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

 15: int MatGetRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *m,int *ia[],int *ja[],PetscTruth *done)
 16: {
 17:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 18:   int        ierr,i,ishift;
 19: 
 21:   *m     = A->m;
 22:   if (!ia) return(0);
 23:   ishift = 0;
 24:   if (symmetric && !A->structurally_symmetric) {
 25:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
 26:   } else if (oshift == 1) {
 27:     int nz = a->i[A->m];
 28:     /* malloc space and  add 1 to i and j indices */
 29:     PetscMalloc((A->m+1)*sizeof(int),ia);
 30:     PetscMalloc((nz+1)*sizeof(int),ja);
 31:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 32:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1;
 33:   } else {
 34:     *ia = a->i; *ja = a->j;
 35:   }
 36:   return(0);
 37: }

 41: int MatRestoreRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int *ia[],int *ja[],PetscTruth *done)
 42: {
 44: 
 46:   if (!ia) return(0);
 47:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
 48:     PetscFree(*ia);
 49:     PetscFree(*ja);
 50:   }
 51:   return(0);
 52: }

 56: int MatGetColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *nn,int *ia[],int *ja[],PetscTruth *done)
 57: {
 58:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
 59:   int        ierr,i,*collengths,*cia,*cja,n = A->n,m = A->m;
 60:   int        nz = a->i[m],row,*jj,mr,col;
 61: 
 63:   *nn     = A->n;
 64:   if (!ia) return(0);
 65:   if (symmetric) {
 66:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,0,oshift,ia,ja);
 67:   } else {
 68:     PetscMalloc((n+1)*sizeof(int),&collengths);
 69:     PetscMemzero(collengths,n*sizeof(int));
 70:     PetscMalloc((n+1)*sizeof(int),&cia);
 71:     PetscMalloc((nz+1)*sizeof(int),&cja);
 72:     jj = a->j;
 73:     for (i=0; i<nz; i++) {
 74:       collengths[jj[i]]++;
 75:     }
 76:     cia[0] = oshift;
 77:     for (i=0; i<n; i++) {
 78:       cia[i+1] = cia[i] + collengths[i];
 79:     }
 80:     PetscMemzero(collengths,n*sizeof(int));
 81:     jj   = a->j;
 82:     for (row=0; row<m; row++) {
 83:       mr = a->i[row+1] - a->i[row];
 84:       for (i=0; i<mr; i++) {
 85:         col = *jj++;
 86:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
 87:       }
 88:     }
 89:     PetscFree(collengths);
 90:     *ia = cia; *ja = cja;
 91:   }
 92:   return(0);
 93: }

 97: int MatRestoreColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int *ia[],int *ja[],PetscTruth *done)
 98: {

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

104:   PetscFree(*ia);
105:   PetscFree(*ja);
106: 
107:   return(0);
108: }

110: #define CHUNKSIZE   15

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

125:   for (k=0; k<m; k++) { /* loop over added rows */
126:     row  = im[k];
127:     if (row < 0) continue;
128: #if defined(PETSC_USE_BOPT_g)  
129:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m-1);
130: #endif
131:     rp   = aj + ai[row]; ap = aa + ai[row];
132:     rmax = imax[row]; nrow = ailen[row];
133:     low = 0;
134:     for (l=0; l<n; l++) { /* loop over added columns */
135:       if (in[l] < 0) continue;
136: #if defined(PETSC_USE_BOPT_g)  
137:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n-1);
138: #endif
139:       col = in[l];
140:       if (roworiented) {
141:         value = v[l + k*n];
142:       } else {
143:         value = v[k + l*m];
144:       }
145:       if (value == 0.0 && ignorezeroentries) continue;

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

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

226:   for (k=0; k<m; k++) { /* loop over rows */
227:     row  = im[k];
228:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",row);
229:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m-1);
230:     rp   = aj + ai[row]; ap = aa + ai[row];
231:     nrow = ailen[row];
232:     for (l=0; l<n; l++) { /* loop over columns */
233:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",in[l]);
234:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n-1);
235:       col = in[l] ;
236:       high = nrow; low = 0; /* assume unsorted */
237:       while (high-low > 5) {
238:         t = (low+high)/2;
239:         if (rp[t] > col) high = t;
240:         else             low  = t;
241:       }
242:       for (i=low; i<high; i++) {
243:         if (rp[i] > col) break;
244:         if (rp[i] == col) {
245:           *v++ = ap[i];
246:           goto finished;
247:         }
248:       }
249:       *v++ = zero;
250:       finished:;
251:     }
252:   }
253:   return(0);
254: }


259: int MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
260: {
261:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
262:   int        i,fd,*col_lens,ierr;

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

272:   /* store lengths of each row and write (including header) to file */
273:   for (i=0; i<A->m; i++) {
274:     col_lens[4+i] = a->i[i+1] - a->i[i];
275:   }
276:   PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,1);
277:   PetscFree(col_lens);

279:   /* store column indices (zero start index) */
280:   PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,0);

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

287: extern int MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

291: int MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
292: {
293:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
294:   int               ierr,i,j,m = A->m,shift=0;
295:   char              *name;
296:   PetscViewerFormat format;

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

318:     for (i=0; i<m; i++) {
319:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
320: #if defined(PETSC_USE_COMPLEX)
321:         PetscViewerASCIIPrintf(viewer,"%d %d  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
322: #else
323:         PetscViewerASCIIPrintf(viewer,"%d %d  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
324: #endif
325:       }
326:     }
327:     if (nofinalvalue) {
328:       PetscViewerASCIIPrintf(viewer,"%d %d  %18.16e\n",m,A->n,0.0);
329:     }
330:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
331:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
332:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO) {
333: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_SINGLE) && !defined(PETSC_USE_COMPLEX)
334:      MatSeqAIJFactorInfo_Matlab(A,viewer);
335: #endif
336:      return(0);
337:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
338:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
339:     for (i=0; i<m; i++) {
340:       PetscViewerASCIIPrintf(viewer,"row %d:",i);
341:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
342: #if defined(PETSC_USE_COMPLEX)
343:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
344:           PetscViewerASCIIPrintf(viewer," (%d, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
345:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
346:           PetscViewerASCIIPrintf(viewer," (%d, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
347:         } else if (PetscRealPart(a->a[j]) != 0.0) {
348:           PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
349:         }
350: #else
351:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%d, %g) ",a->j[j]+shift,a->a[j]);}
352: #endif
353:       }
354:       PetscViewerASCIIPrintf(viewer,"\n");
355:     }
356:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
357:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
358:     int nzd=0,fshift=1,*sptr;
359:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
360:     PetscMalloc((m+1)*sizeof(int),&sptr);
361:     for (i=0; i<m; i++) {
362:       sptr[i] = nzd+1;
363:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
364:         if (a->j[j] >= i) {
365: #if defined(PETSC_USE_COMPLEX)
366:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
367: #else
368:           if (a->a[j] != 0.0) nzd++;
369: #endif
370:         }
371:       }
372:     }
373:     sptr[m] = nzd+1;
374:     PetscViewerASCIIPrintf(viewer," %d %d\n\n",m,nzd);
375:     for (i=0; i<m+1; i+=6) {
376:       if (i+4<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);}
377:       else if (i+3<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);}
378:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
379:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %d %d %d\n",sptr[i],sptr[i+1],sptr[i+2]);}
380:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %d %d\n",sptr[i],sptr[i+1]);}
381:       else            {PetscViewerASCIIPrintf(viewer," %d\n",sptr[i]);}
382:     }
383:     PetscViewerASCIIPrintf(viewer,"\n");
384:     PetscFree(sptr);
385:     for (i=0; i<m; i++) {
386:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
387:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %d ",a->j[j]+fshift);}
388:       }
389:       PetscViewerASCIIPrintf(viewer,"\n");
390:     }
391:     PetscViewerASCIIPrintf(viewer,"\n");
392:     for (i=0; i<m; i++) {
393:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
394:         if (a->j[j] >= i) {
395: #if defined(PETSC_USE_COMPLEX)
396:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
397:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
398:           }
399: #else
400:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
401: #endif
402:         }
403:       }
404:       PetscViewerASCIIPrintf(viewer,"\n");
405:     }
406:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
407:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
408:     int         cnt = 0,jcnt;
409:     PetscScalar value;

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

457: int MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
458: {
459:   Mat               A = (Mat) Aa;
460:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
461:   int               ierr,i,j,m = A->m,color;
462:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
463:   PetscViewer       viewer;
464:   PetscViewerFormat format;

467:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
468:   PetscViewerGetFormat(viewer,&format);

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

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

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

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: }

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

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

589: int MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
590: {
591:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
592:   int          fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax,ierr;
593:   int          m = A->m,*ip,N,*ailen = a->ilen,rmax = 0;
594:   PetscScalar  *aa = a->a,*ap;

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

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

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

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

641:   return(0);
642: }

646: int MatZeroEntries_SeqAIJ(Mat A)
647: {
648:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
649:   int        ierr;

652:   PetscMemzero(a->a,(a->i[A->m])*sizeof(PetscScalar));
653:   return(0);
654: }

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

664: #if defined(PETSC_USE_LOG)
665:   PetscLogObjectState((PetscObject)A,"Rows=%d, Cols=%d, NZ=%d",A->m,A->n,a->nz);
666: #endif
667:   if (a->freedata) {
668:     PetscFree(a->a);
669:     if (!a->singlemalloc) {
670:       PetscFree(a->i);
671:       PetscFree(a->j);
672:     }
673:   }
674:   if (a->row) {
675:     ISDestroy(a->row);
676:   }
677:   if (a->col) {
678:     ISDestroy(a->col);
679:   }
680:   if (a->diag) {PetscFree(a->diag);}
681:   if (a->ilen) {PetscFree(a->ilen);}
682:   if (a->imax) {PetscFree(a->imax);}
683:   if (a->idiag) {PetscFree(a->idiag);}
684:   if (a->solve_work) {PetscFree(a->solve_work);}
685:   if (a->inode.size) {PetscFree(a->inode.size);}
686:   if (a->icol) {ISDestroy(a->icol);}
687:   if (a->saved_values) {PetscFree(a->saved_values);}
688:   if (a->coloring) {ISColoringDestroy(a->coloring);}
689:   if (a->xtoy) {PetscFree(a->xtoy);}
690: 
691:   PetscFree(a);
692:   return(0);
693: }

697: int MatCompress_SeqAIJ(Mat A)
698: {
700:   return(0);
701: }

705: int MatSetOption_SeqAIJ(Mat A,MatOption op)
706: {
707:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

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

779: int MatGetDiagonal_SeqAIJ(Mat A,Vec v)
780: {
781:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
782:   int          i,j,n,ierr;
783:   PetscScalar  *x,zero = 0.0;

786:   VecSet(&zero,v);
787:   VecGetArrayFast(v,&x);
788:   VecGetLocalSize(v,&n);
789:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
790:   for (i=0; i<A->m; i++) {
791:     for (j=a->i[i]; j<a->i[i+1]; j++) {
792:       if (a->j[j] == i) {
793:         x[i] = a->a[j];
794:         break;
795:       }
796:     }
797:   }
798:   VecRestoreArrayFast(v,&x);
799:   return(0);
800: }


805: int MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
806: {
807:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
808:   PetscScalar  *x,*y;
809:   int          ierr,m = A->m;
810: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
811:   PetscScalar  *v,alpha;
812:   int          n,i,*idx;
813: #endif

816:   if (zz != yy) {VecCopy(zz,yy);}
817:   VecGetArrayFast(xx,&x);
818:   VecGetArrayFast(yy,&y);

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

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

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


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

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

868:   VecGetArrayFast(xx,&x);
869:   VecGetArrayFast(yy,&y);
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,v,y);
875: #else
876:   for (i=0; i<m; i++) {
877:     jrow = ii[i];
878:     n    = ii[i+1] - jrow;
879:     sum  = 0.0;
880:     for (j=0; j<n; j++) {
881:       sum += v[jrow]*x[idx[jrow]]; jrow++;
882:      }
883:     y[i] = sum;
884:   }
885: #endif
886:   PetscLogFlops(2*a->nz - m);
887:   VecRestoreArrayFast(xx,&x);
888:   VecRestoreArrayFast(yy,&y);
889:   return(0);
890: }

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,*ii;
899: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
900:   int          n,i,jrow,j;
901: PetscScalar    sum;
902: #endif

905:   VecGetArrayFast(xx,&x);
906:   VecGetArrayFast(yy,&y);
907:   if (zz != yy) {
908:     VecGetArrayFast(zz,&z);
909:   } else {
910:     z = y;
911:   }
912: 
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,v,y,z);
918: #else
919:   for (i=0; i<m; i++) {
920:     jrow = ii[i];
921:     n    = ii[i+1] - jrow;
922:     sum  = y[i];
923:     for (j=0; j<n; j++) {
924:       sum += v[jrow]*x[idx[jrow]]; jrow++;
925:      }
926:     z[i] = sum;
927:   }
928: #endif
929:   PetscLogFlops(2*a->nz);
930:   VecRestoreArrayFast(xx,&x);
931:   VecRestoreArrayFast(yy,&y);
932:   if (zz != yy) {
933:     VecRestoreArrayFast(zz,&z);
934:   }
935:   return(0);
936: }

938: /*
939:      Adds diagonal pointers to sparse matrix structure.
940: */
943: int MatMarkDiagonal_SeqAIJ(Mat A)
944: {
945:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
946:   int        i,j,*diag,m = A->m,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]; j<a->i[i+1]; j++) {
956:       if (a->j[j] == i) {
957:         diag[i] = j;
958:         break;
959:       }
960:     }
961:   }
962:   a->diag = diag;
963:   return(0);
964: }

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

977:   MatMarkDiagonal_SeqAIJ(A);
978:   diag = a->diag;
979:   for (i=0; i<A->m; i++) {
980:     if (jj[diag[i]] != i) {
981:       SETERRQ1(1,"Matrix is missing diagonal number %d",i);
982:     }
983:   }
984:   return(0);
985: }

989: int MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
990: {
991:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
992:   PetscScalar        *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag;
993:   const PetscScalar  *v = a->a, *b, *bs,*xb, *ts;
994:   int                ierr,n = A->n,m = A->m,i;
995:   const int          *idx,*diag;

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

1001:   if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
1002:   diag = a->diag;
1003:   if (!a->idiag) {
1004:     PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);
1005:     a->ssor  = a->idiag + m;
1006:     mdiag    = a->ssor + m;

1008:     v        = a->a;

1010:     /* this is wrong when fshift omega changes each iteration */
1011:     if (omega == 1.0 && fshift == 0.0) {
1012:       for (i=0; i<m; i++) {
1013:         mdiag[i]    = v[diag[i]];
1014:         a->idiag[i] = 1.0/v[diag[i]];
1015:       }
1016:       PetscLogFlops(m);
1017:     } else {
1018:       for (i=0; i<m; i++) {
1019:         mdiag[i]    = v[diag[i]];
1020:         a->idiag[i] = omega/(fshift + v[diag[i]]);
1021:       }
1022:       PetscLogFlops(2*m);
1023:     }
1024:   }
1025:   t     = a->ssor;
1026:   idiag = a->idiag;
1027:   mdiag = a->idiag + 2*m;

1029:   VecGetArrayFast(xx,&x);
1030:   if (xx != bb) {
1031:     VecGetArrayFast(bb,(PetscScalar**)&b);
1032:   } else {
1033:     b = x;
1034:   }

1036:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1037:   xs   = x;
1038:   if (flag == SOR_APPLY_UPPER) {
1039:    /* apply (U + D/omega) to the vector */
1040:     bs = b;
1041:     for (i=0; i<m; i++) {
1042:         d    = fshift + a->a[diag[i]];
1043:         n    = a->i[i+1] - diag[i] - 1;
1044:         idx  = a->j + diag[i] + 1;
1045:         v    = a->a + diag[i] + 1;
1046:         sum  = b[i]*d/omega;
1047:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1048:         x[i] = sum;
1049:     }
1050:     VecRestoreArrayFast(xx,&x);
1051:     if (bb != xx) {VecRestoreArrayFast(bb,(PetscScalar**)&b);}
1052:     PetscLogFlops(a->nz);
1053:     return(0);
1054:   }


1057:     /* Let  A = L + U + D; where L is lower trianglar,
1058:     U is upper triangular, E is diagonal; This routine applies

1060:             (L + E)^{-1} A (U + E)^{-1}

1062:     to a vector efficiently using Eisenstat's trick. This is for
1063:     the case of SSOR preconditioner, so E is D/omega where omega
1064:     is the relaxation factor.
1065:     */

1067:   if (flag == SOR_APPLY_LOWER) {
1068:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1069:   } else if (flag & SOR_EISENSTAT) {
1070:     /* Let  A = L + U + D; where L is lower trianglar,
1071:     U is upper triangular, E is diagonal; This routine applies

1073:             (L + E)^{-1} A (U + E)^{-1}

1075:     to a vector efficiently using Eisenstat's trick. This is for
1076:     the case of SSOR preconditioner, so E is D/omega where omega
1077:     is the relaxation factor.
1078:     */
1079:     scale = (2.0/omega) - 1.0;

1081:     /*  x = (E + U)^{-1} b */
1082:     for (i=m-1; i>=0; i--) {
1083:       n    = a->i[i+1] - diag[i] - 1;
1084:       idx  = a->j + diag[i] + 1;
1085:       v    = a->a + diag[i] + 1;
1086:       sum  = b[i];
1087:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1088:       x[i] = sum*idiag[i];
1089:     }

1091:     /*  t = b - (2*E - D)x */
1092:     v = a->a;
1093:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1095:     /*  t = (E + L)^{-1}t */
1096:     ts = t;
1097:     diag = a->diag;
1098:     for (i=0; i<m; i++) {
1099:       n    = diag[i] - a->i[i];
1100:       idx  = a->j + a->i[i];
1101:       v    = a->a + a->i[i];
1102:       sum  = t[i];
1103:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1104:       t[i] = sum*idiag[i];
1105:       /*  x = x + t */
1106:       x[i] += t[i];
1107:     }

1109:     PetscLogFlops(6*m-1 + 2*a->nz);
1110:     VecRestoreArrayFast(xx,&x);
1111:     if (bb != xx) {VecRestoreArrayFast(bb,(PetscScalar**)&b);}
1112:     return(0);
1113:   }
1114:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1115:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1116: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1117:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(int *)diag,idiag,a->a,(void*)b);
1118: #else
1119:       for (i=0; i<m; i++) {
1120:         n    = diag[i] - a->i[i];
1121:         idx  = a->j + a->i[i];
1122:         v    = a->a + a->i[i];
1123:         sum  = b[i];
1124:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1125:         x[i] = sum*idiag[i];
1126:       }
1127: #endif
1128:       xb = x;
1129:       PetscLogFlops(a->nz);
1130:     } else xb = b;
1131:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1132:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1133:       for (i=0; i<m; i++) {
1134:         x[i] *= mdiag[i];
1135:       }
1136:       PetscLogFlops(m);
1137:     }
1138:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1139: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1140:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(int*)diag,idiag,a->a,(void*)xb);
1141: #else
1142:       for (i=m-1; i>=0; i--) {
1143:         n    = a->i[i+1] - diag[i] - 1;
1144:         idx  = a->j + diag[i] + 1;
1145:         v    = a->a + diag[i] + 1;
1146:         sum  = xb[i];
1147:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1148:         x[i] = sum*idiag[i];
1149:       }
1150: #endif
1151:       PetscLogFlops(a->nz);
1152:     }
1153:     its--;
1154:   }
1155:   while (its--) {
1156:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1157: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1158:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(int*)diag,a->a,(void*)b);
1159: #else
1160:       for (i=0; i<m; i++) {
1161:         d    = fshift + a->a[diag[i]];
1162:         n    = a->i[i+1] - a->i[i];
1163:         idx  = a->j + a->i[i];
1164:         v    = a->a + a->i[i];
1165:         sum  = b[i];
1166:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1167:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1168:       }
1169: #endif 
1170:       PetscLogFlops(a->nz);
1171:     }
1172:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1173: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1174:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(int*)diag,a->a,(void*)b);
1175: #else
1176:       for (i=m-1; i>=0; i--) {
1177:         d    = fshift + a->a[diag[i]];
1178:         n    = a->i[i+1] - a->i[i];
1179:         idx  = a->j + a->i[i];
1180:         v    = a->a + a->i[i];
1181:         sum  = b[i];
1182:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1183:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1184:       }
1185: #endif
1186:       PetscLogFlops(a->nz);
1187:     }
1188:   }
1189:   VecRestoreArrayFast(xx,&x);
1190:   if (bb != xx) {VecRestoreArrayFast(bb,(PetscScalar**)&b);}
1191:   return(0);
1192: }

1196: int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1197: {
1198:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1201:   info->rows_global    = (double)A->m;
1202:   info->columns_global = (double)A->n;
1203:   info->rows_local     = (double)A->m;
1204:   info->columns_local  = (double)A->n;
1205:   info->block_size     = 1.0;
1206:   info->nz_allocated   = (double)a->maxnz;
1207:   info->nz_used        = (double)a->nz;
1208:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1209:   info->assemblies     = (double)A->num_ass;
1210:   info->mallocs        = (double)a->reallocs;
1211:   info->memory         = A->mem;
1212:   if (A->factor) {
1213:     info->fill_ratio_given  = A->info.fill_ratio_given;
1214:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1215:     info->factor_mallocs    = A->info.factor_mallocs;
1216:   } else {
1217:     info->fill_ratio_given  = 0;
1218:     info->fill_ratio_needed = 0;
1219:     info->factor_mallocs    = 0;
1220:   }
1221:   return(0);
1222: }

1226: int MatZeroRows_SeqAIJ(Mat A,IS is,const PetscScalar *diag)
1227: {
1228:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1229:   int         i,ierr,N,*rows,m = A->m - 1;

1232:   ISGetLocalSize(is,&N);
1233:   ISGetIndices(is,&rows);
1234:   if (a->keepzeroedrows) {
1235:     for (i=0; i<N; i++) {
1236:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]);
1237:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1238:     }
1239:     if (diag) {
1240:       MatMissingDiagonal_SeqAIJ(A);
1241:       MatMarkDiagonal_SeqAIJ(A);
1242:       for (i=0; i<N; i++) {
1243:         a->a[a->diag[rows[i]]] = *diag;
1244:       }
1245:     }
1246:   } else {
1247:     if (diag) {
1248:       for (i=0; i<N; i++) {
1249:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]);
1250:         if (a->ilen[rows[i]] > 0) {
1251:           a->ilen[rows[i]]          = 1;
1252:           a->a[a->i[rows[i]]] = *diag;
1253:           a->j[a->i[rows[i]]] = rows[i];
1254:         } else { /* in case row was completely empty */
1255:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);
1256:         }
1257:       }
1258:     } else {
1259:       for (i=0; i<N; i++) {
1260:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %d out of range", rows[i]);
1261:         a->ilen[rows[i]] = 0;
1262:       }
1263:     }
1264:   }
1265:   ISRestoreIndices(is,&rows);
1266:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1267:   return(0);
1268: }

1272: int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1273: {
1274:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1275:   int        *itmp;

1278:   if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row);

1280:   *nz = a->i[row+1] - a->i[row];
1281:   if (v) *v = a->a + a->i[row];
1282:   if (idx) {
1283:     itmp = a->j + a->i[row];
1284:     if (*nz) {
1285:       *idx = itmp;
1286:     }
1287:     else *idx = 0;
1288:   }
1289:   return(0);
1290: }

1292: /* remove this function? */
1295: int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1296: {
1297:   /* Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1301:   /* if (idx) {if (*idx && a->indexshift) {PetscFree(*idx);}} */
1302:   return(0);
1303: }

1307: int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1308: {
1309:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1310:   PetscScalar  *v = a->a;
1311:   PetscReal    sum = 0.0;
1312:   int          i,j,ierr;

1315:   if (type == NORM_FROBENIUS) {
1316:     for (i=0; i<a->nz; i++) {
1317: #if defined(PETSC_USE_COMPLEX)
1318:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1319: #else
1320:       sum += (*v)*(*v); v++;
1321: #endif
1322:     }
1323:     *nrm = sqrt(sum);
1324:   } else if (type == NORM_1) {
1325:     PetscReal *tmp;
1326:     int    *jj = a->j;
1327:     PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);
1328:     PetscMemzero(tmp,A->n*sizeof(PetscReal));
1329:     *nrm = 0.0;
1330:     for (j=0; j<a->nz; j++) {
1331:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
1332:     }
1333:     for (j=0; j<A->n; j++) {
1334:       if (tmp[j] > *nrm) *nrm = tmp[j];
1335:     }
1336:     PetscFree(tmp);
1337:   } else if (type == NORM_INFINITY) {
1338:     *nrm = 0.0;
1339:     for (j=0; j<A->m; j++) {
1340:       v = a->a + a->i[j];
1341:       sum = 0.0;
1342:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1343:         sum += PetscAbsScalar(*v); v++;
1344:       }
1345:       if (sum > *nrm) *nrm = sum;
1346:     }
1347:   } else {
1348:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1349:   }
1350:   return(0);
1351: }

1355: int MatTranspose_SeqAIJ(Mat A,Mat *B)
1356: {
1357:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1358:   Mat          C;
1359:   int          i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col;
1360:   PetscScalar  *array = a->a;

1363:   if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1364:   PetscMalloc((1+A->n)*sizeof(int),&col);
1365:   PetscMemzero(col,(1+A->n)*sizeof(int));
1366: 
1367:   for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1368:   MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);
1369:   PetscFree(col);
1370:   for (i=0; i<m; i++) {
1371:     len    = ai[i+1]-ai[i];
1372:     MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);
1373:     array += len;
1374:     aj    += len;
1375:   }

1377:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1378:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1380:   if (B) {
1381:     *B = C;
1382:   } else {
1383:     MatHeaderCopy(A,C);
1384:   }
1385:   return(0);
1386: }

1388: EXTERN_C_BEGIN
1391: int MatIsSymmetric_SeqAIJ(Mat A,Mat B,PetscTruth *f)
1392: {
1393:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1394:   int *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1395:   int ma,na,mb,nb, i,ierr;

1398:   bij = (Mat_SeqAIJ *) B->data;
1399: 
1400:   MatGetSize(A,&ma,&na);
1401:   MatGetSize(B,&mb,&nb);
1402:   if (ma!=nb || na!=mb)
1403:     SETERRQ(1,"Incompatible A/B sizes for symmetry test");
1404:   aii = aij->i; bii = bij->i;
1405:   adx = aij->j; bdx = bij->j;
1406:   va = aij->a; vb = bij->a;
1407:   PetscMalloc(ma*sizeof(int),&aptr);
1408:   PetscMalloc(mb*sizeof(int),&bptr);
1409:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1410:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1412:   *f = PETSC_TRUE;
1413:   for (i=0; i<ma; i++) {
1414:     /*printf("row %d spans %d--%d; we start @ %d\n",
1415:       i,idx[ii[i]],idx[ii[i+1]-1],idx[aptr[i]]);*/
1416:     while (aptr[i]<aii[i+1]) {
1417:       int idc,idr; PetscScalar vc,vr;
1418:       /* column/row index/value */
1419:       idc = adx[aptr[i]]; idr = bdx[bptr[idc]];
1420:       vc = va[aptr[i]]; vr = vb[bptr[idc]];
1421:       /*printf("comparing %d: (%d,%d)=%e to (%d,%d)=%e\n",
1422:         aptr[i],i,idc,vc,idc,idr,vr);*/
1423:       if (i!=idr || vc!=vr) {
1424:         *f = PETSC_FALSE; goto done;
1425:       } else {
1426:         aptr[i]++; if (B || i!=idc) bptr[idc]++;
1427:       }
1428:     }
1429:   }
1430:  done:
1431:   PetscFree(aptr);
1432:   if (B) {
1433:     PetscFree(bptr);
1434:   }

1436:   return(0);
1437: }
1438: EXTERN_C_END

1442: int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1443: {
1444:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1445:   PetscScalar  *l,*r,x,*v;
1446:   int          ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj;

1449:   if (ll) {
1450:     /* The local size is used so that VecMPI can be passed to this routine
1451:        by MatDiagonalScale_MPIAIJ */
1452:     VecGetLocalSize(ll,&m);
1453:     if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1454:     VecGetArrayFast(ll,&l);
1455:     v = a->a;
1456:     for (i=0; i<m; i++) {
1457:       x = l[i];
1458:       M = a->i[i+1] - a->i[i];
1459:       for (j=0; j<M; j++) { (*v++) *= x;}
1460:     }
1461:     VecRestoreArrayFast(ll,&l);
1462:     PetscLogFlops(nz);
1463:   }
1464:   if (rr) {
1465:     VecGetLocalSize(rr,&n);
1466:     if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1467:     VecGetArrayFast(rr,&r);
1468:     v = a->a; jj = a->j;
1469:     for (i=0; i<nz; i++) {
1470:       (*v++) *= r[*jj++];
1471:     }
1472:     VecRestoreArrayFast(rr,&r);
1473:     PetscLogFlops(nz);
1474:   }
1475:   return(0);
1476: }

1480: int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B)
1481: {
1482:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data,*c;
1483:   int          *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens;
1484:   int          row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1485:   int          *irow,*icol,nrows,ncols;
1486:   int          *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1487:   PetscScalar  *a_new,*mat_a;
1488:   Mat          C;
1489:   PetscTruth   stride;

1492:   ISSorted(isrow,(PetscTruth*)&i);
1493:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1494:   ISSorted(iscol,(PetscTruth*)&i);
1495:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1497:   ISGetIndices(isrow,&irow);
1498:   ISGetLocalSize(isrow,&nrows);
1499:   ISGetLocalSize(iscol,&ncols);

1501:   ISStrideGetInfo(iscol,&first,&step);
1502:   ISStride(iscol,&stride);
1503:   if (stride && step == 1) {
1504:     /* special case of contiguous rows */
1505:     PetscMalloc((2*nrows+1)*sizeof(int),&lens);
1506:     starts = lens + nrows;
1507:     /* loop over new rows determining lens and starting points */
1508:     for (i=0; i<nrows; i++) {
1509:       kstart  = ai[irow[i]];
1510:       kend    = kstart + ailen[irow[i]];
1511:       for (k=kstart; k<kend; k++) {
1512:         if (aj[k] >= first) {
1513:           starts[i] = k;
1514:           break;
1515:         }
1516:       }
1517:       sum = 0;
1518:       while (k < kend) {
1519:         if (aj[k++] >= first+ncols) break;
1520:         sum++;
1521:       }
1522:       lens[i] = sum;
1523:     }
1524:     /* create submatrix */
1525:     if (scall == MAT_REUSE_MATRIX) {
1526:       int n_cols,n_rows;
1527:       MatGetSize(*B,&n_rows,&n_cols);
1528:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1529:       MatZeroEntries(*B);
1530:       C = *B;
1531:     } else {
1532:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1533:     }
1534:     c = (Mat_SeqAIJ*)C->data;

1536:     /* loop over rows inserting into submatrix */
1537:     a_new    = c->a;
1538:     j_new    = c->j;
1539:     i_new    = c->i;

1541:     for (i=0; i<nrows; i++) {
1542:       ii    = starts[i];
1543:       lensi = lens[i];
1544:       for (k=0; k<lensi; k++) {
1545:         *j_new++ = aj[ii+k] - first;
1546:       }
1547:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1548:       a_new      += lensi;
1549:       i_new[i+1]  = i_new[i] + lensi;
1550:       c->ilen[i]  = lensi;
1551:     }
1552:     PetscFree(lens);
1553:   } else {
1554:     ISGetIndices(iscol,&icol);
1555:     PetscMalloc((1+oldcols)*sizeof(int),&smap);
1556: 
1557:     PetscMalloc((1+nrows)*sizeof(int),&lens);
1558:     PetscMemzero(smap,oldcols*sizeof(int));
1559:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1560:     /* determine lens of each row */
1561:     for (i=0; i<nrows; i++) {
1562:       kstart  = ai[irow[i]];
1563:       kend    = kstart + a->ilen[irow[i]];
1564:       lens[i] = 0;
1565:       for (k=kstart; k<kend; k++) {
1566:         if (smap[aj[k]]) {
1567:           lens[i]++;
1568:         }
1569:       }
1570:     }
1571:     /* Create and fill new matrix */
1572:     if (scall == MAT_REUSE_MATRIX) {
1573:       PetscTruth equal;

1575:       c = (Mat_SeqAIJ *)((*B)->data);
1576:       if ((*B)->m  != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1577:       PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);
1578:       if (!equal) {
1579:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1580:       }
1581:       PetscMemzero(c->ilen,(*B)->m*sizeof(int));
1582:       C = *B;
1583:     } else {
1584:       MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1585:     }
1586:     c = (Mat_SeqAIJ *)(C->data);
1587:     for (i=0; i<nrows; i++) {
1588:       row    = irow[i];
1589:       kstart = ai[row];
1590:       kend   = kstart + a->ilen[row];
1591:       mat_i  = c->i[i];
1592:       mat_j  = c->j + mat_i;
1593:       mat_a  = c->a + mat_i;
1594:       mat_ilen = c->ilen + i;
1595:       for (k=kstart; k<kend; k++) {
1596:         if ((tcol=smap[a->j[k]])) {
1597:           *mat_j++ = tcol - 1;
1598:           *mat_a++ = a->a[k];
1599:           (*mat_ilen)++;

1601:         }
1602:       }
1603:     }
1604:     /* Free work space */
1605:     ISRestoreIndices(iscol,&icol);
1606:     PetscFree(smap);
1607:     PetscFree(lens);
1608:   }
1609:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1610:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1612:   ISRestoreIndices(isrow,&irow);
1613:   *B = C;
1614:   return(0);
1615: }

1617: /*
1618: */
1621: int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1622: {
1623:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1624:   int        ierr;
1625:   Mat        outA;
1626:   PetscTruth row_identity,col_identity;

1629:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1630:   ISIdentity(row,&row_identity);
1631:   ISIdentity(col,&col_identity);
1632:   if (!row_identity || !col_identity) {
1633:     SETERRQ(1,"Row and column permutations must be identity for in-place ILU");
1634:   }

1636:   outA          = inA;
1637:   inA->factor   = FACTOR_LU;
1638:   a->row        = row;
1639:   a->col        = col;
1640:   PetscObjectReference((PetscObject)row);
1641:   PetscObjectReference((PetscObject)col);

1643:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1644:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1645:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1646:   PetscLogObjectParent(inA,a->icol);

1648:   if (!a->solve_work) { /* this matrix may have been factored before */
1649:      PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
1650:   }

1652:   if (!a->diag) {
1653:     MatMarkDiagonal_SeqAIJ(inA);
1654:   }
1655:   MatLUFactorNumeric_SeqAIJ(inA,&outA);
1656:   return(0);
1657: }

1659:  #include petscblaslapack.h
1662: int MatScale_SeqAIJ(const PetscScalar *alpha,Mat inA)
1663: {
1664:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1665:   int        one = 1;

1668:   BLscal_(&a->nz,(PetscScalar*)alpha,a->a,&one);
1669:   PetscLogFlops(a->nz);
1670:   return(0);
1671: }

1675: int MatGetSubMatrices_SeqAIJ(Mat A,int n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1676: {
1677:   int ierr,i;

1680:   if (scall == MAT_INITIAL_MATRIX) {
1681:     PetscMalloc((n+1)*sizeof(Mat),B);
1682:   }

1684:   for (i=0; i<n; i++) {
1685:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1686:   }
1687:   return(0);
1688: }

1692: int MatGetBlockSize_SeqAIJ(Mat A,int *bs)
1693: {
1695:   *bs = 1;
1696:   return(0);
1697: }

1701: int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS is[],int ov)
1702: {
1703:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1704:   int        row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val;
1705:   int        start,end,*ai,*aj;
1706:   PetscBT    table;

1709:   m     = A->m;
1710:   ai    = a->i;
1711:   aj    = a->j;

1713:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

1715:   PetscMalloc((m+1)*sizeof(int),&nidx);
1716:   PetscBTCreate(m,table);

1718:   for (i=0; i<is_max; i++) {
1719:     /* Initialize the two local arrays */
1720:     isz  = 0;
1721:     PetscBTMemzero(m,table);
1722: 
1723:     /* Extract the indices, assume there can be duplicate entries */
1724:     ISGetIndices(is[i],&idx);
1725:     ISGetLocalSize(is[i],&n);
1726: 
1727:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1728:     for (j=0; j<n ; ++j){
1729:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1730:     }
1731:     ISRestoreIndices(is[i],&idx);
1732:     ISDestroy(is[i]);
1733: 
1734:     k = 0;
1735:     for (j=0; j<ov; j++){ /* for each overlap */
1736:       n = isz;
1737:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1738:         row   = nidx[k];
1739:         start = ai[row];
1740:         end   = ai[row+1];
1741:         for (l = start; l<end ; l++){
1742:           val = aj[l] ;
1743:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1744:         }
1745:       }
1746:     }
1747:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1748:   }
1749:   PetscBTDestroy(table);
1750:   PetscFree(nidx);
1751:   return(0);
1752: }

1754: /* -------------------------------------------------------------- */
1757: int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1758: {
1759:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
1760:   PetscScalar  *vwork;
1761:   int          i,ierr,nz,m = A->m,n = A->n,*cwork;
1762:   int          *row,*col,*cnew,j,*lens;
1763:   IS           icolp,irowp;

1766:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1767:   ISGetIndices(irowp,&row);
1768:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1769:   ISGetIndices(icolp,&col);
1770: 
1771:   /* determine lengths of permuted rows */
1772:   PetscMalloc((m+1)*sizeof(int),&lens);
1773:   for (i=0; i<m; i++) {
1774:     lens[row[i]] = a->i[i+1] - a->i[i];
1775:   }
1776:   MatCreateSeqAIJ(A->comm,m,n,0,lens,B);
1777:   PetscFree(lens);

1779:   PetscMalloc(n*sizeof(int),&cnew);
1780:   for (i=0; i<m; i++) {
1781:     MatGetRow(A,i,&nz,&cwork,&vwork);
1782:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1783:     MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1784:     MatRestoreRow(A,i,&nz,&cwork,&vwork);
1785:   }
1786:   PetscFree(cnew);
1787:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1788:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1789:   ISRestoreIndices(irowp,&row);
1790:   ISRestoreIndices(icolp,&col);
1791:   ISDestroy(irowp);
1792:   ISDestroy(icolp);
1793:   return(0);
1794: }

1798: int MatPrintHelp_SeqAIJ(Mat A)
1799: {
1800:   static PetscTruth called = PETSC_FALSE;
1801:   MPI_Comm          comm = A->comm;
1802:   int               ierr;

1805:   if (called) {return(0);} else called = PETSC_TRUE;
1806:   (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");
1807:   (*PetscHelpPrintf)(comm,"  -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");
1808:   (*PetscHelpPrintf)(comm,"  -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");
1809:   (*PetscHelpPrintf)(comm,"  -mat_aij_no_inode: Do not use inodes\n");
1810:   (*PetscHelpPrintf)(comm,"  -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)\n");
1811: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
1812:   (*PetscHelpPrintf)(comm,"  -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.\n");
1813: #endif
1814:   return(0);
1815: }

1819: int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1820: {
1821:   int        ierr;

1824:   /* If the two matrices have the same copy implementation, use fast copy. */
1825:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1826:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1827:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1829:     if (a->i[A->m] != b->i[B->m]) {
1830:       SETERRQ(1,"Number of nonzeros in two matrices are different");
1831:     }
1832:     PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));
1833:   } else {
1834:     MatCopy_Basic(A,B,str);
1835:   }
1836:   return(0);
1837: }

1841: int MatSetUpPreallocation_SeqAIJ(Mat A)
1842: {
1843:   int        ierr;

1846:    MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);
1847:   return(0);
1848: }

1852: int MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
1853: {
1854:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1856:   *array = a->a;
1857:   return(0);
1858: }

1862: int MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
1863: {
1865:   return(0);
1866: }

1870: int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1871: {
1872:   int           (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f;
1873:   int           k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1874:   PetscScalar   dx,mone = -1.0,*y,*xx,*w3_array;
1875:   PetscScalar   *vscale_array;
1876:   PetscReal     epsilon = coloring->error_rel,umin = coloring->umin;
1877:   Vec           w1,w2,w3;
1878:   void          *fctx = coloring->fctx;
1879:   PetscTruth    flg;

1882:   if (!coloring->w1) {
1883:     VecDuplicate(x1,&coloring->w1);
1884:     PetscLogObjectParent(coloring,coloring->w1);
1885:     VecDuplicate(x1,&coloring->w2);
1886:     PetscLogObjectParent(coloring,coloring->w2);
1887:     VecDuplicate(x1,&coloring->w3);
1888:     PetscLogObjectParent(coloring,coloring->w3);
1889:   }
1890:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

1892:   MatSetUnfactored(J);
1893:   PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);
1894:   if (flg) {
1895:     PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n");
1896:   } else {
1897:     MatZeroEntries(J);
1898:   }

1900:   VecGetOwnershipRange(x1,&start,&end);
1901:   VecGetSize(x1,&N);

1903:   /*
1904:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
1905:      coloring->F for the coarser grids from the finest
1906:   */
1907:   if (coloring->F) {
1908:     VecGetLocalSize(coloring->F,&m1);
1909:     VecGetLocalSize(w1,&m2);
1910:     if (m1 != m2) {
1911:       coloring->F = 0;
1912:     }
1913:   }

1915:   if (coloring->F) {
1916:     w1          = coloring->F;
1917:     coloring->F = 0;
1918:   } else {
1919:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
1920:     (*f)(sctx,x1,w1,fctx);
1921:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
1922:   }

1924:   /* 
1925:       Compute all the scale factors and share with other processors
1926:   */
1927:   VecGetArrayFast(x1,&xx);xx = xx - start;
1928:   VecGetArrayFast(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
1929:   for (k=0; k<coloring->ncolors; k++) {
1930:     /*
1931:        Loop over each column associated with color adding the 
1932:        perturbation to the vector w3.
1933:     */
1934:     for (l=0; l<coloring->ncolumns[k]; l++) {
1935:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1936:       dx  = xx[col];
1937:       if (dx == 0.0) dx = 1.0;
1938: #if !defined(PETSC_USE_COMPLEX)
1939:       if (dx < umin && dx >= 0.0)      dx = umin;
1940:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1941: #else
1942:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1943:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1944: #endif
1945:       dx                *= epsilon;
1946:       vscale_array[col] = 1.0/dx;
1947:     }
1948:   }
1949:   vscale_array = vscale_array + start;VecRestoreArrayFast(coloring->vscale,&vscale_array);
1950:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
1951:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

1953:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
1954:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

1956:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
1957:   else                        vscaleforrow = coloring->columnsforrow;

1959:   VecGetArrayFast(coloring->vscale,&vscale_array);
1960:   /*
1961:       Loop over each color
1962:   */
1963:   for (k=0; k<coloring->ncolors; k++) {
1964:     coloring->currentcolor = k;
1965:     VecCopy(x1,w3);
1966:     VecGetArrayFast(w3,&w3_array);w3_array = w3_array - start;
1967:     /*
1968:        Loop over each column associated with color adding the 
1969:        perturbation to the vector w3.
1970:     */
1971:     for (l=0; l<coloring->ncolumns[k]; l++) {
1972:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1973:       dx  = xx[col];
1974:       if (dx == 0.0) dx = 1.0;
1975: #if !defined(PETSC_USE_COMPLEX)
1976:       if (dx < umin && dx >= 0.0)      dx = umin;
1977:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1978: #else
1979:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1980:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1981: #endif
1982:       dx            *= epsilon;
1983:       if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter");
1984:       w3_array[col] += dx;
1985:     }
1986:     w3_array = w3_array + start; VecRestoreArrayFast(w3,&w3_array);

1988:     /*
1989:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
1990:     */

1992:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
1993:     (*f)(sctx,w3,w2,fctx);
1994:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
1995:     VecAXPY(&mone,w1,w2);

1997:     /*
1998:        Loop over rows of vector, putting results into Jacobian matrix
1999:     */
2000:     VecGetArrayFast(w2,&y);
2001:     for (l=0; l<coloring->nrows[k]; l++) {
2002:       row    = coloring->rows[k][l];
2003:       col    = coloring->columnsforrow[k][l];
2004:       y[row] *= vscale_array[vscaleforrow[k][l]];
2005:       srow   = row + start;
2006:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2007:     }
2008:     VecRestoreArrayFast(w2,&y);
2009:   }
2010:   coloring->currentcolor = k;
2011:   VecRestoreArrayFast(coloring->vscale,&vscale_array);
2012:   xx = xx + start; VecRestoreArrayFast(x1,&xx);
2013:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2014:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2015:   return(0);
2016: }

2018:  #include petscblaslapack.h
2021: int MatAXPY_SeqAIJ(const PetscScalar a[],Mat X,Mat Y,MatStructure str)
2022: {
2023:   int        ierr,one=1,i;
2024:   Mat_SeqAIJ *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;

2027:   if (str == SAME_NONZERO_PATTERN) {
2028:     BLaxpy_(&x->nz,(PetscScalar*)a,x->a,&one,y->a,&one);
2029:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2030:     if (y->xtoy && y->XtoY != X) {
2031:       PetscFree(y->xtoy);
2032:       MatDestroy(y->XtoY);
2033:     }
2034:     if (!y->xtoy) { /* get xtoy */
2035:       MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2036:       y->XtoY = X;
2037:     }
2038:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += (*a)*(x->a[i]);
2039:     PetscLogInfo(0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz);
2040:   } else {
2041:     MatAXPY_Basic(a,X,Y,str);
2042:   }
2043:   return(0);
2044: }

2046: /* -------------------------------------------------------------------*/
2047: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2048:        MatGetRow_SeqAIJ,
2049:        MatRestoreRow_SeqAIJ,
2050:        MatMult_SeqAIJ,
2051: /* 4*/ MatMultAdd_SeqAIJ,
2052:        MatMultTranspose_SeqAIJ,
2053:        MatMultTransposeAdd_SeqAIJ,
2054:        MatSolve_SeqAIJ,
2055:        MatSolveAdd_SeqAIJ,
2056:        MatSolveTranspose_SeqAIJ,
2057: /*10*/ MatSolveTransposeAdd_SeqAIJ,
2058:        MatLUFactor_SeqAIJ,
2059:        0,
2060:        MatRelax_SeqAIJ,
2061:        MatTranspose_SeqAIJ,
2062: /*15*/ MatGetInfo_SeqAIJ,
2063:        MatEqual_SeqAIJ,
2064:        MatGetDiagonal_SeqAIJ,
2065:        MatDiagonalScale_SeqAIJ,
2066:        MatNorm_SeqAIJ,
2067: /*20*/ 0,
2068:        MatAssemblyEnd_SeqAIJ,
2069:        MatCompress_SeqAIJ,
2070:        MatSetOption_SeqAIJ,
2071:        MatZeroEntries_SeqAIJ,
2072: /*25*/ MatZeroRows_SeqAIJ,
2073:        MatLUFactorSymbolic_SeqAIJ,
2074:        MatLUFactorNumeric_SeqAIJ,
2075:        MatCholeskyFactorSymbolic_SeqAIJ,
2076:        MatCholeskyFactorNumeric_SeqAIJ,
2077: /*30*/ MatSetUpPreallocation_SeqAIJ,
2078:        MatILUFactorSymbolic_SeqAIJ,
2079:        MatICCFactorSymbolic_SeqAIJ,
2080:        MatGetArray_SeqAIJ,
2081:        MatRestoreArray_SeqAIJ,
2082: /*35*/ MatDuplicate_SeqAIJ,
2083:        0,
2084:        0,
2085:        MatILUFactor_SeqAIJ,
2086:        0,
2087: /*40*/ MatAXPY_SeqAIJ,
2088:        MatGetSubMatrices_SeqAIJ,
2089:        MatIncreaseOverlap_SeqAIJ,
2090:        MatGetValues_SeqAIJ,
2091:        MatCopy_SeqAIJ,
2092: /*45*/ MatPrintHelp_SeqAIJ,
2093:        MatScale_SeqAIJ,
2094:        0,
2095:        0,
2096:        MatILUDTFactor_SeqAIJ,
2097: /*50*/ MatGetBlockSize_SeqAIJ,
2098:        MatGetRowIJ_SeqAIJ,
2099:        MatRestoreRowIJ_SeqAIJ,
2100:        MatGetColumnIJ_SeqAIJ,
2101:        MatRestoreColumnIJ_SeqAIJ,
2102: /*55*/ MatFDColoringCreate_SeqAIJ,
2103:        0,
2104:        0,
2105:        MatPermute_SeqAIJ,
2106:        0,
2107: /*60*/ 0,
2108:        MatDestroy_SeqAIJ,
2109:        MatView_SeqAIJ,
2110:        MatGetPetscMaps_Petsc,
2111:        0,
2112: /*65*/ 0,
2113:        0,
2114:        0,
2115:        0,
2116:        0,
2117: /*70*/ 0,
2118:        0,
2119:        MatSetColoring_SeqAIJ,
2120:        MatSetValuesAdic_SeqAIJ,
2121:        MatSetValuesAdifor_SeqAIJ,
2122: /*75*/ MatFDColoringApply_SeqAIJ,
2123:        0,
2124:        0,
2125:        0,
2126:        0,
2127: /*80*/ 0,
2128:        0,
2129:        0,
2130:        0,
2131:        0,
2132: /*85*/ MatLoad_SeqAIJ};

2134: EXTERN_C_BEGIN

2138: int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices)
2139: {
2140:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2141:   int        i,nz,n;


2145:   nz = aij->maxnz;
2146:   n  = mat->n;
2147:   for (i=0; i<nz; i++) {
2148:     aij->j[i] = indices[i];
2149:   }
2150:   aij->nz = nz;
2151:   for (i=0; i<n; i++) {
2152:     aij->ilen[i] = aij->imax[i];
2153:   }

2155:   return(0);
2156: }
2157: EXTERN_C_END

2161: /*@
2162:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2163:        in the matrix.

2165:   Input Parameters:
2166: +  mat - the SeqAIJ matrix
2167: -  indices - the column indices

2169:   Level: advanced

2171:   Notes:
2172:     This can be called if you have precomputed the nonzero structure of the 
2173:   matrix and want to provide it to the matrix object to improve the performance
2174:   of the MatSetValues() operation.

2176:     You MUST have set the correct numbers of nonzeros per row in the call to 
2177:   MatCreateSeqAIJ().

2179:     MUST be called before any calls to MatSetValues();

2181:     The indices should start with zero, not one.

2183: @*/
2184: int MatSeqAIJSetColumnIndices(Mat mat,int *indices)
2185: {
2186:   int ierr,(*f)(Mat,int *);

2190:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2191:   if (f) {
2192:     (*f)(mat,indices);
2193:   } else {
2194:     SETERRQ(1,"Wrong type of matrix to set column indices");
2195:   }
2196:   return(0);
2197: }

2199: /* ----------------------------------------------------------------------------------------*/

2201: EXTERN_C_BEGIN
2204: int MatStoreValues_SeqAIJ(Mat mat)
2205: {
2206:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2207:   size_t       nz = aij->i[mat->m],ierr;

2210:   if (aij->nonew != 1) {
2211:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2212:   }

2214:   /* allocate space for values if not already there */
2215:   if (!aij->saved_values) {
2216:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2217:   }

2219:   /* copy values over */
2220:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2221:   return(0);
2222: }
2223: EXTERN_C_END

2227: /*@
2228:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2229:        example, reuse of the linear part of a Jacobian, while recomputing the 
2230:        nonlinear portion.

2232:    Collect on Mat

2234:   Input Parameters:
2235: .  mat - the matrix (currently on AIJ matrices support this option)

2237:   Level: advanced

2239:   Common Usage, with SNESSolve():
2240: $    Create Jacobian matrix
2241: $    Set linear terms into matrix
2242: $    Apply boundary conditions to matrix, at this time matrix must have 
2243: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2244: $      boundary conditions again will not change the nonzero structure
2245: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2246: $    MatStoreValues(mat);
2247: $    Call SNESSetJacobian() with matrix
2248: $    In your Jacobian routine
2249: $      MatRetrieveValues(mat);
2250: $      Set nonlinear terms in matrix
2251:  
2252:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2253: $    // build linear portion of Jacobian 
2254: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2255: $    MatStoreValues(mat);
2256: $    loop over nonlinear iterations
2257: $       MatRetrieveValues(mat);
2258: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2259: $       // call MatAssemblyBegin/End() on matrix
2260: $       Solve linear system with Jacobian
2261: $    endloop 

2263:   Notes:
2264:     Matrix must already be assemblied before calling this routine
2265:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 
2266:     calling this routine.

2268: .seealso: MatRetrieveValues()

2270: @*/
2271: int MatStoreValues(Mat mat)
2272: {
2273:   int ierr,(*f)(Mat);

2277:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2278:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2280:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2281:   if (f) {
2282:     (*f)(mat);
2283:   } else {
2284:     SETERRQ(1,"Wrong type of matrix to store values");
2285:   }
2286:   return(0);
2287: }

2289: EXTERN_C_BEGIN
2292: int MatRetrieveValues_SeqAIJ(Mat mat)
2293: {
2294:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2295:   int        nz = aij->i[mat->m],ierr;

2298:   if (aij->nonew != 1) {
2299:     SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2300:   }
2301:   if (!aij->saved_values) {
2302:     SETERRQ(1,"Must call MatStoreValues(A);first");
2303:   }

2305:   /* copy values over */
2306:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2307:   return(0);
2308: }
2309: EXTERN_C_END

2313: /*@
2314:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2315:        example, reuse of the linear part of a Jacobian, while recomputing the 
2316:        nonlinear portion.

2318:    Collect on Mat

2320:   Input Parameters:
2321: .  mat - the matrix (currently on AIJ matrices support this option)

2323:   Level: advanced

2325: .seealso: MatStoreValues()

2327: @*/
2328: int MatRetrieveValues(Mat mat)
2329: {
2330:   int ierr,(*f)(Mat);

2334:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2335:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2337:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2338:   if (f) {
2339:     (*f)(mat);
2340:   } else {
2341:     SETERRQ(1,"Wrong type of matrix to retrieve values");
2342:   }
2343:   return(0);
2344: }

2346: /*
2347:    This allows SeqAIJ matrices to be passed to the matlab engine
2348: */
2349: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2350: #include "engine.h"   /* Matlab include file */
2351: #include "mex.h"      /* Matlab include file */
2352: EXTERN_C_BEGIN
2355: int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *mengine)
2356: {
2357:   int         ierr;
2358:   Mat         B = (Mat)obj;
2359:   mxArray     *mat;
2360:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ*)B->data;

2363:   mat  = mxCreateSparse(B->n,B->m,aij->nz,mxREAL);
2364:   PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));
2365:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2366:   PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));
2367:   PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));

2369:   /* Matlab indices start at 0 for sparse (what a surprise) */
2370: 
2371:   PetscObjectName(obj);
2372:   mxSetName(mat,obj->name);
2373:   engPutArray((Engine *)mengine,mat);
2374:   return(0);
2375: }
2376: EXTERN_C_END

2378: EXTERN_C_BEGIN
2381: int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *mengine)
2382: {
2383:   int        ierr,ii;
2384:   Mat        mat = (Mat)obj;
2385:   Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2386:   mxArray    *mmat;

2389:   PetscFree(aij->a);

2391:   mmat = engGetArray((Engine *)mengine,obj->name);

2393:   aij->nz           = (mxGetJc(mmat))[mat->m];
2394:   PetscMalloc(((size_t) aij->nz)*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);
2395:   aij->j            = (int*)(aij->a + aij->nz);
2396:   aij->i            = aij->j + aij->nz;
2397:   aij->singlemalloc = PETSC_TRUE;
2398:   aij->freedata     = PETSC_TRUE;

2400:   PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));
2401:   /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2402:   PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));
2403:   PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));

2405:   for (ii=0; ii<mat->m; ii++) {
2406:     aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii];
2407:   }

2409:   MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
2410:   MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);

2412:   return(0);
2413: }
2414: EXTERN_C_END
2415: #endif

2417: /* --------------------------------------------------------------------------------*/
2420: /*@C
2421:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2422:    (the default parallel PETSc format).  For good matrix assembly performance
2423:    the user should preallocate the matrix storage by setting the parameter nz
2424:    (or the array nnz).  By setting these parameters accurately, performance
2425:    during matrix assembly can be increased by more than a factor of 50.

2427:    Collective on MPI_Comm

2429:    Input Parameters:
2430: +  comm - MPI communicator, set to PETSC_COMM_SELF
2431: .  m - number of rows
2432: .  n - number of columns
2433: .  nz - number of nonzeros per row (same for all rows)
2434: -  nnz - array containing the number of nonzeros in the various rows 
2435:          (possibly different for each row) or PETSC_NULL

2437:    Output Parameter:
2438: .  A - the matrix 

2440:    Notes:
2441:    The AIJ format (also called the Yale sparse matrix format or
2442:    compressed row storage), is fully compatible with standard Fortran 77
2443:    storage.  That is, the stored row and column indices can begin at
2444:    either one (as in Fortran) or zero.  See the users' manual for details.

2446:    Specify the preallocated storage with either nz or nnz (not both).
2447:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2448:    allocation.  For large problems you MUST preallocate memory or you 
2449:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2451:    By default, this format uses inodes (identical nodes) when possible, to 
2452:    improve numerical efficiency of matrix-vector products and solves. We 
2453:    search for consecutive rows with the same nonzero structure, thereby
2454:    reusing matrix information to achieve increased efficiency.

2456:    Options Database Keys:
2457: +  -mat_aij_no_inode  - Do not use inodes
2458: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2459: -  -mat_aij_oneindex - Internally use indexing starting at 1
2460:         rather than 0.  Note that when calling MatSetValues(),
2461:         the user still MUST index entries starting at 0!

2463:    Level: intermediate

2465: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2467: @*/
2468: int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,const int nnz[],Mat *A)
2469: {

2473:   MatCreate(comm,m,n,m,n,A);
2474:   MatSetType(*A,MATSEQAIJ);
2475:   MatSeqAIJSetPreallocation(*A,nz,nnz);
2476:   return(0);
2477: }

2479: #define SKIP_ALLOCATION -4

2483: /*@C
2484:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2485:    the user should preallocate the matrix storage by setting the parameter nz
2486:    (or the array nnz).  By setting these parameters accurately, performance
2487:    during matrix assembly can be increased by more than a factor of 50.

2489:    Collective on MPI_Comm

2491:    Input Parameters:
2492: +  comm - MPI communicator, set to PETSC_COMM_SELF
2493: .  m - number of rows
2494: .  n - number of columns
2495: .  nz - number of nonzeros per row (same for all rows)
2496: -  nnz - array containing the number of nonzeros in the various rows 
2497:          (possibly different for each row) or PETSC_NULL

2499:    Output Parameter:
2500: .  A - the matrix 

2502:    Notes:
2503:    The AIJ format (also called the Yale sparse matrix format or
2504:    compressed row storage), is fully compatible with standard Fortran 77
2505:    storage.  That is, the stored row and column indices can begin at
2506:    either one (as in Fortran) or zero.  See the users' manual for details.

2508:    Specify the preallocated storage with either nz or nnz (not both).
2509:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2510:    allocation.  For large problems you MUST preallocate memory or you 
2511:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2513:    By default, this format uses inodes (identical nodes) when possible, to 
2514:    improve numerical efficiency of matrix-vector products and solves. We 
2515:    search for consecutive rows with the same nonzero structure, thereby
2516:    reusing matrix information to achieve increased efficiency.

2518:    Options Database Keys:
2519: +  -mat_aij_no_inode  - Do not use inodes
2520: .  -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2521: -  -mat_aij_oneindex - Internally use indexing starting at 1
2522:         rather than 0.  Note that when calling MatSetValues(),
2523:         the user still MUST index entries starting at 0!

2525:    Level: intermediate

2527: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2529: @*/
2530: int MatSeqAIJSetPreallocation(Mat B,int nz,const int nnz[])
2531: {
2532:   int ierr,(*f)(Mat,int,const int[]);

2535:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
2536:   if (f) {
2537:     (*f)(B,nz,nnz);
2538:   }
2539:   return(0);
2540: }

2542: EXTERN_C_BEGIN
2545: int MatSeqAIJSetPreallocation_SeqAIJ(Mat B,int nz,int *nnz)
2546: {
2547:   Mat_SeqAIJ *b;
2548:   size_t     len = 0;
2549:   PetscTruth skipallocation = PETSC_FALSE;
2550:   int        i,ierr;

2553: 
2554:   if (nz == SKIP_ALLOCATION) {
2555:     skipallocation = PETSC_TRUE;
2556:     nz             = 0;
2557:   }

2559:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2560:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2561:   if (nnz) {
2562:     for (i=0; i<B->m; i++) {
2563:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2564:       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);
2565:     }
2566:   }

2568:   B->preallocated = PETSC_TRUE;
2569:   b = (Mat_SeqAIJ*)B->data;

2571:   PetscMalloc((B->m+1)*sizeof(int),&b->imax);
2572:   if (!nnz) {
2573:     if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2574:     else if (nz <= 0)        nz = 1;
2575:     for (i=0; i<B->m; i++) b->imax[i] = nz;
2576:     nz = nz*B->m;
2577:   } else {
2578:     nz = 0;
2579:     for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2580:   }

2582:   if (!skipallocation) {
2583:     /* allocate the matrix space */
2584:     len             = ((size_t) nz)*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int);
2585:     PetscMalloc(len,&b->a);
2586:     b->j            = (int*)(b->a + nz);
2587:     PetscMemzero(b->j,nz*sizeof(int));
2588:     b->i            = b->j + nz;
2589:     b->i[0] = 0;
2590:     for (i=1; i<B->m+1; i++) {
2591:       b->i[i] = b->i[i-1] + b->imax[i-1];
2592:     }
2593:     b->singlemalloc = PETSC_TRUE;
2594:     b->freedata     = PETSC_TRUE;
2595:   } else {
2596:     b->freedata     = PETSC_FALSE;
2597:   }

2599:   /* b->ilen will count nonzeros in each row so far. */
2600:   PetscMalloc((B->m+1)*sizeof(int),&b->ilen);
2601:   PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2602:   for (i=0; i<B->m; i++) { b->ilen[i] = 0;}

2604:   b->nz                = 0;
2605:   b->maxnz             = nz;
2606:   B->info.nz_unneeded  = (double)b->maxnz;
2607:   return(0);
2608: }
2609: EXTERN_C_END

2611: /*MC
2612:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
2613:    based on compressed sparse row format.

2615:    Options Database Keys:
2616: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

2618:   Level: beginner

2620: .seealso: MatCreateSeqAIJ
2621: M*/

2623: EXTERN_C_BEGIN
2626: int MatCreate_SeqAIJ(Mat B)
2627: {
2628:   Mat_SeqAIJ *b;
2629:   int        ierr,size;
2630:   PetscTruth flg;

2633:   MPI_Comm_size(B->comm,&size);
2634:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

2636:   B->m = B->M = PetscMax(B->m,B->M);
2637:   B->n = B->N = PetscMax(B->n,B->N);

2639:   PetscNew(Mat_SeqAIJ,&b);
2640:   B->data             = (void*)b;
2641:   PetscMemzero(b,sizeof(Mat_SeqAIJ));
2642:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2643:   B->factor           = 0;
2644:   B->lupivotthreshold = 1.0;
2645:   B->mapping          = 0;
2646:   PetscOptionsGetReal(B->prefix,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);
2647:   PetscOptionsHasName(B->prefix,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);
2648:   b->row              = 0;
2649:   b->col              = 0;
2650:   b->icol             = 0;
2651:   b->reallocs         = 0;
2652: 
2653:   PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
2654:   PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);

2656:   b->sorted            = PETSC_FALSE;
2657:   b->ignorezeroentries = PETSC_FALSE;
2658:   b->roworiented       = PETSC_TRUE;
2659:   b->nonew             = 0;
2660:   b->diag              = 0;
2661:   b->solve_work        = 0;
2662:   B->spptr             = 0;
2663:   b->inode.use         = PETSC_TRUE;
2664:   b->inode.node_count  = 0;
2665:   b->inode.size        = 0;
2666:   b->inode.limit       = 5;
2667:   b->inode.max_limit   = 5;
2668:   b->saved_values      = 0;
2669:   b->idiag             = 0;
2670:   b->ssor              = 0;
2671:   b->keepzeroedrows    = PETSC_FALSE;
2672:   b->xtoy              = 0;
2673:   b->XtoY              = 0;

2675:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

2677:   PetscOptionsHasName(B->prefix,"-mat_aij_matlab",&flg);
2678:   if (flg) {MatUseMatlab_SeqAIJ(B);}
2679:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2680:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
2681:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
2682:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2683:                                      "MatStoreValues_SeqAIJ",
2684:                                      MatStoreValues_SeqAIJ);
2685:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2686:                                      "MatRetrieveValues_SeqAIJ",
2687:                                      MatRetrieveValues_SeqAIJ);
2688:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
2689:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
2690:                                       MatConvert_SeqAIJ_SeqSBAIJ);
2691:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
2692:                                      "MatConvert_SeqAIJ_SeqBAIJ",
2693:                                       MatConvert_SeqAIJ_SeqBAIJ);
2694:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsSymmetric_C",
2695:                                      "MatIsSymmetric_SeqAIJ",
2696:                                       MatIsSymmetric_SeqAIJ);
2697:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
2698:                                      "MatSeqAIJSetPreallocation_SeqAIJ",
2699:                                       MatSeqAIJSetPreallocation_SeqAIJ);
2700:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
2701:                                      "MatReorderForNonzeroDiagonal_SeqAIJ",
2702:                                       MatReorderForNonzeroDiagonal_SeqAIJ);
2703:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatAdjustForInodes_C",
2704:                                      "MatAdjustForInodes_SeqAIJ",
2705:                                       MatAdjustForInodes_SeqAIJ);
2706:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJGetInodeSizes_C",
2707:                                      "MatSeqAIJGetInodeSizes_SeqAIJ",
2708:                                       MatSeqAIJGetInodeSizes_SeqAIJ);
2709: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2710:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);
2711:   PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);
2712: #endif
2713:   RegisterApplyPtAPRoutines_Private(B);
2714:   return(0);
2715: }
2716: EXTERN_C_END

2720: int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2721: {
2722:   Mat        C;
2723:   Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
2724:   int        i,m = A->m,ierr;
2725:   size_t     len;

2728:   *B = 0;
2729:   MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);
2730:   MatSetType(C,MATSEQAIJ);
2731:   c    = (Mat_SeqAIJ*)C->data;

2733:   C->factor         = A->factor;
2734:   c->row            = 0;
2735:   c->col            = 0;
2736:   c->icol           = 0;
2737:   c->keepzeroedrows = a->keepzeroedrows;
2738:   C->assembled      = PETSC_TRUE;

2740:   C->M          = A->m;
2741:   C->N          = A->n;

2743:   PetscMalloc((m+1)*sizeof(int),&c->imax);
2744:   PetscMalloc((m+1)*sizeof(int),&c->ilen);
2745:   for (i=0; i<m; i++) {
2746:     c->imax[i] = a->imax[i];
2747:     c->ilen[i] = a->ilen[i];
2748:   }

2750:   /* allocate the matrix space */
2751:   c->singlemalloc = PETSC_TRUE;
2752:   len   = ((size_t) (m+1))*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int));
2753:   PetscMalloc(len,&c->a);
2754:   c->j  = (int*)(c->a + a->i[m] );
2755:   c->i  = c->j + a->i[m];
2756:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));
2757:   if (m > 0) {
2758:     PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(int));
2759:     if (cpvalues == MAT_COPY_VALUES) {
2760:       PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
2761:     } else {
2762:       PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
2763:     }
2764:   }

2766:   PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2767:   c->sorted      = a->sorted;
2768:   c->roworiented = a->roworiented;
2769:   c->nonew       = a->nonew;
2770:   c->ilu_preserve_row_sums = a->ilu_preserve_row_sums;
2771:   c->saved_values = 0;
2772:   c->idiag        = 0;
2773:   c->ssor         = 0;
2774:   c->ignorezeroentries = a->ignorezeroentries;
2775:   c->freedata     = PETSC_TRUE;

2777:   if (a->diag) {
2778:     PetscMalloc((m+1)*sizeof(int),&c->diag);
2779:     PetscLogObjectMemory(C,(m+1)*sizeof(int));
2780:     for (i=0; i<m; i++) {
2781:       c->diag[i] = a->diag[i];
2782:     }
2783:   } else c->diag        = 0;
2784:   c->inode.use          = a->inode.use;
2785:   c->inode.limit        = a->inode.limit;
2786:   c->inode.max_limit    = a->inode.max_limit;
2787:   if (a->inode.size){
2788:     PetscMalloc((m+1)*sizeof(int),&c->inode.size);
2789:     c->inode.node_count = a->inode.node_count;
2790:     PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));
2791:   } else {
2792:     c->inode.size       = 0;
2793:     c->inode.node_count = 0;
2794:   }
2795:   c->nz                 = a->nz;
2796:   c->maxnz              = a->maxnz;
2797:   c->solve_work         = 0;
2798:   C->spptr              = 0;      /* Dangerous -I'm throwing away a->spptr */
2799:   C->preallocated       = PETSC_TRUE;

2801:   *B = C;
2802:   PetscFListDuplicate(A->qlist,&C->qlist);
2803:   return(0);
2804: }

2808: int MatLoad_SeqAIJ(PetscViewer viewer,MatType type,Mat *A)
2809: {
2810:   Mat_SeqAIJ   *a;
2811:   Mat          B;
2812:   int          i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N;
2813:   MPI_Comm     comm;
2814: 
2816:   PetscObjectGetComm((PetscObject)viewer,&comm);
2817:   MPI_Comm_size(comm,&size);
2818:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
2819:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2820:   PetscBinaryRead(fd,header,4,PETSC_INT);
2821:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
2822:   M = header[1]; N = header[2]; nz = header[3];

2824:   if (nz < 0) {
2825:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
2826:   }

2828:   /* read in row lengths */
2829:   PetscMalloc(M*sizeof(int),&rowlengths);
2830:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

2832:   /* create our matrix */
2833:   MatCreate(comm,PETSC_DECIDE,PETSC_DECIDE,M,N,&B);
2834:   MatSetType(B,type);
2835:   MatSeqAIJSetPreallocation(B,0,rowlengths);
2836:   a = (Mat_SeqAIJ*)B->data;

2838:   /* read in column indices and adjust for Fortran indexing*/
2839:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

2841:   /* read in nonzero values */
2842:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

2844:   /* set matrix "i" values */
2845:   a->i[0] = 0;
2846:   for (i=1; i<= M; i++) {
2847:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
2848:     a->ilen[i-1] = rowlengths[i-1];
2849:   }
2850:   PetscFree(rowlengths);

2852:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2853:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2854:   *A = B;
2855:   return(0);
2856: }

2860: int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
2861: {
2862:   Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
2863:   int        ierr;

2866:   /* If the  matrix dimensions are not equal,or no of nonzeros */
2867:   if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) {
2868:     *flg = PETSC_FALSE;
2869:     return(0);
2870:   }
2871: 
2872:   /* if the a->i are the same */
2873:   PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);
2874:   if (*flg == PETSC_FALSE) return(0);
2875: 
2876:   /* if a->j are the same */
2877:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);
2878:   if (*flg == PETSC_FALSE) return(0);
2879: 
2880:   /* if a->a are the same */
2881:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

2883:   return(0);
2884: 
2885: }

2889: /*@C
2890:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
2891:               provided by the user.

2893:       Coolective on MPI_Comm

2895:    Input Parameters:
2896: +   comm - must be an MPI communicator of size 1
2897: .   m - number of rows
2898: .   n - number of columns
2899: .   i - row indices
2900: .   j - column indices
2901: -   a - matrix values

2903:    Output Parameter:
2904: .   mat - the matrix

2906:    Level: intermediate

2908:    Notes:
2909:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2910:     once the matrix is destroyed

2912:        You cannot set new nonzero locations into this matrix, that will generate an error.

2914:        The i and j indices are 0 based

2916: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()

2918: @*/
2919: int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat)
2920: {
2921:   int        ierr,ii;
2922:   Mat_SeqAIJ *aij;

2925:   MatCreateSeqAIJ(comm,m,n,SKIP_ALLOCATION,0,mat);
2926:   aij  = (Mat_SeqAIJ*)(*mat)->data;

2928:   if (i[0] != 0) {
2929:     SETERRQ(1,"i (row indices) must start with 0");
2930:   }
2931:   aij->i = i;
2932:   aij->j = j;
2933:   aij->a = a;
2934:   aij->singlemalloc = PETSC_FALSE;
2935:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2936:   aij->freedata     = PETSC_FALSE;

2938:   for (ii=0; ii<m; ii++) {
2939:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
2940: #if defined(PETSC_USE_BOPT_g)
2941:     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]);
2942: #endif    
2943:   }
2944: #if defined(PETSC_USE_BOPT_g)
2945:   for (ii=0; ii<aij->i[m]; ii++) {
2946:     if (j[ii] < 0) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]);
2947:     if (j[ii] > n - 1) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]);
2948:   }
2949: #endif    

2951:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2952:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2953:   return(0);
2954: }

2958: int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
2959: {
2960:   int        ierr;
2961:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

2964:   if (coloring->ctype == IS_COLORING_LOCAL) {
2965:     ISColoringReference(coloring);
2966:     a->coloring = coloring;
2967:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
2968:     int             i,*larray;
2969:     ISColoring      ocoloring;
2970:     ISColoringValue *colors;

2972:     /* set coloring for diagonal portion */
2973:     PetscMalloc((A->n+1)*sizeof(int),&larray);
2974:     for (i=0; i<A->n; i++) {
2975:       larray[i] = i;
2976:     }
2977:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);
2978:     PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);
2979:     for (i=0; i<A->n; i++) {
2980:       colors[i] = coloring->colors[larray[i]];
2981:     }
2982:     PetscFree(larray);
2983:     ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);
2984:     a->coloring = ocoloring;
2985:   }
2986:   return(0);
2987: }

2989: #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2990: EXTERN_C_BEGIN
2991: #include "adic/ad_utils.h"
2992: EXTERN_C_END

2996: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2997: {
2998:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
2999:   int             m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen;
3000:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
3001:   ISColoringValue *color;

3004:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
3005:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
3006:   color = a->coloring->colors;
3007:   /* loop over rows */
3008:   for (i=0; i<m; i++) {
3009:     nz = ii[i+1] - ii[i];
3010:     /* loop over columns putting computed value into matrix */
3011:     for (j=0; j<nz; j++) {
3012:       *v++ = values[color[*jj++]];
3013:     }
3014:     values += nlen; /* jump to next row of derivatives */
3015:   }
3016:   return(0);
3017: }

3019: #else

3023: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3024: {
3026:   SETERRQ(1,"PETSc installed without ADIC");
3027: }

3029: #endif

3033: int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues)
3034: {
3035:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3036:   int             m = A->m,*ii = a->i,*jj = a->j,nz,i,j;
3037:   PetscScalar     *v = a->a,*values = (PetscScalar *)advalues;
3038:   ISColoringValue *color;

3041:   if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
3042:   color = a->coloring->colors;
3043:   /* loop over rows */
3044:   for (i=0; i<m; i++) {
3045:     nz = ii[i+1] - ii[i];
3046:     /* loop over columns putting computed value into matrix */
3047:     for (j=0; j<nz; j++) {
3048:       *v++ = values[color[*jj++]];
3049:     }
3050:     values += nl; /* jump to next row of derivatives */
3051:   }
3052:   return(0);
3053: }