Actual source code: lusol.c
1: /*
2: Provides an interface to the LUSOL package of ....
4: */
5: #include <../src/mat/impls/aij/seq/aij.h>
7: #if defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
8: #define LU1FAC lu1fac_
9: #define LU6SOL lu6sol_
10: #define M1PAGE m1page_
11: #define M5SETX m5setx_
12: #define M6RDEL m6rdel_
13: #elif !defined(PETSC_HAVE_FORTRAN_CAPS)
14: #define LU1FAC lu1fac
15: #define LU6SOL lu6sol
16: #define M1PAGE m1page
17: #define M5SETX m5setx
18: #define M6RDEL m6rdel
19: #endif
21: /*
22: Dummy symbols that the MINOS files mi25bfac.f and mi15blas.f may require
23: */
24: PETSC_EXTERN void M1PAGE()
25: {
26: ;
27: }
28: PETSC_EXTERN void M5SETX()
29: {
30: ;
31: }
33: PETSC_EXTERN void M6RDEL()
34: {
35: ;
36: }
38: PETSC_EXTERN void LU1FAC(int *m, int *n, int *nnz, int *size, int *luparm, double *parmlu, double *data, int *indc, int *indr, int *rowperm, int *colperm, int *collen, int *rowlen, int *colstart, int *rowstart, int *rploc, int *cploc, int *rpinv, int *cpinv, double *w, int *inform);
40: PETSC_EXTERN void LU6SOL(int *mode, int *m, int *n, double *rhs, double *x, int *size, int *luparm, double *parmlu, double *data, int *indc, int *indr, int *rowperm, int *colperm, int *collen, int *rowlen, int *colstart, int *rowstart, int *inform);
42: extern PetscErrorCode MatDuplicate_LUSOL(Mat, MatDuplicateOption, Mat *);
44: typedef struct {
45: double *data;
46: int *indc;
47: int *indr;
49: int *ip;
50: int *iq;
51: int *lenc;
52: int *lenr;
53: int *locc;
54: int *locr;
55: int *iploc;
56: int *iqloc;
57: int *ipinv;
58: int *iqinv;
59: double *mnsw;
60: double *mnsv;
62: double elbowroom;
63: double luroom; /* Extra space allocated when factor fails */
64: double parmlu[30]; /* Input/output to LUSOL */
66: int n; /* Number of rows/columns in matrix */
67: int nz; /* Number of nonzeros */
68: int nnz; /* Number of nonzeros allocated for factors */
69: int luparm[30]; /* Input/output to LUSOL */
71: PetscBool CleanUpLUSOL;
73: } Mat_LUSOL;
75: /*
76: LUSOL input/Output Parameters (Description uses C-style indexes
78: Input parameters Typical value
79: luparm(0) = nout File number for printed messages. 6
80: luparm(1) = lprint Print level. 0
81: < 0 suppresses output.
82: = 0 gives error messages.
83: = 1 gives debug output from some of the
84: other routines in LUSOL.
85: >= 2 gives the pivot row and column and the
86: no. of rows and columns involved at
87: each elimination step in lu1fac.
88: luparm(2) = maxcol lu1fac: maximum number of columns 5
89: searched allowed in a Markowitz-type
90: search for the next pivot element.
91: For some of the factorization, the
92: number of rows searched is
93: maxrow = maxcol - 1.
95: Output parameters:
97: luparm(9) = inform Return code from last call to any LU routine.
98: luparm(10) = nsing No. of singularities marked in the
99: output array w(*).
100: luparm(11) = jsing Column index of last singularity.
101: luparm(12) = minlen Minimum recommended value for lena.
102: luparm(13) = maxlen ?
103: luparm(14) = nupdat No. of updates performed by the lu8 routines.
104: luparm(15) = nrank No. of nonempty rows of U.
105: luparm(16) = ndens1 No. of columns remaining when the density of
106: the matrix being factorized reached dens1.
107: luparm(17) = ndens2 No. of columns remaining when the density of
108: the matrix being factorized reached dens2.
109: luparm(18) = jumin The column index associated with dumin.
110: luparm(19) = numl0 No. of columns in initial L.
111: luparm(20) = lenl0 Size of initial L (no. of nonzeros).
112: luparm(21) = lenu0 Size of initial U.
113: luparm(22) = lenl Size of current L.
114: luparm(23) = lenu Size of current U.
115: luparm(24) = lrow Length of row file.
116: luparm(25) = ncp No. of compressions of LU data structures.
117: luparm(26) = mersum lu1fac: sum of Markowitz merit counts.
118: luparm(27) = nutri lu1fac: triangular rows in U.
119: luparm(28) = nltri lu1fac: triangular rows in L.
120: luparm(29) =
122: Input parameters Typical value
123: parmlu(0) = elmax1 Max multiplier allowed in L 10.0
124: during factor.
125: parmlu(1) = elmax2 Max multiplier allowed in L 10.0
126: during updates.
127: parmlu(2) = small Absolute tolerance for eps**0.8
128: treating reals as zero. IBM double: 3.0d-13
129: parmlu(3) = utol1 Absolute tol for flagging eps**0.66667
130: small diagonals of U. IBM double: 3.7d-11
131: parmlu(4) = utol2 Relative tol for flagging eps**0.66667
132: small diagonals of U. IBM double: 3.7d-11
133: parmlu(5) = uspace Factor limiting waste space in U. 3.0
134: In lu1fac, the row or column lists
135: are compressed if their length
136: exceeds uspace times the length of
137: either file after the last compression.
138: parmlu(6) = dens1 The density at which the Markowitz 0.3
139: strategy should search maxcol columns
140: and no rows.
141: parmlu(7) = dens2 the density at which the Markowitz 0.6
142: strategy should search only 1 column
143: or (preferably) use a dense LU for
144: all the remaining rows and columns.
146: Output parameters:
147: parmlu(9) = amax Maximum element in A.
148: parmlu(10) = elmax Maximum multiplier in current L.
149: parmlu(11) = umax Maximum element in current U.
150: parmlu(12) = dumax Maximum diagonal in U.
151: parmlu(13) = dumin Minimum diagonal in U.
152: parmlu(14) =
153: parmlu(15) =
154: parmlu(16) =
155: parmlu(17) =
156: parmlu(18) =
157: parmlu(19) = resid lu6sol: residual after solve with U or U'.
158: ...
159: parmlu(29) =
160: */
162: #define Factorization_Tolerance 1e-1
163: #define Factorization_Pivot_Tolerance pow(2.2204460492503131E-16, 2.0 / 3.0)
164: #define Factorization_Small_Tolerance 1e-15 /* pow(DBL_EPSILON, 0.8) */
166: static PetscErrorCode MatDestroy_LUSOL(Mat A)
167: {
168: Mat_LUSOL *lusol = (Mat_LUSOL *)A->spptr;
170: PetscFunctionBegin;
171: if (lusol && lusol->CleanUpLUSOL) {
172: PetscCall(PetscFree(lusol->ip));
173: PetscCall(PetscFree(lusol->iq));
174: PetscCall(PetscFree(lusol->lenc));
175: PetscCall(PetscFree(lusol->lenr));
176: PetscCall(PetscFree(lusol->locc));
177: PetscCall(PetscFree(lusol->locr));
178: PetscCall(PetscFree(lusol->iploc));
179: PetscCall(PetscFree(lusol->iqloc));
180: PetscCall(PetscFree(lusol->ipinv));
181: PetscCall(PetscFree(lusol->iqinv));
182: PetscCall(PetscFree(lusol->mnsw));
183: PetscCall(PetscFree(lusol->mnsv));
184: PetscCall(PetscFree3(lusol->data, lusol->indc, lusol->indr));
185: }
186: PetscCall(PetscFree(A->spptr));
187: PetscCall(MatDestroy_SeqAIJ(A));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
191: static PetscErrorCode MatSolve_LUSOL(Mat A, Vec b, Vec x)
192: {
193: Mat_LUSOL *lusol = (Mat_LUSOL *)A->spptr;
194: double *xx;
195: const double *bb;
196: int mode = 5;
197: int i, m, n, nnz, status;
199: PetscFunctionBegin;
200: PetscCall(VecGetArray(x, &xx));
201: PetscCall(VecGetArrayRead(b, &bb));
203: m = n = lusol->n;
204: nnz = lusol->nnz;
206: for (i = 0; i < m; i++) lusol->mnsv[i] = bb[i];
208: LU6SOL(&mode, &m, &n, lusol->mnsv, xx, &nnz, lusol->luparm, lusol->parmlu, lusol->data, lusol->indc, lusol->indr, lusol->ip, lusol->iq, lusol->lenc, lusol->lenr, lusol->locc, lusol->locr, &status);
210: PetscCheck(!status, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "solve failed, error code %d", status);
212: PetscCall(VecRestoreArray(x, &xx));
213: PetscCall(VecRestoreArrayRead(b, &bb));
214: PetscFunctionReturn(PETSC_SUCCESS);
215: }
217: static PetscErrorCode MatLUFactorNumeric_LUSOL(Mat F, Mat A, const MatFactorInfo *info)
218: {
219: Mat_SeqAIJ *a;
220: Mat_LUSOL *lusol = (Mat_LUSOL *)F->spptr;
221: int m, n, nz, nnz, status;
222: int i, rs, re;
223: int factorizations;
225: PetscFunctionBegin;
226: PetscCall(MatGetSize(A, &m, &n));
227: a = (Mat_SeqAIJ *)A->data;
229: PetscCheck(m == lusol->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "factorization struct inconsistent");
231: factorizations = 0;
232: do {
233: /*******************************************************************/
234: /* Check the workspace allocation. */
235: /*******************************************************************/
237: nz = a->nz;
238: nnz = PetscMax(lusol->nnz, (int)(lusol->elbowroom * nz));
239: nnz = PetscMax(nnz, 5 * n);
241: if (nnz < lusol->luparm[12]) {
242: nnz = (int)(lusol->luroom * lusol->luparm[12]);
243: } else if ((factorizations > 0) && (lusol->luroom < 6)) {
244: lusol->luroom += 0.1;
245: }
247: nnz = PetscMax(nnz, (int)(lusol->luroom * (lusol->luparm[22] + lusol->luparm[23])));
249: if (nnz > lusol->nnz) {
250: PetscCall(PetscFree3(lusol->data, lusol->indc, lusol->indr));
251: PetscCall(PetscMalloc3(nnz, &lusol->data, nnz, &lusol->indc, nnz, &lusol->indr));
252: lusol->nnz = nnz;
253: }
255: /* Fill in the data for the problem. (1-based Fortran style) */
256: nz = 0;
257: for (i = 0; i < n; i++) {
258: rs = a->i[i];
259: re = a->i[i + 1];
261: while (rs < re) {
262: if (a->a[rs] != 0.0) {
263: lusol->indc[nz] = i + 1;
264: lusol->indr[nz] = a->j[rs] + 1;
265: lusol->data[nz] = a->a[rs];
266: nz++;
267: }
268: rs++;
269: }
270: }
272: /* Do the factorization. */
273: LU1FAC(&m, &n, &nz, &nnz, lusol->luparm, lusol->parmlu, lusol->data, lusol->indc, lusol->indr, lusol->ip, lusol->iq, lusol->lenc, lusol->lenr, lusol->locc, lusol->locr, lusol->iploc, lusol->iqloc, lusol->ipinv, lusol->iqinv, lusol->mnsw, &status);
275: switch (status) {
276: case 0: /* factored */
277: break;
279: case 7: /* insufficient memory */
280: break;
282: case 1:
283: case -1: /* singular */
284: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "Singular matrix");
286: case 3:
287: case 4: /* error conditions */
288: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "matrix error");
290: default: /* unknown condition */
291: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_LIB, "matrix unknown return code");
292: }
294: factorizations++;
295: } while (status == 7);
296: F->ops->solve = MatSolve_LUSOL;
297: F->assembled = PETSC_TRUE;
298: F->preallocated = PETSC_TRUE;
299: PetscFunctionReturn(PETSC_SUCCESS);
300: }
302: static PetscErrorCode MatLUFactorSymbolic_LUSOL(Mat F, Mat A, IS r, IS c, const MatFactorInfo *info)
303: {
304: /*
305: Input
306: A - matrix to factor
307: r - row permutation (ignored)
308: c - column permutation (ignored)
310: Output
311: F - matrix storing the factorization;
312: */
313: Mat_LUSOL *lusol;
314: int i, m, n, nz, nnz;
316: PetscFunctionBegin;
317: /* Check the arguments. */
318: PetscCall(MatGetSize(A, &m, &n));
319: nz = ((Mat_SeqAIJ *)A->data)->nz;
321: /* Create the factorization. */
322: F->ops->lufactornumeric = MatLUFactorNumeric_LUSOL;
323: lusol = (Mat_LUSOL *)F->spptr;
325: /* Initialize parameters */
326: for (i = 0; i < 30; i++) {
327: lusol->luparm[i] = 0;
328: lusol->parmlu[i] = 0;
329: }
331: lusol->luparm[1] = -1;
332: lusol->luparm[2] = 5;
333: lusol->luparm[7] = 1;
335: lusol->parmlu[0] = 1 / Factorization_Tolerance;
336: lusol->parmlu[1] = 1 / Factorization_Tolerance;
337: lusol->parmlu[2] = Factorization_Small_Tolerance;
338: lusol->parmlu[3] = Factorization_Pivot_Tolerance;
339: lusol->parmlu[4] = Factorization_Pivot_Tolerance;
340: lusol->parmlu[5] = 3.0;
341: lusol->parmlu[6] = 0.3;
342: lusol->parmlu[7] = 0.6;
344: /* Allocate the workspace needed by LUSOL. */
345: lusol->elbowroom = PetscMax(lusol->elbowroom, info->fill);
346: nnz = PetscMax((int)(lusol->elbowroom * nz), 5 * n);
348: lusol->n = n;
349: lusol->nz = nz;
350: lusol->nnz = nnz;
351: lusol->luroom = 1.75;
353: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->ip));
354: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->iq));
355: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->lenc));
356: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->lenr));
357: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->locc));
358: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->locr));
359: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->iploc));
360: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->iqloc));
361: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->ipinv));
362: PetscCall(PetscMalloc(sizeof(int) * n, &lusol->iqinv));
363: PetscCall(PetscMalloc(sizeof(double) * n, &lusol->mnsw));
364: PetscCall(PetscMalloc(sizeof(double) * n, &lusol->mnsv));
365: PetscCall(PetscMalloc3(nnz, &lusol->data, nnz, &lusol->indc, nnz, &lusol->indr));
367: lusol->CleanUpLUSOL = PETSC_TRUE;
368: F->ops->lufactornumeric = MatLUFactorNumeric_LUSOL;
369: PetscFunctionReturn(PETSC_SUCCESS);
370: }
372: static PetscErrorCode MatFactorGetSolverType_seqaij_lusol(Mat A, MatSolverType *type)
373: {
374: PetscFunctionBegin;
375: *type = MATSOLVERLUSOL;
376: PetscFunctionReturn(PETSC_SUCCESS);
377: }
379: PETSC_EXTERN PetscErrorCode MatGetFactor_seqaij_lusol(Mat A, MatFactorType ftype, Mat *F)
380: {
381: Mat B;
382: Mat_LUSOL *lusol;
383: int m, n;
385: PetscFunctionBegin;
386: PetscCall(MatGetSize(A, &m, &n));
387: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &B));
388: PetscCall(MatSetSizes(B, PETSC_DECIDE, PETSC_DECIDE, m, n));
389: PetscCall(MatSetType(B, ((PetscObject)A)->type_name));
390: PetscCall(MatSeqAIJSetPreallocation(B, 0, NULL));
392: PetscCall(PetscNew(&lusol));
393: B->spptr = lusol;
395: B->trivialsymbolic = PETSC_TRUE;
396: B->ops->lufactorsymbolic = MatLUFactorSymbolic_LUSOL;
397: B->ops->destroy = MatDestroy_LUSOL;
399: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatFactorGetSolverType_C", MatFactorGetSolverType_seqaij_lusol));
401: B->factortype = MAT_FACTOR_LU;
402: PetscCall(PetscFree(B->solvertype));
403: PetscCall(PetscStrallocpy(MATSOLVERLUSOL, &B->solvertype));
404: PetscFunctionReturn(PETSC_SUCCESS);
405: }
407: PETSC_INTERN PetscErrorCode MatSolverTypeRegister_Lusol(void)
408: {
409: PetscFunctionBegin;
410: PetscCall(MatSolverTypeRegister(MATSOLVERLUSOL, MATSEQAIJ, MAT_FACTOR_LU, MatGetFactor_seqaij_lusol));
411: PetscFunctionReturn(PETSC_SUCCESS);
412: }
414: /*MC
415: MATSOLVERLUSOL - "lusol" - Provides direct solvers, LU, for sequential matrices
416: via the external package LUSOL.
418: Works with `MATSEQAIJ` matrices
420: Level: beginner
422: .seealso: [](ch_matrices), `Mat`, `PCLU`, `PCFactorSetMatSolverType()`, `MatSolverType`
423: M*/