014:        MatGetArray_SeqAIJ,
2015:        MatRestoreArray_SeqAIJ,
2016:        MatDuplicate_SeqAIJ,
2017:        0,
2018:        0,
2019:        MatILUFactor_SeqAIJ,
2020:        0,
2021:        MatAXPY_SeqAIJ,
2022:        MatGetSubMatrices_SeqAIJ,
2023:        MatIncreaseOverlap_SeqAIJ,
2024:        MatGetValues_SeqAIJ,
2025:        MatCopy_SeqAIJ,
2026:        MatPrintHelp_SeqAIJ,
2027:        MatScale_SeqAIJ,
2028:        0,
2029:        0,
2030:        MatILUDTFactor_SeqAIJ,
2031:        MatGetBlockSize_SeqAIJ,
2032:        MatGetRowIJ_SeqAIJ,
2033:        MatRestoreRowIJ_SeqAIJ,
2034:        MatGetColumnIJ_SeqAIJ,
2035:        MatRestoreColumnIJ_SeqAIJ,
2036:        MatFDColoringCreate_SeqAIJ,
2037:        0,
2038:        0,
2039:        MatPermute_SeqAIJ,
2040:        0,
2041:        0,
2042:        MatDestroy_SeqAIJ,
2043:        MatView_SeqAIJ,
2044:        MatGetPetscMaps_Petsc,
2045:        0,
2046:        0,
2047:        0,
2048:        0,
2049:        0,
2050:        0,
2051:        0,
2052:        0,
2053:        MatSetColoring_SeqAIJ,
2054:        MatSetValuesAdic_SeqAIJ,
2055:        MatSetValuesAdifor_SeqAIJ,
2056:        MatFDColoringApply_SeqAIJ};

2058: EXTERN int MatUseSuperLU_SeqAIJ(Mat);
2059: EXTERN int MatUseEssl_SeqAIJ(Mat);
2060: EXTERN int MatUseLUSOL_SeqAIJ(Mat);
2061: EXTERN int MatUseMatlab_SeqAIJ(Mat);
2062: EXTERN int MatUseDXML_SeqAIJ(Mat);

2064: EXTERN_C_BEGIN

2066: int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices)
2067: {
2068:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2069:   int        i,nz,n;


2073:   nz = aij->maxnz;
2074:   n  = mat->n;
2075:   for (i=0; i<nz; i++) {
2076:     aij->j[i] = indices[i];
2077:   }
2078:   aij->nz = nz;
2079:   for (i=0; i<n; i++) {
2080:     aij->ilen[i] = aij->imax[i];
2081:   }

2083:   return(0);
2084: }
2085: EXTERN_C_END

2087: /*@
2088:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2089:        in the matrix.

2091:   Input Parameters:
2092: +  mat - the SeqAIJ matrix
2093: -  indices - the column indices

2095:   Level: advanced

2097:   Notes:
2098:     This can be called if you have precomputed the nonzero structure of the 
2099:   matrix and want to provide it to the matrix object to improve the performance
2100:   of the MatSetValues() operation.

2102:     You MUST have set the correct numbers of nonzeros per row in the call to 
2103:   MatCreateSeqAIJ().

2105:     MUST be called before any calls to MatSetValues();

2107:     The indices should start with zero, not one.

2109: @*/
2110: int MatSeqAIJSetColumnIndices(Mat mat,int *indices)
2111: {
2112:   int ierr,(*f)(Mat,int *);

2116:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2117:   if (f) {
2118:     (*f)(mat,indices);
2119:   } else {
2120:     SETERRQ(1,"Wrong type of matrix to set column indices");
2121:   }
2122:   return(0);
2123: }

2125: /* ----------------------------------------------------------------------------------------*/

2127: EXTERN_C_BEGIN
2128: int MatStoreValues_SeqAIJ(Mat mat)
2129: {
2130:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2131:   int        nz = aij->i[mat->m]+aij->indexshift,ierr;

2134:   if (aij->nonew != 1) {
2135:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2136:   }

2138:   /* allocate space for values if not already there */
2139:   if (!aij->saved_values) {
2140:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2141:   }

2143:   /* copy values over */
2144:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2145:   return(0);
2146: }
2147: EXTERN_C_END

2149: /*@
2150:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2151:        example, reuse of the linear part of a Jacobian, while recomputing the 
2152:        nonlinear portion.

2154:    Collect on Mat

2156:   Input Parameters:
2157: .  mat - the matrix (currently on AIJ matrices support this option)

2159:   Level: advanced

2161:   Common Usage, with SNESSolve():
2162: $    Create Jacobian matrix
2163: $    Set linear terms into matrix
2164: $    Apply boundary conditions to matrix, at this time matrix must have 
2165: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2166: $      boundary conditions again will not change the nonzero structure
2167: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2168: $    MatStoreValues(mat);
2169: $    Call SNESSetJacobian() with matrix
2170: $    In your Jacobian routine
2171: $      MatRetrieveValues(mat);
2172: $      Set nonlinear terms in matrix
2173:  
2174:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2175: $    // build linear portion of Jacobian 
2176: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2177: $    MatStoreValues(mat);
2178: $    loop over nonlinear iterations
2179: $       MatRetrieveValues(mat);
2180: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2181: $       // call MatAssemblyBegin/End() on matrix
2182: $       Solve linear system with Jacobian
2183: $    endloop 

2185:   Notes:
2186:     Matrix must already be assemblied before calling this routine
2187:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 
2188:     calling this routine.

2190: .seealso: MatRetrieveValues()

2192: @*/
2193: int MatStoreValues(Mat mat)
2194: {
2195:   int ierr,(*f)(Mat);

2199:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2200:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2202:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2203:   if (f) {
2204:     (*f)(mat);
2205:   } else {
2206:     SETERRQ(1,"Wrong type of matrix to store values");
2207:   }
2208:   return(0);
2209: }

2211: EXTERN_C_BEGIN
2212: int MatRetrieveValues_SeqAIJ(Mat mat)
2213: {
2214:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2215:   int        nz = aij->i[mat->m]+aij->indexshift,ierr;

2218:   if (aij->nonew != 1) {
2219:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2220:   }
2221:   if (!aij->saved_values) {
2222:     SETERRQ(1,"Must call MatStoreValues(A);first");
2223:   }

2225:   /* copy values over */
2226:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2227:   return(0);
2228: }
2229: EXTERN_C_END

2231: /*@
2232:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2233:        example, reuse of the linear part of a Jacobian, while recomputing the 
2234:        nonlinear portion.

2236:    Collect on Mat

2238:   Input Parameters:
2239: .  mat - the matrix (currently on AIJ matrices support this option)

2241:   Level: advanced

2243: .seealso: MatStoreValues()

2245: @*/
2246: int MatRetrieveValues(Mat mat)
2247: {
2248:   int ierr,(*f)(Mat);

2252:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2253:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2255:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2256:   if (f) {
2257:     (*f)(mat);
2258:   } else {
2259:     SETERRQ(1,"Wrong type of matrix to retrieve values");
2260:   }
2261:   return(0);
2262: }

2264: /*
2265:    This allows SeqAIJ matrices to be passed to the matlab engine
2266: */
2267: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2268: #include "engine.h"   /* Matlab include file */
2269: #include "mex.h"      /* Matlab include file */
2270: EXTERN_C_BEGIN
2271: int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *engine)
2272: {
2273:   int         ierr,i,*ai,*aj;
2274:   Mat         B = (Mat)obj;
2275:   PetscScalar *array;
2276:   mxArray     *mat;
2277:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)B->data;

2280:   mat  = mxCreateSparse(B->n,B->m,aij->nz,mxREAL);
2281:   PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));
2282:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2283:   PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));
2284:   PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));

2286:   /* Matlab indices start at 0 for sparse (what a surprise) */
2287:   if (aij->indexshift) {
2288:     for (i=0; i<B->m+1; i++) {
2289:       ai[i]--;
2290:     }
2291:     for (i=0; i<aij->nz; i++) {
2292:       aj[i]--;
2293:     }
2294:   }
2295:   PetscObjectName(obj);
2296:   mxSetName(mat,obj->name);
2297:   engPutArray((Engine *)engine,mat);
2298:   return(0);
2299: }
2300: EXTERN_C_END

2302: EXTERN_C_BEGIN
2303: int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *engine)
2304: {
2305:   int        ierr,ii;
2306:   Mat        mat = (Mat)obj;
2307:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2308:   mxArray    *mmat;

2311:   PetscFree(aij->a);
2312:   aij->indexshift = 0;

2314:   mmat = engGetArray((Engine *)engine,obj->name);

2316:   aij->nz           = (mxGetJc(mmat))[mat->m];
2317:   ierr              = PetscMalloc(aij->nz*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);
2318:   aij->j            = (int*)(aij->a + aij->nz);
2319:   aij->i            = aij->j + aij->nz;
2320:   aij->singlemalloc = PETSC_TRUE;
2321:   aij->freedata     = PETSC_TRUE;

2323:   PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));
2324:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2325:   PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));
2326:   PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));

2328:   for (ii=0; ii<mat->m; ii++) {
2329:     aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii];
2330:   }

2332:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
2333:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);

2335:   return(0);
2336: }
2337: EXTERN_C_END
2338: #endif

2340: /* --------------------------------------------------------------------------------*/
2341: /*@C
2342:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2343:    (the default parallel PETSc format).  For good matrix assembly performance
2344:    the user should preallocate the matrix storage by setting the parameter nz
2345:    (or the array nnz).  By setting these parameters accurately, performance
2346:    during matrix assembly can be increased by more than a factor of 50.

2348:    Collective on MPI_Comm

2350:    Input Parameters:
2351: +  comm - MPI communicator, set to PETSC_COMM_SELF
2352: .  m - number of rows
2353: .  n - number of columns
2354: .  nz - number of nonzeros per row (same for all rows)
2355: -  nnz - array containing the number of nonzeros in the various rows 
2356:          (possibly different for each row) or PETSC_NULL

2358:    Output Parameter:
2359: .  A - the matrix 

2361:    Notes:
2362:    The AIJ format (also called the Yale sparse matrix format or
2363:    compressed row storage), is fully compatible with standard Fortran 77
2364:    storage.  That is, the stored row and column indices can begin at
2365:    either one (as in Fortran) or zero.  See the users' manual for details.

2367:    Specify the preallocated storage with either nz or nnz (not both).
2368:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2369:    allocation.  For large problems you MUST preallocate memory or you 
2370:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2372:    By default, this format uses inodes (identical nodes) when possible, to 
2373:    improve numerical efficiency of matrix-vector products and solves. We 
2374:    search for consecutive rows with the same nonzero structure, thereby
2375:    reusing matrix information to achieve increased efficiency.

2377:    Options Database Keys:
2378: +  -mat_aij_no_inode  - Do not use inodes
2379: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2380: -  -mat_aij_oneindex - Internally use indexing starting at 1
2381:         rather than 0.  Note that when calling MatSetValues(),
2382:         the user still MUST index entries starting at 0!

2384:    Level: intermediate

2386: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2388: @*/
2389: int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,int *nnz,Mat *A)
2390: {

2394:   MatCreate(comm,m,n,m,n,A);
2395:   MatSetType(*A,MATSEQAIJ);
2396:   MatSeqAIJSetPreallocation(*A,nz,nnz);
2397:   return(0);
2398: }

2400: #define SKIP_ALLOCATION -4

2402: /*@C
2403:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2404:    the user should preallocate the matrix storage by setting the parameter nz
2405:    (or the array nnz).  By setting these parameters accurately, performance
2406:    during matrix assembly can be increased by more than a factor of 50.

2408:    Collective on MPI_Comm

2410:    Input Parameters:
2411: +  comm - MPI communicator, set to PETSC_COMM_SELF
2412: .  m - number of rows
2413: .  n - number of columns
2414: .  nz - number of nonzeros per row (same for all rows)
2415: -  nnz - array containing the number of nonzeros in the various rows 
2416:          (possibly different for each row) or PETSC_NULL

2418:    Output Parameter:
2419: .  A - the matrix 

2421:    Notes:
2422:    The AIJ format (also called the Yale sparse matrix format or
2423:    compressed row storage), is fully compatible with standard Fortran 77
2424:    storage.  That is, the stored row and column indices can begin at
2425:    either one (as in Fortran) or zero.  See the users' manual for details.

2427:    Specify the preallocated storage with either nz or nnz (not both).
2428:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2429:    allocation.  For large problems you MUST preallocate memory or you 
2430:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2432:    By default, this format uses inodes (identical nodes) when possible, to 
2433:    improve numerical efficiency of matrix-vector products and solves. We 
2434:    search for consecutive rows with the same nonzero structure, thereby
2435:    reusing matrix information to achieve increased efficiency.

2437:    Options Database Keys:
2438: +  -mat_aij_no_inode  - Do not use inodes
2439: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2440: -  -mat_aij_oneindex - Internally use indexing starting at 1
2441:         rather than 0.  Note that when calling MatSetValues(),
2442:         the user still MUST index entries starting at 0!

2444:    Level: intermediate

2446: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2448: @*/
2449: int MatSeqAIJSetPreallocation(Mat B,int nz,int *nnz)
2450: {
2451:   Mat_SeqAIJ *b;
2452:   int        i,len=0,ierr;
2453:   PetscTruth flg2,skipallocation = PETSC_FALSE;

2456:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg2);
2457:   if (!flg2) return(0);
2458: 
2459:   if (nz == SKIP_ALLOCATION) {
2460:     skipallocation = PETSC_TRUE;
2461:     nz             = 0;
2462:   }

2464:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2465:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2466:   if (nnz) {
2467:     for (i=0; i<B->m; i++) {
2468:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2469:       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);
2470:     }
2471:   }

2473:   B->preallocated = PETSC_TRUE;
2474:   b = (Mat_SeqAIJ*)B->data;

2476:   PetscMalloc((B->m+1)*sizeof(int),&b->imax);
2477:   if (!nnz) {
2478:     if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2479:     else if (nz <= 0)        nz = 1;
2480:     for (i=0; i<B->m; i++) b->imax[i] = nz;
2481:     nz = nz*B->m;
2482:   } else {
2483:     nz = 0;
2484:     for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2485:   }

2487:   if (!skipallocation) {
2488:     /* allocate the matrix space */
2489:     len             = nz*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int);
2490:     ierr            = PetscMalloc(len,&b->a);
2491:     b->j            = (int*)(b->a + nz);
2492:     ierr            = PetscMemzero(b->j,nz*sizeof(int));
2493:     b->i            = b->j + nz;
2494:     b->i[0] = -b->indexshift;
2495:     for (i=1; i<B->m+1; i++) {
2496:       b->i[i] = b->i[i-1] + b->imax[i-1];
2497:     }
2498:     b->singlemalloc = PETSC_TRUE;
2499:     b->freedata     = PETSC_TRUE;
2500:   } else {
2501:     b->freedata     = PETSC_FALSE;
2502:   }

2504:   /* b->ilen will count nonzeros in each row so far. */
2505:   PetscMalloc((B->m+1)*sizeof(int),&b->ilen);
2506:   PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2507:   for (i=0; i<B->m; i++) { b->ilen[i] = 0;}

2509:   b->nz                = 0;
2510:   b->maxnz             = nz;
2511:   B->info.nz_unneeded  = (double)b->maxnz;
2512:   return(0);
2513: }

2515: EXTERN int RegisterApplyPtAPRoutines_Private(Mat);

2517: EXTERN_C_BEGIN
2518: int MatCreate_SeqAIJ(Mat B)
2519: {
2520:   Mat_SeqAIJ *b;
2521:   int        ierr,size;
2522:   PetscTruth flg;

2525:   MPI_Comm_size(B->comm,&size);
2526:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

2528:   B->m = B->M = PetscMax(B->m,B->M);
2529:   B->n = B->N = PetscMax(B->n,B->N);

2531:   PetscNew(Mat_SeqAIJ,&b);
2532:   B->data             = (void*)b;
2533:   PetscMemzero(b,sizeof(Mat_SeqAIJ));
2534:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2535:   B->factor           = 0;
2536:   B->lupivotthreshold = 1.0;
2537:   B->mapping          = 0;
2538:   PetscOptionsGetReal(PETSC_NULL,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);
2539:   PetscOptionsHasName(PETSC_NULL,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);
2540:   b->row              = 0;
2541:   b->col              = 0;
2542:   b->icol             = 0;
2543:   b->indexshift       = 0;
2544:   b->reallocs         = 0;
2545:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_oneindex",&flg);
2546:   if (flg) b->indexshift = -1;
2547: 
2548:   PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
2549:   PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);

2551:   b->sorted            = PETSC_FALSE;
2552:   b->ignorezeroentries = PETSC_FALSE;
2553:   b->roworiented       = PETSC_TRUE;
2554:   b->nonew             = 0;
2555:   b->diag              = 0;
2556:   b->solve_work        = 0;
2557:   B->spptr             = 0;
2558:   b->inode.use         = PETSC_TRUE;
2559:   b->inode.node_count  = 0;
2560:   b->inode.size        = 0;
2561:   b->inode.limit       = 5;
2562:   b->inode.max_limit   = 5;
2563:   b->saved_values      = 0;
2564:   b->idiag             = 0;
2565:   b->ssor              = 0;
2566:   b->keepzeroedrows    = PETSC_FALSE;

2568:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

2570: #if defined(PETSC_HAVE_SUPERLU)
2571:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_superlu",&flg);
2572:   if (flg) { MatUseSuperLU_SeqAIJ(B); }
2573: #endif
2574:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_essl",&flg);
2575:   if (flg) { MatUseEssl_SeqAIJ(B); }
2576:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_lusol",&flg);
2577:   if (flg) { MatUseLUSOL_SeqAIJ(B); }
2578:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_matlab",&flg);
2579:   if (flg) {MatUseMatlab_SeqAIJ(B);}
2580:   PetscOptionsHasName(PETSC_NULL,"-mat_aij_dxml",&flg);
2581:   if (flg) {
2582:     if (!b->indexshift) SETERRQ(PETSC_ERR_LIB,"need -mat_aij_oneindex with -mat_aij_dxml");
2583:     MatUseDXML_SeqAIJ(B);
2584:   }
2585:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2586:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
2587:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
2588:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2589:                                      "MatStoreValues_SeqAIJ",
2590:                                      MatStoreValues_SeqAIJ);
2591:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2592:                                      "MatRetrieveValues_SeqAIJ",
2593:                                      MatRetrieveValues_SeqAIJ);
2594: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2595:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);
2596:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);
2597: #endif
2598:   RegisterApplyPtAPRoutines_Private(B);
2599:   return(0);
2600: }
2601: EXTERN_C_END

2603: int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2604: {
2605:   Mat        C;
2606:   Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
2607:   int        i,len,m = A->m,shift = a->indexshift,ierr;

2610:   *B = 0;
2611:   MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);
2612:   MatSetType(C,MATSEQAIJ);
2613:   c    = (Mat_SeqAIJ*)C->data;

2615:   C->factor         = A->factor;
2616:   c->row            = 0;
2617:   c->col            = 0;
2618:   c->icol           = 0;
2619:   c->indexshift     = shift;
2620:   c->keepzeroedrows = a->keepzeroedrows;
2621:   C->assembled      = PETSC_TRUE;

2623:   C->M          = A->m;
2624:   C->N          = A->n;

2626:   PetscMalloc((m+1)*sizeof(int),&c->imax);
2627:   PetscMalloc((m+1)*sizeof(int),&c->ilen);
2628:   for (i=0; i<m; i++) {
2629:     c->imax[i] = a->imax[i];
2630:     c->ilen[i] = a->ilen[i];
2631:   }

2633:   /* allocate the matrix space */
2634:   c->singlemalloc = PETSC_TRUE;
2635:   len   = (m+1)*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int));
2636:   ierr  = PetscMalloc(len,&c->a);
2637:   c->j  = (int*)(c->a + a->i[m] + shift);
2638:   c->i  = c->j + a->i[m] + shift;
2639:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));
2640:   if (m > 0) {
2641:     PetscMemcpy(c->j,a->j,(a->i[m]+shift)*sizeof(int));
2642:     if (cpvalues == MAT_COPY_VALUES) {
2643:       PetscMemcpy(c->a,a->a,(a->i[m]+shift)*sizeof(PetscScalar));
2644:     } else {
2645:       PetscMemzero(c->a,(a->i[m]+shift)*sizeof(PetscScalar));
2646:     }
2647:   }

2649:   PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2650:   c->sorted      = a->sorted;
2651:   c->roworiented = a->roworiented;
2652:   c->nonew       = a->nonew;
2653:   c->ilu_preserve_row_sums = a->ilu_preserve_row_sums;
2654:   c->saved_values = 0;
2655:   c->idiag        = 0;
2656:   c->ssor         = 0;
2657:   c->ignorezeroentries = a->ignorezeroentries;
2658:   c->freedata     = PETSC_TRUE;

2660:   if (a->diag) {
2661:     PetscMalloc((m+1)*sizeof(int),&c->diag);
2662:     PetscLogObjectMemory(C,(m+1)*sizeof(int));
2663:     for (i=0; i<m; i++) {
2664:       c->diag[i] = a->diag[i];
2665:     }
2666:   } else c->diag        = 0;
2667:   c->inode.use          = a->inode.use;
2668:   c->inode.limit        = a->inode.limit;
2669:   c->inode.max_limit    = a->inode.max_limit;
2670:   if (a->inode.size){
2671:     ierr                = PetscMalloc((m+1)*sizeof(int),&c->inode.size);
2672:     c->inode.node_count = a->inode.node_count;
2673:     ierr                = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));
2674:   } else {
2675:     c->inode.size       = 0;
2676:     c->inode.node_count = 0;
2677:   }
2678:   c->nz                 = a->nz;
2679:   c->maxnz              = a->maxnz;
2680:   c->solve_work         = 0;
2681:   C->spptr              = 0;      /* Dangerous -I'm throwing away a->spptr */
2682:   C->preallocated       = PETSC_TRUE;

2684:   *B = C;
2685:   PetscFListDuplicate(A->qlist,&C->qlist);
2686:   return(0);
2687: }

2689: EXTERN_C_BEGIN
2690: int MatLoad_SeqAIJ(PetscViewer viewer,MatType type,Mat *A)
2691: {
2692:   Mat_SeqAIJ   *a;
2693:   Mat          B;
2694:   int          i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N,shift;
2695:   MPI_Comm     comm;
2696: 
2698:   PetscObjectGetComm((PetscObject)viewer,&comm);
2699:   MPI_Comm_size(comm,&size);
2700:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
2701:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2702:   PetscBinaryRead(fd,header,4,PETSC_INT);
2703:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
2704:   M = header[1]; N = header[2]; nz = header[3];

2706:   if (nz < 0) {
2707:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
2708:   }

2710:   /* read in row lengths */
2711:   PetscMalloc(M*sizeof(int),&rowlengths);
2712:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

2714:   /* create our matrix */
2715:   MatCreateSeqAIJ(comm,M,N,0,rowlengths,A);
2716:   B = *A;
2717:   a = (Mat_SeqAIJ*)B->data;
2718:   shift = a->indexshift;

2720:   /* read in column indices and adjust for Fortran indexing*/
2721:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);
2722:   if (shift) {
2723:     for (i=0; i<nz; i++) {
2724:       a->j[i] += 1;
2725:     }
2726:   }

2728:   /* read in nonzero values */
2729:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

2731:   /* set matrix "i" values */
2732:   a->i[0] = -shift;
2733:   for (i=1; i<= M; i++) {
2734:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
2735:     a->ilen[i-1] = rowlengths[i-1];
2736:   }
2737:   PetscFree(rowlengths);

2739:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2740:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2741:   return(0);
2742: }
2743: EXTERN_C_END

2745: int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
2746: {
2747:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
2748:   int        ierr;
2749:   PetscTruth flag;

2752:   PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flag);
2753:   if (!flag) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");

2755:   /* If the  matrix dimensions are not equal,or no of nonzeros or shift */
2756:   if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)|| (a->indexshift != b->indexshift)) {
2757:     *flg = PETSC_FALSE;
2758:     return(0);
2759:   }
2760: 
2761:   /* if the a->i are the same */
2762:   PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);
2763:   if (*flg == PETSC_FALSE) return(0);
2764: 
2765:   /* if a->j are the same */
2766:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);
2767:   if (*flg == PETSC_FALSE) return(0);
2768: 
2769:   /* if a->a are the same */
2770:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

2772:   return(0);
2773: 
2774: }

2776: /*@C
2777:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
2778:               provided by the user.

2780:       Coolective on MPI_Comm

2782:    Input Parameters:
2783: +   comm - must be an MPI communicator of size 1
2784: .   m - number of rows
2785: .   n - number of columns
2786: .   i - row indices
2787: .   j - column indices
2788: -   a - matrix values

2790:    Output Parameter:
2791: .   mat - the matrix

2793:    Level: intermediate

2795:    Notes:
2796:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2797:     once the matrix is destroyed

2799:        You cannot set new nonzero locations into this matrix, that will generate an error.

2801:        The i and j indices can be either 0- or 1 based

2803: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()

2805: @*/
2806: int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat)
2807: {
2808:   int        ierr,ii;
2809:   Mat_SeqAIJ *aij;

2812:   MatCreateSeqAIJ(comm,m,n,SKIP_ALLOCATION,0,mat);
2813:   aij  = (Mat_SeqAIJ*)(*mat)->data;

2815:   if (i[0] == 1) {
2816:     aij->indexshift = -1;
2817:   } else if (i[0]) {
2818:     SETERRQ(1,"i (row indices) do not start with 0 or 1");
2819:   }
2820:   aij->i = i;
2821:   aij->j = j;
2822:   aij->a = a;
2823:   aij->singlemalloc = PETSC_FALSE;
2824:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2825:   aij->freedata     = PETSC_FALSE;

2827:   for (ii=0; ii<m; ii++) {
2828:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
2829: #if defined(PETSC_USE_BOPT_g)
2830:     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]);
2831: #endif    
2832:   }
2833: #if defined(PETSC_USE_BOPT_g)
2834:   for (ii=0; ii<aij->i[m]; ii++) {
2835:     if (j[ii] < -aij->indexshift) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]);
2836:     if (j[ii] > n - 1 -aij->indexshift) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]);
2837:   }
2838: #endif    

2840:   /* changes indices to start at 0 */
2841:   if (i[0]) {
2842:     aij->indexshift = 0;
2843:     for (ii=0; ii<m; ii++) {
2844:       i[ii]--;
2845:     }
2846:     for (ii=0; ii<i[m]; ii++) {
2847:       j[ii]--;
2848:     }
2849:   }

2851:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2852:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2853:   return(0);
2854: }

2856: int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
2857: {
2858:   int        ierr;
2859:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2862:   if (coloring->ctype == IS_COLORING_LOCAL) {
2863:     ierr        = ISColoringReference(coloring);
2864:     a->coloring = coloring;
2865:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
2866:     int        *colors,i,*larray;
2867:     ISColoring ocoloring;

2869:     /* set coloring for diagonal portion */
2870:     PetscMalloc((A->n+1)*sizeof(int),&larray);
2871:     for (i=0; i<A->n; i++) {
2872:       larray[i] = i;
2873:     }
2874:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);
2875:     PetscMalloc((A->n+1)*sizeof(int),&colors);
2876:     for (i=0; i<A->n; i++) {
2877:       colors[i] = coloring->colors[larray[i]];
2878:     }
2879:     PetscFree(larray);
2880:     ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);
2881:     a->coloring = ocoloring;
2882:   }
2883:   return(0);
2884: }

2886: #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2887: EXTERN_C_BEGIN
2888: #include "adic/ad_utils.h"
2889: EXTERN_C_END

2891: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2892: {
2893:   Mat_SeqAIJ  *a = (Mat_SeqAIJ*)A->data;
2894:   int         m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j,nlen;
2895:   PetscScalar *v = a->a,*values;
2896:   char        *cadvalues = (char *)advalues;

2899:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
2900:   nlen  = PetscADGetDerivTypeSize();
2901:   color = a->coloring->colors;
2902:   /* loop over rows */
2903:   for (i=0; i<m; i++) {
2904:     nz = ii[i+1] - ii[i];
2905:     /* loop over columns putting computed value into matrix */
2906:     values = PetscADGetGradArray(cadvalues);
2907:     for (j=0; j<nz; j++) {
2908:       *v++ = values[color[*jj++]];
2909:     }
2910:     cadvalues += nlen; /* jump to next row of derivatives */
2911:   }
2912:   return(0);
2913: }

2915: #else

2917: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2918: {
2920:   SETERRQ(1,"PETSc installed without ADIC");
2921: }

2923: #endif

2925: int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues)
2926: {
2927:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
2928:   int          m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j;
2929:   PetscScalar  *v = a->a,*values = (PetscScalar *)advalues;

2932:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
2933:   color = a->coloring->colors;
2934:   /* loop over rows */
2935:   for (i=0; i<m; i++) {
2936:     nz = ii[i+1] - ii[i];
2937:     /* loop over columns putting computed value into matrix */
2938:     for (j=0; j<nz; j++) {
2939:       *v++ = values[color[*jj++]];
2940:     }
2941:     values += nl; /* jump to next row of derivatives */
2942:   }
2943:   return(0);
2944: }