Actual source code: aij.c
1: /*$Id: aij.c,v 1.385 2001/09/07 20:09:22 bsmith Exp $*/
2: /*
3: Defines the basic matrix operations for the AIJ (compressed row)
4: matrix storage format.
5: */
7: #include src/mat/impls/aij/seq/aij.h
8: #include src/vec/vecimpl.h
9: #include src/inline/spops.h
10: #include src/inline/dot.h
11: #include petscbt.h
14: EXTERN int MatToSymmetricIJ_SeqAIJ(int,int*,int*,int,int,int**,int**);
16: int MatGetRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *m,int **ia,int **ja,PetscTruth *done)
17: {
18: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
19: int ierr,i,ishift;
20:
22: *m = A->m;
23: if (!ia) return(0);
24: ishift = a->indexshift;
25: if (symmetric && !A->structurally_symmetric) {
26: MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
27: } else if (oshift == 0 && ishift == -1) {
28: int nz = a->i[A->m] - 1;
29: /* malloc space and subtract 1 from i and j indices */
30: PetscMalloc((A->m+1)*sizeof(int),ia);
31: PetscMalloc((nz+1)*sizeof(int),ja);
32: for (i=0; i<nz; i++) (*ja)[i] = a->j[i] - 1;
33: for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] - 1;
34: } else if (oshift == 1 && ishift == 0) {
35: int nz = a->i[A->m];
36: /* malloc space and add 1 to i and j indices */
37: PetscMalloc((A->m+1)*sizeof(int),ia);
38: PetscMalloc((nz+1)*sizeof(int),ja);
39: for (i=0; i<nz; i++) (*ja)[i] = a->j[i] + 1;
40: for (i=0; i<A->m+1; i++) (*ia)[i] = a->i[i] + 1;
41: } else {
42: *ia = a->i; *ja = a->j;
43: }
44: return(0);
45: }
47: int MatRestoreRowIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int **ia,int **ja,PetscTruth *done)
48: {
49: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
50: int ishift = a->indexshift,ierr;
51:
53: if (!ia) return(0);
54: if ((symmetric && !A->structurally_symmetric) || (oshift == 0 && ishift == -1) || (oshift == 1 && ishift == 0)) {
55: PetscFree(*ia);
56: PetscFree(*ja);
57: }
58: return(0);
59: }
61: int MatGetColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *nn,int **ia,int **ja,PetscTruth *done)
62: {
63: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
64: int ierr,i,ishift = a->indexshift,*collengths,*cia,*cja,n = A->n,m = A->m;
65: int nz = a->i[m]+ishift,row,*jj,mr,col;
66:
68: *nn = A->n;
69: if (!ia) return(0);
70: if (symmetric) {
71: MatToSymmetricIJ_SeqAIJ(A->m,a->i,a->j,ishift,oshift,ia,ja);
72: } else {
73: PetscMalloc((n+1)*sizeof(int),&collengths);
74: PetscMemzero(collengths,n*sizeof(int));
75: PetscMalloc((n+1)*sizeof(int),&cia);
76: PetscMalloc((nz+1)*sizeof(int),&cja);
77: jj = a->j;
78: for (i=0; i<nz; i++) {
79: collengths[jj[i] + ishift]++;
80: }
81: cia[0] = oshift;
82: for (i=0; i<n; i++) {
83: cia[i+1] = cia[i] + collengths[i];
84: }
85: PetscMemzero(collengths,n*sizeof(int));
86: jj = a->j;
87: for (row=0; row<m; row++) {
88: mr = a->i[row+1] - a->i[row];
89: for (i=0; i<mr; i++) {
90: col = *jj++ + ishift;
91: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
92: }
93: }
94: PetscFree(collengths);
95: *ia = cia; *ja = cja;
96: }
97: return(0);
98: }
100: int MatRestoreColumnIJ_SeqAIJ(Mat A,int oshift,PetscTruth symmetric,int *n,int **ia,int **ja,PetscTruth *done)
101: {
105: if (!ia) return(0);
107: PetscFree(*ia);
108: PetscFree(*ja);
109:
110: return(0);
111: }
113: #define CHUNKSIZE 15
115: int MatSetValues_SeqAIJ(Mat A,int m,int *im,int n,int *in,PetscScalar *v,InsertMode is)
116: {
117: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
118: int *rp,k,low,high,t,ii,row,nrow,i,col,l,rmax,N,sorted = a->sorted;
119: int *imax = a->imax,*ai = a->i,*ailen = a->ilen;
120: int *aj = a->j,nonew = a->nonew,shift = a->indexshift,ierr;
121: PetscScalar *ap,value,*aa = a->a;
122: PetscTruth ignorezeroentries = ((a->ignorezeroentries && is == ADD_VALUES) ? PETSC_TRUE:PETSC_FALSE);
123: PetscTruth roworiented = a->roworiented;
126: for (k=0; k<m; k++) { /* loop over added rows */
127: row = im[k];
128: if (row < 0) continue;
129: #if defined(PETSC_USE_BOPT_g)
130: if (row >= A->m) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %d max %d",row,A->m);
131: #endif
132: rp = aj + ai[row] + shift; ap = aa + ai[row] + shift;
133: rmax = imax[row]; nrow = ailen[row];
134: low = 0;
135: for (l=0; l<n; l++) { /* loop over added columns */
136: if (in[l] < 0) continue;
137: #if defined(PETSC_USE_BOPT_g)
138: if (in[l] >= A->n) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: col %d max %d",in[l],A->n);
139: #endif
140: col = in[l] - shift;
141: if (roworiented) {
142: value = v[l + k*n];
143: } else {
144: value = v[k + l*m];
145: }
146: if (value == 0.0 && ignorezeroentries) continue;
148: if (!sorted) low = 0; high = nrow;
149: while (high-low > 5) {
150: t = (low+high)/2;
151: if (rp[t] > col) high = t;
152: else low = t;
153: }
154: for (i=low; i<high; i++) {
155: if (rp[i] > col) break;
156: if (rp[i] == col) {
157: if (is == ADD_VALUES) ap[i] += value;
158: else ap[i] = value;
159: goto noinsert;
160: }
161: }
162: if (nonew == 1) goto noinsert;
163: else if (nonew == -1) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix",row,col);
164: if (nrow >= rmax) {
165: /* there is no extra room in row, therefore enlarge */
166: int new_nz = ai[A->m] + CHUNKSIZE,len,*new_i,*new_j;
167: PetscScalar *new_a;
169: if (nonew == -2) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero at (%d,%d) in the matrix requiring new malloc()",row,col);
171: /* malloc new storage space */
172: len = new_nz*(sizeof(int)+sizeof(PetscScalar))+(A->m+1)*sizeof(int);
173: ierr = PetscMalloc(len,&new_a);
174: new_j = (int*)(new_a + new_nz);
175: new_i = new_j + new_nz;
177: /* copy over old data into new slots */
178: for (ii=0; ii<row+1; ii++) {new_i[ii] = ai[ii];}
179: for (ii=row+1; ii<A->m+1; ii++) {new_i[ii] = ai[ii]+CHUNKSIZE;}
180: PetscMemcpy(new_j,aj,(ai[row]+nrow+shift)*sizeof(int));
181: len = (new_nz - CHUNKSIZE - ai[row] - nrow - shift);
182: PetscMemcpy(new_j+ai[row]+shift+nrow+CHUNKSIZE,aj+ai[row]+shift+nrow,len*sizeof(int));
183: PetscMemcpy(new_a,aa,(ai[row]+nrow+shift)*sizeof(PetscScalar));
184: PetscMemcpy(new_a+ai[row]+shift+nrow+CHUNKSIZE,aa+ai[row]+shift+nrow,len*sizeof(PetscScalar));
185: /* free up old matrix storage */
186: PetscFree(a->a);
187: if (!a->singlemalloc) {
188: PetscFree(a->i);
189: PetscFree(a->j);
190: }
191: aa = a->a = new_a; ai = a->i = new_i; aj = a->j = new_j;
192: a->singlemalloc = PETSC_TRUE;
194: rp = aj + ai[row] + shift; ap = aa + ai[row] + shift;
195: rmax = imax[row] = imax[row] + CHUNKSIZE;
196: PetscLogObjectMemory(A,CHUNKSIZE*(sizeof(int) + sizeof(PetscScalar)));
197: a->maxnz += CHUNKSIZE;
198: a->reallocs++;
199: }
200: N = nrow++ - 1; a->nz++;
201: /* shift up all the later entries in this row */
202: for (ii=N; ii>=i; ii--) {
203: rp[ii+1] = rp[ii];
204: ap[ii+1] = ap[ii];
205: }
206: rp[i] = col;
207: ap[i] = value;
208: noinsert:;
209: low = i + 1;
210: }
211: ailen[row] = nrow;
212: }
213: return(0);
214: }
216: int MatGetValues_SeqAIJ(Mat A,int m,int *im,int n,int *in,PetscScalar *v)
217: {
218: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
219: int *rp,k,low,high,t,row,nrow,i,col,l,*aj = a->j;
220: int *ai = a->i,*ailen = a->ilen,shift = a->indexshift;
221: PetscScalar *ap,*aa = a->a,zero = 0.0;
224: for (k=0; k<m; k++) { /* loop over rows */
225: row = im[k];
226: if (row < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative row: %d",row);
227: if (row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row too large: %d",row);
228: rp = aj + ai[row] + shift; ap = aa + ai[row] + shift;
229: nrow = ailen[row];
230: for (l=0; l<n; l++) { /* loop over columns */
231: if (in[l] < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Negative column: %d",in[l]);
232: if (in[l] >= A->n) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Column too large: %d",in[l]);
233: col = in[l] - shift;
234: high = nrow; low = 0; /* assume unsorted */
235: while (high-low > 5) {
236: t = (low+high)/2;
237: if (rp[t] > col) high = t;
238: else low = t;
239: }
240: for (i=low; i<high; i++) {
241: if (rp[i] > col) break;
242: if (rp[i] == col) {
243: *v++ = ap[i];
244: goto finished;
245: }
246: }
247: *v++ = zero;
248: finished:;
249: }
250: }
251: return(0);
252: }
255: int MatView_SeqAIJ_Binary(Mat A,PetscViewer viewer)
256: {
257: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
258: int i,fd,*col_lens,ierr;
261: PetscViewerBinaryGetDescriptor(viewer,&fd);
262: PetscMalloc((4+A->m)*sizeof(int),&col_lens);
263: col_lens[0] = MAT_FILE_COOKIE;
264: col_lens[1] = A->m;
265: col_lens[2] = A->n;
266: col_lens[3] = a->nz;
268: /* store lengths of each row and write (including header) to file */
269: for (i=0; i<A->m; i++) {
270: col_lens[4+i] = a->i[i+1] - a->i[i];
271: }
272: PetscBinaryWrite(fd,col_lens,4+A->m,PETSC_INT,1);
273: PetscFree(col_lens);
275: /* store column indices (zero start index) */
276: if (a->indexshift) {
277: for (i=0; i<a->nz; i++) a->j[i]--;
278: }
279: PetscBinaryWrite(fd,a->j,a->nz,PETSC_INT,0);
280: if (a->indexshift) {
281: for (i=0; i<a->nz; i++) a->j[i]++;
282: }
284: /* store nonzero values */
285: PetscBinaryWrite(fd,a->a,a->nz,PETSC_SCALAR,0);
286: return(0);
287: }
289: int MatView_SeqAIJ_ASCII(Mat A,PetscViewer viewer)
290: {
291: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
292: int ierr,i,j,m = A->m,shift = a->indexshift;
293: char *name;
294: PetscViewerFormat format;
297: PetscObjectGetName((PetscObject)A,&name);
298: PetscViewerGetFormat(viewer,&format);
299: if (format == PETSC_VIEWER_ASCII_INFO_LONG || format == PETSC_VIEWER_ASCII_INFO) {
300: if (a->inode.size) {
301: PetscViewerASCIIPrintf(viewer,"using I-node routines: found %d nodes, limit used is %dn",a->inode.node_count,a->inode.limit);
302: } else {
303: PetscViewerASCIIPrintf(viewer,"not using I-node routinesn");
304: }
305: } else if (format == PETSC_VIEWER_ASCII_MATLAB) {
306: int nofinalvalue = 0;
307: if ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->n-!shift)) {
308: nofinalvalue = 1;
309: }
310: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
311: PetscViewerASCIIPrintf(viewer,"%% Size = %d %d n",m,A->n);
312: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %d n",a->nz);
313: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%d,3);n",a->nz+nofinalvalue);
314: PetscViewerASCIIPrintf(viewer,"zzz = [n");
316: for (i=0; i<m; i++) {
317: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
318: #if defined(PETSC_USE_COMPLEX)
319: PetscViewerASCIIPrintf(viewer,"%d %d %18.16e + %18.16ei n",i+1,a->j[j]+!shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
320: #else
321: PetscViewerASCIIPrintf(viewer,"%d %d %18.16en",i+1,a->j[j]+!shift,a->a[j]);
322: #endif
323: }
324: }
325: if (nofinalvalue) {
326: PetscViewerASCIIPrintf(viewer,"%d %d %18.16en",m,A->n,0.0);
327: }
328: PetscViewerASCIIPrintf(viewer,"];n %s = spconvert(zzz);n",name);
329: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
330: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
331: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
332: for (i=0; i<m; i++) {
333: PetscViewerASCIIPrintf(viewer,"row %d:",i);
334: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
335: #if defined(PETSC_USE_COMPLEX)
336: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
337: PetscViewerASCIIPrintf(viewer," %d %g + %g i",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
338: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
339: PetscViewerASCIIPrintf(viewer," %d %g - %g i",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
340: } else if (PetscRealPart(a->a[j]) != 0.0) {
341: PetscViewerASCIIPrintf(viewer," %d %g ",a->j[j]+shift,PetscRealPart(a->a[j]));
342: }
343: #else
344: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %d %g ",a->j[j]+shift,a->a[j]);}
345: #endif
346: }
347: PetscViewerASCIIPrintf(viewer,"n");
348: }
349: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
350: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
351: int nzd=0,fshift=1,*sptr;
352: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
353: PetscMalloc((m+1)*sizeof(int),&sptr);
354: for (i=0; i<m; i++) {
355: sptr[i] = nzd+1;
356: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
357: if (a->j[j] >= i) {
358: #if defined(PETSC_USE_COMPLEX)
359: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
360: #else
361: if (a->a[j] != 0.0) nzd++;
362: #endif
363: }
364: }
365: }
366: sptr[m] = nzd+1;
367: PetscViewerASCIIPrintf(viewer," %d %dnn",m,nzd);
368: for (i=0; i<m+1; i+=6) {
369: if (i+4<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4],sptr[i+5]);}
370: else if (i+3<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3],sptr[i+4]);}
371: else if (i+2<m) {PetscViewerASCIIPrintf(viewer," %d %d %d %dn",sptr[i],sptr[i+1],sptr[i+2],sptr[i+3]);}
372: else if (i+1<m) {PetscViewerASCIIPrintf(viewer," %d %d %dn",sptr[i],sptr[i+1],sptr[i+2]);}
373: else if (i<m) {PetscViewerASCIIPrintf(viewer," %d %dn",sptr[i],sptr[i+1]);}
374: else {PetscViewerASCIIPrintf(viewer," %dn",sptr[i]);}
375: }
376: PetscViewerASCIIPrintf(viewer,"n");
377: PetscFree(sptr);
378: for (i=0; i<m; i++) {
379: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
380: if (a->j[j] >= i) {PetscViewerASCIIPrintf(viewer," %d ",a->j[j]+fshift);}
381: }
382: PetscViewerASCIIPrintf(viewer,"n");
383: }
384: PetscViewerASCIIPrintf(viewer,"n");
385: for (i=0; i<m; i++) {
386: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
387: if (a->j[j] >= i) {
388: #if defined(PETSC_USE_COMPLEX)
389: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) {
390: PetscViewerASCIIPrintf(viewer," %18.16e %18.16e ",PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
391: }
392: #else
393: if (a->a[j] != 0.0) {PetscViewerASCIIPrintf(viewer," %18.16e ",a->a[j]);}
394: #endif
395: }
396: }
397: PetscViewerASCIIPrintf(viewer,"n");
398: }
399: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
400: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
401: int cnt = 0,jcnt;
402: PetscScalar value;
404: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
405: for (i=0; i<m; i++) {
406: jcnt = 0;
407: for (j=0; j<A->n; j++) {
408: if (jcnt < a->i[i+1]-a->i[i] && j == a->j[cnt]) {
409: value = a->a[cnt++];
410: jcnt++;
411: } else {
412: value = 0.0;
413: }
414: #if defined(PETSC_USE_COMPLEX)
415: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",PetscRealPart(value),PetscImaginaryPart(value));
416: #else
417: PetscViewerASCIIPrintf(viewer," %7.5e ",value);
418: #endif
419: }
420: PetscViewerASCIIPrintf(viewer,"n");
421: }
422: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
423: } else {
424: PetscViewerASCIIUseTabs(viewer,PETSC_NO);
425: for (i=0; i<m; i++) {
426: PetscViewerASCIIPrintf(viewer,"row %d:",i);
427: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
428: #if defined(PETSC_USE_COMPLEX)
429: if (PetscImaginaryPart(a->a[j]) > 0.0) {
430: PetscViewerASCIIPrintf(viewer," %d %g + %g i",a->j[j]+shift,PetscRealPart(a->a[j]),PetscImaginaryPart(a->a[j]));
431: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
432: PetscViewerASCIIPrintf(viewer," %d %g - %g i",a->j[j]+shift,PetscRealPart(a->a[j]),-PetscImaginaryPart(a->a[j]));
433: } else {
434: PetscViewerASCIIPrintf(viewer," %d %g ",a->j[j]+shift,PetscRealPart(a->a[j]));
435: }
436: #else
437: PetscViewerASCIIPrintf(viewer," %d %g ",a->j[j]+shift,a->a[j]);
438: #endif
439: }
440: PetscViewerASCIIPrintf(viewer,"n");
441: }
442: PetscViewerASCIIUseTabs(viewer,PETSC_YES);
443: }
444: PetscViewerFlush(viewer);
445: return(0);
446: }
448: int MatView_SeqAIJ_Draw_Zoom(PetscDraw draw,void *Aa)
449: {
450: Mat A = (Mat) Aa;
451: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
452: int ierr,i,j,m = A->m,shift = a->indexshift,color;
453: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r,maxv = 0.0;
454: PetscViewer viewer;
455: PetscViewerFormat format;
458: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
459: PetscViewerGetFormat(viewer,&format);
461: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
462: /* loop over matrix elements drawing boxes */
464: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
465: /* Blue for negative, Cyan for zero and Red for positive */
466: color = PETSC_DRAW_BLUE;
467: for (i=0; i<m; i++) {
468: y_l = m - i - 1.0; y_r = y_l + 1.0;
469: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
470: x_l = a->j[j] + shift; x_r = x_l + 1.0;
471: #if defined(PETSC_USE_COMPLEX)
472: if (PetscRealPart(a->a[j]) >= 0.) continue;
473: #else
474: if (a->a[j] >= 0.) continue;
475: #endif
476: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
477: }
478: }
479: color = PETSC_DRAW_CYAN;
480: for (i=0; i<m; i++) {
481: y_l = m - i - 1.0; y_r = y_l + 1.0;
482: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
483: x_l = a->j[j] + shift; x_r = x_l + 1.0;
484: if (a->a[j] != 0.) continue;
485: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
486: }
487: }
488: color = PETSC_DRAW_RED;
489: for (i=0; i<m; i++) {
490: y_l = m - i - 1.0; y_r = y_l + 1.0;
491: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
492: x_l = a->j[j] + shift; x_r = x_l + 1.0;
493: #if defined(PETSC_USE_COMPLEX)
494: if (PetscRealPart(a->a[j]) <= 0.) continue;
495: #else
496: if (a->a[j] <= 0.) continue;
497: #endif
498: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
499: }
500: }
501: } else {
502: /* use contour shading to indicate magnitude of values */
503: /* first determine max of all nonzero values */
504: int nz = a->nz,count;
505: PetscDraw popup;
506: PetscReal scale;
508: for (i=0; i<nz; i++) {
509: if (PetscAbsScalar(a->a[i]) > maxv) maxv = PetscAbsScalar(a->a[i]);
510: }
511: scale = (245.0 - PETSC_DRAW_BASIC_COLORS)/maxv;
512: ierr = PetscDrawGetPopup(draw,&popup);
513: if (popup) {ierr = PetscDrawScalePopup(popup,0.0,maxv);}
514: count = 0;
515: for (i=0; i<m; i++) {
516: y_l = m - i - 1.0; y_r = y_l + 1.0;
517: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
518: x_l = a->j[j] + shift; x_r = x_l + 1.0;
519: color = PETSC_DRAW_BASIC_COLORS + (int)(scale*PetscAbsScalar(a->a[count]));
520: ierr = PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
521: count++;
522: }
523: }
524: }
525: return(0);
526: }
528: int MatView_SeqAIJ_Draw(Mat A,PetscViewer viewer)
529: {
530: int ierr;
531: PetscDraw draw;
532: PetscReal xr,yr,xl,yl,h,w;
533: PetscTruth isnull;
536: PetscViewerDrawGetDraw(viewer,0,&draw);
537: PetscDrawIsNull(draw,&isnull);
538: if (isnull) return(0);
540: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
541: xr = A->n; yr = A->m; h = yr/10.0; w = xr/10.0;
542: xr += w; yr += h; xl = -w; yl = -h;
543: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
544: PetscDrawZoom(draw,MatView_SeqAIJ_Draw_Zoom,A);
545: PetscObjectCompose((PetscObject)A,"Zoomviewer",PETSC_NULL);
546: return(0);
547: }
549: int MatView_SeqAIJ(Mat A,PetscViewer viewer)
550: {
551: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
552: int ierr;
553: PetscTruth issocket,isascii,isbinary,isdraw;
556: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_SOCKET,&issocket);
557: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_ASCII,&isascii);
558: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_BINARY,&isbinary);
559: PetscTypeCompare((PetscObject)viewer,PETSC_VIEWER_DRAW,&isdraw);
560: if (issocket) {
561: if (a->indexshift) {
562: SETERRQ(1,"Can only socket send sparse matrix with 0 based indexing");
563: }
564: PetscViewerSocketPutSparse_Private(viewer,A->m,A->n,a->nz,a->a,a->i,a->j);
565: } else if (isascii) {
566: MatView_SeqAIJ_ASCII(A,viewer);
567: } else if (isbinary) {
568: MatView_SeqAIJ_Binary(A,viewer);
569: } else if (isdraw) {
570: MatView_SeqAIJ_Draw(A,viewer);
571: } else {
572: SETERRQ1(1,"Viewer type %s not supported by SeqAIJ matrices",((PetscObject)viewer)->type_name);
573: }
574: return(0);
575: }
577: EXTERN int Mat_AIJ_CheckInode(Mat,PetscTruth);
578: EXTERN int MatUseSuperLU_DIST_MPIAIJ(Mat);
579: int MatAssemblyEnd_SeqAIJ(Mat A,MatAssemblyType mode)
580: {
581: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
582: int fshift = 0,i,j,*ai = a->i,*aj = a->j,*imax = a->imax,ierr;
583: int m = A->m,*ip,N,*ailen = a->ilen,shift = a->indexshift,rmax = 0;
584: PetscScalar *aa = a->a,*ap;
585: #if defined(PETSC_HAVE_SUPERLUDIST)
586: PetscTruth flag;
587: #endif
590: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
592: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
593: for (i=1; i<m; i++) {
594: /* move each row back by the amount of empty slots (fshift) before it*/
595: fshift += imax[i-1] - ailen[i-1];
596: rmax = PetscMax(rmax,ailen[i]);
597: if (fshift) {
598: ip = aj + ai[i] + shift;
599: ap = aa + ai[i] + shift;
600: N = ailen[i];
601: for (j=0; j<N; j++) {
602: ip[j-fshift] = ip[j];
603: ap[j-fshift] = ap[j];
604: }
605: }
606: ai[i] = ai[i-1] + ailen[i-1];
607: }
608: if (m) {
609: fshift += imax[m-1] - ailen[m-1];
610: ai[m] = ai[m-1] + ailen[m-1];
611: }
612: /* reset ilen and imax for each row */
613: for (i=0; i<m; i++) {
614: ailen[i] = imax[i] = ai[i+1] - ai[i];
615: }
616: a->nz = ai[m] + shift;
618: /* diagonals may have moved, so kill the diagonal pointers */
619: if (fshift && a->diag) {
620: PetscFree(a->diag);
621: PetscLogObjectMemory(A,-(m+1)*sizeof(int));
622: a->diag = 0;
623: }
624: PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Matrix size: %d X %d; storage space: %d unneeded,%d usedn",m,A->n,fshift,a->nz);
625: PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Number of mallocs during MatSetValues() is %dn",a->reallocs);
626: PetscLogInfo(A,"MatAssemblyEnd_SeqAIJ:Most nonzeros in any row is %dn",rmax);
627: a->reallocs = 0;
628: A->info.nz_unneeded = (double)fshift;
629: a->rmax = rmax;
631: /* check out for identical nodes. If found, use inode functions */
632: Mat_AIJ_CheckInode(A,(PetscTruth)(!fshift));
633: #if defined(PETSC_HAVE_SUPERLUDIST)
634: PetscOptionsHasName(PETSC_NULL,"-mat_aij_superlu_dist",&flag);
635: if (flag) { MatUseSuperLU_DIST_MPIAIJ(A); }
636: #endif
638: return(0);
639: }
641: int MatZeroEntries_SeqAIJ(Mat A)
642: {
643: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
644: int ierr;
647: PetscMemzero(a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
648: return(0);
649: }
651: int MatDestroy_SeqAIJ(Mat A)
652: {
653: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
654: int ierr;
657: #if defined(PETSC_USE_LOG)
658: PetscLogObjectState((PetscObject)A,"Rows=%d, Cols=%d, NZ=%d",A->m,A->n,a->nz);
659: #endif
660: if (a->freedata) {
661: PetscFree(a->a);
662: if (!a->singlemalloc) {
663: PetscFree(a->i);
664: PetscFree(a->j);
665: }
666: }
667: if (a->row) {
668: ISDestroy(a->row);
669: }
670: if (a->col) {
671: ISDestroy(a->col);
672: }
673: if (a->diag) {PetscFree(a->diag);}
674: if (a->ilen) {PetscFree(a->ilen);}
675: if (a->imax) {PetscFree(a->imax);}
676: if (a->idiag) {PetscFree(a->idiag);}
677: if (a->solve_work) {PetscFree(a->solve_work);}
678: if (a->inode.size) {PetscFree(a->inode.size);}
679: if (a->icol) {ISDestroy(a->icol);}
680: if (a->saved_values) {PetscFree(a->saved_values);}
681: if (a->coloring) {ISColoringDestroy(a->coloring);}
682: PetscFree(a);
683: return(0);
684: }
686: int MatCompress_SeqAIJ(Mat A)
687: {
689: return(0);
690: }
692: int MatSetOption_SeqAIJ(Mat A,MatOption op)
693: {
694: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
697: switch (op) {
698: case MAT_ROW_ORIENTED:
699: a->roworiented = PETSC_TRUE;
700: break;
701: case MAT_KEEP_ZEROED_ROWS:
702: a->keepzeroedrows = PETSC_TRUE;
703: break;
704: case MAT_COLUMN_ORIENTED:
705: a->roworiented = PETSC_FALSE;
706: break;
707: case MAT_COLUMNS_SORTED:
708: a->sorted = PETSC_TRUE;
709: break;
710: case MAT_COLUMNS_UNSORTED:
711: a->sorted = PETSC_FALSE;
712: break;
713: case MAT_NO_NEW_NONZERO_LOCATIONS:
714: a->nonew = 1;
715: break;
716: case MAT_NEW_NONZERO_LOCATION_ERR:
717: a->nonew = -1;
718: break;
719: case MAT_NEW_NONZERO_ALLOCATION_ERR:
720: a->nonew = -2;
721: break;
722: case MAT_YES_NEW_NONZERO_LOCATIONS:
723: a->nonew = 0;
724: break;
725: case MAT_IGNORE_ZERO_ENTRIES:
726: a->ignorezeroentries = PETSC_TRUE;
727: break;
728: case MAT_USE_INODES:
729: a->inode.use = PETSC_TRUE;
730: break;
731: case MAT_DO_NOT_USE_INODES:
732: a->inode.use = PETSC_FALSE;
733: break;
734: case MAT_ROWS_SORTED:
735: case MAT_ROWS_UNSORTED:
736: case MAT_YES_NEW_DIAGONALS:
737: case MAT_IGNORE_OFF_PROC_ENTRIES:
738: case MAT_USE_HASH_TABLE:
739: case MAT_USE_SINGLE_PRECISION_SOLVES:
740: PetscLogInfo(A,"MatSetOption_SeqAIJ:Option ignoredn");
741: break;
742: case MAT_NO_NEW_DIAGONALS:
743: SETERRQ(PETSC_ERR_SUP,"MAT_NO_NEW_DIAGONALS");
744: case MAT_INODE_LIMIT_1:
745: a->inode.limit = 1;
746: break;
747: case MAT_INODE_LIMIT_2:
748: a->inode.limit = 2;
749: break;
750: case MAT_INODE_LIMIT_3:
751: a->inode.limit = 3;
752: break;
753: case MAT_INODE_LIMIT_4:
754: a->inode.limit = 4;
755: break;
756: case MAT_INODE_LIMIT_5:
757: a->inode.limit = 5;
758: break;
759: default:
760: SETERRQ(PETSC_ERR_SUP,"unknown option");
761: }
762: return(0);
763: }
765: int MatGetDiagonal_SeqAIJ(Mat A,Vec v)
766: {
767: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
768: int i,j,n,shift = a->indexshift,ierr;
769: PetscScalar *x,zero = 0.0;
772: VecSet(&zero,v);
773: VecGetArray(v,&x);
774: VecGetLocalSize(v,&n);
775: if (n != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
776: for (i=0; i<A->m; i++) {
777: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
778: if (a->j[j]+shift == i) {
779: x[i] = a->a[j];
780: break;
781: }
782: }
783: }
784: VecRestoreArray(v,&x);
785: return(0);
786: }
789: int MatMultTransposeAdd_SeqAIJ(Mat A,Vec xx,Vec zz,Vec yy)
790: {
791: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
792: PetscScalar *x,*y;
793: int ierr,m = A->m,shift = a->indexshift;
794: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
795: PetscScalar *v,alpha;
796: int n,i,*idx;
797: #endif
800: if (zz != yy) {VecCopy(zz,yy);}
801: VecGetArray(xx,&x);
802: VecGetArray(yy,&y);
803: y = y + shift; /* shift for Fortran start by 1 indexing */
805: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
806: fortranmulttransposeaddaij_(&m,x,a->i,a->j+shift,a->a+shift,y);
807: #else
808: for (i=0; i<m; i++) {
809: idx = a->j + a->i[i] + shift;
810: v = a->a + a->i[i] + shift;
811: n = a->i[i+1] - a->i[i];
812: alpha = x[i];
813: while (n-->0) {y[*idx++] += alpha * *v++;}
814: }
815: #endif
816: PetscLogFlops(2*a->nz);
817: VecRestoreArray(xx,&x);
818: VecRestoreArray(yy,&y);
819: return(0);
820: }
822: int MatMultTranspose_SeqAIJ(Mat A,Vec xx,Vec yy)
823: {
824: PetscScalar zero = 0.0;
825: int ierr;
828: VecSet(&zero,yy);
829: MatMultTransposeAdd_SeqAIJ(A,xx,yy,yy);
830: return(0);
831: }
834: int MatMult_SeqAIJ(Mat A,Vec xx,Vec yy)
835: {
836: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
837: PetscScalar *x,*y,*v,sum;
838: int ierr,m = A->m,*idx,shift = a->indexshift,*ii;
839: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
840: int n,i,jrow,j;
841: #endif
843: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
844: #pragma disjoint(*x,*y,*v)
845: #endif
848: VecGetArray(xx,&x);
849: VecGetArray(yy,&y);
850: x = x + shift; /* shift for Fortran start by 1 indexing */
851: idx = a->j;
852: v = a->a;
853: ii = a->i;
854: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
855: fortranmultaij_(&m,x,ii,idx+shift,v+shift,y);
856: #else
857: v += shift; /* shift for Fortran start by 1 indexing */
858: idx += shift;
859: for (i=0; i<m; i++) {
860: jrow = ii[i];
861: n = ii[i+1] - jrow;
862: sum = 0.0;
863: for (j=0; j<n; j++) {
864: sum += v[jrow]*x[idx[jrow]]; jrow++;
865: }
866: y[i] = sum;
867: }
868: #endif
869: PetscLogFlops(2*a->nz - m);
870: VecRestoreArray(xx,&x);
871: VecRestoreArray(yy,&y);
872: return(0);
873: }
875: int MatMultAdd_SeqAIJ(Mat A,Vec xx,Vec yy,Vec zz)
876: {
877: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
878: PetscScalar *x,*y,*z,*v,sum;
879: int ierr,m = A->m,*idx,shift = a->indexshift,*ii;
880: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
881: int n,i,jrow,j;
882: #endif
885: VecGetArray(xx,&x);
886: VecGetArray(yy,&y);
887: if (zz != yy) {
888: VecGetArray(zz,&z);
889: } else {
890: z = y;
891: }
892: x = x + shift; /* shift for Fortran start by 1 indexing */
893: idx = a->j;
894: v = a->a;
895: ii = a->i;
896: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
897: fortranmultaddaij_(&m,x,ii,idx+shift,v+shift,y,z);
898: #else
899: v += shift; /* shift for Fortran start by 1 indexing */
900: idx += shift;
901: for (i=0; i<m; i++) {
902: jrow = ii[i];
903: n = ii[i+1] - jrow;
904: sum = y[i];
905: for (j=0; j<n; j++) {
906: sum += v[jrow]*x[idx[jrow]]; jrow++;
907: }
908: z[i] = sum;
909: }
910: #endif
911: PetscLogFlops(2*a->nz);
912: VecRestoreArray(xx,&x);
913: VecRestoreArray(yy,&y);
914: if (zz != yy) {
915: VecRestoreArray(zz,&z);
916: }
917: return(0);
918: }
920: /*
921: Adds diagonal pointers to sparse matrix structure.
922: */
923: int MatMarkDiagonal_SeqAIJ(Mat A)
924: {
925: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
926: int i,j,*diag,m = A->m,shift = a->indexshift,ierr;
929: if (a->diag) return(0);
931: PetscMalloc((m+1)*sizeof(int),&diag);
932: PetscLogObjectMemory(A,(m+1)*sizeof(int));
933: for (i=0; i<A->m; i++) {
934: diag[i] = a->i[i+1];
935: for (j=a->i[i]+shift; j<a->i[i+1]+shift; j++) {
936: if (a->j[j]+shift == i) {
937: diag[i] = j - shift;
938: break;
939: }
940: }
941: }
942: a->diag = diag;
943: return(0);
944: }
946: /*
947: Checks for missing diagonals
948: */
949: int MatMissingDiagonal_SeqAIJ(Mat A)
950: {
951: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
952: int *diag,*jj = a->j,i,shift = a->indexshift,ierr;
955: MatMarkDiagonal_SeqAIJ(A);
956: diag = a->diag;
957: for (i=0; i<A->m; i++) {
958: if (jj[diag[i]+shift] != i-shift) {
959: SETERRQ1(1,"Matrix is missing diagonal number %d",i);
960: }
961: }
962: return(0);
963: }
965: int MatRelax_SeqAIJ(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,int its,int lits,Vec xx)
966: {
967: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
968: PetscScalar *x,*b,*bs, d,*xs,sum,*v = a->a,*t=0,scale,*ts,*xb,*idiag=0;
969: int ierr,*idx,*diag,n = A->n,m = A->m,i,shift = a->indexshift;
972: its = its*lits;
973: if (its <= 0) SETERRQ2(PETSC_ERR_ARG_WRONG,"Relaxation requires global its %d and local its %d both positive",its,lits);
975: VecGetArray(xx,&x);
976: if (xx != bb) {
977: VecGetArray(bb,&b);
978: } else {
979: b = x;
980: }
982: if (!a->diag) {MatMarkDiagonal_SeqAIJ(A);}
983: diag = a->diag;
984: xs = x + shift; /* shifted by one for index start of a or a->j*/
985: if (flag == SOR_APPLY_UPPER) {
986: /* apply (U + D/omega) to the vector */
987: bs = b + shift;
988: for (i=0; i<m; i++) {
989: d = fshift + a->a[diag[i] + shift];
990: n = a->i[i+1] - diag[i] - 1;
991: PetscLogFlops(2*n-1);
992: idx = a->j + diag[i] + (!shift);
993: v = a->a + diag[i] + (!shift);
994: sum = b[i]*d/omega;
995: SPARSEDENSEDOT(sum,bs,v,idx,n);
996: x[i] = sum;
997: }
998: VecRestoreArray(xx,&x);
999: if (bb != xx) {VecRestoreArray(bb,&b);}
1000: return(0);
1001: }
1003: /* setup workspace for Eisenstat */
1004: if (flag & SOR_EISENSTAT) {
1005: if (!a->idiag) {
1006: ierr = PetscMalloc(2*m*sizeof(PetscScalar),&a->idiag);
1007: a->ssor = a->idiag + m;
1008: v = a->a;
1009: for (i=0; i<m; i++) { a->idiag[i] = 1.0/v[diag[i]];}
1010: }
1011: t = a->ssor;
1012: idiag = a->idiag;
1013: }
1014: /* Let A = L + U + D; where L is lower trianglar,
1015: U is upper triangular, E is diagonal; This routine applies
1017: (L + E)^{-1} A (U + E)^{-1}
1019: to a vector efficiently using Eisenstat's trick. This is for
1020: the case of SSOR preconditioner, so E is D/omega where omega
1021: is the relaxation factor.
1022: */
1024: if (flag == SOR_APPLY_LOWER) {
1025: SETERRQ(PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1026: } else if ((flag & SOR_EISENSTAT) && omega == 1.0 && shift == 0 && fshift == 0.0) {
1027: /* special case for omega = 1.0 saves flops and some integer ops */
1028: PetscScalar *v2;
1029:
1030: v2 = a->a;
1031: /* x = (E + U)^{-1} b */
1032: for (i=m-1; i>=0; i--) {
1033: n = a->i[i+1] - diag[i] - 1;
1034: idx = a->j + diag[i] + 1;
1035: v = a->a + diag[i] + 1;
1036: sum = b[i];
1037: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1038: x[i] = sum*idiag[i];
1040: /* t = b - (2*E - D)x */
1041: t[i] = b[i] - (v2[diag[i]])*x[i];
1042: }
1044: /* t = (E + L)^{-1}t */
1045: diag = a->diag;
1046: for (i=0; i<m; i++) {
1047: n = diag[i] - a->i[i];
1048: idx = a->j + a->i[i];
1049: v = a->a + a->i[i];
1050: sum = t[i];
1051: SPARSEDENSEMDOT(sum,t,v,idx,n);
1052: t[i] = sum*idiag[i];
1054: /* x = x + t */
1055: x[i] += t[i];
1056: }
1058: PetscLogFlops(3*m-1 + 2*a->nz);
1059: VecRestoreArray(xx,&x);
1060: if (bb != xx) {VecRestoreArray(bb,&b);}
1061: return(0);
1062: } else if (flag & SOR_EISENSTAT) {
1063: /* Let A = L + U + D; where L is lower trianglar,
1064: U is upper triangular, E is diagonal; This routine applies
1066: (L + E)^{-1} A (U + E)^{-1}
1068: to a vector efficiently using Eisenstat's trick. This is for
1069: the case of SSOR preconditioner, so E is D/omega where omega
1070: is the relaxation factor.
1071: */
1072: scale = (2.0/omega) - 1.0;
1074: /* x = (E + U)^{-1} b */
1075: for (i=m-1; i>=0; i--) {
1076: d = fshift + a->a[diag[i] + shift];
1077: n = a->i[i+1] - diag[i] - 1;
1078: idx = a->j + diag[i] + (!shift);
1079: v = a->a + diag[i] + (!shift);
1080: sum = b[i];
1081: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1082: x[i] = omega*(sum/d);
1083: }
1085: /* t = b - (2*E - D)x */
1086: v = a->a;
1087: for (i=0; i<m; i++) { t[i] = b[i] - scale*(v[*diag++ + shift])*x[i]; }
1089: /* t = (E + L)^{-1}t */
1090: ts = t + shift; /* shifted by one for index start of a or a->j*/
1091: diag = a->diag;
1092: for (i=0; i<m; i++) {
1093: d = fshift + a->a[diag[i]+shift];
1094: n = diag[i] - a->i[i];
1095: idx = a->j + a->i[i] + shift;
1096: v = a->a + a->i[i] + shift;
1097: sum = t[i];
1098: SPARSEDENSEMDOT(sum,ts,v,idx,n);
1099: t[i] = omega*(sum/d);
1100: /* x = x + t */
1101: x[i] += t[i];
1102: }
1104: PetscLogFlops(6*m-1 + 2*a->nz);
1105: VecRestoreArray(xx,&x);
1106: if (bb != xx) {VecRestoreArray(bb,&b);}
1107: return(0);
1108: }
1109: if (flag & SOR_ZERO_INITIAL_GUESS) {
1110: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1111: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1112: fortranrelaxaijforwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1113: #else
1114: for (i=0; i<m; i++) {
1115: d = fshift + a->a[diag[i]+shift];
1116: n = diag[i] - a->i[i];
1117: PetscLogFlops(2*n-1);
1118: idx = a->j + a->i[i] + shift;
1119: v = a->a + a->i[i] + shift;
1120: sum = b[i];
1121: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1122: x[i] = omega*(sum/d);
1123: }
1124: #endif
1125: xb = x;
1126: } else xb = b;
1127: if ((flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) &&
1128: (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1129: for (i=0; i<m; i++) {
1130: x[i] *= a->a[diag[i]+shift];
1131: }
1132: PetscLogFlops(m);
1133: }
1134: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1135: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1136: fortranrelaxaijbackwardzero_(&m,&omega,x,a->i,a->j,diag,a->a,xb);
1137: #else
1138: for (i=m-1; i>=0; i--) {
1139: d = fshift + a->a[diag[i] + shift];
1140: n = a->i[i+1] - diag[i] - 1;
1141: PetscLogFlops(2*n-1);
1142: idx = a->j + diag[i] + (!shift);
1143: v = a->a + diag[i] + (!shift);
1144: sum = xb[i];
1145: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1146: x[i] = omega*(sum/d);
1147: }
1148: #endif
1149: }
1150: its--;
1151: }
1152: while (its--) {
1153: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP){
1154: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1155: fortranrelaxaijforward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1156: #else
1157: for (i=0; i<m; i++) {
1158: d = fshift + a->a[diag[i]+shift];
1159: n = a->i[i+1] - a->i[i];
1160: PetscLogFlops(2*n-1);
1161: idx = a->j + a->i[i] + shift;
1162: v = a->a + a->i[i] + shift;
1163: sum = b[i];
1164: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1165: x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1166: }
1167: #endif
1168: }
1169: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP){
1170: #if defined(PETSC_USE_FORTRAN_KERNEL_RELAXAIJ)
1171: fortranrelaxaijbackward_(&m,&omega,x,a->i,a->j,diag,a->a,b);
1172: #else
1173: for (i=m-1; i>=0; i--) {
1174: d = fshift + a->a[diag[i] + shift];
1175: n = a->i[i+1] - a->i[i];
1176: PetscLogFlops(2*n-1);
1177: idx = a->j + a->i[i] + shift;
1178: v = a->a + a->i[i] + shift;
1179: sum = b[i];
1180: SPARSEDENSEMDOT(sum,xs,v,idx,n);
1181: x[i] = (1. - omega)*x[i] + omega*(sum + a->a[diag[i]+shift]*x[i])/d;
1182: }
1183: #endif
1184: }
1185: }
1186: VecRestoreArray(xx,&x);
1187: if (bb != xx) {VecRestoreArray(bb,&b);}
1188: return(0);
1189: }
1191: int MatGetInfo_SeqAIJ(Mat A,MatInfoType flag,MatInfo *info)
1192: {
1193: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1196: info->rows_global = (double)A->m;
1197: info->columns_global = (double)A->n;
1198: info->rows_local = (double)A->m;
1199: info->columns_local = (double)A->n;
1200: info->block_size = 1.0;
1201: info->nz_allocated = (double)a->maxnz;
1202: info->nz_used = (double)a->nz;
1203: info->nz_unneeded = (double)(a->maxnz - a->nz);
1204: info->assemblies = (double)A->num_ass;
1205: info->mallocs = (double)a->reallocs;
1206: info->memory = A->mem;
1207: if (A->factor) {
1208: info->fill_ratio_given = A->info.fill_ratio_given;
1209: info->fill_ratio_needed = A->info.fill_ratio_needed;
1210: info->factor_mallocs = A->info.factor_mallocs;
1211: } else {
1212: info->fill_ratio_given = 0;
1213: info->fill_ratio_needed = 0;
1214: info->factor_mallocs = 0;
1215: }
1216: return(0);
1217: }
1219: EXTERN int MatLUFactorSymbolic_SeqAIJ(Mat,IS,IS,MatLUInfo*,Mat*);
1220: EXTERN int MatLUFactorNumeric_SeqAIJ(Mat,Mat*);
1221: EXTERN int MatLUFactor_SeqAIJ(Mat,IS,IS,MatLUInfo*);
1222: EXTERN int MatSolve_SeqAIJ(Mat,Vec,Vec);
1223: EXTERN int MatSolveAdd_SeqAIJ(Mat,Vec,Vec,Vec);
1224: EXTERN int MatSolveTranspose_SeqAIJ(Mat,Vec,Vec);
1225: EXTERN int MatSolveTransposeAdd_SeqAIJ(Mat,Vec,Vec,Vec);
1227: int MatZeroRows_SeqAIJ(Mat A,IS is,PetscScalar *diag)
1228: {
1229: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1230: int i,ierr,N,*rows,m = A->m - 1,shift = a->indexshift;
1233: ISGetLocalSize(is,&N);
1234: ISGetIndices(is,&rows);
1235: if (a->keepzeroedrows) {
1236: for (i=0; i<N; i++) {
1237: if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1238: PetscMemzero(&a->a[a->i[rows[i]]+shift],a->ilen[rows[i]]*sizeof(PetscScalar));
1239: }
1240: if (diag) {
1241: MatMissingDiagonal_SeqAIJ(A);
1242: MatMarkDiagonal_SeqAIJ(A);
1243: for (i=0; i<N; i++) {
1244: a->a[a->diag[rows[i]]] = *diag;
1245: }
1246: }
1247: } else {
1248: if (diag) {
1249: for (i=0; i<N; i++) {
1250: if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1251: if (a->ilen[rows[i]] > 0) {
1252: a->ilen[rows[i]] = 1;
1253: a->a[a->i[rows[i]]+shift] = *diag;
1254: a->j[a->i[rows[i]]+shift] = rows[i]+shift;
1255: } else { /* in case row was completely empty */
1256: MatSetValues_SeqAIJ(A,1,&rows[i],1,&rows[i],diag,INSERT_VALUES);
1257: }
1258: }
1259: } else {
1260: for (i=0; i<N; i++) {
1261: if (rows[i] < 0 || rows[i] > m) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"row out of range");
1262: a->ilen[rows[i]] = 0;
1263: }
1264: }
1265: }
1266: ISRestoreIndices(is,&rows);
1267: MatAssemblyEnd_SeqAIJ(A,MAT_FINAL_ASSEMBLY);
1268: return(0);
1269: }
1271: int MatGetRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1272: {
1273: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1274: int *itmp,i,shift = a->indexshift,ierr;
1277: if (row < 0 || row >= A->m) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"Row %d out of range",row);
1279: *nz = a->i[row+1] - a->i[row];
1280: if (v) *v = a->a + a->i[row] + shift;
1281: if (idx) {
1282: itmp = a->j + a->i[row] + shift;
1283: if (*nz && shift) {
1284: PetscMalloc((*nz)*sizeof(int),idx);
1285: for (i=0; i<(*nz); i++) {(*idx)[i] = itmp[i] + shift;}
1286: } else if (*nz) {
1287: *idx = itmp;
1288: }
1289: else *idx = 0;
1290: }
1291: return(0);
1292: }
1294: int MatRestoreRow_SeqAIJ(Mat A,int row,int *nz,int **idx,PetscScalar **v)
1295: {
1296: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1300: if (idx) {if (*idx && a->indexshift) {PetscFree(*idx);}}
1301: return(0);
1302: }
1304: int MatNorm_SeqAIJ(Mat A,NormType type,PetscReal *nrm)
1305: {
1306: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1307: PetscScalar *v = a->a;
1308: PetscReal sum = 0.0;
1309: int i,j,shift = a->indexshift,ierr;
1312: if (type == NORM_FROBENIUS) {
1313: for (i=0; i<a->nz; i++) {
1314: #if defined(PETSC_USE_COMPLEX)
1315: sum += PetscRealPart(PetscConj(*v)*(*v)); v++;
1316: #else
1317: sum += (*v)*(*v); v++;
1318: #endif
1319: }
1320: *nrm = sqrt(sum);
1321: } else if (type == NORM_1) {
1322: PetscReal *tmp;
1323: int *jj = a->j;
1324: PetscMalloc((A->n+1)*sizeof(PetscReal),&tmp);
1325: PetscMemzero(tmp,A->n*sizeof(PetscReal));
1326: *nrm = 0.0;
1327: for (j=0; j<a->nz; j++) {
1328: tmp[*jj++ + shift] += PetscAbsScalar(*v); v++;
1329: }
1330: for (j=0; j<A->n; j++) {
1331: if (tmp[j] > *nrm) *nrm = tmp[j];
1332: }
1333: PetscFree(tmp);
1334: } else if (type == NORM_INFINITY) {
1335: *nrm = 0.0;
1336: for (j=0; j<A->m; j++) {
1337: v = a->a + a->i[j] + shift;
1338: sum = 0.0;
1339: for (i=0; i<a->i[j+1]-a->i[j]; i++) {
1340: sum += PetscAbsScalar(*v); v++;
1341: }
1342: if (sum > *nrm) *nrm = sum;
1343: }
1344: } else {
1345: SETERRQ(PETSC_ERR_SUP,"No support for two norm");
1346: }
1347: return(0);
1348: }
1350: int MatTranspose_SeqAIJ(Mat A,Mat *B)
1351: {
1352: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1353: Mat C;
1354: int i,ierr,*aj = a->j,*ai = a->i,m = A->m,len,*col;
1355: int shift = a->indexshift;
1356: PetscScalar *array = a->a;
1359: if (!B && m != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Square matrix only for in-place");
1360: PetscMalloc((1+A->n)*sizeof(int),&col);
1361: PetscMemzero(col,(1+A->n)*sizeof(int));
1362: if (shift) {
1363: for (i=0; i<ai[m]-1; i++) aj[i] -= 1;
1364: }
1365: for (i=0; i<ai[m]+shift; i++) col[aj[i]] += 1;
1366: MatCreateSeqAIJ(A->comm,A->n,m,0,col,&C);
1367: PetscFree(col);
1368: for (i=0; i<m; i++) {
1369: len = ai[i+1]-ai[i];
1370: ierr = MatSetValues(C,len,aj,1,&i,array,INSERT_VALUES);
1371: array += len;
1372: aj += len;
1373: }
1374: if (shift) {
1375: for (i=0; i<ai[m]-1; i++) aj[i] += 1;
1376: }
1378: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1379: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1381: if (B) {
1382: *B = C;
1383: } else {
1384: MatHeaderCopy(A,C);
1385: }
1386: return(0);
1387: }
1389: int MatDiagonalScale_SeqAIJ(Mat A,Vec ll,Vec rr)
1390: {
1391: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1392: PetscScalar *l,*r,x,*v;
1393: int ierr,i,j,m = A->m,n = A->n,M,nz = a->nz,*jj,shift = a->indexshift;
1396: if (ll) {
1397: /* The local size is used so that VecMPI can be passed to this routine
1398: by MatDiagonalScale_MPIAIJ */
1399: VecGetLocalSize(ll,&m);
1400: if (m != A->m) SETERRQ(PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
1401: VecGetArray(ll,&l);
1402: v = a->a;
1403: for (i=0; i<m; i++) {
1404: x = l[i];
1405: M = a->i[i+1] - a->i[i];
1406: for (j=0; j<M; j++) { (*v++) *= x;}
1407: }
1408: VecRestoreArray(ll,&l);
1409: PetscLogFlops(nz);
1410: }
1411: if (rr) {
1412: VecGetLocalSize(rr,&n);
1413: if (n != A->n) SETERRQ(PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
1414: VecGetArray(rr,&r);
1415: v = a->a; jj = a->j;
1416: for (i=0; i<nz; i++) {
1417: (*v++) *= r[*jj++ + shift];
1418: }
1419: VecRestoreArray(rr,&r);
1420: PetscLogFlops(nz);
1421: }
1422: return(0);
1423: }
1425: int MatGetSubMatrix_SeqAIJ(Mat A,IS isrow,IS iscol,int csize,MatReuse scall,Mat *B)
1426: {
1427: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data,*c;
1428: int *smap,i,k,kstart,kend,ierr,oldcols = A->n,*lens;
1429: int row,mat_i,*mat_j,tcol,first,step,*mat_ilen,sum,lensi;
1430: int *irow,*icol,nrows,ncols,shift = a->indexshift,*ssmap;
1431: int *starts,*j_new,*i_new,*aj = a->j,*ai = a->i,ii,*ailen = a->ilen;
1432: PetscScalar *a_new,*mat_a;
1433: Mat C;
1434: PetscTruth stride;
1437: ISSorted(isrow,(PetscTruth*)&i);
1438: if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"ISrow is not sorted");
1439: ISSorted(iscol,(PetscTruth*)&i);
1440: if (!i) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"IScol is not sorted");
1442: ISGetIndices(isrow,&irow);
1443: ISGetLocalSize(isrow,&nrows);
1444: ISGetLocalSize(iscol,&ncols);
1446: ISStrideGetInfo(iscol,&first,&step);
1447: ISStride(iscol,&stride);
1448: if (stride && step == 1) {
1449: /* special case of contiguous rows */
1450: ierr = PetscMalloc((2*nrows+1)*sizeof(int),&lens);
1451: starts = lens + nrows;
1452: /* loop over new rows determining lens and starting points */
1453: for (i=0; i<nrows; i++) {
1454: kstart = ai[irow[i]]+shift;
1455: kend = kstart + ailen[irow[i]];
1456: for (k=kstart; k<kend; k++) {
1457: if (aj[k]+shift >= first) {
1458: starts[i] = k;
1459: break;
1460: }
1461: }
1462: sum = 0;
1463: while (k < kend) {
1464: if (aj[k++]+shift >= first+ncols) break;
1465: sum++;
1466: }
1467: lens[i] = sum;
1468: }
1469: /* create submatrix */
1470: if (scall == MAT_REUSE_MATRIX) {
1471: int n_cols,n_rows;
1472: MatGetSize(*B,&n_rows,&n_cols);
1473: if (n_rows != nrows || n_cols != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Reused submatrix wrong size");
1474: MatZeroEntries(*B);
1475: C = *B;
1476: } else {
1477: MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1478: }
1479: c = (Mat_SeqAIJ*)C->data;
1481: /* loop over rows inserting into submatrix */
1482: a_new = c->a;
1483: j_new = c->j;
1484: i_new = c->i;
1485: i_new[0] = -shift;
1486: for (i=0; i<nrows; i++) {
1487: ii = starts[i];
1488: lensi = lens[i];
1489: for (k=0; k<lensi; k++) {
1490: *j_new++ = aj[ii+k] - first;
1491: }
1492: PetscMemcpy(a_new,a->a + starts[i],lensi*sizeof(PetscScalar));
1493: a_new += lensi;
1494: i_new[i+1] = i_new[i] + lensi;
1495: c->ilen[i] = lensi;
1496: }
1497: PetscFree(lens);
1498: } else {
1499: ierr = ISGetIndices(iscol,&icol);
1500: ierr = PetscMalloc((1+oldcols)*sizeof(int),&smap);
1501: ssmap = smap + shift;
1502: ierr = PetscMalloc((1+nrows)*sizeof(int),&lens);
1503: ierr = PetscMemzero(smap,oldcols*sizeof(int));
1504: for (i=0; i<ncols; i++) smap[icol[i]] = i+1;
1505: /* determine lens of each row */
1506: for (i=0; i<nrows; i++) {
1507: kstart = ai[irow[i]]+shift;
1508: kend = kstart + a->ilen[irow[i]];
1509: lens[i] = 0;
1510: for (k=kstart; k<kend; k++) {
1511: if (ssmap[aj[k]]) {
1512: lens[i]++;
1513: }
1514: }
1515: }
1516: /* Create and fill new matrix */
1517: if (scall == MAT_REUSE_MATRIX) {
1518: PetscTruth equal;
1520: c = (Mat_SeqAIJ *)((*B)->data);
1521: if ((*B)->m != nrows || (*B)->n != ncols) SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong size");
1522: PetscMemcmp(c->ilen,lens,(*B)->m*sizeof(int),&equal);
1523: if (!equal) {
1524: SETERRQ(PETSC_ERR_ARG_SIZ,"Cannot reuse matrix. wrong no of nonzeros");
1525: }
1526: PetscMemzero(c->ilen,(*B)->m*sizeof(int));
1527: C = *B;
1528: } else {
1529: MatCreateSeqAIJ(A->comm,nrows,ncols,0,lens,&C);
1530: }
1531: c = (Mat_SeqAIJ *)(C->data);
1532: for (i=0; i<nrows; i++) {
1533: row = irow[i];
1534: kstart = ai[row]+shift;
1535: kend = kstart + a->ilen[row];
1536: mat_i = c->i[i]+shift;
1537: mat_j = c->j + mat_i;
1538: mat_a = c->a + mat_i;
1539: mat_ilen = c->ilen + i;
1540: for (k=kstart; k<kend; k++) {
1541: if ((tcol=ssmap[a->j[k]])) {
1542: *mat_j++ = tcol - (!shift);
1543: *mat_a++ = a->a[k];
1544: (*mat_ilen)++;
1546: }
1547: }
1548: }
1549: /* Free work space */
1550: ISRestoreIndices(iscol,&icol);
1551: PetscFree(smap);
1552: PetscFree(lens);
1553: }
1554: MatAssemblyBegin(C,MAT_FINAL_ASSEMBLY);
1555: MatAssemblyEnd(C,MAT_FINAL_ASSEMBLY);
1557: ISRestoreIndices(isrow,&irow);
1558: *B = C;
1559: return(0);
1560: }
1562: /*
1563: */
1564: int MatILUFactor_SeqAIJ(Mat inA,IS row,IS col,MatILUInfo *info)
1565: {
1566: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1567: int ierr;
1568: Mat outA;
1569: PetscTruth row_identity,col_identity;
1572: if (info && info->levels != 0) SETERRQ(PETSC_ERR_SUP,"Only levels=0 supported for in-place ilu");
1573: ISIdentity(row,&row_identity);
1574: ISIdentity(col,&col_identity);
1575: if (!row_identity || !col_identity) {
1576: SETERRQ(1,"Row and column permutations must be identity for in-place ILU");
1577: }
1579: outA = inA;
1580: inA->factor = FACTOR_LU;
1581: a->row = row;
1582: a->col = col;
1583: PetscObjectReference((PetscObject)row);
1584: PetscObjectReference((PetscObject)col);
1586: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
1587: if (a->icol) {ISDestroy(a->icol);} /* need to remove old one */
1588: ISInvertPermutation(col,PETSC_DECIDE,&a->icol);
1589: PetscLogObjectParent(inA,a->icol);
1591: if (!a->solve_work) { /* this matrix may have been factored before */
1592: PetscMalloc((inA->m+1)*sizeof(PetscScalar),&a->solve_work);
1593: }
1595: if (!a->diag) {
1596: MatMarkDiagonal_SeqAIJ(inA);
1597: }
1598: MatLUFactorNumeric_SeqAIJ(inA,&outA);
1599: return(0);
1600: }
1602: #include petscblaslapack.h
1603: int MatScale_SeqAIJ(PetscScalar *alpha,Mat inA)
1604: {
1605: Mat_SeqAIJ *a = (Mat_SeqAIJ*)inA->data;
1606: int one = 1;
1609: BLscal_(&a->nz,alpha,a->a,&one);
1610: PetscLogFlops(a->nz);
1611: return(0);
1612: }
1614: int MatGetSubMatrices_SeqAIJ(Mat A,int n,IS *irow,IS *icol,MatReuse scall,Mat **B)
1615: {
1616: int ierr,i;
1619: if (scall == MAT_INITIAL_MATRIX) {
1620: PetscMalloc((n+1)*sizeof(Mat),B);
1621: }
1623: for (i=0; i<n; i++) {
1624: MatGetSubMatrix_SeqAIJ(A,irow[i],icol[i],PETSC_DECIDE,scall,&(*B)[i]);
1625: }
1626: return(0);
1627: }
1629: int MatGetBlockSize_SeqAIJ(Mat A,int *bs)
1630: {
1632: *bs = 1;
1633: return(0);
1634: }
1636: int MatIncreaseOverlap_SeqAIJ(Mat A,int is_max,IS *is,int ov)
1637: {
1638: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1639: int shift,row,i,j,k,l,m,n,*idx,ierr,*nidx,isz,val;
1640: int start,end,*ai,*aj;
1641: PetscBT table;
1644: shift = a->indexshift;
1645: m = A->m;
1646: ai = a->i;
1647: aj = a->j+shift;
1649: if (ov < 0) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"illegal overlap value used");
1651: PetscMalloc((m+1)*sizeof(int),&nidx);
1652: PetscBTCreate(m,table);
1654: for (i=0; i<is_max; i++) {
1655: /* Initialize the two local arrays */
1656: isz = 0;
1657: PetscBTMemzero(m,table);
1658:
1659: /* Extract the indices, assume there can be duplicate entries */
1660: ISGetIndices(is[i],&idx);
1661: ISGetLocalSize(is[i],&n);
1662:
1663: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
1664: for (j=0; j<n ; ++j){
1665: if(!PetscBTLookupSet(table,idx[j])) { nidx[isz++] = idx[j];}
1666: }
1667: ISRestoreIndices(is[i],&idx);
1668: ISDestroy(is[i]);
1669:
1670: k = 0;
1671: for (j=0; j<ov; j++){ /* for each overlap */
1672: n = isz;
1673: for (; k<n ; k++){ /* do only those rows in nidx[k], which are not done yet */
1674: row = nidx[k];
1675: start = ai[row];
1676: end = ai[row+1];
1677: for (l = start; l<end ; l++){
1678: val = aj[l] + shift;
1679: if (!PetscBTLookupSet(table,val)) {nidx[isz++] = val;}
1680: }
1681: }
1682: }
1683: ISCreateGeneral(PETSC_COMM_SELF,isz,nidx,(is+i));
1684: }
1685: PetscBTDestroy(table);
1686: PetscFree(nidx);
1687: return(0);
1688: }
1690: /* -------------------------------------------------------------- */
1691: int MatPermute_SeqAIJ(Mat A,IS rowp,IS colp,Mat *B)
1692: {
1693: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1694: PetscScalar *vwork;
1695: int i,ierr,nz,m = A->m,n = A->n,*cwork;
1696: int *row,*col,*cnew,j,*lens;
1697: IS icolp,irowp;
1700: ISInvertPermutation(rowp,PETSC_DECIDE,&irowp);
1701: ISGetIndices(irowp,&row);
1702: ISInvertPermutation(colp,PETSC_DECIDE,&icolp);
1703: ISGetIndices(icolp,&col);
1704:
1705: /* determine lengths of permuted rows */
1706: PetscMalloc((m+1)*sizeof(int),&lens);
1707: for (i=0; i<m; i++) {
1708: lens[row[i]] = a->i[i+1] - a->i[i];
1709: }
1710: MatCreateSeqAIJ(A->comm,m,n,0,lens,B);
1711: PetscFree(lens);
1713: PetscMalloc(n*sizeof(int),&cnew);
1714: for (i=0; i<m; i++) {
1715: MatGetRow(A,i,&nz,&cwork,&vwork);
1716: for (j=0; j<nz; j++) { cnew[j] = col[cwork[j]];}
1717: MatSetValues(*B,1,&row[i],nz,cnew,vwork,INSERT_VALUES);
1718: MatRestoreRow(A,i,&nz,&cwork,&vwork);
1719: }
1720: PetscFree(cnew);
1721: MatAssemblyBegin(*B,MAT_FINAL_ASSEMBLY);
1722: MatAssemblyEnd(*B,MAT_FINAL_ASSEMBLY);
1723: ISRestoreIndices(irowp,&row);
1724: ISRestoreIndices(icolp,&col);
1725: ISDestroy(irowp);
1726: ISDestroy(icolp);
1727: return(0);
1728: }
1730: int MatPrintHelp_SeqAIJ(Mat A)
1731: {
1732: static PetscTruth called = PETSC_FALSE;
1733: MPI_Comm comm = A->comm;
1734: int ierr;
1737: if (called) {return(0);} else called = PETSC_TRUE;
1738: (*PetscHelpPrintf)(comm," Options for MATSEQAIJ and MATMPIAIJ matrix formats (the defaults):n");
1739: (*PetscHelpPrintf)(comm," -mat_lu_pivotthreshold <threshold>: Set pivoting thresholdn");
1740: (*PetscHelpPrintf)(comm," -mat_aij_oneindex: internal indices begin at 1 instead of the default 0.n");
1741: (*PetscHelpPrintf)(comm," -mat_aij_no_inode: Do not use inodesn");
1742: (*PetscHelpPrintf)(comm," -mat_aij_inode_limit <limit>: Set inode limit (max limit=5)n");
1743: #if defined(PETSC_HAVE_ESSL)
1744: (*PetscHelpPrintf)(comm," -mat_aij_essl: Use IBM sparse LU factorization and solve.n");
1745: #endif
1746: #if defined(PETSC_HAVE_LUSOL)
1747: (*PetscHelpPrintf)(comm," -mat_aij_lusol: Use the Stanford LUSOL sparse factorization and solve.n");
1748: #endif
1749: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
1750: (*PetscHelpPrintf)(comm," -mat_aij_matlab: Use Matlab engine sparse LU factorization and solve.n");
1751: #endif
1752: return(0);
1753: }
1754: EXTERN int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg);
1755: EXTERN int MatFDColoringCreate_SeqAIJ(Mat,ISColoring,MatFDColoring);
1756: EXTERN int MatILUDTFactor_SeqAIJ(Mat,MatILUInfo*,IS,IS,Mat*);
1757: int MatCopy_SeqAIJ(Mat A,Mat B,MatStructure str)
1758: {
1759: int ierr;
1760: PetscTruth flg;
1763: PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg);
1764: if (str == SAME_NONZERO_PATTERN && flg) {
1765: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1766: Mat_SeqAIJ *b = (Mat_SeqAIJ*)B->data;
1768: if (a->i[A->m]+a->indexshift != b->i[B->m]+a->indexshift) {
1769: SETERRQ(1,"Number of nonzeros in two matrices are different");
1770: }
1771: PetscMemcpy(b->a,a->a,(a->i[A->m]+a->indexshift)*sizeof(PetscScalar));
1772: } else {
1773: MatCopy_Basic(A,B,str);
1774: }
1775: return(0);
1776: }
1778: int MatSetUpPreallocation_SeqAIJ(Mat A)
1779: {
1780: int ierr;
1783: MatSeqAIJSetPreallocation(A,PETSC_DEFAULT,0);
1784: return(0);
1785: }
1787: int MatGetArray_SeqAIJ(Mat A,PetscScalar **array)
1788: {
1789: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
1791: *array = a->a;
1792: return(0);
1793: }
1795: int MatRestoreArray_SeqAIJ(Mat A,PetscScalar **array)
1796: {
1798: return(0);
1799: }
1801: int MatFDColoringApply_SeqAIJ(Mat J,MatFDColoring coloring,Vec x1,MatStructure *flag,void *sctx)
1802: {
1803: int (*f)(void *,Vec,Vec,void*) = (int (*)(void *,Vec,Vec,void *))coloring->f;
1804: int k,ierr,N,start,end,l,row,col,srow,**vscaleforrow,m1,m2;
1805: PetscScalar dx,mone = -1.0,*y,*xx,*w3_array;
1806: PetscScalar *vscale_array;
1807: PetscReal epsilon = coloring->error_rel,umin = coloring->umin;
1808: Vec w1,w2,w3;
1809: void *fctx = coloring->fctx;
1810: PetscTruth flg;
1813: if (!coloring->w1) {
1814: VecDuplicate(x1,&coloring->w1);
1815: PetscLogObjectParent(coloring,coloring->w1);
1816: VecDuplicate(x1,&coloring->w2);
1817: PetscLogObjectParent(coloring,coloring->w2);
1818: VecDuplicate(x1,&coloring->w3);
1819: PetscLogObjectParent(coloring,coloring->w3);
1820: }
1821: w1 = coloring->w1; w2 = coloring->w2; w3 = coloring->w3;
1823: MatSetUnfactored(J);
1824: PetscOptionsHasName(PETSC_NULL,"-mat_fd_coloring_dont_rezero",&flg);
1825: if (flg) {
1826: PetscLogInfo(coloring,"MatFDColoringApply_SeqAIJ: Not calling MatZeroEntries()n");
1827: } else {
1828: MatZeroEntries(J);
1829: }
1831: VecGetOwnershipRange(x1,&start,&end);
1832: VecGetSize(x1,&N);
1834: /*
1835: This is a horrible, horrible, hack. See DMMGComputeJacobian_Multigrid() it inproperly sets
1836: coloring->F for the coarser grids from the finest
1837: */
1838: if (coloring->F) {
1839: VecGetLocalSize(coloring->F,&m1);
1840: VecGetLocalSize(w1,&m2);
1841: if (m1 != m2) {
1842: coloring->F = 0;
1843: }
1844: }
1846: if (coloring->F) {
1847: w1 = coloring->F;
1848: coloring->F = 0;
1849: } else {
1850: (*f)(sctx,x1,w1,fctx);
1851: }
1853: /*
1854: Compute all the scale factors and share with other processors
1855: */
1856: VecGetArray(x1,&xx);xx = xx - start;
1857: VecGetArray(coloring->vscale,&vscale_array);vscale_array = vscale_array - start;
1858: for (k=0; k<coloring->ncolors; k++) {
1859: /*
1860: Loop over each column associated with color adding the
1861: perturbation to the vector w3.
1862: */
1863: for (l=0; l<coloring->ncolumns[k]; l++) {
1864: col = coloring->columns[k][l]; /* column of the matrix we are probing for */
1865: dx = xx[col];
1866: if (dx == 0.0) dx = 1.0;
1867: #if !defined(PETSC_USE_COMPLEX)
1868: if (dx < umin && dx >= 0.0) dx = umin;
1869: else if (dx < 0.0 && dx > -umin) dx = -umin;
1870: #else
1871: if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin;
1872: else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1873: #endif
1874: dx *= epsilon;
1875: vscale_array[col] = 1.0/dx;
1876: }
1877: }
1878: vscale_array = vscale_array + start;VecRestoreArray(coloring->vscale,&vscale_array);
1879: VecGhostUpdateBegin(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
1880: VecGhostUpdateEnd(coloring->vscale,INSERT_VALUES,SCATTER_FORWARD);
1882: /* VecView(coloring->vscale,PETSC_VIEWER_STDOUT_WORLD);
1883: VecView(x1,PETSC_VIEWER_STDOUT_WORLD);*/
1885: if (coloring->vscaleforrow) vscaleforrow = coloring->vscaleforrow;
1886: else vscaleforrow = coloring->columnsforrow;
1888: VecGetArray(coloring->vscale,&vscale_array);
1889: /*
1890: Loop over each color
1891: */
1892: for (k=0; k<coloring->ncolors; k++) {
1893: VecCopy(x1,w3);
1894: VecGetArray(w3,&w3_array);w3_array = w3_array - start;
1895: /*
1896: Loop over each column associated with color adding the
1897: perturbation to the vector w3.
1898: */
1899: for (l=0; l<coloring->ncolumns[k]; l++) {
1900: col = coloring->columns[k][l]; /* column of the matrix we are probing for */
1901: dx = xx[col];
1902: if (dx == 0.0) dx = 1.0;
1903: #if !defined(PETSC_USE_COMPLEX)
1904: if (dx < umin && dx >= 0.0) dx = umin;
1905: else if (dx < 0.0 && dx > -umin) dx = -umin;
1906: #else
1907: if (PetscAbsScalar(dx) < umin && PetscRealPart(dx) >= 0.0) dx = umin;
1908: else if (PetscRealPart(dx) < 0.0 && PetscAbsScalar(dx) < umin) dx = -umin;
1909: #endif
1910: dx *= epsilon;
1911: if (!PetscAbsScalar(dx)) SETERRQ(1,"Computed 0 differencing parameter");
1912: w3_array[col] += dx;
1913: }
1914: w3_array = w3_array + start; VecRestoreArray(w3,&w3_array);
1916: /*
1917: Evaluate function at x1 + dx (here dx is a vector of perturbations)
1918: */
1920: (*f)(sctx,w3,w2,fctx);
1921: VecAXPY(&mone,w1,w2);
1923: /*
1924: Loop over rows of vector, putting results into Jacobian matrix
1925: */
1926: VecGetArray(w2,&y);
1927: for (l=0; l<coloring->nrows[k]; l++) {
1928: row = coloring->rows[k][l];
1929: col = coloring->columnsforrow[k][l];
1930: y[row] *= vscale_array[vscaleforrow[k][l]];
1931: srow = row + start;
1932: ierr = MatSetValues_SeqAIJ(J,1,&srow,1,&col,y+row,INSERT_VALUES);
1933: }
1934: VecRestoreArray(w2,&y);
1935: }
1936: VecRestoreArray(coloring->vscale,&vscale_array);
1937: xx = xx + start; ierr = VecRestoreArray(x1,&xx);
1938: ierr = MatAssemblyBegin(J,MAT_FINAL_ASSEMBLY);
1939: ierr = MatAssemblyEnd(J,MAT_FINAL_ASSEMBLY);
1940: return(0);
1941: }
1943: #include petscblaslapack.h
1945: int MatAXPY_SeqAIJ(PetscScalar *a,Mat X,Mat Y,MatStructure str)
1946: {
1947: int ierr,one;
1948: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data,*y = (Mat_SeqAIJ *)Y->data;
1951: if (str == SAME_NONZERO_PATTERN) {
1952: BLaxpy_(&x->nz,a,x->a,&one,y->a,&one);
1953: } else {
1954: MatAXPY_Basic(a,X,Y,str);
1955: }
1956: return(0);
1957: }
1960: /* -------------------------------------------------------------------*/
1961: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
1962: MatGetRow_SeqAIJ,
1963: MatRestoreRow_SeqAIJ,
1964: MatMult_SeqAIJ,
1965: MatMultAdd_SeqAIJ,
1966: MatMultTranspose_SeqAIJ,
1967: MatMultTransposeAdd_SeqAIJ,
1968: MatSolve_SeqAIJ,
1969: MatSolveAdd_SeqAIJ,
1970: MatSolveTranspose_SeqAIJ,
1971: MatSolveTransposeAdd_SeqAIJ,
1972: MatLUFactor_SeqAIJ,
1973: 0,
1974: MatRelax_SeqAIJ,
1975: MatTranspose_SeqAIJ,
1976: MatGetInfo_SeqAIJ,
1977: MatEqual_SeqAIJ,
1978: MatGetDiagonal_SeqAIJ,
1979: MatDiagonalScale_SeqAIJ,
1980: MatNorm_SeqAIJ,
1981: 0,
1982: MatAssemblyEnd_SeqAIJ,
1983: MatCompress_SeqAIJ,
1984: MatSetOption_SeqAIJ,
1985: MatZeroEntries_SeqAIJ,
1986: MatZeroRows_SeqAIJ,
1987: MatLUFactorSymbolic_SeqAIJ,
1988: MatLUFactorNumeric_SeqAIJ,
1989: 0,
1990: 0,
1991: MatSetUpPreallocation_SeqAIJ,
1992: MatILUFactorSymbolic_SeqAIJ,
1993: 0,
1994: MatGetArray_SeqAIJ,
1995: MatRestoreArray_SeqAIJ,
1996: MatDuplicate_SeqAIJ,
1997: 0,
1998: 0,
1999: MatILUFactor_SeqAIJ,
2000: 0,
2001: MatAXPY_SeqAIJ,
2002: MatGetSubMatrices_SeqAIJ,
2003: MatIncreaseOverlap_SeqAIJ,
2004: MatGetValues_SeqAIJ,
2005: MatCopy_SeqAIJ,
2006: MatPrintHelp_SeqAIJ,
2007: MatScale_SeqAIJ,
2008: 0,
2009: 0,
2010: MatILUDTFactor_SeqAIJ,
2011: MatGetBlockSize_SeqAIJ,
2012: MatGetRowIJ_SeqAIJ,
2013: MatRestoreRowIJ_SeqAIJ,
2014: MatGetColumnIJ_SeqAIJ,
2015: MatRestoreColumnIJ_SeqAIJ,
2016: MatFDColoringCreate_SeqAIJ,
2017: 0,
2018: 0,
2019: MatPermute_SeqAIJ,
2020: 0,
2021: 0,
2022: MatDestroy_SeqAIJ,
2023: MatView_SeqAIJ,
2024: MatGetPetscMaps_Petsc,
2025: 0,
2026: 0,
2027: 0,
2028: 0,
2029: 0,
2030: 0,
2031: 0,
2032: 0,
2033: MatSetColoring_SeqAIJ,
2034: MatSetValuesAdic_SeqAIJ,
2035: MatSetValuesAdifor_SeqAIJ,
2036: MatFDColoringApply_SeqAIJ};
2038: EXTERN int MatUseSuperLU_SeqAIJ(Mat);
2039: EXTERN int MatUseEssl_SeqAIJ(Mat);
2040: EXTERN int MatUseLUSOL_SeqAIJ(Mat);
2041: EXTERN int MatUseMatlab_SeqAIJ(Mat);
2042: EXTERN int MatUseDXML_SeqAIJ(Mat);
2044: EXTERN_C_BEGIN
2046: int MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat,int *indices)
2047: {
2048: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2049: int i,nz,n;
2053: nz = aij->maxnz;
2054: n = mat->n;
2055: for (i=0; i<nz; i++) {
2056: aij->j[i] = indices[i];
2057: }
2058: aij->nz = nz;
2059: for (i=0; i<n; i++) {
2060: aij->ilen[i] = aij->imax[i];
2061: }
2063: return(0);
2064: }
2065: EXTERN_C_END
2067: /*@
2068: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
2069: in the matrix.
2071: Input Parameters:
2072: + mat - the SeqAIJ matrix
2073: - indices - the column indices
2075: Level: advanced
2077: Notes:
2078: This can be called if you have precomputed the nonzero structure of the
2079: matrix and want to provide it to the matrix object to improve the performance
2080: of the MatSetValues() operation.
2082: You MUST have set the correct numbers of nonzeros per row in the call to
2083: MatCreateSeqAIJ().
2085: MUST be called before any calls to MatSetValues();
2087: The indices should start with zero, not one.
2089: @*/
2090: int MatSeqAIJSetColumnIndices(Mat mat,int *indices)
2091: {
2092: int ierr,(*f)(Mat,int *);
2096: PetscObjectQueryFunction((PetscObject)mat,"MatSeqAIJSetColumnIndices_C",(void (**)(void))&f);
2097: if (f) {
2098: (*f)(mat,indices);
2099: } else {
2100: SETERRQ(1,"Wrong type of matrix to set column indices");
2101: }
2102: return(0);
2103: }
2105: /* ----------------------------------------------------------------------------------------*/
2107: EXTERN_C_BEGIN
2108: int MatStoreValues_SeqAIJ(Mat mat)
2109: {
2110: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2111: int nz = aij->i[mat->m]+aij->indexshift,ierr;
2114: if (aij->nonew != 1) {
2115: SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2116: }
2118: /* allocate space for values if not already there */
2119: if (!aij->saved_values) {
2120: PetscMalloc((nz+1)*sizeof(PetscScalar),&aij->saved_values);
2121: }
2123: /* copy values over */
2124: PetscMemcpy(aij->saved_values,aij->a,nz*sizeof(PetscScalar));
2125: return(0);
2126: }
2127: EXTERN_C_END
2129: /*@
2130: MatStoreValues - Stashes a copy of the matrix values; this allows, for
2131: example, reuse of the linear part of a Jacobian, while recomputing the
2132: nonlinear portion.
2134: Collect on Mat
2136: Input Parameters:
2137: . mat - the matrix (currently on AIJ matrices support this option)
2139: Level: advanced
2141: Common Usage, with SNESSolve():
2142: $ Create Jacobian matrix
2143: $ Set linear terms into matrix
2144: $ Apply boundary conditions to matrix, at this time matrix must have
2145: $ final nonzero structure (i.e. setting the nonlinear terms and applying
2146: $ boundary conditions again will not change the nonzero structure
2147: $ MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2148: $ MatStoreValues(mat);
2149: $ Call SNESSetJacobian() with matrix
2150: $ In your Jacobian routine
2151: $ MatRetrieveValues(mat);
2152: $ Set nonlinear terms in matrix
2153:
2154: Common Usage without SNESSolve(), i.e. when you handle nonlinear solve yourself:
2155: $ // build linear portion of Jacobian
2156: $ MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS);
2157: $ MatStoreValues(mat);
2158: $ loop over nonlinear iterations
2159: $ MatRetrieveValues(mat);
2160: $ // call MatSetValues(mat,...) to set nonliner portion of Jacobian
2161: $ // call MatAssemblyBegin/End() on matrix
2162: $ Solve linear system with Jacobian
2163: $ endloop
2165: Notes:
2166: Matrix must already be assemblied before calling this routine
2167: Must set the matrix option MatSetOption(mat,MAT_NO_NEW_NONZERO_LOCATIONS); before
2168: calling this routine.
2170: .seealso: MatRetrieveValues()
2172: @*/
2173: int MatStoreValues(Mat mat)
2174: {
2175: int ierr,(*f)(Mat);
2179: if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2180: if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2182: PetscObjectQueryFunction((PetscObject)mat,"MatStoreValues_C",(void (**)(void))&f);
2183: if (f) {
2184: (*f)(mat);
2185: } else {
2186: SETERRQ(1,"Wrong type of matrix to store values");
2187: }
2188: return(0);
2189: }
2191: EXTERN_C_BEGIN
2192: int MatRetrieveValues_SeqAIJ(Mat mat)
2193: {
2194: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
2195: int nz = aij->i[mat->m]+aij->indexshift,ierr;
2198: if (aij->nonew != 1) {
2199: SETERRQ(1,"Must call MatSetOption(A,MAT_NO_NEW_NONZERO_LOCATIONS);first");
2200: }
2201: if (!aij->saved_values) {
2202: SETERRQ(1,"Must call MatStoreValues(A);first");
2203: }
2205: /* copy values over */
2206: PetscMemcpy(aij->a,aij->saved_values,nz*sizeof(PetscScalar));
2207: return(0);
2208: }
2209: EXTERN_C_END
2211: /*@
2212: MatRetrieveValues - Retrieves the copy of the matrix values; this allows, for
2213: example, reuse of the linear part of a Jacobian, while recomputing the
2214: nonlinear portion.
2216: Collect on Mat
2218: Input Parameters:
2219: . mat - the matrix (currently on AIJ matrices support this option)
2221: Level: advanced
2223: .seealso: MatStoreValues()
2225: @*/
2226: int MatRetrieveValues(Mat mat)
2227: {
2228: int ierr,(*f)(Mat);
2232: if (!mat->assembled) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for unassembled matrix");
2233: if (mat->factor) SETERRQ(PETSC_ERR_ARG_WRONGSTATE,"Not for factored matrix");
2235: PetscObjectQueryFunction((PetscObject)mat,"MatRetrieveValues_C",(void (**)(void))&f);
2236: if (f) {
2237: (*f)(mat);
2238: } else {
2239: SETERRQ(1,"Wrong type of matrix to retrieve values");
2240: }
2241: return(0);
2242: }
2244: /*
2245: This allows SeqAIJ matrices to be passed to the matlab engine
2246: */
2247: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2248: #include "engine.h" /* Matlab include file */
2249: #include "mex.h" /* Matlab include file */
2250: EXTERN_C_BEGIN
2251: int MatMatlabEnginePut_SeqAIJ(PetscObject obj,void *engine)
2252: {
2253: int ierr,i,*ai,*aj;
2254: Mat B = (Mat)obj;
2255: PetscScalar *array;
2256: mxArray *mat;
2257: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)B->data;
2260: mat = mxCreateSparse(B->n,B->m,aij->nz,mxREAL);
2261: PetscMemcpy(mxGetPr(mat),aij->a,aij->nz*sizeof(PetscScalar));
2262: /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2263: PetscMemcpy(mxGetIr(mat),aij->j,aij->nz*sizeof(int));
2264: PetscMemcpy(mxGetJc(mat),aij->i,(B->m+1)*sizeof(int));
2266: /* Matlab indices start at 0 for sparse (what a surprise) */
2267: if (aij->indexshift) {
2268: for (i=0; i<B->m+1; i++) {
2269: ai[i]--;
2270: }
2271: for (i=0; i<aij->nz; i++) {
2272: aj[i]--;
2273: }
2274: }
2275: PetscObjectName(obj);
2276: mxSetName(mat,obj->name);
2277: engPutArray((Engine *)engine,mat);
2278: return(0);
2279: }
2280: EXTERN_C_END
2282: EXTERN_C_BEGIN
2283: int MatMatlabEngineGet_SeqAIJ(PetscObject obj,void *engine)
2284: {
2285: int ierr,ii;
2286: Mat mat = (Mat)obj;
2287: Mat_SeqAIJ *aij = (Mat_SeqAIJ*)mat->data;
2288: mxArray *mmat;
2291: PetscFree(aij->a);
2292: aij->indexshift = 0;
2294: mmat = engGetArray((Engine *)engine,obj->name);
2296: aij->nz = (mxGetJc(mmat))[mat->m];
2297: ierr = PetscMalloc(aij->nz*(sizeof(int)+sizeof(PetscScalar))+(mat->m+1)*sizeof(int),&aij->a);
2298: aij->j = (int*)(aij->a + aij->nz);
2299: aij->i = aij->j + aij->nz;
2300: aij->singlemalloc = PETSC_TRUE;
2301: aij->freedata = PETSC_TRUE;
2303: PetscMemcpy(aij->a,mxGetPr(mmat),aij->nz*sizeof(PetscScalar));
2304: /* Matlab stores by column, not row so we pass in the transpose of the matrix */
2305: PetscMemcpy(aij->j,mxGetIr(mmat),aij->nz*sizeof(int));
2306: PetscMemcpy(aij->i,mxGetJc(mmat),(mat->m+1)*sizeof(int));
2308: for (ii=0; ii<mat->m; ii++) {
2309: aij->ilen[ii] = aij->imax[ii] = aij->i[ii+1] - aij->i[ii];
2310: }
2312: MatAssemblyBegin(mat,MAT_FINAL_ASSEMBLY);
2313: MatAssemblyEnd(mat,MAT_FINAL_ASSEMBLY);
2315: return(0);
2316: }
2317: EXTERN_C_END
2318: #endif
2320: /* --------------------------------------------------------------------------------*/
2322: /*@C
2323: MatCreateSeqAIJ - Creates a sparse matrix in AIJ (compressed row) format
2324: (the default parallel PETSc format). For good matrix assembly performance
2325: the user should preallocate the matrix storage by setting the parameter nz
2326: (or the array nnz). By setting these parameters accurately, performance
2327: during matrix assembly can be increased by more than a factor of 50.
2329: Collective on MPI_Comm
2331: Input Parameters:
2332: + comm - MPI communicator, set to PETSC_COMM_SELF
2333: . m - number of rows
2334: . n - number of columns
2335: . nz - number of nonzeros per row (same for all rows)
2336: - nnz - array containing the number of nonzeros in the various rows
2337: (possibly different for each row) or PETSC_NULL
2339: Output Parameter:
2340: . A - the matrix
2342: Notes:
2343: The AIJ format (also called the Yale sparse matrix format or
2344: compressed row storage), is fully compatible with standard Fortran 77
2345: storage. That is, the stored row and column indices can begin at
2346: either one (as in Fortran) or zero. See the users' manual for details.
2348: Specify the preallocated storage with either nz or nnz (not both).
2349: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2350: allocation. For large problems you MUST preallocate memory or you
2351: will get TERRIBLE performance, see the users' manual chapter on matrices.
2353: By default, this format uses inodes (identical nodes) when possible, to
2354: improve numerical efficiency of matrix-vector products and solves. We
2355: search for consecutive rows with the same nonzero structure, thereby
2356: reusing matrix information to achieve increased efficiency.
2358: Options Database Keys:
2359: + -mat_aij_no_inode - Do not use inodes
2360: . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2361: - -mat_aij_oneindex - Internally use indexing starting at 1
2362: rather than 0. Note that when calling MatSetValues(),
2363: the user still MUST index entries starting at 0!
2365: Level: intermediate
2367: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
2369: @*/
2370: int MatCreateSeqAIJ(MPI_Comm comm,int m,int n,int nz,int *nnz,Mat *A)
2371: {
2375: MatCreate(comm,m,n,m,n,A);
2376: MatSetType(*A,MATSEQAIJ);
2377: MatSeqAIJSetPreallocation(*A,nz,nnz);
2378: return(0);
2379: }
2381: /*@C
2382: MatSeqAIJSetPreallocation - For good matrix assembly performance
2383: the user should preallocate the matrix storage by setting the parameter nz
2384: (or the array nnz). By setting these parameters accurately, performance
2385: during matrix assembly can be increased by more than a factor of 50.
2387: Collective on MPI_Comm
2389: Input Parameters:
2390: + comm - MPI communicator, set to PETSC_COMM_SELF
2391: . m - number of rows
2392: . n - number of columns
2393: . nz - number of nonzeros per row (same for all rows)
2394: - nnz - array containing the number of nonzeros in the various rows
2395: (possibly different for each row) or PETSC_NULL
2397: Output Parameter:
2398: . A - the matrix
2400: Notes:
2401: The AIJ format (also called the Yale sparse matrix format or
2402: compressed row storage), is fully compatible with standard Fortran 77
2403: storage. That is, the stored row and column indices can begin at
2404: either one (as in Fortran) or zero. See the users' manual for details.
2406: Specify the preallocated storage with either nz or nnz (not both).
2407: Set nz=PETSC_DEFAULT and nnz=PETSC_NULL for PETSc to control dynamic memory
2408: allocation. For large problems you MUST preallocate memory or you
2409: will get TERRIBLE performance, see the users' manual chapter on matrices.
2411: By default, this format uses inodes (identical nodes) when possible, to
2412: improve numerical efficiency of matrix-vector products and solves. We
2413: search for consecutive rows with the same nonzero structure, thereby
2414: reusing matrix information to achieve increased efficiency.
2416: Options Database Keys:
2417: + -mat_aij_no_inode - Do not use inodes
2418: . -mat_aij_inode_limit <limit> - Sets inode limit (max limit=5)
2419: - -mat_aij_oneindex - Internally use indexing starting at 1
2420: rather than 0. Note that when calling MatSetValues(),
2421: the user still MUST index entries starting at 0!
2423: Level: intermediate
2425: .seealso: MatCreate(), MatCreateMPIAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays()
2427: @*/
2428: int MatSeqAIJSetPreallocation(Mat B,int nz,int *nnz)
2429: {
2430: Mat_SeqAIJ *b;
2431: int i,len,ierr;
2432: PetscTruth flg2;
2435: PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flg2);
2436: if (!flg2) return(0);
2437:
2438: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
2439: if (nz < 0) SETERRQ1(PETSC_ERR_ARG_OUTOFRANGE,"nz cannot be less than 0: value %d",nz);
2440: if (nnz) {
2441: for (i=0; i<B->m; i++) {
2442: if (nnz[i] < 0) SETERRQ2(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be less than 0: local row %d value %d",i,nnz[i]);
2443: if (nnz[i] > B->n) SETERRQ3(PETSC_ERR_ARG_OUTOFRANGE,"nnz cannot be greater than row length: local row %d value %d rowlength %d",i,nnz[i],B->n);
2444: }
2445: }
2447: B->preallocated = PETSC_TRUE;
2448: b = (Mat_SeqAIJ*)B->data;
2450: PetscMalloc((B->m+1)*sizeof(int),&b->imax);
2451: if (!nnz) {
2452: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
2453: else if (nz <= 0) nz = 1;
2454: for (i=0; i<B->m; i++) b->imax[i] = nz;
2455: nz = nz*B->m;
2456: } else {
2457: nz = 0;
2458: for (i=0; i<B->m; i++) {b->imax[i] = nnz[i]; nz += nnz[i];}
2459: }
2461: /* allocate the matrix space */
2462: len = nz*(sizeof(int) + sizeof(PetscScalar)) + (B->m+1)*sizeof(int);
2463: ierr = PetscMalloc(len,&b->a);
2464: b->j = (int*)(b->a + nz);
2465: ierr = PetscMemzero(b->j,nz*sizeof(int));
2466: b->i = b->j + nz;
2467: b->singlemalloc = PETSC_TRUE;
2468: b->freedata = PETSC_TRUE;
2470: b->i[0] = -b->indexshift;
2471: for (i=1; i<B->m+1; i++) {
2472: b->i[i] = b->i[i-1] + b->imax[i-1];
2473: }
2475: /* b->ilen will count nonzeros in each row so far. */
2476: PetscMalloc((B->m+1)*sizeof(int),&b->ilen);
2477: PetscLogObjectMemory(B,len+2*(B->m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2478: for (i=0; i<B->m; i++) { b->ilen[i] = 0;}
2480: b->nz = 0;
2481: b->maxnz = nz;
2482: B->info.nz_unneeded = (double)b->maxnz;
2483: return(0);
2484: }
2486: EXTERN_C_BEGIN
2487: int MatCreate_SeqAIJ(Mat B)
2488: {
2489: Mat_SeqAIJ *b;
2490: int ierr,size;
2491: PetscTruth flg;
2494: MPI_Comm_size(B->comm,&size);
2495: if (size > 1) SETERRQ(PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
2497: B->m = B->M = PetscMax(B->m,B->M);
2498: B->n = B->N = PetscMax(B->n,B->N);
2500: PetscNew(Mat_SeqAIJ,&b);
2501: B->data = (void*)b;
2502: PetscMemzero(b,sizeof(Mat_SeqAIJ));
2503: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
2504: B->factor = 0;
2505: B->lupivotthreshold = 1.0;
2506: B->mapping = 0;
2507: PetscOptionsGetReal(PETSC_NULL,"-mat_lu_pivotthreshold",&B->lupivotthreshold,PETSC_NULL);
2508: PetscOptionsHasName(PETSC_NULL,"-pc_ilu_preserve_row_sums",&b->ilu_preserve_row_sums);
2509: b->row = 0;
2510: b->col = 0;
2511: b->icol = 0;
2512: b->indexshift = 0;
2513: b->reallocs = 0;
2514: PetscOptionsHasName(PETSC_NULL,"-mat_aij_oneindex",&flg);
2515: if (flg) b->indexshift = -1;
2516:
2517: PetscMapCreateMPI(B->comm,B->m,B->m,&B->rmap);
2518: PetscMapCreateMPI(B->comm,B->n,B->n,&B->cmap);
2520: b->sorted = PETSC_FALSE;
2521: b->ignorezeroentries = PETSC_FALSE;
2522: b->roworiented = PETSC_TRUE;
2523: b->nonew = 0;
2524: b->diag = 0;
2525: b->solve_work = 0;
2526: b->spptr = 0;
2527: b->inode.use = PETSC_TRUE;
2528: b->inode.node_count = 0;
2529: b->inode.size = 0;
2530: b->inode.limit = 5;
2531: b->inode.max_limit = 5;
2532: b->saved_values = 0;
2533: b->idiag = 0;
2534: b->ssor = 0;
2535: b->keepzeroedrows = PETSC_FALSE;
2537: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJ);
2539: #if defined(PETSC_HAVE_SUPERLU)
2540: PetscOptionsHasName(PETSC_NULL,"-mat_aij_superlu",&flg);
2541: if (flg) { MatUseSuperLU_SeqAIJ(B); }
2542: #endif
2543: PetscOptionsHasName(PETSC_NULL,"-mat_aij_essl",&flg);
2544: if (flg) { MatUseEssl_SeqAIJ(B); }
2545: PetscOptionsHasName(PETSC_NULL,"-mat_aij_lusol",&flg);
2546: if (flg) { MatUseLUSOL_SeqAIJ(B); }
2547: PetscOptionsHasName(PETSC_NULL,"-mat_aij_matlab",&flg);
2548: if (flg) {MatUseMatlab_SeqAIJ(B);}
2549: PetscOptionsHasName(PETSC_NULL,"-mat_aij_dxml",&flg);
2550: if (flg) {
2551: if (!b->indexshift) SETERRQ(PETSC_ERR_LIB,"need -mat_aij_oneindex with -mat_aij_dxml");
2552: MatUseDXML_SeqAIJ(B);
2553: }
2554: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatSeqAIJSetColumnIndices_C",
2555: "MatSeqAIJSetColumnIndices_SeqAIJ",
2556: MatSeqAIJSetColumnIndices_SeqAIJ);
2557: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatStoreValues_C",
2558: "MatStoreValues_SeqAIJ",
2559: MatStoreValues_SeqAIJ);
2560: PetscObjectComposeFunctionDynamic((PetscObject)B,"MatRetrieveValues_C",
2561: "MatRetrieveValues_SeqAIJ",
2562: MatRetrieveValues_SeqAIJ);
2563: #if defined(PETSC_HAVE_MATLAB_ENGINE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2564: PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEnginePut_C","MatMatlabEnginePut_SeqAIJ",MatMatlabEnginePut_SeqAIJ);
2565: PetscObjectComposeFunctionDynamic((PetscObject)B,"PetscMatlabEngineGet_C","MatMatlabEngineGet_SeqAIJ",MatMatlabEngineGet_SeqAIJ);
2566: #endif
2567: return(0);
2568: }
2569: EXTERN_C_END
2571: int MatDuplicate_SeqAIJ(Mat A,MatDuplicateOption cpvalues,Mat *B)
2572: {
2573: Mat C;
2574: Mat_SeqAIJ *c,*a = (Mat_SeqAIJ*)A->data;
2575: int i,len,m = A->m,shift = a->indexshift,ierr;
2578: *B = 0;
2579: MatCreate(A->comm,A->m,A->n,A->m,A->n,&C);
2580: MatSetType(C,MATSEQAIJ);
2581: c = (Mat_SeqAIJ*)C->data;
2583: C->factor = A->factor;
2584: c->row = 0;
2585: c->col = 0;
2586: c->icol = 0;
2587: c->indexshift = shift;
2588: c->keepzeroedrows = a->keepzeroedrows;
2589: C->assembled = PETSC_TRUE;
2591: C->M = A->m;
2592: C->N = A->n;
2594: PetscMalloc((m+1)*sizeof(int),&c->imax);
2595: PetscMalloc((m+1)*sizeof(int),&c->ilen);
2596: for (i=0; i<m; i++) {
2597: c->imax[i] = a->imax[i];
2598: c->ilen[i] = a->ilen[i];
2599: }
2601: /* allocate the matrix space */
2602: c->singlemalloc = PETSC_TRUE;
2603: len = (m+1)*sizeof(int)+(a->i[m])*(sizeof(PetscScalar)+sizeof(int));
2604: ierr = PetscMalloc(len,&c->a);
2605: c->j = (int*)(c->a + a->i[m] + shift);
2606: c->i = c->j + a->i[m] + shift;
2607: PetscMemcpy(c->i,a->i,(m+1)*sizeof(int));
2608: if (m > 0) {
2609: PetscMemcpy(c->j,a->j,(a->i[m]+shift)*sizeof(int));
2610: if (cpvalues == MAT_COPY_VALUES) {
2611: PetscMemcpy(c->a,a->a,(a->i[m]+shift)*sizeof(PetscScalar));
2612: } else {
2613: PetscMemzero(c->a,(a->i[m]+shift)*sizeof(PetscScalar));
2614: }
2615: }
2617: PetscLogObjectMemory(C,len+2*(m+1)*sizeof(int)+sizeof(struct _p_Mat)+sizeof(Mat_SeqAIJ));
2618: c->sorted = a->sorted;
2619: c->roworiented = a->roworiented;
2620: c->nonew = a->nonew;
2621: c->ilu_preserve_row_sums = a->ilu_preserve_row_sums;
2622: c->saved_values = 0;
2623: c->idiag = 0;
2624: c->ssor = 0;
2625: c->ignorezeroentries = a->ignorezeroentries;
2626: c->freedata = PETSC_TRUE;
2628: if (a->diag) {
2629: PetscMalloc((m+1)*sizeof(int),&c->diag);
2630: PetscLogObjectMemory(C,(m+1)*sizeof(int));
2631: for (i=0; i<m; i++) {
2632: c->diag[i] = a->diag[i];
2633: }
2634: } else c->diag = 0;
2635: c->inode.use = a->inode.use;
2636: c->inode.limit = a->inode.limit;
2637: c->inode.max_limit = a->inode.max_limit;
2638: if (a->inode.size){
2639: ierr = PetscMalloc((m+1)*sizeof(int),&c->inode.size);
2640: c->inode.node_count = a->inode.node_count;
2641: ierr = PetscMemcpy(c->inode.size,a->inode.size,(m+1)*sizeof(int));
2642: } else {
2643: c->inode.size = 0;
2644: c->inode.node_count = 0;
2645: }
2646: c->nz = a->nz;
2647: c->maxnz = a->maxnz;
2648: c->solve_work = 0;
2649: c->spptr = 0; /* Dangerous -I'm throwing away a->spptr */
2650: C->preallocated = PETSC_TRUE;
2652: *B = C;
2653: PetscFListDuplicate(A->qlist,&C->qlist);
2654: return(0);
2655: }
2657: EXTERN_C_BEGIN
2658: int MatLoad_SeqAIJ(PetscViewer viewer,MatType type,Mat *A)
2659: {
2660: Mat_SeqAIJ *a;
2661: Mat B;
2662: int i,nz,ierr,fd,header[4],size,*rowlengths = 0,M,N,shift;
2663: MPI_Comm comm;
2664:
2666: PetscObjectGetComm((PetscObject)viewer,&comm);
2667: MPI_Comm_size(comm,&size);
2668: if (size > 1) SETERRQ(PETSC_ERR_ARG_SIZ,"view must have one processor");
2669: PetscViewerBinaryGetDescriptor(viewer,&fd);
2670: PetscBinaryRead(fd,header,4,PETSC_INT);
2671: if (header[0] != MAT_FILE_COOKIE) SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"not matrix object in file");
2672: M = header[1]; N = header[2]; nz = header[3];
2674: if (nz < 0) {
2675: SETERRQ(PETSC_ERR_FILE_UNEXPECTED,"Matrix stored in special format on disk,cannot load as SeqAIJ");
2676: }
2678: /* read in row lengths */
2679: PetscMalloc(M*sizeof(int),&rowlengths);
2680: PetscBinaryRead(fd,rowlengths,M,PETSC_INT);
2682: /* create our matrix */
2683: MatCreateSeqAIJ(comm,M,N,0,rowlengths,A);
2684: B = *A;
2685: a = (Mat_SeqAIJ*)B->data;
2686: shift = a->indexshift;
2688: /* read in column indices and adjust for Fortran indexing*/
2689: PetscBinaryRead(fd,a->j,nz,PETSC_INT);
2690: if (shift) {
2691: for (i=0; i<nz; i++) {
2692: a->j[i] += 1;
2693: }
2694: }
2696: /* read in nonzero values */
2697: PetscBinaryRead(fd,a->a,nz,PETSC_SCALAR);
2699: /* set matrix "i" values */
2700: a->i[0] = -shift;
2701: for (i=1; i<= M; i++) {
2702: a->i[i] = a->i[i-1] + rowlengths[i-1];
2703: a->ilen[i-1] = rowlengths[i-1];
2704: }
2705: PetscFree(rowlengths);
2707: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
2708: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
2709: return(0);
2710: }
2711: EXTERN_C_END
2713: int MatEqual_SeqAIJ(Mat A,Mat B,PetscTruth* flg)
2714: {
2715: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data,*b = (Mat_SeqAIJ *)B->data;
2716: int ierr;
2717: PetscTruth flag;
2720: PetscTypeCompare((PetscObject)B,MATSEQAIJ,&flag);
2721: if (!flag) SETERRQ(PETSC_ERR_ARG_INCOMP,"Matrices must be same type");
2723: /* If the matrix dimensions are not equal,or no of nonzeros or shift */
2724: if ((A->m != B->m) || (A->n != B->n) ||(a->nz != b->nz)|| (a->indexshift != b->indexshift)) {
2725: *flg = PETSC_FALSE;
2726: return(0);
2727: }
2728:
2729: /* if the a->i are the same */
2730: PetscMemcmp(a->i,b->i,(A->m+1)*sizeof(int),flg);
2731: if (*flg == PETSC_FALSE) return(0);
2732:
2733: /* if a->j are the same */
2734: PetscMemcmp(a->j,b->j,(a->nz)*sizeof(int),flg);
2735: if (*flg == PETSC_FALSE) return(0);
2736:
2737: /* if a->a are the same */
2738: PetscMemcmp(a->a,b->a,(a->nz)*sizeof(PetscScalar),flg);
2740: return(0);
2741:
2742: }
2744: /*@C
2745: MatCreateSeqAIJWithArrays - Creates an sequential AIJ matrix using matrix elements (in CSR format)
2746: provided by the user.
2748: Coolective on MPI_Comm
2750: Input Parameters:
2751: + comm - must be an MPI communicator of size 1
2752: . m - number of rows
2753: . n - number of columns
2754: . i - row indices
2755: . j - column indices
2756: - a - matrix values
2758: Output Parameter:
2759: . mat - the matrix
2761: Level: intermediate
2763: Notes:
2764: The i, j, and a arrays are not copied by this routine, the user must free these arrays
2765: once the matrix is destroyed
2767: You cannot set new nonzero locations into this matrix, that will generate an error.
2769: The i and j indices can be either 0- or 1 based
2771: .seealso: MatCreate(), MatCreateMPIAIJ(), MatCreateSeqAIJ()
2773: @*/
2774: int MatCreateSeqAIJWithArrays(MPI_Comm comm,int m,int n,int* i,int*j,PetscScalar *a,Mat *mat)
2775: {
2776: int ierr,ii;
2777: Mat_SeqAIJ *aij;
2780: MatCreateSeqAIJ(comm,m,n,0,0,mat);
2781: aij = (Mat_SeqAIJ*)(*mat)->data;
2782: PetscFree(aij->a);
2784: if (i[0] == 1) {
2785: aij->indexshift = -1;
2786: } else if (i[0]) {
2787: SETERRQ(1,"i (row indices) do not start with 0 or 1");
2788: }
2789: aij->i = i;
2790: aij->j = j;
2791: aij->a = a;
2792: aij->singlemalloc = PETSC_FALSE;
2793: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
2794: aij->freedata = PETSC_FALSE;
2796: for (ii=0; ii<m; ii++) {
2797: aij->ilen[ii] = aij->imax[ii] = i[ii+1] - i[ii];
2798: #if defined(PETSC_USE_BOPT_g)
2799: if (i[ii+1] - i[ii] < 0) SETERRQ2(1,"Negative row length in i (row indices) row = %d length = %d",ii,i[ii+1] - i[ii]);
2800: #endif
2801: }
2802: #if defined(PETSC_USE_BOPT_g)
2803: for (ii=0; ii<aij->i[m]; ii++) {
2804: if (j[ii] < -aij->indexshift) SETERRQ2(1,"Negative column index at location = %d index = %d",ii,j[ii]);
2805: if (j[ii] > n - 1 -aij->indexshift) SETERRQ2(1,"Column index to large at location = %d index = %d",ii,j[ii]);
2806: }
2807: #endif
2809: /* changes indices to start at 0 */
2810: if (i[0]) {
2811: aij->indexshift = 0;
2812: for (ii=0; ii<m; ii++) {
2813: i[ii]--;
2814: }
2815: for (ii=0; ii<i[m]; ii++) {
2816: j[ii]--;
2817: }
2818: }
2820: MatAssemblyBegin(*mat,MAT_FINAL_ASSEMBLY);
2821: MatAssemblyEnd(*mat,MAT_FINAL_ASSEMBLY);
2822: return(0);
2823: }
2825: int MatSetColoring_SeqAIJ(Mat A,ISColoring coloring)
2826: {
2827: int ierr;
2828: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2831: ierr = ISColoringReference(coloring);
2832: a->coloring = coloring;
2833: return(0);
2834: }
2836: #if defined(PETSC_HAVE_ADIC) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_SINGLE)
2837: EXTERN_C_BEGIN
2838: #include "adic/ad_utils.h"
2839: EXTERN_C_END
2841: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2842: {
2843: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2844: int m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j,nlen;
2845: PetscScalar *v = a->a,*values;
2846: char *cadvalues = (char *)advalues;
2849: if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
2850: nlen = PetscADGetDerivTypeSize();
2851: color = a->coloring->colors;
2852: /* loop over rows */
2853: for (i=0; i<m; i++) {
2854: nz = ii[i+1] - ii[i];
2855: /* loop over columns putting computed value into matrix */
2856: values = PetscADGetGradArray(cadvalues);
2857: for (j=0; j<nz; j++) {
2858: *v++ = values[color[*jj++]];
2859: }
2860: cadvalues += nlen; /* jump to next row of derivatives */
2861: }
2862: return(0);
2863: }
2865: #else
2867: int MatSetValuesAdic_SeqAIJ(Mat A,void *advalues)
2868: {
2870: SETERRQ(1,"PETSc installed without ADIC");
2871: }
2873: #endif
2875: int MatSetValuesAdifor_SeqAIJ(Mat A,int nl,void *advalues)
2876: {
2877: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
2878: int m = A->m,*ii = a->i,*jj = a->j,nz,i,*color,j;
2879: PetscScalar *v = a->a,*values = (PetscScalar *)advalues;
2882: if (!a->coloring) SETERRQ(1,"Coloring not set for matrix");
2883: color = a->coloring->colors;
2884: /* loop over rows */
2885: for (i=0; i<m; i++) {
2886: nz = ii[i+1] - ii[i];
2887: /* loop over columns putting computed value into matrix */
2888: for (j=0; j<nz; j++) {
2889: *v++ = values[color[*jj++]];
2890: }
2891: values += nl; /* jump to next row of derivatives */
2892: }
2893: return(0);
2894: }