Actual source code: aij.c
1: #define PETSCMAT_DLL
3: /*
4: Defines the basic matrix operations for the AIJ (compressed row)
5: matrix storage format.
6: */
8: #include src/mat/impls/aij/seq/aij.h
9: #include src/inline/spops.h
10: #include src/inline/dot.h
11: #include petscbt.h
15: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
16: {
18: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) Y->data;
19: PetscInt i,*diag, m = Y->rmap.n;
20: PetscScalar *v,*aa = aij->a;
21: PetscTruth missing;
24: if (Y->assembled) {
25: MatMissingDiagonal_SeqAIJ(Y,&missing,PETSC_NULL);
26: if (!missing) {
27: diag = aij->diag;
28: VecGetArray(D,&v);
29: if (is == INSERT_VALUES) {
30: for (i=0; i<m; i++) {
31: aa[diag[i]] = v[i];
32: }
33: } else {
34: for (i=0; i<m; i++) {
35: aa[diag[i]] += v[i];
36: }
37: }
38: VecRestoreArray(D,&v);
39: return(0);
40: }
41: }
42: MatDiagonalSet_Default(Y,D,is);
43: return(0);
44: }
48: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *m,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
49: {
50: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
52: PetscInt i,ishift;
53:
55: *m = A->rmap.n;
56: if (!ia) return(0);
57: ishift = 0;
58: if (symmetric && !A->structurally_symmetric) {
59: MatToSymmetricIJ_SeqAIJ(A->rmap.n,a->i,a->j,ishift,oshift,ia,ja);
60: } else if (oshift == 1) {
61: PetscInt nz = a->i[A->rmap.n];
62: /* malloc space and add 1 to i and j indices */
63: PetscMalloc((A->rmap.n+1)*sizeof(PetscInt),ia);
64: for (i=0; i<A->rmap.n+1; i++) (*ia)[i] = a->i[i] + 1;
65: if (ja) {
66: PetscMalloc((nz+1)*sizeof(PetscInt),ja);
67: for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
68: }
69: } else {
70: *ia = a->i;
71: if (ja) *ja = a->j;
72: }
73: return(0);
74: }
78: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
79: {
81:
83: if (!ia) return(0);
84: if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
85: PetscFree(*ia);
86: if (ja) {PetscFree(*ja);}
87: }
88: return(0);
89: }
93: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *nn,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
94: {
95: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
97: PetscInt i,*collengths,*cia,*cja,n = A->cmap.n,m = A->rmap.n;
98: PetscInt nz = a->i[m],row,*jj,mr,col;
99:
101: *nn = n;
102: if (!ia) return(0);
103: if (symmetric) {
104: MatToSymmetricIJ_SeqAIJ(A->rmap.n,a->i,a->j,0,oshift,ia,ja);
105: } else {
106: PetscMalloc((n+1)*sizeof(PetscInt),&collengths);
107: PetscMemzero(collengths,n*sizeof(PetscInt));
108: PetscMalloc((n+1)*sizeof(PetscInt),&cia);
109: PetscMalloc((nz+1)*sizeof(PetscInt),&cja);
110: jj = a->j;
111: for (i=0; i<nz; i++) {
112: collengths[jj[i]]++;
113: }
114: cia[0] = oshift;
115: for (i=0; i<n; i++) {
116: cia[i+1] = cia[i] + collengths[i];
117: }
118: PetscMemzero(collengths,n*sizeof(PetscInt));
119: jj = a->j;
120: for (row=0; row<m; row++) {
121: mr = a->i[row+1] - a->i[row];
122: for (i=0; i<mr; i++) {
123: col = *jj++;
124: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
125: }
126: }
127: PetscFree(collengths);
128: *ia = cia; *ja = cja;
129: }
130: return(0);
131: }
135: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscTruth symmetric,PetscTruth blockcompressed,PetscInt *n,PetscInt *ia[],PetscInt *ja[],PetscTruth *done)
136: {
140: if (!ia) return(0);
142: PetscFree(*ia);
143: PetscFree(*ja);
144:
145: return(0);
146: }
150: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
151: {
152: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
153: PetscInt *ai = a->i;
157: PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
158: return(0);
159: }
161: #define CHUNKSIZE 15
165: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
166: {
167: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
168: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
169: PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen;
171: PetscInt *aj = a->j,nonew = a->nonew,lastcol = -1;
172: PetscScalar *ap,value,*aa = a->a;
173: PetscTruth ignorezeroentries = a->ignorezeroentries;
174: PetscTruth roworiented = a->roworiented;
177: for (k=0; k<m; k++) { /* loop over added rows */
178: row = im[k];
179: if (row < 0) continue;
180: #if defined(PETSC_USE_DEBUG)
181: if (row >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.n-1);
182: #endif
183: rp = aj + ai[row]; ap = aa + ai[row];
184: rmax = imax[row]; nrow = ailen[row];
185: low = 0;
186: high = nrow;
187: for (l=0; l<n; l++) { /* loop over added columns */
188: if (in[l] < 0) continue;
189: #if defined(PETSC_USE_DEBUG)
190: if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
191: #endif
192: col = in[l];
193: if (roworiented) {
194: value = v[l + k*n];
195: } else {
196: value = v[k + l*m];
197: }
198: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
200: if (col <= lastcol) low = 0; else high = nrow;
201: lastcol = col;
202: while (high-low > 5) {
203: t = (low+high)/2;
204: if (rp[t] > col) high = t;
205: else low = t;
206: }
207: for (i=low; i<high; i++) {
208: if (rp[i] > col) break;
209: if (rp[i] == col) {
210: if (is == ADD_VALUES) ap[i] += value;
211: else ap[i] = value;
212: goto noinsert;
213: }
214: }
215: if (value == 0.0 && ignorezeroentries) goto noinsert;
216: if (nonew == 1) goto noinsert;
217: if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
218: MatSeqXAIJReallocateAIJ(A,A->rmap.n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
219: N = nrow++ - 1; a->nz++; high++;
220: /* shift up all the later entries in this row */
221: for (ii=N; ii>=i; ii--) {
222: rp[ii+1] = rp[ii];
223: ap[ii+1] = ap[ii];
224: }
225: rp[i] = col;
226: ap[i] = value;
227: noinsert:;
228: low = i + 1;
229: }
230: ailen[row] = nrow;
231: }
232: A->same_nonzero = PETSC_FALSE;
233: return(0);
234: }
239: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
240: {
241: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
242: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
243: PetscInt *ai = a->i,*ailen = a->ilen;
244: PetscScalar *ap,*aa = a->a;
247: for (k=0; k<m; k++) { /* loop over rows */
248: row = im[k];
249: if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
250: if (row >= A->rmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap.n-1);
251: rp = aj + ai[row]; ap = aa + ai[row];
252: nrow = ailen[row];
253: for (l=0; l<n; l++) { /* loop over columns */
254: if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
255: if (in[l] >= A->cmap.n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap.n-1);
256: col = in[l] ;
257: high = nrow; low = 0; /* assume unsorted */
258: while (high-low > 5) {
259: t = (low+high)/2;
260: if (rp[t] > col) high = t;
261: else low = t;
262: }
263: for (i=low; i<high; i++) {
264: if (rp[i] > col) break;
265: if (rp[i] == col) {
266: *v++ = ap[i];
267: goto finished;
268: }
269: }
270: *v++ = 0.0;
271: finished:;
272: }
273: }
274: return(0);
275: }
280: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
281: {
282: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
284: PetscInt i,*col_lens;
285: int fd;
288: PetscViewerBinaryGetDescriptor(viewer,&fd);
289: PetscMalloc((4+A->rmap.n)*sizeof(PetscInt),&col_lens);
290: col_lens[0] = MAT_FILE_COOKIE;
291: col_lens[1] = A->rmap.n;
292: col_lens[2] = A->cmap.n;
293: col_lens[3] = a->nz;
295: /* store lengths of each row and write (including header) to file */
296: for (i=0; i<A->rmap.n; i++) {
297: col_lens[4+i] = a->i[i+1] - a->i[i];
298: }
299: PetscBinaryWrite(fd,col_lens,4+A->rmap.n,PETSC_INT,PETSC_TRUE);
300: PetscFree(col_lens);
302: /* store column indices (zero start index) */
303: PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);
305: /* store nonzero values */
306: PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
307: return(0);
308: }
310: EXTERN PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);
314: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
315: {
316: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
317: PetscErrorCode ierr;
318: PetscInt i,j,m = A->rmap.n,shift=0;
319: const char *name;
320: PetscViewerFormat format;
323: PetscObjectGetName((PetscObject)A,&name);
324: PetscViewerGetFormat(viewer,&format);
325: if (format == PETSC_VIEWER_ASCII_MATLAB) {
326: PetscInt nofinalvalue = 0;
327: if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap.n-!shift)) {
328: nofinalvalue = 1;
329: }
330: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
331: PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap.n);
332: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
333: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
334: PetscViewerASCIIPrintf(viewer,"zzz = [\n");
336: for (i=0; i<m; i++) {
337: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
338: #if defined(PETSC_USE_COMPLEX)
339: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e + %18.16ei \n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
340: #else
341: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+!shift,a->a[j]);
342: #endif
343: }
344: }
345: if (nofinalvalue) {
346: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap.n,0.0);
347: }
348: PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
349: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
350: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
351: return(0);
352: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
353: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
354: for (i=0; i<m; i++) {
355: PetscViewerASCIIPrintf(viewer,"row %D:",i);
356: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
357: #if defined(PETSC_USE_COMPLEX)
358: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
359: PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
360: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
361: PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
362: } else if (PetscRealPart(a->a[j]) != 0.0) {
363: PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
364: }
365: #else
366: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);}
367: #endif
368: }
369: PetscViewerASCIIPrintf(viewer,"\n");
370: }
371: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
372: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
373: PetscInt nzd=0,fshift=1,*sptr;
374: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
375: PetscMalloc((m+1)*sizeof(PetscInt),&sptr);
376: for (i=0; i<m; i++) {
377: sptr[i] = nzd+1;
378: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
379: if (a->j[j] >= i) {
380: #if defined(PETSC_USE_COMPLEX)
381: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
382: #else
383: if (a->a[j] != 0.0) nzd++;
384: #endif
385: }
386: }
387: }
388: sptr[m] = nzd+1;
389: PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
390: for (i=0; i<m+1; i+=6) {
391: if (i+4<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);}
392: else if (i+3<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);}
393: else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
394: else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);}
395: else if (i<m) {PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);}
396: else {PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);}
397: }
398: PetscViewerASCIIPrintf(viewer,"\n");
399: PetscFree(sptr);
400: for (i=0; i<m; i++) {
401: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
402: if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
403: }
404: PetscViewerASCIIPrintf(viewer,"\n");
405: }
406: PetscViewerASCIIPrintf(viewer,"\n");
407: for (i=0; i<m; i++) {
408: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
409: if (a->j[j] >= i) {
410: #if defined(PETSC_USE_COMPLEX)
411: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
412: PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
413: }
414: #else
415: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
416: #endif
417: }
418: }
419: PetscViewerASCIIPrintf(viewer,"\n");
420: }
421: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
422: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
423: PetscInt cnt = 0,jcnt;
424: PetscScalar value;
426: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
427: for (i=0; i<m; i++) {
428: jcnt = 0;
429: for (j=0; j<A->cmap.n; j++) {
430: if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
431: value = a->a[cnt++];
432: jcnt++;
433: } else {
434: value = 0.0;
435: }
436: #if defined(PETSC_USE_COMPLEX)
437: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
438: #else
439: PetscViewerASCIIPrintf(viewer," %7.5e ",value);
440: #endif
441: }
442: PetscViewerASCIIPrintf(viewer,"\n");
443: }
444: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
445: } else {
446: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
447: for (i=0; i<m; i++) {
448: PetscViewerASCIIPrintf(viewer,"row %D:",i);
449: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
450: #if defined(PETSC_USE_COMPLEX)
451: if (PetscImaginaryPart(a->a[j]) > 0.0) {
452: PetscViewerASCIIPrintf(viewer," (%D, %G + %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
453: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
454: PetscViewerASCIIPrintf(viewer," (%D, %G - %G i)",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
455: } else {
456: PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,PetscRealPart(a->a[j]));
457: }
458: #else
459: PetscViewerASCIIPrintf(viewer," (%D, %G) ",a->j[j]+shift,a->a[j]);
460: #endif
461: }
462: PetscViewerASCIIPrintf(viewer,"\n");
463: }
464: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
465: }
466: PetscViewerFlush(viewer);
467: return(0);
468: }
472: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
473: {
474: Mat A = (Mat) Aa;
475: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
476: PetscErrorCode ierr;
477: PetscInt i,j,m = A->rmap.n,color;
478: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
479: PetscViewer viewer;
480: PetscViewerFormat format;
483: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
484: PetscViewerGetFormat(viewer,&format);
486: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
487: /* loop over matrix elements drawing boxes */
489: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
490: /* Blue for negative, Cyan for zero and Red for positive */
491: color = PETSC_DRAW_BLUE;
492: for (i=0; i<m; i++) {
493: y_l = m - i - 1.0; y_r = y_l + 1.0;
494: for (j=a->i[i]; j<a->i[i+1]; j++) {
495: x_l = a->j[j] ; x_r = x_l + 1.0;
496: #if defined(PETSC_USE_COMPLEX)
497: if (PetscRealPart(a->a[j]) >= 0.) continue;
498: #else
499: if (a->a[j] >= 0.) continue;
500: #endif
501: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
502: }
503: }
504: color = PETSC_DRAW_CYAN;
505: for (i=0; i<m; i++) {
506: y_l = m - i - 1.0; y_r = y_l + 1.0;
507: for (j=a->i[i]; j<a->i[i+1]; j++) {
508: x_l = a->j[j]; x_r = x_l + 1.0;
509: if (a->a[j] != 0.) continue;
510: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
511: }
512: }
513: color = PETSC_DRAW_RED;
514: for (i=0; i<m; i++) {
515: y_l = m - i - 1.0; y_r = y_l + 1.0;
516: for (j=a->i[i]; j<a->i[i+1]; j++) {
517: x_l = a->j[j]; x_r = x_l + 1.0;
518: #if defined(PETSC_USE_COMPLEX)
519: if (PetscRealPart(a->a[j]) <= 0.) continue;
520: #else
521: if (a->a[j] <= 0.) continue;
522: #endif
523: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
524: }
525: }
526: } else {
527: /* use contour shading to indicate magnitude of values */
528: /* first determine max of all nonzero values */
529: PetscInt nz = a->nz,count;
530: PetscDraw popup;
531: PetscReal scale;
533: for (i=0; i<nz; i++) {
534: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
535: }
536: scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
537: PetscDrawGetPopup(draw,&popup);
538: if (popup) {PetscDrawScalePopup(popup,0.0,maxv);}
539: count = 0;
540: for (i=0; i<m; i++) {
541: y_l = m - i - 1.0; y_r = y_l + 1.0;
542: for (j=a->i[i]; j<a->i[i+1]; j++) {
543: x_l = a->j[j]; x_r = x_l + 1.0;
544: color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
545: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
546: count++;
547: }
548: }
549: }
550: return(0);
551: }
555: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
556: {
558: PetscDraw draw;
559: PetscReal xr,yr,xl,yl,h,w;
560: PetscTruth isnull;
563: PetscViewerDrawGetDraw(viewer,0,&draw);
564: PetscDrawIsNull(draw,&isnull);
565: if (isnull) return(0);
567: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
568: xr = A->cmap.n; yr = A->rmap.n; h = yr/10.0; w = xr/10.0;
569: xr += w; yr += h; xl = -w; yl = -h;
570: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
571: PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
572: PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
573: return(0);
574: }
578: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
579: {
581: PetscTruth iascii,isbinary,isdraw;
584: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&iascii);
585: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
586: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
587: if (iascii) {
588: MatView_SeqAIJ_ASCII(A,viewer);
589: } else if (isbinary) {
590: MatView_SeqAIJ_Binary(A,viewer);
591: } else if (isdraw) {
592: MatView_SeqAIJ_Draw(A,viewer);
593: } else {
594: SETERRQ1(PETSC_ERR_SUP,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
595: }
596: MatView_Inode(A,viewer);
597: return(0);
598: }
602: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
603: {
604: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
606: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
607: PetscInt m = A->rmap.n,*ip,N,*ailen = a->ilen,rmax = 0;
608: PetscScalar *aa = a->a,*ap;
609: PetscReal ratio=0.6;
612: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
614: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
615: for (i=1; i<m; i++) {
616: /* move each row back by the amount of empty slots (fshift) before it*/
617: fshift += imax[i-1] - ailen[i-1];
618: rmax = PetscMax(rmax,ailen[i]);
619: if (fshift) {
620: ip = aj + ai[i] ;
621: ap = aa + ai[i] ;
622: N = ailen[i];
623: for (j=0; j<N; j++) {
624: ip[j-fshift] = ip[j];
625: ap[j-fshift] = ap[j];
626: }
627: }
628: ai[i] = ai[i-1] + ailen[i-1];
629: }
630: if (m) {
631: fshift += imax[m-1] - ailen[m-1];
632: ai[m] = ai[m-1] + ailen[m-1];
633: }
634: /* reset ilen and imax for each row */
635: for (i=0; i<m; i++) {
636: ailen[i] = imax[i] = ai[i+1] - ai[i];
637: }
638: a->nz = ai[m];
640: MatMarkDiagonal_SeqAIJ(A);
641: PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap.n,fshift,a->nz);
642: PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
643: PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);
645: a->reallocs = 0;
646: A->info.nz_unneeded = (double)fshift;
647: a->rmax = rmax;
649: /* check for zero rows. If found a large number of zero rows, use CompressedRow functions */
650: Mat_CheckCompressedRow(A,&a->compressedrow,a->i,m,ratio);
651: A->same_nonzero = PETSC_TRUE;
653: MatAssemblyEnd_Inode(A,mode);
655: a->idiagvalid = PETSC_FALSE;
656: return(0);
657: }
661: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
662: {
663: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
664: PetscInt i,nz = a->nz;
665: PetscScalar *aa = a->a;
668: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
669: return(0);
670: }
674: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
675: {
676: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
677: PetscInt i,nz = a->nz;
678: PetscScalar *aa = a->a;
681: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
682: return(0);
683: }
687: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
688: {
689: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
693: PetscMemzero(a->a,(a->i[A->rmap.n])*sizeof(PetscScalar));
694: return(0);
695: }
699: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
700: {
701: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
705: #if defined(PETSC_USE_LOG)
706: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap.n,A->cmap.n,a->nz);
707: #endif
708: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
709: if (a->row) {
710: ISDestroy(a->row);
711: }
712: if (a->col) {
713: ISDestroy(a->col);
714: }
715: PetscFree(a->diag);
716: PetscFree2(a->imax,a->ilen);
717: PetscFree3(a->idiag,a->mdiag,a->ssor_work);
718: PetscFree(a->solve_work);
719: if (a->icol) {ISDestroy(a->icol);}
720: PetscFree(a->saved_values);
721: if (a->coloring) {ISColoringDestroy(a->coloring);}
722: PetscFree(a->xtoy);
723: if (a->XtoY) {MatDestroy(a->XtoY);}
724: if (a->compressedrow.use){PetscFree(a->compressedrow.i);}
726: MatDestroy_Inode(A);
728: PetscFree(a);
730: PetscObjectChangeTypeName((PetscObject)A,0);
731: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetColumnIndices_C","",PETSC_NULL);
732: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatStoreValues_C","",PETSC_NULL);
733: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatRetrieveValues_C","",PETSC_NULL);
734: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqsbaij_C","",PETSC_NULL);
735: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqbaij_C","",PETSC_NULL);
736: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatConvert_seqaij_seqcsrperm_C","",PETSC_NULL);
737: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatIsTranspose_C","",PETSC_NULL);
738: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocation_C","",PETSC_NULL);
739: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C","",PETSC_NULL);
740: PetscObjectComposeFunctionDynamic((PetscObject)A,"MatReorderForNonzeroDiagonal_C","",PETSC_NULL);
741: return(0);
742: }
746: PetscErrorCode MatCompress_SeqAIJ(Mat A)
747: {
749: return(0);
750: }
754: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscTruth flg)
755: {
756: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
760: switch (op) {
761: case MAT_ROW_ORIENTED:
762: a->roworiented = flg;
763: break;
764: case MAT_KEEP_ZEROED_ROWS:
765: a->keepzeroedrows = flg;
766: break;
767: case MAT_NEW_NONZERO_LOCATIONS:
768: a->nonew = (flg ? 0 : 1);
769: break;
770: case MAT_NEW_NONZERO_LOCATION_ERR:
771: a->nonew = (flg ? -1 : 0);
772: break;
773: case MAT_NEW_NONZERO_ALLOCATION_ERR:
774: a->nonew = (flg ? -2 : 0);
775: break;
776: case MAT_IGNORE_ZERO_ENTRIES:
777: a->ignorezeroentries = flg;
778: break;
779: case MAT_USE_COMPRESSEDROW:
780: a->compressedrow.use = flg;
781: break;
782: case MAT_NEW_DIAGONALS:
783: case MAT_IGNORE_OFF_PROC_ENTRIES:
784: case MAT_USE_HASH_TABLE:
785: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
786: break;
787: default:
788: break;
789: }
790: MatSetOption_Inode(A,op,flg);
791: return(0);
792: }
796: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
797: {
798: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
800: PetscInt i,j,n;
801: PetscScalar *x,zero = 0.0;
804: VecSet(v,zero);
805: VecGetArray(v,&x);
806: VecGetLocalSize(v,&n);
807: if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
808: for (i=0; i<A->rmap.n; i++) {
809: for (j=a->i[i]; j<a->i[i+1]; j++) {
810: if (a->j[j] == i) {
811: x[i] = a->a[j];
812: break;
813: }
814: }
815: }
816: VecRestoreArray(v,&x);
817: return(0);
818: }
822: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
823: {
824: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
825: PetscScalar *x,*y;
826: PetscErrorCode ierr;
827: PetscInt m = A->rmap.n;
828: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
829: PetscScalar *v,alpha;
830: PetscInt n,i,*idx,*ii,*ridx=PETSC_NULL;
831: Mat_CompressedRow cprow = a->compressedrow;
832: PetscTruth usecprow = cprow.use;
833: #endif
836: if (zz != yy) {VecCopy(zz,yy);}
837: VecGetArray(xx,&x);
838: VecGetArray(yy,&y);
840: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
841: fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
842: #else
843: if (usecprow){
844: m = cprow.nrows;
845: ii = cprow.i;
846: ridx = cprow.rindex;
847: } else {
848: ii = a->i;
849: }
850: for (i=0; i<m; i++) {
851: idx = a->j + ii[i] ;
852: v = a->a + ii[i] ;
853: n = ii[i+1] - ii[i];
854: if (usecprow){
855: alpha = x[ridx[i]];
856: } else {
857: alpha = x[i];
858: }
859: while (n-->0) {y[*idx++] += alpha * *v++;}
860: }
861: #endif
862: PetscLogFlops(2*a->nz);
863: VecRestoreArray(xx,&x);
864: VecRestoreArray(yy,&y);
865: return(0);
866: }
870: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
871: {
872: PetscScalar zero = 0.0;
876: VecSet(yy,zero);
877: MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
878: return(0);
879: }
884: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
885: {
886: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
887: PetscScalar *x,*y,*aa;
889: PetscInt m=A->rmap.n,*aj,*ii;
890: PetscInt n,i,j,nonzerorow=0,*ridx=PETSC_NULL;
891: PetscScalar sum;
892: PetscTruth usecprow=a->compressedrow.use;
893: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
894: PetscInt jrow;
895: #endif
897: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
898: #pragma disjoint(*x,*y,*aa)
899: #endif
902: VecGetArray(xx,&x);
903: VecGetArray(yy,&y);
904: aj = a->j;
905: aa = a->a;
906: ii = a->i;
907: if (usecprow){ /* use compressed row format */
908: m = a->compressedrow.nrows;
909: ii = a->compressedrow.i;
910: ridx = a->compressedrow.rindex;
911: for (i=0; i<m; i++){
912: n = ii[i+1] - ii[i];
913: aj = a->j + ii[i];
914: aa = a->a + ii[i];
915: sum = 0.0;
916: nonzerorow += (n>0);
917: for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
918: y[*ridx++] = sum;
919: }
920: } else { /* do not use compressed row format */
921: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
922: fortranmultaij_(&m,x,ii,aj,aa,y);
923: #else
924: for (i=0; i<m; i++) {
925: jrow = ii[i];
926: n = ii[i+1] - jrow;
927: sum = 0.0;
928: nonzerorow += (n>0);
929: for (j=0; j<n; j++) {
930: sum += aa[jrow]*x[aj[jrow]]; jrow++;
931: }
932: y[i] = sum;
933: }
934: #endif
935: }
936: PetscLogFlops(2*a->nz - nonzerorow);
937: VecRestoreArray(xx,&x);
938: VecRestoreArray(yy,&y);
939: return(0);
940: }
944: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
945: {
946: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
947: PetscScalar *x,*y,*z,*aa;
949: PetscInt m = A->rmap.n,*aj,*ii;
950: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
951: PetscInt n,i,jrow,j,*ridx=PETSC_NULL;
952: PetscScalar sum;
953: PetscTruth usecprow=a->compressedrow.use;
954: #endif
957: VecGetArray(xx,&x);
958: VecGetArray(yy,&y);
959: if (zz != yy) {
960: VecGetArray(zz,&z);
961: } else {
962: z = y;
963: }
965: aj = a->j;
966: aa = a->a;
967: ii = a->i;
968: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
969: fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
970: #else
971: if (usecprow){ /* use compressed row format */
972: if (zz != yy){
973: PetscMemcpy(z,y,m*sizeof(PetscScalar));
974: }
975: m = a->compressedrow.nrows;
976: ii = a->compressedrow.i;
977: ridx = a->compressedrow.rindex;
978: for (i=0; i<m; i++){
979: n = ii[i+1] - ii[i];
980: aj = a->j + ii[i];
981: aa = a->a + ii[i];
982: sum = y[*ridx];
983: for (j=0; j<n; j++) sum += (*aa++)*x[*aj++];
984: z[*ridx++] = sum;
985: }
986: } else { /* do not use compressed row format */
987: for (i=0; i<m; i++) {
988: jrow = ii[i];
989: n = ii[i+1] - jrow;
990: sum = y[i];
991: for (j=0; j<n; j++) {
992: sum += aa[jrow]*x[aj[jrow]]; jrow++;
993: }
994: z[i] = sum;
995: }
996: }
997: #endif
998: PetscLogFlops(2*a->nz);
999: VecRestoreArray(xx,&x);
1000: VecRestoreArray(yy,&y);
1001: if (zz != yy) {
1002: VecRestoreArray(zz,&z);
1003: }
1004: return(0);
1005: }
1007: /*
1008: Adds diagonal pointers to sparse matrix structure.
1009: */
1012: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1013: {
1014: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1016: PetscInt i,j,m = A->rmap.n;
1019: if (!a->diag) {
1020: PetscMalloc(m*sizeof(PetscInt),&a->diag);
1021: PetscLogObjectMemory(A, m*sizeof(PetscInt));
1022: }
1023: for (i=0; i<A->rmap.n; i++) {
1024: a->diag[i] = a->i[i+1];
1025: for (j=a->i[i]; j<a->i[i+1]; j++) {
1026: if (a->j[j] == i) {
1027: a->diag[i] = j;
1028: break;
1029: }
1030: }
1031: }
1032: return(0);
1033: }
1035: /*
1036: Checks for missing diagonals
1037: */
1040: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscTruth *missing,PetscInt *d)
1041: {
1042: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1043: PetscInt *diag,*jj = a->j,i;
1046: *missing = PETSC_FALSE;
1047: if (A->rmap.n > 0 && !jj) {
1048: *missing = PETSC_TRUE;
1049: if (d) *d = 0;
1050: PetscInfo(A,"Matrix has no entries therefor is missing diagonal");
1051: } else {
1052: diag = a->diag;
1053: for (i=0; i<A->rmap.n; i++) {
1054: if (jj[diag[i]] != i) {
1055: *missing = PETSC_TRUE;
1056: if (d) *d = i;
1057: PetscInfo1(A,"Matrix is missing diagonal number %D",i);
1058: }
1059: }
1060: }
1061: return(0);
1062: }
1067: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1068: {
1069: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
1071: PetscInt i,*diag,m = A->rmap.n;
1072: PetscScalar *v = a->a,*idiag,*mdiag;
1075: if (a->idiagvalid) return(0);
1076: MatMarkDiagonal_SeqAIJ(A);
1077: diag = a->diag;
1078: if (!a->idiag) {
1079: PetscMalloc3(m,PetscScalar,&a->idiag,m,PetscScalar,&a->mdiag,m,PetscScalar,&a->ssor_work);
1080: PetscLogObjectMemory(A, 3*m*sizeof(PetscScalar));
1081: v = a->a;
1082: }
1083: mdiag = a->mdiag;
1084: idiag = a->idiag;
1085:
1086: if (omega == 1.0 && !PetscAbsScalar(fshift)) {
1087: for (i=0; i<m; i++) {
1088: mdiag[i] = v[diag[i]];
1089: if (!PetscAbsScalar(mdiag[i])) SETERRQ1(PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1090: idiag[i] = 1.0/v[diag[i]];
1091: }
1092: PetscLogFlops(m);
1093: } else {
1094: for (i=0; i<m; i++) {
1095: mdiag[i] = v[diag[i]];
1096: idiag[i] = omega/(fshift + v[diag[i]]);
1097: }
1098: PetscLogFlops(2*m);
1099: }
1100: a->idiagvalid = PETSC_TRUE;
1101: return(0);
1102: }
1107: PetscErrorCode MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1108: {
1109: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1110: PetscScalar *x,d,*xs,sum,*t,scale,*idiag=0,*mdiag;
1111: const PetscScalar *v = a->a, *b, *bs,*xb, *ts;
1112: PetscErrorCode ierr;
1113: PetscInt n = A->cmap.n,m = A->rmap.n,i;
1114: const PetscInt *idx,*diag;
1117: its = its*lits;
1119: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1120: if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1121: a->fshift = fshift;
1122: a->omega = omega;
1124: diag = a->diag;
1125: t = a->ssor_work;
1126: idiag = a->idiag;
1127: mdiag = a->mdiag;
1129: VecGetArray(xx,&x);
1130: if (xx != bb) {
1131: VecGetArray(bb,(PetscScalar**)&b);
1132: } else {
1133: b = x;
1134: }
1135: CHKMEMQ;
1136: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1137: xs = x;
1138: if (flag == SOR_APPLY_UPPER) {
1139: /* apply (U + D/omega) to the vector */
1140: bs = b;
1141: for (i=0; i<m; i++) {
1142: d = fshift + mdiag[i];
1143: n = a->i[i+1] - diag[i] - 1;
1144: idx = a->j + diag[i] + 1;
1145: v = a->a + diag[i] + 1;
1146: sum = b[i]*d/omega;
1147: SPARSEDENSEDOT(sum,bs,v,idx,n);
1148: x[i] = sum;
1149: }
1150: VecRestoreArray(xx,&x);
1151: if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1152: PetscLogFlops(a->nz);
1153: return(0);
1154: }
1156: if (flag == SOR_APPLY_LOWER) {
1157: SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1158: } else if (flag & SOR_EISENSTAT) {
1159: /* Let A = L + U + D; where L is lower trianglar,
1160: U is upper triangular, E is diagonal; This routine applies
1162: (L + E)^{-1} A (U + E)^{-1}
1164: to a vector efficiently using Eisenstat's trick. This is for
1165: the case of SSOR preconditioner, so E is D/omega where omega
1166: is the relaxation factor.
1167: */
1168: scale = (2.0/omega) - 1.0;
1170: /* x = (E + U)^{-1} b */
1171: for (i=m-1; i>=0; i--) {
1172: n = a->i[i+1] - diag[i] - 1;
1173: idx = a->j + diag[i] + 1;
1174: v = a->a + diag[i] + 1;
1175: sum = b[i];
1176: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1177: x[i] = sum*idiag[i];
1178: }
1180: /* t = b - (2*E - D)x */
1181: v = a->a;
1182: for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++])*x[i]; }
1184: /* t = (E + L)^{-1}t */
1185: ts = t;
1186: diag = a->diag;
1187: for (i=0; i<m; i++) {
1188: n = diag[i] - a->i[i];
1189: idx = a->j + a->i[i];
1190: v = a->a + a->i[i];
1191: sum = t[i];
1192: SPARSEDENSEMDOT(sum,ts,v,idx,n);
1193: t[i] = sum*idiag[i];
1194: /* x = x + t */
1195: x[i] += t[i];
1196: }
1198: PetscLogFlops(6*m-1 + 2*a->nz);
1199: VecRestoreArray(xx,&x);
1200: if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1201: return(0);
1202: }
1203: if (flag & SOR_ZERO_INITIAL_GUESS) {
1204: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1205: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1206: fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)b);
1207: #else
1208: for (i=0; i<m; i++) {
1209: n = diag[i] - a->i[i];
1210: idx = a->j + a->i[i];
1211: v = a->a + a->i[i];
1212: sum = b[i];
1213: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1214: x[i] = sum*idiag[i];
1215: }
1216: #endif
1217: xb = x;
1218: PetscLogFlops(a->nz);
1219: } else xb = b;
1220: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1221: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1222: for (i=0; i<m; i++) {
1223: x[i] *= mdiag[i];
1224: }
1225: PetscLogFlops(m);
1226: }
1227: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1228: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1229: fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,idiag,a->a,(void*)xb);
1230: #else
1231: for (i=m-1; i>=0; i--) {
1232: n = a->i[i+1] - diag[i] - 1;
1233: idx = a->j + diag[i] + 1;
1234: v = a->a + diag[i] + 1;
1235: sum = xb[i];
1236: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1237: x[i] = sum*idiag[i];
1238: }
1239: #endif
1240: PetscLogFlops(a->nz);
1241: }
1242: its--;
1243: }
1244: while (its--) {
1245: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1246: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1247: fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1248: #else
1249: for (i=0; i<m; i++) {
1250: n = a->i[i+1] - a->i[i];
1251: idx = a->j + a->i[i];
1252: v = a->a + a->i[i];
1253: sum = b[i];
1254: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1255: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1256: }
1257: #endif
1258: PetscLogFlops(a->nz);
1259: }
1260: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1261: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1262: fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,(PetscInt*)diag,a->a,(void*)b);
1263: #else
1264: for (i=m-1; i>=0; i--) {
1265: n = a->i[i+1] - a->i[i];
1266: idx = a->j + a->i[i];
1267: v = a->a + a->i[i];
1268: sum = b[i];
1269: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1270: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1271: }
1272: #endif
1273: PetscLogFlops(a->nz);
1274: }
1275: }
1276: VecRestoreArray(xx,&x);
1277: if (bb != xx) {VecRestoreArray(bb,(PetscScalar**)&b);}
1278: CHKMEMQ; return(0);
1279: }
1284: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1285: {
1286: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1289: info->rows_global = (double)A->rmap.n;
1290: info->columns_global = (double)A->cmap.n;
1291: info->rows_local = (double)A->rmap.n;
1292: info->columns_local = (double)A->cmap.n;
1293: info->block_size = 1.0;
1294: info->nz_allocated = (double)a->maxnz;
1295: info->nz_used = (double)a->nz;
1296: info->nz_unneeded = (double)(a->maxnz - a->nz);
1297: info->assemblies = (double)A->num_ass;
1298: info->mallocs = (double)a->reallocs;
1299: info->memory = ((PetscObject)A)->mem;
1300: if (A->factor) {
1301: info->fill_ratio_given = A->info.fill_ratio_given;
1302: info->fill_ratio_needed = A->info.fill_ratio_needed;
1303: info->factor_mallocs = A->info.factor_mallocs;
1304: } else {
1305: info->fill_ratio_given = 0;
1306: info->fill_ratio_needed = 0;
1307: info->factor_mallocs = 0;
1308: }
1309: return(0);
1310: }
1314: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag)
1315: {
1316: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1317: PetscInt i,m = A->rmap.n - 1,d;
1319: PetscTruth missing;
1322: if (a->keepzeroedrows) {
1323: for (i=0; i<N; i++) {
1324: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1325: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1326: }
1327: if (diag != 0.0) {
1328: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1329: if (missing) SETERRQ1(PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1330: for (i=0; i<N; i++) {
1331: a->a[a->diag[rows[i]]] = diag;
1332: }
1333: }
1334: A->same_nonzero = PETSC_TRUE;
1335: } else {
1336: if (diag != 0.0) {
1337: for (i=0; i<N; i++) {
1338: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1339: if (a->ilen[rows[i]] > 0) {
1340: a->ilen[rows[i]] = 1;
1341: a->a[a->i[rows[i]]] = diag;
1342: a->j[a->i[rows[i]]] = rows[i];
1343: } else { /* in case row was completely empty */
1344: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1345: }
1346: }
1347: } else {
1348: for (i=0; i<N; i++) {
1349: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1350: a->ilen[rows[i]] = 0;
1351: }
1352: }
1353: A->same_nonzero = PETSC_FALSE;
1354: }
1355: MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1356: return(0);
1357: }
1361: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1362: {
1363: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1364: PetscInt *itmp;
1367: if (row < 0 || row >= A->rmap.n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);
1369: *nz = a->i[row+1] - a->i[row];
1370: if (v) *v = a->a + a->i[row];
1371: if (idx) {
1372: itmp = a->j + a->i[row];
1373: if (*nz) {
1374: *idx = itmp;
1375: }
1376: else *idx = 0;
1377: }
1378: return(0);
1379: }
1381: /* remove this function? */
1384: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1385: {
1387: return(0);
1388: }
1392: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1393: {
1394: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1395: PetscScalar *v = a->a;
1396: PetscReal sum = 0.0;
1398: PetscInt i,j;
1401: if (type == NORM_FROBENIUS) {
1402: for (i=0; i<a->nz; i++) {
1403: #if defined(PETSC_USE_COMPLEX)
1404: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1405: #else
1406: sum += (*v)*(*v); v++;
1407: #endif
1408: }
1409: *nrm = sqrt(sum);
1410: } else if (type == NORM_1) {
1411: PetscReal *tmp;
1412: PetscInt *jj = a->j;
1413: PetscMalloc((A->cmap.n+1)*sizeof(PetscReal),&tmp);
1414: PetscMemzero(tmp,A->cmap.n*sizeof(PetscReal));
1415: *nrm = 0.0;
1416: for (j=0; j<a->nz; j++) {
1417: tmp[*jj++] += PetscAbsScalar(*v); v++;
1418: }
1419: for (j=0; j<A->cmap.n; j++) {
1420: if (tmp[j] > *nrm) *nrm = tmp[j];
1421: }
1422: PetscFree(tmp);
1423: } else if (type == NORM_INFINITY) {
1424: *nrm = 0.0;
1425: for (j=0; j<A->rmap.n; j++) {
1426: v = a->a + a->i[j];
1427: sum = 0.0;
1428: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1429: sum += PetscAbsScalar(*v); v++;
1430: }
1431: if (sum > *nrm) *nrm = sum;
1432: }
1433: } else {
1434: SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1435: }
1436: return(0);
1437: }
1441: PetscErrorCode MatTranspose_SeqAIJ(Mat A,Mat *B)
1442: {
1443: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1444: Mat C;
1446: PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap.n,len,*col;
1447: PetscScalar *array = a->a;
1450: if (!B && m != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1451: PetscMalloc((1+A->cmap.n)*sizeof(PetscInt),&col);
1452: PetscMemzero(col,(1+A->cmap.n)*sizeof(PetscInt));
1453:
1454: for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
1455: MatCreate(((PetscObject)A)->comm,&C);
1456: MatSetSizes(C,A->cmap.n,m,A->cmap.n,m);
1457: MatSetType(C,((PetscObject)A)->type_name);
1458: MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
1459: PetscFree(col);
1460: for (i=0; i<m; i++) {
1461: len = ai[i+1]-ai[i];
1462: MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
1463: array += len;
1464: aj += len;
1465: }
1467: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1468: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1470: if (B) {
1471: *B = C;
1472: } else {
1473: MatHeaderCopy(A,C);
1474: }
1475: return(0);
1476: }
1481: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1482: {
1483: Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1484: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1486: PetscInt ma,na,mb,nb, i;
1489: bij = (Mat_SeqAIJ *) B->data;
1490:
1491: MatGetSize(A,&ma,&na);
1492: MatGetSize(B,&mb,&nb);
1493: if (ma!=nb || na!=mb){
1494: *f = PETSC_FALSE;
1495: return(0);
1496: }
1497: aii = aij->i; bii = bij->i;
1498: adx = aij->j; bdx = bij->j;
1499: va = aij->a; vb = bij->a;
1500: PetscMalloc(ma*sizeof(PetscInt),&aptr);
1501: PetscMalloc(mb*sizeof(PetscInt),&bptr);
1502: for (i=0; i<ma; i++) aptr[i] = aii[i];
1503: for (i=0; i<mb; i++) bptr[i] = bii[i];
1505: *f = PETSC_TRUE;
1506: for (i=0; i<ma; i++) {
1507: while (aptr[i]<aii[i+1]) {
1508: PetscInt idc,idr;
1509: PetscScalar vc,vr;
1510: /* column/row index/value */
1511: idc = adx[aptr[i]];
1512: idr = bdx[bptr[idc]];
1513: vc = va[aptr[i]];
1514: vr = vb[bptr[idc]];
1515: if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
1516: *f = PETSC_FALSE;
1517: goto done;
1518: } else {
1519: aptr[i]++;
1520: if (B || i!=idc) bptr[idc]++;
1521: }
1522: }
1523: }
1524: done:
1525: PetscFree(aptr);
1526: if (B) {
1527: PetscFree(bptr);
1528: }
1529: return(0);
1530: }
1536: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscTruth *f)
1537: {
1538: Mat_SeqAIJ *aij = (Mat_SeqAIJ *) A->data,*bij = (Mat_SeqAIJ*) A->data;
1539: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr; PetscScalar *va,*vb;
1541: PetscInt ma,na,mb,nb, i;
1544: bij = (Mat_SeqAIJ *) B->data;
1545:
1546: MatGetSize(A,&ma,&na);
1547: MatGetSize(B,&mb,&nb);
1548: if (ma!=nb || na!=mb){
1549: *f = PETSC_FALSE;
1550: return(0);
1551: }
1552: aii = aij->i; bii = bij->i;
1553: adx = aij->j; bdx = bij->j;
1554: va = aij->a; vb = bij->a;
1555: PetscMalloc(ma*sizeof(PetscInt),&aptr);
1556: PetscMalloc(mb*sizeof(PetscInt),&bptr);
1557: for (i=0; i<ma; i++) aptr[i] = aii[i];
1558: for (i=0; i<mb; i++) bptr[i] = bii[i];
1560: *f = PETSC_TRUE;
1561: for (i=0; i<ma; i++) {
1562: while (aptr[i]<aii[i+1]) {
1563: PetscInt idc,idr;
1564: PetscScalar vc,vr;
1565: /* column/row index/value */
1566: idc = adx[aptr[i]];
1567: idr = bdx[bptr[idc]];
1568: vc = va[aptr[i]];
1569: vr = vb[bptr[idc]];
1570: if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
1571: *f = PETSC_FALSE;
1572: goto done;
1573: } else {
1574: aptr[i]++;
1575: if (B || i!=idc) bptr[idc]++;
1576: }
1577: }
1578: }
1579: done:
1580: PetscFree(aptr);
1581: if (B) {
1582: PetscFree(bptr);
1583: }
1584: return(0);
1585: }
1590: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1591: {
1594: MatIsTranspose_SeqAIJ(A,A,tol,f);
1595: return(0);
1596: }
1600: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscTruth *f)
1601: {
1604: MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
1605: return(0);
1606: }
1610: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1611: {
1612: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1613: PetscScalar *l,*r,x,*v;
1615: PetscInt i,j,m = A->rmap.n,n = A->cmap.n,M,nz = a->nz,*jj;
1618: if (ll) {
1619: /* The local size is used so that VecMPI can be passed to this routine
1620: by MatDiagonalScale_MPIAIJ */
1621: VecGetLocalSize(ll,&m);
1622: if (m != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1623: VecGetArray(ll,&l);
1624: v = a->a;
1625: for (i=0; i<m; i++) {
1626: x = l[i];
1627: M = a->i[i+1] - a->i[i];
1628: for (j=0; j<M; j++) { (*v++) *= x;}
1629: }
1630: VecRestoreArray(ll,&l);
1631: PetscLogFlops(nz);
1632: }
1633: if (rr) {
1634: VecGetLocalSize(rr,&n);
1635: if (n != A->cmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1636: VecGetArray(rr,&r);
1637: v = a->a; jj = a->j;
1638: for (i=0; i<nz; i++) {
1639: (*v++) *= r[*jj++];
1640: }
1641: VecRestoreArray(rr,&r);
1642: PetscLogFlops(nz);
1643: }
1644: return(0);
1645: }
1649: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
1650: {
1651: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
1653: PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap.n,*lens;
1654: PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1655: PetscInt *irow,*icol,nrows,ncols;
1656: PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1657: PetscScalar *a_new,*mat_a;
1658: Mat C;
1659: PetscTruth stride;
1662: ISSorted(isrow,(PetscTruth*)&i);
1663: if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1664: ISSorted(iscol,(PetscTruth*)&i);
1665: if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");
1667: ISGetIndices(isrow,&irow);
1668: ISGetLocalSize(isrow,&nrows);
1669: ISGetLocalSize(iscol,&ncols);
1671: ISStrideGetInfo(iscol,&first,&step);
1672: ISStride(iscol,&stride);
1673: if (stride && step == 1) {
1674: /* special case of contiguous rows */
1675: PetscMalloc((2*nrows+1)*sizeof(PetscInt),&lens);
1676: starts = lens + nrows;
1677: /* loop over new rows determining lens and starting points */
1678: for (i=0; i<nrows; i++) {
1679: kstart = ai[irow[i]];
1680: kend = kstart + ailen[irow[i]];
1681: for (k=kstart; k<kend; k++) {
1682: if (aj[k] >= first) {
1683: starts[i] = k;
1684: break;
1685: }
1686: }
1687: sum = 0;
1688: while (k < kend) {
1689: if (aj[k++] >= first+ncols) break;
1690: sum++;
1691: }
1692: lens[i] = sum;
1693: }
1694: /* create submatrix */
1695: if (scall == MAT_REUSE_MATRIX) {
1696: PetscInt n_cols,n_rows;
1697: MatGetSize(*B,&n_rows,&n_cols);
1698: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1699: MatZeroEntries(*B);
1700: C = *B;
1701: } else {
1702: MatCreate(((PetscObject)A)->comm,&C);
1703: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1704: MatSetType(C,((PetscObject)A)->type_name);
1705: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1706: }
1707: c = (Mat_SeqAIJ*)C->data;
1709: /* loop over rows inserting into submatrix */
1710: a_new = c->a;
1711: j_new = c->j;
1712: i_new = c->i;
1714: for (i=0; i<nrows; i++) {
1715: ii = starts[i];
1716: lensi = lens[i];
1717: for (k=0; k<lensi; k++) {
1718: *j_new++ = aj[ii+k] - first;
1719: }
1720: PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1721: a_new += lensi;
1722: i_new[i+1] = i_new[i] + lensi;
1723: c->ilen[i] = lensi;
1724: }
1725: PetscFree(lens);
1726: } else {
1727: ISGetIndices(iscol,&icol);
1728: PetscMalloc((1+oldcols)*sizeof(PetscInt),&smap);
1729:
1730: PetscMalloc((1+nrows)*sizeof(PetscInt),&lens);
1731: PetscMemzero(smap,oldcols*sizeof(PetscInt));
1732: for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1733: /* determine lens of each row */
1734: for (i=0; i<nrows; i++) {
1735: kstart = ai[irow[i]];
1736: kend = kstart + a->ilen[irow[i]];
1737: lens[i] = 0;
1738: for (k=kstart; k<kend; k++) {
1739: if (smap[aj[k]]) {
1740: lens[i]++;
1741: }
1742: }
1743: }
1744: /* Create and fill new matrix */
1745: if (scall == MAT_REUSE_MATRIX) {
1746: PetscTruth equal;
1748: c = (Mat_SeqAIJ *)((*B)->data);
1749: if ((*B)->rmap.n != nrows || (*B)->cmap.n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1750: PetscMemcmp(c->ilen,lens,(*B)->rmap.n*sizeof(PetscInt),&equal);
1751: if (!equal) {
1752: SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1753: }
1754: PetscMemzero(c->ilen,(*B)->rmap.n*sizeof(PetscInt));
1755: C = *B;
1756: } else {
1757: MatCreate(((PetscObject)A)->comm,&C);
1758: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
1759: MatSetType(C,((PetscObject)A)->type_name);
1760: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
1761: }
1762: c = (Mat_SeqAIJ *)(C->data);
1763: for (i=0; i<nrows; i++) {
1764: row = irow[i];
1765: kstart = ai[row];
1766: kend = kstart + a->ilen[row];
1767: mat_i = c->i[i];
1768: mat_j = c->j + mat_i;
1769: mat_a = c->a + mat_i;
1770: mat_ilen = c->ilen + i;
1771: for (k=kstart; k<kend; k++) {
1772: if ((tcol=smap[a->j[k]])) {
1773: *mat_j++ = tcol - 1;
1774: *mat_a++ = a->a[k];
1775: (*mat_ilen)++;
1777: }
1778: }
1779: }
1780: /* Free work space */
1781: ISRestoreIndices(iscol,&icol);
1782: PetscFree(smap);
1783: PetscFree(lens);
1784: }
1785: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1786: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1788: ISRestoreIndices(isrow,&irow);
1789: *B = C;
1790: return(0);
1791: }
1793: /*
1794: */
1797: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatFactorInfo *info)
1798: {
1799: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1801: Mat outA;
1802: PetscTruth row_identity,col_identity;
1805: if (info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1806: ISIdentity(row,&row_identity);
1807: ISIdentity(col,&col_identity);
1809: outA = inA;
1810: inA->factor = FACTOR_LU;
1811: PetscObjectReference((PetscObject)row);
1812: if (a->row) { ISDestroy(a->row); }
1813: a->row = row;
1814: PetscObjectReference((PetscObject)col);
1815: if (a->col) { ISDestroy(a->col); }
1816: a->col = col;
1818: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1819: if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1820: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1821: PetscLogObjectParent(inA,a->icol);
1823: if (!a->solve_work) { /* this matrix may have been factored before */
1824: PetscMalloc((inA->rmap.n+1)*sizeof(PetscScalar),&a->solve_work);
1825: PetscLogObjectMemory(inA, (inA->rmap.n+1)*sizeof(PetscScalar));
1826: }
1828: MatMarkDiagonal_SeqAIJ(inA);
1829: if (row_identity && col_identity) {
1830: MatLUFactorNumeric_SeqAIJ(inA,info,&outA);
1831: } else {
1832: MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(inA,info,&outA);
1833: }
1834: return(0);
1835: }
1837: #include petscblaslapack.h
1840: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
1841: {
1842: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1843: PetscBLASInt bnz = (PetscBLASInt)a->nz,one = 1;
1844: PetscScalar oalpha = alpha;
1849: BLASscal_(&bnz,&oalpha,a->a,&one);
1850: PetscLogFlops(a->nz);
1851: return(0);
1852: }
1856: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
1857: {
1859: PetscInt i;
1862: if (scall == MAT_INITIAL_MATRIX) {
1863: PetscMalloc((n+1)*sizeof(Mat),B);
1864: }
1866: for (i=0; i<n; i++) {
1867: MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1868: }
1869: return(0);
1870: }
1874: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
1875: {
1876: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1878: PetscInt row,i,j,k,l,m,n,*idx,*nidx,isz,val;
1879: PetscInt start,end,*ai,*aj;
1880: PetscBT table;
1883: m = A->rmap.n;
1884: ai = a->i;
1885: aj = a->j;
1887: if (ov < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");
1889: PetscMalloc((m+1)*sizeof(PetscInt),&nidx);
1890: PetscBTCreate(m,table);
1892: for (i=0; i<is_max; i++) {
1893: /* Initialize the two local arrays */
1894: isz = 0;
1895: PetscBTMemzero(m,table);
1896:
1897: /* Extract the indices, assume there can be duplicate entries */
1898: ISGetIndices(is[i],&idx);
1899: ISGetLocalSize(is[i],&n);
1900:
1901: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1902: for (j=0; j<n ; ++j){
1903: if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1904: }
1905: ISRestoreIndices(is[i],&idx);
1906: ISDestroy(is[i]);
1907:
1908: k = 0;
1909: for (j=0; j<ov; j++){ /* for each overlap */
1910: n = isz;
1911: for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1912: row = nidx[k];
1913: start = ai[row];
1914: end = ai[row+1];
1915: for (l = start; l<end ; l++){
1916: val = aj[l] ;
1917: if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1918: }
1919: }
1920: }
1921: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1922: }
1923: PetscBTDestroy(table);
1924: PetscFree(nidx);
1925: return(0);
1926: }
1928: /* -------------------------------------------------------------- */
1931: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1932: {
1933: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1935: PetscInt i,nz,m = A->rmap.n,n = A->cmap.n,*col;
1936: PetscInt *row,*cnew,j,*lens;
1937: IS icolp,irowp;
1938: PetscInt *cwork;
1939: PetscScalar *vwork;
1942: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1943: ISGetIndices(irowp,&row);
1944: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1945: ISGetIndices(icolp,&col);
1946:
1947: /* determine lengths of permuted rows */
1948: PetscMalloc((m+1)*sizeof(PetscInt),&lens);
1949: for (i=0; i<m; i++) {
1950: lens[row[i]] = a->i[i+1] - a->i[i];
1951: }
1952: MatCreate(((PetscObject)A)->comm,B);
1953: MatSetSizes(*B,m,n,m,n);
1954: MatSetType(*B,((PetscObject)A)->type_name);
1955: MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
1956: PetscFree(lens);
1958: PetscMalloc(n*sizeof(PetscInt),&cnew);
1959: for (i=0; i<m; i++) {
1960: MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1961: for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1962: MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1963: MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
1964: }
1965: PetscFree(cnew);
1966: (*B)->assembled = PETSC_FALSE;
1967: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1968: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1969: ISRestoreIndices(irowp,&row);
1970: ISRestoreIndices(icolp,&col);
1971: ISDestroy(irowp);
1972: ISDestroy(icolp);
1973: return(0);
1974: }
1978: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1979: {
1983: /* If the two matrices have the same copy implementation, use fast copy. */
1984: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1985: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1986: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
1988: if (a->i[A->rmap.n] != b->i[B->rmap.n]) {
1989: SETERRQ(PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1990: }
1991: PetscMemcpy(b->a,a->a,(a->i[A->rmap.n])*sizeof(PetscScalar));
1992: } else {
1993: MatCopy_Basic(A,B,str);
1994: }
1995: return(0);
1996: }
2000: PetscErrorCode MatSetUpPreallocation_SeqAIJ(Mat A)
2001: {
2005: MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2006: return(0);
2007: }
2011: PetscErrorCode MatGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2012: {
2013: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2015: *array = a->a;
2016: return(0);
2017: }
2021: PetscErrorCode MatRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2022: {
2024: return(0);
2025: }
2029: PetscErrorCode MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
2030: {
2031: PetscErrorCode (*f)(void*,Vec,Vec,void*) = (PetscErrorCode (*)(void*,Vec,Vec,void *))coloring->f;
2033: PetscInt k,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
2034: PetscScalar dx,*y,*xx,*w3_array;
2035: PetscScalar *vscale_array;
2036: PetscReal epsilon = coloring->error_rel,umin = coloring->umin;
2037: Vec w1,w2,w3;
2038: void *fctx = coloring->fctx;
2039: PetscTruth flg;
2042: if (!coloring->w1) {
2043: VecDuplicate(x1,&coloring->w1);
2044: PetscLogObjectParent(coloring,coloring->w1);
2045: VecDuplicate(x1,&coloring->w2);
2046: PetscLogObjectParent(coloring,coloring->w2);
2047: VecDuplicate(x1,&coloring->w3);
2048: PetscLogObjectParent(coloring,coloring->w3);
2049: }
2050: w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;
2052: MatSetUnfactored(J);
2053: PetscOptionsHasName(((PetscObject)coloring)->prefix,"-mat_fd_coloring_dont_rezero",&flg);
2054: if (flg) {
2055: PetscInfo(coloring,"Not calling MatZeroEntries()\n");
2056: } else {
2057: PetscTruth assembled;
2058: MatAssembled(J,&assembled);
2059: if (assembled) {
2060: MatZeroEntries(J);
2061: }
2062: }
2064: VecGetOwnershipRange(x1,&start,&end);
2065: VecGetSize(x1,&N);
2067: /*
2068: This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
2069: coloring->F for the coarser grids from the finest
2070: */
2071: if (coloring->F) {
2072: VecGetLocalSize(coloring->F,&m1);
2073: VecGetLocalSize(w1,&m2);
2074: if (m1 != m2) {
2075: coloring->F = 0;
2076: }
2077: }
2079: if (coloring->F) {
2080: w1 = coloring->F;
2081: coloring->F = 0;
2082: } else {
2084: (*f)(sctx,x1,w1,fctx);
2086: }
2088: /*
2089: Compute all the scale factors and share with other processors
2090: */
2091: VecGetArray(x1,&xx);xx = xx - start;
2092: VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
2093: for (k=0; k<coloring->ncolors; k++) {
2094: /*
2095: Loop over each column associated with color adding the
2096: perturbation to the vector w3.
2097: */
2098: for (l=0; l<coloring->ncolumns[k]; l++) {
2099: col = coloring->columns[k][l]; /* column of the matrix we are probing for */
2100: dx = xx[col];
2101: if (dx == 0.0) dx = 1.0;
2102: #if !defined(PETSC_USE_COMPLEX)
2103: if (dx < umin && dx >= 0.0) dx = umin;
2104: else if (dx < 0.0 && dx > -umin) dx = -umin;
2105: #else
2106: if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin;
2107: else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2108: #endif
2109: dx *= epsilon;
2110: vscale_array[col] = 1.0/dx;
2111: }
2112: }
2113: vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
2114: VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2115: VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
2117: /* VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
2118: VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/
2120: if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
2121: else vscaleforrow = coloring->columnsforrow;
2123: VecGetArray(coloring->vscale,&vscale_array);
2124: /*
2125: Loop over each color
2126: */
2127: for (k=0; k<coloring->ncolors; k++) {
2128: coloring->currentcolor = k;
2129: VecCopy(x1,w3);
2130: VecGetArray(w3,&w3_array);w3_array = w3_array - start;
2131: /*
2132: Loop over each column associated with color adding the
2133: perturbation to the vector w3.
2134: */
2135: for (l=0; l<coloring->ncolumns[k]; l++) {
2136: col = coloring->columns[k][l]; /* column of the matrix we are probing for */
2137: dx = xx[col];
2138: if (dx == 0.0) dx = 1.0;
2139: #if !defined(PETSC_USE_COMPLEX)
2140: if (dx < umin && dx >= 0.0) dx = umin;
2141: else if (dx < 0.0 && dx > -umin) dx = -umin;
2142: #else
2143: if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin;
2144: else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
2145: #endif
2146: dx *= epsilon;
2147: if (!PetscAbsScalar(dx)) SETERRQ(PETSC_ERR_PLIB,"Computed 0 differencing parameter");
2148: w3_array[col] += dx;
2149: }
2150: w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);
2152: /*
2153: Evaluate function at x1 + dx (here dx is a vector of perturbations)
2154: */
2157: (*f)(sctx,w3,w2,fctx);
2159: VecAXPY(w2,-1.0,w1);
2161: /*
2162: Loop over rows of vector, putting results into Jacobian matrix
2163: */
2164: VecGetArray(w2,&y);
2165: for (l=0; l<coloring->nrows[k]; l++) {
2166: row = coloring->rows[k][l];
2167: col = coloring->columnsforrow[k][l];
2168: y[row] *= vscale_array[vscaleforrow[k][l]];
2169: srow = row + start;
2170: MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
2171: }
2172: VecRestoreArray(w2,&y);
2173: }
2174: coloring->currentcolor = k;
2175: VecRestoreArray(coloring->vscale,&vscale_array);
2176: xx = xx + start; VecRestoreArray(x1,&xx);
2177: MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
2178: MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
2179: return(0);
2180: }
2182: #include petscblaslapack.h
2185: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2186: {
2188: PetscInt i;
2189: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
2190: PetscBLASInt one=1,bnz = (PetscBLASInt)x->nz;
2193: if (str == SAME_NONZERO_PATTERN) {
2194: PetscScalar alpha = a;
2195: BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one);
2196: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2197: if (y->xtoy && y->XtoY != X) {
2198: PetscFree(y->xtoy);
2199: MatDestroy(y->XtoY);
2200: }
2201: if (!y->xtoy) { /* get xtoy */
2202: MatAXPYGetxtoy_Private(X->rmap.n,x->i,x->j,PETSC_NULL, y->i,y->j,PETSC_NULL, &y->xtoy);
2203: y->XtoY = X;
2204: PetscObjectReference((PetscObject)X);
2205: }
2206: for (i=0; i<x->nz; i++) y->a[y->xtoy[i]] += a*(x->a[i]);
2207: PetscInfo3(Y,"ratio of nnz(X)/nnz(Y): %d/%d = %G\n",x->nz,y->nz,(PetscReal)(x->nz)/y->nz);
2208: } else {
2209: MatAXPY_Basic(Y,a,X,str);
2210: }
2211: return(0);
2212: }
2216: PetscErrorCode MatSetBlockSize_SeqAIJ(Mat A,PetscInt bs)
2217: {
2219: return(0);
2220: }
2224: PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
2225: {
2226: #if defined(PETSC_USE_COMPLEX)
2227: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2228: PetscInt i,nz;
2229: PetscScalar *a;
2232: nz = aij->nz;
2233: a = aij->a;
2234: for (i=0; i<nz; i++) {
2235: a[i] = PetscConj(a[i]);
2236: }
2237: #else
2239: #endif
2240: return(0);
2241: }
2245: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2246: {
2247: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2249: PetscInt i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2250: PetscReal atmp;
2251: PetscScalar *x;
2252: MatScalar *aa;
2255: if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2256: aa = a->a;
2257: ai = a->i;
2258: aj = a->j;
2260: VecSet(v,0.0);
2261: VecGetArray(v,&x);
2262: VecGetLocalSize(v,&n);
2263: if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2264: for (i=0; i<m; i++) {
2265: ncols = ai[1] - ai[0]; ai++;
2266: x[i] = 0.0; if (idx) idx[i] = 0;
2267: for (j=0; j<ncols; j++){
2268: atmp = PetscAbsScalar(*aa);
2269: if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2270: aa++; aj++;
2271: }
2272: }
2273: VecRestoreArray(v,&x);
2274: return(0);
2275: }
2279: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2280: {
2281: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2283: PetscInt i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2284: PetscScalar *x;
2285: MatScalar *aa;
2288: if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2289: aa = a->a;
2290: ai = a->i;
2291: aj = a->j;
2293: VecSet(v,0.0);
2294: VecGetArray(v,&x);
2295: VecGetLocalSize(v,&n);
2296: if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2297: for (i=0; i<m; i++) {
2298: ncols = ai[1] - ai[0]; ai++;
2299: if (ncols == A->cmap.n) { /* row is dense */
2300: x[i] = *aa; if (idx) idx[i] = 0;
2301: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
2302: x[i] = 0.0;
2303: if (idx) {
2304: idx[i] = 0; /* in case ncols is zero */
2305: for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2306: if (aj[j] > j) {
2307: idx[i] = j;
2308: break;
2309: }
2310: }
2311: }
2312: }
2313: for (j=0; j<ncols; j++){
2314: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2315: aa++; aj++;
2316: }
2317: }
2318: VecRestoreArray(v,&x);
2319: return(0);
2320: }
2324: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2325: {
2326: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2328: PetscInt i,j,m = A->rmap.n,*ai,*aj,ncols,n;
2329: PetscScalar *x;
2330: MatScalar *aa;
2333: if (A->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2334: aa = a->a;
2335: ai = a->i;
2336: aj = a->j;
2338: VecSet(v,0.0);
2339: VecGetArray(v,&x);
2340: VecGetLocalSize(v,&n);
2341: if (n != A->rmap.n) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2342: for (i=0; i<m; i++) {
2343: ncols = ai[1] - ai[0]; ai++;
2344: if (ncols == A->cmap.n) { /* row is dense */
2345: x[i] = *aa; if (idx) idx[i] = 0;
2346: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
2347: x[i] = 0.0;
2348: if (idx) { /* find first implicit 0.0 in the row */
2349: idx[i] = 0; /* in case ncols is zero */
2350: for (j=0;j<ncols;j++) {
2351: if (aj[j] > j) {
2352: idx[i] = j;
2353: break;
2354: }
2355: }
2356: }
2357: }
2358: for (j=0; j<ncols; j++){
2359: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2360: aa++; aj++;
2361: }
2362: }
2363: VecRestoreArray(v,&x);
2364: return(0);
2365: }
2367: /* -------------------------------------------------------------------*/
2368: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
2369: MatGetRow_SeqAIJ,
2370: MatRestoreRow_SeqAIJ,
2371: MatMult_SeqAIJ,
2372: /* 4*/ MatMultAdd_SeqAIJ,
2373: MatMultTranspose_SeqAIJ,
2374: MatMultTransposeAdd_SeqAIJ,
2375: MatSolve_SeqAIJ,
2376: MatSolveAdd_SeqAIJ,
2377: MatSolveTranspose_SeqAIJ,
2378: /*10*/ MatSolveTransposeAdd_SeqAIJ,
2379: MatLUFactor_SeqAIJ,
2380: 0,
2381: MatRelax_SeqAIJ,
2382: MatTranspose_SeqAIJ,
2383: /*15*/ MatGetInfo_SeqAIJ,
2384: MatEqual_SeqAIJ,
2385: MatGetDiagonal_SeqAIJ,
2386: MatDiagonalScale_SeqAIJ,
2387: MatNorm_SeqAIJ,
2388: /*20*/ 0,
2389: MatAssemblyEnd_SeqAIJ,
2390: MatCompress_SeqAIJ,
2391: MatSetOption_SeqAIJ,
2392: MatZeroEntries_SeqAIJ,
2393: /*25*/ MatZeroRows_SeqAIJ,
2394: MatLUFactorSymbolic_SeqAIJ,
2395: MatLUFactorNumeric_SeqAIJ,
2396: MatCholeskyFactorSymbolic_SeqAIJ,
2397: MatCholeskyFactorNumeric_SeqAIJ,
2398: /*30*/ MatSetUpPreallocation_SeqAIJ,
2399: MatILUFactorSymbolic_SeqAIJ,
2400: MatICCFactorSymbolic_SeqAIJ,
2401: MatGetArray_SeqAIJ,
2402: MatRestoreArray_SeqAIJ,
2403: /*35*/ MatDuplicate_SeqAIJ,
2404: 0,
2405: 0,
2406: MatILUFactor_SeqAIJ,
2407: 0,
2408: /*40*/ MatAXPY_SeqAIJ,
2409: MatGetSubMatrices_SeqAIJ,
2410: MatIncreaseOverlap_SeqAIJ,
2411: MatGetValues_SeqAIJ,
2412: MatCopy_SeqAIJ,
2413: /*45*/ MatGetRowMax_SeqAIJ,
2414: MatScale_SeqAIJ,
2415: 0,
2416: MatDiagonalSet_SeqAIJ,
2417: MatILUDTFactor_SeqAIJ,
2418: /*50*/ MatSetBlockSize_SeqAIJ,
2419: MatGetRowIJ_SeqAIJ,
2420: MatRestoreRowIJ_SeqAIJ,
2421: MatGetColumnIJ_SeqAIJ,
2422: MatRestoreColumnIJ_SeqAIJ,
2423: /*55*/ MatFDColoringCreate_SeqAIJ,
2424: 0,
2425: 0,
2426: MatPermute_SeqAIJ,
2427: 0,
2428: /*60*/ 0,
2429: MatDestroy_SeqAIJ,
2430: MatView_SeqAIJ,
2431: 0,
2432: 0,
2433: /*65*/ 0,
2434: 0,
2435: 0,
2436: 0,
2437: 0,
2438: /*70*/ MatGetRowMaxAbs_SeqAIJ,
2439: 0,
2440: MatSetColoring_SeqAIJ,
2441: #if defined(PETSC_HAVE_ADIC)
2442: MatSetValuesAdic_SeqAIJ,
2443: #else
2444: 0,
2445: #endif
2446: MatSetValuesAdifor_SeqAIJ,
2447: /*75*/ 0,
2448: 0,
2449: 0,
2450: 0,
2451: 0,
2452: /*80*/ 0,
2453: 0,
2454: 0,
2455: 0,
2456: MatLoad_SeqAIJ,
2457: /*85*/ MatIsSymmetric_SeqAIJ,
2458: MatIsHermitian_SeqAIJ,
2459: 0,
2460: 0,
2461: 0,
2462: /*90*/ MatMatMult_SeqAIJ_SeqAIJ,
2463: MatMatMultSymbolic_SeqAIJ_SeqAIJ,
2464: MatMatMultNumeric_SeqAIJ_SeqAIJ,
2465: MatPtAP_Basic,
2466: MatPtAPSymbolic_SeqAIJ,
2467: /*95*/ MatPtAPNumeric_SeqAIJ,
2468: MatMatMultTranspose_SeqAIJ_SeqAIJ,
2469: MatMatMultTransposeSymbolic_SeqAIJ_SeqAIJ,
2470: MatMatMultTransposeNumeric_SeqAIJ_SeqAIJ,
2471: MatPtAPSymbolic_SeqAIJ_SeqAIJ,
2472: /*100*/MatPtAPNumeric_SeqAIJ_SeqAIJ,
2473: 0,
2474: 0,
2475: MatConjugate_SeqAIJ,
2476: 0,
2477: /*105*/MatSetValuesRow_SeqAIJ,
2478: MatRealPart_SeqAIJ,
2479: MatImaginaryPart_SeqAIJ,
2480: 0,
2481: 0,
2482: /*110*/MatMatSolve_SeqAIJ,
2483: 0,
2484: MatGetRowMin_SeqAIJ,
2485: 0,
2486: MatMissingDiagonal_SeqAIJ
2487: };
2492: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
2493: {
2494: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2495: PetscInt i,nz,n;
2499: nz = aij->maxnz;
2500: n = mat->rmap.n;
2501: for (i=0; i<nz; i++) {
2502: aij->j[i] = indices[i];
2503: }
2504: aij->nz = nz;
2505: for (i=0; i<n; i++) {
2506: aij->ilen[i] = aij->imax[i];
2507: }
2509: return(0);
2510: }
2515: /*@
2516: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2517: in the matrix.
2519: Input Parameters:
2520: + mat - the SeqAIJ matrix
2521: - indices - the column indices
2523: Level: advanced
2525: Notes:
2526: This can be called if you have precomputed the nonzero structure of the
2527: matrix and want to provide it to the matrix object to improve the performance
2528: of the MatSetValues() operation.
2530: You MUST have set the correct numbers of nonzeros per row in the call to
2531: MatCreateSeqAIJ(), and the columns indices MUST be sorted.
2533: MUST be called before any calls to MatSetValues();
2535: The indices should start with zero, not one.
2537: @*/
2538: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
2539: {
2540: PetscErrorCode ierr,(*f)(Mat,PetscInt *);
2545: PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2546: if (f) {
2547: (*f)(mat,indices);
2548: } else {
2549: SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to set column indices");
2550: }
2551: return(0);
2552: }
2554: /* ----------------------------------------------------------------------------------------*/
2559: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
2560: {
2561: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2563: size_t nz = aij->i[mat->rmap.n];
2566: if (aij->nonew != 1) {
2567: SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2568: }
2570: /* allocate space for values if not already there */
2571: if (!aij->saved_values) {
2572: PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2573: PetscLogObjectMemory(mat,(nz+1)*sizeof(PetscScalar));
2574: }
2576: /* copy values over */
2577: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2578: return(0);
2579: }
2584: /*@
2585: MatStoreValues - Stashes a copy of the matrix values; this allows, for
2586: example, reuse of the linear part of a Jacobian, while recomputing the
2587: nonlinear portion.
2589: Collect on Mat
2591: Input Parameters:
2592: . mat - the matrix (currently only AIJ matrices support this option)
2594: Level: advanced
2596: Common Usage, with SNESSolve():
2597: $ Create Jacobian matrix
2598: $ Set linear terms into matrix
2599: $ Apply boundary conditions to matrix, at this time matrix must have
2600: $ final nonzero structure (i.e. setting the nonlinear terms and applying
2601: $ boundary conditions again will not change the nonzero structure
2602: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
2603: $ MatStoreValues(mat);
2604: $ Call SNESSetJacobian() with matrix
2605: $ In your Jacobian routine
2606: $ MatRetrieveValues(mat);
2607: $ Set nonlinear terms in matrix
2608:
2609: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2610: $ // build linear portion of Jacobian
2611: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
2612: $ MatStoreValues(mat);
2613: $ loop over nonlinear iterations
2614: $ MatRetrieveValues(mat);
2615: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
2616: $ // call MatAssemblyBegin/End() on matrix
2617: $ Solve linear system with Jacobian
2618: $ endloop
2620: Notes:
2621: Matrix must already be assemblied before calling this routine
2622: Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
2623: calling this routine.
2625: When this is called multiple times it overwrites the previous set of stored values
2626: and does not allocated additional space.
2628: .seealso: MatRetrieveValues()
2630: @*/
2631: PetscErrorCode MatStoreValues(Mat mat)
2632: {
2633: PetscErrorCode ierr,(*f)(Mat);
2637: if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2638: if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2640: PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2641: if (f) {
2642: (*f)(mat);
2643: } else {
2644: SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to store values");
2645: }
2646: return(0);
2647: }
2652: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
2653: {
2654: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2656: PetscInt nz = aij->i[mat->rmap.n];
2659: if (aij->nonew != 1) {
2660: SETERRQ(PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
2661: }
2662: if (!aij->saved_values) {
2663: SETERRQ(PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
2664: }
2665: /* copy values over */
2666: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2667: return(0);
2668: }
2673: /*@
2674: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
2675: example, reuse of the linear part of a Jacobian, while recomputing the
2676: nonlinear portion.
2678: Collect on Mat
2680: Input Parameters:
2681: . mat - the matrix (currently on AIJ matrices support this option)
2683: Level: advanced
2685: .seealso: MatStoreValues()
2687: @*/
2688: PetscErrorCode MatRetrieveValues(Mat mat)
2689: {
2690: PetscErrorCode ierr,(*f)(Mat);
2694: if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2695: if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2697: PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2698: if (f) {
2699: (*f)(mat);
2700: } else {
2701: SETERRQ(PETSC_ERR_SUP,"Wrong type of matrix to retrieve values");
2702: }
2703: return(0);
2704: }
2707: /* --------------------------------------------------------------------------------*/
2710: /*@C
2711: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2712: (the default parallel PETSc format). For good matrix assembly performance
2713: the user should preallocate the matrix storage by setting the parameter nz
2714: (or the array nnz). By setting these parameters accurately, performance
2715: during matrix assembly can be increased by more than a factor of 50.
2717: Collective on MPI_Comm
2719: Input Parameters:
2720: + comm - MPI communicator, set to PETSC_COMM_SELF
2721: . m - number of rows
2722: . n - number of columns
2723: . nz - number of nonzeros per row (same for all rows)
2724: - nnz - array containing the number of nonzeros in the various rows
2725: (possibly different for each row) or PETSC_NULL
2727: Output Parameter:
2728: . A - the matrix
2730: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
2731: MatXXXXSetPreallocation() paradgm instead of this routine directly. This is definitely
2732: true if you plan to use the external direct solvers such as SuperLU, MUMPS or Spooles.
2733: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
2735: Notes:
2736: If nnz is given then nz is ignored
2738: The AIJ format (also called the Yale sparse matrix format or
2739: compressed row storage), is fully compatible with standard Fortran 77
2740: storage. That is, the stored row and column indices can begin at
2741: either one (as in Fortran) or zero. See the users' manual for details.
2743: Specify the preallocated storage with either nz or nnz (not both).
2744: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2745: allocation. For large problems you MUST preallocate memory or you
2746: will get TERRIBLE performance, see the users' manual chapter on matrices.
2748: By default, this format uses inodes (identical nodes) when possible, to
2749: improve numerical efficiency of matrix-vector products and solves. We
2750: search for consecutive rows with the same nonzero structure, thereby
2751: reusing matrix information to achieve increased efficiency.
2753: Options Database Keys:
2754: + -mat_no_inode - Do not use inodes
2755: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2756: - -mat_aij_oneindex - Internally use indexing starting at 1
2757: rather than 0. Note that when calling MatSetValues(),
2758: the user still MUST index entries starting at 0!
2760: Level: intermediate
2762: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
2764: @*/
2765: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
2766: {
2770: MatCreate(comm,A);
2771: MatSetSizes(*A,m,n,m,n);
2772: MatSetType(*A,MATSEQAIJ);
2773: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
2774: return(0);
2775: }
2779: /*@C
2780: MatSeqAIJSetPreallocation - For good matrix assembly performance
2781: the user should preallocate the matrix storage by setting the parameter nz
2782: (or the array nnz). By setting these parameters accurately, performance
2783: during matrix assembly can be increased by more than a factor of 50.
2785: Collective on MPI_Comm
2787: Input Parameters:
2788: + B - The matrix
2789: . nz - number of nonzeros per row (same for all rows)
2790: - nnz - array containing the number of nonzeros in the various rows
2791: (possibly different for each row) or PETSC_NULL
2793: Notes:
2794: If nnz is given then nz is ignored
2796: The AIJ format (also called the Yale sparse matrix format or
2797: compressed row storage), is fully compatible with standard Fortran 77
2798: storage. That is, the stored row and column indices can begin at
2799: either one (as in Fortran) or zero. See the users' manual for details.
2801: Specify the preallocated storage with either nz or nnz (not both).
2802: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2803: allocation. For large problems you MUST preallocate memory or you
2804: will get TERRIBLE performance, see the users' manual chapter on matrices.
2806: You can call MatGetInfo() to get information on how effective the preallocation was;
2807: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
2808: You can also run with the option -info and look for messages with the string
2809: malloc in them to see if additional memory allocation was needed.
2811: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
2812: entries or columns indices
2814: By default, this format uses inodes (identical nodes) when possible, to
2815: improve numerical efficiency of matrix-vector products and solves. We
2816: search for consecutive rows with the same nonzero structure, thereby
2817: reusing matrix information to achieve increased efficiency.
2819: Options Database Keys:
2820: + -mat_no_inode - Do not use inodes
2821: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
2822: - -mat_aij_oneindex - Internally use indexing starting at 1
2823: rather than 0. Note that when calling MatSetValues(),
2824: the user still MUST index entries starting at 0!
2826: Level: intermediate
2828: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()
2830: @*/
2831: PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
2832: {
2833: PetscErrorCode ierr,(*f)(Mat,PetscInt,const PetscInt[]);
2836: PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",(void (**)(void))&f);
2837: if (f) {
2838: (*f)(B,nz,nnz);
2839: }
2840: return(0);
2841: }
2846: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,PetscInt *nnz)
2847: {
2848: Mat_SeqAIJ *b;
2849: PetscTruth skipallocation = PETSC_FALSE;
2851: PetscInt i;
2854:
2855: if (nz == MAT_SKIP_ALLOCATION) {
2856: skipallocation = PETSC_TRUE;
2857: nz = 0;
2858: }
2860: B->rmap.bs = B->cmap.bs = 1;
2861: PetscMapSetUp(&B->rmap);
2862: PetscMapSetUp(&B->cmap);
2864: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2865: if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2866: if (nnz) {
2867: for (i=0; i<B->rmap.n; i++) {
2868: if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2869: if (nnz[i] > B->cmap.n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->cmap.n);
2870: }
2871: }
2873: B->preallocated = PETSC_TRUE;
2874: b = (Mat_SeqAIJ*)B->data;
2876: if (!skipallocation) {
2877: if (!b->imax) {
2878: PetscMalloc2(B->rmap.n,PetscInt,&b->imax,B->rmap.n,PetscInt,&b->ilen);
2879: PetscLogObjectMemory(B,2*B->rmap.n*sizeof(PetscInt));
2880: }
2881: if (!nnz) {
2882: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2883: else if (nz <= 0) nz = 1;
2884: for (i=0; i<B->rmap.n; i++) b->imax[i] = nz;
2885: nz = nz*B->rmap.n;
2886: } else {
2887: nz = 0;
2888: for (i=0; i<B->rmap.n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2889: }
2890: /* b->ilen will count nonzeros in each row so far. */
2891: for (i=0; i<B->rmap.n; i++) { b->ilen[i] = 0; }
2893: /* allocate the matrix space */
2894: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
2895: PetscMalloc3(nz,PetscScalar,&b->a,nz,PetscInt,&b->j,B->rmap.n+1,PetscInt,&b->i);
2896: PetscLogObjectMemory(B,(B->rmap.n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
2897: b->i[0] = 0;
2898: for (i=1; i<B->rmap.n+1; i++) {
2899: b->i[i] = b->i[i-1] + b->imax[i-1];
2900: }
2901: b->singlemalloc = PETSC_TRUE;
2902: b->free_a = PETSC_TRUE;
2903: b->free_ij = PETSC_TRUE;
2904: } else {
2905: b->free_a = PETSC_FALSE;
2906: b->free_ij = PETSC_FALSE;
2907: }
2909: b->nz = 0;
2910: b->maxnz = nz;
2911: B->info.nz_unneeded = (double)b->maxnz;
2912: return(0);
2913: }
2916: #undef __FUNCT__
2918: /*@
2919: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.
2921: Input Parameters:
2922: + B - the matrix
2923: . i - the indices into j for the start of each row (starts with zero)
2924: . j - the column indices for each row (starts with zero) these must be sorted for each row
2925: - v - optional values in the matrix
2927: Contributed by: Lisandro Dalchin
2929: Level: developer
2931: The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()
2933: .keywords: matrix, aij, compressed row, sparse, sequential
2935: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
2936: @*/
2937: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
2938: {
2939: PetscErrorCode (*f)(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]);
2944: PetscObjectQueryFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",(void (**)(void))&f);
2945: if (f) {
2946: (*f)(B,i,j,v);
2947: }
2948: return(0);
2949: }
2952: #undef __FUNCT__
2954: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
2955: {
2956: PetscInt i;
2957: PetscInt m,n;
2958: PetscInt nz;
2959: PetscInt *nnz, nz_max = 0;
2960: PetscScalar *values;
2964: MatGetSize(B, &m, &n);
2966: if (Ii[0]) {
2967: SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
2968: }
2969: PetscMalloc((m+1) * sizeof(PetscInt), &nnz);
2970: for(i = 0; i < m; i++) {
2971: nz = Ii[i+1]- Ii[i];
2972: nz_max = PetscMax(nz_max, nz);
2973: if (nz < 0) {
2974: SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
2975: }
2976: nnz[i] = nz;
2977: }
2978: MatSeqAIJSetPreallocation(B, 0, nnz);
2979: PetscFree(nnz);
2981: if (v) {
2982: values = (PetscScalar*) v;
2983: } else {
2984: PetscMalloc((nz_max+1)*sizeof(PetscScalar), &values);
2985: PetscMemzero(values, nz_max*sizeof(PetscScalar));
2986: }
2988: for(i = 0; i < m; i++) {
2989: nz = Ii[i+1] - Ii[i];
2990: MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
2991: }
2993: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2994: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2996: if (!v) {
2997: PetscFree(values);
2998: }
2999: return(0);
3000: }
3003: /*MC
3004: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
3005: based on compressed sparse row format.
3007: Options Database Keys:
3008: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
3010: Level: beginner
3012: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3013: M*/
3022: PetscErrorCode MatCreate_SeqAIJ(Mat B)
3023: {
3024: Mat_SeqAIJ *b;
3026: PetscMPIInt size;
3029: MPI_Comm_size(((PetscObject)B)->comm,&size);
3030: if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
3032: PetscNewLog(B,Mat_SeqAIJ,&b);
3033: B->data = (void*)b;
3034: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3035: B->factor = 0;
3036: B->mapping = 0;
3037: b->row = 0;
3038: b->col = 0;
3039: b->icol = 0;
3040: b->reallocs = 0;
3041: b->ignorezeroentries = PETSC_FALSE;
3042: b->roworiented = PETSC_TRUE;
3043: b->nonew = 0;
3044: b->diag = 0;
3045: b->solve_work = 0;
3046: B->spptr = 0;
3047: b->saved_values = 0;
3048: b->idiag = 0;
3049: b->mdiag = 0;
3050: b->ssor_work = 0;
3051: b->omega = 1.0;
3052: b->fshift = 0.0;
3053: b->idiagvalid = PETSC_FALSE;
3054: b->keepzeroedrows = PETSC_FALSE;
3055: b->xtoy = 0;
3056: b->XtoY = 0;
3057: b->compressedrow.use = PETSC_FALSE;
3058: b->compressedrow.nrows = B->rmap.n;
3059: b->compressedrow.i = PETSC_NULL;
3060: b->compressedrow.rindex = PETSC_NULL;
3061: b->compressedrow.checked = PETSC_FALSE;
3062: B->same_nonzero = PETSC_FALSE;
3064: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3065: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
3066: "MatSeqAIJSetColumnIndices_SeqAIJ",
3067: MatSeqAIJSetColumnIndices_SeqAIJ);
3068: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
3069: "MatStoreValues_SeqAIJ",
3070: MatStoreValues_SeqAIJ);
3071: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
3072: "MatRetrieveValues_SeqAIJ",
3073: MatRetrieveValues_SeqAIJ);
3074: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",
3075: "MatConvert_SeqAIJ_SeqSBAIJ",
3076: MatConvert_SeqAIJ_SeqSBAIJ);
3077: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqbaij_C",
3078: "MatConvert_SeqAIJ_SeqBAIJ",
3079: MatConvert_SeqAIJ_SeqBAIJ);
3080: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcsrperm_C",
3081: "MatConvert_SeqAIJ_SeqCSRPERM",
3082: MatConvert_SeqAIJ_SeqCSRPERM);
3083: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatConvert_seqaij_seqcrl_C",
3084: "MatConvert_SeqAIJ_SeqCRL",
3085: MatConvert_SeqAIJ_SeqCRL);
3086: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsTranspose_C",
3087: "MatIsTranspose_SeqAIJ",
3088: MatIsTranspose_SeqAIJ);
3089: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatIsHermitianTranspose_C",
3090: "MatIsHermitianTranspose_SeqAIJ",
3091: MatIsTranspose_SeqAIJ);
3092: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocation_C",
3093: "MatSeqAIJSetPreallocation_SeqAIJ",
3094: MatSeqAIJSetPreallocation_SeqAIJ);
3095: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",
3096: "MatSeqAIJSetPreallocationCSR_SeqAIJ",
3097: MatSeqAIJSetPreallocationCSR_SeqAIJ);
3098: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatReorderForNonzeroDiagonal_C",
3099: "MatReorderForNonzeroDiagonal_SeqAIJ",
3100: MatReorderForNonzeroDiagonal_SeqAIJ);
3101: MatCreate_Inode(B);
3102: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3103: return(0);
3104: }
3109: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
3110: {
3111: Mat C;
3112: Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
3114: PetscInt i,m = A->rmap.n;
3117: *B = 0;
3118: MatCreate(((PetscObject)A)->comm,&C);
3119: MatSetSizes(C,A->rmap.n,A->cmap.n,A->rmap.n,A->cmap.n);
3120: MatSetType(C,((PetscObject)A)->type_name);
3121: PetscMemcpy(C->ops,A->ops,sizeof(struct _MatOps));
3122:
3123: PetscMapCopy(((PetscObject)A)->comm,&A->rmap,&C->rmap);
3124: PetscMapCopy(((PetscObject)A)->comm,&A->cmap,&C->cmap);
3126: c = (Mat_SeqAIJ*)C->data;
3128: C->factor = A->factor;
3130: c->row = 0;
3131: c->col = 0;
3132: c->icol = 0;
3133: c->reallocs = 0;
3135: C->assembled = PETSC_TRUE;
3136:
3137: PetscMalloc2(m,PetscInt,&c->imax,m,PetscInt,&c->ilen);
3138: PetscLogObjectMemory(C, 2*m*sizeof(PetscInt));
3139: for (i=0; i<m; i++) {
3140: c->imax[i] = a->imax[i];
3141: c->ilen[i] = a->ilen[i];
3142: }
3144: /* allocate the matrix space */
3145: PetscMalloc3(a->i[m],PetscScalar,&c->a,a->i[m],PetscInt,&c->j,m+1,PetscInt,&c->i);
3146: PetscLogObjectMemory(C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
3147: c->singlemalloc = PETSC_TRUE;
3148: PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
3149: if (m > 0) {
3150: PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
3151: if (cpvalues == MAT_COPY_VALUES) {
3152: PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
3153: } else {
3154: PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
3155: }
3156: }
3158: c->ignorezeroentries = a->ignorezeroentries;
3159: c->roworiented = a->roworiented;
3160: c->nonew = a->nonew;
3161: if (a->diag) {
3162: PetscMalloc((m+1)*sizeof(PetscInt),&c->diag);
3163: PetscLogObjectMemory(C,(m+1)*sizeof(PetscInt));
3164: for (i=0; i<m; i++) {
3165: c->diag[i] = a->diag[i];
3166: }
3167: } else c->diag = 0;
3168: c->solve_work = 0;
3169: c->saved_values = 0;
3170: c->idiag = 0;
3171: c->ssor_work = 0;
3172: c->keepzeroedrows = a->keepzeroedrows;
3173: c->free_a = PETSC_TRUE;
3174: c->free_ij = PETSC_TRUE;
3175: c->xtoy = 0;
3176: c->XtoY = 0;
3178: c->nz = a->nz;
3179: c->maxnz = a->maxnz;
3180: C->preallocated = PETSC_TRUE;
3182: c->compressedrow.use = a->compressedrow.use;
3183: c->compressedrow.nrows = a->compressedrow.nrows;
3184: c->compressedrow.checked = a->compressedrow.checked;
3185: if ( a->compressedrow.checked && a->compressedrow.use){
3186: i = a->compressedrow.nrows;
3187: PetscMalloc((2*i+1)*sizeof(PetscInt),&c->compressedrow.i);
3188: c->compressedrow.rindex = c->compressedrow.i + i + 1;
3189: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
3190: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
3191: } else {
3192: c->compressedrow.use = PETSC_FALSE;
3193: c->compressedrow.i = PETSC_NULL;
3194: c->compressedrow.rindex = PETSC_NULL;
3195: }
3196: C->same_nonzero = A->same_nonzero;
3197: MatDuplicate_Inode(A,cpvalues,&C);
3199: *B = C;
3200: PetscFListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
3201: return(0);
3202: }
3206: PetscErrorCode MatLoad_SeqAIJ(PetscViewer viewer, MatType type,Mat *A)
3207: {
3208: Mat_SeqAIJ *a;
3209: Mat B;
3211: PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N;
3212: int fd;
3213: PetscMPIInt size;
3214: MPI_Comm comm;
3215:
3217: PetscObjectGetComm((PetscObject)viewer,&comm);
3218: MPI_Comm_size(comm,&size);
3219: if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
3220: PetscViewerBinaryGetDescriptor(viewer,&fd);
3221: PetscBinaryRead(fd,header,4,PETSC_INT);
3222: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
3223: M = header[1]; N = header[2]; nz = header[3];
3225: if (nz < 0) {
3226: SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
3227: }
3229: /* read in row lengths */
3230: PetscMalloc(M*sizeof(PetscInt),&rowlengths);
3231: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
3233: /* check if sum of rowlengths is same as nz */
3234: for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
3235: if (sum != nz) SETERRQ2(PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %d, sum-row-lengths = %d\n",nz,sum);
3237: /* create our matrix */
3238: MatCreate(comm,&B);
3239: MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,M,N);
3240: MatSetType(B,type);
3241: MatSeqAIJSetPreallocation_SeqAIJ(B,0,rowlengths);
3242: a = (Mat_SeqAIJ*)B->data;
3244: PetscBinaryRead(fd,a->j,nz,PETSC_INT);
3246: /* read in nonzero values */
3247: PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);
3249: /* set matrix "i" values */
3250: a->i[0] = 0;
3251: for (i=1; i<= M; i++) {
3252: a->i[i] = a->i[i-1] + rowlengths[i-1];
3253: a->ilen[i-1] = rowlengths[i-1];
3254: }
3255: PetscFree(rowlengths);
3257: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3258: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3259: *A = B;
3260: return(0);
3261: }
3265: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
3266: {
3267: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
3271: /* If the matrix dimensions are not equal,or no of nonzeros */
3272: if ((A->rmap.n != B->rmap.n) || (A->cmap.n != B->cmap.n) ||(a->nz != b->nz)) {
3273: *flg = PETSC_FALSE;
3274: return(0);
3275: }
3276:
3277: /* if the a->i are the same */
3278: PetscMemcmp(a->i,b->i,(A->rmap.n+1)*sizeof(PetscInt),flg);
3279: if (!*flg) return(0);
3280:
3281: /* if a->j are the same */
3282: PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
3283: if (!*flg) return(0);
3284:
3285: /* if a->a are the same */
3286: PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
3288: return(0);
3289:
3290: }
3294: /*@
3295: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
3296: provided by the user.
3298: Collective on MPI_Comm
3300: Input Parameters:
3301: + comm - must be an MPI communicator of size 1
3302: . m - number of rows
3303: . n - number of columns
3304: . i - row indices
3305: . j - column indices
3306: - a - matrix values
3308: Output Parameter:
3309: . mat - the matrix
3311: Level: intermediate
3313: Notes:
3314: The i, j, and a arrays are not copied by this routine, the user must free these arrays
3315: once the matrix is destroyed
3317: You cannot set new nonzero locations into this matrix, that will generate an error.
3319: The i and j indices are 0 based
3321: The format which is used for the sparse matrix input, is equivalent to a
3322: row-major ordering.. i.e for the following matrix, the input data expected is
3323: as shown:
3325: 1 0 0
3326: 2 0 3
3327: 4 5 6
3329: i = {0,1,3,6} [size = nrow+1 = 3+1]
3330: j = {0,0,2,0,1,2} [size = nz = 6]; values must be sorted for each row
3331: v = {1,2,3,4,5,6} [size = nz = 6]
3333:
3334: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
3336: @*/
3337: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt* i,PetscInt*j,PetscScalar *a,Mat *mat)
3338: {
3340: PetscInt ii,jj;
3341: Mat_SeqAIJ *aij;
3344: if (i[0]) {
3345: SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
3346: }
3347: MatCreate(comm,mat);
3348: MatSetSizes(*mat,m,n,m,n);
3349: MatSetType(*mat,MATSEQAIJ);
3350: MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
3351: aij = (Mat_SeqAIJ*)(*mat)->data;
3352: PetscMalloc2(m,PetscInt,&aij->imax,m,PetscInt,&aij->ilen);
3354: aij->i = i;
3355: aij->j = j;
3356: aij->a = a;
3357: aij->singlemalloc = PETSC_FALSE;
3358: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
3359: aij->free_a = PETSC_FALSE;
3360: aij->free_ij = PETSC_FALSE;
3362: for (ii=0; ii<m; ii++) {
3363: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
3364: #if defined(PETSC_USE_DEBUG)
3365: 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]);
3366: for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
3367: if (j[jj] < j[jj-1]) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
3368: if (j[jj] == j[jj]-1) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is identical to previous entry",jj-i[ii],j[jj],ii);
3369: }
3370: #endif
3371: }
3372: #if defined(PETSC_USE_DEBUG)
3373: for (ii=0; ii<aij->i[m]; ii++) {
3374: if (j[ii] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %d index = %d",ii,j[ii]);
3375: if (j[ii] > n - 1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %d index = %d",ii,j[ii]);
3376: }
3377: #endif
3379: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
3380: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
3381: return(0);
3382: }
3386: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
3387: {
3389: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3392: if (coloring->ctype == IS_COLORING_GLOBAL) {
3393: ISColoringReference(coloring);
3394: a->coloring = coloring;
3395: } else if (coloring->ctype == IS_COLORING_GHOSTED) {
3396: PetscInt i,*larray;
3397: ISColoring ocoloring;
3398: ISColoringValue *colors;
3400: /* set coloring for diagonal portion */
3401: PetscMalloc((A->cmap.n+1)*sizeof(PetscInt),&larray);
3402: for (i=0; i<A->cmap.n; i++) {
3403: larray[i] = i;
3404: }
3405: ISGlobalToLocalMappingApply(A->mapping,IS_GTOLM_MASK,A->cmap.n,larray,PETSC_NULL,larray);
3406: PetscMalloc((A->cmap.n+1)*sizeof(ISColoringValue),&colors);
3407: for (i=0; i<A->cmap.n; i++) {
3408: colors[i] = coloring->colors[larray[i]];
3409: }
3410: PetscFree(larray);
3411: ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap.n,colors,&ocoloring);
3412: a->coloring = ocoloring;
3413: }
3414: return(0);
3415: }
3417: #if defined(PETSC_HAVE_ADIC)
3419: #include "adic/ad_utils.h"
3424: PetscErrorCode MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
3425: {
3426: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3427: PetscInt m = A->rmap.n,*ii = a->i,*jj = a->j,nz,i,j,nlen;
3428: PetscScalar *v = a->a,*values = ((PetscScalar*)advalues)+1;
3429: ISColoringValue *color;
3432: if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3433: nlen = PetscADGetDerivTypeSize()/sizeof(PetscScalar);
3434: color = a->coloring->colors;
3435: /* loop over rows */
3436: for (i=0; i<m; i++) {
3437: nz = ii[i+1] - ii[i];
3438: /* loop over columns putting computed value into matrix */
3439: for (j=0; j<nz; j++) {
3440: *v++ = values[color[*jj++]];
3441: }
3442: values += nlen; /* jump to next row of derivatives */
3443: }
3444: return(0);
3445: }
3446: #endif
3450: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
3451: {
3452: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3453: PetscInt m = A->rmap.n,*ii = a->i,*jj = a->j,nz,i,j;
3454: PetscScalar *v = a->a,*values = (PetscScalar *)advalues;
3455: ISColoringValue *color;
3458: if (!a->coloring) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
3459: color = a->coloring->colors;
3460: /* loop over rows */
3461: for (i=0; i<m; i++) {
3462: nz = ii[i+1] - ii[i];
3463: /* loop over columns putting computed value into matrix */
3464: for (j=0; j<nz; j++) {
3465: *v++ = values[color[*jj++]];
3466: }
3467: values += nl; /* jump to next row of derivatives */
3468: }
3469: return(0);
3470: }
3472: /*
3473: Special version for direct calls from Fortran
3474: */
3475: #if defined(PETSC_HAVE_FORTRAN_CAPS)
3476: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
3477: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
3478: #define matsetvaluesseqaij_ matsetvaluesseqaij
3479: #endif
3481: /* Change these macros so can be used in void function */
3482: #undef CHKERRQ
3483: #define CHKERRQ(ierr) CHKERRABORT(((PetscObject)A)->comm,ierr)
3484: #undef SETERRQ2
3485: #define SETERRQ2(ierr,b,c,d) CHKERRABORT(((PetscObject)A)->comm,ierr)
3490: void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
3491: {
3492: Mat A = *AA;
3493: PetscInt m = *mm, n = *nn;
3494: InsertMode is = *isis;
3495: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
3496: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
3497: PetscInt *imax,*ai,*ailen;
3499: PetscInt *aj,nonew = a->nonew,lastcol = -1;
3500: PetscScalar *ap,value,*aa;
3501: PetscTruth ignorezeroentries = a->ignorezeroentries;
3502: PetscTruth roworiented = a->roworiented;
3505: MatPreallocated(A);
3506: imax = a->imax;
3507: ai = a->i;
3508: ailen = a->ilen;
3509: aj = a->j;
3510: aa = a->a;
3512: for (k=0; k<m; k++) { /* loop over added rows */
3513: row = im[k];
3514: if (row < 0) continue;
3515: #if defined(PETSC_USE_DEBUG)
3516: if (row >= A->rmap.n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
3517: #endif
3518: rp = aj + ai[row]; ap = aa + ai[row];
3519: rmax = imax[row]; nrow = ailen[row];
3520: low = 0;
3521: high = nrow;
3522: for (l=0; l<n; l++) { /* loop over added columns */
3523: if (in[l] < 0) continue;
3524: #if defined(PETSC_USE_DEBUG)
3525: if (in[l] >= A->cmap.n) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
3526: #endif
3527: col = in[l];
3528: if (roworiented) {
3529: value = v[l + k*n];
3530: } else {
3531: value = v[k + l*m];
3532: }
3533: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
3535: if (col <= lastcol) low = 0; else high = nrow;
3536: lastcol = col;
3537: while (high-low > 5) {
3538: t = (low+high)/2;
3539: if (rp[t] > col) high = t;
3540: else low = t;
3541: }
3542: for (i=low; i<high; i++) {
3543: if (rp[i] > col) break;
3544: if (rp[i] == col) {
3545: if (is == ADD_VALUES) ap[i] += value;
3546: else ap[i] = value;
3547: goto noinsert;
3548: }
3549: }
3550: if (value == 0.0 && ignorezeroentries) goto noinsert;
3551: if (nonew == 1) goto noinsert;
3552: if (nonew == -1) SETERRABORT(((PetscObject)A)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
3553: MatSeqXAIJReallocateAIJ(A,A->rmap.n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
3554: N = nrow++ - 1; a->nz++; high++;
3555: /* shift up all the later entries in this row */
3556: for (ii=N; ii>=i; ii--) {
3557: rp[ii+1] = rp[ii];
3558: ap[ii+1] = ap[ii];
3559: }
3560: rp[i] = col;
3561: ap[i] = value;
3562: noinsert:;
3563: low = i + 1;
3564: }
3565: ailen[row] = nrow;
3566: }
3567: A->same_nonzero = PETSC_FALSE;
3568: PetscFunctionReturnVoid();
3569: }