Actual source code: aij.c

  1: #define PETSCMAT_DLL

  3: /*
  4:     Defines the basic matrix operations for the AIJ (compressed row)
  5:   matrix storage format.
  6: */

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

 15: PetscErrorCode  MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
 16: {
 18:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ*) Y->data;
 19:   PetscInt       i,*diag, m = Y->rmap.n;
 20:   PetscScalar    *v,*aa = aij->a;
 21:   PetscTruth     missing;

 24:   if (Y->assembled) {
 25:     MatMissingDiagonal_SeqAIJ(Y,&missing,PETSC_NULL);
 26:     if (!missing) {
 27:       diag = aij->diag;
 28:       VecGetArray(D,&v);
 29:       if (is == INSERT_VALUES) {
 30:         for (i=0; i<m; i++) {
 31:           aa[diag[i]] = v[i];
 32:         }
 33:       } else {
 34:         for (i=0; i<m; i++) {
 35:           aa[diag[i]] += v[i];
 36:         }
 37:       }
 38:       VecRestoreArray(D,&v);
 39:       return(0);
 40:     }
 41:   }
 42:   MatDiagonalSet_Default(Y,D,is);
 43:   return(0);
 44: }

 48: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 49: {
 50:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 52:   PetscInt       i,ishift;
 53: 
 55:   *m     = A->rmap.n;
 56:   if (!ia) return(0);
 57:   ishift = 0;
 58:   if (symmetric && !A->structurally_symmetric) {
 59:     MatToSymmetricIJ_SeqAIJ(A->rmap.n,a->i,a->j,ishift,oshift,ia,ja);
 60:   } else if (oshift == 1) {
 61:     PetscInt nz = a->i[A->rmap.n];
 62:     /* malloc space and  add 1 to i and j indices */
 63:     PetscMalloc((A->rmap.n+1)*sizeof(PetscInt),ia);
 64:     for (i=0; i<A->rmap.n+1; i++) (*ia)[i] = a->i[i] + 1;
 65:     if (ja) {
 66:       PetscMalloc((nz+1)*sizeof(PetscInt),ja);
 67:       for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 68:     }
 69:   } else {
 70:     *ia = a->i;
 71:     if (ja) *ja = a->j;
 72:   }
 73:   return(0);
 74: }

 78: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 79: {
 81: 
 83:   if (!ia) return(0);
 84:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
 85:     PetscFree(*ia);
 86:     if (ja) {PetscFree(*ja);}
 87:   }
 88:   return(0);
 89: }

 93: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 94: {
 95:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 97:   PetscInt       i,*collengths,*cia,*cja,n = A->cmap.n,m = A->rmap.n;
 98:   PetscInt       nz = a->i[m],row,*jj,mr,col;
 99: 
101:   *nn = n;
102:   if (!ia) return(0);
103:   if (symmetric) {
104:     MatToSymmetricIJ_SeqAIJ(A->rmap.n,a->i,a->j,0,oshift,ia,ja);
105:   } else {
106:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
107:     PetscMemzero(collengths,n*sizeof(PetscInt));
108:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
109:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
110:     jj = a->j;
111:     for (i=0; i<nz; i++) {
112:       collengths[jj[i]]++;
113:     }
114:     cia[0] = oshift;
115:     for (i=0; i<n; i++) {
116:       cia[i+1] = cia[i] + collengths[i];
117:     }
118:     PetscMemzero(collengths,n*sizeof(PetscInt));
119:     jj   = a->j;
120:     for (row=0; row<m; row++) {
121:       mr = a->i[row+1] - a->i[row];
122:       for (i=0; i<mr; i++) {
123:         col = *jj++;
124:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
125:       }
126:     }
127:     PetscFree(collengths);
128:     *ia = cia; *ja = cja;
129:   }
130:   return(0);
131: }

135: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
136: {

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

142:   PetscFree(*ia);
143:   PetscFree(*ja);
144: 
145:   return(0);
146: }

150: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
151: {
152:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
153:   PetscInt       *ai = a->i;

157:   PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
158:   return(0);
159: }

161: #define CHUNKSIZE   15

165: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
166: {
167:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
168:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
169:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
171:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
172:   PetscScalar    *ap,value,*aa = a->a;
173:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
174:   PetscTruth     roworiented = a->roworiented;

177:   for (k=0; k<m; k++) { /* loop over added rows */
178:     row  = im[k];
179:     if (row < 0) continue;
180: #if defined(PETSC_USE_DEBUG)  
181:     if (row >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.n-1);
182: #endif
183:     rp   = aj + ai[row]; ap = aa + ai[row];
184:     rmax = imax[row]; nrow = ailen[row];
185:     low  = 0;
186:     high = nrow;
187:     for (l=0; l<n; l++) { /* loop over added columns */
188:       if (in[l] < 0) continue;
189: #if defined(PETSC_USE_DEBUG)  
190:       if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
191: #endif
192:       col = in[l];
193:       if (roworiented) {
194:         value = v[l + k*n];
195:       } else {
196:         value = v[k + l*m];
197:       }
198:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

200:       if (col <= lastcol) low = 0; else high = nrow;
201:       lastcol = col;
202:       while (high-low > 5) {
203:         t = (low+high)/2;
204:         if (rp[t] > col) high = t;
205:         else             low  = t;
206:       }
207:       for (i=low; i<high; i++) {
208:         if (rp[i] > col) break;
209:         if (rp[i] == col) {
210:           if (is == ADD_VALUES) ap[i] += value;
211:           else                  ap[i] = value;
212:           goto noinsert;
213:         }
214:       }
215:       if (value == 0.0 && ignorezeroentries) goto noinsert;
216:       if (nonew == 1) goto noinsert;
217:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
218:       MatSeqXAIJReallocateAIJ(A,A->rmap.n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
219:       N = nrow++ - 1; a->nz++; high++;
220:       /* shift up all the later entries in this row */
221:       for (ii=N; ii>=i; ii--) {
222:         rp[ii+1] = rp[ii];
223:         ap[ii+1] = ap[ii];
224:       }
225:       rp[i] = col;
226:       ap[i] = value;
227:       noinsert:;
228:       low = i + 1;
229:     }
230:     ailen[row] = nrow;
231:   }
232:   A->same_nonzero = PETSC_FALSE;
233:   return(0);
234: }


239: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
240: {
241:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
242:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
243:   PetscInt     *ai = a->i,*ailen = a->ilen;
244:   PetscScalar  *ap,*aa = a->a;

247:   for (k=0; k<m; k++) { /* loop over rows */
248:     row  = im[k];
249:     if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
250:     if (row >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.n-1);
251:     rp   = aj + ai[row]; ap = aa + ai[row];
252:     nrow = ailen[row];
253:     for (l=0; l<n; l++) { /* loop over columns */
254:       if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
255:       if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
256:       col = in[l] ;
257:       high = nrow; low = 0; /* assume unsorted */
258:       while (high-low > 5) {
259:         t = (low+high)/2;
260:         if (rp[t] > col) high = t;
261:         else             low  = t;
262:       }
263:       for (i=low; i<high; i++) {
264:         if (rp[i] > col) break;
265:         if (rp[i] == col) {
266:           *v++ = ap[i];
267:           goto finished;
268:         }
269:       }
270:       *v++ = 0.0;
271:       finished:;
272:     }
273:   }
274:   return(0);
275: }


280: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
281: {
282:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
284:   PetscInt       i,*col_lens;
285:   int            fd;

288:   PetscViewerBinaryGetDescriptor(viewer,&fd);
289:   PetscMalloc((4+A->rmap.n)*sizeof(PetscInt),&col_lens);
290:   col_lens[0] = MAT_FILE_COOKIE;
291:   col_lens[1] = A->rmap.n;
292:   col_lens[2] = A->cmap.n;
293:   col_lens[3] = a->nz;

295:   /* store lengths of each row and write (including header) to file */
296:   for (i=0; i<A->rmap.n; i++) {
297:     col_lens[4+i] = a->i[i+1] - a->i[i];
298:   }
299:   PetscBinaryWrite(fd,col_lens,4+A->rmap.n,PETSC_INT,PETSC_TRUE);
300:   PetscFree(col_lens);

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

305:   /* store nonzero values */
306:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
307:   return(0);
308: }

310: EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

314: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
315: {
316:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
317:   PetscErrorCode    ierr;
318:   PetscInt          i,j,m = A->rmap.n,shift=0;
319:   const char        *name;
320:   PetscViewerFormat format;

323:   PetscObjectGetName((PetscObject)A,&name);
324:   PetscViewerGetFormat(viewer,&format);
325:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
326:     PetscInt nofinalvalue = 0;
327:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap.n-!shift)) {
328:       nofinalvalue = 1;
329:     }
330:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
331:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap.n);
332:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
333:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
334:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

336:     for (i=0; i<m; i++) {
337:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
338: #if defined(PETSC_USE_COMPLEX)
339:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
340: #else
341:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
342: #endif
343:       }
344:     }
345:     if (nofinalvalue) {
346:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->cmap.n,0.0);
347:     }
348:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
349:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
350:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
351:      return(0);
352:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
353:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
354:     for (i=0; i<m; i++) {
355:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
356:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
357: #if defined(PETSC_USE_COMPLEX)
358:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
359:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
360:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
361:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
362:         } else if (PetscRealPart(a->a[j]) != 0.0) {
363:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
364:         }
365: #else
366:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);}
367: #endif
368:       }
369:       PetscViewerASCIIPrintf(viewer,"\n");
370:     }
371:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
372:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
373:     PetscInt nzd=0,fshift=1,*sptr;
374:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
375:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
376:     for (i=0; i<m; i++) {
377:       sptr[i] = nzd+1;
378:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
379:         if (a->j[j] >= i) {
380: #if defined(PETSC_USE_COMPLEX)
381:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
382: #else
383:           if (a->a[j] != 0.0) nzd++;
384: #endif
385:         }
386:       }
387:     }
388:     sptr[m] = nzd+1;
389:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
390:     for (i=0; i<m+1; i+=6) {
391:       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]);}
392:       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]);}
393:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
394:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
395:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
396:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
397:     }
398:     PetscViewerASCIIPrintf(viewer,"\n");
399:     PetscFree(sptr);
400:     for (i=0; i<m; i++) {
401:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
402:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
403:       }
404:       PetscViewerASCIIPrintf(viewer,"\n");
405:     }
406:     PetscViewerASCIIPrintf(viewer,"\n");
407:     for (i=0; i<m; i++) {
408:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
409:         if (a->j[j] >= i) {
410: #if defined(PETSC_USE_COMPLEX)
411:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
412:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
413:           }
414: #else
415:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
416: #endif
417:         }
418:       }
419:       PetscViewerASCIIPrintf(viewer,"\n");
420:     }
421:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
422:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
423:     PetscInt         cnt = 0,jcnt;
424:     PetscScalar value;

426:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
427:     for (i=0; i<m; i++) {
428:       jcnt = 0;
429:       for (j=0; j<A->cmap.n; j++) {
430:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
431:           value = a->a[cnt++];
432:           jcnt++;
433:         } else {
434:           value = 0.0;
435:         }
436: #if defined(PETSC_USE_COMPLEX)
437:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
438: #else
439:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
440: #endif
441:       }
442:       PetscViewerASCIIPrintf(viewer,"\n");
443:     }
444:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
445:   } else {
446:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
447:     for (i=0; i<m; i++) {
448:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
449:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
450: #if defined(PETSC_USE_COMPLEX)
451:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
452:           PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
453:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
454:           PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
455:         } else {
456:           PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
457:         }
458: #else
459:         PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
460: #endif
461:       }
462:       PetscViewerASCIIPrintf(viewer,"\n");
463:     }
464:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
465:   }
466:   PetscViewerFlush(viewer);
467:   return(0);
468: }

472: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
473: {
474:   Mat               A = (Mat) Aa;
475:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
476:   PetscErrorCode    ierr;
477:   PetscInt          i,j,m = A->rmap.n,color;
478:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
479:   PetscViewer       viewer;
480:   PetscViewerFormat format;

483:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
484:   PetscViewerGetFormat(viewer,&format);

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

489:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
490:     /* Blue for negative, Cyan for zero and  Red for positive */
491:     color = PETSC_DRAW_BLUE;
492:     for (i=0; i<m; i++) {
493:       y_l = m - i - 1.0; y_r = y_l + 1.0;
494:       for (j=a->i[i]; j<a->i[i+1]; j++) {
495:         x_l = a->j[j] ; x_r = x_l + 1.0;
496: #if defined(PETSC_USE_COMPLEX)
497:         if (PetscRealPart(a->a[j]) >=  0.) continue;
498: #else
499:         if (a->a[j] >=  0.) continue;
500: #endif
501:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
502:       }
503:     }
504:     color = PETSC_DRAW_CYAN;
505:     for (i=0; i<m; i++) {
506:       y_l = m - i - 1.0; y_r = y_l + 1.0;
507:       for (j=a->i[i]; j<a->i[i+1]; j++) {
508:         x_l = a->j[j]; x_r = x_l + 1.0;
509:         if (a->a[j] !=  0.) continue;
510:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
511:       }
512:     }
513:     color = PETSC_DRAW_RED;
514:     for (i=0; i<m; i++) {
515:       y_l = m - i - 1.0; y_r = y_l + 1.0;
516:       for (j=a->i[i]; j<a->i[i+1]; j++) {
517:         x_l = a->j[j]; x_r = x_l + 1.0;
518: #if defined(PETSC_USE_COMPLEX)
519:         if (PetscRealPart(a->a[j]) <=  0.) continue;
520: #else
521:         if (a->a[j] <=  0.) continue;
522: #endif
523:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
524:       }
525:     }
526:   } else {
527:     /* use contour shading to indicate magnitude of values */
528:     /* first determine max of all nonzero values */
529:     PetscInt    nz = a->nz,count;
530:     PetscDraw   popup;
531:     PetscReal scale;

533:     for (i=0; i<nz; i++) {
534:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
535:     }
536:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
537:     PetscDrawGetPopup(draw,&popup);
538:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
539:     count = 0;
540:     for (i=0; i<m; i++) {
541:       y_l = m - i - 1.0; y_r = y_l + 1.0;
542:       for (j=a->i[i]; j<a->i[i+1]; j++) {
543:         x_l = a->j[j]; x_r = x_l + 1.0;
544:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
545:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
546:         count++;
547:       }
548:     }
549:   }
550:   return(0);
551: }

555: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
556: {
558:   PetscDraw      draw;
559:   PetscReal      xr,yr,xl,yl,h,w;
560:   PetscTruth     isnull;

563:   PetscViewerDrawGetDraw(viewer,0,&draw);
564:   PetscDrawIsNull(draw,&isnull);
565:   if (isnull) return(0);

567:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
568:   xr  = A->cmap.n; yr = A->rmap.n; h = yr/10.0; w = xr/10.0;
569:   xr += w;    yr += h;  xl = -w;     yl = -h;
570:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
571:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
572:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
573:   return(0);
574: }

578: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
579: {
581:   PetscTruth     iascii,isbinary,isdraw;

584:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
585:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
586:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
587:   if (iascii) {
588:     MatView_SeqAIJ_ASCII(A,viewer);
589:   } else if (isbinary) {
590:     MatView_SeqAIJ_Binary(A,viewer);
591:   } else if (isdraw) {
592:     MatView_SeqAIJ_Draw(A,viewer);
593:   } else {
594:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
595:   }
596:   MatView_Inode(A,viewer);
597:   return(0);
598: }

602: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
603: {
604:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
606:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
607:   PetscInt       m = A->rmap.n,*ip,N,*ailen = a->ilen,rmax = 0;
608:   PetscScalar    *aa = a->a,*ap;
609:   PetscReal      ratio=0.6;

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

614:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
615:   for (i=1; i<m; i++) {
616:     /* move each row back by the amount of empty slots (fshift) before it*/
617:     fshift += imax[i-1] - ailen[i-1];
618:     rmax   = PetscMax(rmax,ailen[i]);
619:     if (fshift) {
620:       ip = aj + ai[i] ;
621:       ap = aa + ai[i] ;
622:       N  = ailen[i];
623:       for (j=0; j<N; j++) {
624:         ip[j-fshift] = ip[j];
625:         ap[j-fshift] = ap[j];
626:       }
627:     }
628:     ai[i] = ai[i-1] + ailen[i-1];
629:   }
630:   if (m) {
631:     fshift += imax[m-1] - ailen[m-1];
632:     ai[m]  = ai[m-1] + ailen[m-1];
633:   }
634:   /* reset ilen and imax for each row */
635:   for (i=0; i<m; i++) {
636:     ailen[i] = imax[i] = ai[i+1] - ai[i];
637:   }
638:   a->nz = ai[m];

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

645:   a->reallocs          = 0;
646:   A->info.nz_unneeded  = (double)fshift;
647:   a->rmax              = rmax;

649:   /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
650:   Mat_CheckCompressedRow(A,&a->compressedrow,a->i,m,ratio);
651:   A->same_nonzero = PETSC_TRUE;

653:   MatAssemblyEnd_Inode(A,mode);

655:   a->idiagvalid = PETSC_FALSE;
656:   return(0);
657: }

661: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
662: {
663:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
664:   PetscInt       i,nz = a->nz;
665:   PetscScalar    *aa = a->a;

668:   for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
669:   return(0);
670: }

674: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
675: {
676:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
677:   PetscInt       i,nz = a->nz;
678:   PetscScalar    *aa = a->a;

681:   for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
682:   return(0);
683: }

687: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
688: {
689:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

693:   PetscMemzero(a->a,(a->i[A->rmap.n])*sizeof(PetscScalar));
694:   return(0);
695: }

699: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
700: {
701:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

705: #if defined(PETSC_USE_LOG)
706:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.n,A->cmap.n,a->nz);
707: #endif
708:   MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
709:   if (a->row) {
710:     ISDestroy(a->row);
711:   }
712:   if (a->col) {
713:     ISDestroy(a->col);
714:   }
715:   PetscFree(a->diag);
716:   PetscFree2(a->imax,a->ilen);
717:   PetscFree3(a->idiag,a->mdiag,a->ssor_work);
718:   PetscFree(a->solve_work);
719:   if (a->icol) {ISDestroy(a->icol);}
720:   PetscFree(a->saved_values);
721:   if (a->coloring) {ISColoringDestroy(a->coloring);}
722:   PetscFree(a->xtoy);
723:   if (a->XtoY) {MatDestroy(a->XtoY);}
724:   if (a->compressedrow.use){PetscFree(a->compressedrow.i);}

726:   MatDestroy_Inode(A);

728:   PetscFree(a);

730:   PetscObjectChangeTypeName((PetscObject)A,0);
731:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
732:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
733:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
734:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
735:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
736:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqcsrperm_C","",PETSC_NULL);
737:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
738:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
739:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C","",PETSC_NULL);
740:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
741:   return(0);
742: }

746: PetscErrorCode MatCompress_SeqAIJ(Mat A)
747: {
749:   return(0);
750: }

754: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscTruth flg)
755: {
756:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

760:   switch (op) {
761:     case MAT_ROW_ORIENTED:
762:       a->roworiented       = flg;
763:       break;
764:     case MAT_KEEP_ZEROED_ROWS:
765:       a->keepzeroedrows    = flg;
766:       break;
767:     case MAT_NEW_NONZERO_LOCATIONS:
768:       a->nonew             = (flg ? 0 : 1);
769:       break;
770:     case MAT_NEW_NONZERO_LOCATION_ERR:
771:       a->nonew             = (flg ? -1 : 0);
772:       break;
773:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
774:       a->nonew             = (flg ? -2 : 0);
775:       break;
776:     case MAT_IGNORE_ZERO_ENTRIES:
777:       a->ignorezeroentries = flg;
778:       break;
779:     case MAT_USE_COMPRESSEDROW:
780:       a->compressedrow.use = flg;
781:       break;
782:     case MAT_NEW_DIAGONALS:
783:     case MAT_IGNORE_OFF_PROC_ENTRIES:
784:     case MAT_USE_HASH_TABLE:
785:       PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
786:       break;
787:     default:
788:       break;
789:   }
790:   MatSetOption_Inode(A,op,flg);
791:   return(0);
792: }

796: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
797: {
798:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
800:   PetscInt       i,j,n;
801:   PetscScalar    *x,zero = 0.0;

804:   VecSet(v,zero);
805:   VecGetArray(v,&x);
806:   VecGetLocalSize(v,&n);
807:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
808:   for (i=0; i<A->rmap.n; i++) {
809:     for (j=a->i[i]; j<a->i[i+1]; j++) {
810:       if (a->j[j] == i) {
811:         x[i] = a->a[j];
812:         break;
813:       }
814:     }
815:   }
816:   VecRestoreArray(v,&x);
817:   return(0);
818: }

822: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
823: {
824:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
825:   PetscScalar       *x,*y;
826:   PetscErrorCode    ierr;
827:   PetscInt          m = A->rmap.n;
828: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
829:   PetscScalar       *v,alpha;
830:   PetscInt          n,i,*idx,*ii,*ridx=PETSC_NULL;
831:   Mat_CompressedRow cprow = a->compressedrow;
832:   PetscTruth        usecprow = cprow.use;
833: #endif

836:   if (zz != yy) {VecCopy(zz,yy);}
837:   VecGetArray(xx,&x);
838:   VecGetArray(yy,&y);

840: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
841:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
842: #else
843:   if (usecprow){
844:     m    = cprow.nrows;
845:     ii   = cprow.i;
846:     ridx = cprow.rindex;
847:   } else {
848:     ii = a->i;
849:   }
850:   for (i=0; i<m; i++) {
851:     idx   = a->j + ii[i] ;
852:     v     = a->a + ii[i] ;
853:     n     = ii[i+1] - ii[i];
854:     if (usecprow){
855:       alpha = x[ridx[i]];
856:     } else {
857:       alpha = x[i];
858:     }
859:     while (n-->0) {y[*idx++] += alpha * *v++;}
860:   }
861: #endif
862:   PetscLogFlops(2*a->nz);
863:   VecRestoreArray(xx,&x);
864:   VecRestoreArray(yy,&y);
865:   return(0);
866: }

870: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
871: {
872:   PetscScalar    zero = 0.0;

876:   VecSet(yy,zero);
877:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
878:   return(0);
879: }


884: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
885: {
886:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
887:   PetscScalar       *y;
888:   const PetscScalar *x,*aa;
889:   PetscErrorCode    ierr;
890:   PetscInt          m=A->rmap.n,*aj,*ii;
891:   PetscInt          n,i,j,nonzerorow=0,*ridx=PETSC_NULL;
892:   PetscScalar       sum;
893:   PetscTruth        usecprow=a->compressedrow.use;
894: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
895:   PetscInt         jrow;
896: #endif

898: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
899: #pragma disjoint(*x,*y,*aa)
900: #endif

903:   VecGetArray(xx,(PetscScalar**)&x);
904:   VecGetArray(yy,&y);
905:   aj  = a->j;
906:   aa  = a->a;
907:   ii  = a->i;
908:   if (usecprow){ /* use compressed row format */
909:     m    = a->compressedrow.nrows;
910:     ii   = a->compressedrow.i;
911:     ridx = a->compressedrow.rindex;
912:     for (i=0; i<m; i++){
913:       n   = ii[i+1] - ii[i];
914:       aj  = a->j + ii[i];
915:       aa  = a->a + ii[i];
916:       sum = 0.0;
917:       nonzerorow += (n>0);
918:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
919:       y[*ridx++] = sum;
920:     }
921:   } else { /* do not use compressed row format */
922: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
923:     fortranmultaij_(&m,x,ii,aj,aa,y);
924: #else
925:     for (i=0; i<m; i++) {
926:       jrow = ii[i];
927:       n    = ii[i+1] - jrow;
928:       sum  = 0.0;
929:       nonzerorow += (n>0);
930:       for (j=0; j<n; j++) {
931:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
932:       }
933:       y[i] = sum;
934:     }
935: #endif
936:   }
937:   PetscLogFlops(2*a->nz - nonzerorow);
938:   VecRestoreArray(xx,(PetscScalar**)&x);
939:   VecRestoreArray(yy,&y);
940:   return(0);
941: }

945: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
946: {
947:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
948:   PetscScalar    *x,*y,*z,*aa;
950:   PetscInt       m = A->rmap.n,*aj,*ii;
951: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
952:   PetscInt       n,i,jrow,j,*ridx=PETSC_NULL;
953:   PetscScalar    sum;
954:   PetscTruth     usecprow=a->compressedrow.use;
955: #endif

958:   VecGetArray(xx,&x);
959:   VecGetArray(yy,&y);
960:   if (zz != yy) {
961:     VecGetArray(zz,&z);
962:   } else {
963:     z = y;
964:   }

966:   aj  = a->j;
967:   aa  = a->a;
968:   ii  = a->i;
969: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
970:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
971: #else
972:   if (usecprow){ /* use compressed row format */
973:     if (zz != yy){
974:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
975:     }
976:     m    = a->compressedrow.nrows;
977:     ii   = a->compressedrow.i;
978:     ridx = a->compressedrow.rindex;
979:     for (i=0; i<m; i++){
980:       n  = ii[i+1] - ii[i];
981:       aj  = a->j + ii[i];
982:       aa  = a->a + ii[i];
983:       sum = y[*ridx];
984:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
985:       z[*ridx++] = sum;
986:     }
987:   } else { /* do not use compressed row format */
988:     for (i=0; i<m; i++) {
989:       jrow = ii[i];
990:       n    = ii[i+1] - jrow;
991:       sum  = y[i];
992:       for (j=0; j<n; j++) {
993:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
994:       }
995:       z[i] = sum;
996:     }
997:   }
998: #endif
999:   PetscLogFlops(2*a->nz);
1000:   VecRestoreArray(xx,&x);
1001:   VecRestoreArray(yy,&y);
1002:   if (zz != yy) {
1003:     VecRestoreArray(zz,&z);
1004:   }
1005:   return(0);
1006: }

1008: /*
1009:      Adds diagonal pointers to sparse matrix structure.
1010: */
1013: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1014: {
1015:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1017:   PetscInt       i,j,m = A->rmap.n;

1020:   if (!a->diag) {
1021:     PetscMalloc(m*sizeof(PetscInt),&a->diag);
1022:     PetscLogObjectMemory(A, m*sizeof(PetscInt));
1023:   }
1024:   for (i=0; i<A->rmap.n; i++) {
1025:     a->diag[i] = a->i[i+1];
1026:     for (j=a->i[i]; j<a->i[i+1]; j++) {
1027:       if (a->j[j] == i) {
1028:         a->diag[i] = j;
1029:         break;
1030:       }
1031:     }
1032:   }
1033:   return(0);
1034: }

1036: /*
1037:      Checks for missing diagonals
1038: */
1041: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscTruth *missing,PetscInt *d)
1042: {
1043:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1044:   PetscInt       *diag,*jj = a->j,i;

1047:   *missing = PETSC_FALSE;
1048:   if (A->rmap.n > 0 && !jj) {
1049:     *missing  = PETSC_TRUE;
1050:     if (d) *d = 0;
1051:     PetscInfo(A,"Matrix has no entries therefor is missing diagonal");
1052:   } else {
1053:     diag = a->diag;
1054:     for (i=0; i<A->rmap.n; i++) {
1055:       if (jj[diag[i]] != i) {
1056:         *missing = PETSC_TRUE;
1057:         if (d) *d = i;
1058:         PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1059:       }
1060:     }
1061:   }
1062:   return(0);
1063: }

1068: PetscErrorCode  MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1069: {
1070:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*) A->data;
1072:   PetscInt       i,*diag,m = A->rmap.n;
1073:   PetscScalar    *v = a->a,*idiag,*mdiag;

1076:   if (a->idiagvalid) return(0);
1077:   MatMarkDiagonal_SeqAIJ(A);
1078:   diag = a->diag;
1079:   if (!a->idiag) {
1080:     PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
1081:     PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1082:     v        = a->a;
1083:   }
1084:   mdiag = a->mdiag;
1085:   idiag = a->idiag;
1086: 
1087:   if (omega == 1.0 && !PetscAbsScalar(fshift)) {
1088:     for (i=0; i<m; i++) {
1089:       mdiag[i] = v[diag[i]];
1090:       if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1091:       idiag[i] = 1.0/v[diag[i]];
1092:     }
1093:     PetscLogFlops(m);
1094:   } else {
1095:     for (i=0; i<m; i++) {
1096:       mdiag[i] = v[diag[i]];
1097:       idiag[i] = omega/(fshift + v[diag[i]]);
1098:     }
1099:     PetscLogFlops(2*m);
1100:   }
1101:   a->idiagvalid = PETSC_TRUE;
1102:   return(0);
1103: }

1108: PetscErrorCode MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1109: {
1110:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
1111:   PetscScalar        *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag;
1112:   const PetscScalar  *v = a->a, *b, *bs,*xb, *ts;
1113:   PetscErrorCode     ierr;
1114:   PetscInt           n = A->cmap.n,m = A->rmap.n,i;
1115:   const PetscInt     *idx,*diag;

1118:   its = its*lits;

1120:   if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1121:   if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1122:   a->fshift = fshift;
1123:   a->omega  = omega;

1125:   diag = a->diag;
1126:   t     = a->ssor_work;
1127:   idiag = a->idiag;
1128:   mdiag = a->mdiag;

1130:   VecGetArray(xx,&x);
1131:   if (xx != bb) {
1132:     VecGetArray(bb,(PetscScalar**)&b);
1133:   } else {
1134:     b = x;
1135:   }
1136:   CHKMEMQ;
1137:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1138:   xs   = x;
1139:   if (flag == SOR_APPLY_UPPER) {
1140:    /* apply (U + D/omega) to the vector */
1141:     bs = b;
1142:     for (i=0; i<m; i++) {
1143:         d    = fshift + mdiag[i];
1144:         n    = a->i[i+1] - diag[i] - 1;
1145:         idx  = a->j + diag[i] + 1;
1146:         v    = a->a + diag[i] + 1;
1147:         sum  = b[i]*d/omega;
1148:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1149:         x[i] = sum;
1150:     }
1151:     VecRestoreArray(xx,&x);
1152:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1153:     PetscLogFlops(a->nz);
1154:     return(0);
1155:   }

1157:   if (flag == SOR_APPLY_LOWER) {
1158:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1159:   } else if (flag & SOR_EISENSTAT) {
1160:     /* Let  A = L + U + D; where L is lower trianglar,
1161:     U is upper triangular, E is diagonal; This routine applies

1163:             (L + E)^{-1} A (U + E)^{-1}

1165:     to a vector efficiently using Eisenstat's trick. This is for
1166:     the case of SSOR preconditioner, so E is D/omega where omega
1167:     is the relaxation factor.
1168:     */
1169:     scale = (2.0/omega) - 1.0;

1171:     /*  x = (E + U)^{-1} b */
1172:     for (i=m-1; i>=0; i--) {
1173:       n    = a->i[i+1] - diag[i] - 1;
1174:       idx  = a->j + diag[i] + 1;
1175:       v    = a->a + diag[i] + 1;
1176:       sum  = b[i];
1177:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1178:       x[i] = sum*idiag[i];
1179:     }

1181:     /*  t = b - (2*E - D)x */
1182:     v = a->a;
1183:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1185:     /*  t = (E + L)^{-1}t */
1186:     ts = t;
1187:     diag = a->diag;
1188:     for (i=0; i<m; i++) {
1189:       n    = diag[i] - a->i[i];
1190:       idx  = a->j + a->i[i];
1191:       v    = a->a + a->i[i];
1192:       sum  = t[i];
1193:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1194:       t[i] = sum*idiag[i];
1195:       /*  x = x + t */
1196:       x[i] += t[i];
1197:     }

1199:     PetscLogFlops(6*m-1 + 2*a->nz);
1200:     VecRestoreArray(xx,&x);
1201:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1202:     return(0);
1203:   }
1204:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1205:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1206: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1207:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)b);
1208: #else
1209:       for (i=0; i<m; i++) {
1210:         n    = diag[i] - a->i[i];
1211:         idx  = a->j + a->i[i];
1212:         v    = a->a + a->i[i];
1213:         sum  = b[i];
1214:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1215:         x[i] = sum*idiag[i];
1216:       }
1217: #endif
1218:       xb = x;
1219:       PetscLogFlops(a->nz);
1220:     } else xb = b;
1221:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1222:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1223:       for (i=0; i<m; i++) {
1224:         x[i] *= mdiag[i];
1225:       }
1226:       PetscLogFlops(m);
1227:     }
1228:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1229: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1230:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)xb);
1231: #else
1232:       for (i=m-1; i>=0; i--) {
1233:         n    = a->i[i+1] - diag[i] - 1;
1234:         idx  = a->j + diag[i] + 1;
1235:         v    = a->a + diag[i] + 1;
1236:         sum  = xb[i];
1237:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1238:         x[i] = sum*idiag[i];
1239:       }
1240: #endif
1241:       PetscLogFlops(a->nz);
1242:     }
1243:     its--;
1244:   }
1245:   while (its--) {
1246:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1247: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1248:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1249: #else
1250:       for (i=0; i<m; i++) {
1251:         n    = a->i[i+1] - a->i[i];
1252:         idx  = a->j + a->i[i];
1253:         v    = a->a + a->i[i];
1254:         sum  = b[i];
1255:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1256:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1257:       }
1258: #endif 
1259:       PetscLogFlops(a->nz);
1260:     }
1261:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1262: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1263:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1264: #else
1265:       for (i=m-1; i>=0; i--) {
1266:         n    = a->i[i+1] - a->i[i];
1267:         idx  = a->j + a->i[i];
1268:         v    = a->a + a->i[i];
1269:         sum  = b[i];
1270:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1271:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1272:       }
1273: #endif
1274:       PetscLogFlops(a->nz);
1275:     }
1276:   }
1277:   VecRestoreArray(xx,&x);
1278:   if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1279:   CHKMEMQ;  return(0);
1280: }


1285: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1286: {
1287:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1290:   info->rows_global    = (double)A->rmap.n;
1291:   info->columns_global = (double)A->cmap.n;
1292:   info->rows_local     = (double)A->rmap.n;
1293:   info->columns_local  = (double)A->cmap.n;
1294:   info->block_size     = 1.0;
1295:   info->nz_allocated   = (double)a->maxnz;
1296:   info->nz_used        = (double)a->nz;
1297:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1298:   info->assemblies     = (double)A->num_ass;
1299:   info->mallocs        = (double)a->reallocs;
1300:   info->memory         = ((PetscObject)A)->mem;
1301:   if (A->factor) {
1302:     info->fill_ratio_given  = A->info.fill_ratio_given;
1303:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1304:     info->factor_mallocs    = A->info.factor_mallocs;
1305:   } else {
1306:     info->fill_ratio_given  = 0;
1307:     info->fill_ratio_needed = 0;
1308:     info->factor_mallocs    = 0;
1309:   }
1310:   return(0);
1311: }

1315: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1316: {
1317:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1318:   PetscInt       i,m = A->rmap.n - 1,d;
1320:   PetscTruth     missing;

1323:   if (a->keepzeroedrows) {
1324:     for (i=0; i<N; i++) {
1325:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1326:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1327:     }
1328:     if (diag != 0.0) {
1329:       MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1330:       if (missing) SETERRQ1(PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1331:       for (i=0; i<N; i++) {
1332:         a->a[a->diag[rows[i]]] = diag;
1333:       }
1334:     }
1335:     A->same_nonzero = PETSC_TRUE;
1336:   } else {
1337:     if (diag != 0.0) {
1338:       for (i=0; i<N; i++) {
1339:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1340:         if (a->ilen[rows[i]] > 0) {
1341:           a->ilen[rows[i]]          = 1;
1342:           a->a[a->i[rows[i]]] = diag;
1343:           a->j[a->i[rows[i]]] = rows[i];
1344:         } else { /* in case row was completely empty */
1345:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1346:         }
1347:       }
1348:     } else {
1349:       for (i=0; i<N; i++) {
1350:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1351:         a->ilen[rows[i]] = 0;
1352:       }
1353:     }
1354:     A->same_nonzero = PETSC_FALSE;
1355:   }
1356:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1357:   return(0);
1358: }

1362: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1363: {
1364:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1365:   PetscInt   *itmp;

1368:   if (row < 0 || row >= A->rmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1370:   *nz = a->i[row+1] - a->i[row];
1371:   if (v) *v = a->a + a->i[row];
1372:   if (idx) {
1373:     itmp = a->j + a->i[row];
1374:     if (*nz) {
1375:       *idx = itmp;
1376:     }
1377:     else *idx = 0;
1378:   }
1379:   return(0);
1380: }

1382: /* remove this function? */
1385: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1386: {
1388:   return(0);
1389: }

1393: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1394: {
1395:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1396:   PetscScalar    *v = a->a;
1397:   PetscReal      sum = 0.0;
1399:   PetscInt       i,j;

1402:   if (type == NORM_FROBENIUS) {
1403:     for (i=0; i<a->nz; i++) {
1404: #if defined(PETSC_USE_COMPLEX)
1405:       sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1406: #else
1407:       sum += (*v)*(*v); v++;
1408: #endif
1409:     }
1410:     *nrm = sqrt(sum);
1411:   } else if (type == NORM_1) {
1412:     PetscReal *tmp;
1413:     PetscInt    *jj = a->j;
1414:     PetscMalloc((A->cmap.n+1)*sizeof(PetscReal),&tmp);
1415:     PetscMemzero(tmp,A->cmap.n*sizeof(PetscReal));
1416:     *nrm = 0.0;
1417:     for (j=0; j<a->nz; j++) {
1418:         tmp[*jj++] += PetscAbsScalar(*v);  v++;
1419:     }
1420:     for (j=0; j<A->cmap.n; j++) {
1421:       if (tmp[j] > *nrm) *nrm = tmp[j];
1422:     }
1423:     PetscFree(tmp);
1424:   } else if (type == NORM_INFINITY) {
1425:     *nrm = 0.0;
1426:     for (j=0; j<A->rmap.n; j++) {
1427:       v = a->a + a->i[j];
1428:       sum = 0.0;
1429:       for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1430:         sum += PetscAbsScalar(*v); v++;
1431:       }
1432:       if (sum > *nrm) *nrm = sum;
1433:     }
1434:   } else {
1435:     SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1436:   }
1437:   return(0);
1438: }

1442: PetscErrorCode MatTranspose_SeqAIJ(Mat A,Mat *B)
1443: {
1444:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1445:   Mat            C;
1447:   PetscInt       i,*aj = a->j,*ai = a->i,m = A->rmap.n,len,*col;
1448:   PetscScalar    *array = a->a;

1451:   if (!B && m != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1452:   PetscMalloc((1+A->cmap.n)*sizeof(PetscInt),&col);
1453:   PetscMemzero(col,(1+A->cmap.n)*sizeof(PetscInt));
1454: 
1455:   for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1456:   MatCreate(((PetscObject)A)->comm,&C);
1457:   MatSetSizes(C,A->cmap.n,m,A->cmap.n,m);
1458:   MatSetType(C,((PetscObject)A)->type_name);
1459:   MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1460:   PetscFree(col);
1461:   for (i=0; i<m; i++) {
1462:     len    = ai[i+1]-ai[i];
1463:     MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1464:     array += len;
1465:     aj    += len;
1466:   }

1468:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1469:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1471:   if (B) {
1472:     *B = C;
1473:   } else {
1474:     MatHeaderCopy(A,C);
1475:   }
1476:   return(0);
1477: }

1482: PetscErrorCode  MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1483: {
1484:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1485:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1487:   PetscInt       ma,na,mb,nb, i;

1490:   bij = (Mat_SeqAIJ *) B->data;
1491: 
1492:   MatGetSize(A,&ma,&na);
1493:   MatGetSize(B,&mb,&nb);
1494:   if (ma!=nb || na!=mb){
1495:     *f = PETSC_FALSE;
1496:     return(0);
1497:   }
1498:   aii = aij->i; bii = bij->i;
1499:   adx = aij->j; bdx = bij->j;
1500:   va  = aij->a; vb = bij->a;
1501:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1502:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1503:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1504:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1506:   *f = PETSC_TRUE;
1507:   for (i=0; i<ma; i++) {
1508:     while (aptr[i]<aii[i+1]) {
1509:       PetscInt         idc,idr;
1510:       PetscScalar vc,vr;
1511:       /* column/row index/value */
1512:       idc = adx[aptr[i]];
1513:       idr = bdx[bptr[idc]];
1514:       vc  = va[aptr[i]];
1515:       vr  = vb[bptr[idc]];
1516:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1517:         *f = PETSC_FALSE;
1518:         goto done;
1519:       } else {
1520:         aptr[i]++;
1521:         if (B || i!=idc) bptr[idc]++;
1522:       }
1523:     }
1524:   }
1525:  done:
1526:   PetscFree(aptr);
1527:   if (B) {
1528:     PetscFree(bptr);
1529:   }
1530:   return(0);
1531: }

1537: PetscErrorCode  MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1538: {
1539:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1540:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1542:   PetscInt       ma,na,mb,nb, i;

1545:   bij = (Mat_SeqAIJ *) B->data;
1546: 
1547:   MatGetSize(A,&ma,&na);
1548:   MatGetSize(B,&mb,&nb);
1549:   if (ma!=nb || na!=mb){
1550:     *f = PETSC_FALSE;
1551:     return(0);
1552:   }
1553:   aii = aij->i; bii = bij->i;
1554:   adx = aij->j; bdx = bij->j;
1555:   va  = aij->a; vb = bij->a;
1556:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1557:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1558:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1559:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1561:   *f = PETSC_TRUE;
1562:   for (i=0; i<ma; i++) {
1563:     while (aptr[i]<aii[i+1]) {
1564:       PetscInt         idc,idr;
1565:       PetscScalar vc,vr;
1566:       /* column/row index/value */
1567:       idc = adx[aptr[i]];
1568:       idr = bdx[bptr[idc]];
1569:       vc  = va[aptr[i]];
1570:       vr  = vb[bptr[idc]];
1571:       if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
1572:         *f = PETSC_FALSE;
1573:         goto done;
1574:       } else {
1575:         aptr[i]++;
1576:         if (B || i!=idc) bptr[idc]++;
1577:       }
1578:     }
1579:   }
1580:  done:
1581:   PetscFree(aptr);
1582:   if (B) {
1583:     PetscFree(bptr);
1584:   }
1585:   return(0);
1586: }

1591: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1592: {
1595:   MatIsTranspose_SeqAIJ(A,A,tol,f);
1596:   return(0);
1597: }

1601: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1602: {
1605:   MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
1606:   return(0);
1607: }

1611: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1612: {
1613:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1614:   PetscScalar    *l,*r,x,*v;
1616:   PetscInt       i,j,m = A->rmap.n,n = A->cmap.n,M,nz = a->nz,*jj;

1619:   if (ll) {
1620:     /* The local size is used so that VecMPI can be passed to this routine
1621:        by MatDiagonalScale_MPIAIJ */
1622:     VecGetLocalSize(ll,&m);
1623:     if (m != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1624:     VecGetArray(ll,&l);
1625:     v = a->a;
1626:     for (i=0; i<m; i++) {
1627:       x = l[i];
1628:       M = a->i[i+1] - a->i[i];
1629:       for (j=0; j<M; j++) { (*v++) *= x;}
1630:     }
1631:     VecRestoreArray(ll,&l);
1632:     PetscLogFlops(nz);
1633:   }
1634:   if (rr) {
1635:     VecGetLocalSize(rr,&n);
1636:     if (n != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1637:     VecGetArray(rr,&r);
1638:     v = a->a; jj = a->j;
1639:     for (i=0; i<nz; i++) {
1640:       (*v++) *= r[*jj++];
1641:     }
1642:     VecRestoreArray(rr,&r);
1643:     PetscLogFlops(nz);
1644:   }
1645:   return(0);
1646: }

1650: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
1651: {
1652:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
1654:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->cmap.n,*lens;
1655:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1656:   PetscInt       *irow,*icol,nrows,ncols;
1657:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1658:   PetscScalar    *a_new,*mat_a;
1659:   Mat            C;
1660:   PetscTruth     stride;

1663:   ISSorted(isrow,(PetscTruth*)&i);
1664:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1665:   ISSorted(iscol,(PetscTruth*)&i);
1666:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1668:   ISGetIndices(isrow,&irow);
1669:   ISGetLocalSize(isrow,&nrows);
1670:   ISGetLocalSize(iscol,&ncols);

1672:   ISStrideGetInfo(iscol,&first,&step);
1673:   ISStride(iscol,&stride);
1674:   if (stride && step == 1) {
1675:     /* special case of contiguous rows */
1676:     PetscMalloc((2*nrows+1)*sizeof(PetscInt),&lens);
1677:     starts = lens + nrows;
1678:     /* loop over new rows determining lens and starting points */
1679:     for (i=0; i<nrows; i++) {
1680:       kstart  = ai[irow[i]];
1681:       kend    = kstart + ailen[irow[i]];
1682:       for (k=kstart; k<kend; k++) {
1683:         if (aj[k] >= first) {
1684:           starts[i] = k;
1685:           break;
1686:         }
1687:       }
1688:       sum = 0;
1689:       while (k < kend) {
1690:         if (aj[k++] >= first+ncols) break;
1691:         sum++;
1692:       }
1693:       lens[i] = sum;
1694:     }
1695:     /* create submatrix */
1696:     if (scall == MAT_REUSE_MATRIX) {
1697:       PetscInt n_cols,n_rows;
1698:       MatGetSize(*B,&n_rows,&n_cols);
1699:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1700:       MatZeroEntries(*B);
1701:       C = *B;
1702:     } else {
1703:       MatCreate(((PetscObject)A)->comm,&C);
1704:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1705:       MatSetType(C,((PetscObject)A)->type_name);
1706:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1707:     }
1708:     c = (Mat_SeqAIJ*)C->data;

1710:     /* loop over rows inserting into submatrix */
1711:     a_new    = c->a;
1712:     j_new    = c->j;
1713:     i_new    = c->i;

1715:     for (i=0; i<nrows; i++) {
1716:       ii    = starts[i];
1717:       lensi = lens[i];
1718:       for (k=0; k<lensi; k++) {
1719:         *j_new++ = aj[ii+k] - first;
1720:       }
1721:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1722:       a_new      += lensi;
1723:       i_new[i+1]  = i_new[i] + lensi;
1724:       c->ilen[i]  = lensi;
1725:     }
1726:     PetscFree(lens);
1727:   } else {
1728:     ISGetIndices(iscol,&icol);
1729:     PetscMalloc((1+oldcols)*sizeof(PetscInt),&smap);
1730: 
1731:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
1732:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
1733:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1734:     /* determine lens of each row */
1735:     for (i=0; i<nrows; i++) {
1736:       kstart  = ai[irow[i]];
1737:       kend    = kstart + a->ilen[irow[i]];
1738:       lens[i] = 0;
1739:       for (k=kstart; k<kend; k++) {
1740:         if (smap[aj[k]]) {
1741:           lens[i]++;
1742:         }
1743:       }
1744:     }
1745:     /* Create and fill new matrix */
1746:     if (scall == MAT_REUSE_MATRIX) {
1747:       PetscTruth equal;

1749:       c = (Mat_SeqAIJ *)((*B)->data);
1750:       if ((*B)->rmap.n  != nrows || (*B)->cmap.n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1751:       PetscMemcmp(c->ilen,lens,(*B)->rmap.n*sizeof(PetscInt),&equal);
1752:       if (!equal) {
1753:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1754:       }
1755:       PetscMemzero(c->ilen,(*B)->rmap.n*sizeof(PetscInt));
1756:       C = *B;
1757:     } else {
1758:       MatCreate(((PetscObject)A)->comm,&C);
1759:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1760:       MatSetType(C,((PetscObject)A)->type_name);
1761:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1762:     }
1763:     c = (Mat_SeqAIJ *)(C->data);
1764:     for (i=0; i<nrows; i++) {
1765:       row    = irow[i];
1766:       kstart = ai[row];
1767:       kend   = kstart + a->ilen[row];
1768:       mat_i  = c->i[i];
1769:       mat_j  = c->j + mat_i;
1770:       mat_a  = c->a + mat_i;
1771:       mat_ilen = c->ilen + i;
1772:       for (k=kstart; k<kend; k++) {
1773:         if ((tcol=smap[a->j[k]])) {
1774:           *mat_j++ = tcol - 1;
1775:           *mat_a++ = a->a[k];
1776:           (*mat_ilen)++;

1778:         }
1779:       }
1780:     }
1781:     /* Free work space */
1782:     ISRestoreIndices(iscol,&icol);
1783:     PetscFree(smap);
1784:     PetscFree(lens);
1785:   }
1786:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1787:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1789:   ISRestoreIndices(isrow,&irow);
1790:   *B = C;
1791:   return(0);
1792: }

1794: /*
1795: */
1798: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1799: {
1800:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
1802:   Mat            outA;
1803:   PetscTruth     row_identity,col_identity;

1806:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1807:   ISIdentity(row,&row_identity);
1808:   ISIdentity(col,&col_identity);

1810:   outA          = inA;
1811:   inA->factor   = FACTOR_LU;
1812:   PetscObjectReference((PetscObject)row);
1813:   if (a->row) { ISDestroy(a->row); }
1814:   a->row = row;
1815:   PetscObjectReference((PetscObject)col);
1816:   if (a->col) { ISDestroy(a->col); }
1817:   a->col = col;

1819:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1820:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1821:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1822:   PetscLogObjectParent(inA,a->icol);

1824:   if (!a->solve_work) { /* this matrix may have been factored before */
1825:      PetscMalloc((inA->rmap.n+1)*sizeof(PetscScalar),&a->solve_work);
1826:      PetscLogObjectMemory(inA, (inA->rmap.n+1)*sizeof(PetscScalar));
1827:   }

1829:   MatMarkDiagonal_SeqAIJ(inA);
1830:   if (row_identity && col_identity) {
1831:     MatLUFactorNumeric_SeqAIJ(inA,info,&outA);
1832:   } else {
1833:     MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(inA,info,&outA);
1834:   }
1835:   return(0);
1836: }

1838:  #include petscblaslapack.h
1841: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
1842: {
1843:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)inA->data;
1844:   PetscBLASInt bnz = (PetscBLASInt)a->nz,one = 1;
1845:   PetscScalar oalpha = alpha;


1850:   BLASscal_(&bnz,&oalpha,a->a,&one);
1851:   PetscLogFlops(a->nz);
1852:   return(0);
1853: }

1857: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1858: {
1860:   PetscInt       i;

1863:   if (scall == MAT_INITIAL_MATRIX) {
1864:     PetscMalloc((n+1)*sizeof(Mat),B);
1865:   }

1867:   for (i=0; i<n; i++) {
1868:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1869:   }
1870:   return(0);
1871: }

1875: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
1876: {
1877:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1879:   PetscInt       row,i,j,k,l,m,n,*idx,*nidx,isz,val;
1880:   PetscInt       start,end,*ai,*aj;
1881:   PetscBT        table;

1884:   m     = A->rmap.n;
1885:   ai    = a->i;
1886:   aj    = a->j;

1888:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

1890:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
1891:   PetscBTCreate(m,table);

1893:   for (i=0; i<is_max; i++) {
1894:     /* Initialize the two local arrays */
1895:     isz  = 0;
1896:     PetscBTMemzero(m,table);
1897: 
1898:     /* Extract the indices, assume there can be duplicate entries */
1899:     ISGetIndices(is[i],&idx);
1900:     ISGetLocalSize(is[i],&n);
1901: 
1902:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1903:     for (j=0; j<n ; ++j){
1904:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1905:     }
1906:     ISRestoreIndices(is[i],&idx);
1907:     ISDestroy(is[i]);
1908: 
1909:     k = 0;
1910:     for (j=0; j<ov; j++){ /* for each overlap */
1911:       n = isz;
1912:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1913:         row   = nidx[k];
1914:         start = ai[row];
1915:         end   = ai[row+1];
1916:         for (l = start; l<end ; l++){
1917:           val = aj[l] ;
1918:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1919:         }
1920:       }
1921:     }
1922:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1923:   }
1924:   PetscBTDestroy(table);
1925:   PetscFree(nidx);
1926:   return(0);
1927: }

1929: /* -------------------------------------------------------------- */
1932: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1933: {
1934:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1936:   PetscInt       i,nz,m = A->rmap.n,n = A->cmap.n,*col;
1937:   PetscInt       *row,*cnew,j,*lens;
1938:   IS             icolp,irowp;
1939:   PetscInt       *cwork;
1940:   PetscScalar    *vwork;

1943:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1944:   ISGetIndices(irowp,&row);
1945:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1946:   ISGetIndices(icolp,&col);
1947: 
1948:   /* determine lengths of permuted rows */
1949:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
1950:   for (i=0; i<m; i++) {
1951:     lens[row[i]] = a->i[i+1] - a->i[i];
1952:   }
1953:   MatCreate(((PetscObject)A)->comm,B);
1954:   MatSetSizes(*B,m,n,m,n);
1955:   MatSetType(*B,((PetscObject)A)->type_name);
1956:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
1957:   PetscFree(lens);

1959:   PetscMalloc(n*sizeof(PetscInt),&cnew);
1960:   for (i=0; i<m; i++) {
1961:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1962:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1963:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1964:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1965:   }
1966:   PetscFree(cnew);
1967:   (*B)->assembled     = PETSC_FALSE;
1968:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1969:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1970:   ISRestoreIndices(irowp,&row);
1971:   ISRestoreIndices(icolp,&col);
1972:   ISDestroy(irowp);
1973:   ISDestroy(icolp);
1974:   return(0);
1975: }

1979: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1980: {

1984:   /* If the two matrices have the same copy implementation, use fast copy. */
1985:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1986:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1987:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1989:     if (a->i[A->rmap.n] != b->i[B->rmap.n]) {
1990:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1991:     }
1992:     PetscMemcpy(b->a,a->a,(a->i[A->rmap.n])*sizeof(PetscScalar));
1993:   } else {
1994:     MatCopy_Basic(A,B,str);
1995:   }
1996:   return(0);
1997: }

2001: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
2002: {

2006:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2007:   return(0);
2008: }

2012: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2013: {
2014:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2016:   *array = a->a;
2017:   return(0);
2018: }

2022: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2023: {
2025:   return(0);
2026: }

2030: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2031: {
2032:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
2034:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
2035:   PetscScalar    dx,*y,*xx,*w3_array;
2036:   PetscScalar    *vscale_array;
2037:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
2038:   Vec            w1,w2,w3;
2039:   void           *fctx = coloring->fctx;
2040:   PetscTruth     flg;

2043:   if (!coloring->w1) {
2044:     VecDuplicate(x1,&coloring->w1);
2045:     PetscLogObjectParent(coloring,coloring->w1);
2046:     VecDuplicate(x1,&coloring->w2);
2047:     PetscLogObjectParent(coloring,coloring->w2);
2048:     VecDuplicate(x1,&coloring->w3);
2049:     PetscLogObjectParent(coloring,coloring->w3);
2050:   }
2051:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

2053:   MatSetUnfactored(J);
2054:   PetscOptionsHasName(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg);
2055:   if (flg) {
2056:     PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2057:   } else {
2058:     PetscTruth assembled;
2059:     MatAssembled(J,&assembled);
2060:     if (assembled) {
2061:       MatZeroEntries(J);
2062:     }
2063:   }

2065:   VecGetOwnershipRange(x1,&start,&end);
2066:   VecGetSize(x1,&N);

2068:   /*
2069:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
2070:      coloring->F for the coarser grids from the finest
2071:   */
2072:   if (coloring->F) {
2073:     VecGetLocalSize(coloring->F,&m1);
2074:     VecGetLocalSize(w1,&m2);
2075:     if (m1 != m2) {
2076:       coloring->F = 0;
2077:     }
2078:   }

2080:   if (coloring->F) {
2081:     w1          = coloring->F;
2082:     coloring->F = 0;
2083:   } else {
2085:     (*f)(sctx,x1,w1,fctx);
2087:   }

2089:   /* 
2090:       Compute all the scale factors and share with other processors
2091:   */
2092:   VecGetArray(x1,&xx);xx = xx - start;
2093:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2094:   for (k=0; k<coloring->ncolors; k++) {
2095:     /*
2096:        Loop over each column associated with color adding the 
2097:        perturbation to the vector w3.
2098:     */
2099:     for (l=0; l<coloring->ncolumns[k]; l++) {
2100:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2101:       dx  = xx[col];
2102:       if (dx == 0.0) dx = 1.0;
2103: #if !defined(PETSC_USE_COMPLEX)
2104:       if (dx < umin && dx >= 0.0)      dx = umin;
2105:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2106: #else
2107:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2108:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2109: #endif
2110:       dx                *= epsilon;
2111:       vscale_array[col] = 1.0/dx;
2112:     }
2113:   }
2114:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2115:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2116:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

2118:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2119:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2121:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2122:   else                        vscaleforrow = coloring->columnsforrow;

2124:   VecGetArray(coloring->vscale,&vscale_array);
2125:   /*
2126:       Loop over each color
2127:   */
2128:   for (k=0; k<coloring->ncolors; k++) {
2129:     coloring->currentcolor = k;
2130:     VecCopy(x1,w3);
2131:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2132:     /*
2133:        Loop over each column associated with color adding the 
2134:        perturbation to the vector w3.
2135:     */
2136:     for (l=0; l<coloring->ncolumns[k]; l++) {
2137:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2138:       dx  = xx[col];
2139:       if (dx == 0.0) dx = 1.0;
2140: #if !defined(PETSC_USE_COMPLEX)
2141:       if (dx < umin && dx >= 0.0)      dx = umin;
2142:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2143: #else
2144:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2145:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2146: #endif
2147:       dx            *= epsilon;
2148:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2149:       w3_array[col] += dx;
2150:     }
2151:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2153:     /*
2154:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2155:     */

2158:     (*f)(sctx,w3,w2,fctx);
2160:     VecAXPY(w2,-1.0,w1);

2162:     /*
2163:        Loop over rows of vector, putting results into Jacobian matrix
2164:     */
2165:     VecGetArray(w2,&y);
2166:     for (l=0; l<coloring->nrows[k]; l++) {
2167:       row    = coloring->rows[k][l];
2168:       col    = coloring->columnsforrow[k][l];
2169:       y[row] *= vscale_array[vscaleforrow[k][l]];
2170:       srow   = row + start;
2171:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2172:     }
2173:     VecRestoreArray(w2,&y);
2174:   }
2175:   coloring->currentcolor = k;
2176:   VecRestoreArray(coloring->vscale,&vscale_array);
2177:   xx = xx + start; VecRestoreArray(x1,&xx);
2178:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2179:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2180:   return(0);
2181: }

2183:  #include petscblaslapack.h
2186: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2187: {
2189:   PetscInt       i;
2190:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2191:   PetscBLASInt   one=1,bnz = (PetscBLASInt)x->nz;

2194:   if (str == SAME_NONZERO_PATTERN) {
2195:     PetscScalar alpha = a;
2196:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2197:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2198:     if (y->xtoy && y->XtoY != X) {
2199:       PetscFree(y->xtoy);
2200:       MatDestroy(y->XtoY);
2201:     }
2202:     if (!y->xtoy) { /* get xtoy */
2203:       MatAXPYGetxtoy_Private(X->rmap.n,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2204:       y->XtoY = X;
2205:       PetscObjectReference((PetscObject)X);
2206:     }
2207:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2208:     PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %G\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz);
2209:   } else {
2210:     MatAXPY_Basic(Y,a,X,str);
2211:   }
2212:   return(0);
2213: }

2217: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
2218: {
2220:   return(0);
2221: }

2225: PetscErrorCode  MatConjugate_SeqAIJ(Mat mat)
2226: {
2227: #if defined(PETSC_USE_COMPLEX)
2228:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
2229:   PetscInt    i,nz;
2230:   PetscScalar *a;

2233:   nz = aij->nz;
2234:   a  = aij->a;
2235:   for (i=0; i<nz; i++) {
2236:     a[i] = PetscConj(a[i]);
2237:   }
2238: #else
2240: #endif
2241:   return(0);
2242: }

2246: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2247: {
2248:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2250:   PetscInt       i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2251:   PetscReal      atmp;
2252:   PetscScalar    *x;
2253:   MatScalar      *aa;

2256:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2257:   aa   = a->a;
2258:   ai   = a->i;
2259:   aj   = a->j;

2261:   VecSet(v,0.0);
2262:   VecGetArray(v,&x);
2263:   VecGetLocalSize(v,&n);
2264:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2265:   for (i=0; i<m; i++) {
2266:     ncols = ai[1] - ai[0]; ai++;
2267:     x[i] = 0.0; if (idx) idx[i] = 0;
2268:     for (j=0; j<ncols; j++){
2269:       atmp = PetscAbsScalar(*aa);
2270:       if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2271:       aa++; aj++;
2272:     }
2273:   }
2274:   VecRestoreArray(v,&x);
2275:   return(0);
2276: }

2280: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2281: {
2282:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2284:   PetscInt       i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2285:   PetscScalar    *x;
2286:   MatScalar      *aa;

2289:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2290:   aa   = a->a;
2291:   ai   = a->i;
2292:   aj   = a->j;

2294:   VecSet(v,0.0);
2295:   VecGetArray(v,&x);
2296:   VecGetLocalSize(v,&n);
2297:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2298:   for (i=0; i<m; i++) {
2299:     ncols = ai[1] - ai[0]; ai++;
2300:     if (ncols == A->cmap.n) { /* row is dense */
2301:       x[i] = *aa; if (idx) idx[i] = 0;
2302:     } else {  /* row is sparse so already KNOW maximum is 0.0 or higher */
2303:       x[i] = 0.0;
2304:       if (idx) {
2305:         idx[i] = 0; /* in case ncols is zero */
2306:         for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2307:           if (aj[j] > j) {
2308:             idx[i] = j;
2309:             break;
2310:           }
2311:         }
2312:       }
2313:     }
2314:     for (j=0; j<ncols; j++){
2315:       if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2316:       aa++; aj++;
2317:     }
2318:   }
2319:   VecRestoreArray(v,&x);
2320:   return(0);
2321: }

2325: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2326: {
2327:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2329:   PetscInt       i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2330:   PetscScalar    *x;
2331:   MatScalar      *aa;

2334:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2335:   aa   = a->a;
2336:   ai   = a->i;
2337:   aj   = a->j;

2339:   VecSet(v,0.0);
2340:   VecGetArray(v,&x);
2341:   VecGetLocalSize(v,&n);
2342:   if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2343:   for (i=0; i<m; i++) {
2344:     ncols = ai[1] - ai[0]; ai++;
2345:     if (ncols == A->cmap.n) { /* row is dense */
2346:       x[i] = *aa; if (idx) idx[i] = 0;
2347:     } else {  /* row is sparse so already KNOW minimum is 0.0 or lower */
2348:       x[i] = 0.0;
2349:       if (idx) {   /* find first implicit 0.0 in the row */
2350:         idx[i] = 0; /* in case ncols is zero */
2351:         for (j=0;j<ncols;j++) {
2352:           if (aj[j] > j) {
2353:             idx[i] = j;
2354:             break;
2355:           }
2356:         }
2357:       }
2358:     }
2359:     for (j=0; j<ncols; j++){
2360:       if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2361:       aa++; aj++;
2362:     }
2363:   }
2364:   VecRestoreArray(v,&x);
2365:   return(0);
2366: }

2368: /* -------------------------------------------------------------------*/
2369: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2370:        MatGetRow_SeqAIJ,
2371:        MatRestoreRow_SeqAIJ,
2372:        MatMult_SeqAIJ,
2373: /* 4*/ MatMultAdd_SeqAIJ,
2374:        MatMultTranspose_SeqAIJ,
2375:        MatMultTransposeAdd_SeqAIJ,
2376:        MatSolve_SeqAIJ,
2377:        MatSolveAdd_SeqAIJ,
2378:        MatSolveTranspose_SeqAIJ,
2379: /*10*/ MatSolveTransposeAdd_SeqAIJ,
2380:        MatLUFactor_SeqAIJ,
2381:        0,
2382:        MatRelax_SeqAIJ,
2383:        MatTranspose_SeqAIJ,
2384: /*15*/ MatGetInfo_SeqAIJ,
2385:        MatEqual_SeqAIJ,
2386:        MatGetDiagonal_SeqAIJ,
2387:        MatDiagonalScale_SeqAIJ,
2388:        MatNorm_SeqAIJ,
2389: /*20*/ 0,
2390:        MatAssemblyEnd_SeqAIJ,
2391:        MatCompress_SeqAIJ,
2392:        MatSetOption_SeqAIJ,
2393:        MatZeroEntries_SeqAIJ,
2394: /*25*/ MatZeroRows_SeqAIJ,
2395:        MatLUFactorSymbolic_SeqAIJ,
2396:        MatLUFactorNumeric_SeqAIJ,
2397:        MatCholeskyFactorSymbolic_SeqAIJ,
2398:        MatCholeskyFactorNumeric_SeqAIJ,
2399: /*30*/ MatSetUpPreallocation_SeqAIJ,
2400:        MatILUFactorSymbolic_SeqAIJ,
2401:        MatICCFactorSymbolic_SeqAIJ,
2402:        MatGetArray_SeqAIJ,
2403:        MatRestoreArray_SeqAIJ,
2404: /*35*/ MatDuplicate_SeqAIJ,
2405:        0,
2406:        0,
2407:        MatILUFactor_SeqAIJ,
2408:        0,
2409: /*40*/ MatAXPY_SeqAIJ,
2410:        MatGetSubMatrices_SeqAIJ,
2411:        MatIncreaseOverlap_SeqAIJ,
2412:        MatGetValues_SeqAIJ,
2413:        MatCopy_SeqAIJ,
2414: /*45*/ MatGetRowMax_SeqAIJ,
2415:        MatScale_SeqAIJ,
2416:        0,
2417:        MatDiagonalSet_SeqAIJ,
2418:        MatILUDTFactor_SeqAIJ,
2419: /*50*/ MatSetBlockSize_SeqAIJ,
2420:        MatGetRowIJ_SeqAIJ,
2421:        MatRestoreRowIJ_SeqAIJ,
2422:        MatGetColumnIJ_SeqAIJ,
2423:        MatRestoreColumnIJ_SeqAIJ,
2424: /*55*/ MatFDColoringCreate_SeqAIJ,
2425:        0,
2426:        0,
2427:        MatPermute_SeqAIJ,
2428:        0,
2429: /*60*/ 0,
2430:        MatDestroy_SeqAIJ,
2431:        MatView_SeqAIJ,
2432:        0,
2433:        0,
2434: /*65*/ 0,
2435:        0,
2436:        0,
2437:        0,
2438:        0,
2439: /*70*/ MatGetRowMaxAbs_SeqAIJ,
2440:        0,
2441:        MatSetColoring_SeqAIJ,
2442: #if defined(PETSC_HAVE_ADIC)
2443:        MatSetValuesAdic_SeqAIJ,
2444: #else
2445:        0,
2446: #endif
2447:        MatSetValuesAdifor_SeqAIJ,
2448: /*75*/ 0,
2449:        0,
2450:        0,
2451:        0,
2452:        0,
2453: /*80*/ 0,
2454:        0,
2455:        0,
2456:        0,
2457:        MatLoad_SeqAIJ,
2458: /*85*/ MatIsSymmetric_SeqAIJ,
2459:        MatIsHermitian_SeqAIJ,
2460:        0,
2461:        0,
2462:        0,
2463: /*90*/ MatMatMult_SeqAIJ_SeqAIJ,
2464:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
2465:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
2466:        MatPtAP_Basic,
2467:        MatPtAPSymbolic_SeqAIJ,
2468: /*95*/ MatPtAPNumeric_SeqAIJ,
2469:        MatMatMultTranspose_SeqAIJ_SeqAIJ,
2470:        MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
2471:        MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
2472:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
2473: /*100*/MatPtAPNumeric_SeqAIJ_SeqAIJ,
2474:        0,
2475:        0,
2476:        MatConjugate_SeqAIJ,
2477:        0,
2478: /*105*/MatSetValuesRow_SeqAIJ,
2479:        MatRealPart_SeqAIJ,
2480:        MatImaginaryPart_SeqAIJ,
2481:        0,
2482:        0,
2483: /*110*/MatMatSolve_SeqAIJ,
2484:        0,
2485:        MatGetRowMin_SeqAIJ,
2486:        0,
2487:        MatMissingDiagonal_SeqAIJ
2488: };

2493: PetscErrorCode  MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
2494: {
2495:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2496:   PetscInt   i,nz,n;


2500:   nz = aij->maxnz;
2501:   n  = mat->rmap.n;
2502:   for (i=0; i<nz; i++) {
2503:     aij->j[i] = indices[i];
2504:   }
2505:   aij->nz = nz;
2506:   for (i=0; i<n; i++) {
2507:     aij->ilen[i] = aij->imax[i];
2508:   }

2510:   return(0);
2511: }

2516: /*@
2517:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2518:        in the matrix.

2520:   Input Parameters:
2521: +  mat - the SeqAIJ matrix
2522: -  indices - the column indices

2524:   Level: advanced

2526:   Notes:
2527:     This can be called if you have precomputed the nonzero structure of the 
2528:   matrix and want to provide it to the matrix object to improve the performance
2529:   of the MatSetValues() operation.

2531:     You MUST have set the correct numbers of nonzeros per row in the call to 
2532:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

2534:     MUST be called before any calls to MatSetValues();

2536:     The indices should start with zero, not one.

2538: @*/
2539: PetscErrorCode  MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
2540: {
2541:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

2546:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2547:   if (f) {
2548:     (*f)(mat,indices);
2549:   } else {
2550:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
2551:   }
2552:   return(0);
2553: }

2555: /* ----------------------------------------------------------------------------------------*/

2560: PetscErrorCode  MatStoreValues_SeqAIJ(Mat mat)
2561: {
2562:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2564:   size_t         nz = aij->i[mat->rmap.n];

2567:   if (aij->nonew != 1) {
2568:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2569:   }

2571:   /* allocate space for values if not already there */
2572:   if (!aij->saved_values) {
2573:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2574:     PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
2575:   }

2577:   /* copy values over */
2578:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2579:   return(0);
2580: }

2585: /*@
2586:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2587:        example, reuse of the linear part of a Jacobian, while recomputing the 
2588:        nonlinear portion.

2590:    Collect on Mat

2592:   Input Parameters:
2593: .  mat - the matrix (currently only AIJ matrices support this option)

2595:   Level: advanced

2597:   Common Usage, with SNESSolve():
2598: $    Create Jacobian matrix
2599: $    Set linear terms into matrix
2600: $    Apply boundary conditions to matrix, at this time matrix must have 
2601: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2602: $      boundary conditions again will not change the nonzero structure
2603: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
2604: $    MatStoreValues(mat);
2605: $    Call SNESSetJacobian() with matrix
2606: $    In your Jacobian routine
2607: $      MatRetrieveValues(mat);
2608: $      Set nonlinear terms in matrix
2609:  
2610:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2611: $    // build linear portion of Jacobian 
2612: $    MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
2613: $    MatStoreValues(mat);
2614: $    loop over nonlinear iterations
2615: $       MatRetrieveValues(mat);
2616: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2617: $       // call MatAssemblyBegin/End() on matrix
2618: $       Solve linear system with Jacobian
2619: $    endloop 

2621:   Notes:
2622:     Matrix must already be assemblied before calling this routine
2623:     Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before 
2624:     calling this routine.

2626:     When this is called multiple times it overwrites the previous set of stored values
2627:     and does not allocated additional space.

2629: .seealso: MatRetrieveValues()

2631: @*/
2632: PetscErrorCode  MatStoreValues(Mat mat)
2633: {
2634:   PetscErrorCode ierr,(*f)(Mat);

2638:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2639:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2641:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2642:   if (f) {
2643:     (*f)(mat);
2644:   } else {
2645:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values");
2646:   }
2647:   return(0);
2648: }

2653: PetscErrorCode  MatRetrieveValues_SeqAIJ(Mat mat)
2654: {
2655:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2657:   PetscInt       nz = aij->i[mat->rmap.n];

2660:   if (aij->nonew != 1) {
2661:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2662:   }
2663:   if (!aij->saved_values) {
2664:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2665:   }
2666:   /* copy values over */
2667:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2668:   return(0);
2669: }

2674: /*@
2675:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2676:        example, reuse of the linear part of a Jacobian, while recomputing the 
2677:        nonlinear portion.

2679:    Collect on Mat

2681:   Input Parameters:
2682: .  mat - the matrix (currently on AIJ matrices support this option)

2684:   Level: advanced

2686: .seealso: MatStoreValues()

2688: @*/
2689: PetscErrorCode  MatRetrieveValues(Mat mat)
2690: {
2691:   PetscErrorCode ierr,(*f)(Mat);

2695:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2696:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2698:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2699:   if (f) {
2700:     (*f)(mat);
2701:   } else {
2702:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values");
2703:   }
2704:   return(0);
2705: }


2708: /* --------------------------------------------------------------------------------*/
2711: /*@C
2712:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2713:    (the default parallel PETSc format).  For good matrix assembly performance
2714:    the user should preallocate the matrix storage by setting the parameter nz
2715:    (or the array nnz).  By setting these parameters accurately, performance
2716:    during matrix assembly can be increased by more than a factor of 50.

2718:    Collective on MPI_Comm

2720:    Input Parameters:
2721: +  comm - MPI communicator, set to PETSC_COMM_SELF
2722: .  m - number of rows
2723: .  n - number of columns
2724: .  nz - number of nonzeros per row (same for all rows)
2725: -  nnz - array containing the number of nonzeros in the various rows 
2726:          (possibly different for each row) or PETSC_NULL

2728:    Output Parameter:
2729: .  A - the matrix 

2731:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
2732:    MatXXXXSetPreallocation() paradgm instead of this routine directly. This is definitely
2733:    true if you plan to use the external direct solvers such as SuperLU, MUMPS or Spooles.
2734:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

2736:    Notes:
2737:    If nnz is given then nz is ignored

2739:    The AIJ format (also called the Yale sparse matrix format or
2740:    compressed row storage), is fully compatible with standard Fortran 77
2741:    storage.  That is, the stored row and column indices can begin at
2742:    either one (as in Fortran) or zero.  See the users' manual for details.

2744:    Specify the preallocated storage with either nz or nnz (not both).
2745:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2746:    allocation.  For large problems you MUST preallocate memory or you 
2747:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2749:    By default, this format uses inodes (identical nodes) when possible, to 
2750:    improve numerical efficiency of matrix-vector products and solves. We 
2751:    search for consecutive rows with the same nonzero structure, thereby
2752:    reusing matrix information to achieve increased efficiency.

2754:    Options Database Keys:
2755: +  -mat_no_inode  - Do not use inodes
2756: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2757: -  -mat_aij_oneindex - Internally use indexing starting at 1
2758:         rather than 0.  Note that when calling MatSetValues(),
2759:         the user still MUST index entries starting at 0!

2761:    Level: intermediate

2763: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2765: @*/
2766: PetscErrorCode  MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2767: {

2771:   MatCreate(comm,A);
2772:   MatSetSizes(*A,m,n,m,n);
2773:   MatSetType(*A,MATSEQAIJ);
2774:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
2775:   return(0);
2776: }

2780: /*@C
2781:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2782:    the user should preallocate the matrix storage by setting the parameter nz
2783:    (or the array nnz).  By setting these parameters accurately, performance
2784:    during matrix assembly can be increased by more than a factor of 50.

2786:    Collective on MPI_Comm

2788:    Input Parameters:
2789: +  B - The matrix
2790: .  nz - number of nonzeros per row (same for all rows)
2791: -  nnz - array containing the number of nonzeros in the various rows 
2792:          (possibly different for each row) or PETSC_NULL

2794:    Notes:
2795:      If nnz is given then nz is ignored

2797:     The AIJ format (also called the Yale sparse matrix format or
2798:    compressed row storage), is fully compatible with standard Fortran 77
2799:    storage.  That is, the stored row and column indices can begin at
2800:    either one (as in Fortran) or zero.  See the users' manual for details.

2802:    Specify the preallocated storage with either nz or nnz (not both).
2803:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2804:    allocation.  For large problems you MUST preallocate memory or you 
2805:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2807:    You can call MatGetInfo() to get information on how effective the preallocation was;
2808:    for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
2809:    You can also run with the option -info and look for messages with the string 
2810:    malloc in them to see if additional memory allocation was needed.

2812:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
2813:    entries or columns indices

2815:    By default, this format uses inodes (identical nodes) when possible, to 
2816:    improve numerical efficiency of matrix-vector products and solves. We 
2817:    search for consecutive rows with the same nonzero structure, thereby
2818:    reusing matrix information to achieve increased efficiency.

2820:    Options Database Keys:
2821: +  -mat_no_inode  - Do not use inodes
2822: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2823: -  -mat_aij_oneindex - Internally use indexing starting at 1
2824:         rather than 0.  Note that when calling MatSetValues(),
2825:         the user still MUST index entries starting at 0!

2827:    Level: intermediate

2829: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()

2831: @*/
2832: PetscErrorCode  MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
2833: {
2834:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]);

2837:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
2838:   if (f) {
2839:     (*f)(B,nz,nnz);
2840:   }
2841:   return(0);
2842: }

2847: PetscErrorCode  MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz)
2848: {
2849:   Mat_SeqAIJ     *b;
2850:   PetscTruth     skipallocation = PETSC_FALSE;
2852:   PetscInt       i;

2855: 
2856:   if (nz == MAT_SKIP_ALLOCATION) {
2857:     skipallocation = PETSC_TRUE;
2858:     nz             = 0;
2859:   }

2861:   B->rmap.bs = B->cmap.bs = 1;
2862:   PetscMapSetUp(&B->rmap);
2863:   PetscMapSetUp(&B->cmap);

2865:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2866:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2867:   if (nnz) {
2868:     for (i=0; i<B->rmap.n; i++) {
2869:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2870:       if (nnz[i] > B->cmap.n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->cmap.n);
2871:     }
2872:   }

2874:   B->preallocated = PETSC_TRUE;
2875:   b = (Mat_SeqAIJ*)B->data;

2877:   if (!skipallocation) {
2878:     if (!b->imax) {
2879:       PetscMalloc2(B->rmap.n,PetscInt,&b->imax,B->rmap.n,PetscInt,&b->ilen);
2880:       PetscLogObjectMemory(B,2*B->rmap.n*sizeof(PetscInt));
2881:     }
2882:     if (!nnz) {
2883:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2884:       else if (nz <= 0)        nz = 1;
2885:       for (i=0; i<B->rmap.n; i++) b->imax[i] = nz;
2886:       nz = nz*B->rmap.n;
2887:     } else {
2888:       nz = 0;
2889:       for (i=0; i<B->rmap.n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2890:     }
2891:     /* b->ilen will count nonzeros in each row so far. */
2892:     for (i=0; i<B->rmap.n; i++) { b->ilen[i] = 0; }

2894:     /* allocate the matrix space */
2895:     MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
2896:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.n+1,PetscInt,&b->i);
2897:     PetscLogObjectMemory(B,(B->rmap.n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
2898:     b->i[0] = 0;
2899:     for (i=1; i<B->rmap.n+1; i++) {
2900:       b->i[i] = b->i[i-1] + b->imax[i-1];
2901:     }
2902:     b->singlemalloc = PETSC_TRUE;
2903:     b->free_a       = PETSC_TRUE;
2904:     b->free_ij      = PETSC_TRUE;
2905:   } else {
2906:     b->free_a       = PETSC_FALSE;
2907:     b->free_ij      = PETSC_FALSE;
2908:   }

2910:   b->nz                = 0;
2911:   b->maxnz             = nz;
2912:   B->info.nz_unneeded  = (double)b->maxnz;
2913:   return(0);
2914: }

2917: #undef  __FUNCT__
2919: /*@
2920:    MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.  

2922:    Input Parameters:
2923: +  B - the matrix 
2924: .  i - the indices into j for the start of each row (starts with zero)
2925: .  j - the column indices for each row (starts with zero) these must be sorted for each row
2926: -  v - optional values in the matrix

2928:    Contributed by: Lisandro Dalchin

2930:    Level: developer

2932:    The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()

2934: .keywords: matrix, aij, compressed row, sparse, sequential

2936: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
2937: @*/
2938: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
2939: {
2940:   PetscErrorCode (*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);

2945:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",(void (**)(void))&f);
2946:   if (f) {
2947:     (*f)(B,i,j,v);
2948:   }
2949:   return(0);
2950: }

2953: #undef  __FUNCT__
2955: PetscErrorCode  MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2956: {
2957:   PetscInt       i;
2958:   PetscInt       m,n;
2959:   PetscInt       nz;
2960:   PetscInt       *nnz, nz_max = 0;
2961:   PetscScalar    *values;

2965:   MatGetSize(B, &m, &n);

2967:   if (Ii[0]) {
2968:     SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
2969:   }
2970:   PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
2971:   for(i = 0; i < m; i++) {
2972:     nz     = Ii[i+1]- Ii[i];
2973:     nz_max = PetscMax(nz_max, nz);
2974:     if (nz < 0) {
2975:       SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
2976:     }
2977:     nnz[i] = nz;
2978:   }
2979:   MatSeqAIJSetPreallocation(B, 0, nnz);
2980:   PetscFree(nnz);

2982:   if (v) {
2983:     values = (PetscScalar*) v;
2984:   } else {
2985:     PetscMalloc((nz_max+1)*sizeof(PetscScalar), &values);
2986:     PetscMemzero(values, nz_max*sizeof(PetscScalar));
2987:   }

2989:   for(i = 0; i < m; i++) {
2990:     nz  = Ii[i+1] - Ii[i];
2991:     MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
2992:   }

2994:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2995:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);

2997:   if (!v) {
2998:     PetscFree(values);
2999:   }
3000:   return(0);
3001: }

3004: /*MC
3005:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
3006:    based on compressed sparse row format.

3008:    Options Database Keys:
3009: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

3011:   Level: beginner

3013: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3014: M*/


3023: PetscErrorCode  MatCreate_SeqAIJ(Mat B)
3024: {
3025:   Mat_SeqAIJ     *b;
3027:   PetscMPIInt    size;

3030:   MPI_Comm_size(((PetscObject)B)->comm,&size);
3031:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

3033:   PetscNewLog(B,Mat_SeqAIJ,&b);
3034:   B->data             = (void*)b;
3035:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3036:   B->factor           = 0;
3037:   B->mapping          = 0;
3038:   b->row              = 0;
3039:   b->col              = 0;
3040:   b->icol             = 0;
3041:   b->reallocs         = 0;
3042:   b->ignorezeroentries = PETSC_FALSE;
3043:   b->roworiented       = PETSC_TRUE;
3044:   b->nonew             = 0;
3045:   b->diag              = 0;
3046:   b->solve_work        = 0;
3047:   B->spptr             = 0;
3048:   b->saved_values      = 0;
3049:   b->idiag             = 0;
3050:   b->mdiag             = 0;
3051:   b->ssor_work         = 0;
3052:   b->omega             = 1.0;
3053:   b->fshift            = 0.0;
3054:   b->idiagvalid        = PETSC_FALSE;
3055:   b->keepzeroedrows    = PETSC_FALSE;
3056:   b->xtoy              = 0;
3057:   b->XtoY              = 0;
3058:   b->compressedrow.use     = PETSC_FALSE;
3059:   b->compressedrow.nrows   = B->rmap.n;
3060:   b->compressedrow.i       = PETSC_NULL;
3061:   b->compressedrow.rindex  = PETSC_NULL;
3062:   b->compressedrow.checked = PETSC_FALSE;
3063:   B->same_nonzero          = PETSC_FALSE;

3065:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3066:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
3067:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
3068:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
3069:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
3070:                                      "MatStoreValues_SeqAIJ",
3071:                                      MatStoreValues_SeqAIJ);
3072:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
3073:                                      "MatRetrieveValues_SeqAIJ",
3074:                                      MatRetrieveValues_SeqAIJ);
3075:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
3076:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
3077:                                       MatConvert_SeqAIJ_SeqSBAIJ);
3078:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
3079:                                      "MatConvert_SeqAIJ_SeqBAIJ",
3080:                                       MatConvert_SeqAIJ_SeqBAIJ);
3081:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcsrperm_C",
3082:                                      "MatConvert_SeqAIJ_SeqCSRPERM",
3083:                                       MatConvert_SeqAIJ_SeqCSRPERM);
3084:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcrl_C",
3085:                                      "MatConvert_SeqAIJ_SeqCRL",
3086:                                       MatConvert_SeqAIJ_SeqCRL);
3087:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
3088:                                      "MatIsTranspose_SeqAIJ",
3089:                                       MatIsTranspose_SeqAIJ);
3090:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsHermitianTranspose_C",
3091:                                      "MatIsHermitianTranspose_SeqAIJ",
3092:                                       MatIsTranspose_SeqAIJ);
3093:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
3094:                                      "MatSeqAIJSetPreallocation_SeqAIJ",
3095:                                       MatSeqAIJSetPreallocation_SeqAIJ);
3096:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",
3097:                                      "MatSeqAIJSetPreallocationCSR_SeqAIJ",
3098:                                       MatSeqAIJSetPreallocationCSR_SeqAIJ);
3099:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
3100:                                      "MatReorderForNonzeroDiagonal_SeqAIJ",
3101:                                       MatReorderForNonzeroDiagonal_SeqAIJ);
3102:   MatCreate_Inode(B);
3103:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3104:   return(0);
3105: }

3110: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3111: {
3112:   Mat            C;
3113:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
3115:   PetscInt       i,m = A->rmap.n;

3118:   *B = 0;
3119:   MatCreate(((PetscObject)A)->comm,&C);
3120:   MatSetSizes(C,A->rmap.n,A->cmap.n,A->rmap.n,A->cmap.n);
3121:   MatSetType(C,((PetscObject)A)->type_name);
3122:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
3123: 
3124:   PetscMapCopy(((PetscObject)A)->comm,&A->rmap,&C->rmap);
3125:   PetscMapCopy(((PetscObject)A)->comm,&A->cmap,&C->cmap);

3127:   c = (Mat_SeqAIJ*)C->data;

3129:   C->factor           = A->factor;

3131:   c->row            = 0;
3132:   c->col            = 0;
3133:   c->icol           = 0;
3134:   c->reallocs       = 0;

3136:   C->assembled      = PETSC_TRUE;
3137: 
3138:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
3139:   PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
3140:   for (i=0; i<m; i++) {
3141:     c->imax[i] = a->imax[i];
3142:     c->ilen[i] = a->ilen[i];
3143:   }

3145:   /* allocate the matrix space */
3146:   PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
3147:   PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
3148:   c->singlemalloc = PETSC_TRUE;
3149:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
3150:   if (m > 0) {
3151:     PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
3152:     if (cpvalues == MAT_COPY_VALUES) {
3153:       PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
3154:     } else {
3155:       PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
3156:     }
3157:   }

3159:   c->ignorezeroentries = a->ignorezeroentries;
3160:   c->roworiented       = a->roworiented;
3161:   c->nonew             = a->nonew;
3162:   if (a->diag) {
3163:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
3164:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
3165:     for (i=0; i<m; i++) {
3166:       c->diag[i] = a->diag[i];
3167:     }
3168:   } else c->diag           = 0;
3169:   c->solve_work            = 0;
3170:   c->saved_values          = 0;
3171:   c->idiag                 = 0;
3172:   c->ssor_work             = 0;
3173:   c->keepzeroedrows        = a->keepzeroedrows;
3174:   c->free_a                = PETSC_TRUE;
3175:   c->free_ij               = PETSC_TRUE;
3176:   c->xtoy                  = 0;
3177:   c->XtoY                  = 0;

3179:   c->nz                 = a->nz;
3180:   c->maxnz              = a->maxnz;
3181:   C->preallocated       = PETSC_TRUE;

3183:   c->compressedrow.use     = a->compressedrow.use;
3184:   c->compressedrow.nrows   = a->compressedrow.nrows;
3185:   c->compressedrow.checked = a->compressedrow.checked;
3186:   if ( a->compressedrow.checked && a->compressedrow.use){
3187:     i = a->compressedrow.nrows;
3188:     PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
3189:     c->compressedrow.rindex = c->compressedrow.i + i + 1;
3190:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3191:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3192:   } else {
3193:     c->compressedrow.use    = PETSC_FALSE;
3194:     c->compressedrow.i      = PETSC_NULL;
3195:     c->compressedrow.rindex = PETSC_NULL;
3196:   }
3197:   C->same_nonzero = A->same_nonzero;
3198:   MatDuplicate_Inode(A,cpvalues,&C);

3200:   *B = C;
3201:   PetscFListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3202:   return(0);
3203: }

3207: PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer, MatType type,Mat *A)
3208: {
3209:   Mat_SeqAIJ     *a;
3210:   Mat            B;
3212:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N;
3213:   int            fd;
3214:   PetscMPIInt    size;
3215:   MPI_Comm       comm;
3216: 
3218:   PetscObjectGetComm((PetscObject)viewer,&comm);
3219:   MPI_Comm_size(comm,&size);
3220:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
3221:   PetscViewerBinaryGetDescriptor(viewer,&fd);
3222:   PetscBinaryRead(fd,header,4,PETSC_INT);
3223:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
3224:   M = header[1]; N = header[2]; nz = header[3];

3226:   if (nz < 0) {
3227:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
3228:   }

3230:   /* read in row lengths */
3231:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
3232:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

3234:   /* check if sum of rowlengths is same as nz */
3235:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
3236:   if (sum != nz) SETERRQ2(PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

3238:   /* create our matrix */
3239:   MatCreate(comm,&B);
3240:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M,N);
3241:   MatSetType(B,type);
3242:   MatSeqAIJSetPreallocation_SeqAIJ(B,0,rowlengths);
3243:   a = (Mat_SeqAIJ*)B->data;

3245:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

3247:   /* read in nonzero values */
3248:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

3250:   /* set matrix "i" values */
3251:   a->i[0] = 0;
3252:   for (i=1; i<= M; i++) {
3253:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
3254:     a->ilen[i-1] = rowlengths[i-1];
3255:   }
3256:   PetscFree(rowlengths);

3258:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3259:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3260:   *A = B;
3261:   return(0);
3262: }

3266: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
3267: {
3268:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;

3272:   /* If the  matrix dimensions are not equal,or no of nonzeros */
3273:   if ((A->rmap.n != B->rmap.n) || (A->cmap.n != B->cmap.n) ||(a->nz != b->nz)) {
3274:     *flg = PETSC_FALSE;
3275:     return(0);
3276:   }
3277: 
3278:   /* if the a->i are the same */
3279:   PetscMemcmp(a->i,b->i,(A->rmap.n+1)*sizeof(PetscInt),flg);
3280:   if (!*flg) return(0);
3281: 
3282:   /* if a->j are the same */
3283:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
3284:   if (!*flg) return(0);
3285: 
3286:   /* if a->a are the same */
3287:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

3289:   return(0);
3290: 
3291: }

3295: /*@
3296:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
3297:               provided by the user.

3299:       Collective on MPI_Comm

3301:    Input Parameters:
3302: +   comm - must be an MPI communicator of size 1
3303: .   m - number of rows
3304: .   n - number of columns
3305: .   i - row indices
3306: .   j - column indices
3307: -   a - matrix values

3309:    Output Parameter:
3310: .   mat - the matrix

3312:    Level: intermediate

3314:    Notes:
3315:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
3316:     once the matrix is destroyed

3318:        You cannot set new nonzero locations into this matrix, that will generate an error.

3320:        The i and j indices are 0 based

3322:        The format which is used for the sparse matrix input, is equivalent to a
3323:     row-major ordering.. i.e for the following matrix, the input data expected is
3324:     as shown:

3326:         1 0 0
3327:         2 0 3
3328:         4 5 6

3330:         i =  {0,1,3,6}  [size = nrow+1  = 3+1]
3331:         j =  {0,0,2,0,1,2}  [size = nz = 6]; values must be sorted for each row
3332:         v =  {1,2,3,4,5,6}  [size = nz = 6]

3334:         
3335: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()

3337: @*/
3338: PetscErrorCode  MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
3339: {
3341:   PetscInt       ii;
3342:   Mat_SeqAIJ     *aij;
3343: #if defined(PETSC_USE_DEBUG)
3344:   PetscInt       jj;
3345: #endif

3348:   if (i[0]) {
3349:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3350:   }
3351:   MatCreate(comm,mat);
3352:   MatSetSizes(*mat,m,n,m,n);
3353:   MatSetType(*mat,MATSEQAIJ);
3354:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
3355:   aij  = (Mat_SeqAIJ*)(*mat)->data;
3356:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

3358:   aij->i = i;
3359:   aij->j = j;
3360:   aij->a = a;
3361:   aij->singlemalloc = PETSC_FALSE;
3362:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3363:   aij->free_a       = PETSC_FALSE;
3364:   aij->free_ij      = PETSC_FALSE;

3366:   for (ii=0; ii<m; ii++) {
3367:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
3368: #if defined(PETSC_USE_DEBUG)
3369:     if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
3370:     for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
3371:       if (j[jj] < j[jj-1]) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
3372:       if (j[jj] == j[jj]-1) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
3373:     }
3374: #endif    
3375:   }
3376: #if defined(PETSC_USE_DEBUG)
3377:   for (ii=0; ii<aij->i[m]; ii++) {
3378:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3379:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
3380:   }
3381: #endif    

3383:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3384:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3385:   return(0);
3386: }

3390: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
3391: {
3393:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

3396:   if (coloring->ctype == IS_COLORING_GLOBAL) {
3397:     ISColoringReference(coloring);
3398:     a->coloring = coloring;
3399:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3400:     PetscInt             i,*larray;
3401:     ISColoring      ocoloring;
3402:     ISColoringValue *colors;

3404:     /* set coloring for diagonal portion */
3405:     PetscMalloc((A->cmap.n+1)*sizeof(PetscInt),&larray);
3406:     for (i=0; i<A->cmap.n; i++) {
3407:       larray[i] = i;
3408:     }
3409:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->cmap.n,larray,PETSC_NULL,larray);
3410:     PetscMalloc((A->cmap.n+1)*sizeof(ISColoringValue),&colors);
3411:     for (i=0; i<A->cmap.n; i++) {
3412:       colors[i] = coloring->colors[larray[i]];
3413:     }
3414:     PetscFree(larray);
3415:     ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap.n,colors,&ocoloring);
3416:     a->coloring = ocoloring;
3417:   }
3418:   return(0);
3419: }

3421: #if defined(PETSC_HAVE_ADIC)
3423: #include "adic/ad_utils.h"

3428: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3429: {
3430:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3431:   PetscInt        m = A->rmap.n,*ii = a->i,*jj = a->j,nz,i,j,nlen;
3432:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
3433:   ISColoringValue *color;

3436:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3437:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
3438:   color = a->coloring->colors;
3439:   /* loop over rows */
3440:   for (i=0; i<m; i++) {
3441:     nz = ii[i+1] - ii[i];
3442:     /* loop over columns putting computed value into matrix */
3443:     for (j=0; j<nz; j++) {
3444:       *v++ = values[color[*jj++]];
3445:     }
3446:     values += nlen; /* jump to next row of derivatives */
3447:   }
3448:   return(0);
3449: }
3450: #endif

3454: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
3455: {
3456:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3457:   PetscInt             m = A->rmap.n,*ii = a->i,*jj = a->j,nz,i,j;
3458:   PetscScalar     *v = a->a,*values = (PetscScalar *)advalues;
3459:   ISColoringValue *color;

3462:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3463:   color = a->coloring->colors;
3464:   /* loop over rows */
3465:   for (i=0; i<m; i++) {
3466:     nz = ii[i+1] - ii[i];
3467:     /* loop over columns putting computed value into matrix */
3468:     for (j=0; j<nz; j++) {
3469:       *v++ = values[color[*jj++]];
3470:     }
3471:     values += nl; /* jump to next row of derivatives */
3472:   }
3473:   return(0);
3474: }

3476: /*
3477:     Special version for direct calls from Fortran 
3478: */
3479: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3480: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
3481: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3482: #define matsetvaluesseqaij_ matsetvaluesseqaij
3483: #endif

3485: /* Change these macros so can be used in void function */
3486: #undef CHKERRQ
3487: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)A)->comm,ierr) 
3488: #undef SETERRQ2
3489: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(((PetscObject)A)->comm,ierr) 

3494: void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
3495: {
3496:   Mat            A = *AA;
3497:   PetscInt       m = *mm, n = *nn;
3498:   InsertMode     is = *isis;
3499:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
3500:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
3501:   PetscInt       *imax,*ai,*ailen;
3503:   PetscInt       *aj,nonew = a->nonew,lastcol = -1;
3504:   PetscScalar    *ap,value,*aa;
3505:   PetscTruth     ignorezeroentries = a->ignorezeroentries;
3506:   PetscTruth     roworiented = a->roworiented;

3509:   MatPreallocated(A);
3510:   imax = a->imax;
3511:   ai = a->i;
3512:   ailen = a->ilen;
3513:   aj = a->j;
3514:   aa = a->a;

3516:   for (k=0; k<m; k++) { /* loop over added rows */
3517:     row  = im[k];
3518:     if (row < 0) continue;
3519: #if defined(PETSC_USE_DEBUG)  
3520:     if (row >= A->rmap.n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
3521: #endif
3522:     rp   = aj + ai[row]; ap = aa + ai[row];
3523:     rmax = imax[row]; nrow = ailen[row];
3524:     low  = 0;
3525:     high = nrow;
3526:     for (l=0; l<n; l++) { /* loop over added columns */
3527:       if (in[l] < 0) continue;
3528: #if defined(PETSC_USE_DEBUG)  
3529:       if (in[l] >= A->cmap.n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
3530: #endif
3531:       col = in[l];
3532:       if (roworiented) {
3533:         value = v[l + k*n];
3534:       } else {
3535:         value = v[k + l*m];
3536:       }
3537:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

3539:       if (col <= lastcol) low = 0; else high = nrow;
3540:       lastcol = col;
3541:       while (high-low > 5) {
3542:         t = (low+high)/2;
3543:         if (rp[t] > col) high = t;
3544:         else             low  = t;
3545:       }
3546:       for (i=low; i<high; i++) {
3547:         if (rp[i] > col) break;
3548:         if (rp[i] == col) {
3549:           if (is == ADD_VALUES) ap[i] += value;
3550:           else                  ap[i] = value;
3551:           goto noinsert;
3552:         }
3553:       }
3554:       if (value == 0.0 && ignorezeroentries) goto noinsert;
3555:       if (nonew == 1) goto noinsert;
3556:       if (nonew == -1) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
3557:       MatSeqXAIJReallocateAIJ(A,A->rmap.n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
3558:       N = nrow++ - 1; a->nz++; high++;
3559:       /* shift up all the later entries in this row */
3560:       for (ii=N; ii>=i; ii--) {
3561:         rp[ii+1] = rp[ii];
3562:         ap[ii+1] = ap[ii];
3563:       }
3564:       rp[i] = col;
3565:       ap[i] = value;
3566:       noinsert:;
3567:       low = i + 1;
3568:     }
3569:     ailen[row] = nrow;
3570:   }
3571:   A->same_nonzero = PETSC_FALSE;
3572:   PetscFunctionReturnVoid();
3573: }