Actual source code: glle.c
2: #include <../src/ts/impls/implicit/glle/glle.h>
3: #include <petscdm.h>
4: #include <petscblaslapack.h>
6: static const char *TSGLLEErrorDirections[] = {"FORWARD","BACKWARD","TSGLLEErrorDirection","TSGLLEERROR_",NULL};
7: static PetscFunctionList TSGLLEList;
8: static PetscFunctionList TSGLLEAcceptList;
9: static PetscBool TSGLLEPackageInitialized;
10: static PetscBool TSGLLERegisterAllCalled;
12: /* This function is pure */
13: static PetscScalar Factorial(PetscInt n)
14: {
15: PetscInt i;
16: if (n < 12) { /* Can compute with 32-bit integers */
17: PetscInt f = 1;
18: for (i=2; i<=n; i++) f *= i;
19: return (PetscScalar)f;
20: } else {
21: PetscScalar f = 1.;
22: for (i=2; i<=n; i++) f *= (PetscScalar)i;
23: return f;
24: }
25: }
27: /* This function is pure */
28: static PetscScalar CPowF(PetscScalar c,PetscInt p)
29: {
30: return PetscPowRealInt(PetscRealPart(c),p)/Factorial(p);
31: }
33: static PetscErrorCode TSGLLEGetVecs(TS ts,DM dm,Vec *Z,Vec *Ydotstage)
34: {
35: TS_GLLE *gl = (TS_GLLE*)ts->data;
37: if (Z) {
38: if (dm && dm != ts->dm) {
39: DMGetNamedGlobalVector(dm,"TSGLLE_Z",Z);
40: } else *Z = gl->Z;
41: }
42: if (Ydotstage) {
43: if (dm && dm != ts->dm) {
44: DMGetNamedGlobalVector(dm,"TSGLLE_Ydot",Ydotstage);
45: } else *Ydotstage = gl->Ydot[gl->stage];
46: }
47: return 0;
48: }
50: static PetscErrorCode TSGLLERestoreVecs(TS ts,DM dm,Vec *Z,Vec *Ydotstage)
51: {
52: if (Z) {
53: if (dm && dm != ts->dm) {
54: DMRestoreNamedGlobalVector(dm,"TSGLLE_Z",Z);
55: }
56: }
57: if (Ydotstage) {
59: if (dm && dm != ts->dm) {
60: DMRestoreNamedGlobalVector(dm,"TSGLLE_Ydot",Ydotstage);
61: }
62: }
63: return 0;
64: }
66: static PetscErrorCode DMCoarsenHook_TSGLLE(DM fine,DM coarse,void *ctx)
67: {
68: return 0;
69: }
71: static PetscErrorCode DMRestrictHook_TSGLLE(DM fine,Mat restrct,Vec rscale,Mat inject,DM coarse,void *ctx)
72: {
73: TS ts = (TS)ctx;
74: Vec Ydot,Ydot_c;
76: TSGLLEGetVecs(ts,fine,NULL,&Ydot);
77: TSGLLEGetVecs(ts,coarse,NULL,&Ydot_c);
78: MatRestrict(restrct,Ydot,Ydot_c);
79: VecPointwiseMult(Ydot_c,rscale,Ydot_c);
80: TSGLLERestoreVecs(ts,fine,NULL,&Ydot);
81: TSGLLERestoreVecs(ts,coarse,NULL,&Ydot_c);
82: return 0;
83: }
85: static PetscErrorCode DMSubDomainHook_TSGLLE(DM dm,DM subdm,void *ctx)
86: {
87: return 0;
88: }
90: static PetscErrorCode DMSubDomainRestrictHook_TSGLLE(DM dm,VecScatter gscat, VecScatter lscat,DM subdm,void *ctx)
91: {
92: TS ts = (TS)ctx;
93: Vec Ydot,Ydot_s;
95: TSGLLEGetVecs(ts,dm,NULL,&Ydot);
96: TSGLLEGetVecs(ts,subdm,NULL,&Ydot_s);
98: VecScatterBegin(gscat,Ydot,Ydot_s,INSERT_VALUES,SCATTER_FORWARD);
99: VecScatterEnd(gscat,Ydot,Ydot_s,INSERT_VALUES,SCATTER_FORWARD);
101: TSGLLERestoreVecs(ts,dm,NULL,&Ydot);
102: TSGLLERestoreVecs(ts,subdm,NULL,&Ydot_s);
103: return 0;
104: }
106: static PetscErrorCode TSGLLESchemeCreate(PetscInt p,PetscInt q,PetscInt r,PetscInt s,const PetscScalar *c,
107: const PetscScalar *a,const PetscScalar *b,const PetscScalar *u,const PetscScalar *v,TSGLLEScheme *inscheme)
108: {
109: TSGLLEScheme scheme;
110: PetscInt j;
116: *inscheme = NULL;
117: PetscNew(&scheme);
118: scheme->p = p;
119: scheme->q = q;
120: scheme->r = r;
121: scheme->s = s;
123: PetscMalloc5(s,&scheme->c,s*s,&scheme->a,r*s,&scheme->b,r*s,&scheme->u,r*r,&scheme->v);
124: PetscArraycpy(scheme->c,c,s);
125: for (j=0; j<s*s; j++) scheme->a[j] = (PetscAbsScalar(a[j]) < 1e-12) ? 0 : a[j];
126: for (j=0; j<r*s; j++) scheme->b[j] = (PetscAbsScalar(b[j]) < 1e-12) ? 0 : b[j];
127: for (j=0; j<s*r; j++) scheme->u[j] = (PetscAbsScalar(u[j]) < 1e-12) ? 0 : u[j];
128: for (j=0; j<r*r; j++) scheme->v[j] = (PetscAbsScalar(v[j]) < 1e-12) ? 0 : v[j];
130: PetscMalloc6(r,&scheme->alpha,r,&scheme->beta,r,&scheme->gamma,3*s,&scheme->phi,3*r,&scheme->psi,r,&scheme->stage_error);
131: {
132: PetscInt i,j,k,ss=s+2;
133: PetscBLASInt m,n,one=1,*ipiv,lwork=4*((s+3)*3+3),info,ldb;
134: PetscReal rcond,*sing,*workreal;
135: PetscScalar *ImV,*H,*bmat,*workscalar,*c=scheme->c,*a=scheme->a,*b=scheme->b,*u=scheme->u,*v=scheme->v;
136: PetscBLASInt rank;
137: PetscMalloc7(PetscSqr(r),&ImV,3*s,&H,3*ss,&bmat,lwork,&workscalar,5*(3+r),&workreal,r+s,&sing,r+s,&ipiv);
139: /* column-major input */
140: for (i=0; i<r-1; i++) {
141: for (j=0; j<r-1; j++) ImV[i+j*r] = 1.0*(i==j) - v[(i+1)*r+j+1];
142: }
143: /* Build right hand side for alpha (tp - glm.B(2:end,:)*(glm.c.^(p)./factorial(p))) */
144: for (i=1; i<r; i++) {
145: scheme->alpha[i] = 1./Factorial(p+1-i);
146: for (j=0; j<s; j++) scheme->alpha[i] -= b[i*s+j]*CPowF(c[j],p);
147: }
148: PetscBLASIntCast(r-1,&m);
149: PetscBLASIntCast(r,&n);
150: PetscStackCallBLAS("LAPACKgesv",LAPACKgesv_(&m,&one,ImV,&n,ipiv,scheme->alpha+1,&n,&info));
154: /* Build right hand side for beta (tp1 - glm.B(2:end,:)*(glm.c.^(p+1)./factorial(p+1)) - e.alpha) */
155: for (i=1; i<r; i++) {
156: scheme->beta[i] = 1./Factorial(p+2-i) - scheme->alpha[i];
157: for (j=0; j<s; j++) scheme->beta[i] -= b[i*s+j]*CPowF(c[j],p+1);
158: }
159: PetscStackCallBLAS("LAPACKgetrs",LAPACKgetrs_("No transpose",&m,&one,ImV,&n,ipiv,scheme->beta+1,&n,&info));
163: /* Build stage_error vector
164: xi = glm.c.^(p+1)/factorial(p+1) - glm.A*glm.c.^p/factorial(p) + glm.U(:,2:end)*e.alpha;
165: */
166: for (i=0; i<s; i++) {
167: scheme->stage_error[i] = CPowF(c[i],p+1);
168: for (j=0; j<s; j++) scheme->stage_error[i] -= a[i*s+j]*CPowF(c[j],p);
169: for (j=1; j<r; j++) scheme->stage_error[i] += u[i*r+j]*scheme->alpha[j];
170: }
172: /* alpha[0] (epsilon in B,J,W 2007)
173: epsilon = 1/factorial(p+1) - B(1,:)*c.^p/factorial(p) + V(1,2:end)*e.alpha;
174: */
175: scheme->alpha[0] = 1./Factorial(p+1);
176: for (j=0; j<s; j++) scheme->alpha[0] -= b[0*s+j]*CPowF(c[j],p);
177: for (j=1; j<r; j++) scheme->alpha[0] += v[0*r+j]*scheme->alpha[j];
179: /* right hand side for gamma (glm.B(2:end,:)*e.xi - e.epsilon*eye(s-1,1)) */
180: for (i=1; i<r; i++) {
181: scheme->gamma[i] = (i==1 ? -1. : 0)*scheme->alpha[0];
182: for (j=0; j<s; j++) scheme->gamma[i] += b[i*s+j]*scheme->stage_error[j];
183: }
184: PetscStackCallBLAS("LAPACKgetrs",LAPACKgetrs_("No transpose",&m,&one,ImV,&n,ipiv,scheme->gamma+1,&n,&info));
188: /* beta[0] (rho in B,J,W 2007)
189: e.rho = 1/factorial(p+2) - glm.B(1,:)*glm.c.^(p+1)/factorial(p+1) ...
190: + glm.V(1,2:end)*e.beta;% - e.epsilon;
191: % Note: The paper (B,J,W 2007) includes the last term in their definition
192: * */
193: scheme->beta[0] = 1./Factorial(p+2);
194: for (j=0; j<s; j++) scheme->beta[0] -= b[0*s+j]*CPowF(c[j],p+1);
195: for (j=1; j<r; j++) scheme->beta[0] += v[0*r+j]*scheme->beta[j];
197: /* gamma[0] (sigma in B,J,W 2007)
198: * e.sigma = glm.B(1,:)*e.xi + glm.V(1,2:end)*e.gamma;
199: * */
200: scheme->gamma[0] = 0.0;
201: for (j=0; j<s; j++) scheme->gamma[0] += b[0*s+j]*scheme->stage_error[j];
202: for (j=1; j<r; j++) scheme->gamma[0] += v[0*s+j]*scheme->gamma[j];
204: /* Assemble H
205: * % Determine the error estimators phi
206: H = [[cpow(glm.c,p) + C*e.alpha] [cpow(glm.c,p+1) + C*e.beta] ...
207: [e.xi - C*(e.gamma + 0*e.epsilon*eye(s-1,1))]]';
208: % Paper has formula above without the 0, but that term must be left
209: % out to satisfy the conditions they propose and to make the
210: % example schemes work
211: e.H = H;
212: e.phi = (H \ [1 0 0;1 1 0;0 0 -1])';
213: e.psi = -e.phi*C;
214: * */
215: for (j=0; j<s; j++) {
216: H[0+j*3] = CPowF(c[j],p);
217: H[1+j*3] = CPowF(c[j],p+1);
218: H[2+j*3] = scheme->stage_error[j];
219: for (k=1; k<r; k++) {
220: H[0+j*3] += CPowF(c[j],k-1)*scheme->alpha[k];
221: H[1+j*3] += CPowF(c[j],k-1)*scheme->beta[k];
222: H[2+j*3] -= CPowF(c[j],k-1)*scheme->gamma[k];
223: }
224: }
225: bmat[0+0*ss] = 1.; bmat[0+1*ss] = 0.; bmat[0+2*ss] = 0.;
226: bmat[1+0*ss] = 1.; bmat[1+1*ss] = 1.; bmat[1+2*ss] = 0.;
227: bmat[2+0*ss] = 0.; bmat[2+1*ss] = 0.; bmat[2+2*ss] = -1.;
228: m = 3;
229: PetscBLASIntCast(s,&n);
230: PetscBLASIntCast(ss,&ldb);
231: rcond = 1e-12;
232: #if defined(PETSC_USE_COMPLEX)
233: /* ZGELSS( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, RWORK, INFO) */
234: PetscStackCallBLAS("LAPACKgelss",LAPACKgelss_(&m,&n,&m,H,&m,bmat,&ldb,sing,&rcond,&rank,workscalar,&lwork,workreal,&info));
235: #else
236: /* DGELSS( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, INFO) */
237: PetscStackCallBLAS("LAPACKgelss",LAPACKgelss_(&m,&n,&m,H,&m,bmat,&ldb,sing,&rcond,&rank,workscalar,&lwork,&info));
238: #endif
242: for (j=0; j<3; j++) {
243: for (k=0; k<s; k++) scheme->phi[k+j*s] = bmat[k+j*ss];
244: }
246: /* the other part of the error estimator, psi in B,J,W 2007 */
247: scheme->psi[0*r+0] = 0.;
248: scheme->psi[1*r+0] = 0.;
249: scheme->psi[2*r+0] = 0.;
250: for (j=1; j<r; j++) {
251: scheme->psi[0*r+j] = 0.;
252: scheme->psi[1*r+j] = 0.;
253: scheme->psi[2*r+j] = 0.;
254: for (k=0; k<s; k++) {
255: scheme->psi[0*r+j] -= CPowF(c[k],j-1)*scheme->phi[0*s+k];
256: scheme->psi[1*r+j] -= CPowF(c[k],j-1)*scheme->phi[1*s+k];
257: scheme->psi[2*r+j] -= CPowF(c[k],j-1)*scheme->phi[2*s+k];
258: }
259: }
260: PetscFree7(ImV,H,bmat,workscalar,workreal,sing,ipiv);
261: }
262: /* Check which properties are satisfied */
263: scheme->stiffly_accurate = PETSC_TRUE;
264: if (scheme->c[s-1] != 1.) scheme->stiffly_accurate = PETSC_FALSE;
265: for (j=0; j<s; j++) if (a[(s-1)*s+j] != b[j]) scheme->stiffly_accurate = PETSC_FALSE;
266: for (j=0; j<r; j++) if (u[(s-1)*r+j] != v[j]) scheme->stiffly_accurate = PETSC_FALSE;
267: scheme->fsal = scheme->stiffly_accurate; /* FSAL is stronger */
268: for (j=0; j<s-1; j++) if (r>1 && b[1*s+j] != 0.) scheme->fsal = PETSC_FALSE;
269: if (b[1*s+r-1] != 1.) scheme->fsal = PETSC_FALSE;
270: for (j=0; j<r; j++) if (r>1 && v[1*r+j] != 0.) scheme->fsal = PETSC_FALSE;
272: *inscheme = scheme;
273: return 0;
274: }
276: static PetscErrorCode TSGLLESchemeDestroy(TSGLLEScheme sc)
277: {
278: PetscFree5(sc->c,sc->a,sc->b,sc->u,sc->v);
279: PetscFree6(sc->alpha,sc->beta,sc->gamma,sc->phi,sc->psi,sc->stage_error);
280: PetscFree(sc);
281: return 0;
282: }
284: static PetscErrorCode TSGLLEDestroy_Default(TS_GLLE *gl)
285: {
286: PetscInt i;
288: for (i=0; i<gl->nschemes; i++) {
289: if (gl->schemes[i]) TSGLLESchemeDestroy(gl->schemes[i]);
290: }
291: PetscFree(gl->schemes);
292: gl->nschemes = 0;
293: PetscMemzero(gl->type_name,sizeof(gl->type_name));
294: return 0;
295: }
297: static PetscErrorCode TSGLLEViewTable_Private(PetscViewer viewer,PetscInt m,PetscInt n,const PetscScalar a[],const char name[])
298: {
299: PetscBool iascii;
300: PetscInt i,j;
302: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
303: if (iascii) {
304: PetscViewerASCIIPrintf(viewer,"%30s = [",name);
305: for (i=0; i<m; i++) {
306: if (i) PetscViewerASCIIPrintf(viewer,"%30s [","");
307: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
308: for (j=0; j<n; j++) {
309: PetscViewerASCIIPrintf(viewer," %12.8g",PetscRealPart(a[i*n+j]));
310: }
311: PetscViewerASCIIPrintf(viewer,"]\n");
312: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
313: }
314: }
315: return 0;
316: }
318: static PetscErrorCode TSGLLESchemeView(TSGLLEScheme sc,PetscBool view_details,PetscViewer viewer)
319: {
320: PetscBool iascii;
322: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
323: if (iascii) {
324: PetscViewerASCIIPrintf(viewer,"GL scheme p,q,r,s = %d,%d,%d,%d\n",sc->p,sc->q,sc->r,sc->s);
325: PetscViewerASCIIPushTab(viewer);
326: PetscViewerASCIIPrintf(viewer,"Stiffly accurate: %s, FSAL: %s\n",sc->stiffly_accurate ? "yes" : "no",sc->fsal ? "yes" : "no");
327: PetscCall(PetscViewerASCIIPrintf(viewer,"Leading error constants: %10.3e %10.3e %10.3e\n",
328: PetscRealPart(sc->alpha[0]),PetscRealPart(sc->beta[0]),PetscRealPart(sc->gamma[0])));
329: TSGLLEViewTable_Private(viewer,1,sc->s,sc->c,"Abscissas c");
330: if (view_details) {
331: TSGLLEViewTable_Private(viewer,sc->s,sc->s,sc->a,"A");
332: TSGLLEViewTable_Private(viewer,sc->r,sc->s,sc->b,"B");
333: TSGLLEViewTable_Private(viewer,sc->s,sc->r,sc->u,"U");
334: TSGLLEViewTable_Private(viewer,sc->r,sc->r,sc->v,"V");
336: TSGLLEViewTable_Private(viewer,3,sc->s,sc->phi,"Error estimate phi");
337: TSGLLEViewTable_Private(viewer,3,sc->r,sc->psi,"Error estimate psi");
338: TSGLLEViewTable_Private(viewer,1,sc->r,sc->alpha,"Modify alpha");
339: TSGLLEViewTable_Private(viewer,1,sc->r,sc->beta,"Modify beta");
340: TSGLLEViewTable_Private(viewer,1,sc->r,sc->gamma,"Modify gamma");
341: TSGLLEViewTable_Private(viewer,1,sc->s,sc->stage_error,"Stage error xi");
342: }
343: PetscViewerASCIIPopTab(viewer);
344: } else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"Viewer type %s not supported",((PetscObject)viewer)->type_name);
345: return 0;
346: }
348: static PetscErrorCode TSGLLEEstimateHigherMoments_Default(TSGLLEScheme sc,PetscReal h,Vec Ydot[],Vec Xold[],Vec hm[])
349: {
350: PetscInt i;
353: /* build error vectors*/
354: for (i=0; i<3; i++) {
355: PetscScalar phih[64];
356: PetscInt j;
357: for (j=0; j<sc->s; j++) phih[j] = sc->phi[i*sc->s+j]*h;
358: VecZeroEntries(hm[i]);
359: VecMAXPY(hm[i],sc->s,phih,Ydot);
360: VecMAXPY(hm[i],sc->r,&sc->psi[i*sc->r],Xold);
361: }
362: return 0;
363: }
365: static PetscErrorCode TSGLLECompleteStep_Rescale(TSGLLEScheme sc,PetscReal h,TSGLLEScheme next_sc,PetscReal next_h,Vec Ydot[],Vec Xold[],Vec X[])
366: {
367: PetscScalar brow[32],vrow[32];
368: PetscInt i,j,r,s;
370: /* Build the new solution from (X,Ydot) */
371: r = sc->r;
372: s = sc->s;
373: for (i=0; i<r; i++) {
374: VecZeroEntries(X[i]);
375: for (j=0; j<s; j++) brow[j] = h*sc->b[i*s+j];
376: VecMAXPY(X[i],s,brow,Ydot);
377: for (j=0; j<r; j++) vrow[j] = sc->v[i*r+j];
378: VecMAXPY(X[i],r,vrow,Xold);
379: }
380: return 0;
381: }
383: static PetscErrorCode TSGLLECompleteStep_RescaleAndModify(TSGLLEScheme sc,PetscReal h,TSGLLEScheme next_sc,PetscReal next_h,Vec Ydot[],Vec Xold[],Vec X[])
384: {
385: PetscScalar brow[32],vrow[32];
386: PetscReal ratio;
387: PetscInt i,j,p,r,s;
389: /* Build the new solution from (X,Ydot) */
390: p = sc->p;
391: r = sc->r;
392: s = sc->s;
393: ratio = next_h/h;
394: for (i=0; i<r; i++) {
395: VecZeroEntries(X[i]);
396: for (j=0; j<s; j++) {
397: brow[j] = h*(PetscPowRealInt(ratio,i)*sc->b[i*s+j]
398: + (PetscPowRealInt(ratio,i) - PetscPowRealInt(ratio,p+1))*(+ sc->alpha[i]*sc->phi[0*s+j])
399: + (PetscPowRealInt(ratio,i) - PetscPowRealInt(ratio,p+2))*(+ sc->beta [i]*sc->phi[1*s+j]
400: + sc->gamma[i]*sc->phi[2*s+j]));
401: }
402: VecMAXPY(X[i],s,brow,Ydot);
403: for (j=0; j<r; j++) {
404: vrow[j] = (PetscPowRealInt(ratio,i)*sc->v[i*r+j]
405: + (PetscPowRealInt(ratio,i) - PetscPowRealInt(ratio,p+1))*(+ sc->alpha[i]*sc->psi[0*r+j])
406: + (PetscPowRealInt(ratio,i) - PetscPowRealInt(ratio,p+2))*(+ sc->beta [i]*sc->psi[1*r+j]
407: + sc->gamma[i]*sc->psi[2*r+j]));
408: }
409: VecMAXPY(X[i],r,vrow,Xold);
410: }
411: if (r < next_sc->r) {
413: VecZeroEntries(X[r]);
414: for (j=0; j<s; j++) brow[j] = h*PetscPowRealInt(ratio,p+1)*sc->phi[0*s+j];
415: VecMAXPY(X[r],s,brow,Ydot);
416: for (j=0; j<r; j++) vrow[j] = PetscPowRealInt(ratio,p+1)*sc->psi[0*r+j];
417: VecMAXPY(X[r],r,vrow,Xold);
418: }
419: return 0;
420: }
422: static PetscErrorCode TSGLLECreate_IRKS(TS ts)
423: {
424: TS_GLLE *gl = (TS_GLLE*)ts->data;
426: gl->Destroy = TSGLLEDestroy_Default;
427: gl->EstimateHigherMoments = TSGLLEEstimateHigherMoments_Default;
428: gl->CompleteStep = TSGLLECompleteStep_RescaleAndModify;
429: PetscMalloc1(10,&gl->schemes);
430: gl->nschemes = 0;
432: {
433: /* p=1,q=1, r=s=2, A- and L-stable with error estimates of order 2 and 3
434: * Listed in Butcher & Podhaisky 2006. On error estimation in general linear methods for stiff ODE.
435: * irks(0.3,0,[.3,1],[1],1)
436: * Note: can be made to have classical order (not stage order) 2 by replacing 0.3 with 1-sqrt(1/2)
437: * but doing so would sacrifice the error estimator.
438: */
439: const PetscScalar c[2] = {3./10., 1.};
440: const PetscScalar a[2][2] = {{3./10., 0}, {7./10., 3./10.}};
441: const PetscScalar b[2][2] = {{7./10., 3./10.}, {0,1}};
442: const PetscScalar u[2][2] = {{1,0},{1,0}};
443: const PetscScalar v[2][2] = {{1,0},{0,0}};
444: TSGLLESchemeCreate(1,1,2,2,c,*a,*b,*u,*v,&gl->schemes[gl->nschemes++]);
445: }
447: {
448: /* p=q=2, r=s=3: irks(4/9,0,[1:3]/3,[0.33852],1) */
449: /* http://www.math.auckland.ac.nz/~hpod/atlas/i2a.html */
450: const PetscScalar c[3] = {1./3., 2./3., 1}
451: ,a[3][3] = {{4./9. ,0 , 0},
452: {1.03750643704090e+00 , 4./9., 0},
453: {7.67024779410304e-01 , -3.81140216918943e-01, 4./9.}}
454: ,b[3][3] = {{0.767024779410304, -0.381140216918943, 4./9.},
455: {0.000000000000000, 0.000000000000000, 1.000000000000000},
456: {-2.075048385225385, 0.621728385225383, 1.277197204924873}}
457: ,u[3][3] = {{1.0000000000000000, -0.1111111111111109, -0.0925925925925922},
458: {1.0000000000000000, -0.8152842148186744, -0.4199095530877056},
459: {1.0000000000000000, 0.1696709930641948, 0.0539741070314165}}
460: ,v[3][3] = {{1.0000000000000000, 0.1696709930641948, 0.0539741070314165},
461: {0.000000000000000, 0.000000000000000, 0.000000000000000},
462: {0.000000000000000, 0.176122795075129, 0.000000000000000}};
463: TSGLLESchemeCreate(2,2,3,3,c,*a,*b,*u,*v,&gl->schemes[gl->nschemes++]);
464: }
465: {
466: /* p=q=3, r=s=4: irks(9/40,0,[1:4]/4,[0.3312 1.0050],[0.49541 1;1 0]) */
467: const PetscScalar c[4] = {0.25,0.5,0.75,1.0}
468: ,a[4][4] = {{9./40. , 0, 0, 0},
469: {2.11286958887701e-01 , 9./40. , 0, 0},
470: {9.46338294287584e-01 , -3.42942861246094e-01, 9./40. , 0},
471: {0.521490453970721 , -0.662474225622980, 0.490476425459734, 9./40. }}
472: ,b[4][4] = {{0.521490453970721 , -0.662474225622980, 0.490476425459734, 9./40. },
473: {0.000000000000000 , 0.000000000000000, 0.000000000000000, 1.000000000000000},
474: {-0.084677029310348 , 1.390757514776085, -1.568157386206001, 2.023192696767826},
475: {0.465383797936408 , 1.478273530625148, -1.930836081010182, 1.644872111193354}}
476: ,u[4][4] = {{1.00000000000000000 , 0.02500000000001035, -0.02499999999999053, -0.00442708333332865},
477: {1.00000000000000000 , 0.06371304111232945, -0.04032173972189845, -0.01389438413189452},
478: {1.00000000000000000 , -0.07839543304147778, 0.04738685705116663, 0.02032603595928376},
479: {1.00000000000000000 , 0.42550734619251651, 0.10800718022400080, -0.01726712647760034}}
480: ,v[4][4] = {{1.00000000000000000 , 0.42550734619251651, 0.10800718022400080, -0.01726712647760034},
481: {0.000000000000000 , 0.000000000000000, 0.000000000000000, 0.000000000000000},
482: {0.000000000000000 , -1.761115796027561, -0.521284157173780, 0.258249384305463},
483: {0.000000000000000 , -1.657693358744728, -1.052227765232394, 0.521284157173780}};
484: TSGLLESchemeCreate(3,3,4,4,c,*a,*b,*u,*v,&gl->schemes[gl->nschemes++]);
485: }
486: {
487: /* p=q=4, r=s=5:
488: irks(3/11,0,[1:5]/5, [0.1715 -0.1238 0.6617],...
489: [ -0.0812 0.4079 1.0000
490: 1.0000 0 0
491: 0.8270 1.0000 0])
492: */
493: const PetscScalar c[5] = {0.2,0.4,0.6,0.8,1.0}
494: ,a[5][5] = {{2.72727272727352e-01 , 0.00000000000000e+00, 0.00000000000000e+00 , 0.00000000000000e+00 , 0.00000000000000e+00},
495: {-1.03980153733431e-01, 2.72727272727405e-01, 0.00000000000000e+00, 0.00000000000000e+00 , 0.00000000000000e+00},
496: {-1.58615400341492e+00, 7.44168951881122e-01, 2.72727272727309e-01, 0.00000000000000e+00 , 0.00000000000000e+00},
497: {-8.73658042865628e-01, 5.37884671894595e-01, -1.63298538799523e-01, 2.72727272726996e-01 , 0.00000000000000e+00},
498: {2.95489397443992e-01 , -1.18481693910097e+00 , -6.68029812659953e-01 , 1.00716687860943e+00 , 2.72727272727288e-01}}
499: ,b[5][5] = {{2.95489397443992e-01 , -1.18481693910097e+00 , -6.68029812659953e-01 , 1.00716687860943e+00 , 2.72727272727288e-01},
500: {0.00000000000000e+00 , 1.11022302462516e-16 , -2.22044604925031e-16 , 0.00000000000000e+00 , 1.00000000000000e+00},
501: {-4.05882503986005e+00, -4.00924006567769e+00, -1.38930610972481e+00, 4.45223930308488e+00 , 6.32331093108427e-01},
502: {8.35690179937017e+00 , -2.26640927349732e+00 , 6.86647884973826e+00 , -5.22595158025740e+00 , 4.50893068837431e+00},
503: {1.27656267027479e+01 , 2.80882153840821e+00 , 8.91173096522890e+00 , -1.07936444078906e+01 , 4.82534148988854e+00}}
504: ,u[5][5] = {{1.00000000000000e+00 , -7.27272727273551e-02 , -3.45454545454419e-02 , -4.12121212119565e-03 ,-2.96969696964014e-04},
505: {1.00000000000000e+00 , 2.31252881006154e-01 , -8.29487834416481e-03 , -9.07191207681020e-03 ,-1.70378403743473e-03},
506: {1.00000000000000e+00 , 1.16925777880663e+00 , 3.59268562942635e-02 , -4.09013451730615e-02 ,-1.02411119670164e-02},
507: {1.00000000000000e+00 , 1.02634463704356e+00 , 1.59375044913405e-01 , 1.89673015035370e-03 ,-4.89987231897569e-03},
508: {1.00000000000000e+00 , 1.27746320298021e+00 , 2.37186008132728e-01 , -8.28694373940065e-02 ,-5.34396510196430e-02}}
509: ,v[5][5] = {{1.00000000000000e+00 , 1.27746320298021e+00 , 2.37186008132728e-01 , -8.28694373940065e-02 ,-5.34396510196430e-02},
510: {0.00000000000000e+00 , -1.77635683940025e-15 , -1.99840144432528e-15 , -9.99200722162641e-16 ,-3.33066907387547e-16},
511: {0.00000000000000e+00 , 4.37280081906924e+00 , 5.49221645016377e-02 , -8.88913177394943e-02 , 1.12879077989154e-01},
512: {0.00000000000000e+00 , -1.22399504837280e+01 , -5.21287338448645e+00 , -8.03952325565291e-01 , 4.60298678047147e-01},
513: {0.00000000000000e+00 , -1.85178762883829e+01 , -5.21411849862624e+00 , -1.04283436528809e+00 , 7.49030161063651e-01}};
514: TSGLLESchemeCreate(4,4,5,5,c,*a,*b,*u,*v,&gl->schemes[gl->nschemes++]);
515: }
516: {
517: /* p=q=5, r=s=6;
518: irks(1/3,0,[1:6]/6,...
519: [-0.0489 0.4228 -0.8814 0.9021],...
520: [-0.3474 -0.6617 0.6294 0.2129
521: 0.0044 -0.4256 -0.1427 -0.8936
522: -0.8267 0.4821 0.1371 -0.2557
523: -0.4426 -0.3855 -0.7514 0.3014])
524: */
525: const PetscScalar c[6] = {1./6, 2./6, 3./6, 4./6, 5./6, 1.}
526: ,a[6][6] = {{ 3.33333333333940e-01, 0 , 0 , 0 , 0 , 0 },
527: { -8.64423857333350e-02, 3.33333333332888e-01, 0 , 0 , 0 , 0 },
528: { -2.16850174258252e+00, -2.23619072028839e+00, 3.33333333335204e-01, 0 , 0 , 0 },
529: { -4.73160970138997e+00, -3.89265344629268e+00, -2.76318716520933e-01, 3.33333333335759e-01, 0 , 0 },
530: { -6.75187540297338e+00, -7.90756533769377e+00, 7.90245051802259e-01, -4.48352364517632e-01, 3.33333333328483e-01, 0 },
531: { -4.26488287921548e+00, -1.19320395589302e+01, 3.38924509887755e+00, -2.23969848002481e+00, 6.62807710124007e-01, 3.33333333335440e-01}}
532: ,b[6][6] = {{ -4.26488287921548e+00, -1.19320395589302e+01, 3.38924509887755e+00, -2.23969848002481e+00, 6.62807710124007e-01, 3.33333333335440e-01},
533: { -8.88178419700125e-16, 4.44089209850063e-16, -1.54737334057131e-15, -8.88178419700125e-16, 0.00000000000000e+00, 1.00000000000001e+00},
534: { -2.87780425770651e+01, -1.13520448264971e+01, 2.62002318943161e+01, 2.56943874812797e+01, -3.06702268304488e+01, 6.68067773510103e+00},
535: { 5.47971245256474e+01, 6.80366875868284e+01, -6.50952588861999e+01, -8.28643975339097e+01, 8.17416943896414e+01, -1.17819043489036e+01},
536: { -2.33332114788869e+02, 6.12942539462634e+01, -4.91850135865944e+01, 1.82716844135480e+02, -1.29788173979395e+02, 3.09968095651099e+01},
537: { -1.72049132343751e+02, 8.60194713593999e+00, 7.98154219170200e-01, 1.50371386053218e+02, -1.18515423962066e+02, 2.50898277784663e+01}}
538: ,u[6][6] = {{ 1.00000000000000e+00, -1.66666666666870e-01, -4.16666666664335e-02, -3.85802469124815e-03, -2.25051440302250e-04, -9.64506172339142e-06},
539: { 1.00000000000000e+00, 8.64423857327162e-02, -4.11484912671353e-02, -1.11450903217645e-02, -1.47651050487126e-03, -1.34395070766826e-04},
540: { 1.00000000000000e+00, 4.57135912953434e+00, 1.06514719719137e+00, 1.33517564218007e-01, 1.11365952968659e-02, 6.12382756769504e-04},
541: { 1.00000000000000e+00, 9.23391519753404e+00, 2.22431212392095e+00, 2.91823807741891e-01, 2.52058456411084e-02, 1.22800542949647e-03},
542: { 1.00000000000000e+00, 1.48175480533865e+01, 3.73439117461835e+00, 5.14648336541804e-01, 4.76430038853402e-02, 2.56798515502156e-03},
543: { 1.00000000000000e+00, 1.50512347758335e+01, 4.10099701165164e+00, 5.66039141003603e-01, 3.91213893800891e-02, -2.99136269067853e-03}}
544: ,v[6][6] = {{ 1.00000000000000e+00, 1.50512347758335e+01, 4.10099701165164e+00, 5.66039141003603e-01, 3.91213893800891e-02, -2.99136269067853e-03},
545: { 0.00000000000000e+00, -4.88498130835069e-15, -6.43929354282591e-15, -3.55271367880050e-15, -1.22124532708767e-15, -3.12250225675825e-16},
546: { 0.00000000000000e+00, 1.22250171233141e+01, -1.77150760606169e+00, 3.54516769879390e-01, 6.22298845883398e-01, 2.31647447450276e-01},
547: { 0.00000000000000e+00, -4.48339457331040e+01, -3.57363126641880e-01, 5.18750173123425e-01, 6.55727990241799e-02, 1.63175368287079e-01},
548: { 0.00000000000000e+00, 1.37297394708005e+02, -1.60145272991317e+00, -5.05319555199441e+00, 1.55328940390990e-01, 9.16629423682464e-01},
549: { 0.00000000000000e+00, 1.05703241119022e+02, -1.16610260983038e+00, -2.99767252773859e+00, -1.13472315553890e-01, 1.09742849254729e+00}};
550: TSGLLESchemeCreate(5,5,6,6,c,*a,*b,*u,*v,&gl->schemes[gl->nschemes++]);
551: }
552: return 0;
553: }
555: /*@C
556: TSGLLESetType - sets the class of general linear method to use for time-stepping
558: Collective on TS
560: Input Parameters:
561: + ts - the TS context
562: - type - a method
564: Options Database Key:
565: . -ts_gl_type <type> - sets the method, use -help for a list of available method (e.g. irks)
567: Notes:
568: See "petsc/include/petscts.h" for available methods (for instance)
569: . TSGLLE_IRKS - Diagonally implicit methods with inherent Runge-Kutta stability (for stiff problems)
571: Normally, it is best to use the TSSetFromOptions() command and
572: then set the TSGLLE type from the options database rather than by using
573: this routine. Using the options database provides the user with
574: maximum flexibility in evaluating the many different solvers.
575: The TSGLLESetType() routine is provided for those situations where it
576: is necessary to set the timestepping solver independently of the
577: command line or options database. This might be the case, for example,
578: when the choice of solver changes during the execution of the
579: program, and the user's application is taking responsibility for
580: choosing the appropriate method.
582: Level: intermediate
584: @*/
585: PetscErrorCode TSGLLESetType(TS ts,TSGLLEType type)
586: {
589: PetscTryMethod(ts,"TSGLLESetType_C",(TS,TSGLLEType),(ts,type));
590: return 0;
591: }
593: /*@C
594: TSGLLESetAcceptType - sets the acceptance test
596: Time integrators that need to control error must have the option to reject a time step based on local error
597: estimates. This function allows different schemes to be set.
599: Logically Collective on TS
601: Input Parameters:
602: + ts - the TS context
603: - type - the type
605: Options Database Key:
606: . -ts_gl_accept_type <type> - sets the method used to determine whether to accept or reject a step
608: Level: intermediate
610: .seealso: TS, TSGLLE, TSGLLEAcceptRegister(), TSGLLEAdapt, set type
611: @*/
612: PetscErrorCode TSGLLESetAcceptType(TS ts,TSGLLEAcceptType type)
613: {
616: PetscTryMethod(ts,"TSGLLESetAcceptType_C",(TS,TSGLLEAcceptType),(ts,type));
617: return 0;
618: }
620: /*@C
621: TSGLLEGetAdapt - gets the TSGLLEAdapt object from the TS
623: Not Collective
625: Input Parameter:
626: . ts - the TS context
628: Output Parameter:
629: . adapt - the TSGLLEAdapt context
631: Notes:
632: This allows the user set options on the TSGLLEAdapt object. Usually it is better to do this using the options
633: database, so this function is rarely needed.
635: Level: advanced
637: .seealso: TSGLLEAdapt, TSGLLEAdaptRegister()
638: @*/
639: PetscErrorCode TSGLLEGetAdapt(TS ts,TSGLLEAdapt *adapt)
640: {
643: PetscUseMethod(ts,"TSGLLEGetAdapt_C",(TS,TSGLLEAdapt*),(ts,adapt));
644: return 0;
645: }
647: static PetscErrorCode TSGLLEAccept_Always(TS ts,PetscReal tleft,PetscReal h,const PetscReal enorms[],PetscBool *accept)
648: {
649: *accept = PETSC_TRUE;
650: return 0;
651: }
653: static PetscErrorCode TSGLLEUpdateWRMS(TS ts)
654: {
655: TS_GLLE *gl = (TS_GLLE*)ts->data;
656: PetscScalar *x,*w;
657: PetscInt n,i;
659: VecGetArray(gl->X[0],&x);
660: VecGetArray(gl->W,&w);
661: VecGetLocalSize(gl->W,&n);
662: for (i=0; i<n; i++) w[i] = 1./(gl->wrms_atol + gl->wrms_rtol*PetscAbsScalar(x[i]));
663: VecRestoreArray(gl->X[0],&x);
664: VecRestoreArray(gl->W,&w);
665: return 0;
666: }
668: static PetscErrorCode TSGLLEVecNormWRMS(TS ts,Vec X,PetscReal *nrm)
669: {
670: TS_GLLE *gl = (TS_GLLE*)ts->data;
671: PetscScalar *x,*w;
672: PetscReal sum = 0.0,gsum;
673: PetscInt n,N,i;
675: VecGetArray(X,&x);
676: VecGetArray(gl->W,&w);
677: VecGetLocalSize(gl->W,&n);
678: for (i=0; i<n; i++) sum += PetscAbsScalar(PetscSqr(x[i]*w[i]));
679: VecRestoreArray(X,&x);
680: VecRestoreArray(gl->W,&w);
681: MPIU_Allreduce(&sum,&gsum,1,MPIU_REAL,MPIU_SUM,PetscObjectComm((PetscObject)ts));
682: VecGetSize(gl->W,&N);
683: *nrm = PetscSqrtReal(gsum/(1.*N));
684: return 0;
685: }
687: static PetscErrorCode TSGLLESetType_GLLE(TS ts,TSGLLEType type)
688: {
689: PetscBool same;
690: TS_GLLE *gl = (TS_GLLE*)ts->data;
691: PetscErrorCode (*r)(TS);
693: if (gl->type_name[0]) {
694: PetscStrcmp(gl->type_name,type,&same);
695: if (same) return 0;
696: (*gl->Destroy)(gl);
697: }
699: PetscFunctionListFind(TSGLLEList,type,&r);
701: (*r)(ts);
702: PetscStrcpy(gl->type_name,type);
703: return 0;
704: }
706: static PetscErrorCode TSGLLESetAcceptType_GLLE(TS ts,TSGLLEAcceptType type)
707: {
708: TSGLLEAcceptFunction r;
709: TS_GLLE *gl = (TS_GLLE*)ts->data;
711: PetscFunctionListFind(TSGLLEAcceptList,type,&r);
713: gl->Accept = r;
714: PetscStrncpy(gl->accept_name,type,sizeof(gl->accept_name));
715: return 0;
716: }
718: static PetscErrorCode TSGLLEGetAdapt_GLLE(TS ts,TSGLLEAdapt *adapt)
719: {
720: TS_GLLE *gl = (TS_GLLE*)ts->data;
722: if (!gl->adapt) {
723: TSGLLEAdaptCreate(PetscObjectComm((PetscObject)ts),&gl->adapt);
724: PetscObjectIncrementTabLevel((PetscObject)gl->adapt,(PetscObject)ts,1);
725: PetscLogObjectParent((PetscObject)ts,(PetscObject)gl->adapt);
726: }
727: *adapt = gl->adapt;
728: return 0;
729: }
731: static PetscErrorCode TSGLLEChooseNextScheme(TS ts,PetscReal h,const PetscReal hmnorm[],PetscInt *next_scheme,PetscReal *next_h,PetscBool *finish)
732: {
733: TS_GLLE *gl = (TS_GLLE*)ts->data;
734: PetscInt i,n,cur_p,cur,next_sc,candidates[64],orders[64];
735: PetscReal errors[64],costs[64],tleft;
737: cur = -1;
738: cur_p = gl->schemes[gl->current_scheme]->p;
739: tleft = ts->max_time - (ts->ptime + ts->time_step);
740: for (i=0,n=0; i<gl->nschemes; i++) {
741: TSGLLEScheme sc = gl->schemes[i];
742: if (sc->p < gl->min_order || gl->max_order < sc->p) continue;
743: if (sc->p == cur_p - 1) errors[n] = PetscAbsScalar(sc->alpha[0])*hmnorm[0];
744: else if (sc->p == cur_p) errors[n] = PetscAbsScalar(sc->alpha[0])*hmnorm[1];
745: else if (sc->p == cur_p+1) errors[n] = PetscAbsScalar(sc->alpha[0])*(hmnorm[2]+hmnorm[3]);
746: else continue;
747: candidates[n] = i;
748: orders[n] = PetscMin(sc->p,sc->q); /* order of global truncation error */
749: costs[n] = sc->s; /* estimate the cost as the number of stages */
750: if (i == gl->current_scheme) cur = n;
751: n++;
752: }
754: TSGLLEAdaptChoose(gl->adapt,n,orders,errors,costs,cur,h,tleft,&next_sc,next_h,finish);
755: *next_scheme = candidates[next_sc];
756: PetscInfo(ts,"Adapt chose scheme %d (%d,%d,%d,%d) with step size %6.2e, finish=%d\n",*next_scheme,gl->schemes[*next_scheme]->p,gl->schemes[*next_scheme]->q,gl->schemes[*next_scheme]->r,gl->schemes[*next_scheme]->s,*next_h,*finish);
757: return 0;
758: }
760: static PetscErrorCode TSGLLEGetMaxSizes(TS ts,PetscInt *max_r,PetscInt *max_s)
761: {
762: TS_GLLE *gl = (TS_GLLE*)ts->data;
764: *max_r = gl->schemes[gl->nschemes-1]->r;
765: *max_s = gl->schemes[gl->nschemes-1]->s;
766: return 0;
767: }
769: static PetscErrorCode TSSolve_GLLE(TS ts)
770: {
771: TS_GLLE *gl = (TS_GLLE*)ts->data;
772: PetscInt i,k,its,lits,max_r,max_s;
773: PetscBool final_step,finish;
774: SNESConvergedReason snesreason;
776: TSMonitor(ts,ts->steps,ts->ptime,ts->vec_sol);
778: TSGLLEGetMaxSizes(ts,&max_r,&max_s);
779: VecCopy(ts->vec_sol,gl->X[0]);
780: for (i=1; i<max_r; i++) {
781: VecZeroEntries(gl->X[i]);
782: }
783: TSGLLEUpdateWRMS(ts);
785: if (0) {
786: /* Find consistent initial data for DAE */
787: gl->stage_time = ts->ptime + ts->time_step;
788: gl->scoeff = 1.;
789: gl->stage = 0;
791: VecCopy(ts->vec_sol,gl->Z);
792: VecCopy(ts->vec_sol,gl->Y);
793: SNESSolve(ts->snes,NULL,gl->Y);
794: SNESGetIterationNumber(ts->snes,&its);
795: SNESGetLinearSolveIterations(ts->snes,&lits);
796: SNESGetConvergedReason(ts->snes,&snesreason);
798: ts->snes_its += its; ts->ksp_its += lits;
799: if (snesreason < 0 && ts->max_snes_failures > 0 && ++ts->num_snes_failures >= ts->max_snes_failures) {
800: ts->reason = TS_DIVERGED_NONLINEAR_SOLVE;
801: PetscInfo(ts,"Step=%D, nonlinear solve solve failures %D greater than current TS allowed, stopping solve\n",ts->steps,ts->num_snes_failures);
802: return 0;
803: }
804: }
808: for (k=0,final_step=PETSC_FALSE,finish=PETSC_FALSE; k<ts->max_steps && !finish; k++) {
809: PetscInt j,r,s,next_scheme = 0,rejections;
810: PetscReal h,hmnorm[4],enorm[3],next_h;
811: PetscBool accept;
812: const PetscScalar *c,*a,*u;
813: Vec *X,*Ydot,Y;
814: TSGLLEScheme scheme = gl->schemes[gl->current_scheme];
816: r = scheme->r; s = scheme->s;
817: c = scheme->c;
818: a = scheme->a; u = scheme->u;
819: h = ts->time_step;
820: X = gl->X; Ydot = gl->Ydot; Y = gl->Y;
822: if (ts->ptime > ts->max_time) break;
824: /*
825: We only call PreStep at the start of each STEP, not each STAGE. This is because it is
826: possible to fail (have to restart a step) after multiple stages.
827: */
828: TSPreStep(ts);
830: rejections = 0;
831: while (1) {
832: for (i=0; i<s; i++) {
833: PetscScalar shift;
834: gl->scoeff = 1./PetscRealPart(a[i*s+i]);
835: shift = gl->scoeff/ts->time_step;
836: gl->stage = i;
837: gl->stage_time = ts->ptime + PetscRealPart(c[i])*h;
839: /*
840: * Stage equation: Y = h A Y' + U X
841: * We assume that A is lower-triangular so that we can solve the stages (Y,Y') sequentially
842: * Build the affine vector z_i = -[1/(h a_ii)](h sum_j a_ij y'_j + sum_j u_ij x_j)
843: * Then y'_i = z + 1/(h a_ii) y_i
844: */
845: VecZeroEntries(gl->Z);
846: for (j=0; j<r; j++) {
847: VecAXPY(gl->Z,-shift*u[i*r+j],X[j]);
848: }
849: for (j=0; j<i; j++) {
850: VecAXPY(gl->Z,-shift*h*a[i*s+j],Ydot[j]);
851: }
852: /* Note: Z is used within function evaluation, Ydot = Z + shift*Y */
854: /* Compute an estimate of Y to start Newton iteration */
855: if (gl->extrapolate) {
856: if (i==0) {
857: /* Linear extrapolation on the first stage */
858: VecWAXPY(Y,c[i]*h,X[1],X[0]);
859: } else {
860: /* Linear extrapolation from the last stage */
861: VecAXPY(Y,(c[i]-c[i-1])*h,Ydot[i-1]);
862: }
863: } else if (i==0) { /* Directly use solution from the last step, otherwise reuse the last stage (do nothing) */
864: VecCopy(X[0],Y);
865: }
867: /* Solve this stage (Ydot[i] is computed during function evaluation) */
868: SNESSolve(ts->snes,NULL,Y);
869: SNESGetIterationNumber(ts->snes,&its);
870: SNESGetLinearSolveIterations(ts->snes,&lits);
871: SNESGetConvergedReason(ts->snes,&snesreason);
872: ts->snes_its += its; ts->ksp_its += lits;
873: if (snesreason < 0 && ts->max_snes_failures > 0 && ++ts->num_snes_failures >= ts->max_snes_failures) {
874: ts->reason = TS_DIVERGED_NONLINEAR_SOLVE;
875: PetscInfo(ts,"Step=%D, nonlinear solve solve failures %D greater than current TS allowed, stopping solve\n",ts->steps,ts->num_snes_failures);
876: return 0;
877: }
878: }
880: gl->stage_time = ts->ptime + ts->time_step;
882: (*gl->EstimateHigherMoments)(scheme,h,Ydot,gl->X,gl->himom);
883: /* hmnorm[i] = h^{p+i}x^{(p+i)} with i=0,1,2; hmnorm[3] = h^{p+2}(dx'/dx) x^{(p+1)} */
884: for (i=0; i<3; i++) {
885: TSGLLEVecNormWRMS(ts,gl->himom[i],&hmnorm[i+1]);
886: }
887: enorm[0] = PetscRealPart(scheme->alpha[0])*hmnorm[1];
888: enorm[1] = PetscRealPart(scheme->beta[0]) *hmnorm[2];
889: enorm[2] = PetscRealPart(scheme->gamma[0])*hmnorm[3];
890: (*gl->Accept)(ts,ts->max_time-gl->stage_time,h,enorm,&accept);
891: if (accept) goto accepted;
892: rejections++;
893: PetscInfo(ts,"Step %D (t=%g) not accepted, rejections=%D\n",k,gl->stage_time,rejections);
894: if (rejections > gl->max_step_rejections) break;
895: /*
896: There are lots of reasons why a step might be rejected, including solvers not converging and other factors that
897: TSGLLEChooseNextScheme does not support. Additionally, the error estimates may be very screwed up, so I'm not
898: convinced that it's safe to just compute a new error estimate using the same interface as the current adaptor
899: (the adaptor interface probably has to change). Here we make an arbitrary and naive choice. This assumes that
900: steps were written in Nordsieck form. The "correct" method would be to re-complete the previous time step with
901: the correct "next" step size. It is unclear to me whether the present ad-hoc method of rescaling X is stable.
902: */
903: h *= 0.5;
904: for (i=1; i<scheme->r; i++) {
905: VecScale(X[i],PetscPowRealInt(0.5,i));
906: }
907: }
908: SETERRQ(PETSC_COMM_SELF,PETSC_ERR_CONV_FAILED,"Time step %D (t=%g) not accepted after %D failures",k,gl->stage_time,rejections);
910: accepted:
911: /* This term is not error, but it *would* be the leading term for a lower order method */
912: TSGLLEVecNormWRMS(ts,gl->X[scheme->r-1],&hmnorm[0]);
913: /* Correct scaling so that these are equivalent to norms of the Nordsieck vectors */
915: PetscInfo(ts,"Last moment norm %10.2e, estimated error norms %10.2e %10.2e %10.2e\n",hmnorm[0],enorm[0],enorm[1],enorm[2]);
916: if (!final_step) {
917: TSGLLEChooseNextScheme(ts,h,hmnorm,&next_scheme,&next_h,&final_step);
918: } else {
919: /* Dummy values to complete the current step in a consistent manner */
920: next_scheme = gl->current_scheme;
921: next_h = h;
922: finish = PETSC_TRUE;
923: }
925: X = gl->Xold;
926: gl->Xold = gl->X;
927: gl->X = X;
928: (*gl->CompleteStep)(scheme,h,gl->schemes[next_scheme],next_h,Ydot,gl->Xold,gl->X);
930: TSGLLEUpdateWRMS(ts);
932: /* Post the solution for the user, we could avoid this copy with a small bit of cleverness */
933: VecCopy(gl->X[0],ts->vec_sol);
934: ts->ptime += h;
935: ts->steps++;
937: TSPostEvaluate(ts);
938: TSPostStep(ts);
939: TSMonitor(ts,ts->steps,ts->ptime,ts->vec_sol);
941: gl->current_scheme = next_scheme;
942: ts->time_step = next_h;
943: }
944: return 0;
945: }
947: /*------------------------------------------------------------*/
949: static PetscErrorCode TSReset_GLLE(TS ts)
950: {
951: TS_GLLE *gl = (TS_GLLE*)ts->data;
952: PetscInt max_r,max_s;
954: if (gl->setupcalled) {
955: TSGLLEGetMaxSizes(ts,&max_r,&max_s);
956: VecDestroyVecs(max_r,&gl->Xold);
957: VecDestroyVecs(max_r,&gl->X);
958: VecDestroyVecs(max_s,&gl->Ydot);
959: VecDestroyVecs(3,&gl->himom);
960: VecDestroy(&gl->W);
961: VecDestroy(&gl->Y);
962: VecDestroy(&gl->Z);
963: }
964: gl->setupcalled = PETSC_FALSE;
965: return 0;
966: }
968: static PetscErrorCode TSDestroy_GLLE(TS ts)
969: {
970: TS_GLLE *gl = (TS_GLLE*)ts->data;
972: TSReset_GLLE(ts);
973: if (ts->dm) {
974: DMCoarsenHookRemove(ts->dm,DMCoarsenHook_TSGLLE,DMRestrictHook_TSGLLE,ts);
975: DMSubDomainHookRemove(ts->dm,DMSubDomainHook_TSGLLE,DMSubDomainRestrictHook_TSGLLE,ts);
976: }
977: if (gl->adapt) TSGLLEAdaptDestroy(&gl->adapt);
978: if (gl->Destroy) (*gl->Destroy)(gl);
979: PetscFree(ts->data);
980: PetscObjectComposeFunction((PetscObject)ts,"TSGLLESetType_C",NULL);
981: PetscObjectComposeFunction((PetscObject)ts,"TSGLLESetAcceptType_C",NULL);
982: PetscObjectComposeFunction((PetscObject)ts,"TSGLLEGetAdapt_C",NULL);
983: return 0;
984: }
986: /*
987: This defines the nonlinear equation that is to be solved with SNES
988: g(x) = f(t,x,z+shift*x) = 0
989: */
990: static PetscErrorCode SNESTSFormFunction_GLLE(SNES snes,Vec x,Vec f,TS ts)
991: {
992: TS_GLLE *gl = (TS_GLLE*)ts->data;
993: Vec Z,Ydot;
994: DM dm,dmsave;
996: SNESGetDM(snes,&dm);
997: TSGLLEGetVecs(ts,dm,&Z,&Ydot);
998: VecWAXPY(Ydot,gl->scoeff/ts->time_step,x,Z);
999: dmsave = ts->dm;
1000: ts->dm = dm;
1001: TSComputeIFunction(ts,gl->stage_time,x,Ydot,f,PETSC_FALSE);
1002: ts->dm = dmsave;
1003: TSGLLERestoreVecs(ts,dm,&Z,&Ydot);
1004: return 0;
1005: }
1007: static PetscErrorCode SNESTSFormJacobian_GLLE(SNES snes,Vec x,Mat A,Mat B,TS ts)
1008: {
1009: TS_GLLE *gl = (TS_GLLE*)ts->data;
1010: Vec Z,Ydot;
1011: DM dm,dmsave;
1013: SNESGetDM(snes,&dm);
1014: TSGLLEGetVecs(ts,dm,&Z,&Ydot);
1015: dmsave = ts->dm;
1016: ts->dm = dm;
1017: /* gl->Xdot will have already been computed in SNESTSFormFunction_GLLE */
1018: TSComputeIJacobian(ts,gl->stage_time,x,gl->Ydot[gl->stage],gl->scoeff/ts->time_step,A,B,PETSC_FALSE);
1019: ts->dm = dmsave;
1020: TSGLLERestoreVecs(ts,dm,&Z,&Ydot);
1021: return 0;
1022: }
1024: static PetscErrorCode TSSetUp_GLLE(TS ts)
1025: {
1026: TS_GLLE *gl = (TS_GLLE*)ts->data;
1027: PetscInt max_r,max_s;
1028: DM dm;
1030: gl->setupcalled = PETSC_TRUE;
1031: TSGLLEGetMaxSizes(ts,&max_r,&max_s);
1032: VecDuplicateVecs(ts->vec_sol,max_r,&gl->X);
1033: VecDuplicateVecs(ts->vec_sol,max_r,&gl->Xold);
1034: VecDuplicateVecs(ts->vec_sol,max_s,&gl->Ydot);
1035: VecDuplicateVecs(ts->vec_sol,3,&gl->himom);
1036: VecDuplicate(ts->vec_sol,&gl->W);
1037: VecDuplicate(ts->vec_sol,&gl->Y);
1038: VecDuplicate(ts->vec_sol,&gl->Z);
1040: /* Default acceptance tests and adaptivity */
1041: if (!gl->Accept) TSGLLESetAcceptType(ts,TSGLLEACCEPT_ALWAYS);
1042: if (!gl->adapt) TSGLLEGetAdapt(ts,&gl->adapt);
1044: if (gl->current_scheme < 0) {
1045: PetscInt i;
1046: for (i=0;; i++) {
1047: if (gl->schemes[i]->p == gl->start_order) break;
1049: }
1050: gl->current_scheme = i;
1051: }
1052: TSGetDM(ts,&dm);
1053: DMCoarsenHookAdd(dm,DMCoarsenHook_TSGLLE,DMRestrictHook_TSGLLE,ts);
1054: DMSubDomainHookAdd(dm,DMSubDomainHook_TSGLLE,DMSubDomainRestrictHook_TSGLLE,ts);
1055: return 0;
1056: }
1057: /*------------------------------------------------------------*/
1059: static PetscErrorCode TSSetFromOptions_GLLE(PetscOptionItems *PetscOptionsObject,TS ts)
1060: {
1061: TS_GLLE *gl = (TS_GLLE*)ts->data;
1062: char tname[256] = TSGLLE_IRKS,completef[256] = "rescale-and-modify";
1064: PetscOptionsHead(PetscOptionsObject,"General Linear ODE solver options");
1065: {
1066: PetscBool flg;
1067: PetscOptionsFList("-ts_gl_type","Type of GL method","TSGLLESetType",TSGLLEList,gl->type_name[0] ? gl->type_name : tname,tname,sizeof(tname),&flg);
1068: if (flg || !gl->type_name[0]) {
1069: TSGLLESetType(ts,tname);
1070: }
1071: PetscOptionsInt("-ts_gl_max_step_rejections","Maximum number of times to attempt a step","None",gl->max_step_rejections,&gl->max_step_rejections,NULL);
1072: PetscOptionsInt("-ts_gl_max_order","Maximum order to try","TSGLLESetMaxOrder",gl->max_order,&gl->max_order,NULL);
1073: PetscOptionsInt("-ts_gl_min_order","Minimum order to try","TSGLLESetMinOrder",gl->min_order,&gl->min_order,NULL);
1074: PetscOptionsInt("-ts_gl_start_order","Initial order to try","TSGLLESetMinOrder",gl->start_order,&gl->start_order,NULL);
1075: PetscOptionsEnum("-ts_gl_error_direction","Which direction to look when estimating error","TSGLLESetErrorDirection",TSGLLEErrorDirections,(PetscEnum)gl->error_direction,(PetscEnum*)&gl->error_direction,NULL);
1076: PetscOptionsBool("-ts_gl_extrapolate","Extrapolate stage solution from previous solution (sometimes unstable)","TSGLLESetExtrapolate",gl->extrapolate,&gl->extrapolate,NULL);
1077: PetscOptionsReal("-ts_gl_atol","Absolute tolerance","TSGLLESetTolerances",gl->wrms_atol,&gl->wrms_atol,NULL);
1078: PetscOptionsReal("-ts_gl_rtol","Relative tolerance","TSGLLESetTolerances",gl->wrms_rtol,&gl->wrms_rtol,NULL);
1079: PetscOptionsString("-ts_gl_complete","Method to use for completing the step","none",completef,completef,sizeof(completef),&flg);
1080: if (flg) {
1081: PetscBool match1,match2;
1082: PetscStrcmp(completef,"rescale",&match1);
1083: PetscStrcmp(completef,"rescale-and-modify",&match2);
1084: if (match1) gl->CompleteStep = TSGLLECompleteStep_Rescale;
1085: else if (match2) gl->CompleteStep = TSGLLECompleteStep_RescaleAndModify;
1086: else SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_UNKNOWN_TYPE,"%s",completef);
1087: }
1088: {
1089: char type[256] = TSGLLEACCEPT_ALWAYS;
1090: PetscOptionsFList("-ts_gl_accept_type","Method to use for determining whether to accept a step","TSGLLESetAcceptType",TSGLLEAcceptList,gl->accept_name[0] ? gl->accept_name : type,type,sizeof(type),&flg);
1091: if (flg || !gl->accept_name[0]) {
1092: TSGLLESetAcceptType(ts,type);
1093: }
1094: }
1095: {
1096: TSGLLEAdapt adapt;
1097: TSGLLEGetAdapt(ts,&adapt);
1098: TSGLLEAdaptSetFromOptions(PetscOptionsObject,adapt);
1099: }
1100: }
1101: PetscOptionsTail();
1102: return 0;
1103: }
1105: static PetscErrorCode TSView_GLLE(TS ts,PetscViewer viewer)
1106: {
1107: TS_GLLE *gl = (TS_GLLE*)ts->data;
1108: PetscInt i;
1109: PetscBool iascii,details;
1111: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1112: if (iascii) {
1113: PetscViewerASCIIPrintf(viewer," min order %D, max order %D, current order %D\n",gl->min_order,gl->max_order,gl->schemes[gl->current_scheme]->p);
1114: PetscViewerASCIIPrintf(viewer," Error estimation: %s\n",TSGLLEErrorDirections[gl->error_direction]);
1115: PetscViewerASCIIPrintf(viewer," Extrapolation: %s\n",gl->extrapolate ? "yes" : "no");
1116: PetscViewerASCIIPrintf(viewer," Acceptance test: %s\n",gl->accept_name[0] ? gl->accept_name : "(not yet set)");
1117: PetscViewerASCIIPushTab(viewer);
1118: TSGLLEAdaptView(gl->adapt,viewer);
1119: PetscViewerASCIIPopTab(viewer);
1120: PetscViewerASCIIPrintf(viewer," type: %s\n",gl->type_name[0] ? gl->type_name : "(not yet set)");
1121: PetscViewerASCIIPrintf(viewer,"Schemes within family (%d):\n",gl->nschemes);
1122: details = PETSC_FALSE;
1123: PetscOptionsGetBool(((PetscObject)ts)->options,((PetscObject)ts)->prefix,"-ts_gl_view_detailed",&details,NULL);
1124: PetscViewerASCIIPushTab(viewer);
1125: for (i=0; i<gl->nschemes; i++) {
1126: TSGLLESchemeView(gl->schemes[i],details,viewer);
1127: }
1128: if (gl->View) {
1129: (*gl->View)(gl,viewer);
1130: }
1131: PetscViewerASCIIPopTab(viewer);
1132: }
1133: return 0;
1134: }
1136: /*@C
1137: TSGLLERegister - adds a TSGLLE implementation
1139: Not Collective
1141: Input Parameters:
1142: + name_scheme - name of user-defined general linear scheme
1143: - routine_create - routine to create method context
1145: Notes:
1146: TSGLLERegister() may be called multiple times to add several user-defined families.
1148: Sample usage:
1149: .vb
1150: TSGLLERegister("my_scheme",MySchemeCreate);
1151: .ve
1153: Then, your scheme can be chosen with the procedural interface via
1154: $ TSGLLESetType(ts,"my_scheme")
1155: or at runtime via the option
1156: $ -ts_gl_type my_scheme
1158: Level: advanced
1160: .seealso: TSGLLERegisterAll()
1161: @*/
1162: PetscErrorCode TSGLLERegister(const char sname[],PetscErrorCode (*function)(TS))
1163: {
1164: TSGLLEInitializePackage();
1165: PetscFunctionListAdd(&TSGLLEList,sname,function);
1166: return 0;
1167: }
1169: /*@C
1170: TSGLLEAcceptRegister - adds a TSGLLE acceptance scheme
1172: Not Collective
1174: Input Parameters:
1175: + name_scheme - name of user-defined acceptance scheme
1176: - routine_create - routine to create method context
1178: Notes:
1179: TSGLLEAcceptRegister() may be called multiple times to add several user-defined families.
1181: Sample usage:
1182: .vb
1183: TSGLLEAcceptRegister("my_scheme",MySchemeCreate);
1184: .ve
1186: Then, your scheme can be chosen with the procedural interface via
1187: $ TSGLLESetAcceptType(ts,"my_scheme")
1188: or at runtime via the option
1189: $ -ts_gl_accept_type my_scheme
1191: Level: advanced
1193: .seealso: TSGLLERegisterAll()
1194: @*/
1195: PetscErrorCode TSGLLEAcceptRegister(const char sname[],TSGLLEAcceptFunction function)
1196: {
1197: PetscFunctionListAdd(&TSGLLEAcceptList,sname,function);
1198: return 0;
1199: }
1201: /*@C
1202: TSGLLERegisterAll - Registers all of the general linear methods in TSGLLE
1204: Not Collective
1206: Level: advanced
1208: .seealso: TSGLLERegisterDestroy()
1209: @*/
1210: PetscErrorCode TSGLLERegisterAll(void)
1211: {
1212: if (TSGLLERegisterAllCalled) return 0;
1213: TSGLLERegisterAllCalled = PETSC_TRUE;
1215: TSGLLERegister(TSGLLE_IRKS, TSGLLECreate_IRKS);
1216: TSGLLEAcceptRegister(TSGLLEACCEPT_ALWAYS,TSGLLEAccept_Always);
1217: return 0;
1218: }
1220: /*@C
1221: TSGLLEInitializePackage - This function initializes everything in the TSGLLE package. It is called
1222: from TSInitializePackage().
1224: Level: developer
1226: .seealso: PetscInitialize()
1227: @*/
1228: PetscErrorCode TSGLLEInitializePackage(void)
1229: {
1230: if (TSGLLEPackageInitialized) return 0;
1231: TSGLLEPackageInitialized = PETSC_TRUE;
1232: TSGLLERegisterAll();
1233: PetscRegisterFinalize(TSGLLEFinalizePackage);
1234: return 0;
1235: }
1237: /*@C
1238: TSGLLEFinalizePackage - This function destroys everything in the TSGLLE package. It is
1239: called from PetscFinalize().
1241: Level: developer
1243: .seealso: PetscFinalize()
1244: @*/
1245: PetscErrorCode TSGLLEFinalizePackage(void)
1246: {
1247: PetscFunctionListDestroy(&TSGLLEList);
1248: PetscFunctionListDestroy(&TSGLLEAcceptList);
1249: TSGLLEPackageInitialized = PETSC_FALSE;
1250: TSGLLERegisterAllCalled = PETSC_FALSE;
1251: return 0;
1252: }
1254: /* ------------------------------------------------------------ */
1255: /*MC
1256: TSGLLE - DAE solver using implicit General Linear methods
1258: These methods contain Runge-Kutta and multistep schemes as special cases. These special cases have some fundamental
1259: limitations. For example, diagonally implicit Runge-Kutta cannot have stage order greater than 1 which limits their
1260: applicability to very stiff systems. Meanwhile, multistep methods cannot be A-stable for order greater than 2 and BDF
1261: are not 0-stable for order greater than 6. GL methods can be A- and L-stable with arbitrarily high stage order and
1262: reliable error estimates for both 1 and 2 orders higher to facilitate adaptive step sizes and adaptive order schemes.
1263: All this is possible while preserving a singly diagonally implicit structure.
1265: Options database keys:
1266: + -ts_gl_type <type> - the class of general linear method (irks)
1267: . -ts_gl_rtol <tol> - relative error
1268: . -ts_gl_atol <tol> - absolute error
1269: . -ts_gl_min_order <p> - minimum order method to consider (default=1)
1270: . -ts_gl_max_order <p> - maximum order method to consider (default=3)
1271: . -ts_gl_start_order <p> - order of starting method (default=1)
1272: . -ts_gl_complete <method> - method to use for completing the step (rescale-and-modify or rescale)
1273: - -ts_adapt_type <method> - adaptive controller to use (none step both)
1275: Notes:
1276: This integrator can be applied to DAE.
1278: Diagonally implicit general linear (DIGL) methods are a generalization of diagonally implicit Runge-Kutta (DIRK).
1279: They are represented by the tableau
1281: .vb
1282: A | U
1283: -------
1284: B | V
1285: .ve
1287: combined with a vector c of abscissa. "Diagonally implicit" means that A is lower triangular.
1288: A step of the general method reads
1290: .vb
1291: [ Y ] = [A U] [ Y' ]
1292: [X^k] = [B V] [X^{k-1}]
1293: .ve
1295: where Y is the multivector of stage values, Y' is the multivector of stage derivatives, X^k is the Nordsieck vector of
1296: the solution at step k. The Nordsieck vector consists of the first r moments of the solution, given by
1298: .vb
1299: X = [x_0,x_1,...,x_{r-1}] = [x, h x', h^2 x'', ..., h^{r-1} x^{(r-1)} ]
1300: .ve
1302: If A is lower triangular, we can solve the stages (Y,Y') sequentially
1304: .vb
1305: y_i = h sum_{j=0}^{s-1} (a_ij y'_j) + sum_{j=0}^{r-1} u_ij x_j, i=0,...,{s-1}
1306: .ve
1308: and then construct the pieces to carry to the next step
1310: .vb
1311: xx_i = h sum_{j=0}^{s-1} b_ij y'_j + sum_{j=0}^{r-1} v_ij x_j, i=0,...,{r-1}
1312: .ve
1314: Note that when the equations are cast in implicit form, we are using the stage equation to define y'_i
1315: in terms of y_i and known stuff (y_j for j<i and x_j for all j).
1317: Error estimation
1319: At present, the most attractive GL methods for stiff problems are singly diagonally implicit schemes which posses
1320: Inherent Runge-Kutta Stability (IRKS). These methods have r=s, the number of items passed between steps is equal to
1321: the number of stages. The order and stage-order are one less than the number of stages. We use the error estimates
1322: in the 2007 paper which provide the following estimates
1324: .vb
1325: h^{p+1} X^{(p+1)} = phi_0^T Y' + [0 psi_0^T] Xold
1326: h^{p+2} X^{(p+2)} = phi_1^T Y' + [0 psi_1^T] Xold
1327: h^{p+2} (dx'/dx) X^{(p+1)} = phi_2^T Y' + [0 psi_2^T] Xold
1328: .ve
1330: These estimates are accurate to O(h^{p+3}).
1332: Changing the step size
1334: We use the generalized "rescale and modify" scheme, see equation (4.5) of the 2007 paper.
1336: Level: beginner
1338: References:
1339: + * - John Butcher and Z. Jackieweicz and W. Wright, On error propagation in general linear methods for
1340: ordinary differential equations, Journal of Complexity, Vol 23, 2007.
1341: - * - John Butcher, Numerical methods for ordinary differential equations, second edition, Wiley, 2009.
1343: .seealso: TSCreate(), TS, TSSetType()
1345: M*/
1346: PETSC_EXTERN PetscErrorCode TSCreate_GLLE(TS ts)
1347: {
1348: TS_GLLE *gl;
1350: TSGLLEInitializePackage();
1352: PetscNewLog(ts,&gl);
1353: ts->data = (void*)gl;
1355: ts->ops->reset = TSReset_GLLE;
1356: ts->ops->destroy = TSDestroy_GLLE;
1357: ts->ops->view = TSView_GLLE;
1358: ts->ops->setup = TSSetUp_GLLE;
1359: ts->ops->solve = TSSolve_GLLE;
1360: ts->ops->setfromoptions = TSSetFromOptions_GLLE;
1361: ts->ops->snesfunction = SNESTSFormFunction_GLLE;
1362: ts->ops->snesjacobian = SNESTSFormJacobian_GLLE;
1364: ts->usessnes = PETSC_TRUE;
1366: gl->max_step_rejections = 1;
1367: gl->min_order = 1;
1368: gl->max_order = 3;
1369: gl->start_order = 1;
1370: gl->current_scheme = -1;
1371: gl->extrapolate = PETSC_FALSE;
1373: gl->wrms_atol = 1e-8;
1374: gl->wrms_rtol = 1e-5;
1376: PetscObjectComposeFunction((PetscObject)ts,"TSGLLESetType_C", &TSGLLESetType_GLLE);
1377: PetscObjectComposeFunction((PetscObject)ts,"TSGLLESetAcceptType_C",&TSGLLESetAcceptType_GLLE);
1378: PetscObjectComposeFunction((PetscObject)ts,"TSGLLEGetAdapt_C", &TSGLLEGetAdapt_GLLE);
1379: return 0;
1380: }