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 MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 16: {
 17:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 19:   PetscInt       i,ishift;
 20: 
 22:   *m     = A->m;
 23:   if (!ia) return(0);
 24:   ishift = 0;
 25:   if (symmetric && !A->structurally_symmetric) {
 26:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
 27:   } else if (oshift == 1) {
 28:     PetscInt nz = a->i[A->m];
 29:     /* malloc space and  add 1 to i and j indices */
 30:     PetscMalloc((A->m+1)*sizeof(PetscInt),ia);
 31:     PetscMalloc((nz+1)*sizeof(PetscInt),ja);
 32:     for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
 33:     for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1;
 34:   } else {
 35:     *ia = a->i; *ja = a->j;
 36:   }
 37:   return(0);
 38: }

 42: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 43: {
 45: 
 47:   if (!ia) return(0);
 48:   if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
 49:     PetscFree(*ia);
 50:     PetscFree(*ja);
 51:   }
 52:   return(0);
 53: }

 57: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
 58: {
 59:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
 61:   PetscInt       i,*collengths,*cia,*cja,n = A->n,m = A->m;
 62:   PetscInt       nz = a->i[m],row,*jj,mr,col;
 63: 
 65:   *nn     = A->n;
 66:   if (!ia) return(0);
 67:   if (symmetric) {
 68:     MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,0,oshift,ia,ja);
 69:   } else {
 70:     PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
 71:     PetscMemzero(collengths,n*sizeof(PetscInt));
 72:     PetscMalloc((n+1)*sizeof(PetscInt),&cia);
 73:     PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
 74:     jj = a->j;
 75:     for (i=0; i<nz; i++) {
 76:       collengths[jj[i]]++;
 77:     }
 78:     cia[0] = oshift;
 79:     for (i=0; i<n; i++) {
 80:       cia[i+1] = cia[i] + collengths[i];
 81:     }
 82:     PetscMemzero(collengths,n*sizeof(PetscInt));
 83:     jj   = a->j;
 84:     for (row=0; row<m; row++) {
 85:       mr = a->i[row+1] - a->i[row];
 86:       for (i=0; i<mr; i++) {
 87:         col = *jj++;
 88:         cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
 89:       }
 90:     }
 91:     PetscFree(collengths);
 92:     *ia = cia; *ja = cja;
 93:   }
 94:   return(0);
 95: }

 99: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
100: {

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

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

112: #define CHUNKSIZE   15

116: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
117: {
118:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
119:   PetscInt       *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
120:   PetscInt       *imax = a->imax,*ai = a->i,*ailen = a->ilen;
122:   PetscInt       *aj = a->j,nonew = a->nonew,lastcol = -1;
123:   PetscScalar    *ap,value,*aa = a->a;
124:   PetscTruth     ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE);
125:   PetscTruth     roworiented = a->roworiented;

128:   for (k=0; k<m; k++) { /* loop over added rows */
129:     row  = im[k];
130:     if (row < 0) continue;
131: #if defined(PETSC_USE_DEBUG)  
132:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->m-1);
133: #endif
134:     rp   = aj + ai[row]; ap = aa + ai[row];
135:     rmax = imax[row]; nrow = ailen[row];
136:     low  = 0;
137:     high = nrow;
138:     for (l=0; l<n; l++) { /* loop over added columns */
139:       if (in[l] < 0) continue;
140: #if defined(PETSC_USE_DEBUG)  
141:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->n-1);
142: #endif
143:       col = in[l];
144:       if (roworiented) {
145:         value = v[l + k*n];
146:       } else {
147:         value = v[k + l*m];
148:       }
149:       if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;

151:       if (col <= lastcol) low = 0; else high = nrow;
152:       lastcol = col;
153:       while (high-low > 5) {
154:         t = (low+high)/2;
155:         if (rp[t] > col) high = t;
156:         else             low  = t;
157:       }
158:       for (i=low; i<high; i++) {
159:         if (rp[i] > col) break;
160:         if (rp[i] == col) {
161:           if (is == ADD_VALUES) ap[i] += value;
162:           else                  ap[i] = value;
163:           goto noinsert;
164:         }
165:       }
166:       if (value == 0.0 && ignorezeroentries) goto noinsert;
167:       if (nonew == 1) goto noinsert;
168:       if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
169:       MatSeqXAIJReallocateAIJ(a,1,nrow,row,col,rmax,aa,ai,aj,A->m,rp,ap,imax,nonew);
170:       N = nrow++ - 1; a->nz++;
171:       /* shift up all the later entries in this row */
172:       for (ii=N; ii>=i; ii--) {
173:         rp[ii+1] = rp[ii];
174:         ap[ii+1] = ap[ii];
175:       }
176:       rp[i] = col;
177:       ap[i] = value;
178:       noinsert:;
179:       low = i + 1;
180:     }
181:     ailen[row] = nrow;
182:   }
183:   A->same_nonzero = PETSC_FALSE;
184:   return(0);
185: }

189: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
190: {
191:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)A->data;
192:   PetscInt     *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
193:   PetscInt     *ai = a->i,*ailen = a->ilen;
194:   PetscScalar  *ap,*aa = a->a,zero = 0.0;

197:   for (k=0; k<m; k++) { /* loop over rows */
198:     row  = im[k];
199:     if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row);
200:     if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->m-1);
201:     rp   = aj + ai[row]; ap = aa + ai[row];
202:     nrow = ailen[row];
203:     for (l=0; l<n; l++) { /* loop over columns */
204:       if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]);
205:       if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->n-1);
206:       col = in[l] ;
207:       high = nrow; low = 0; /* assume unsorted */
208:       while (high-low > 5) {
209:         t = (low+high)/2;
210:         if (rp[t] > col) high = t;
211:         else             low  = t;
212:       }
213:       for (i=low; i<high; i++) {
214:         if (rp[i] > col) break;
215:         if (rp[i] == col) {
216:           *v++ = ap[i];
217:           goto finished;
218:         }
219:       }
220:       *v++ = zero;
221:       finished:;
222:     }
223:   }
224:   return(0);
225: }


230: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
231: {
232:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
234:   PetscInt       i,*col_lens;
235:   int            fd;

238:   PetscViewerBinaryGetDescriptor(viewer,&fd);
239:   PetscMalloc((4+A->m)*sizeof(PetscInt),&col_lens);
240:   col_lens[0] = MAT_FILE_COOKIE;
241:   col_lens[1] = A->m;
242:   col_lens[2] = A->n;
243:   col_lens[3] = a->nz;

245:   /* store lengths of each row and write (including header) to file */
246:   for (i=0; i<A->m; i++) {
247:     col_lens[4+i] = a->i[i+1] - a->i[i];
248:   }
249:   PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,PETSC_TRUE);
250:   PetscFree(col_lens);

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

255:   /* store nonzero values */
256:   PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
257:   return(0);
258: }

260: EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);

264: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
265: {
266:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
267:   PetscErrorCode    ierr;
268:   PetscInt          i,j,m = A->m,shift=0;
269:   const char        *name;
270:   PetscViewerFormat format;

273:   PetscObjectGetName((PetscObject)A,&name);
274:   PetscViewerGetFormat(viewer,&format);
275:   if (format == PETSC_VIEWER_ASCII_MATLAB) {
276:     PetscInt nofinalvalue = 0;
277:     if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) {
278:       nofinalvalue = 1;
279:     }
280:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
281:     PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->n);
282:     PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
283:     PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
284:     PetscViewerASCIIPrintf(viewer,"zzz = [\n");

286:     for (i=0; i<m; i++) {
287:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
288: #if defined(PETSC_USE_COMPLEX)
289:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
290: #else
291:         PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
292: #endif
293:       }
294:     }
295:     if (nofinalvalue) {
296:       PetscViewerASCIIPrintf(viewer,"%D %D  %18.16e\n",m,A->n,0.0);
297:     }
298:     PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
299:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
300:   } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
301:      return(0);
302:   } else if (format == PETSC_VIEWER_ASCII_COMMON) {
303:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
304:     for (i=0; i<m; i++) {
305:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
306:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
307: #if defined(PETSC_USE_COMPLEX)
308:         if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
309:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
310:         } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
311:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
312:         } else if (PetscRealPart(a->a[j]) != 0.0) {
313:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
314:         }
315: #else
316:         if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,a->a[j]);}
317: #endif
318:       }
319:       PetscViewerASCIIPrintf(viewer,"\n");
320:     }
321:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
322:   } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
323:     PetscInt nzd=0,fshift=1,*sptr;
324:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
325:     PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
326:     for (i=0; i<m; i++) {
327:       sptr[i] = nzd+1;
328:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
329:         if (a->j[j] >= i) {
330: #if defined(PETSC_USE_COMPLEX)
331:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
332: #else
333:           if (a->a[j] != 0.0) nzd++;
334: #endif
335:         }
336:       }
337:     }
338:     sptr[m] = nzd+1;
339:     PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
340:     for (i=0; i<m+1; i+=6) {
341:       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]);}
342:       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]);}
343:       else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
344:       else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
345:       else if (i<m)   {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
346:       else            {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
347:     }
348:     PetscViewerASCIIPrintf(viewer,"\n");
349:     PetscFree(sptr);
350:     for (i=0; i<m; i++) {
351:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
352:         if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
353:       }
354:       PetscViewerASCIIPrintf(viewer,"\n");
355:     }
356:     PetscViewerASCIIPrintf(viewer,"\n");
357:     for (i=0; i<m; i++) {
358:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
359:         if (a->j[j] >= i) {
360: #if defined(PETSC_USE_COMPLEX)
361:           if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
362:             PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
363:           }
364: #else
365:           if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
366: #endif
367:         }
368:       }
369:       PetscViewerASCIIPrintf(viewer,"\n");
370:     }
371:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
372:   } else if (format == PETSC_VIEWER_ASCII_DENSE) {
373:     PetscInt         cnt = 0,jcnt;
374:     PetscScalar value;

376:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
377:     for (i=0; i<m; i++) {
378:       jcnt = 0;
379:       for (j=0; j<A->n; j++) {
380:         if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
381:           value = a->a[cnt++];
382:           jcnt++;
383:         } else {
384:           value = 0.0;
385:         }
386: #if defined(PETSC_USE_COMPLEX)
387:         PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
388: #else
389:         PetscViewerASCIIPrintf(viewer," %7.5e ",value);
390: #endif
391:       }
392:       PetscViewerASCIIPrintf(viewer,"\n");
393:     }
394:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
395:   } else {
396:     PetscViewerASCIIUseTabs(viewer,PETSC_NO);
397:     for (i=0; i<m; i++) {
398:       PetscViewerASCIIPrintf(viewer,"row %D:",i);
399:       for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
400: #if defined(PETSC_USE_COMPLEX)
401:         if (PetscImaginaryPart(a->a[j]) > 0.0) {
402:           PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
403:         } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
404:           PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
405:         } else {
406:           PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,PetscRealPart(a->a[j]));
407:         }
408: #else
409:         PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j]+shift,a->a[j]);
410: #endif
411:       }
412:       PetscViewerASCIIPrintf(viewer,"\n");
413:     }
414:     PetscViewerASCIIUseTabs(viewer,PETSC_YES);
415:   }
416:   PetscViewerFlush(viewer);
417:   return(0);
418: }

422: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
423: {
424:   Mat               A = (Mat) Aa;
425:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
426:   PetscErrorCode    ierr;
427:   PetscInt          i,j,m = A->m,color;
428:   PetscReal         xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
429:   PetscViewer       viewer;
430:   PetscViewerFormat format;

433:   PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
434:   PetscViewerGetFormat(viewer,&format);

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

439:   if (format != PETSC_VIEWER_DRAW_CONTOUR) {
440:     /* Blue for negative, Cyan for zero and  Red for positive */
441:     color = PETSC_DRAW_BLUE;
442:     for (i=0; i<m; i++) {
443:       y_l = m - i - 1.0; y_r = y_l + 1.0;
444:       for (j=a->i[i]; j<a->i[i+1]; j++) {
445:         x_l = a->j[j] ; x_r = x_l + 1.0;
446: #if defined(PETSC_USE_COMPLEX)
447:         if (PetscRealPart(a->a[j]) >=  0.) continue;
448: #else
449:         if (a->a[j] >=  0.) continue;
450: #endif
451:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
452:       }
453:     }
454:     color = PETSC_DRAW_CYAN;
455:     for (i=0; i<m; i++) {
456:       y_l = m - i - 1.0; y_r = y_l + 1.0;
457:       for (j=a->i[i]; j<a->i[i+1]; j++) {
458:         x_l = a->j[j]; x_r = x_l + 1.0;
459:         if (a->a[j] !=  0.) continue;
460:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
461:       }
462:     }
463:     color = PETSC_DRAW_RED;
464:     for (i=0; i<m; i++) {
465:       y_l = m - i - 1.0; y_r = y_l + 1.0;
466:       for (j=a->i[i]; j<a->i[i+1]; j++) {
467:         x_l = a->j[j]; x_r = x_l + 1.0;
468: #if defined(PETSC_USE_COMPLEX)
469:         if (PetscRealPart(a->a[j]) <=  0.) continue;
470: #else
471:         if (a->a[j] <=  0.) continue;
472: #endif
473:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
474:       }
475:     }
476:   } else {
477:     /* use contour shading to indicate magnitude of values */
478:     /* first determine max of all nonzero values */
479:     PetscInt    nz = a->nz,count;
480:     PetscDraw   popup;
481:     PetscReal scale;

483:     for (i=0; i<nz; i++) {
484:       if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
485:     }
486:     scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
487:     PetscDrawGetPopup(draw,&popup);
488:     if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
489:     count = 0;
490:     for (i=0; i<m; i++) {
491:       y_l = m - i - 1.0; y_r = y_l + 1.0;
492:       for (j=a->i[i]; j<a->i[i+1]; j++) {
493:         x_l = a->j[j]; x_r = x_l + 1.0;
494:         color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
495:         PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
496:         count++;
497:       }
498:     }
499:   }
500:   return(0);
501: }

505: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
506: {
508:   PetscDraw      draw;
509:   PetscReal      xr,yr,xl,yl,h,w;
510:   PetscTruth     isnull;

513:   PetscViewerDrawGetDraw(viewer,0,&draw);
514:   PetscDrawIsNull(draw,&isnull);
515:   if (isnull) return(0);

517:   PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
518:   xr  = A->n; yr = A->m; h = yr/10.0; w = xr/10.0;
519:   xr += w;    yr += h;  xl = -w;     yl = -h;
520:   PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
521:   PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
522:   PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
523:   return(0);
524: }

528: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
529: {
531:   PetscTruth     issocket,iascii,isbinary,isdraw;

534:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
535:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
536:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
537:   PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
538:   if (iascii) {
539:     MatView_SeqAIJ_ASCII(A,viewer);
540: #if defined(PETSC_USE_SOCKET_VIEWER)
541:   } else if (issocket) {
542:     Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
543:     PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);
544: #endif
545:   } else if (isbinary) {
546:     MatView_SeqAIJ_Binary(A,viewer);
547:   } else if (isdraw) {
548:     MatView_SeqAIJ_Draw(A,viewer);
549:   } else {
550:     SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
551:   }
552:   MatView_Inode(A,viewer);
553:   return(0);
554: }

558: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
559: {
560:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
562:   PetscInt       fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
563:   PetscInt       m = A->m,*ip,N,*ailen = a->ilen,rmax = 0;
564:   PetscScalar    *aa = a->a,*ap;
565:   PetscReal      ratio=0.6;

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

570:   if (m) rmax = ailen[0]; /* determine row with most nonzeros */
571:   for (i=1; i<m; i++) {
572:     /* move each row back by the amount of empty slots (fshift) before it*/
573:     fshift += imax[i-1] - ailen[i-1];
574:     rmax   = PetscMax(rmax,ailen[i]);
575:     if (fshift) {
576:       ip = aj + ai[i] ;
577:       ap = aa + ai[i] ;
578:       N  = ailen[i];
579:       for (j=0; j<N; j++) {
580:         ip[j-fshift] = ip[j];
581:         ap[j-fshift] = ap[j];
582:       }
583:     }
584:     ai[i] = ai[i-1] + ailen[i-1];
585:   }
586:   if (m) {
587:     fshift += imax[m-1] - ailen[m-1];
588:     ai[m]  = ai[m-1] + ailen[m-1];
589:   }
590:   /* reset ilen and imax for each row */
591:   for (i=0; i<m; i++) {
592:     ailen[i] = imax[i] = ai[i+1] - ai[i];
593:   }
594:   a->nz = ai[m];

596:   /* diagonals may have moved, so kill the diagonal pointers */
597:   if (fshift && a->diag) {
598:     PetscFree(a->diag);
599:     PetscLogObjectMemory(A,-(m+1)*sizeof(PetscInt));
600:     a->diag = 0;
601:   }
602:   PetscLogInfo((A,"MatAssemblyEnd_SeqAIJ:Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->n,fshift,a->nz));
603:   PetscLogInfo((A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %D\n",a->reallocs));
604:   PetscLogInfo((A,"MatAssemblyEnd_SeqAIJ:Maximum nonzeros in any row is %D\n",rmax));
605:   a->reallocs          = 0;
606:   A->info.nz_unneeded  = (double)fshift;
607:   a->rmax              = rmax;

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

613:   MatAssemblyEnd_Inode(A,mode);
614:   return(0);
615: }

619: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
620: {
621:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

625:   PetscMemzero(a->a,(a->i[A->m])*sizeof(PetscScalar));
626:   return(0);
627: }

631: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
632: {
633:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

637: #if defined(PETSC_USE_LOG)
638:   PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->m,A->n,a->nz);
639: #endif
640:   if (a->freedata){
641:     MatSeqXAIJFreeAIJ(a->singlemalloc,&a->a,&a->j,&a->i);
642:   }
643:   if (a->row) {
644:     ISDestroy(a->row);
645:   }
646:   if (a->col) {
647:     ISDestroy(a->col);
648:   }
649:   if (a->diag) {PetscFree(a->diag);}
650:   if (a->ilen) {PetscFree2(a->imax,a->ilen);}
651:   if (a->idiag) {PetscFree(a->idiag);}
652:   if (a->solve_work) {PetscFree(a->solve_work);}
653:   if (a->icol) {ISDestroy(a->icol);}
654:   if (a->saved_values) {PetscFree(a->saved_values);}
655:   if (a->coloring) {ISColoringDestroy(a->coloring);}
656:   if (a->xtoy) {PetscFree(a->xtoy);}
657:   if (a->compressedrow.use){PetscFree(a->compressedrow.i);}

659:   MatDestroy_Inode(A);

661:   PetscFree(a);

663:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
664:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
665:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
666:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
667:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
668:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
669:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
670:   PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
671:   return(0);
672: }

676: PetscErrorCode MatCompress_SeqAIJ(Mat A)
677: {
679:   return(0);
680: }

684: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op)
685: {
686:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

690:   switch (op) {
691:     case MAT_ROW_ORIENTED:
692:       a->roworiented       = PETSC_TRUE;
693:       break;
694:     case MAT_KEEP_ZEROED_ROWS:
695:       a->keepzeroedrows    = PETSC_TRUE;
696:       break;
697:     case MAT_COLUMN_ORIENTED:
698:       a->roworiented       = PETSC_FALSE;
699:       break;
700:     case MAT_COLUMNS_SORTED:
701:       a->sorted            = PETSC_TRUE;
702:       break;
703:     case MAT_COLUMNS_UNSORTED:
704:       a->sorted            = PETSC_FALSE;
705:       break;
706:     case MAT_NO_NEW_NONZERO_LOCATIONS:
707:       a->nonew             = 1;
708:       break;
709:     case MAT_NEW_NONZERO_LOCATION_ERR:
710:       a->nonew             = -1;
711:       break;
712:     case MAT_NEW_NONZERO_ALLOCATION_ERR:
713:       a->nonew             = -2;
714:       break;
715:     case MAT_YES_NEW_NONZERO_LOCATIONS:
716:       a->nonew             = 0;
717:       break;
718:     case MAT_IGNORE_ZERO_ENTRIES:
719:       a->ignorezeroentries = PETSC_TRUE;
720:       break;
721:     case MAT_USE_COMPRESSEDROW:
722:       a->compressedrow.use = PETSC_TRUE;
723:       break;
724:     case MAT_DO_NOT_USE_COMPRESSEDROW:
725:       a->compressedrow.use = PETSC_FALSE;
726:       break;
727:     case MAT_ROWS_SORTED:
728:     case MAT_ROWS_UNSORTED:
729:     case MAT_YES_NEW_DIAGONALS:
730:     case MAT_IGNORE_OFF_PROC_ENTRIES:
731:     case MAT_USE_HASH_TABLE:
732:       PetscLogInfo((A,"MatSetOption_SeqAIJ:Option ignored\n"));
733:       break;
734:     case MAT_NO_NEW_DIAGONALS:
735:       SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
736:     default:
737:       break;
738:   }
739:   MatSetOption_Inode(A,op);
740:   return(0);
741: }

745: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
746: {
747:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
749:   PetscInt       i,j,n;
750:   PetscScalar    *x,zero = 0.0;

753:   VecSet(v,zero);
754:   VecGetArray(v,&x);
755:   VecGetLocalSize(v,&n);
756:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
757:   for (i=0; i<A->m; i++) {
758:     for (j=a->i[i]; j<a->i[i+1]; j++) {
759:       if (a->j[j] == i) {
760:         x[i] = a->a[j];
761:         break;
762:       }
763:     }
764:   }
765:   VecRestoreArray(v,&x);
766:   return(0);
767: }

771: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
772: {
773:   Mat_SeqAIJ        *a = (Mat_SeqAIJ*)A->data;
774:   PetscScalar       *x,*y;
775:   PetscErrorCode    ierr;
776:   PetscInt          m = A->m;
777: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
778:   PetscScalar       *v,alpha;
779:   PetscInt          n,i,*idx,*ii,*ridx=PETSC_NULL;
780:   Mat_CompressedRow cprow = a->compressedrow;
781:   PetscTruth        usecprow = cprow.use;
782: #endif

785:   if (zz != yy) {VecCopy(zz,yy);}
786:   VecGetArray(xx,&x);
787:   VecGetArray(yy,&y);

789: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
790:   fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
791: #else
792:   if (usecprow){
793:     m    = cprow.nrows;
794:     ii   = cprow.i;
795:     ridx = cprow.rindex;
796:   } else {
797:     ii = a->i;
798:   }
799:   for (i=0; i<m; i++) {
800:     idx   = a->j + ii[i] ;
801:     v     = a->a + ii[i] ;
802:     n     = ii[i+1] - ii[i];
803:     if (usecprow){
804:       alpha = x[ridx[i]];
805:     } else {
806:       alpha = x[i];
807:     }
808:     while (n-->0) {y[*idx++] += alpha * *v++;}
809:   }
810: #endif
811:   PetscLogFlops(2*a->nz);
812:   VecRestoreArray(xx,&x);
813:   VecRestoreArray(yy,&y);
814:   return(0);
815: }

819: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
820: {
821:   PetscScalar    zero = 0.0;

825:   VecSet(yy,zero);
826:   MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
827:   return(0);
828: }


833: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
834: {
835:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
836:   PetscScalar    *x,*y,*aa;
838:   PetscInt       m=A->m,*aj,*ii;
839: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
840:   PetscInt       n,i,jrow,j,*ridx=PETSC_NULL;
841:   PetscScalar    sum;
842:   PetscTruth     usecprow=a->compressedrow.use;
843: #endif

845: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
846: #pragma disjoint(*x,*y,*aa)
847: #endif

850:   VecGetArray(xx,&x);
851:   VecGetArray(yy,&y);
852:   aj  = a->j;
853:   aa    = a->a;
854:   ii   = a->i;
855: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
856:   fortranmultaij_(&m,x,ii,aj,aa,y);
857: #else
858:   if (usecprow){ /* use compressed row format */
859:     m    = a->compressedrow.nrows;
860:     ii   = a->compressedrow.i;
861:     ridx = a->compressedrow.rindex;
862:     for (i=0; i<m; i++){
863:       n   = ii[i+1] - ii[i];
864:       aj  = a->j + ii[i];
865:       aa  = a->a + ii[i];
866:       sum = 0.0;
867:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
868:       y[*ridx++] = sum;
869:     }
870:   } else { /* do not use compressed row format */
871:     for (i=0; i<m; i++) {
872:       jrow = ii[i];
873:       n    = ii[i+1] - jrow;
874:       sum  = 0.0;
875:       for (j=0; j<n; j++) {
876:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
877:       }
878:       y[i] = sum;
879:     }
880:   }
881: #endif
882:   PetscLogFlops(2*a->nz - m);
883:   VecRestoreArray(xx,&x);
884:   VecRestoreArray(yy,&y);
885:   return(0);
886: }

890: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
891: {
892:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
893:   PetscScalar    *x,*y,*z,*aa;
895:   PetscInt       m = A->m,*aj,*ii;
896: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
897:   PetscInt       n,i,jrow,j,*ridx=PETSC_NULL;
898:   PetscScalar    sum;
899:   PetscTruth     usecprow=a->compressedrow.use;
900: #endif

903:   VecGetArray(xx,&x);
904:   VecGetArray(yy,&y);
905:   if (zz != yy) {
906:     VecGetArray(zz,&z);
907:   } else {
908:     z = y;
909:   }

911:   aj  = a->j;
912:   aa  = a->a;
913:   ii  = a->i;
914: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
915:   fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
916: #else
917:   if (usecprow){ /* use compressed row format */
918:     if (zz != yy){
919:       PetscMemcpy(z,y,m*sizeof(PetscScalar));
920:     }
921:     m    = a->compressedrow.nrows;
922:     ii   = a->compressedrow.i;
923:     ridx = a->compressedrow.rindex;
924:     for (i=0; i<m; i++){
925:       n  = ii[i+1] - ii[i];
926:       aj  = a->j + ii[i];
927:       aa  = a->a + ii[i];
928:       sum = y[*ridx];
929:       for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
930:       z[*ridx++] = sum;
931:     }
932:   } else { /* do not use compressed row format */
933:     for (i=0; i<m; i++) {
934:       jrow = ii[i];
935:       n    = ii[i+1] - jrow;
936:       sum  = y[i];
937:       for (j=0; j<n; j++) {
938:         sum += aa[jrow]*x[aj[jrow]]; jrow++;
939:       }
940:       z[i] = sum;
941:     }
942:   }
943: #endif
944:   PetscLogFlops(2*a->nz);
945:   VecRestoreArray(xx,&x);
946:   VecRestoreArray(yy,&y);
947:   if (zz != yy) {
948:     VecRestoreArray(zz,&z);
949:   }
950:   return(0);
951: }

953: /*
954:      Adds diagonal pointers to sparse matrix structure.
955: */
958: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
959: {
960:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
962:   PetscInt       i,j,*diag,m = A->m;

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

967:   PetscMalloc((m+1)*sizeof(PetscInt),&diag);
968:   PetscLogObjectMemory(A,(m+1)*sizeof(PetscInt));
969:   for (i=0; i<A->m; i++) {
970:     diag[i] = a->i[i+1];
971:     for (j=a->i[i]; j<a->i[i+1]; j++) {
972:       if (a->j[j] == i) {
973:         diag[i] = j;
974:         break;
975:       }
976:     }
977:   }
978:   a->diag = diag;
979:   return(0);
980: }

982: /*
983:      Checks for missing diagonals
984: */
987: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A)
988: {
989:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
991:   PetscInt       *diag,*jj = a->j,i;

994:   MatMarkDiagonal_SeqAIJ(A);
995:   diag = a->diag;
996:   for (i=0; i<A->m; i++) {
997:     if (jj[diag[i]] != i) {
998:       SETERRQ1(PETSC_ERR_PLIB,"Matrix is missing diagonal number %D",i);
999:     }
1000:   }
1001:   return(0);
1002: }

1006: PetscErrorCode MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1007: {
1008:   Mat_SeqAIJ         *a = (Mat_SeqAIJ*)A->data;
1009:   PetscScalar        *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag;
1010:   const PetscScalar  *v = a->a, *b, *bs,*xb, *ts;
1011:   PetscErrorCode     ierr;
1012:   PetscInt           n = A->n,m = A->m,i;
1013:   const PetscInt     *idx,*diag;

1016:   its = its*lits;
1017:   if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %D and local its %D both positive",its,lits);

1019:   if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
1020:   diag = a->diag;
1021:   if (!a->idiag) {
1022:     PetscMalloc(3*m*sizeof(PetscScalar),&a->idiag);
1023:     a->ssor  = a->idiag + m;
1024:     mdiag    = a->ssor + m;

1026:     v        = a->a;

1028:     /* this is wrong when fshift omega changes each iteration */
1029:     if (omega == 1.0 && !fshift) {
1030:       for (i=0; i<m; i++) {
1031:         mdiag[i]    = v[diag[i]];
1032:         a->idiag[i] = 1.0/v[diag[i]];
1033:       }
1034:       PetscLogFlops(m);
1035:     } else {
1036:       for (i=0; i<m; i++) {
1037:         mdiag[i]    = v[diag[i]];
1038:         a->idiag[i] = omega/(fshift + v[diag[i]]);
1039:       }
1040:       PetscLogFlops(2*m);
1041:     }
1042:   }
1043:   t     = a->ssor;
1044:   idiag = a->idiag;
1045:   mdiag = a->idiag + 2*m;

1047:   VecGetArray(xx,&x);
1048:   if (xx != bb) {
1049:     VecGetArray(bb,(PetscScalar**)&b);
1050:   } else {
1051:     b = x;
1052:   }

1054:   /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1055:   xs   = x;
1056:   if (flag == SOR_APPLY_UPPER) {
1057:    /* apply (U + D/omega) to the vector */
1058:     bs = b;
1059:     for (i=0; i<m; i++) {
1060:         d    = fshift + a->a[diag[i]];
1061:         n    = a->i[i+1] - diag[i] - 1;
1062:         idx  = a->j + diag[i] + 1;
1063:         v    = a->a + diag[i] + 1;
1064:         sum  = b[i]*d/omega;
1065:         SPARSEDENSEDOT(sum,bs,v,idx,n);
1066:         x[i] = sum;
1067:     }
1068:     VecRestoreArray(xx,&x);
1069:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1070:     PetscLogFlops(a->nz);
1071:     return(0);
1072:   }


1075:     /* Let  A = L + U + D; where L is lower trianglar,
1076:     U is upper triangular, E is diagonal; This routine applies

1078:             (L + E)^{-1} A (U + E)^{-1}

1080:     to a vector efficiently using Eisenstat's trick. This is for
1081:     the case of SSOR preconditioner, so E is D/omega where omega
1082:     is the relaxation factor.
1083:     */

1085:   if (flag == SOR_APPLY_LOWER) {
1086:     SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1087:   } else if (flag & SOR_EISENSTAT) {
1088:     /* Let  A = L + U + D; where L is lower trianglar,
1089:     U is upper triangular, E is diagonal; This routine applies

1091:             (L + E)^{-1} A (U + E)^{-1}

1093:     to a vector efficiently using Eisenstat's trick. This is for
1094:     the case of SSOR preconditioner, so E is D/omega where omega
1095:     is the relaxation factor.
1096:     */
1097:     scale = (2.0/omega) - 1.0;

1099:     /*  x = (E + U)^{-1} b */
1100:     for (i=m-1; i>=0; i--) {
1101:       n    = a->i[i+1] - diag[i] - 1;
1102:       idx  = a->j + diag[i] + 1;
1103:       v    = a->a + diag[i] + 1;
1104:       sum  = b[i];
1105:       SPARSEDENSEMDOT(sum,xs,v,idx,n);
1106:       x[i] = sum*idiag[i];
1107:     }

1109:     /*  t = b - (2*E - D)x */
1110:     v = a->a;
1111:     for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }

1113:     /*  t = (E + L)^{-1}t */
1114:     ts = t;
1115:     diag = a->diag;
1116:     for (i=0; i<m; i++) {
1117:       n    = diag[i] - a->i[i];
1118:       idx  = a->j + a->i[i];
1119:       v    = a->a + a->i[i];
1120:       sum  = t[i];
1121:       SPARSEDENSEMDOT(sum,ts,v,idx,n);
1122:       t[i] = sum*idiag[i];
1123:       /*  x = x + t */
1124:       x[i] += t[i];
1125:     }

1127:     PetscLogFlops(6*m-1 + 2*a->nz);
1128:     VecRestoreArray(xx,&x);
1129:     if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1130:     return(0);
1131:   }
1132:   if (flag & SOR_ZERO_INITIAL_GUESS) {
1133:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1134: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1135:       fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)b);
1136: #else
1137:       for (i=0; i<m; i++) {
1138:         n    = diag[i] - a->i[i];
1139:         idx  = a->j + a->i[i];
1140:         v    = a->a + a->i[i];
1141:         sum  = b[i];
1142:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1143:         x[i] = sum*idiag[i];
1144:       }
1145: #endif
1146:       xb = x;
1147:       PetscLogFlops(a->nz);
1148:     } else xb = b;
1149:     if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1150:         (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1151:       for (i=0; i<m; i++) {
1152:         x[i] *= mdiag[i];
1153:       }
1154:       PetscLogFlops(m);
1155:     }
1156:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1157: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1158:       fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)xb);
1159: #else
1160:       for (i=m-1; i>=0; i--) {
1161:         n    = a->i[i+1] - diag[i] - 1;
1162:         idx  = a->j + diag[i] + 1;
1163:         v    = a->a + diag[i] + 1;
1164:         sum  = xb[i];
1165:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1166:         x[i] = sum*idiag[i];
1167:       }
1168: #endif
1169:       PetscLogFlops(a->nz);
1170:     }
1171:     its--;
1172:   }
1173:   while (its--) {
1174:     if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1175: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1176:       fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1177: #else
1178:       for (i=0; i<m; i++) {
1179:         d    = fshift + a->a[diag[i]];
1180:         n    = a->i[i+1] - a->i[i];
1181:         idx  = a->j + a->i[i];
1182:         v    = a->a + a->i[i];
1183:         sum  = b[i];
1184:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1185:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1186:       }
1187: #endif 
1188:       PetscLogFlops(a->nz);
1189:     }
1190:     if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1191: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1192:       fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1193: #else
1194:       for (i=m-1; i>=0; i--) {
1195:         d    = fshift + a->a[diag[i]];
1196:         n    = a->i[i+1] - a->i[i];
1197:         idx  = a->j + a->i[i];
1198:         v    = a->a + a->i[i];
1199:         sum  = b[i];
1200:         SPARSEDENSEMDOT(sum,xs,v,idx,n);
1201:         x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1202:       }
1203: #endif
1204:       PetscLogFlops(a->nz);
1205:     }
1206:   }
1207:   VecRestoreArray(xx,&x);
1208:   if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1209:   return(0);
1210: }

1214: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1215: {
1216:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;

1219:   info->rows_global    = (double)A->m;
1220:   info->columns_global = (double)A->n;
1221:   info->rows_local     = (double)A->m;
1222:   info->columns_local  = (double)A->n;
1223:   info->block_size     = 1.0;
1224:   info->nz_allocated   = (double)a->maxnz;
1225:   info->nz_used        = (double)a->nz;
1226:   info->nz_unneeded    = (double)(a->maxnz - a->nz);
1227:   info->assemblies     = (double)A->num_ass;
1228:   info->mallocs        = (double)a->reallocs;
1229:   info->memory         = A->mem;
1230:   if (A->factor) {
1231:     info->fill_ratio_given  = A->info.fill_ratio_given;
1232:     info->fill_ratio_needed = A->info.fill_ratio_needed;
1233:     info->factor_mallocs    = A->info.factor_mallocs;
1234:   } else {
1235:     info->fill_ratio_given  = 0;
1236:     info->fill_ratio_needed = 0;
1237:     info->factor_mallocs    = 0;
1238:   }
1239:   return(0);
1240: }

1244: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1245: {
1246:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1247:   PetscInt       i,m = A->m - 1;

1251:   if (a->keepzeroedrows) {
1252:     for (i=0; i<N; i++) {
1253:       if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1254:       PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1255:     }
1256:     if (diag != 0.0) {
1257:       MatMissingDiagonal_SeqAIJ(A);
1258:       MatMarkDiagonal_SeqAIJ(A);
1259:       for (i=0; i<N; i++) {
1260:         a->a[a->diag[rows[i]]] = diag;
1261:       }
1262:     }
1263:     A->same_nonzero = PETSC_TRUE;
1264:   } else {
1265:     if (diag != 0.0) {
1266:       for (i=0; i<N; i++) {
1267:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1268:         if (a->ilen[rows[i]] > 0) {
1269:           a->ilen[rows[i]]          = 1;
1270:           a->a[a->i[rows[i]]] = diag;
1271:           a->j[a->i[rows[i]]] = rows[i];
1272:         } else { /* in case row was completely empty */
1273:           MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1274:         }
1275:       }
1276:     } else {
1277:       for (i=0; i<N; i++) {
1278:         if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1279:         a->ilen[rows[i]] = 0;
1280:       }
1281:     }
1282:     A->same_nonzero = PETSC_FALSE;
1283:   }
1284:   MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1285:   return(0);
1286: }

1290: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1291: {
1292:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1293:   PetscInt   *itmp;

1296:   if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);

1298:   *nz = a->i[row+1] - a->i[row];
1299:   if (v) *v = a->a + a->i[row];
1300:   if (idx) {
1301:     itmp = a->j + a->i[row];
1302:     if (*nz) {
1303:       *idx = itmp;
1304:     }
1305:     else *idx = 0;
1306:   }
1307:   return(0);
1308: }

1310: /* remove this function? */
1313: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1314: {
1316:   return(0);
1317: }

1321: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1322: {
1323:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1324:   PetscScalar    *v = a->a;
1325:   PetscReal      sum = 0.0;
1327:   PetscInt       i,j;

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

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

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

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

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

1410: PetscErrorCode PETSCMAT_DLLEXPORT MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1411: {
1412:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1413:   PetscInt       *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1415:   PetscInt       ma,na,mb,nb, i;

1418:   bij = (Mat_SeqAIJ *) B->data;
1419: 
1420:   MatGetSize(A,&ma,&na);
1421:   MatGetSize(B,&mb,&nb);
1422:   if (ma!=nb || na!=mb){
1423:     *f = PETSC_FALSE;
1424:     return(0);
1425:   }
1426:   aii = aij->i; bii = bij->i;
1427:   adx = aij->j; bdx = bij->j;
1428:   va  = aij->a; vb = bij->a;
1429:   PetscMalloc(ma*sizeof(PetscInt),&aptr);
1430:   PetscMalloc(mb*sizeof(PetscInt),&bptr);
1431:   for (i=0; i<ma; i++) aptr[i] = aii[i];
1432:   for (i=0; i<mb; i++) bptr[i] = bii[i];

1434:   *f = PETSC_TRUE;
1435:   for (i=0; i<ma; i++) {
1436:     while (aptr[i]<aii[i+1]) {
1437:       PetscInt         idc,idr;
1438:       PetscScalar vc,vr;
1439:       /* column/row index/value */
1440:       idc = adx[aptr[i]];
1441:       idr = bdx[bptr[idc]];
1442:       vc  = va[aptr[i]];
1443:       vr  = vb[bptr[idc]];
1444:       if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1445:         *f = PETSC_FALSE;
1446:         goto done;
1447:       } else {
1448:         aptr[i]++;
1449:         if (B || i!=idc) bptr[idc]++;
1450:       }
1451:     }
1452:   }
1453:  done:
1454:   PetscFree(aptr);
1455:   if (B) {
1456:     PetscFree(bptr);
1457:   }
1458:   return(0);
1459: }

1464: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1465: {
1468:   MatIsTranspose_SeqAIJ(A,A,tol,f);
1469:   return(0);
1470: }

1474: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1475: {
1476:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1477:   PetscScalar    *l,*r,x,*v;
1479:   PetscInt       i,j,m = A->m,n = A->n,M,nz = a->nz,*jj;

1482:   if (ll) {
1483:     /* The local size is used so that VecMPI can be passed to this routine
1484:        by MatDiagonalScale_MPIAIJ */
1485:     VecGetLocalSize(ll,&m);
1486:     if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1487:     VecGetArray(ll,&l);
1488:     v = a->a;
1489:     for (i=0; i<m; i++) {
1490:       x = l[i];
1491:       M = a->i[i+1] - a->i[i];
1492:       for (j=0; j<M; j++) { (*v++) *= x;}
1493:     }
1494:     VecRestoreArray(ll,&l);
1495:     PetscLogFlops(nz);
1496:   }
1497:   if (rr) {
1498:     VecGetLocalSize(rr,&n);
1499:     if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1500:     VecGetArray(rr,&r);
1501:     v = a->a; jj = a->j;
1502:     for (i=0; i<nz; i++) {
1503:       (*v++) *= r[*jj++];
1504:     }
1505:     VecRestoreArray(rr,&r);
1506:     PetscLogFlops(nz);
1507:   }
1508:   return(0);
1509: }

1513: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
1514: {
1515:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data,*c;
1517:   PetscInt       *smap,i,k,kstart,kend,oldcols = A->n,*lens;
1518:   PetscInt       row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1519:   PetscInt       *irow,*icol,nrows,ncols;
1520:   PetscInt       *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1521:   PetscScalar    *a_new,*mat_a;
1522:   Mat            C;
1523:   PetscTruth     stride;

1526:   ISSorted(isrow,(PetscTruth*)&i);
1527:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1528:   ISSorted(iscol,(PetscTruth*)&i);
1529:   if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");

1531:   ISGetIndices(isrow,&irow);
1532:   ISGetLocalSize(isrow,&nrows);
1533:   ISGetLocalSize(iscol,&ncols);

1535:   ISStrideGetInfo(iscol,&first,&step);
1536:   ISStride(iscol,&stride);
1537:   if (stride && step == 1) {
1538:     /* special case of contiguous rows */
1539:     PetscMalloc((2*nrows+1)*sizeof(PetscInt),&lens);
1540:     starts = lens + nrows;
1541:     /* loop over new rows determining lens and starting points */
1542:     for (i=0; i<nrows; i++) {
1543:       kstart  = ai[irow[i]];
1544:       kend    = kstart + ailen[irow[i]];
1545:       for (k=kstart; k<kend; k++) {
1546:         if (aj[k] >= first) {
1547:           starts[i] = k;
1548:           break;
1549:         }
1550:       }
1551:       sum = 0;
1552:       while (k < kend) {
1553:         if (aj[k++] >= first+ncols) break;
1554:         sum++;
1555:       }
1556:       lens[i] = sum;
1557:     }
1558:     /* create submatrix */
1559:     if (scall == MAT_REUSE_MATRIX) {
1560:       PetscInt n_cols,n_rows;
1561:       MatGetSize(*B,&n_rows,&n_cols);
1562:       if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1563:       MatZeroEntries(*B);
1564:       C = *B;
1565:     } else {
1566:       MatCreate(A->comm,&C);
1567:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1568:       MatSetType(C,A->type_name);
1569:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1570:     }
1571:     c = (Mat_SeqAIJ*)C->data;

1573:     /* loop over rows inserting into submatrix */
1574:     a_new    = c->a;
1575:     j_new    = c->j;
1576:     i_new    = c->i;

1578:     for (i=0; i<nrows; i++) {
1579:       ii    = starts[i];
1580:       lensi = lens[i];
1581:       for (k=0; k<lensi; k++) {
1582:         *j_new++ = aj[ii+k] - first;
1583:       }
1584:       PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1585:       a_new      += lensi;
1586:       i_new[i+1]  = i_new[i] + lensi;
1587:       c->ilen[i]  = lensi;
1588:     }
1589:     PetscFree(lens);
1590:   } else {
1591:     ISGetIndices(iscol,&icol);
1592:     PetscMalloc((1+oldcols)*sizeof(PetscInt),&smap);
1593: 
1594:     PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
1595:     PetscMemzero(smap,oldcols*sizeof(PetscInt));
1596:     for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1597:     /* determine lens of each row */
1598:     for (i=0; i<nrows; i++) {
1599:       kstart  = ai[irow[i]];
1600:       kend    = kstart + a->ilen[irow[i]];
1601:       lens[i] = 0;
1602:       for (k=kstart; k<kend; k++) {
1603:         if (smap[aj[k]]) {
1604:           lens[i]++;
1605:         }
1606:       }
1607:     }
1608:     /* Create and fill new matrix */
1609:     if (scall == MAT_REUSE_MATRIX) {
1610:       PetscTruth equal;

1612:       c = (Mat_SeqAIJ *)((*B)->data);
1613:       if ((*B)->m  != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1614:       PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(PetscInt),&equal);
1615:       if (!equal) {
1616:         SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1617:       }
1618:       PetscMemzero(c->ilen,(*B)->m*sizeof(PetscInt));
1619:       C = *B;
1620:     } else {
1621:       MatCreate(A->comm,&C);
1622:       MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1623:       MatSetType(C,A->type_name);
1624:       MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1625:     }
1626:     c = (Mat_SeqAIJ *)(C->data);
1627:     for (i=0; i<nrows; i++) {
1628:       row    = irow[i];
1629:       kstart = ai[row];
1630:       kend   = kstart + a->ilen[row];
1631:       mat_i  = c->i[i];
1632:       mat_j  = c->j + mat_i;
1633:       mat_a  = c->a + mat_i;
1634:       mat_ilen = c->ilen + i;
1635:       for (k=kstart; k<kend; k++) {
1636:         if ((tcol=smap[a->j[k]])) {
1637:           *mat_j++ = tcol - 1;
1638:           *mat_a++ = a->a[k];
1639:           (*mat_ilen)++;

1641:         }
1642:       }
1643:     }
1644:     /* Free work space */
1645:     ISRestoreIndices(iscol,&icol);
1646:     PetscFree(smap);
1647:     PetscFree(lens);
1648:   }
1649:   MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1650:   MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);

1652:   ISRestoreIndices(isrow,&irow);
1653:   *B = C;
1654:   return(0);
1655: }

1657: /*
1658: */
1661: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1662: {
1663:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)inA->data;
1665:   Mat            outA;
1666:   PetscTruth     row_identity,col_identity;

1669:   if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1670:   ISIdentity(row,&row_identity);
1671:   ISIdentity(col,&col_identity);
1672:   if (!row_identity || !col_identity) {
1673:     SETERRQ(PETSC_ERR_ARG_WRONG,"Row and column permutations must be identity for in-place ILU");
1674:   }

1676:   outA          = inA;
1677:   inA->factor   = FACTOR_LU;
1678:   a->row        = row;
1679:   a->col        = col;
1680:   PetscObjectReference((PetscObject)row);
1681:   PetscObjectReference((PetscObject)col);

1683:   /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1684:   if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1685:   ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1686:   PetscLogObjectParent(inA,a->icol);

1688:   if (!a->solve_work) { /* this matrix may have been factored before */
1689:      PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
1690:   }

1692:   if (!a->diag) {
1693:     MatMarkDiagonal_SeqAIJ(inA);
1694:   }
1695:   MatLUFactorNumeric_SeqAIJ(inA,info,&outA);
1696:   return(0);
1697: }

1699:  #include petscblaslapack.h
1702: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
1703: {
1704:   Mat_SeqAIJ   *a = (Mat_SeqAIJ*)inA->data;
1705:   PetscBLASInt bnz = (PetscBLASInt)a->nz,one = 1;
1706:   PetscScalar oalpha = alpha;


1711:   BLASscal_(&bnz,&oalpha,a->a,&one);
1712:   PetscLogFlops(a->nz);
1713:   return(0);
1714: }

1718: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1719: {
1721:   PetscInt       i;

1724:   if (scall == MAT_INITIAL_MATRIX) {
1725:     PetscMalloc((n+1)*sizeof(Mat),B);
1726:   }

1728:   for (i=0; i<n; i++) {
1729:     MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1730:   }
1731:   return(0);
1732: }

1736: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
1737: {
1738:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1740:   PetscInt       row,i,j,k,l,m,n,*idx,*nidx,isz,val;
1741:   PetscInt       start,end,*ai,*aj;
1742:   PetscBT        table;

1745:   m     = A->m;
1746:   ai    = a->i;
1747:   aj    = a->j;

1749:   if (ov < 0)  SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");

1751:   PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
1752:   PetscBTCreate(m,table);

1754:   for (i=0; i<is_max; i++) {
1755:     /* Initialize the two local arrays */
1756:     isz  = 0;
1757:     PetscBTMemzero(m,table);
1758: 
1759:     /* Extract the indices, assume there can be duplicate entries */
1760:     ISGetIndices(is[i],&idx);
1761:     ISGetLocalSize(is[i],&n);
1762: 
1763:     /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1764:     for (j=0; j<n ; ++j){
1765:       if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1766:     }
1767:     ISRestoreIndices(is[i],&idx);
1768:     ISDestroy(is[i]);
1769: 
1770:     k = 0;
1771:     for (j=0; j<ov; j++){ /* for each overlap */
1772:       n = isz;
1773:       for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1774:         row   = nidx[k];
1775:         start = ai[row];
1776:         end   = ai[row+1];
1777:         for (l = start; l<end ; l++){
1778:           val = aj[l] ;
1779:           if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1780:         }
1781:       }
1782:     }
1783:     ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1784:   }
1785:   PetscBTDestroy(table);
1786:   PetscFree(nidx);
1787:   return(0);
1788: }

1790: /* -------------------------------------------------------------- */
1793: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1794: {
1795:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
1797:   PetscInt       i,nz,m = A->m,n = A->n,*col;
1798:   PetscInt       *row,*cnew,j,*lens;
1799:   IS             icolp,irowp;
1800:   PetscInt       *cwork;
1801:   PetscScalar    *vwork;

1804:   ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1805:   ISGetIndices(irowp,&row);
1806:   ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1807:   ISGetIndices(icolp,&col);
1808: 
1809:   /* determine lengths of permuted rows */
1810:   PetscMalloc((m+1)*sizeof(PetscInt),&lens);
1811:   for (i=0; i<m; i++) {
1812:     lens[row[i]] = a->i[i+1] - a->i[i];
1813:   }
1814:   MatCreate(A->comm,B);
1815:   MatSetSizes(*B,m,n,m,n);
1816:   MatSetType(*B,A->type_name);
1817:   MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
1818:   PetscFree(lens);

1820:   PetscMalloc(n*sizeof(PetscInt),&cnew);
1821:   for (i=0; i<m; i++) {
1822:     MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1823:     for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1824:     MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1825:     MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1826:   }
1827:   PetscFree(cnew);
1828:   (*B)->assembled     = PETSC_FALSE;
1829:   MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1830:   MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1831:   ISRestoreIndices(irowp,&row);
1832:   ISRestoreIndices(icolp,&col);
1833:   ISDestroy(irowp);
1834:   ISDestroy(icolp);
1835:   return(0);
1836: }

1840: PetscErrorCode MatPrintHelp_SeqAIJ(Mat A)
1841: {
1842:   static PetscTruth called = PETSC_FALSE;
1843:   MPI_Comm          comm = A->comm;
1844:   PetscErrorCode    ierr;

1847:   MatPrintHelp_Inode(A);
1848:   if (called) {return(0);} else called = PETSC_TRUE;
1849:   (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):\n");
1850:   (*PetscHelpPrintf)(comm,"  -mat_lu_pivotthreshold <threshold>: Set pivoting threshold\n");
1851:   (*PetscHelpPrintf)(comm,"  -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.\n");
1852:   (*PetscHelpPrintf)(comm,"  -mat_no_compressedrow: Do not use compressedrow\n");
1853:   return(0);
1854: }

1858: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1859: {

1863:   /* If the two matrices have the same copy implementation, use fast copy. */
1864:   if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1865:     Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1866:     Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;

1868:     if (a->i[A->m] != b->i[B->m]) {
1869:       SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1870:     }
1871:     PetscMemcpy(b->a,a->a,(a->i[A->m])*sizeof(PetscScalar));
1872:   } else {
1873:     MatCopy_Basic(A,B,str);
1874:   }
1875:   return(0);
1876: }

1880: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
1881: {

1885:    MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
1886:   return(0);
1887: }

1891: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
1892: {
1893:   Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1895:   *array = a->a;
1896:   return(0);
1897: }

1901: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
1902: {
1904:   return(0);
1905: }

1909: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1910: {
1911:   PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
1913:   PetscInt       k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1914:   PetscScalar    dx,mone = -1.0,*y,*xx,*w3_array;
1915:   PetscScalar    *vscale_array;
1916:   PetscReal      epsilon = coloring->error_rel,umin = coloring->umin;
1917:   Vec            w1,w2,w3;
1918:   void           *fctx = coloring->fctx;
1919:   PetscTruth     flg;

1922:   if (!coloring->w1) {
1923:     VecDuplicate(x1,&coloring->w1);
1924:     PetscLogObjectParent(coloring,coloring->w1);
1925:     VecDuplicate(x1,&coloring->w2);
1926:     PetscLogObjectParent(coloring,coloring->w2);
1927:     VecDuplicate(x1,&coloring->w3);
1928:     PetscLogObjectParent(coloring,coloring->w3);
1929:   }
1930:   w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;

1932:   MatSetUnfactored(J);
1933:   PetscOptionsHasName(coloring->prefix,"-mat_fd_coloring_dont_rezero",&flg);
1934:   if (flg) {
1935:     PetscLogInfo((coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()\n"));
1936:   } else {
1937:     PetscTruth assembled;
1938:     MatAssembled(J,&assembled);
1939:     if (assembled) {
1940:       MatZeroEntries(J);
1941:     }
1942:   }

1944:   VecGetOwnershipRange(x1,&start,&end);
1945:   VecGetSize(x1,&N);

1947:   /*
1948:        This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
1949:      coloring->F for the coarser grids from the finest
1950:   */
1951:   if (coloring->F) {
1952:     VecGetLocalSize(coloring->F,&m1);
1953:     VecGetLocalSize(w1,&m2);
1954:     if (m1 != m2) {
1955:       coloring->F = 0;
1956:     }
1957:   }

1959:   if (coloring->F) {
1960:     w1          = coloring->F;
1961:     coloring->F = 0;
1962:   } else {
1963:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
1964:     (*f)(sctx,x1,w1,fctx);
1965:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
1966:   }

1968:   /* 
1969:       Compute all the scale factors and share with other processors
1970:   */
1971:   VecGetArray(x1,&xx);xx = xx - start;
1972:   VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
1973:   for (k=0; k<coloring->ncolors; k++) {
1974:     /*
1975:        Loop over each column associated with color adding the 
1976:        perturbation to the vector w3.
1977:     */
1978:     for (l=0; l<coloring->ncolumns[k]; l++) {
1979:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
1980:       dx  = xx[col];
1981:       if (dx == 0.0) dx = 1.0;
1982: #if !defined(PETSC_USE_COMPLEX)
1983:       if (dx < umin && dx >= 0.0)      dx = umin;
1984:       else if (dx < 0.0 && dx > -umin) dx = -umin;
1985: #else
1986:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
1987:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1988: #endif
1989:       dx                *= epsilon;
1990:       vscale_array[col] = 1.0/dx;
1991:     }
1992:   }
1993:   vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
1994:   VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
1995:   VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);

1997:   /*  VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
1998:       VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/

2000:   if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2001:   else                        vscaleforrow = coloring->columnsforrow;

2003:   VecGetArray(coloring->vscale,&vscale_array);
2004:   /*
2005:       Loop over each color
2006:   */
2007:   for (k=0; k<coloring->ncolors; k++) {
2008:     coloring->currentcolor = k;
2009:     VecCopy(x1,w3);
2010:     VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2011:     /*
2012:        Loop over each column associated with color adding the 
2013:        perturbation to the vector w3.
2014:     */
2015:     for (l=0; l<coloring->ncolumns[k]; l++) {
2016:       col = coloring->columns[k][l];    /* column of the matrix we are probing for */
2017:       dx  = xx[col];
2018:       if (dx == 0.0) dx = 1.0;
2019: #if !defined(PETSC_USE_COMPLEX)
2020:       if (dx < umin && dx >= 0.0)      dx = umin;
2021:       else if (dx < 0.0 && dx > -umin) dx = -umin;
2022: #else
2023:       if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0)     dx = umin;
2024:       else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2025: #endif
2026:       dx            *= epsilon;
2027:       if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2028:       w3_array[col] += dx;
2029:     }
2030:     w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);

2032:     /*
2033:        Evaluate function at x1 + dx (here dx is a vector of perturbations)
2034:     */

2036:     PetscLogEventBegin(MAT_FDColoringFunction,0,0,0,0);
2037:     (*f)(sctx,w3,w2,fctx);
2038:     PetscLogEventEnd(MAT_FDColoringFunction,0,0,0,0);
2039:     VecAXPY(w2,mone,w1);

2041:     /*
2042:        Loop over rows of vector, putting results into Jacobian matrix
2043:     */
2044:     VecGetArray(w2,&y);
2045:     for (l=0; l<coloring->nrows[k]; l++) {
2046:       row    = coloring->rows[k][l];
2047:       col    = coloring->columnsforrow[k][l];
2048:       y[row] *= vscale_array[vscaleforrow[k][l]];
2049:       srow   = row + start;
2050:       MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2051:     }
2052:     VecRestoreArray(w2,&y);
2053:   }
2054:   coloring->currentcolor = k;
2055:   VecRestoreArray(coloring->vscale,&vscale_array);
2056:   xx = xx + start; VecRestoreArray(x1,&xx);
2057:   MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2058:   MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2059:   return(0);
2060: }

2062:  #include petscblaslapack.h
2065: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2066: {
2068:   PetscInt       i;
2069:   Mat_SeqAIJ     *x  = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2070:   PetscBLASInt   one=1,bnz = (PetscBLASInt)x->nz;

2073:   if (str == SAME_NONZERO_PATTERN) {
2074:     PetscScalar alpha = a;
2075:     BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2076:   } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2077:     if (y->xtoy && y->XtoY != X) {
2078:       PetscFree(y->xtoy);
2079:       MatDestroy(y->XtoY);
2080:     }
2081:     if (!y->xtoy) { /* get xtoy */
2082:       MatAXPYGetxtoy_Private(X->m,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2083:       y->XtoY = X;
2084:     }
2085:     for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2086:     PetscLogInfo((0,"MatAXPY_SeqAIJ: ratio of nnz(X)/nnz(Y): %d/%d = %g\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz));
2087:   } else {
2088:     MatAXPY_Basic(Y,a,X,str);
2089:   }
2090:   return(0);
2091: }

2095: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
2096: {
2098:   return(0);
2099: }

2103: PetscErrorCode PETSCMAT_DLLEXPORT MatConjugate_SeqAIJ(Mat mat)
2104: {
2105: #if defined(PETSC_USE_COMPLEX)
2106:   Mat_SeqAIJ  *aij = (Mat_SeqAIJ *)mat->data;
2107:   PetscInt    i,nz;
2108:   PetscScalar *a;

2111:   nz = aij->nz;
2112:   a  = aij->a;
2113:   for (i=0; i<nz; i++) {
2114:     a[i] = PetscConj(a[i]);
2115:   }
2116: #else
2118: #endif
2119:   return(0);
2120: }

2124: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v)
2125: {
2126:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;
2128:   PetscInt       i,j,m = A->m,*ai,*aj,ncols,n;
2129:   PetscReal      atmp;
2130:   PetscScalar    *x,zero = 0.0;
2131:   MatScalar      *aa;

2134:   if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2135:   aa   = a->a;
2136:   ai   = a->i;
2137:   aj   = a->j;

2139:   VecSet(v,zero);
2140:   VecGetArray(v,&x);
2141:   VecGetLocalSize(v,&n);
2142:   if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2143:   for (i=0; i<m; i++) {
2144:     ncols = ai[1] - ai[0]; ai++;
2145:     for (j=0; j<ncols; j++){
2146:       atmp = PetscAbsScalar(*aa); aa++;
2147:       if (PetscAbsScalar(x[i]) < atmp) x[i] = atmp;
2148:       aj++;
2149:     }
2150:   }
2151:   VecRestoreArray(v,&x);
2152:   return(0);
2153: }

2155: /* -------------------------------------------------------------------*/
2156: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2157:        MatGetRow_SeqAIJ,
2158:        MatRestoreRow_SeqAIJ,
2159:        MatMult_SeqAIJ,
2160: /* 4*/ MatMultAdd_SeqAIJ,
2161:        MatMultTranspose_SeqAIJ,
2162:        MatMultTransposeAdd_SeqAIJ,
2163:        MatSolve_SeqAIJ,
2164:        MatSolveAdd_SeqAIJ,
2165:        MatSolveTranspose_SeqAIJ,
2166: /*10*/ MatSolveTransposeAdd_SeqAIJ,
2167:        MatLUFactor_SeqAIJ,
2168:        0,
2169:        MatRelax_SeqAIJ,
2170:        MatTranspose_SeqAIJ,
2171: /*15*/ MatGetInfo_SeqAIJ,
2172:        MatEqual_SeqAIJ,
2173:        MatGetDiagonal_SeqAIJ,
2174:        MatDiagonalScale_SeqAIJ,
2175:        MatNorm_SeqAIJ,
2176: /*20*/ 0,
2177:        MatAssemblyEnd_SeqAIJ,
2178:        MatCompress_SeqAIJ,
2179:        MatSetOption_SeqAIJ,
2180:        MatZeroEntries_SeqAIJ,
2181: /*25*/ MatZeroRows_SeqAIJ,
2182:        MatLUFactorSymbolic_SeqAIJ,
2183:        MatLUFactorNumeric_SeqAIJ,
2184:        MatCholeskyFactorSymbolic_SeqAIJ,
2185:        MatCholeskyFactorNumeric_SeqAIJ,
2186: /*30*/ MatSetUpPreallocation_SeqAIJ,
2187:        MatILUFactorSymbolic_SeqAIJ,
2188:        MatICCFactorSymbolic_SeqAIJ,
2189:        MatGetArray_SeqAIJ,
2190:        MatRestoreArray_SeqAIJ,
2191: /*35*/ MatDuplicate_SeqAIJ,
2192:        0,
2193:        0,
2194:        MatILUFactor_SeqAIJ,
2195:        0,
2196: /*40*/ MatAXPY_SeqAIJ,
2197:        MatGetSubMatrices_SeqAIJ,
2198:        MatIncreaseOverlap_SeqAIJ,
2199:        MatGetValues_SeqAIJ,
2200:        MatCopy_SeqAIJ,
2201: /*45*/ MatPrintHelp_SeqAIJ,
2202:        MatScale_SeqAIJ,
2203:        0,
2204:        0,
2205:        MatILUDTFactor_SeqAIJ,
2206: /*50*/ MatSetBlockSize_SeqAIJ,
2207:        MatGetRowIJ_SeqAIJ,
2208:        MatRestoreRowIJ_SeqAIJ,
2209:        MatGetColumnIJ_SeqAIJ,
2210:        MatRestoreColumnIJ_SeqAIJ,
2211: /*55*/ MatFDColoringCreate_SeqAIJ,
2212:        0,
2213:        0,
2214:        MatPermute_SeqAIJ,
2215:        0,
2216: /*60*/ 0,
2217:        MatDestroy_SeqAIJ,
2218:        MatView_SeqAIJ,
2219:        MatGetPetscMaps_Petsc,
2220:        0,
2221: /*65*/ 0,
2222:        0,
2223:        0,
2224:        0,
2225:        0,
2226: /*70*/ MatGetRowMax_SeqAIJ,
2227:        0,
2228:        MatSetColoring_SeqAIJ,
2229: #if defined(PETSC_HAVE_ADIC)
2230:        MatSetValuesAdic_SeqAIJ,
2231: #else
2232:        0,
2233: #endif
2234:        MatSetValuesAdifor_SeqAIJ,
2235: /*75*/ MatFDColoringApply_SeqAIJ,
2236:        0,
2237:        0,
2238:        0,
2239:        0,
2240: /*80*/ 0,
2241:        0,
2242:        0,
2243:        0,
2244:        MatLoad_SeqAIJ,
2245: /*85*/ MatIsSymmetric_SeqAIJ,
2246:        0,
2247:        0,
2248:        0,
2249:        0,
2250: /*90*/ MatMatMult_SeqAIJ_SeqAIJ,
2251:        MatMatMultSymbolic_SeqAIJ_SeqAIJ,
2252:        MatMatMultNumeric_SeqAIJ_SeqAIJ,
2253:        MatPtAP_Basic,
2254:        MatPtAPSymbolic_SeqAIJ,
2255: /*95*/ MatPtAPNumeric_SeqAIJ,
2256:        MatMatMultTranspose_SeqAIJ_SeqAIJ,
2257:        MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
2258:        MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
2259:        MatPtAPSymbolic_SeqAIJ_SeqAIJ,
2260: /*100*/MatPtAPNumeric_SeqAIJ_SeqAIJ,
2261:        0,
2262:        0,
2263:        MatConjugate_SeqAIJ
2264: };

2269: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
2270: {
2271:   Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2272:   PetscInt   i,nz,n;


2276:   nz = aij->maxnz;
2277:   n  = mat->n;
2278:   for (i=0; i<nz; i++) {
2279:     aij->j[i] = indices[i];
2280:   }
2281:   aij->nz = nz;
2282:   for (i=0; i<n; i++) {
2283:     aij->ilen[i] = aij->imax[i];
2284:   }

2286:   return(0);
2287: }

2292: /*@
2293:     MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2294:        in the matrix.

2296:   Input Parameters:
2297: +  mat - the SeqAIJ matrix
2298: -  indices - the column indices

2300:   Level: advanced

2302:   Notes:
2303:     This can be called if you have precomputed the nonzero structure of the 
2304:   matrix and want to provide it to the matrix object to improve the performance
2305:   of the MatSetValues() operation.

2307:     You MUST have set the correct numbers of nonzeros per row in the call to 
2308:   MatCreateSeqAIJ(), and the columns indices MUST be sorted.

2310:     MUST be called before any calls to MatSetValues();

2312:     The indices should start with zero, not one.

2314: @*/
2315: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
2316: {
2317:   PetscErrorCode ierr,(*f)(Mat,PetscInt *);

2322:   PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2323:   if (f) {
2324:     (*f)(mat,indices);
2325:   } else {
2326:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
2327:   }
2328:   return(0);
2329: }

2331: /* ----------------------------------------------------------------------------------------*/

2336: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues_SeqAIJ(Mat mat)
2337: {
2338:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2340:   size_t         nz = aij->i[mat->m];

2343:   if (aij->nonew != 1) {
2344:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2345:   }

2347:   /* allocate space for values if not already there */
2348:   if (!aij->saved_values) {
2349:     PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2350:   }

2352:   /* copy values over */
2353:   PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2354:   return(0);
2355: }

2360: /*@
2361:     MatStoreValues - Stashes a copy of the matrix values; this allows, for 
2362:        example, reuse of the linear part of a Jacobian, while recomputing the 
2363:        nonlinear portion.

2365:    Collect on Mat

2367:   Input Parameters:
2368: .  mat - the matrix (currently only AIJ matrices support this option)

2370:   Level: advanced

2372:   Common Usage, with SNESSolve():
2373: $    Create Jacobian matrix
2374: $    Set linear terms into matrix
2375: $    Apply boundary conditions to matrix, at this time matrix must have 
2376: $      final nonzero structure (i.e. setting the nonlinear terms and applying 
2377: $      boundary conditions again will not change the nonzero structure
2378: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2379: $    MatStoreValues(mat);
2380: $    Call SNESSetJacobian() with matrix
2381: $    In your Jacobian routine
2382: $      MatRetrieveValues(mat);
2383: $      Set nonlinear terms in matrix
2384:  
2385:   Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2386: $    // build linear portion of Jacobian 
2387: $    MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2388: $    MatStoreValues(mat);
2389: $    loop over nonlinear iterations
2390: $       MatRetrieveValues(mat);
2391: $       // call MatSetValues(mat,...) to set nonliner portion of Jacobian 
2392: $       // call MatAssemblyBegin/End() on matrix
2393: $       Solve linear system with Jacobian
2394: $    endloop 

2396:   Notes:
2397:     Matrix must already be assemblied before calling this routine
2398:     Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before 
2399:     calling this routine.

2401:     When this is called multiple times it overwrites the previous set of stored values
2402:     and does not allocated additional space.

2404: .seealso: MatRetrieveValues()

2406: @*/
2407: PetscErrorCode PETSCMAT_DLLEXPORT MatStoreValues(Mat mat)
2408: {
2409:   PetscErrorCode ierr,(*f)(Mat);

2413:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2414:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2416:   PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2417:   if (f) {
2418:     (*f)(mat);
2419:   } else {
2420:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values");
2421:   }
2422:   return(0);
2423: }

2428: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues_SeqAIJ(Mat mat)
2429: {
2430:   Mat_SeqAIJ     *aij = (Mat_SeqAIJ *)mat->data;
2432:   PetscInt       nz = aij->i[mat->m];

2435:   if (aij->nonew != 1) {
2436:     SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2437:   }
2438:   if (!aij->saved_values) {
2439:     SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2440:   }
2441:   /* copy values over */
2442:   PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2443:   return(0);
2444: }

2449: /*@
2450:     MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for 
2451:        example, reuse of the linear part of a Jacobian, while recomputing the 
2452:        nonlinear portion.

2454:    Collect on Mat

2456:   Input Parameters:
2457: .  mat - the matrix (currently on AIJ matrices support this option)

2459:   Level: advanced

2461: .seealso: MatStoreValues()

2463: @*/
2464: PetscErrorCode PETSCMAT_DLLEXPORT MatRetrieveValues(Mat mat)
2465: {
2466:   PetscErrorCode ierr,(*f)(Mat);

2470:   if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2471:   if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");

2473:   PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2474:   if (f) {
2475:     (*f)(mat);
2476:   } else {
2477:     SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values");
2478:   }
2479:   return(0);
2480: }


2483: /* --------------------------------------------------------------------------------*/
2486: /*@C
2487:    MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2488:    (the default parallel PETSc format).  For good matrix assembly performance
2489:    the user should preallocate the matrix storage by setting the parameter nz
2490:    (or the array nnz).  By setting these parameters accurately, performance
2491:    during matrix assembly can be increased by more than a factor of 50.

2493:    Collective on MPI_Comm

2495:    Input Parameters:
2496: +  comm - MPI communicator, set to PETSC_COMM_SELF
2497: .  m - number of rows
2498: .  n - number of columns
2499: .  nz - number of nonzeros per row (same for all rows)
2500: -  nnz - array containing the number of nonzeros in the various rows 
2501:          (possibly different for each row) or PETSC_NULL

2503:    Output Parameter:
2504: .  A - the matrix 

2506:    Notes:
2507:    If nnz is given then nz is ignored

2509:    The AIJ format (also called the Yale sparse matrix format or
2510:    compressed row storage), is fully compatible with standard Fortran 77
2511:    storage.  That is, the stored row and column indices can begin at
2512:    either one (as in Fortran) or zero.  See the users' manual for details.

2514:    Specify the preallocated storage with either nz or nnz (not both).
2515:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2516:    allocation.  For large problems you MUST preallocate memory or you 
2517:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2519:    By default, this format uses inodes (identical nodes) when possible, to 
2520:    improve numerical efficiency of matrix-vector products and solves. We 
2521:    search for consecutive rows with the same nonzero structure, thereby
2522:    reusing matrix information to achieve increased efficiency.

2524:    Options Database Keys:
2525: +  -mat_no_inode  - Do not use inodes
2526: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2527: -  -mat_aij_oneindex - Internally use indexing starting at 1
2528:         rather than 0.  Note that when calling MatSetValues(),
2529:         the user still MUST index entries starting at 0!

2531:    Level: intermediate

2533: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2535: @*/
2536: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2537: {

2541:   MatCreate(comm,A);
2542:   MatSetSizes(*A,m,n,m,n);
2543:   MatSetType(*A,MATSEQAIJ);
2544:   MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
2545:   return(0);
2546: }

2550: /*@C
2551:    MatSeqAIJSetPreallocation - For good matrix assembly performance
2552:    the user should preallocate the matrix storage by setting the parameter nz
2553:    (or the array nnz).  By setting these parameters accurately, performance
2554:    during matrix assembly can be increased by more than a factor of 50.

2556:    Collective on MPI_Comm

2558:    Input Parameters:
2559: +  B - The matrix
2560: .  nz - number of nonzeros per row (same for all rows)
2561: -  nnz - array containing the number of nonzeros in the various rows 
2562:          (possibly different for each row) or PETSC_NULL

2564:    Notes:
2565:      If nnz is given then nz is ignored

2567:     The AIJ format (also called the Yale sparse matrix format or
2568:    compressed row storage), is fully compatible with standard Fortran 77
2569:    storage.  That is, the stored row and column indices can begin at
2570:    either one (as in Fortran) or zero.  See the users' manual for details.

2572:    Specify the preallocated storage with either nz or nnz (not both).
2573:    Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory 
2574:    allocation.  For large problems you MUST preallocate memory or you 
2575:    will get TERRIBLE performance, see the users' manual chapter on matrices.

2577:    Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
2578:    entries or columns indices

2580:    By default, this format uses inodes (identical nodes) when possible, to 
2581:    improve numerical efficiency of matrix-vector products and solves. We 
2582:    search for consecutive rows with the same nonzero structure, thereby
2583:    reusing matrix information to achieve increased efficiency.

2585:    Options Database Keys:
2586: +  -mat_no_inode  - Do not use inodes
2587: .  -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2588: -  -mat_aij_oneindex - Internally use indexing starting at 1
2589:         rather than 0.  Note that when calling MatSetValues(),
2590:         the user still MUST index entries starting at 0!

2592:    Level: intermediate

2594: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()

2596: @*/
2597: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
2598: {
2599:   PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]);

2602:   PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
2603:   if (f) {
2604:     (*f)(B,nz,nnz);
2605:   }
2606:   return(0);
2607: }

2612: PetscErrorCode PETSCMAT_DLLEXPORT MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz)
2613: {
2614:   Mat_SeqAIJ     *b;
2615:   PetscTruth     skipallocation = PETSC_FALSE;
2617:   PetscInt       i;

2620: 
2621:   if (nz == MAT_SKIP_ALLOCATION) {
2622:     skipallocation = PETSC_TRUE;
2623:     nz             = 0;
2624:   }

2626:   if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2627:   if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2628:   if (nnz) {
2629:     for (i=0; i<B->m; i++) {
2630:       if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2631:       if (nnz[i] > B->n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->n);
2632:     }
2633:   }

2635:   B->preallocated = PETSC_TRUE;
2636:   b = (Mat_SeqAIJ*)B->data;

2638:   if (!skipallocation) {
2639:     PetscMalloc2(B->m,PetscInt,&b->imax,B->m,PetscInt,&b->ilen);
2640:     if (!nnz) {
2641:       if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2642:       else if (nz <= 0)        nz = 1;
2643:       for (i=0; i<B->m; i++) b->imax[i] = nz;
2644:       nz = nz*B->m;
2645:     } else {
2646:       nz = 0;
2647:       for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2648:     }

2650:     /* b->ilen will count nonzeros in each row so far. */
2651:     for (i=0; i<B->m; i++) { b->ilen[i] = 0;}

2653:     /* allocate the matrix space */
2654:     PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->m+1,PetscInt,&b->i);
2655:     b->i[0] = 0;
2656:     for (i=1; i<B->m+1; i++) {
2657:       b->i[i] = b->i[i-1] + b->imax[i-1];
2658:     }
2659:     b->singlemalloc = PETSC_TRUE;
2660:     b->freedata     = PETSC_TRUE;
2661:   } else {
2662:     b->freedata     = PETSC_FALSE;
2663:   }

2665:   b->nz                = 0;
2666:   b->maxnz             = nz;
2667:   B->info.nz_unneeded  = (double)b->maxnz;
2668:   return(0);
2669: }

2672: /*MC
2673:    MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices, 
2674:    based on compressed sparse row format.

2676:    Options Database Keys:
2677: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()

2679:   Level: beginner

2681: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
2682: M*/

2687: PetscErrorCode PETSCMAT_DLLEXPORT MatCreate_SeqAIJ(Mat B)
2688: {
2689:   Mat_SeqAIJ     *b;
2691:   PetscMPIInt    size;

2694:   MPI_Comm_size(B->comm,&size);
2695:   if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");

2697:   B->m = B->M = PetscMax(B->m,B->M);
2698:   B->n = B->N = PetscMax(B->n,B->N);

2700:   PetscNew(Mat_SeqAIJ,&b);
2701:   B->data             = (void*)b;
2702:   PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2703:   B->factor           = 0;
2704:   B->mapping          = 0;
2705:   b->row              = 0;
2706:   b->col              = 0;
2707:   b->icol             = 0;
2708:   b->reallocs         = 0;
2709: 
2710:   PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
2711:   PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);

2713:   b->sorted            = PETSC_FALSE;
2714:   b->ignorezeroentries = PETSC_FALSE;
2715:   b->roworiented       = PETSC_TRUE;
2716:   b->nonew             = 0;
2717:   b->diag              = 0;
2718:   b->solve_work        = 0;
2719:   B->spptr             = 0;
2720:   b->saved_values      = 0;
2721:   b->idiag             = 0;
2722:   b->ssor              = 0;
2723:   b->keepzeroedrows    = PETSC_FALSE;
2724:   b->xtoy              = 0;
2725:   b->XtoY              = 0;
2726:   b->compressedrow.use     = PETSC_FALSE;
2727:   b->compressedrow.nrows   = B->m;
2728:   b->compressedrow.i       = PETSC_NULL;
2729:   b->compressedrow.rindex  = PETSC_NULL;
2730:   b->compressedrow.checked = PETSC_FALSE;
2731:   B->same_nonzero          = PETSC_FALSE;

2733:   PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);

2735:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2736:                                      "MatSeqAIJSetColumnIndices_SeqAIJ",
2737:                                      MatSeqAIJSetColumnIndices_SeqAIJ);
2738:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2739:                                      "MatStoreValues_SeqAIJ",
2740:                                      MatStoreValues_SeqAIJ);
2741:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2742:                                      "MatRetrieveValues_SeqAIJ",
2743:                                      MatRetrieveValues_SeqAIJ);
2744:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
2745:                                      "MatConvert_SeqAIJ_SeqSBAIJ",
2746:                                       MatConvert_SeqAIJ_SeqSBAIJ);
2747:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
2748:                                      "MatConvert_SeqAIJ_SeqBAIJ",
2749:                                       MatConvert_SeqAIJ_SeqBAIJ);
2750:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
2751:                                      "MatIsTranspose_SeqAIJ",
2752:                                       MatIsTranspose_SeqAIJ);
2753:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
2754:                                      "MatSeqAIJSetPreallocation_SeqAIJ",
2755:                                       MatSeqAIJSetPreallocation_SeqAIJ);
2756:   PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
2757:                                      "MatReorderForNonzeroDiagonal_SeqAIJ",
2758:                                       MatReorderForNonzeroDiagonal_SeqAIJ);
2759:   MatCreate_Inode(B);
2760:   return(0);
2761: }

2766: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2767: {
2768:   Mat            C;
2769:   Mat_SeqAIJ     *c,*a = (Mat_SeqAIJ*)A->data;
2771:   PetscInt       i,m = A->m;

2774:   *B = 0;
2775:   MatCreate(A->comm,&C);
2776:   MatSetSizes(C,A->m,A->n,A->m,A->n);
2777:   MatSetType(C,A->type_name);
2778:   PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
2779: 
2780:   c = (Mat_SeqAIJ*)C->data;

2782:   C->factor           = A->factor;

2784:   c->row            = 0;
2785:   c->col            = 0;
2786:   c->icol           = 0;
2787:   c->reallocs       = 0;

2789:   C->assembled      = PETSC_TRUE;
2790: 
2791:   PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
2792:   for (i=0; i<m; i++) {
2793:     c->imax[i] = a->imax[i];
2794:     c->ilen[i] = a->ilen[i];
2795:   }

2797:   /* allocate the matrix space */
2798:   PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
2799:   c->singlemalloc = PETSC_TRUE;
2800:   PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
2801:   if (m > 0) {
2802:     PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
2803:     if (cpvalues == MAT_COPY_VALUES) {
2804:       PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
2805:     } else {
2806:       PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
2807:     }
2808:   }

2810:   c->sorted            = a->sorted;
2811:   c->ignorezeroentries = a->ignorezeroentries;
2812:   c->roworiented       = a->roworiented;
2813:   c->nonew             = a->nonew;
2814:   if (a->diag) {
2815:     PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
2816:     PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
2817:     for (i=0; i<m; i++) {
2818:       c->diag[i] = a->diag[i];
2819:     }
2820:   } else c->diag        = 0;
2821:   c->solve_work         = 0;
2822:   c->saved_values          = 0;
2823:   c->idiag                 = 0;
2824:   c->ssor                  = 0;
2825:   c->keepzeroedrows        = a->keepzeroedrows;
2826:   c->freedata              = PETSC_TRUE;
2827:   c->xtoy                  = 0;
2828:   c->XtoY                  = 0;

2830:   c->nz                 = a->nz;
2831:   c->maxnz              = a->maxnz;
2832:   C->preallocated       = PETSC_TRUE;

2834:   c->compressedrow.use     = a->compressedrow.use;
2835:   c->compressedrow.nrows   = a->compressedrow.nrows;
2836:   c->compressedrow.checked = a->compressedrow.checked;
2837:   if ( a->compressedrow.checked && a->compressedrow.use){
2838:     i = a->compressedrow.nrows;
2839:     PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
2840:     c->compressedrow.rindex = c->compressedrow.i + i + 1;
2841:     PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
2842:     PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
2843:   } else {
2844:     c->compressedrow.use    = PETSC_FALSE;
2845:     c->compressedrow.i      = PETSC_NULL;
2846:     c->compressedrow.rindex = PETSC_NULL;
2847:   }
2848:   C->same_nonzero = A->same_nonzero;

2850:   MatDuplicate_Inode(A,cpvalues,&C);

2852:   *B = C;
2853:   PetscFListDuplicate(A->qlist,&C->qlist);
2854:   return(0);
2855: }

2859: PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer, MatType type,Mat *A)
2860: {
2861:   Mat_SeqAIJ     *a;
2862:   Mat            B;
2864:   PetscInt       i,sum,nz,header[4],*rowlengths = 0,M,N;
2865:   int            fd;
2866:   PetscMPIInt    size;
2867:   MPI_Comm       comm;
2868: 
2870:   PetscObjectGetComm((PetscObject)viewer,&comm);
2871:   MPI_Comm_size(comm,&size);
2872:   if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
2873:   PetscViewerBinaryGetDescriptor(viewer,&fd);
2874:   PetscBinaryRead(fd,header,4,PETSC_INT);
2875:   if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
2876:   M = header[1]; N = header[2]; nz = header[3];

2878:   if (nz < 0) {
2879:     SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
2880:   }

2882:   /* read in row lengths */
2883:   PetscMalloc(M*sizeof(PetscInt),&rowlengths);
2884:   PetscBinaryRead(fd,rowlengths,M,PETSC_INT);

2886:   /* check if sum of rowlengths is same as nz */
2887:   for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
2888:   if (sum != nz) SETERRQ2(PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);

2890:   /* create our matrix */
2891:   MatCreate(comm,&B);
2892:   MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M,N);
2893:   MatSetType(B,type);
2894:   MatSeqAIJSetPreallocation_SeqAIJ(B,0,rowlengths);
2895:   a = (Mat_SeqAIJ*)B->data;

2897:   /* read in column indices and adjust for Fortran indexing*/
2898:   PetscBinaryRead(fd,a->j,nz,PETSC_INT);

2900:   /* read in nonzero values */
2901:   PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);

2903:   /* set matrix "i" values */
2904:   a->i[0] = 0;
2905:   for (i=1; i<= M; i++) {
2906:     a->i[i]      = a->i[i-1] + rowlengths[i-1];
2907:     a->ilen[i-1] = rowlengths[i-1];
2908:   }
2909:   PetscFree(rowlengths);

2911:   MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2912:   MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2913:   *A = B;
2914:   return(0);
2915: }

2919: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
2920: {
2921:   Mat_SeqAIJ     *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;

2925:   /* If the  matrix dimensions are not equal,or no of nonzeros */
2926:   if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)) {
2927:     *flg = PETSC_FALSE;
2928:     return(0);
2929:   }
2930: 
2931:   /* if the a->i are the same */
2932:   PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(PetscInt),flg);
2933:   if (!*flg) return(0);
2934: 
2935:   /* if a->j are the same */
2936:   PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
2937:   if (!*flg) return(0);
2938: 
2939:   /* if a->a are the same */
2940:   PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);

2942:   return(0);
2943: 
2944: }

2948: /*@C
2949:      MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
2950:               provided by the user.

2952:       Coolective on MPI_Comm

2954:    Input Parameters:
2955: +   comm - must be an MPI communicator of size 1
2956: .   m - number of rows
2957: .   n - number of columns
2958: .   i - row indices
2959: .   j - column indices
2960: -   a - matrix values

2962:    Output Parameter:
2963: .   mat - the matrix

2965:    Level: intermediate

2967:    Notes:
2968:        The i, j, and a arrays are not copied by this routine, the user must free these arrays
2969:     once the matrix is destroyed

2971:        You cannot set new nonzero locations into this matrix, that will generate an error.

2973:        The i and j indices are 0 based

2975: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()

2977: @*/
2978: PetscErrorCode PETSCMAT_DLLEXPORT MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
2979: {
2981:   PetscInt       ii;
2982:   Mat_SeqAIJ     *aij;

2985:   if (i[0]) {
2986:     SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
2987:   }
2988:   MatCreate(comm,mat);
2989:   MatSetSizes(*mat,m,n,m,n);
2990:   MatSetType(*mat,MATSEQAIJ);
2991:   MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
2992:   aij  = (Mat_SeqAIJ*)(*mat)->data;
2993:   PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);

2995:   aij->i = i;
2996:   aij->j = j;
2997:   aij->a = a;
2998:   aij->singlemalloc = PETSC_FALSE;
2999:   aij->nonew        = -1;             /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3000:   aij->freedata     = PETSC_FALSE;

3002:   for (ii=0; ii<m; ii++) {
3003:     aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
3004: #if defined(PETSC_USE_DEBUG)
3005:     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]);
3006: #endif    
3007:   }
3008: #if defined(PETSC_USE_DEBUG)
3009:   for (ii=0; ii<aij->i[m]; ii++) {
3010:     if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3011:     if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
3012:   }
3013: #endif    

3015:   MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3016:   MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3017:   return(0);
3018: }

3022: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
3023: {
3025:   Mat_SeqAIJ     *a = (Mat_SeqAIJ*)A->data;

3028:   if (coloring->ctype == IS_COLORING_LOCAL) {
3029:     ISColoringReference(coloring);
3030:     a->coloring = coloring;
3031:   } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3032:     PetscInt             i,*larray;
3033:     ISColoring      ocoloring;
3034:     ISColoringValue *colors;

3036:     /* set coloring for diagonal portion */
3037:     PetscMalloc((A->n+1)*sizeof(PetscInt),&larray);
3038:     for (i=0; i<A->n; i++) {
3039:       larray[i] = i;
3040:     }
3041:     ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->n,larray,PETSC_NULL,larray);
3042:     PetscMalloc((A->n+1)*sizeof(ISColoringValue),&colors);
3043:     for (i=0; i<A->n; i++) {
3044:       colors[i] = coloring->colors[larray[i]];
3045:     }
3046:     PetscFree(larray);
3047:     ISColoringCreate(PETSC_COMM_SELF,A->n,colors,&ocoloring);
3048:     a->coloring = ocoloring;
3049:   }
3050:   return(0);
3051: }

3053: #if defined(PETSC_HAVE_ADIC)
3055: #include "adic/ad_utils.h"

3060: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3061: {
3062:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3063:   PetscInt        m = A->m,*ii = a->i,*jj = a->j,nz,i,j,nlen;
3064:   PetscScalar     *v = a->a,*values = ((PetscScalar*)advalues)+1;
3065:   ISColoringValue *color;

3068:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3069:   nlen  = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
3070:   color = a->coloring->colors;
3071:   /* loop over rows */
3072:   for (i=0; i<m; i++) {
3073:     nz = ii[i+1] - ii[i];
3074:     /* loop over columns putting computed value into matrix */
3075:     for (j=0; j<nz; j++) {
3076:       *v++ = values[color[*jj++]];
3077:     }
3078:     values += nlen; /* jump to next row of derivatives */
3079:   }
3080:   return(0);
3081: }
3082: #endif

3086: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
3087: {
3088:   Mat_SeqAIJ      *a = (Mat_SeqAIJ*)A->data;
3089:   PetscInt             m = A->m,*ii = a->i,*jj = a->j,nz,i,j;
3090:   PetscScalar     *v = a->a,*values = (PetscScalar *)advalues;
3091:   ISColoringValue *color;

3094:   if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3095:   color = a->coloring->colors;
3096:   /* loop over rows */
3097:   for (i=0; i<m; i++) {
3098:     nz = ii[i+1] - ii[i];
3099:     /* loop over columns putting computed value into matrix */
3100:     for (j=0; j<nz; j++) {
3101:       *v++ = values[color[*jj++]];
3102:     }
3103:     values += nl; /* jump to next row of derivatives */
3104:   }
3105:   return(0);
3106: }