Actual source code: aij.c
petsc-3.6.0 2015-06-09
2: /*
3: Defines the basic matrix operations for the AIJ (compressed row)
4: matrix storage format.
5: */
8: #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/
9: #include <petscblaslapack.h>
10: #include <petscbt.h>
11: #include <petsc/private/kernels/blocktranspose.h>
15: PetscErrorCode MatGetColumnNorms_SeqAIJ(Mat A,NormType type,PetscReal *norms)
16: {
18: PetscInt i,m,n;
19: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
22: MatGetSize(A,&m,&n);
23: PetscMemzero(norms,n*sizeof(PetscReal));
24: if (type == NORM_2) {
25: for (i=0; i<aij->i[m]; i++) {
26: norms[aij->j[i]] += PetscAbsScalar(aij->a[i]*aij->a[i]);
27: }
28: } else if (type == NORM_1) {
29: for (i=0; i<aij->i[m]; i++) {
30: norms[aij->j[i]] += PetscAbsScalar(aij->a[i]);
31: }
32: } else if (type == NORM_INFINITY) {
33: for (i=0; i<aij->i[m]; i++) {
34: norms[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]),norms[aij->j[i]]);
35: }
36: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONG,"Unknown NormType");
38: if (type == NORM_2) {
39: for (i=0; i<n; i++) norms[i] = PetscSqrtReal(norms[i]);
40: }
41: return(0);
42: }
46: PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A,IS *is)
47: {
48: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
49: PetscInt i,m=A->rmap->n,cnt = 0, bs = A->rmap->bs;
50: const PetscInt *jj = a->j,*ii = a->i;
51: PetscInt *rows;
52: PetscErrorCode ierr;
55: for (i=0; i<m; i++) {
56: if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
57: cnt++;
58: }
59: }
60: PetscMalloc1(cnt,&rows);
61: cnt = 0;
62: for (i=0; i<m; i++) {
63: if ((ii[i] != ii[i+1]) && ((jj[ii[i]] < bs*(i/bs)) || (jj[ii[i+1]-1] > bs*((i+bs)/bs)-1))) {
64: rows[cnt] = i;
65: cnt++;
66: }
67: }
68: ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,is);
69: return(0);
70: }
74: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A,PetscInt *nrows,PetscInt **zrows)
75: {
76: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
77: const MatScalar *aa = a->a;
78: PetscInt i,m=A->rmap->n,cnt = 0;
79: const PetscInt *jj = a->j,*diag;
80: PetscInt *rows;
81: PetscErrorCode ierr;
84: MatMarkDiagonal_SeqAIJ(A);
85: diag = a->diag;
86: for (i=0; i<m; i++) {
87: if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
88: cnt++;
89: }
90: }
91: PetscMalloc1(cnt,&rows);
92: cnt = 0;
93: for (i=0; i<m; i++) {
94: if ((jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) {
95: rows[cnt++] = i;
96: }
97: }
98: *nrows = cnt;
99: *zrows = rows;
100: return(0);
101: }
105: PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A,IS *zrows)
106: {
107: PetscInt nrows,*rows;
111: *zrows = NULL;
112: MatFindZeroDiagonals_SeqAIJ_Private(A,&nrows,&rows);
113: ISCreateGeneral(PetscObjectComm((PetscObject)A),nrows,rows,PETSC_OWN_POINTER,zrows);
114: return(0);
115: }
119: PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A,IS *keptrows)
120: {
121: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
122: const MatScalar *aa;
123: PetscInt m=A->rmap->n,cnt = 0;
124: const PetscInt *ii;
125: PetscInt n,i,j,*rows;
126: PetscErrorCode ierr;
129: *keptrows = 0;
130: ii = a->i;
131: for (i=0; i<m; i++) {
132: n = ii[i+1] - ii[i];
133: if (!n) {
134: cnt++;
135: goto ok1;
136: }
137: aa = a->a + ii[i];
138: for (j=0; j<n; j++) {
139: if (aa[j] != 0.0) goto ok1;
140: }
141: cnt++;
142: ok1:;
143: }
144: if (!cnt) return(0);
145: PetscMalloc1(A->rmap->n-cnt,&rows);
146: cnt = 0;
147: for (i=0; i<m; i++) {
148: n = ii[i+1] - ii[i];
149: if (!n) continue;
150: aa = a->a + ii[i];
151: for (j=0; j<n; j++) {
152: if (aa[j] != 0.0) {
153: rows[cnt++] = i;
154: break;
155: }
156: }
157: }
158: ISCreateGeneral(PETSC_COMM_SELF,cnt,rows,PETSC_OWN_POINTER,keptrows);
159: return(0);
160: }
164: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y,Vec D,InsertMode is)
165: {
166: PetscErrorCode ierr;
167: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) Y->data;
168: PetscInt i,m = Y->rmap->n;
169: const PetscInt *diag;
170: MatScalar *aa = aij->a;
171: const PetscScalar *v;
172: PetscBool missing;
175: if (Y->assembled) {
176: MatMissingDiagonal_SeqAIJ(Y,&missing,NULL);
177: if (!missing) {
178: diag = aij->diag;
179: VecGetArrayRead(D,&v);
180: if (is == INSERT_VALUES) {
181: for (i=0; i<m; i++) {
182: aa[diag[i]] = v[i];
183: }
184: } else {
185: for (i=0; i<m; i++) {
186: aa[diag[i]] += v[i];
187: }
188: }
189: VecRestoreArrayRead(D,&v);
190: return(0);
191: }
192: MatSeqAIJInvalidateDiagonal(Y);
193: }
194: MatDiagonalSet_Default(Y,D,is);
195: return(0);
196: }
200: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *m,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
201: {
202: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
204: PetscInt i,ishift;
207: *m = A->rmap->n;
208: if (!ia) return(0);
209: ishift = 0;
210: if (symmetric && !A->structurally_symmetric) {
211: MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,ishift,oshift,(PetscInt**)ia,(PetscInt**)ja);
212: } else if (oshift == 1) {
213: PetscInt *tia;
214: PetscInt nz = a->i[A->rmap->n];
215: /* malloc space and add 1 to i and j indices */
216: PetscMalloc1(A->rmap->n+1,&tia);
217: for (i=0; i<A->rmap->n+1; i++) tia[i] = a->i[i] + 1;
218: *ia = tia;
219: if (ja) {
220: PetscInt *tja;
221: PetscMalloc1(nz+1,&tja);
222: for (i=0; i<nz; i++) tja[i] = a->j[i] + 1;
223: *ja = tja;
224: }
225: } else {
226: *ia = a->i;
227: if (ja) *ja = a->j;
228: }
229: return(0);
230: }
234: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
235: {
239: if (!ia) return(0);
240: if ((symmetric && !A->structurally_symmetric) || oshift == 1) {
241: PetscFree(*ia);
242: if (ja) {PetscFree(*ja);}
243: }
244: return(0);
245: }
249: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
250: {
251: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
253: PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
254: PetscInt nz = a->i[m],row,*jj,mr,col;
257: *nn = n;
258: if (!ia) return(0);
259: if (symmetric) {
260: MatToSymmetricIJ_SeqAIJ(A->rmap->n,a->i,a->j,0,oshift,(PetscInt**)ia,(PetscInt**)ja);
261: } else {
262: PetscCalloc1(n+1,&collengths);
263: PetscMalloc1(n+1,&cia);
264: PetscMalloc1(nz+1,&cja);
265: jj = a->j;
266: for (i=0; i<nz; i++) {
267: collengths[jj[i]]++;
268: }
269: cia[0] = oshift;
270: for (i=0; i<n; i++) {
271: cia[i+1] = cia[i] + collengths[i];
272: }
273: PetscMemzero(collengths,n*sizeof(PetscInt));
274: jj = a->j;
275: for (row=0; row<m; row++) {
276: mr = a->i[row+1] - a->i[row];
277: for (i=0; i<mr; i++) {
278: col = *jj++;
280: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
281: }
282: }
283: PetscFree(collengths);
284: *ia = cia; *ja = cja;
285: }
286: return(0);
287: }
291: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscBool *done)
292: {
296: if (!ia) return(0);
298: PetscFree(*ia);
299: PetscFree(*ja);
300: return(0);
301: }
303: /*
304: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
305: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
306: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
307: */
310: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *nn,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
311: {
312: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
314: PetscInt i,*collengths,*cia,*cja,n = A->cmap->n,m = A->rmap->n;
315: PetscInt nz = a->i[m],row,*jj,mr,col;
316: PetscInt *cspidx;
319: *nn = n;
320: if (!ia) return(0);
322: PetscCalloc1(n+1,&collengths);
323: PetscMalloc1(n+1,&cia);
324: PetscMalloc1(nz+1,&cja);
325: PetscMalloc1(nz+1,&cspidx);
326: jj = a->j;
327: for (i=0; i<nz; i++) {
328: collengths[jj[i]]++;
329: }
330: cia[0] = oshift;
331: for (i=0; i<n; i++) {
332: cia[i+1] = cia[i] + collengths[i];
333: }
334: PetscMemzero(collengths,n*sizeof(PetscInt));
335: jj = a->j;
336: for (row=0; row<m; row++) {
337: mr = a->i[row+1] - a->i[row];
338: for (i=0; i<mr; i++) {
339: col = *jj++;
340: cspidx[cia[col] + collengths[col] - oshift] = a->i[row] + i; /* index of a->j */
341: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
342: }
343: }
344: PetscFree(collengths);
345: *ia = cia; *ja = cja;
346: *spidx = cspidx;
347: return(0);
348: }
352: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A,PetscInt oshift,PetscBool symmetric,PetscBool inodecompressed,PetscInt *n,const PetscInt *ia[],const PetscInt *ja[],PetscInt *spidx[],PetscBool *done)
353: {
357: MatRestoreColumnIJ_SeqAIJ(A,oshift,symmetric,inodecompressed,n,ia,ja,done);
358: PetscFree(*spidx);
359: return(0);
360: }
364: PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A,PetscInt row,const PetscScalar v[])
365: {
366: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
367: PetscInt *ai = a->i;
371: PetscMemcpy(a->a+ai[row],v,(ai[row+1]-ai[row])*sizeof(PetscScalar));
372: return(0);
373: }
375: /*
376: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
378: - a single row of values is set with each call
379: - no row or column indices are negative or (in error) larger than the number of rows or columns
380: - the values are always added to the matrix, not set
381: - no new locations are introduced in the nonzero structure of the matrix
383: This does NOT assume the global column indices are sorted
385: */
387: #include <petsc/private/isimpl.h>
390: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
391: {
392: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
393: PetscInt low,high,t,row,nrow,i,col,l;
394: const PetscInt *rp,*ai = a->i,*ailen = a->ilen,*aj = a->j;
395: PetscInt lastcol = -1;
396: MatScalar *ap,value,*aa = a->a;
397: const PetscInt *ridx = A->rmap->mapping->indices,*cidx = A->cmap->mapping->indices;
399: row = ridx[im[0]];
400: rp = aj + ai[row];
401: ap = aa + ai[row];
402: nrow = ailen[row];
403: low = 0;
404: high = nrow;
405: for (l=0; l<n; l++) { /* loop over added columns */
406: col = cidx[in[l]];
407: value = v[l];
409: if (col <= lastcol) low = 0;
410: else high = nrow;
411: lastcol = col;
412: while (high-low > 5) {
413: t = (low+high)/2;
414: if (rp[t] > col) high = t;
415: else low = t;
416: }
417: for (i=low; i<high; i++) {
418: if (rp[i] == col) {
419: ap[i] += value;
420: low = i + 1;
421: break;
422: }
423: }
424: }
425: return 0;
426: }
430: PetscErrorCode MatSetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
431: {
432: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
433: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
434: PetscInt *imax = a->imax,*ai = a->i,*ailen = a->ilen;
436: PetscInt *aj = a->j,nonew = a->nonew,lastcol = -1;
437: MatScalar *ap,value,*aa = a->a;
438: PetscBool ignorezeroentries = a->ignorezeroentries;
439: PetscBool roworiented = a->roworiented;
442: for (k=0; k<m; k++) { /* loop over added rows */
443: row = im[k];
444: if (row < 0) continue;
445: #if defined(PETSC_USE_DEBUG)
446: if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
447: #endif
448: rp = aj + ai[row]; ap = aa + ai[row];
449: rmax = imax[row]; nrow = ailen[row];
450: low = 0;
451: high = nrow;
452: for (l=0; l<n; l++) { /* loop over added columns */
453: if (in[l] < 0) continue;
454: #if defined(PETSC_USE_DEBUG)
455: if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
456: #endif
457: col = in[l];
458: if (roworiented) {
459: value = v[l + k*n];
460: } else {
461: value = v[k + l*m];
462: }
463: if ((value == 0.0 && ignorezeroentries) && (is == ADD_VALUES)) continue;
465: if (col <= lastcol) low = 0;
466: else high = nrow;
467: lastcol = col;
468: while (high-low > 5) {
469: t = (low+high)/2;
470: if (rp[t] > col) high = t;
471: else low = t;
472: }
473: for (i=low; i<high; i++) {
474: if (rp[i] > col) break;
475: if (rp[i] == col) {
476: if (is == ADD_VALUES) ap[i] += value;
477: else ap[i] = value;
478: low = i + 1;
479: goto noinsert;
480: }
481: }
482: if (value == 0.0 && ignorezeroentries) goto noinsert;
483: if (nonew == 1) goto noinsert;
484: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%D,%D) in the matrix",row,col);
485: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
486: N = nrow++ - 1; a->nz++; high++;
487: /* shift up all the later entries in this row */
488: for (ii=N; ii>=i; ii--) {
489: rp[ii+1] = rp[ii];
490: ap[ii+1] = ap[ii];
491: }
492: rp[i] = col;
493: ap[i] = value;
494: low = i + 1;
495: A->nonzerostate++;
496: noinsert:;
497: }
498: ailen[row] = nrow;
499: }
500: return(0);
501: }
506: PetscErrorCode MatGetValues_SeqAIJ(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
507: {
508: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
509: PetscInt *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
510: PetscInt *ai = a->i,*ailen = a->ilen;
511: MatScalar *ap,*aa = a->a;
514: for (k=0; k<m; k++) { /* loop over rows */
515: row = im[k];
516: if (row < 0) {v += n; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %D",row); */
517: if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
518: rp = aj + ai[row]; ap = aa + ai[row];
519: nrow = ailen[row];
520: for (l=0; l<n; l++) { /* loop over columns */
521: if (in[l] < 0) {v++; continue;} /* SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %D",in[l]); */
522: if (in[l] >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %D max %D",in[l],A->cmap->n-1);
523: col = in[l];
524: high = nrow; low = 0; /* assume unsorted */
525: while (high-low > 5) {
526: t = (low+high)/2;
527: if (rp[t] > col) high = t;
528: else low = t;
529: }
530: for (i=low; i<high; i++) {
531: if (rp[i] > col) break;
532: if (rp[i] == col) {
533: *v++ = ap[i];
534: goto finished;
535: }
536: }
537: *v++ = 0.0;
538: finished:;
539: }
540: }
541: return(0);
542: }
547: PetscErrorCode MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
548: {
549: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
551: PetscInt i,*col_lens;
552: int fd;
553: FILE *file;
556: PetscViewerBinaryGetDescriptor(viewer,&fd);
557: PetscMalloc1(4+A->rmap->n,&col_lens);
559: col_lens[0] = MAT_FILE_CLASSID;
560: col_lens[1] = A->rmap->n;
561: col_lens[2] = A->cmap->n;
562: col_lens[3] = a->nz;
564: /* store lengths of each row and write (including header) to file */
565: for (i=0; i<A->rmap->n; i++) {
566: col_lens[4+i] = a->i[i+1] - a->i[i];
567: }
568: PetscBinaryWrite(fd,col_lens,4+A->rmap->n,PETSC_INT,PETSC_TRUE);
569: PetscFree(col_lens);
571: /* store column indices (zero start index) */
572: PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,PETSC_FALSE);
574: /* store nonzero values */
575: PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,PETSC_FALSE);
577: PetscViewerBinaryGetInfoPointer(viewer,&file);
578: if (file) {
579: fprintf(file,"-matload_block_size %d\n",(int)PetscAbs(A->rmap->bs));
580: }
581: return(0);
582: }
584: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat,PetscViewer);
588: PetscErrorCode MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
589: {
590: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
591: PetscErrorCode ierr;
592: PetscInt i,j,m = A->rmap->n;
593: const char *name;
594: PetscViewerFormat format;
597: PetscViewerGetFormat(viewer,&format);
598: if (format == PETSC_VIEWER_ASCII_MATLAB) {
599: PetscInt nofinalvalue = 0;
600: if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
601: /* Need a dummy value to ensure the dimension of the matrix. */
602: nofinalvalue = 1;
603: }
604: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
605: PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
606: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
607: #if defined(PETSC_USE_COMPLEX)
608: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
609: #else
610: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
611: #endif
612: PetscViewerASCIIPrintf(viewer,"zzz = [\n");
614: for (i=0; i<m; i++) {
615: for (j=a->i[i]; j<a->i[i+1]; j++) {
616: #if defined(PETSC_USE_COMPLEX)
617: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",i+1,a->j[j]+1,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
618: #else
619: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->j[j]+1,(double)a->a[j]);
620: #endif
621: }
622: }
623: if (nofinalvalue) {
624: #if defined(PETSC_USE_COMPLEX)
625: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
626: #else
627: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);
628: #endif
629: }
630: PetscObjectGetName((PetscObject)A,&name);
631: PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
632: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
633: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
634: return(0);
635: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
636: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
637: for (i=0; i<m; i++) {
638: PetscViewerASCIIPrintf(viewer,"row %D:",i);
639: for (j=a->i[i]; j<a->i[i+1]; j++) {
640: #if defined(PETSC_USE_COMPLEX)
641: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
642: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
643: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
644: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
645: } else if (PetscRealPart(a->a[j]) != 0.0) {
646: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
647: }
648: #else
649: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);}
650: #endif
651: }
652: PetscViewerASCIIPrintf(viewer,"\n");
653: }
654: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
655: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
656: PetscInt nzd=0,fshift=1,*sptr;
657: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
658: PetscMalloc1(m+1,&sptr);
659: for (i=0; i<m; i++) {
660: sptr[i] = nzd+1;
661: for (j=a->i[i]; j<a->i[i+1]; j++) {
662: if (a->j[j] >= i) {
663: #if defined(PETSC_USE_COMPLEX)
664: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
665: #else
666: if (a->a[j] != 0.0) nzd++;
667: #endif
668: }
669: }
670: }
671: sptr[m] = nzd+1;
672: PetscViewerASCIIPrintf(viewer," %D %D\n\n",m,nzd);
673: for (i=0; i<m+1; i+=6) {
674: if (i+4<m) {
675: 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]);
676: } else if (i+3<m) {
677: PetscViewerASCIIPrintf(viewer," %D %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);
678: } else if (i+2<m) {
679: PetscViewerASCIIPrintf(viewer," %D %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);
680: } else if (i+1<m) {
681: PetscViewerASCIIPrintf(viewer," %D %D %D\n",sptr[i],sptr[i+1],sptr[i+2]);
682: } else if (i<m) {
683: PetscViewerASCIIPrintf(viewer," %D %D\n",sptr[i],sptr[i+1]);
684: } else {
685: PetscViewerASCIIPrintf(viewer," %D\n",sptr[i]);
686: }
687: }
688: PetscViewerASCIIPrintf(viewer,"\n");
689: PetscFree(sptr);
690: for (i=0; i<m; i++) {
691: for (j=a->i[i]; j<a->i[i+1]; j++) {
692: if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %D ",a->j[j]+fshift);}
693: }
694: PetscViewerASCIIPrintf(viewer,"\n");
695: }
696: PetscViewerASCIIPrintf(viewer,"\n");
697: for (i=0; i<m; i++) {
698: for (j=a->i[i]; j<a->i[i+1]; j++) {
699: if (a->j[j] >= i) {
700: #if defined(PETSC_USE_COMPLEX)
701: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
702: PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
703: }
704: #else
705: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",(double)a->a[j]);}
706: #endif
707: }
708: }
709: PetscViewerASCIIPrintf(viewer,"\n");
710: }
711: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
712: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
713: PetscInt cnt = 0,jcnt;
714: PetscScalar value;
715: #if defined(PETSC_USE_COMPLEX)
716: PetscBool realonly = PETSC_TRUE;
718: for (i=0; i<a->i[m]; i++) {
719: if (PetscImaginaryPart(a->a[i]) != 0.0) {
720: realonly = PETSC_FALSE;
721: break;
722: }
723: }
724: #endif
726: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
727: for (i=0; i<m; i++) {
728: jcnt = 0;
729: for (j=0; j<A->cmap->n; j++) {
730: if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
731: value = a->a[cnt++];
732: jcnt++;
733: } else {
734: value = 0.0;
735: }
736: #if defined(PETSC_USE_COMPLEX)
737: if (realonly) {
738: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
739: } else {
740: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
741: }
742: #else
743: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
744: #endif
745: }
746: PetscViewerASCIIPrintf(viewer,"\n");
747: }
748: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
749: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
750: PetscInt fshift=1;
751: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
752: #if defined(PETSC_USE_COMPLEX)
753: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
754: #else
755: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
756: #endif
757: PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
758: for (i=0; i<m; i++) {
759: for (j=a->i[i]; j<a->i[i+1]; j++) {
760: #if defined(PETSC_USE_COMPLEX)
761: if (PetscImaginaryPart(a->a[j]) > 0.0) {
762: PetscViewerASCIIPrintf(viewer,"%D %D, %g %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
763: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
764: PetscViewerASCIIPrintf(viewer,"%D %D, %g -%g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
765: } else {
766: PetscViewerASCIIPrintf(viewer,"%D %D, %g\n", i+fshift,a->j[j]+fshift,(double)PetscRealPart(a->a[j]));
767: }
768: #else
769: PetscViewerASCIIPrintf(viewer,"%D %D %g\n", i+fshift, a->j[j]+fshift, (double)a->a[j]);
770: #endif
771: }
772: }
773: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
774: } else {
775: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
776: if (A->factortype) {
777: for (i=0; i<m; i++) {
778: PetscViewerASCIIPrintf(viewer,"row %D:",i);
779: /* L part */
780: for (j=a->i[i]; j<a->i[i+1]; j++) {
781: #if defined(PETSC_USE_COMPLEX)
782: if (PetscImaginaryPart(a->a[j]) > 0.0) {
783: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
784: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
785: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
786: } else {
787: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
788: }
789: #else
790: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
791: #endif
792: }
793: /* diagonal */
794: j = a->diag[i];
795: #if defined(PETSC_USE_COMPLEX)
796: if (PetscImaginaryPart(a->a[j]) > 0.0) {
797: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)PetscImaginaryPart(1.0/a->a[j]));
798: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
799: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(1.0/a->a[j]),(double)(-PetscImaginaryPart(1.0/a->a[j])));
800: } else {
801: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(1.0/a->a[j]));
802: }
803: #else
804: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)(1.0/a->a[j]));
805: #endif
807: /* U part */
808: for (j=a->diag[i+1]+1; j<a->diag[i]; j++) {
809: #if defined(PETSC_USE_COMPLEX)
810: if (PetscImaginaryPart(a->a[j]) > 0.0) {
811: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
812: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
813: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)(-PetscImaginaryPart(a->a[j])));
814: } else {
815: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
816: }
817: #else
818: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
819: #endif
820: }
821: PetscViewerASCIIPrintf(viewer,"\n");
822: }
823: } else {
824: for (i=0; i<m; i++) {
825: PetscViewerASCIIPrintf(viewer,"row %D:",i);
826: for (j=a->i[i]; j<a->i[i+1]; j++) {
827: #if defined(PETSC_USE_COMPLEX)
828: if (PetscImaginaryPart(a->a[j]) > 0.0) {
829: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)PetscImaginaryPart(a->a[j]));
830: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
831: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->j[j],(double)PetscRealPart(a->a[j]),(double)-PetscImaginaryPart(a->a[j]));
832: } else {
833: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)PetscRealPart(a->a[j]));
834: }
835: #else
836: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->j[j],(double)a->a[j]);
837: #endif
838: }
839: PetscViewerASCIIPrintf(viewer,"\n");
840: }
841: }
842: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
843: }
844: PetscViewerFlush(viewer);
845: return(0);
846: }
848: #include <petscdraw.h>
851: PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
852: {
853: Mat A = (Mat) Aa;
854: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
855: PetscErrorCode ierr;
856: PetscInt i,j,m = A->rmap->n,color;
857: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
858: PetscViewer viewer;
859: PetscViewerFormat format;
862: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
863: PetscViewerGetFormat(viewer,&format);
865: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
866: /* loop over matrix elements drawing boxes */
868: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
869: /* Blue for negative, Cyan for zero and Red for positive */
870: color = PETSC_DRAW_BLUE;
871: for (i=0; i<m; i++) {
872: y_l = m - i - 1.0; y_r = y_l + 1.0;
873: for (j=a->i[i]; j<a->i[i+1]; j++) {
874: x_l = a->j[j]; x_r = x_l + 1.0;
875: if (PetscRealPart(a->a[j]) >= 0.) continue;
876: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
877: }
878: }
879: color = PETSC_DRAW_CYAN;
880: for (i=0; i<m; i++) {
881: y_l = m - i - 1.0; y_r = y_l + 1.0;
882: for (j=a->i[i]; j<a->i[i+1]; j++) {
883: x_l = a->j[j]; x_r = x_l + 1.0;
884: if (a->a[j] != 0.) continue;
885: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
886: }
887: }
888: color = PETSC_DRAW_RED;
889: for (i=0; i<m; i++) {
890: y_l = m - i - 1.0; y_r = y_l + 1.0;
891: for (j=a->i[i]; j<a->i[i+1]; j++) {
892: x_l = a->j[j]; x_r = x_l + 1.0;
893: if (PetscRealPart(a->a[j]) <= 0.) continue;
894: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
895: }
896: }
897: } else {
898: /* use contour shading to indicate magnitude of values */
899: /* first determine max of all nonzero values */
900: PetscInt nz = a->nz,count;
901: PetscDraw popup;
902: PetscReal scale;
904: for (i=0; i<nz; i++) {
905: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
906: }
907: scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
908: PetscDrawGetPopup(draw,&popup);
909: if (popup) {
910: PetscDrawScalePopup(popup,0.0,maxv);
911: }
912: count = 0;
913: for (i=0; i<m; i++) {
914: y_l = m - i - 1.0; y_r = y_l + 1.0;
915: for (j=a->i[i]; j<a->i[i+1]; j++) {
916: x_l = a->j[j]; x_r = x_l + 1.0;
917: color = PETSC_DRAW_BASIC_COLORS + (PetscInt)(scale*PetscAbsScalar(a->a[count]));
918: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
919: count++;
920: }
921: }
922: }
923: return(0);
924: }
926: #include <petscdraw.h>
929: PetscErrorCode MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
930: {
932: PetscDraw draw;
933: PetscReal xr,yr,xl,yl,h,w;
934: PetscBool isnull;
937: PetscViewerDrawGetDraw(viewer,0,&draw);
938: PetscDrawIsNull(draw,&isnull);
939: if (isnull) return(0);
941: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
942: xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
943: xr += w; yr += h; xl = -w; yl = -h;
944: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
945: PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
946: PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
947: return(0);
948: }
952: PetscErrorCode MatView_SeqAIJ(Mat A,PetscViewer viewer)
953: {
955: PetscBool iascii,isbinary,isdraw;
958: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
959: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
960: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
961: if (iascii) {
962: MatView_SeqAIJ_ASCII(A,viewer);
963: } else if (isbinary) {
964: MatView_SeqAIJ_Binary(A,viewer);
965: } else if (isdraw) {
966: MatView_SeqAIJ_Draw(A,viewer);
967: }
968: MatView_SeqAIJ_Inode(A,viewer);
969: return(0);
970: }
974: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
975: {
976: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
978: PetscInt fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax;
979: PetscInt m = A->rmap->n,*ip,N,*ailen = a->ilen,rmax = 0;
980: MatScalar *aa = a->a,*ap;
981: PetscReal ratio = 0.6;
984: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
986: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
987: for (i=1; i<m; i++) {
988: /* move each row back by the amount of empty slots (fshift) before it*/
989: fshift += imax[i-1] - ailen[i-1];
990: rmax = PetscMax(rmax,ailen[i]);
991: if (fshift) {
992: ip = aj + ai[i];
993: ap = aa + ai[i];
994: N = ailen[i];
995: for (j=0; j<N; j++) {
996: ip[j-fshift] = ip[j];
997: ap[j-fshift] = ap[j];
998: }
999: }
1000: ai[i] = ai[i-1] + ailen[i-1];
1001: }
1002: if (m) {
1003: fshift += imax[m-1] - ailen[m-1];
1004: ai[m] = ai[m-1] + ailen[m-1];
1005: }
1007: /* reset ilen and imax for each row */
1008: a->nonzerorowcnt = 0;
1009: for (i=0; i<m; i++) {
1010: ailen[i] = imax[i] = ai[i+1] - ai[i];
1011: a->nonzerorowcnt += ((ai[i+1] - ai[i]) > 0);
1012: }
1013: a->nz = ai[m];
1014: if (fshift && a->nounused == -1) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_PLIB, "Unused space detected in matrix: %D X %D, %D unneeded", m, A->cmap->n, fshift);
1016: MatMarkDiagonal_SeqAIJ(A);
1017: PetscInfo4(A,"Matrix size: %D X %D; storage space: %D unneeded,%D used\n",m,A->cmap->n,fshift,a->nz);
1018: PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1019: PetscInfo1(A,"Maximum nonzeros in any row is %D\n",rmax);
1021: A->info.mallocs += a->reallocs;
1022: a->reallocs = 0;
1023: A->info.nz_unneeded = (PetscReal)fshift;
1024: a->rmax = rmax;
1026: MatCheckCompressedRow(A,a->nonzerorowcnt,&a->compressedrow,a->i,m,ratio);
1027: MatAssemblyEnd_SeqAIJ_Inode(A,mode);
1028: MatSeqAIJInvalidateDiagonal(A);
1029: return(0);
1030: }
1034: PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1035: {
1036: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1037: PetscInt i,nz = a->nz;
1038: MatScalar *aa = a->a;
1042: for (i=0; i<nz; i++) aa[i] = PetscRealPart(aa[i]);
1043: MatSeqAIJInvalidateDiagonal(A);
1044: return(0);
1045: }
1049: PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1050: {
1051: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1052: PetscInt i,nz = a->nz;
1053: MatScalar *aa = a->a;
1057: for (i=0; i<nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1058: MatSeqAIJInvalidateDiagonal(A);
1059: return(0);
1060: }
1064: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1065: {
1066: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1070: PetscMemzero(a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
1071: MatSeqAIJInvalidateDiagonal(A);
1072: return(0);
1073: }
1077: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1078: {
1079: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1083: #if defined(PETSC_USE_LOG)
1084: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
1085: #endif
1086: MatSeqXAIJFreeAIJ(A,&a->a,&a->j,&a->i);
1087: ISDestroy(&a->row);
1088: ISDestroy(&a->col);
1089: PetscFree(a->diag);
1090: PetscFree(a->ibdiag);
1091: PetscFree2(a->imax,a->ilen);
1092: PetscFree3(a->idiag,a->mdiag,a->ssor_work);
1093: PetscFree(a->solve_work);
1094: ISDestroy(&a->icol);
1095: PetscFree(a->saved_values);
1096: ISColoringDestroy(&a->coloring);
1097: PetscFree2(a->compressedrow.i,a->compressedrow.rindex);
1098: PetscFree(a->matmult_abdense);
1100: MatDestroy_SeqAIJ_Inode(A);
1101: PetscFree(A->data);
1103: PetscObjectChangeTypeName((PetscObject)A,0);
1104: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetColumnIndices_C",NULL);
1105: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
1106: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
1107: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqsbaij_C",NULL);
1108: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqbaij_C",NULL);
1109: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqaijperm_C",NULL);
1110: #if defined(PETSC_HAVE_ELEMENTAL)
1111: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_elemental_C",NULL);
1112: #endif
1113: PetscObjectComposeFunction((PetscObject)A,"MatConvert_seqaij_seqdense_C",NULL);
1114: PetscObjectComposeFunction((PetscObject)A,"MatIsTranspose_C",NULL);
1115: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocation_C",NULL);
1116: PetscObjectComposeFunction((PetscObject)A,"MatSeqAIJSetPreallocationCSR_C",NULL);
1117: PetscObjectComposeFunction((PetscObject)A,"MatReorderForNonzeroDiagonal_C",NULL);
1118: return(0);
1119: }
1123: PetscErrorCode MatSetOption_SeqAIJ(Mat A,MatOption op,PetscBool flg)
1124: {
1125: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1129: switch (op) {
1130: case MAT_ROW_ORIENTED:
1131: a->roworiented = flg;
1132: break;
1133: case MAT_KEEP_NONZERO_PATTERN:
1134: a->keepnonzeropattern = flg;
1135: break;
1136: case MAT_NEW_NONZERO_LOCATIONS:
1137: a->nonew = (flg ? 0 : 1);
1138: break;
1139: case MAT_NEW_NONZERO_LOCATION_ERR:
1140: a->nonew = (flg ? -1 : 0);
1141: break;
1142: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1143: a->nonew = (flg ? -2 : 0);
1144: break;
1145: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1146: a->nounused = (flg ? -1 : 0);
1147: break;
1148: case MAT_IGNORE_ZERO_ENTRIES:
1149: a->ignorezeroentries = flg;
1150: break;
1151: case MAT_SPD:
1152: case MAT_SYMMETRIC:
1153: case MAT_STRUCTURALLY_SYMMETRIC:
1154: case MAT_HERMITIAN:
1155: case MAT_SYMMETRY_ETERNAL:
1156: /* These options are handled directly by MatSetOption() */
1157: break;
1158: case MAT_NEW_DIAGONALS:
1159: case MAT_IGNORE_OFF_PROC_ENTRIES:
1160: case MAT_USE_HASH_TABLE:
1161: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
1162: break;
1163: case MAT_USE_INODES:
1164: /* Not an error because MatSetOption_SeqAIJ_Inode handles this one */
1165: break;
1166: default:
1167: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
1168: }
1169: MatSetOption_SeqAIJ_Inode(A,op,flg);
1170: return(0);
1171: }
1175: PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A,Vec v)
1176: {
1177: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1179: PetscInt i,j,n,*ai=a->i,*aj=a->j,nz;
1180: PetscScalar *aa=a->a,*x,zero=0.0;
1183: VecGetLocalSize(v,&n);
1184: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
1186: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1187: PetscInt *diag=a->diag;
1188: VecGetArray(v,&x);
1189: for (i=0; i<n; i++) x[i] = 1.0/aa[diag[i]];
1190: VecRestoreArray(v,&x);
1191: return(0);
1192: }
1194: VecSet(v,zero);
1195: VecGetArray(v,&x);
1196: for (i=0; i<n; i++) {
1197: nz = ai[i+1] - ai[i];
1198: if (!nz) x[i] = 0.0;
1199: for (j=ai[i]; j<ai[i+1]; j++) {
1200: if (aj[j] == i) {
1201: x[i] = aa[j];
1202: break;
1203: }
1204: }
1205: }
1206: VecRestoreArray(v,&x);
1207: return(0);
1208: }
1210: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1213: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
1214: {
1215: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1216: PetscScalar *y;
1217: const PetscScalar *x;
1218: PetscErrorCode ierr;
1219: PetscInt m = A->rmap->n;
1220: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1221: const MatScalar *v;
1222: PetscScalar alpha;
1223: PetscInt n,i,j;
1224: const PetscInt *idx,*ii,*ridx=NULL;
1225: Mat_CompressedRow cprow = a->compressedrow;
1226: PetscBool usecprow = cprow.use;
1227: #endif
1230: if (zz != yy) {VecCopy(zz,yy);}
1231: VecGetArrayRead(xx,&x);
1232: VecGetArray(yy,&y);
1234: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1235: fortranmulttransposeaddaij_(&m,x,a->i,a->j,a->a,y);
1236: #else
1237: if (usecprow) {
1238: m = cprow.nrows;
1239: ii = cprow.i;
1240: ridx = cprow.rindex;
1241: } else {
1242: ii = a->i;
1243: }
1244: for (i=0; i<m; i++) {
1245: idx = a->j + ii[i];
1246: v = a->a + ii[i];
1247: n = ii[i+1] - ii[i];
1248: if (usecprow) {
1249: alpha = x[ridx[i]];
1250: } else {
1251: alpha = x[i];
1252: }
1253: for (j=0; j<n; j++) y[idx[j]] += alpha*v[j];
1254: }
1255: #endif
1256: PetscLogFlops(2.0*a->nz);
1257: VecRestoreArrayRead(xx,&x);
1258: VecRestoreArray(yy,&y);
1259: return(0);
1260: }
1264: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
1265: {
1269: VecSet(yy,0.0);
1270: MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
1271: return(0);
1272: }
1274: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1278: PetscErrorCode MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
1279: {
1280: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1281: PetscScalar *y;
1282: const PetscScalar *x;
1283: const MatScalar *aa;
1284: PetscErrorCode ierr;
1285: PetscInt m=A->rmap->n;
1286: const PetscInt *aj,*ii,*ridx=NULL;
1287: PetscInt n,i;
1288: PetscScalar sum;
1289: PetscBool usecprow=a->compressedrow.use;
1291: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1292: #pragma disjoint(*x,*y,*aa)
1293: #endif
1296: VecGetArrayRead(xx,&x);
1297: VecGetArray(yy,&y);
1298: aj = a->j;
1299: aa = a->a;
1300: ii = a->i;
1301: if (usecprow) { /* use compressed row format */
1302: PetscMemzero(y,m*sizeof(PetscScalar));
1303: m = a->compressedrow.nrows;
1304: ii = a->compressedrow.i;
1305: ridx = a->compressedrow.rindex;
1306: for (i=0; i<m; i++) {
1307: n = ii[i+1] - ii[i];
1308: aj = a->j + ii[i];
1309: aa = a->a + ii[i];
1310: sum = 0.0;
1311: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1312: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1313: y[*ridx++] = sum;
1314: }
1315: } else { /* do not use compressed row format */
1316: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1317: fortranmultaij_(&m,x,ii,aj,aa,y);
1318: #else
1319: for (i=0; i<m; i++) {
1320: n = ii[i+1] - ii[i];
1321: aj = a->j + ii[i];
1322: aa = a->a + ii[i];
1323: sum = 0.0;
1324: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1325: y[i] = sum;
1326: }
1327: #endif
1328: }
1329: PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
1330: VecRestoreArrayRead(xx,&x);
1331: VecRestoreArray(yy,&y);
1332: return(0);
1333: }
1337: PetscErrorCode MatMultMax_SeqAIJ(Mat A,Vec xx,Vec yy)
1338: {
1339: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1340: PetscScalar *y;
1341: const PetscScalar *x;
1342: const MatScalar *aa;
1343: PetscErrorCode ierr;
1344: PetscInt m=A->rmap->n;
1345: const PetscInt *aj,*ii,*ridx=NULL;
1346: PetscInt n,i,nonzerorow=0;
1347: PetscScalar sum;
1348: PetscBool usecprow=a->compressedrow.use;
1350: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1351: #pragma disjoint(*x,*y,*aa)
1352: #endif
1355: VecGetArrayRead(xx,&x);
1356: VecGetArray(yy,&y);
1357: aj = a->j;
1358: aa = a->a;
1359: ii = a->i;
1360: if (usecprow) { /* use compressed row format */
1361: m = a->compressedrow.nrows;
1362: ii = a->compressedrow.i;
1363: ridx = a->compressedrow.rindex;
1364: for (i=0; i<m; i++) {
1365: n = ii[i+1] - ii[i];
1366: aj = a->j + ii[i];
1367: aa = a->a + ii[i];
1368: sum = 0.0;
1369: nonzerorow += (n>0);
1370: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1371: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1372: y[*ridx++] = sum;
1373: }
1374: } else { /* do not use compressed row format */
1375: for (i=0; i<m; i++) {
1376: n = ii[i+1] - ii[i];
1377: aj = a->j + ii[i];
1378: aa = a->a + ii[i];
1379: sum = 0.0;
1380: nonzerorow += (n>0);
1381: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1382: y[i] = sum;
1383: }
1384: }
1385: PetscLogFlops(2.0*a->nz - nonzerorow);
1386: VecRestoreArrayRead(xx,&x);
1387: VecRestoreArray(yy,&y);
1388: return(0);
1389: }
1393: PetscErrorCode MatMultAddMax_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1394: {
1395: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1396: PetscScalar *y,*z;
1397: const PetscScalar *x;
1398: const MatScalar *aa;
1399: PetscErrorCode ierr;
1400: PetscInt m = A->rmap->n,*aj,*ii;
1401: PetscInt n,i,*ridx=NULL;
1402: PetscScalar sum;
1403: PetscBool usecprow=a->compressedrow.use;
1406: VecGetArrayRead(xx,&x);
1407: VecGetArrayPair(yy,zz,&y,&z);
1409: aj = a->j;
1410: aa = a->a;
1411: ii = a->i;
1412: if (usecprow) { /* use compressed row format */
1413: if (zz != yy) {
1414: PetscMemcpy(z,y,m*sizeof(PetscScalar));
1415: }
1416: m = a->compressedrow.nrows;
1417: ii = a->compressedrow.i;
1418: ridx = a->compressedrow.rindex;
1419: for (i=0; i<m; i++) {
1420: n = ii[i+1] - ii[i];
1421: aj = a->j + ii[i];
1422: aa = a->a + ii[i];
1423: sum = y[*ridx];
1424: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1425: z[*ridx++] = sum;
1426: }
1427: } else { /* do not use compressed row format */
1428: for (i=0; i<m; i++) {
1429: n = ii[i+1] - ii[i];
1430: aj = a->j + ii[i];
1431: aa = a->a + ii[i];
1432: sum = y[i];
1433: PetscSparseDenseMaxDot(sum,x,aa,aj,n);
1434: z[i] = sum;
1435: }
1436: }
1437: PetscLogFlops(2.0*a->nz);
1438: VecRestoreArrayRead(xx,&x);
1439: VecRestoreArrayPair(yy,zz,&y,&z);
1440: return(0);
1441: }
1443: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1446: PetscErrorCode MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
1447: {
1448: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1449: PetscScalar *y,*z;
1450: const PetscScalar *x;
1451: const MatScalar *aa;
1452: PetscErrorCode ierr;
1453: const PetscInt *aj,*ii,*ridx=NULL;
1454: PetscInt m = A->rmap->n,n,i;
1455: PetscScalar sum;
1456: PetscBool usecprow=a->compressedrow.use;
1459: VecGetArrayRead(xx,&x);
1460: VecGetArrayPair(yy,zz,&y,&z);
1462: aj = a->j;
1463: aa = a->a;
1464: ii = a->i;
1465: if (usecprow) { /* use compressed row format */
1466: if (zz != yy) {
1467: PetscMemcpy(z,y,m*sizeof(PetscScalar));
1468: }
1469: m = a->compressedrow.nrows;
1470: ii = a->compressedrow.i;
1471: ridx = a->compressedrow.rindex;
1472: for (i=0; i<m; i++) {
1473: n = ii[i+1] - ii[i];
1474: aj = a->j + ii[i];
1475: aa = a->a + ii[i];
1476: sum = y[*ridx];
1477: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1478: z[*ridx++] = sum;
1479: }
1480: } else { /* do not use compressed row format */
1481: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1482: fortranmultaddaij_(&m,x,ii,aj,aa,y,z);
1483: #else
1484: for (i=0; i<m; i++) {
1485: n = ii[i+1] - ii[i];
1486: aj = a->j + ii[i];
1487: aa = a->a + ii[i];
1488: sum = y[i];
1489: PetscSparseDensePlusDot(sum,x,aa,aj,n);
1490: z[i] = sum;
1491: }
1492: #endif
1493: }
1494: PetscLogFlops(2.0*a->nz);
1495: VecRestoreArrayRead(xx,&x);
1496: VecRestoreArrayPair(yy,zz,&y,&z);
1497: #if defined(PETSC_HAVE_CUSP)
1498: /*
1499: VecView(xx,0);
1500: VecView(zz,0);
1501: MatView(A,0);
1502: */
1503: #endif
1504: return(0);
1505: }
1507: /*
1508: Adds diagonal pointers to sparse matrix structure.
1509: */
1512: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1513: {
1514: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1516: PetscInt i,j,m = A->rmap->n;
1519: if (!a->diag) {
1520: PetscMalloc1(m,&a->diag);
1521: PetscLogObjectMemory((PetscObject)A, m*sizeof(PetscInt));
1522: }
1523: for (i=0; i<A->rmap->n; i++) {
1524: a->diag[i] = a->i[i+1];
1525: for (j=a->i[i]; j<a->i[i+1]; j++) {
1526: if (a->j[j] == i) {
1527: a->diag[i] = j;
1528: break;
1529: }
1530: }
1531: }
1532: return(0);
1533: }
1535: /*
1536: Checks for missing diagonals
1537: */
1540: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A,PetscBool *missing,PetscInt *d)
1541: {
1542: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1543: PetscInt *diag,*ii = a->i,i;
1546: *missing = PETSC_FALSE;
1547: if (A->rmap->n > 0 && !ii) {
1548: *missing = PETSC_TRUE;
1549: if (d) *d = 0;
1550: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
1551: } else {
1552: diag = a->diag;
1553: for (i=0; i<A->rmap->n; i++) {
1554: if (diag[i] >= ii[i+1]) {
1555: *missing = PETSC_TRUE;
1556: if (d) *d = i;
1557: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
1558: break;
1559: }
1560: }
1561: }
1562: return(0);
1563: }
1567: /*
1568: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1569: */
1570: PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A,PetscScalar omega,PetscScalar fshift)
1571: {
1572: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
1574: PetscInt i,*diag,m = A->rmap->n;
1575: MatScalar *v = a->a;
1576: PetscScalar *idiag,*mdiag;
1579: if (a->idiagvalid) return(0);
1580: MatMarkDiagonal_SeqAIJ(A);
1581: diag = a->diag;
1582: if (!a->idiag) {
1583: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
1584: PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
1585: v = a->a;
1586: }
1587: mdiag = a->mdiag;
1588: idiag = a->idiag;
1590: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1591: for (i=0; i<m; i++) {
1592: mdiag[i] = v[diag[i]];
1593: if (!PetscAbsScalar(mdiag[i]) && !PetscRealPart(fshift)) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
1594: idiag[i] = 1.0/v[diag[i]];
1595: }
1596: PetscLogFlops(m);
1597: } else {
1598: for (i=0; i<m; i++) {
1599: mdiag[i] = v[diag[i]];
1600: idiag[i] = omega/(fshift + v[diag[i]]);
1601: }
1602: PetscLogFlops(2.0*m);
1603: }
1604: a->idiagvalid = PETSC_TRUE;
1605: return(0);
1606: }
1608: #include <../src/mat/impls/aij/seq/ftn-kernels/frelax.h>
1611: PetscErrorCode MatSOR_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1612: {
1613: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1614: PetscScalar *x,d,sum,*t,scale;
1615: const MatScalar *v = a->a,*idiag=0,*mdiag;
1616: const PetscScalar *b, *bs,*xb, *ts;
1617: PetscErrorCode ierr;
1618: PetscInt n = A->cmap->n,m = A->rmap->n,i;
1619: const PetscInt *idx,*diag;
1622: its = its*lits;
1624: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1625: if (!a->idiagvalid) {MatInvertDiagonal_SeqAIJ(A,omega,fshift);}
1626: a->fshift = fshift;
1627: a->omega = omega;
1629: diag = a->diag;
1630: t = a->ssor_work;
1631: idiag = a->idiag;
1632: mdiag = a->mdiag;
1634: VecGetArray(xx,&x);
1635: VecGetArrayRead(bb,&b);
1636: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1637: if (flag == SOR_APPLY_UPPER) {
1638: /* apply (U + D/omega) to the vector */
1639: bs = b;
1640: for (i=0; i<m; i++) {
1641: d = fshift + mdiag[i];
1642: n = a->i[i+1] - diag[i] - 1;
1643: idx = a->j + diag[i] + 1;
1644: v = a->a + diag[i] + 1;
1645: sum = b[i]*d/omega;
1646: PetscSparseDensePlusDot(sum,bs,v,idx,n);
1647: x[i] = sum;
1648: }
1649: VecRestoreArray(xx,&x);
1650: VecRestoreArrayRead(bb,&b);
1651: PetscLogFlops(a->nz);
1652: return(0);
1653: }
1655: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1656: else if (flag & SOR_EISENSTAT) {
1657: /* Let A = L + U + D; where L is lower trianglar,
1658: U is upper triangular, E = D/omega; This routine applies
1660: (L + E)^{-1} A (U + E)^{-1}
1662: to a vector efficiently using Eisenstat's trick.
1663: */
1664: scale = (2.0/omega) - 1.0;
1666: /* x = (E + U)^{-1} b */
1667: for (i=m-1; i>=0; i--) {
1668: n = a->i[i+1] - diag[i] - 1;
1669: idx = a->j + diag[i] + 1;
1670: v = a->a + diag[i] + 1;
1671: sum = b[i];
1672: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1673: x[i] = sum*idiag[i];
1674: }
1676: /* t = b - (2*E - D)x */
1677: v = a->a;
1678: for (i=0; i<m; i++) t[i] = b[i] - scale*(v[*diag++])*x[i];
1680: /* t = (E + L)^{-1}t */
1681: ts = t;
1682: diag = a->diag;
1683: for (i=0; i<m; i++) {
1684: n = diag[i] - a->i[i];
1685: idx = a->j + a->i[i];
1686: v = a->a + a->i[i];
1687: sum = t[i];
1688: PetscSparseDenseMinusDot(sum,ts,v,idx,n);
1689: t[i] = sum*idiag[i];
1690: /* x = x + t */
1691: x[i] += t[i];
1692: }
1694: PetscLogFlops(6.0*m-1 + 2.0*a->nz);
1695: VecRestoreArray(xx,&x);
1696: VecRestoreArrayRead(bb,&b);
1697: return(0);
1698: }
1699: if (flag & SOR_ZERO_INITIAL_GUESS) {
1700: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1701: for (i=0; i<m; i++) {
1702: n = diag[i] - a->i[i];
1703: idx = a->j + a->i[i];
1704: v = a->a + a->i[i];
1705: sum = b[i];
1706: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1707: t[i] = sum;
1708: x[i] = sum*idiag[i];
1709: }
1710: xb = t;
1711: PetscLogFlops(a->nz);
1712: } else xb = b;
1713: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1714: for (i=m-1; i>=0; i--) {
1715: n = a->i[i+1] - diag[i] - 1;
1716: idx = a->j + diag[i] + 1;
1717: v = a->a + diag[i] + 1;
1718: sum = xb[i];
1719: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1720: if (xb == b) {
1721: x[i] = sum*idiag[i];
1722: } else {
1723: x[i] = (1-omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1724: }
1725: }
1726: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1727: }
1728: its--;
1729: }
1730: while (its--) {
1731: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
1732: for (i=0; i<m; i++) {
1733: /* lower */
1734: n = diag[i] - a->i[i];
1735: idx = a->j + a->i[i];
1736: v = a->a + a->i[i];
1737: sum = b[i];
1738: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1739: t[i] = sum; /* save application of the lower-triangular part */
1740: /* upper */
1741: n = a->i[i+1] - diag[i] - 1;
1742: idx = a->j + diag[i] + 1;
1743: v = a->a + diag[i] + 1;
1744: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1745: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1746: }
1747: xb = t;
1748: PetscLogFlops(2.0*a->nz);
1749: } else xb = b;
1750: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
1751: for (i=m-1; i>=0; i--) {
1752: sum = xb[i];
1753: if (xb == b) {
1754: /* whole matrix (no checkpointing available) */
1755: n = a->i[i+1] - a->i[i];
1756: idx = a->j + a->i[i];
1757: v = a->a + a->i[i];
1758: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1759: x[i] = (1. - omega)*x[i] + (sum + mdiag[i]*x[i])*idiag[i];
1760: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1761: n = a->i[i+1] - diag[i] - 1;
1762: idx = a->j + diag[i] + 1;
1763: v = a->a + diag[i] + 1;
1764: PetscSparseDenseMinusDot(sum,x,v,idx,n);
1765: x[i] = (1. - omega)*x[i] + sum*idiag[i]; /* omega in idiag */
1766: }
1767: }
1768: if (xb == b) {
1769: PetscLogFlops(2.0*a->nz);
1770: } else {
1771: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1772: }
1773: }
1774: }
1775: VecRestoreArray(xx,&x);
1776: VecRestoreArrayRead(bb,&b);
1777: return(0);
1778: }
1783: PetscErrorCode MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1784: {
1785: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1788: info->block_size = 1.0;
1789: info->nz_allocated = (double)a->maxnz;
1790: info->nz_used = (double)a->nz;
1791: info->nz_unneeded = (double)(a->maxnz - a->nz);
1792: info->assemblies = (double)A->num_ass;
1793: info->mallocs = (double)A->info.mallocs;
1794: info->memory = ((PetscObject)A)->mem;
1795: if (A->factortype) {
1796: info->fill_ratio_given = A->info.fill_ratio_given;
1797: info->fill_ratio_needed = A->info.fill_ratio_needed;
1798: info->factor_mallocs = A->info.factor_mallocs;
1799: } else {
1800: info->fill_ratio_given = 0;
1801: info->fill_ratio_needed = 0;
1802: info->factor_mallocs = 0;
1803: }
1804: return(0);
1805: }
1809: PetscErrorCode MatZeroRows_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1810: {
1811: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1812: PetscInt i,m = A->rmap->n - 1,d = 0;
1813: PetscErrorCode ierr;
1814: const PetscScalar *xx;
1815: PetscScalar *bb;
1816: PetscBool missing;
1819: if (x && b) {
1820: VecGetArrayRead(x,&xx);
1821: VecGetArray(b,&bb);
1822: for (i=0; i<N; i++) {
1823: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1824: bb[rows[i]] = diag*xx[rows[i]];
1825: }
1826: VecRestoreArrayRead(x,&xx);
1827: VecRestoreArray(b,&bb);
1828: }
1830: if (a->keepnonzeropattern) {
1831: for (i=0; i<N; i++) {
1832: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1833: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1834: }
1835: if (diag != 0.0) {
1836: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1837: if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1838: for (i=0; i<N; i++) {
1839: a->a[a->diag[rows[i]]] = diag;
1840: }
1841: }
1842: } else {
1843: if (diag != 0.0) {
1844: for (i=0; i<N; i++) {
1845: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1846: if (a->ilen[rows[i]] > 0) {
1847: a->ilen[rows[i]] = 1;
1848: a->a[a->i[rows[i]]] = diag;
1849: a->j[a->i[rows[i]]] = rows[i];
1850: } else { /* in case row was completely empty */
1851: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],&diag,INSERT_VALUES);
1852: }
1853: }
1854: } else {
1855: for (i=0; i<N; i++) {
1856: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1857: a->ilen[rows[i]] = 0;
1858: }
1859: }
1860: A->nonzerostate++;
1861: }
1862: MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1863: return(0);
1864: }
1868: PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A,PetscInt N,const PetscInt rows[],PetscScalar diag,Vec x,Vec b)
1869: {
1870: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1871: PetscInt i,j,m = A->rmap->n - 1,d = 0;
1872: PetscErrorCode ierr;
1873: PetscBool missing,*zeroed,vecs = PETSC_FALSE;
1874: const PetscScalar *xx;
1875: PetscScalar *bb;
1878: if (x && b) {
1879: VecGetArrayRead(x,&xx);
1880: VecGetArray(b,&bb);
1881: vecs = PETSC_TRUE;
1882: }
1883: PetscCalloc1(A->rmap->n,&zeroed);
1884: for (i=0; i<N; i++) {
1885: if (rows[i] < 0 || rows[i] > m) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"row %D out of range", rows[i]);
1886: PetscMemzero(&a->a[a->i[rows[i]]],a->ilen[rows[i]]*sizeof(PetscScalar));
1888: zeroed[rows[i]] = PETSC_TRUE;
1889: }
1890: for (i=0; i<A->rmap->n; i++) {
1891: if (!zeroed[i]) {
1892: for (j=a->i[i]; j<a->i[i+1]; j++) {
1893: if (zeroed[a->j[j]]) {
1894: if (vecs) bb[i] -= a->a[j]*xx[a->j[j]];
1895: a->a[j] = 0.0;
1896: }
1897: }
1898: } else if (vecs) bb[i] = diag*xx[i];
1899: }
1900: if (x && b) {
1901: VecRestoreArrayRead(x,&xx);
1902: VecRestoreArray(b,&bb);
1903: }
1904: PetscFree(zeroed);
1905: if (diag != 0.0) {
1906: MatMissingDiagonal_SeqAIJ(A,&missing,&d);
1907: if (missing) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Matrix is missing diagonal entry in row %D",d);
1908: for (i=0; i<N; i++) {
1909: a->a[a->diag[rows[i]]] = diag;
1910: }
1911: }
1912: MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1913: return(0);
1914: }
1918: PetscErrorCode MatGetRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1919: {
1920: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1921: PetscInt *itmp;
1924: if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);
1926: *nz = a->i[row+1] - a->i[row];
1927: if (v) *v = a->a + a->i[row];
1928: if (idx) {
1929: itmp = a->j + a->i[row];
1930: if (*nz) *idx = itmp;
1931: else *idx = 0;
1932: }
1933: return(0);
1934: }
1936: /* remove this function? */
1939: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
1940: {
1942: return(0);
1943: }
1947: PetscErrorCode MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1948: {
1949: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1950: MatScalar *v = a->a;
1951: PetscReal sum = 0.0;
1953: PetscInt i,j;
1956: if (type == NORM_FROBENIUS) {
1957: for (i=0; i<a->nz; i++) {
1958: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1959: }
1960: *nrm = PetscSqrtReal(sum);
1961: } else if (type == NORM_1) {
1962: PetscReal *tmp;
1963: PetscInt *jj = a->j;
1964: PetscCalloc1(A->cmap->n+1,&tmp);
1965: *nrm = 0.0;
1966: for (j=0; j<a->nz; j++) {
1967: tmp[*jj++] += PetscAbsScalar(*v); v++;
1968: }
1969: for (j=0; j<A->cmap->n; j++) {
1970: if (tmp[j] > *nrm) *nrm = tmp[j];
1971: }
1972: PetscFree(tmp);
1973: } else if (type == NORM_INFINITY) {
1974: *nrm = 0.0;
1975: for (j=0; j<A->rmap->n; j++) {
1976: v = a->a + a->i[j];
1977: sum = 0.0;
1978: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1979: sum += PetscAbsScalar(*v); v++;
1980: }
1981: if (sum > *nrm) *nrm = sum;
1982: }
1983: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support for two norm");
1984: return(0);
1985: }
1987: /* Merged from MatGetSymbolicTranspose_SeqAIJ() - replace MatGetSymbolicTranspose_SeqAIJ()? */
1990: PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat A,Mat *B)
1991: {
1993: PetscInt i,j,anzj;
1994: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data,*b;
1995: PetscInt an=A->cmap->N,am=A->rmap->N;
1996: PetscInt *ati,*atj,*atfill,*ai=a->i,*aj=a->j;
1999: /* Allocate space for symbolic transpose info and work array */
2000: PetscCalloc1(an+1,&ati);
2001: PetscMalloc1(ai[am],&atj);
2002: PetscMalloc1(an,&atfill);
2004: /* Walk through aj and count ## of non-zeros in each row of A^T. */
2005: /* Note: offset by 1 for fast conversion into csr format. */
2006: for (i=0;i<ai[am];i++) ati[aj[i]+1] += 1;
2007: /* Form ati for csr format of A^T. */
2008: for (i=0;i<an;i++) ati[i+1] += ati[i];
2010: /* Copy ati into atfill so we have locations of the next free space in atj */
2011: PetscMemcpy(atfill,ati,an*sizeof(PetscInt));
2013: /* Walk through A row-wise and mark nonzero entries of A^T. */
2014: for (i=0;i<am;i++) {
2015: anzj = ai[i+1] - ai[i];
2016: for (j=0;j<anzj;j++) {
2017: atj[atfill[*aj]] = i;
2018: atfill[*aj++] += 1;
2019: }
2020: }
2022: /* Clean up temporary space and complete requests. */
2023: PetscFree(atfill);
2024: MatCreateSeqAIJWithArrays(PetscObjectComm((PetscObject)A),an,am,ati,atj,NULL,B);
2025: MatSetBlockSizes(*B,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2027: b = (Mat_SeqAIJ*)((*B)->data);
2028: b->free_a = PETSC_FALSE;
2029: b->free_ij = PETSC_TRUE;
2030: b->nonew = 0;
2031: return(0);
2032: }
2036: PetscErrorCode MatTranspose_SeqAIJ(Mat A,MatReuse reuse,Mat *B)
2037: {
2038: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2039: Mat C;
2041: PetscInt i,*aj = a->j,*ai = a->i,m = A->rmap->n,len,*col;
2042: MatScalar *array = a->a;
2045: if (reuse == MAT_REUSE_MATRIX && A == *B && m != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
2047: if (reuse == MAT_INITIAL_MATRIX || *B == A) {
2048: PetscCalloc1(1+A->cmap->n,&col);
2050: for (i=0; i<ai[m]; i++) col[aj[i]] += 1;
2051: MatCreate(PetscObjectComm((PetscObject)A),&C);
2052: MatSetSizes(C,A->cmap->n,m,A->cmap->n,m);
2053: MatSetBlockSizes(C,PetscAbs(A->cmap->bs),PetscAbs(A->rmap->bs));
2054: MatSetType(C,((PetscObject)A)->type_name);
2055: MatSeqAIJSetPreallocation_SeqAIJ(C,0,col);
2056: PetscFree(col);
2057: } else {
2058: C = *B;
2059: }
2061: for (i=0; i<m; i++) {
2062: len = ai[i+1]-ai[i];
2063: MatSetValues_SeqAIJ(C,len,aj,1,&i,array,INSERT_VALUES);
2064: array += len;
2065: aj += len;
2066: }
2067: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2068: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2070: if (reuse == MAT_INITIAL_MATRIX || *B != A) {
2071: *B = C;
2072: } else {
2073: MatHeaderMerge(A,C);
2074: }
2075: return(0);
2076: }
2080: PetscErrorCode MatIsTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2081: {
2082: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
2083: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2084: MatScalar *va,*vb;
2086: PetscInt ma,na,mb,nb, i;
2089: bij = (Mat_SeqAIJ*) B->data;
2091: MatGetSize(A,&ma,&na);
2092: MatGetSize(B,&mb,&nb);
2093: if (ma!=nb || na!=mb) {
2094: *f = PETSC_FALSE;
2095: return(0);
2096: }
2097: aii = aij->i; bii = bij->i;
2098: adx = aij->j; bdx = bij->j;
2099: va = aij->a; vb = bij->a;
2100: PetscMalloc1(ma,&aptr);
2101: PetscMalloc1(mb,&bptr);
2102: for (i=0; i<ma; i++) aptr[i] = aii[i];
2103: for (i=0; i<mb; i++) bptr[i] = bii[i];
2105: *f = PETSC_TRUE;
2106: for (i=0; i<ma; i++) {
2107: while (aptr[i]<aii[i+1]) {
2108: PetscInt idc,idr;
2109: PetscScalar vc,vr;
2110: /* column/row index/value */
2111: idc = adx[aptr[i]];
2112: idr = bdx[bptr[idc]];
2113: vc = va[aptr[i]];
2114: vr = vb[bptr[idc]];
2115: if (i!=idr || PetscAbsScalar(vc-vr) > tol) {
2116: *f = PETSC_FALSE;
2117: goto done;
2118: } else {
2119: aptr[i]++;
2120: if (B || i!=idc) bptr[idc]++;
2121: }
2122: }
2123: }
2124: done:
2125: PetscFree(aptr);
2126: PetscFree(bptr);
2127: return(0);
2128: }
2132: PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A,Mat B,PetscReal tol,PetscBool *f)
2133: {
2134: Mat_SeqAIJ *aij = (Mat_SeqAIJ*) A->data,*bij = (Mat_SeqAIJ*) A->data;
2135: PetscInt *adx,*bdx,*aii,*bii,*aptr,*bptr;
2136: MatScalar *va,*vb;
2138: PetscInt ma,na,mb,nb, i;
2141: bij = (Mat_SeqAIJ*) B->data;
2143: MatGetSize(A,&ma,&na);
2144: MatGetSize(B,&mb,&nb);
2145: if (ma!=nb || na!=mb) {
2146: *f = PETSC_FALSE;
2147: return(0);
2148: }
2149: aii = aij->i; bii = bij->i;
2150: adx = aij->j; bdx = bij->j;
2151: va = aij->a; vb = bij->a;
2152: PetscMalloc1(ma,&aptr);
2153: PetscMalloc1(mb,&bptr);
2154: for (i=0; i<ma; i++) aptr[i] = aii[i];
2155: for (i=0; i<mb; i++) bptr[i] = bii[i];
2157: *f = PETSC_TRUE;
2158: for (i=0; i<ma; i++) {
2159: while (aptr[i]<aii[i+1]) {
2160: PetscInt idc,idr;
2161: PetscScalar vc,vr;
2162: /* column/row index/value */
2163: idc = adx[aptr[i]];
2164: idr = bdx[bptr[idc]];
2165: vc = va[aptr[i]];
2166: vr = vb[bptr[idc]];
2167: if (i!=idr || PetscAbsScalar(vc-PetscConj(vr)) > tol) {
2168: *f = PETSC_FALSE;
2169: goto done;
2170: } else {
2171: aptr[i]++;
2172: if (B || i!=idc) bptr[idc]++;
2173: }
2174: }
2175: }
2176: done:
2177: PetscFree(aptr);
2178: PetscFree(bptr);
2179: return(0);
2180: }
2184: PetscErrorCode MatIsSymmetric_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2185: {
2189: MatIsTranspose_SeqAIJ(A,A,tol,f);
2190: return(0);
2191: }
2195: PetscErrorCode MatIsHermitian_SeqAIJ(Mat A,PetscReal tol,PetscBool *f)
2196: {
2200: MatIsHermitianTranspose_SeqAIJ(A,A,tol,f);
2201: return(0);
2202: }
2206: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
2207: {
2208: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2209: PetscScalar *l,*r,x;
2210: MatScalar *v;
2212: PetscInt i,j,m = A->rmap->n,n = A->cmap->n,M,nz = a->nz,*jj;
2215: if (ll) {
2216: /* The local size is used so that VecMPI can be passed to this routine
2217: by MatDiagonalScale_MPIAIJ */
2218: VecGetLocalSize(ll,&m);
2219: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
2220: VecGetArray(ll,&l);
2221: v = a->a;
2222: for (i=0; i<m; i++) {
2223: x = l[i];
2224: M = a->i[i+1] - a->i[i];
2225: for (j=0; j<M; j++) (*v++) *= x;
2226: }
2227: VecRestoreArray(ll,&l);
2228: PetscLogFlops(nz);
2229: }
2230: if (rr) {
2231: VecGetLocalSize(rr,&n);
2232: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
2233: VecGetArray(rr,&r);
2234: v = a->a; jj = a->j;
2235: for (i=0; i<nz; i++) (*v++) *= r[*jj++];
2236: VecRestoreArray(rr,&r);
2237: PetscLogFlops(nz);
2238: }
2239: MatSeqAIJInvalidateDiagonal(A);
2240: return(0);
2241: }
2245: PetscErrorCode MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,PetscInt csize,MatReuse scall,Mat *B)
2246: {
2247: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
2249: PetscInt *smap,i,k,kstart,kend,oldcols = A->cmap->n,*lens;
2250: PetscInt row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
2251: const PetscInt *irow,*icol;
2252: PetscInt nrows,ncols;
2253: PetscInt *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
2254: MatScalar *a_new,*mat_a;
2255: Mat C;
2256: PetscBool stride;
2260: ISGetIndices(isrow,&irow);
2261: ISGetLocalSize(isrow,&nrows);
2262: ISGetLocalSize(iscol,&ncols);
2264: PetscObjectTypeCompare((PetscObject)iscol,ISSTRIDE,&stride);
2265: if (stride) {
2266: ISStrideGetInfo(iscol,&first,&step);
2267: } else {
2268: first = 0;
2269: step = 0;
2270: }
2271: if (stride && step == 1) {
2272: /* special case of contiguous rows */
2273: PetscMalloc2(nrows,&lens,nrows,&starts);
2274: /* loop over new rows determining lens and starting points */
2275: for (i=0; i<nrows; i++) {
2276: kstart = ai[irow[i]];
2277: kend = kstart + ailen[irow[i]];
2278: starts[i] = kstart;
2279: for (k=kstart; k<kend; k++) {
2280: if (aj[k] >= first) {
2281: starts[i] = k;
2282: break;
2283: }
2284: }
2285: sum = 0;
2286: while (k < kend) {
2287: if (aj[k++] >= first+ncols) break;
2288: sum++;
2289: }
2290: lens[i] = sum;
2291: }
2292: /* create submatrix */
2293: if (scall == MAT_REUSE_MATRIX) {
2294: PetscInt n_cols,n_rows;
2295: MatGetSize(*B,&n_rows,&n_cols);
2296: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
2297: MatZeroEntries(*B);
2298: C = *B;
2299: } else {
2300: PetscInt rbs,cbs;
2301: MatCreate(PetscObjectComm((PetscObject)A),&C);
2302: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2303: ISGetBlockSize(isrow,&rbs);
2304: ISGetBlockSize(iscol,&cbs);
2305: MatSetBlockSizes(C,rbs,cbs);
2306: MatSetType(C,((PetscObject)A)->type_name);
2307: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2308: }
2309: c = (Mat_SeqAIJ*)C->data;
2311: /* loop over rows inserting into submatrix */
2312: a_new = c->a;
2313: j_new = c->j;
2314: i_new = c->i;
2316: for (i=0; i<nrows; i++) {
2317: ii = starts[i];
2318: lensi = lens[i];
2319: for (k=0; k<lensi; k++) {
2320: *j_new++ = aj[ii+k] - first;
2321: }
2322: PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
2323: a_new += lensi;
2324: i_new[i+1] = i_new[i] + lensi;
2325: c->ilen[i] = lensi;
2326: }
2327: PetscFree2(lens,starts);
2328: } else {
2329: ISGetIndices(iscol,&icol);
2330: PetscCalloc1(oldcols,&smap);
2331: PetscMalloc1(1+nrows,&lens);
2332: for (i=0; i<ncols; i++) {
2333: #if defined(PETSC_USE_DEBUG)
2334: if (icol[i] >= oldcols) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Requesting column beyond largest column icol[%D] %D <= A->cmap->n %D",i,icol[i],oldcols);
2335: #endif
2336: smap[icol[i]] = i+1;
2337: }
2339: /* determine lens of each row */
2340: for (i=0; i<nrows; i++) {
2341: kstart = ai[irow[i]];
2342: kend = kstart + a->ilen[irow[i]];
2343: lens[i] = 0;
2344: for (k=kstart; k<kend; k++) {
2345: if (smap[aj[k]]) {
2346: lens[i]++;
2347: }
2348: }
2349: }
2350: /* Create and fill new matrix */
2351: if (scall == MAT_REUSE_MATRIX) {
2352: PetscBool equal;
2354: c = (Mat_SeqAIJ*)((*B)->data);
2355: if ((*B)->rmap->n != nrows || (*B)->cmap->n != ncols) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
2356: PetscMemcmp(c->ilen,lens,(*B)->rmap->n*sizeof(PetscInt),&equal);
2357: if (!equal) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
2358: PetscMemzero(c->ilen,(*B)->rmap->n*sizeof(PetscInt));
2359: C = *B;
2360: } else {
2361: PetscInt rbs,cbs;
2362: MatCreate(PetscObjectComm((PetscObject)A),&C);
2363: MatSetSizes(C,nrows,ncols,PETSC_DETERMINE,PETSC_DETERMINE);
2364: ISGetBlockSize(isrow,&rbs);
2365: ISGetBlockSize(iscol,&cbs);
2366: MatSetBlockSizes(C,rbs,cbs);
2367: MatSetType(C,((PetscObject)A)->type_name);
2368: MatSeqAIJSetPreallocation_SeqAIJ(C,0,lens);
2369: }
2370: c = (Mat_SeqAIJ*)(C->data);
2371: for (i=0; i<nrows; i++) {
2372: row = irow[i];
2373: kstart = ai[row];
2374: kend = kstart + a->ilen[row];
2375: mat_i = c->i[i];
2376: mat_j = c->j + mat_i;
2377: mat_a = c->a + mat_i;
2378: mat_ilen = c->ilen + i;
2379: for (k=kstart; k<kend; k++) {
2380: if ((tcol=smap[a->j[k]])) {
2381: *mat_j++ = tcol - 1;
2382: *mat_a++ = a->a[k];
2383: (*mat_ilen)++;
2385: }
2386: }
2387: }
2388: /* Free work space */
2389: ISRestoreIndices(iscol,&icol);
2390: PetscFree(smap);
2391: PetscFree(lens);
2392: /* sort */
2393: for (i = 0; i < nrows; i++) {
2394: PetscInt ilen;
2396: mat_i = c->i[i];
2397: mat_j = c->j + mat_i;
2398: mat_a = c->a + mat_i;
2399: ilen = c->ilen[i];
2400: PetscSortIntWithMatScalarArray(ilen,mat_j,mat_a);
2401: }
2402: }
2403: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
2404: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
2406: ISRestoreIndices(isrow,&irow);
2407: *B = C;
2408: return(0);
2409: }
2413: PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat,MPI_Comm subComm,MatReuse scall,Mat *subMat)
2414: {
2416: Mat B;
2419: if (scall == MAT_INITIAL_MATRIX) {
2420: MatCreate(subComm,&B);
2421: MatSetSizes(B,mat->rmap->n,mat->cmap->n,mat->rmap->n,mat->cmap->n);
2422: MatSetBlockSizesFromMats(B,mat,mat);
2423: MatSetType(B,MATSEQAIJ);
2424: MatDuplicateNoCreate_SeqAIJ(B,mat,MAT_COPY_VALUES,PETSC_TRUE);
2425: *subMat = B;
2426: } else {
2427: MatCopy_SeqAIJ(mat,*subMat,SAME_NONZERO_PATTERN);
2428: }
2429: return(0);
2430: }
2434: PetscErrorCode MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,const MatFactorInfo *info)
2435: {
2436: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2438: Mat outA;
2439: PetscBool row_identity,col_identity;
2442: if (info->levels != 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
2444: ISIdentity(row,&row_identity);
2445: ISIdentity(col,&col_identity);
2447: outA = inA;
2448: outA->factortype = MAT_FACTOR_LU;
2450: PetscObjectReference((PetscObject)row);
2451: ISDestroy(&a->row);
2453: a->row = row;
2455: PetscObjectReference((PetscObject)col);
2456: ISDestroy(&a->col);
2458: a->col = col;
2460: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2461: ISDestroy(&a->icol);
2462: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
2463: PetscLogObjectParent((PetscObject)inA,(PetscObject)a->icol);
2465: if (!a->solve_work) { /* this matrix may have been factored before */
2466: PetscMalloc1(inA->rmap->n+1,&a->solve_work);
2467: PetscLogObjectMemory((PetscObject)inA, (inA->rmap->n+1)*sizeof(PetscScalar));
2468: }
2470: MatMarkDiagonal_SeqAIJ(inA);
2471: if (row_identity && col_identity) {
2472: MatLUFactorNumeric_SeqAIJ_inplace(outA,inA,info);
2473: } else {
2474: MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA,inA,info);
2475: }
2476: return(0);
2477: }
2481: PetscErrorCode MatScale_SeqAIJ(Mat inA,PetscScalar alpha)
2482: {
2483: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
2484: PetscScalar oalpha = alpha;
2486: PetscBLASInt one = 1,bnz;
2489: PetscBLASIntCast(a->nz,&bnz);
2490: PetscStackCallBLAS("BLASscal",BLASscal_(&bnz,&oalpha,a->a,&one));
2491: PetscLogFlops(a->nz);
2492: MatSeqAIJInvalidateDiagonal(inA);
2493: return(0);
2494: }
2498: PetscErrorCode MatGetSubMatrices_SeqAIJ(Mat A,PetscInt n,const IS irow[],const IS icol[],MatReuse scall,Mat *B[])
2499: {
2501: PetscInt i;
2504: if (scall == MAT_INITIAL_MATRIX) {
2505: PetscMalloc1(n+1,B);
2506: }
2508: for (i=0; i<n; i++) {
2509: MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
2510: }
2511: return(0);
2512: }
2516: PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A,PetscInt is_max,IS is[],PetscInt ov)
2517: {
2518: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2520: PetscInt row,i,j,k,l,m,n,*nidx,isz,val;
2521: const PetscInt *idx;
2522: PetscInt start,end,*ai,*aj;
2523: PetscBT table;
2526: m = A->rmap->n;
2527: ai = a->i;
2528: aj = a->j;
2530: if (ov < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"illegal negative overlap value used");
2532: PetscMalloc1(m+1,&nidx);
2533: PetscBTCreate(m,&table);
2535: for (i=0; i<is_max; i++) {
2536: /* Initialize the two local arrays */
2537: isz = 0;
2538: PetscBTMemzero(m,table);
2540: /* Extract the indices, assume there can be duplicate entries */
2541: ISGetIndices(is[i],&idx);
2542: ISGetLocalSize(is[i],&n);
2544: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2545: for (j=0; j<n; ++j) {
2546: if (!PetscBTLookupSet(table,idx[j])) nidx[isz++] = idx[j];
2547: }
2548: ISRestoreIndices(is[i],&idx);
2549: ISDestroy(&is[i]);
2551: k = 0;
2552: for (j=0; j<ov; j++) { /* for each overlap */
2553: n = isz;
2554: for (; k<n; k++) { /* do only those rows in nidx[k], which are not done yet */
2555: row = nidx[k];
2556: start = ai[row];
2557: end = ai[row+1];
2558: for (l = start; l<end; l++) {
2559: val = aj[l];
2560: if (!PetscBTLookupSet(table,val)) nidx[isz++] = val;
2561: }
2562: }
2563: }
2564: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,PETSC_COPY_VALUES,(is+i));
2565: }
2566: PetscBTDestroy(&table);
2567: PetscFree(nidx);
2568: return(0);
2569: }
2571: /* -------------------------------------------------------------- */
2574: PetscErrorCode MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
2575: {
2576: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2578: PetscInt i,nz = 0,m = A->rmap->n,n = A->cmap->n;
2579: const PetscInt *row,*col;
2580: PetscInt *cnew,j,*lens;
2581: IS icolp,irowp;
2582: PetscInt *cwork = NULL;
2583: PetscScalar *vwork = NULL;
2586: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
2587: ISGetIndices(irowp,&row);
2588: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
2589: ISGetIndices(icolp,&col);
2591: /* determine lengths of permuted rows */
2592: PetscMalloc1(m+1,&lens);
2593: for (i=0; i<m; i++) lens[row[i]] = a->i[i+1] - a->i[i];
2594: MatCreate(PetscObjectComm((PetscObject)A),B);
2595: MatSetSizes(*B,m,n,m,n);
2596: MatSetBlockSizesFromMats(*B,A,A);
2597: MatSetType(*B,((PetscObject)A)->type_name);
2598: MatSeqAIJSetPreallocation_SeqAIJ(*B,0,lens);
2599: PetscFree(lens);
2601: PetscMalloc1(n,&cnew);
2602: for (i=0; i<m; i++) {
2603: MatGetRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2604: for (j=0; j<nz; j++) cnew[j] = col[cwork[j]];
2605: MatSetValues_SeqAIJ(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
2606: MatRestoreRow_SeqAIJ(A,i,&nz,&cwork,&vwork);
2607: }
2608: PetscFree(cnew);
2610: (*B)->assembled = PETSC_FALSE;
2612: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
2613: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
2614: ISRestoreIndices(irowp,&row);
2615: ISRestoreIndices(icolp,&col);
2616: ISDestroy(&irowp);
2617: ISDestroy(&icolp);
2618: return(0);
2619: }
2623: PetscErrorCode MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
2624: {
2628: /* If the two matrices have the same copy implementation, use fast copy. */
2629: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2630: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2631: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
2633: if (a->i[A->rmap->n] != b->i[B->rmap->n]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
2634: PetscMemcpy(b->a,a->a,(a->i[A->rmap->n])*sizeof(PetscScalar));
2635: } else {
2636: MatCopy_Basic(A,B,str);
2637: }
2638: return(0);
2639: }
2643: PetscErrorCode MatSetUp_SeqAIJ(Mat A)
2644: {
2648: MatSeqAIJSetPreallocation_SeqAIJ(A,PETSC_DEFAULT,0);
2649: return(0);
2650: }
2654: PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A,PetscScalar *array[])
2655: {
2656: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2659: *array = a->a;
2660: return(0);
2661: }
2665: PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A,PetscScalar *array[])
2666: {
2668: return(0);
2669: }
2671: /*
2672: Computes the number of nonzeros per row needed for preallocation when X and Y
2673: have different nonzero structure.
2674: */
2677: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m,const PetscInt *xi,const PetscInt *xj,const PetscInt *yi,const PetscInt *yj,PetscInt *nnz)
2678: {
2679: PetscInt i,j,k,nzx,nzy;
2682: /* Set the number of nonzeros in the new matrix */
2683: for (i=0; i<m; i++) {
2684: const PetscInt *xjj = xj+xi[i],*yjj = yj+yi[i];
2685: nzx = xi[i+1] - xi[i];
2686: nzy = yi[i+1] - yi[i];
2687: nnz[i] = 0;
2688: for (j=0,k=0; j<nzx; j++) { /* Point in X */
2689: for (; k<nzy && yjj[k]<xjj[j]; k++) nnz[i]++; /* Catch up to X */
2690: if (k<nzy && yjj[k]==xjj[j]) k++; /* Skip duplicate */
2691: nnz[i]++;
2692: }
2693: for (; k<nzy; k++) nnz[i]++;
2694: }
2695: return(0);
2696: }
2700: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y,Mat X,PetscInt *nnz)
2701: {
2702: PetscInt m = Y->rmap->N;
2703: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data;
2704: Mat_SeqAIJ *y = (Mat_SeqAIJ*)Y->data;
2708: /* Set the number of nonzeros in the new matrix */
2709: MatAXPYGetPreallocation_SeqX_private(m,x->i,x->j,y->i,y->j,nnz);
2710: return(0);
2711: }
2715: PetscErrorCode MatAXPY_SeqAIJ(Mat Y,PetscScalar a,Mat X,MatStructure str)
2716: {
2718: Mat_SeqAIJ *x = (Mat_SeqAIJ*)X->data,*y = (Mat_SeqAIJ*)Y->data;
2719: PetscBLASInt one=1,bnz;
2722: PetscBLASIntCast(x->nz,&bnz);
2723: if (str == SAME_NONZERO_PATTERN) {
2724: PetscScalar alpha = a;
2725: PetscStackCallBLAS("BLASaxpy",BLASaxpy_(&bnz,&alpha,x->a,&one,y->a,&one));
2726: MatSeqAIJInvalidateDiagonal(Y);
2727: PetscObjectStateIncrease((PetscObject)Y);
2728: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
2729: MatAXPY_Basic(Y,a,X,str);
2730: } else {
2731: Mat B;
2732: PetscInt *nnz;
2733: PetscMalloc1(Y->rmap->N,&nnz);
2734: MatCreate(PetscObjectComm((PetscObject)Y),&B);
2735: PetscObjectSetName((PetscObject)B,((PetscObject)Y)->name);
2736: MatSetSizes(B,Y->rmap->n,Y->cmap->n,Y->rmap->N,Y->cmap->N);
2737: MatSetBlockSizesFromMats(B,Y,Y);
2738: MatSetType(B,(MatType) ((PetscObject)Y)->type_name);
2739: MatAXPYGetPreallocation_SeqAIJ(Y,X,nnz);
2740: MatSeqAIJSetPreallocation(B,0,nnz);
2741: MatAXPY_BasicWithPreallocation(B,Y,a,X,str);
2742: MatHeaderReplace(Y,B);
2743: PetscFree(nnz);
2744: }
2745: return(0);
2746: }
2750: PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
2751: {
2752: #if defined(PETSC_USE_COMPLEX)
2753: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2754: PetscInt i,nz;
2755: PetscScalar *a;
2758: nz = aij->nz;
2759: a = aij->a;
2760: for (i=0; i<nz; i++) a[i] = PetscConj(a[i]);
2761: #else
2763: #endif
2764: return(0);
2765: }
2769: PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2770: {
2771: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2773: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2774: PetscReal atmp;
2775: PetscScalar *x;
2776: MatScalar *aa;
2779: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2780: aa = a->a;
2781: ai = a->i;
2782: aj = a->j;
2784: VecSet(v,0.0);
2785: VecGetArray(v,&x);
2786: VecGetLocalSize(v,&n);
2787: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2788: for (i=0; i<m; i++) {
2789: ncols = ai[1] - ai[0]; ai++;
2790: x[i] = 0.0;
2791: for (j=0; j<ncols; j++) {
2792: atmp = PetscAbsScalar(*aa);
2793: if (PetscAbsScalar(x[i]) < atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2794: aa++; aj++;
2795: }
2796: }
2797: VecRestoreArray(v,&x);
2798: return(0);
2799: }
2803: PetscErrorCode MatGetRowMax_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2804: {
2805: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2807: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2808: PetscScalar *x;
2809: MatScalar *aa;
2812: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2813: aa = a->a;
2814: ai = a->i;
2815: aj = a->j;
2817: VecSet(v,0.0);
2818: VecGetArray(v,&x);
2819: VecGetLocalSize(v,&n);
2820: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2821: for (i=0; i<m; i++) {
2822: ncols = ai[1] - ai[0]; ai++;
2823: if (ncols == A->cmap->n) { /* row is dense */
2824: x[i] = *aa; if (idx) idx[i] = 0;
2825: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
2826: x[i] = 0.0;
2827: if (idx) {
2828: idx[i] = 0; /* in case ncols is zero */
2829: for (j=0;j<ncols;j++) { /* find first implicit 0.0 in the row */
2830: if (aj[j] > j) {
2831: idx[i] = j;
2832: break;
2833: }
2834: }
2835: }
2836: }
2837: for (j=0; j<ncols; j++) {
2838: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2839: aa++; aj++;
2840: }
2841: }
2842: VecRestoreArray(v,&x);
2843: return(0);
2844: }
2848: PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2849: {
2850: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2852: PetscInt i,j,m = A->rmap->n,*ai,*aj,ncols,n;
2853: PetscReal atmp;
2854: PetscScalar *x;
2855: MatScalar *aa;
2858: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2859: aa = a->a;
2860: ai = a->i;
2861: aj = a->j;
2863: VecSet(v,0.0);
2864: VecGetArray(v,&x);
2865: VecGetLocalSize(v,&n);
2866: if (n != A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector, %D vs. %D rows", A->rmap->n, n);
2867: for (i=0; i<m; i++) {
2868: ncols = ai[1] - ai[0]; ai++;
2869: if (ncols) {
2870: /* Get first nonzero */
2871: for (j = 0; j < ncols; j++) {
2872: atmp = PetscAbsScalar(aa[j]);
2873: if (atmp > 1.0e-12) {
2874: x[i] = atmp;
2875: if (idx) idx[i] = aj[j];
2876: break;
2877: }
2878: }
2879: if (j == ncols) {x[i] = PetscAbsScalar(*aa); if (idx) idx[i] = *aj;}
2880: } else {
2881: x[i] = 0.0; if (idx) idx[i] = 0;
2882: }
2883: for (j = 0; j < ncols; j++) {
2884: atmp = PetscAbsScalar(*aa);
2885: if (atmp > 1.0e-12 && PetscAbsScalar(x[i]) > atmp) {x[i] = atmp; if (idx) idx[i] = *aj;}
2886: aa++; aj++;
2887: }
2888: }
2889: VecRestoreArray(v,&x);
2890: return(0);
2891: }
2895: PetscErrorCode MatGetRowMin_SeqAIJ(Mat A,Vec v,PetscInt idx[])
2896: {
2897: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2898: PetscErrorCode ierr;
2899: PetscInt i,j,m = A->rmap->n,ncols,n;
2900: const PetscInt *ai,*aj;
2901: PetscScalar *x;
2902: const MatScalar *aa;
2905: if (A->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2906: aa = a->a;
2907: ai = a->i;
2908: aj = a->j;
2910: VecSet(v,0.0);
2911: VecGetArray(v,&x);
2912: VecGetLocalSize(v,&n);
2913: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
2914: for (i=0; i<m; i++) {
2915: ncols = ai[1] - ai[0]; ai++;
2916: if (ncols == A->cmap->n) { /* row is dense */
2917: x[i] = *aa; if (idx) idx[i] = 0;
2918: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
2919: x[i] = 0.0;
2920: if (idx) { /* find first implicit 0.0 in the row */
2921: idx[i] = 0; /* in case ncols is zero */
2922: for (j=0; j<ncols; j++) {
2923: if (aj[j] > j) {
2924: idx[i] = j;
2925: break;
2926: }
2927: }
2928: }
2929: }
2930: for (j=0; j<ncols; j++) {
2931: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {x[i] = *aa; if (idx) idx[i] = *aj;}
2932: aa++; aj++;
2933: }
2934: }
2935: VecRestoreArray(v,&x);
2936: return(0);
2937: }
2939: #include <petscblaslapack.h>
2940: #include <petsc/private/kernels/blockinvert.h>
2944: PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A,const PetscScalar **values)
2945: {
2946: Mat_SeqAIJ *a = (Mat_SeqAIJ*) A->data;
2948: PetscInt i,bs = PetscAbs(A->rmap->bs),mbs = A->rmap->n/bs,ipvt[5],bs2 = bs*bs,*v_pivots,ij[7],*IJ,j;
2949: MatScalar *diag,work[25],*v_work;
2950: PetscReal shift = 0.0;
2953: if (a->ibdiagvalid) {
2954: if (values) *values = a->ibdiag;
2955: return(0);
2956: }
2957: MatMarkDiagonal_SeqAIJ(A);
2958: if (!a->ibdiag) {
2959: PetscMalloc1(bs2*mbs,&a->ibdiag);
2960: PetscLogObjectMemory((PetscObject)A,bs2*mbs*sizeof(PetscScalar));
2961: }
2962: diag = a->ibdiag;
2963: if (values) *values = a->ibdiag;
2964: /* factor and invert each block */
2965: switch (bs) {
2966: case 1:
2967: for (i=0; i<mbs; i++) {
2968: MatGetValues(A,1,&i,1,&i,diag+i);
2969: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
2970: }
2971: break;
2972: case 2:
2973: for (i=0; i<mbs; i++) {
2974: ij[0] = 2*i; ij[1] = 2*i + 1;
2975: MatGetValues(A,2,ij,2,ij,diag);
2976: PetscKernel_A_gets_inverse_A_2(diag,shift);
2977: PetscKernel_A_gets_transpose_A_2(diag);
2978: diag += 4;
2979: }
2980: break;
2981: case 3:
2982: for (i=0; i<mbs; i++) {
2983: ij[0] = 3*i; ij[1] = 3*i + 1; ij[2] = 3*i + 2;
2984: MatGetValues(A,3,ij,3,ij,diag);
2985: PetscKernel_A_gets_inverse_A_3(diag,shift);
2986: PetscKernel_A_gets_transpose_A_3(diag);
2987: diag += 9;
2988: }
2989: break;
2990: case 4:
2991: for (i=0; i<mbs; i++) {
2992: ij[0] = 4*i; ij[1] = 4*i + 1; ij[2] = 4*i + 2; ij[3] = 4*i + 3;
2993: MatGetValues(A,4,ij,4,ij,diag);
2994: PetscKernel_A_gets_inverse_A_4(diag,shift);
2995: PetscKernel_A_gets_transpose_A_4(diag);
2996: diag += 16;
2997: }
2998: break;
2999: case 5:
3000: for (i=0; i<mbs; i++) {
3001: ij[0] = 5*i; ij[1] = 5*i + 1; ij[2] = 5*i + 2; ij[3] = 5*i + 3; ij[4] = 5*i + 4;
3002: MatGetValues(A,5,ij,5,ij,diag);
3003: PetscKernel_A_gets_inverse_A_5(diag,ipvt,work,shift);
3004: PetscKernel_A_gets_transpose_A_5(diag);
3005: diag += 25;
3006: }
3007: break;
3008: case 6:
3009: for (i=0; i<mbs; i++) {
3010: ij[0] = 6*i; ij[1] = 6*i + 1; ij[2] = 6*i + 2; ij[3] = 6*i + 3; ij[4] = 6*i + 4; ij[5] = 6*i + 5;
3011: MatGetValues(A,6,ij,6,ij,diag);
3012: PetscKernel_A_gets_inverse_A_6(diag,shift);
3013: PetscKernel_A_gets_transpose_A_6(diag);
3014: diag += 36;
3015: }
3016: break;
3017: case 7:
3018: for (i=0; i<mbs; i++) {
3019: ij[0] = 7*i; ij[1] = 7*i + 1; ij[2] = 7*i + 2; ij[3] = 7*i + 3; ij[4] = 7*i + 4; ij[5] = 7*i + 5; ij[5] = 7*i + 6;
3020: MatGetValues(A,7,ij,7,ij,diag);
3021: PetscKernel_A_gets_inverse_A_7(diag,shift);
3022: PetscKernel_A_gets_transpose_A_7(diag);
3023: diag += 49;
3024: }
3025: break;
3026: default:
3027: PetscMalloc3(bs,&v_work,bs,&v_pivots,bs,&IJ);
3028: for (i=0; i<mbs; i++) {
3029: for (j=0; j<bs; j++) {
3030: IJ[j] = bs*i + j;
3031: }
3032: MatGetValues(A,bs,IJ,bs,IJ,diag);
3033: PetscKernel_A_gets_inverse_A(bs,diag,v_pivots,v_work);
3034: PetscKernel_A_gets_transpose_A_N(diag,bs);
3035: diag += bs2;
3036: }
3037: PetscFree3(v_work,v_pivots,IJ);
3038: }
3039: a->ibdiagvalid = PETSC_TRUE;
3040: return(0);
3041: }
3045: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x,PetscRandom rctx)
3046: {
3048: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)x->data;
3049: PetscScalar a;
3050: PetscInt m,n,i,j,col;
3053: if (!x->assembled) {
3054: MatGetSize(x,&m,&n);
3055: for (i=0; i<m; i++) {
3056: for (j=0; j<aij->imax[i]; j++) {
3057: PetscRandomGetValue(rctx,&a);
3058: col = (PetscInt)(n*PetscRealPart(a));
3059: MatSetValues(x,1,&i,1,&col,&a,ADD_VALUES);
3060: }
3061: }
3062: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Not yet coded");
3063: MatAssemblyBegin(x,MAT_FINAL_ASSEMBLY);
3064: MatAssemblyEnd(x,MAT_FINAL_ASSEMBLY);
3065: return(0);
3066: }
3070: PetscErrorCode MatShift_SeqAIJ(Mat Y,PetscScalar a)
3071: {
3073: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)Y->data;
3076: if (!aij->nz) {
3077: MatSeqAIJSetPreallocation(Y,1,NULL);
3078: }
3079: MatShift_Basic(Y,a);
3080: return(0);
3081: }
3083: /* -------------------------------------------------------------------*/
3084: static struct _MatOps MatOps_Values = { MatSetValues_SeqAIJ,
3085: MatGetRow_SeqAIJ,
3086: MatRestoreRow_SeqAIJ,
3087: MatMult_SeqAIJ,
3088: /* 4*/ MatMultAdd_SeqAIJ,
3089: MatMultTranspose_SeqAIJ,
3090: MatMultTransposeAdd_SeqAIJ,
3091: 0,
3092: 0,
3093: 0,
3094: /* 10*/ 0,
3095: MatLUFactor_SeqAIJ,
3096: 0,
3097: MatSOR_SeqAIJ,
3098: MatTranspose_SeqAIJ,
3099: /*1 5*/ MatGetInfo_SeqAIJ,
3100: MatEqual_SeqAIJ,
3101: MatGetDiagonal_SeqAIJ,
3102: MatDiagonalScale_SeqAIJ,
3103: MatNorm_SeqAIJ,
3104: /* 20*/ 0,
3105: MatAssemblyEnd_SeqAIJ,
3106: MatSetOption_SeqAIJ,
3107: MatZeroEntries_SeqAIJ,
3108: /* 24*/ MatZeroRows_SeqAIJ,
3109: 0,
3110: 0,
3111: 0,
3112: 0,
3113: /* 29*/ MatSetUp_SeqAIJ,
3114: 0,
3115: 0,
3116: 0,
3117: 0,
3118: /* 34*/ MatDuplicate_SeqAIJ,
3119: 0,
3120: 0,
3121: MatILUFactor_SeqAIJ,
3122: 0,
3123: /* 39*/ MatAXPY_SeqAIJ,
3124: MatGetSubMatrices_SeqAIJ,
3125: MatIncreaseOverlap_SeqAIJ,
3126: MatGetValues_SeqAIJ,
3127: MatCopy_SeqAIJ,
3128: /* 44*/ MatGetRowMax_SeqAIJ,
3129: MatScale_SeqAIJ,
3130: MatShift_SeqAIJ,
3131: MatDiagonalSet_SeqAIJ,
3132: MatZeroRowsColumns_SeqAIJ,
3133: /* 49*/ MatSetRandom_SeqAIJ,
3134: MatGetRowIJ_SeqAIJ,
3135: MatRestoreRowIJ_SeqAIJ,
3136: MatGetColumnIJ_SeqAIJ,
3137: MatRestoreColumnIJ_SeqAIJ,
3138: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3139: 0,
3140: 0,
3141: MatPermute_SeqAIJ,
3142: 0,
3143: /* 59*/ 0,
3144: MatDestroy_SeqAIJ,
3145: MatView_SeqAIJ,
3146: 0,
3147: MatMatMatMult_SeqAIJ_SeqAIJ_SeqAIJ,
3148: /* 64*/ MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ,
3149: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3150: 0,
3151: 0,
3152: 0,
3153: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3154: MatGetRowMinAbs_SeqAIJ,
3155: 0,
3156: MatSetColoring_SeqAIJ,
3157: 0,
3158: /* 74*/ MatSetValuesAdifor_SeqAIJ,
3159: MatFDColoringApply_AIJ,
3160: 0,
3161: 0,
3162: 0,
3163: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3164: 0,
3165: 0,
3166: 0,
3167: MatLoad_SeqAIJ,
3168: /* 84*/ MatIsSymmetric_SeqAIJ,
3169: MatIsHermitian_SeqAIJ,
3170: 0,
3171: 0,
3172: 0,
3173: /* 89*/ MatMatMult_SeqAIJ_SeqAIJ,
3174: MatMatMultSymbolic_SeqAIJ_SeqAIJ,
3175: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3176: MatPtAP_SeqAIJ_SeqAIJ,
3177: MatPtAPSymbolic_SeqAIJ_SeqAIJ_DenseAxpy,
3178: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ,
3179: MatMatTransposeMult_SeqAIJ_SeqAIJ,
3180: MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ,
3181: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3182: 0,
3183: /* 99*/ 0,
3184: 0,
3185: 0,
3186: MatConjugate_SeqAIJ,
3187: 0,
3188: /*104*/ MatSetValuesRow_SeqAIJ,
3189: MatRealPart_SeqAIJ,
3190: MatImaginaryPart_SeqAIJ,
3191: 0,
3192: 0,
3193: /*109*/ MatMatSolve_SeqAIJ,
3194: 0,
3195: MatGetRowMin_SeqAIJ,
3196: 0,
3197: MatMissingDiagonal_SeqAIJ,
3198: /*114*/ 0,
3199: 0,
3200: 0,
3201: 0,
3202: 0,
3203: /*119*/ 0,
3204: 0,
3205: 0,
3206: 0,
3207: MatGetMultiProcBlock_SeqAIJ,
3208: /*124*/ MatFindNonzeroRows_SeqAIJ,
3209: MatGetColumnNorms_SeqAIJ,
3210: MatInvertBlockDiagonal_SeqAIJ,
3211: 0,
3212: 0,
3213: /*129*/ 0,
3214: MatTransposeMatMult_SeqAIJ_SeqAIJ,
3215: MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ,
3216: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3217: MatTransposeColoringCreate_SeqAIJ,
3218: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3219: MatTransColoringApplyDenToSp_SeqAIJ,
3220: MatRARt_SeqAIJ_SeqAIJ,
3221: MatRARtSymbolic_SeqAIJ_SeqAIJ,
3222: MatRARtNumeric_SeqAIJ_SeqAIJ,
3223: /*139*/0,
3224: 0,
3225: 0,
3226: MatFDColoringSetUp_SeqXAIJ,
3227: MatFindOffBlockDiagonalEntries_SeqAIJ,
3228: /*144*/MatCreateMPIMatConcatenateSeqMat_SeqAIJ
3229: };
3233: PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,PetscInt *indices)
3234: {
3235: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3236: PetscInt i,nz,n;
3239: nz = aij->maxnz;
3240: n = mat->rmap->n;
3241: for (i=0; i<nz; i++) {
3242: aij->j[i] = indices[i];
3243: }
3244: aij->nz = nz;
3245: for (i=0; i<n; i++) {
3246: aij->ilen[i] = aij->imax[i];
3247: }
3248: return(0);
3249: }
3253: /*@
3254: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3255: in the matrix.
3257: Input Parameters:
3258: + mat - the SeqAIJ matrix
3259: - indices - the column indices
3261: Level: advanced
3263: Notes:
3264: This can be called if you have precomputed the nonzero structure of the
3265: matrix and want to provide it to the matrix object to improve the performance
3266: of the MatSetValues() operation.
3268: You MUST have set the correct numbers of nonzeros per row in the call to
3269: MatCreateSeqAIJ(), and the columns indices MUST be sorted.
3271: MUST be called before any calls to MatSetValues();
3273: The indices should start with zero, not one.
3275: @*/
3276: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat,PetscInt *indices)
3277: {
3283: PetscUseMethod(mat,"MatSeqAIJSetColumnIndices_C",(Mat,PetscInt*),(mat,indices));
3284: return(0);
3285: }
3287: /* ----------------------------------------------------------------------------------------*/
3291: PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3292: {
3293: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3295: size_t nz = aij->i[mat->rmap->n];
3298: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3300: /* allocate space for values if not already there */
3301: if (!aij->saved_values) {
3302: PetscMalloc1(nz+1,&aij->saved_values);
3303: PetscLogObjectMemory((PetscObject)mat,(nz+1)*sizeof(PetscScalar));
3304: }
3306: /* copy values over */
3307: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
3308: return(0);
3309: }
3313: /*@
3314: MatStoreValues - Stashes a copy of the matrix values; this allows, for
3315: example, reuse of the linear part of a Jacobian, while recomputing the
3316: nonlinear portion.
3318: Collect on Mat
3320: Input Parameters:
3321: . mat - the matrix (currently only AIJ matrices support this option)
3323: Level: advanced
3325: Common Usage, with SNESSolve():
3326: $ Create Jacobian matrix
3327: $ Set linear terms into matrix
3328: $ Apply boundary conditions to matrix, at this time matrix must have
3329: $ final nonzero structure (i.e. setting the nonlinear terms and applying
3330: $ boundary conditions again will not change the nonzero structure
3331: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3332: $ MatStoreValues(mat);
3333: $ Call SNESSetJacobian() with matrix
3334: $ In your Jacobian routine
3335: $ MatRetrieveValues(mat);
3336: $ Set nonlinear terms in matrix
3338: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
3339: $ // build linear portion of Jacobian
3340: $ MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);
3341: $ MatStoreValues(mat);
3342: $ loop over nonlinear iterations
3343: $ MatRetrieveValues(mat);
3344: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3345: $ // call MatAssemblyBegin/End() on matrix
3346: $ Solve linear system with Jacobian
3347: $ endloop
3349: Notes:
3350: Matrix must already be assemblied before calling this routine
3351: Must set the matrix option MatSetOption(mat,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE); before
3352: calling this routine.
3354: When this is called multiple times it overwrites the previous set of stored values
3355: and does not allocated additional space.
3357: .seealso: MatRetrieveValues()
3359: @*/
3360: PetscErrorCode MatStoreValues(Mat mat)
3361: {
3366: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3367: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3368: PetscUseMethod(mat,"MatStoreValues_C",(Mat),(mat));
3369: return(0);
3370: }
3374: PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3375: {
3376: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
3378: PetscInt nz = aij->i[mat->rmap->n];
3381: if (!aij->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3382: if (!aij->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
3383: /* copy values over */
3384: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
3385: return(0);
3386: }
3390: /*@
3391: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
3392: example, reuse of the linear part of a Jacobian, while recomputing the
3393: nonlinear portion.
3395: Collect on Mat
3397: Input Parameters:
3398: . mat - the matrix (currently on AIJ matrices support this option)
3400: Level: advanced
3402: .seealso: MatStoreValues()
3404: @*/
3405: PetscErrorCode MatRetrieveValues(Mat mat)
3406: {
3411: if (!mat->assembled) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
3412: if (mat->factortype) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
3413: PetscUseMethod(mat,"MatRetrieveValues_C",(Mat),(mat));
3414: return(0);
3415: }
3418: /* --------------------------------------------------------------------------------*/
3421: /*@C
3422: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
3423: (the default parallel PETSc format). For good matrix assembly performance
3424: the user should preallocate the matrix storage by setting the parameter nz
3425: (or the array nnz). By setting these parameters accurately, performance
3426: during matrix assembly can be increased by more than a factor of 50.
3428: Collective on MPI_Comm
3430: Input Parameters:
3431: + comm - MPI communicator, set to PETSC_COMM_SELF
3432: . m - number of rows
3433: . n - number of columns
3434: . nz - number of nonzeros per row (same for all rows)
3435: - nnz - array containing the number of nonzeros in the various rows
3436: (possibly different for each row) or NULL
3438: Output Parameter:
3439: . A - the matrix
3441: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
3442: MatXXXXSetPreallocation() paradgm instead of this routine directly.
3443: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
3445: Notes:
3446: If nnz is given then nz is ignored
3448: The AIJ format (also called the Yale sparse matrix format or
3449: compressed row storage), is fully compatible with standard Fortran 77
3450: storage. That is, the stored row and column indices can begin at
3451: either one (as in Fortran) or zero. See the users' manual for details.
3453: Specify the preallocated storage with either nz or nnz (not both).
3454: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3455: allocation. For large problems you MUST preallocate memory or you
3456: will get TERRIBLE performance, see the users' manual chapter on matrices.
3458: By default, this format uses inodes (identical nodes) when possible, to
3459: improve numerical efficiency of matrix-vector products and solves. We
3460: search for consecutive rows with the same nonzero structure, thereby
3461: reusing matrix information to achieve increased efficiency.
3463: Options Database Keys:
3464: + -mat_no_inode - Do not use inodes
3465: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3467: Level: intermediate
3469: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
3471: @*/
3472: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
3473: {
3477: MatCreate(comm,A);
3478: MatSetSizes(*A,m,n,m,n);
3479: MatSetType(*A,MATSEQAIJ);
3480: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,nnz);
3481: return(0);
3482: }
3486: /*@C
3487: MatSeqAIJSetPreallocation - For good matrix assembly performance
3488: the user should preallocate the matrix storage by setting the parameter nz
3489: (or the array nnz). By setting these parameters accurately, performance
3490: during matrix assembly can be increased by more than a factor of 50.
3492: Collective on MPI_Comm
3494: Input Parameters:
3495: + B - The matrix
3496: . nz - number of nonzeros per row (same for all rows)
3497: - nnz - array containing the number of nonzeros in the various rows
3498: (possibly different for each row) or NULL
3500: Notes:
3501: If nnz is given then nz is ignored
3503: The AIJ format (also called the Yale sparse matrix format or
3504: compressed row storage), is fully compatible with standard Fortran 77
3505: storage. That is, the stored row and column indices can begin at
3506: either one (as in Fortran) or zero. See the users' manual for details.
3508: Specify the preallocated storage with either nz or nnz (not both).
3509: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
3510: allocation. For large problems you MUST preallocate memory or you
3511: will get TERRIBLE performance, see the users' manual chapter on matrices.
3513: You can call MatGetInfo() to get information on how effective the preallocation was;
3514: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3515: You can also run with the option -info and look for messages with the string
3516: malloc in them to see if additional memory allocation was needed.
3518: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
3519: entries or columns indices
3521: By default, this format uses inodes (identical nodes) when possible, to
3522: improve numerical efficiency of matrix-vector products and solves. We
3523: search for consecutive rows with the same nonzero structure, thereby
3524: reusing matrix information to achieve increased efficiency.
3526: Options Database Keys:
3527: + -mat_no_inode - Do not use inodes
3528: . -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3529: - -mat_aij_oneindex - Internally use indexing starting at 1
3530: rather than 0. Note that when calling MatSetValues(),
3531: the user still MUST index entries starting at 0!
3533: Level: intermediate
3535: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatGetInfo()
3537: @*/
3538: PetscErrorCode MatSeqAIJSetPreallocation(Mat B,PetscInt nz,const PetscInt nnz[])
3539: {
3545: PetscTryMethod(B,"MatSeqAIJSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,nz,nnz));
3546: return(0);
3547: }
3551: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B,PetscInt nz,const PetscInt *nnz)
3552: {
3553: Mat_SeqAIJ *b;
3554: PetscBool skipallocation = PETSC_FALSE,realalloc = PETSC_FALSE;
3556: PetscInt i;
3559: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3560: if (nz == MAT_SKIP_ALLOCATION) {
3561: skipallocation = PETSC_TRUE;
3562: nz = 0;
3563: }
3565: PetscLayoutSetUp(B->rmap);
3566: PetscLayoutSetUp(B->cmap);
3568: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3569: if (nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %D",nz);
3570: if (nnz) {
3571: for (i=0; i<B->rmap->n; i++) {
3572: if (nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %D value %D",i,nnz[i]);
3573: if (nnz[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %D value %d rowlength %D",i,nnz[i],B->cmap->n);
3574: }
3575: }
3577: B->preallocated = PETSC_TRUE;
3579: b = (Mat_SeqAIJ*)B->data;
3581: if (!skipallocation) {
3582: if (!b->imax) {
3583: PetscMalloc2(B->rmap->n,&b->imax,B->rmap->n,&b->ilen);
3584: PetscLogObjectMemory((PetscObject)B,2*B->rmap->n*sizeof(PetscInt));
3585: }
3586: if (!nnz) {
3587: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
3588: else if (nz < 0) nz = 1;
3589: for (i=0; i<B->rmap->n; i++) b->imax[i] = nz;
3590: nz = nz*B->rmap->n;
3591: } else {
3592: nz = 0;
3593: for (i=0; i<B->rmap->n; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
3594: }
3595: /* b->ilen will count nonzeros in each row so far. */
3596: for (i=0; i<B->rmap->n; i++) b->ilen[i] = 0;
3598: /* allocate the matrix space */
3599: /* FIXME: should B's old memory be unlogged? */
3600: MatSeqXAIJFreeAIJ(B,&b->a,&b->j,&b->i);
3601: PetscMalloc3(nz,&b->a,nz,&b->j,B->rmap->n+1,&b->i);
3602: PetscLogObjectMemory((PetscObject)B,(B->rmap->n+1)*sizeof(PetscInt)+nz*(sizeof(PetscScalar)+sizeof(PetscInt)));
3603: b->i[0] = 0;
3604: for (i=1; i<B->rmap->n+1; i++) {
3605: b->i[i] = b->i[i-1] + b->imax[i-1];
3606: }
3607: b->singlemalloc = PETSC_TRUE;
3608: b->free_a = PETSC_TRUE;
3609: b->free_ij = PETSC_TRUE;
3610: } else {
3611: b->free_a = PETSC_FALSE;
3612: b->free_ij = PETSC_FALSE;
3613: }
3615: b->nz = 0;
3616: b->maxnz = nz;
3617: B->info.nz_unneeded = (double)b->maxnz;
3618: if (realalloc) {
3619: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
3620: }
3621: return(0);
3622: }
3624: #undef __FUNCT__
3626: /*@
3627: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in AIJ format.
3629: Input Parameters:
3630: + B - the matrix
3631: . i - the indices into j for the start of each row (starts with zero)
3632: . j - the column indices for each row (starts with zero) these must be sorted for each row
3633: - v - optional values in the matrix
3635: Level: developer
3637: The i,j,v values are COPIED with this routine; to avoid the copy use MatCreateSeqAIJWithArrays()
3639: .keywords: matrix, aij, compressed row, sparse, sequential
3641: .seealso: MatCreate(), MatCreateSeqAIJ(), MatSetValues(), MatSeqAIJSetPreallocation(), MatCreateSeqAIJ(), SeqAIJ
3642: @*/
3643: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B,const PetscInt i[],const PetscInt j[],const PetscScalar v[])
3644: {
3650: PetscTryMethod(B,"MatSeqAIJSetPreallocationCSR_C",(Mat,const PetscInt[],const PetscInt[],const PetscScalar[]),(B,i,j,v));
3651: return(0);
3652: }
3654: #undef __FUNCT__
3656: PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B,const PetscInt Ii[],const PetscInt J[],const PetscScalar v[])
3657: {
3658: PetscInt i;
3659: PetscInt m,n;
3660: PetscInt nz;
3661: PetscInt *nnz, nz_max = 0;
3662: PetscScalar *values;
3666: if (Ii[0]) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %D", Ii[0]);
3668: PetscLayoutSetUp(B->rmap);
3669: PetscLayoutSetUp(B->cmap);
3671: MatGetSize(B, &m, &n);
3672: PetscMalloc1(m+1, &nnz);
3673: for (i = 0; i < m; i++) {
3674: nz = Ii[i+1]- Ii[i];
3675: nz_max = PetscMax(nz_max, nz);
3676: if (nz < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE, "Local row %D has a negative number of columns %D", i, nnz);
3677: nnz[i] = nz;
3678: }
3679: MatSeqAIJSetPreallocation(B, 0, nnz);
3680: PetscFree(nnz);
3682: if (v) {
3683: values = (PetscScalar*) v;
3684: } else {
3685: PetscCalloc1(nz_max, &values);
3686: }
3688: for (i = 0; i < m; i++) {
3689: nz = Ii[i+1] - Ii[i];
3690: MatSetValues_SeqAIJ(B, 1, &i, nz, J+Ii[i], values + (v ? Ii[i] : 0), INSERT_VALUES);
3691: }
3693: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
3694: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
3696: if (!v) {
3697: PetscFree(values);
3698: }
3699: MatSetOption(B,MAT_NEW_NONZERO_LOCATION_ERR,PETSC_TRUE);
3700: return(0);
3701: }
3703: #include <../src/mat/impls/dense/seq/dense.h>
3704: #include <petsc/private/kernels/petscaxpy.h>
3708: /*
3709: Computes (B'*A')' since computing B*A directly is untenable
3711: n p p
3712: ( ) ( ) ( )
3713: m ( A ) * n ( B ) = m ( C )
3714: ( ) ( ) ( )
3716: */
3717: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A,Mat B,Mat C)
3718: {
3719: PetscErrorCode ierr;
3720: Mat_SeqDense *sub_a = (Mat_SeqDense*)A->data;
3721: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ*)B->data;
3722: Mat_SeqDense *sub_c = (Mat_SeqDense*)C->data;
3723: PetscInt i,n,m,q,p;
3724: const PetscInt *ii,*idx;
3725: const PetscScalar *b,*a,*a_q;
3726: PetscScalar *c,*c_q;
3729: m = A->rmap->n;
3730: n = A->cmap->n;
3731: p = B->cmap->n;
3732: a = sub_a->v;
3733: b = sub_b->a;
3734: c = sub_c->v;
3735: PetscMemzero(c,m*p*sizeof(PetscScalar));
3737: ii = sub_b->i;
3738: idx = sub_b->j;
3739: for (i=0; i<n; i++) {
3740: q = ii[i+1] - ii[i];
3741: while (q-->0) {
3742: c_q = c + m*(*idx);
3743: a_q = a + m*i;
3744: PetscKernelAXPY(c_q,*b,a_q,m);
3745: idx++;
3746: b++;
3747: }
3748: }
3749: return(0);
3750: }
3754: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A,Mat B,PetscReal fill,Mat *C)
3755: {
3757: PetscInt m=A->rmap->n,n=B->cmap->n;
3758: Mat Cmat;
3761: if (A->cmap->n != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"A->cmap->n %D != B->rmap->n %D\n",A->cmap->n,B->rmap->n);
3762: MatCreate(PetscObjectComm((PetscObject)A),&Cmat);
3763: MatSetSizes(Cmat,m,n,m,n);
3764: MatSetBlockSizesFromMats(Cmat,A,B);
3765: MatSetType(Cmat,MATSEQDENSE);
3766: MatSeqDenseSetPreallocation(Cmat,NULL);
3768: Cmat->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
3770: *C = Cmat;
3771: return(0);
3772: }
3774: /* ----------------------------------------------------------------*/
3777: PetscErrorCode MatMatMult_SeqDense_SeqAIJ(Mat A,Mat B,MatReuse scall,PetscReal fill,Mat *C)
3778: {
3782: if (scall == MAT_INITIAL_MATRIX) {
3783: PetscLogEventBegin(MAT_MatMultSymbolic,A,B,0,0);
3784: MatMatMultSymbolic_SeqDense_SeqAIJ(A,B,fill,C);
3785: PetscLogEventEnd(MAT_MatMultSymbolic,A,B,0,0);
3786: }
3787: PetscLogEventBegin(MAT_MatMultNumeric,A,B,0,0);
3788: MatMatMultNumeric_SeqDense_SeqAIJ(A,B,*C);
3789: PetscLogEventEnd(MAT_MatMultNumeric,A,B,0,0);
3790: return(0);
3791: }
3794: /*MC
3795: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
3796: based on compressed sparse row format.
3798: Options Database Keys:
3799: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
3801: Level: beginner
3803: .seealso: MatCreateSeqAIJ(), MatSetFromOptions(), MatSetType(), MatCreate(), MatType
3804: M*/
3806: /*MC
3807: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
3809: This matrix type is identical to MATSEQAIJ when constructed with a single process communicator,
3810: and MATMPIAIJ otherwise. As a result, for single process communicators,
3811: MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
3812: for communicators controlling multiple processes. It is recommended that you call both of
3813: the above preallocation routines for simplicity.
3815: Options Database Keys:
3816: . -mat_type aij - sets the matrix type to "aij" during a call to MatSetFromOptions()
3818: Developer Notes: Subclasses include MATAIJCUSP, MATAIJPERM, MATAIJCRL, and also automatically switches over to use inodes when
3819: enough exist.
3821: Level: beginner
3823: .seealso: MatCreateAIJ(), MatCreateSeqAIJ(), MATSEQAIJ,MATMPIAIJ
3824: M*/
3826: /*MC
3827: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
3829: This matrix type is identical to MATSEQAIJCRL when constructed with a single process communicator,
3830: and MATMPIAIJCRL otherwise. As a result, for single process communicators,
3831: MatSeqAIJSetPreallocation() is supported, and similarly MatMPIAIJSetPreallocation() is supported
3832: for communicators controlling multiple processes. It is recommended that you call both of
3833: the above preallocation routines for simplicity.
3835: Options Database Keys:
3836: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to MatSetFromOptions()
3838: Level: beginner
3840: .seealso: MatCreateMPIAIJCRL,MATSEQAIJCRL,MATMPIAIJCRL, MATSEQAIJCRL, MATMPIAIJCRL
3841: M*/
3843: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat,MatType,MatReuse,Mat*);
3844: #if defined(PETSC_HAVE_ELEMENTAL)
3845: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat,MatType,MatReuse,Mat*);
3846: #endif
3847: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat,MatType,MatReuse,Mat*);
3849: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3850: PETSC_EXTERN PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject,void*);
3851: PETSC_EXTERN PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject,void*);
3852: #endif
3857: /*@C
3858: MatSeqAIJGetArray - gives access to the array where the data for a SeqSeqAIJ matrix is stored
3860: Not Collective
3862: Input Parameter:
3863: . mat - a MATSEQAIJ matrix
3865: Output Parameter:
3866: . array - pointer to the data
3868: Level: intermediate
3870: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3871: @*/
3872: PetscErrorCode MatSeqAIJGetArray(Mat A,PetscScalar **array)
3873: {
3877: PetscUseMethod(A,"MatSeqAIJGetArray_C",(Mat,PetscScalar**),(A,array));
3878: return(0);
3879: }
3883: /*@C
3884: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
3886: Not Collective
3888: Input Parameter:
3889: . mat - a MATSEQAIJ matrix
3891: Output Parameter:
3892: . nz - the maximum number of nonzeros in any row
3894: Level: intermediate
3896: .seealso: MatSeqAIJRestoreArray(), MatSeqAIJGetArrayF90()
3897: @*/
3898: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A,PetscInt *nz)
3899: {
3900: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)A->data;
3903: *nz = aij->rmax;
3904: return(0);
3905: }
3909: /*@C
3910: MatSeqAIJRestoreArray - returns access to the array where the data for a MATSEQAIJ matrix is stored obtained by MatSeqAIJGetArray()
3912: Not Collective
3914: Input Parameters:
3915: . mat - a MATSEQAIJ matrix
3916: . array - pointer to the data
3918: Level: intermediate
3920: .seealso: MatSeqAIJGetArray(), MatSeqAIJRestoreArrayF90()
3921: @*/
3922: PetscErrorCode MatSeqAIJRestoreArray(Mat A,PetscScalar **array)
3923: {
3927: PetscUseMethod(A,"MatSeqAIJRestoreArray_C",(Mat,PetscScalar**),(A,array));
3928: return(0);
3929: }
3933: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
3934: {
3935: Mat_SeqAIJ *b;
3937: PetscMPIInt size;
3940: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
3941: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
3943: PetscNewLog(B,&b);
3945: B->data = (void*)b;
3947: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
3949: b->row = 0;
3950: b->col = 0;
3951: b->icol = 0;
3952: b->reallocs = 0;
3953: b->ignorezeroentries = PETSC_FALSE;
3954: b->roworiented = PETSC_TRUE;
3955: b->nonew = 0;
3956: b->diag = 0;
3957: b->solve_work = 0;
3958: B->spptr = 0;
3959: b->saved_values = 0;
3960: b->idiag = 0;
3961: b->mdiag = 0;
3962: b->ssor_work = 0;
3963: b->omega = 1.0;
3964: b->fshift = 0.0;
3965: b->idiagvalid = PETSC_FALSE;
3966: b->ibdiagvalid = PETSC_FALSE;
3967: b->keepnonzeropattern = PETSC_FALSE;
3969: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3970: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJGetArray_C",MatSeqAIJGetArray_SeqAIJ);
3971: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJRestoreArray_C",MatSeqAIJRestoreArray_SeqAIJ);
3973: #if defined(PETSC_HAVE_MATLAB_ENGINE)
3974: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEnginePut_C",MatlabEnginePut_SeqAIJ);
3975: PetscObjectComposeFunction((PetscObject)B,"PetscMatlabEngineGet_C",MatlabEngineGet_SeqAIJ);
3976: #endif
3978: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetColumnIndices_C",MatSeqAIJSetColumnIndices_SeqAIJ);
3979: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqAIJ);
3980: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqAIJ);
3981: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqsbaij_C",MatConvert_SeqAIJ_SeqSBAIJ);
3982: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqbaij_C",MatConvert_SeqAIJ_SeqBAIJ);
3983: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijperm_C",MatConvert_SeqAIJ_SeqAIJPERM);
3984: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqaijcrl_C",MatConvert_SeqAIJ_SeqAIJCRL);
3985: #if defined(PETSC_HAVE_ELEMENTAL)
3986: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_elemental_C",MatConvert_SeqAIJ_Elemental);
3987: #endif
3988: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqaij_seqdense_C",MatConvert_SeqAIJ_SeqDense);
3989: PetscObjectComposeFunction((PetscObject)B,"MatIsTranspose_C",MatIsTranspose_SeqAIJ);
3990: PetscObjectComposeFunction((PetscObject)B,"MatIsHermitianTranspose_C",MatIsTranspose_SeqAIJ);
3991: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocation_C",MatSeqAIJSetPreallocation_SeqAIJ);
3992: PetscObjectComposeFunction((PetscObject)B,"MatSeqAIJSetPreallocationCSR_C",MatSeqAIJSetPreallocationCSR_SeqAIJ);
3993: PetscObjectComposeFunction((PetscObject)B,"MatReorderForNonzeroDiagonal_C",MatReorderForNonzeroDiagonal_SeqAIJ);
3994: PetscObjectComposeFunction((PetscObject)B,"MatMatMult_seqdense_seqaij_C",MatMatMult_SeqDense_SeqAIJ);
3995: PetscObjectComposeFunction((PetscObject)B,"MatMatMultSymbolic_seqdense_seqaij_C",MatMatMultSymbolic_SeqDense_SeqAIJ);
3996: PetscObjectComposeFunction((PetscObject)B,"MatMatMultNumeric_seqdense_seqaij_C",MatMatMultNumeric_SeqDense_SeqAIJ);
3997: MatCreate_SeqAIJ_Inode(B);
3998: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
3999: return(0);
4000: }
4004: /*
4005: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
4006: */
4007: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
4008: {
4009: Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
4011: PetscInt i,m = A->rmap->n;
4014: c = (Mat_SeqAIJ*)C->data;
4016: C->factortype = A->factortype;
4017: c->row = 0;
4018: c->col = 0;
4019: c->icol = 0;
4020: c->reallocs = 0;
4022: C->assembled = PETSC_TRUE;
4024: PetscLayoutReference(A->rmap,&C->rmap);
4025: PetscLayoutReference(A->cmap,&C->cmap);
4027: PetscMalloc2(m,&c->imax,m,&c->ilen);
4028: PetscLogObjectMemory((PetscObject)C, 2*m*sizeof(PetscInt));
4029: for (i=0; i<m; i++) {
4030: c->imax[i] = a->imax[i];
4031: c->ilen[i] = a->ilen[i];
4032: }
4034: /* allocate the matrix space */
4035: if (mallocmatspace) {
4036: PetscMalloc3(a->i[m],&c->a,a->i[m],&c->j,m+1,&c->i);
4037: PetscLogObjectMemory((PetscObject)C, a->i[m]*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
4039: c->singlemalloc = PETSC_TRUE;
4041: PetscMemcpy(c->i,a->i,(m+1)*sizeof(PetscInt));
4042: if (m > 0) {
4043: PetscMemcpy(c->j,a->j,(a->i[m])*sizeof(PetscInt));
4044: if (cpvalues == MAT_COPY_VALUES) {
4045: PetscMemcpy(c->a,a->a,(a->i[m])*sizeof(PetscScalar));
4046: } else {
4047: PetscMemzero(c->a,(a->i[m])*sizeof(PetscScalar));
4048: }
4049: }
4050: }
4052: c->ignorezeroentries = a->ignorezeroentries;
4053: c->roworiented = a->roworiented;
4054: c->nonew = a->nonew;
4055: if (a->diag) {
4056: PetscMalloc1(m+1,&c->diag);
4057: PetscLogObjectMemory((PetscObject)C,(m+1)*sizeof(PetscInt));
4058: for (i=0; i<m; i++) {
4059: c->diag[i] = a->diag[i];
4060: }
4061: } else c->diag = 0;
4063: c->solve_work = 0;
4064: c->saved_values = 0;
4065: c->idiag = 0;
4066: c->ssor_work = 0;
4067: c->keepnonzeropattern = a->keepnonzeropattern;
4068: c->free_a = PETSC_TRUE;
4069: c->free_ij = PETSC_TRUE;
4071: c->rmax = a->rmax;
4072: c->nz = a->nz;
4073: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
4074: C->preallocated = PETSC_TRUE;
4076: c->compressedrow.use = a->compressedrow.use;
4077: c->compressedrow.nrows = a->compressedrow.nrows;
4078: if (a->compressedrow.use) {
4079: i = a->compressedrow.nrows;
4080: PetscMalloc2(i+1,&c->compressedrow.i,i,&c->compressedrow.rindex);
4081: PetscMemcpy(c->compressedrow.i,a->compressedrow.i,(i+1)*sizeof(PetscInt));
4082: PetscMemcpy(c->compressedrow.rindex,a->compressedrow.rindex,i*sizeof(PetscInt));
4083: } else {
4084: c->compressedrow.use = PETSC_FALSE;
4085: c->compressedrow.i = NULL;
4086: c->compressedrow.rindex = NULL;
4087: }
4088: c->nonzerorowcnt = a->nonzerorowcnt;
4089: C->nonzerostate = A->nonzerostate;
4091: MatDuplicate_SeqAIJ_Inode(A,cpvalues,&C);
4092: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
4093: return(0);
4094: }
4098: PetscErrorCode MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
4099: {
4103: MatCreate(PetscObjectComm((PetscObject)A),B);
4104: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
4105: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
4106: MatSetBlockSizesFromMats(*B,A,A);
4107: }
4108: MatSetType(*B,((PetscObject)A)->type_name);
4109: MatDuplicateNoCreate_SeqAIJ(*B,A,cpvalues,PETSC_TRUE);
4110: return(0);
4111: }
4115: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
4116: {
4117: Mat_SeqAIJ *a;
4119: PetscInt i,sum,nz,header[4],*rowlengths = 0,M,N,rows,cols;
4120: int fd;
4121: PetscMPIInt size;
4122: MPI_Comm comm;
4123: PetscInt bs = newMat->rmap->bs;
4126: /* force binary viewer to load .info file if it has not yet done so */
4127: PetscViewerSetUp(viewer);
4128: PetscObjectGetComm((PetscObject)viewer,&comm);
4129: MPI_Comm_size(comm,&size);
4130: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"view must have one processor");
4132: PetscOptionsBegin(comm,NULL,"Options for loading SEQAIJ matrix","Mat");
4133: PetscOptionsInt("-matload_block_size","Set the blocksize used to store the matrix","MatLoad",bs,&bs,NULL);
4134: PetscOptionsEnd();
4135: if (bs < 0) bs = 1;
4136: MatSetBlockSize(newMat,bs);
4138: PetscViewerBinaryGetDescriptor(viewer,&fd);
4139: PetscBinaryRead(fd,header,4,PETSC_INT);
4140: if (header[0] != MAT_FILE_CLASSID) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
4141: M = header[1]; N = header[2]; nz = header[3];
4143: if (nz < 0) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
4145: /* read in row lengths */
4146: PetscMalloc1(M,&rowlengths);
4147: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
4149: /* check if sum of rowlengths is same as nz */
4150: for (i=0,sum=0; i< M; i++) sum +=rowlengths[i];
4151: if (sum != nz) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_FILE_READ,"Inconsistant matrix data in file. no-nonzeros = %dD, sum-row-lengths = %D\n",nz,sum);
4153: /* set global size if not set already*/
4154: if (newMat->rmap->n < 0 && newMat->rmap->N < 0 && newMat->cmap->n < 0 && newMat->cmap->N < 0) {
4155: MatSetSizes(newMat,PETSC_DECIDE,PETSC_DECIDE,M,N);
4156: } else {
4157: /* if sizes and type are already set, check if the matrix global sizes are correct */
4158: MatGetSize(newMat,&rows,&cols);
4159: if (rows < 0 && cols < 0) { /* user might provide local size instead of global size */
4160: MatGetLocalSize(newMat,&rows,&cols);
4161: }
4162: if (M != rows || N != cols) SETERRQ4(PETSC_COMM_SELF,PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%D, %D) than the input matrix (%D, %D)",M,N,rows,cols);
4163: }
4164: MatSeqAIJSetPreallocation_SeqAIJ(newMat,0,rowlengths);
4165: a = (Mat_SeqAIJ*)newMat->data;
4167: PetscBinaryRead(fd,a->j,nz,PETSC_INT);
4169: /* read in nonzero values */
4170: PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);
4172: /* set matrix "i" values */
4173: a->i[0] = 0;
4174: for (i=1; i<= M; i++) {
4175: a->i[i] = a->i[i-1] + rowlengths[i-1];
4176: a->ilen[i-1] = rowlengths[i-1];
4177: }
4178: PetscFree(rowlengths);
4180: MatAssemblyBegin(newMat,MAT_FINAL_ASSEMBLY);
4181: MatAssemblyEnd(newMat,MAT_FINAL_ASSEMBLY);
4182: return(0);
4183: }
4187: PetscErrorCode MatEqual_SeqAIJ(Mat A,Mat B,PetscBool * flg)
4188: {
4189: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*b = (Mat_SeqAIJ*)B->data;
4191: #if defined(PETSC_USE_COMPLEX)
4192: PetscInt k;
4193: #endif
4196: /* If the matrix dimensions are not equal,or no of nonzeros */
4197: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz)) {
4198: *flg = PETSC_FALSE;
4199: return(0);
4200: }
4202: /* if the a->i are the same */
4203: PetscMemcmp(a->i,b->i,(A->rmap->n+1)*sizeof(PetscInt),flg);
4204: if (!*flg) return(0);
4206: /* if a->j are the same */
4207: PetscMemcmp(a->j,b->j,(a->nz)*sizeof(PetscInt),flg);
4208: if (!*flg) return(0);
4210: /* if a->a are the same */
4211: #if defined(PETSC_USE_COMPLEX)
4212: for (k=0; k<a->nz; k++) {
4213: if (PetscRealPart(a->a[k]) != PetscRealPart(b->a[k]) || PetscImaginaryPart(a->a[k]) != PetscImaginaryPart(b->a[k])) {
4214: *flg = PETSC_FALSE;
4215: return(0);
4216: }
4217: }
4218: #else
4219: PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
4220: #endif
4221: return(0);
4222: }
4226: /*@
4227: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
4228: provided by the user.
4230: Collective on MPI_Comm
4232: Input Parameters:
4233: + comm - must be an MPI communicator of size 1
4234: . m - number of rows
4235: . n - number of columns
4236: . i - row indices
4237: . j - column indices
4238: - a - matrix values
4240: Output Parameter:
4241: . mat - the matrix
4243: Level: intermediate
4245: Notes:
4246: The i, j, and a arrays are not copied by this routine, the user must free these arrays
4247: once the matrix is destroyed and not before
4249: You cannot set new nonzero locations into this matrix, that will generate an error.
4251: The i and j indices are 0 based
4253: The format which is used for the sparse matrix input, is equivalent to a
4254: row-major ordering.. i.e for the following matrix, the input data expected is
4255: as shown:
4257: 1 0 0
4258: 2 0 3
4259: 4 5 6
4261: i = {0,1,3,6} [size = nrow+1 = 3+1]
4262: j = {0,0,2,0,1,2} [size = nz = 6]; values must be sorted for each row
4263: v = {1,2,3,4,5,6} [size = nz = 6]
4266: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateMPIAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4268: @*/
4269: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat)
4270: {
4272: PetscInt ii;
4273: Mat_SeqAIJ *aij;
4274: #if defined(PETSC_USE_DEBUG)
4275: PetscInt jj;
4276: #endif
4279: if (i[0]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"i (row indices) must start with 0");
4280: MatCreate(comm,mat);
4281: MatSetSizes(*mat,m,n,m,n);
4282: /* MatSetBlockSizes(*mat,,); */
4283: MatSetType(*mat,MATSEQAIJ);
4284: MatSeqAIJSetPreallocation_SeqAIJ(*mat,MAT_SKIP_ALLOCATION,0);
4285: aij = (Mat_SeqAIJ*)(*mat)->data;
4286: PetscMalloc2(m,&aij->imax,m,&aij->ilen);
4288: aij->i = i;
4289: aij->j = j;
4290: aij->a = a;
4291: aij->singlemalloc = PETSC_FALSE;
4292: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
4293: aij->free_a = PETSC_FALSE;
4294: aij->free_ij = PETSC_FALSE;
4296: for (ii=0; ii<m; ii++) {
4297: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
4298: #if defined(PETSC_USE_DEBUG)
4299: if (i[ii+1] - i[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative row length in i (row indices) row = %D length = %D",ii,i[ii+1] - i[ii]);
4300: for (jj=i[ii]+1; jj<i[ii+1]; jj++) {
4301: if (j[jj] < j[jj-1]) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column entry number %D (actual colum %D) in row %D is not sorted",jj-i[ii],j[jj],ii);
4302: if (j[jj] == j[jj]-1) SETERRQ3(PETSC_COMM_SELF,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);
4303: }
4304: #endif
4305: }
4306: #if defined(PETSC_USE_DEBUG)
4307: for (ii=0; ii<aij->i[m]; ii++) {
4308: if (j[ii] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Negative column index at location = %D index = %D",ii,j[ii]);
4309: if (j[ii] > n - 1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column index to large at location = %D index = %D",ii,j[ii]);
4310: }
4311: #endif
4313: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4314: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4315: return(0);
4316: }
4319: /*@C
4320: MatCreateSeqAIJFromTriple - Creates an sequential AIJ matrix using matrix elements (in COO format)
4321: provided by the user.
4323: Collective on MPI_Comm
4325: Input Parameters:
4326: + comm - must be an MPI communicator of size 1
4327: . m - number of rows
4328: . n - number of columns
4329: . i - row indices
4330: . j - column indices
4331: . a - matrix values
4332: . nz - number of nonzeros
4333: - idx - 0 or 1 based
4335: Output Parameter:
4336: . mat - the matrix
4338: Level: intermediate
4340: Notes:
4341: The i and j indices are 0 based
4343: The format which is used for the sparse matrix input, is equivalent to a
4344: row-major ordering.. i.e for the following matrix, the input data expected is
4345: as shown:
4347: 1 0 0
4348: 2 0 3
4349: 4 5 6
4351: i = {0,1,1,2,2,2}
4352: j = {0,0,2,0,1,2}
4353: v = {1,2,3,4,5,6}
4356: .seealso: MatCreate(), MatCreateAIJ(), MatCreateSeqAIJ(), MatCreateSeqAIJWithArrays(), MatMPIAIJSetPreallocationCSR()
4358: @*/
4359: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt *i,PetscInt *j,PetscScalar *a,Mat *mat,PetscInt nz,PetscBool idx)
4360: {
4362: PetscInt ii, *nnz, one = 1,row,col;
4366: PetscCalloc1(m,&nnz);
4367: for (ii = 0; ii < nz; ii++) {
4368: nnz[i[ii] - !!idx] += 1;
4369: }
4370: MatCreate(comm,mat);
4371: MatSetSizes(*mat,m,n,m,n);
4372: MatSetType(*mat,MATSEQAIJ);
4373: MatSeqAIJSetPreallocation_SeqAIJ(*mat,0,nnz);
4374: for (ii = 0; ii < nz; ii++) {
4375: if (idx) {
4376: row = i[ii] - 1;
4377: col = j[ii] - 1;
4378: } else {
4379: row = i[ii];
4380: col = j[ii];
4381: }
4382: MatSetValues(*mat,one,&row,one,&col,&a[ii],ADD_VALUES);
4383: }
4384: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
4385: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
4386: PetscFree(nnz);
4387: return(0);
4388: }
4392: PetscErrorCode MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
4393: {
4395: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
4398: if (coloring->ctype == IS_COLORING_GLOBAL) {
4399: ISColoringReference(coloring);
4400: a->coloring = coloring;
4401: } else if (coloring->ctype == IS_COLORING_GHOSTED) {
4402: PetscInt i,*larray;
4403: ISColoring ocoloring;
4404: ISColoringValue *colors;
4406: /* set coloring for diagonal portion */
4407: PetscMalloc1(A->cmap->n,&larray);
4408: for (i=0; i<A->cmap->n; i++) larray[i] = i;
4409: ISGlobalToLocalMappingApply(A->cmap->mapping,IS_GTOLM_MASK,A->cmap->n,larray,NULL,larray);
4410: PetscMalloc1(A->cmap->n,&colors);
4411: for (i=0; i<A->cmap->n; i++) colors[i] = coloring->colors[larray[i]];
4412: PetscFree(larray);
4413: ISColoringCreate(PETSC_COMM_SELF,coloring->n,A->cmap->n,colors,PETSC_OWN_POINTER,&ocoloring);
4414: a->coloring = ocoloring;
4415: }
4416: return(0);
4417: }
4421: PetscErrorCode MatSetValuesAdifor_SeqAIJ(Mat A,PetscInt nl,void *advalues)
4422: {
4423: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
4424: PetscInt m = A->rmap->n,*ii = a->i,*jj = a->j,nz,i,j;
4425: MatScalar *v = a->a;
4426: PetscScalar *values = (PetscScalar*)advalues;
4427: ISColoringValue *color;
4430: if (!a->coloring) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Coloring not set for matrix");
4431: color = a->coloring->colors;
4432: /* loop over rows */
4433: for (i=0; i<m; i++) {
4434: nz = ii[i+1] - ii[i];
4435: /* loop over columns putting computed value into matrix */
4436: for (j=0; j<nz; j++) *v++ = values[color[*jj++]];
4437: values += nl; /* jump to next row of derivatives */
4438: }
4439: return(0);
4440: }
4444: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
4445: {
4446: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
4450: a->idiagvalid = PETSC_FALSE;
4451: a->ibdiagvalid = PETSC_FALSE;
4453: MatSeqAIJInvalidateDiagonal_Inode(A);
4454: return(0);
4455: }
4459: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm,Mat inmat,PetscInt n,MatReuse scall,Mat *outmat)
4460: {
4464: MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm,inmat,n,scall,outmat);
4465: return(0);
4466: }
4468: /*
4469: Permute A into C's *local* index space using rowemb,colemb.
4470: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
4471: of [0,m), colemb is in [0,n).
4472: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
4473: */
4476: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C,IS rowemb,IS colemb,MatStructure pattern,Mat B)
4477: {
4478: /* If making this function public, change the error returned in this function away from _PLIB. */
4480: Mat_SeqAIJ *Baij;
4481: PetscBool seqaij;
4482: PetscInt m,n,*nz,i,j,count;
4483: PetscScalar v;
4484: const PetscInt *rowindices,*colindices;
4487: if (!B) return(0);
4488: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
4489: PetscObjectTypeCompare((PetscObject)B,MATSEQAIJ,&seqaij);
4490: if (!seqaij) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is of wrong type");
4491: if (rowemb) {
4492: ISGetLocalSize(rowemb,&m);
4493: if (m != B->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Row IS of size %D is incompatible with matrix row size %D",m,B->rmap->n);
4494: } else {
4495: if (C->rmap->n != B->rmap->n) {
4496: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is row-incompatible with the target matrix");
4497: }
4498: }
4499: if (colemb) {
4500: ISGetLocalSize(colemb,&n);
4501: if (n != B->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Diag col IS of size %D is incompatible with input matrix col size %D",n,B->cmap->n);
4502: } else {
4503: if (C->cmap->n != B->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_PLIB,"Input matrix is col-incompatible with the target matrix");
4504: }
4506: Baij = (Mat_SeqAIJ*)(B->data);
4507: if (pattern == DIFFERENT_NONZERO_PATTERN) {
4508: PetscMalloc1(B->rmap->n,&nz);
4509: for (i=0; i<B->rmap->n; i++) {
4510: nz[i] = Baij->i[i+1] - Baij->i[i];
4511: }
4512: MatSeqAIJSetPreallocation(C,0,nz);
4513: PetscFree(nz);
4514: }
4515: if (pattern == SUBSET_NONZERO_PATTERN) {
4516: MatZeroEntries(C);
4517: }
4518: count = 0;
4519: rowindices = NULL;
4520: colindices = NULL;
4521: if (rowemb) {
4522: ISGetIndices(rowemb,&rowindices);
4523: }
4524: if (colemb) {
4525: ISGetIndices(colemb,&colindices);
4526: }
4527: for (i=0; i<B->rmap->n; i++) {
4528: PetscInt row;
4529: row = i;
4530: if (rowindices) row = rowindices[i];
4531: for (j=Baij->i[i]; j<Baij->i[i+1]; j++) {
4532: PetscInt col;
4533: col = Baij->j[count];
4534: if (colindices) col = colindices[col];
4535: v = Baij->a[count];
4536: MatSetValues(C,1,&row,1,&col,&v,INSERT_VALUES);
4537: ++count;
4538: }
4539: }
4540: /* FIXME: set C's nonzerostate correctly. */
4541: /* Assembly for C is necessary. */
4542: C->preallocated = PETSC_TRUE;
4543: C->assembled = PETSC_TRUE;
4544: C->was_assembled = PETSC_FALSE;
4545: return(0);
4546: }
4549: /*
4550: Special version for direct calls from Fortran
4551: */
4552: #include <petsc/private/fortranimpl.h>
4553: #if defined(PETSC_HAVE_FORTRAN_CAPS)
4554: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
4555: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
4556: #define matsetvaluesseqaij_ matsetvaluesseqaij
4557: #endif
4559: /* Change these macros so can be used in void function */
4560: #undef CHKERRQ
4561: #define CHKERRQ(ierr) CHKERRABORT(PetscObjectComm((PetscObject)A),ierr)
4562: #undef SETERRQ2
4563: #define SETERRQ2(comm,ierr,b,c,d) CHKERRABORT(comm,ierr)
4564: #undef SETERRQ3
4565: #define SETERRQ3(comm,ierr,b,c,d,e) CHKERRABORT(comm,ierr)
4569: PETSC_EXTERN void PETSC_STDCALL matsetvaluesseqaij_(Mat *AA,PetscInt *mm,const PetscInt im[],PetscInt *nn,const PetscInt in[],const PetscScalar v[],InsertMode *isis, PetscErrorCode *_ierr)
4570: {
4571: Mat A = *AA;
4572: PetscInt m = *mm, n = *nn;
4573: InsertMode is = *isis;
4574: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
4575: PetscInt *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N;
4576: PetscInt *imax,*ai,*ailen;
4578: PetscInt *aj,nonew = a->nonew,lastcol = -1;
4579: MatScalar *ap,value,*aa;
4580: PetscBool ignorezeroentries = a->ignorezeroentries;
4581: PetscBool roworiented = a->roworiented;
4584: MatCheckPreallocated(A,1);
4585: imax = a->imax;
4586: ai = a->i;
4587: ailen = a->ilen;
4588: aj = a->j;
4589: aa = a->a;
4591: for (k=0; k<m; k++) { /* loop over added rows */
4592: row = im[k];
4593: if (row < 0) continue;
4594: #if defined(PETSC_USE_DEBUG)
4595: if (row >= A->rmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Row too large");
4596: #endif
4597: rp = aj + ai[row]; ap = aa + ai[row];
4598: rmax = imax[row]; nrow = ailen[row];
4599: low = 0;
4600: high = nrow;
4601: for (l=0; l<n; l++) { /* loop over added columns */
4602: if (in[l] < 0) continue;
4603: #if defined(PETSC_USE_DEBUG)
4604: if (in[l] >= A->cmap->n) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Column too large");
4605: #endif
4606: col = in[l];
4607: if (roworiented) value = v[l + k*n];
4608: else value = v[k + l*m];
4610: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
4612: if (col <= lastcol) low = 0;
4613: else high = nrow;
4614: lastcol = col;
4615: while (high-low > 5) {
4616: t = (low+high)/2;
4617: if (rp[t] > col) high = t;
4618: else low = t;
4619: }
4620: for (i=low; i<high; i++) {
4621: if (rp[i] > col) break;
4622: if (rp[i] == col) {
4623: if (is == ADD_VALUES) ap[i] += value;
4624: else ap[i] = value;
4625: goto noinsert;
4626: }
4627: }
4628: if (value == 0.0 && ignorezeroentries) goto noinsert;
4629: if (nonew == 1) goto noinsert;
4630: if (nonew == -1) SETERRABORT(PetscObjectComm((PetscObject)A),PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero in the matrix");
4631: MatSeqXAIJReallocateAIJ(A,A->rmap->n,1,nrow,row,col,rmax,aa,ai,aj,rp,ap,imax,nonew,MatScalar);
4632: N = nrow++ - 1; a->nz++; high++;
4633: /* shift up all the later entries in this row */
4634: for (ii=N; ii>=i; ii--) {
4635: rp[ii+1] = rp[ii];
4636: ap[ii+1] = ap[ii];
4637: }
4638: rp[i] = col;
4639: ap[i] = value;
4640: A->nonzerostate++;
4641: noinsert:;
4642: low = i + 1;
4643: }
4644: ailen[row] = nrow;
4645: }
4646: PetscFunctionReturnVoid();
4647: }