Actual source code: tssen.c

  1: #include <petsc/private/tsimpl.h>
  2: #include <petscdraw.h>

  4: PetscLogEvent TS_AdjointStep,TS_ForwardStep,TS_JacobianPEval;

  6: /* #define TSADJOINT_STAGE */

  8: /* ------------------------ Sensitivity Context ---------------------------*/

 10: /*@C
 11:   TSSetRHSJacobianP - Sets the function that computes the Jacobian of G w.r.t. the parameters P where U_t = G(U,P,t), as well as the location to store the matrix.

 13:   Logically Collective on TS

 15:   Input Parameters:
 16: + ts - TS context obtained from TSCreate()
 17: . Amat - JacobianP matrix
 18: . func - function
 19: - ctx - [optional] user-defined function context

 21:   Calling sequence of func:
 22: $ func (TS ts,PetscReal t,Vec y,Mat A,void *ctx);
 23: +   t - current timestep
 24: .   U - input vector (current ODE solution)
 25: .   A - output matrix
 26: -   ctx - [optional] user-defined function context

 28:   Level: intermediate

 30:   Notes:
 31:     Amat has the same number of rows and the same row parallel layout as u, Amat has the same number of columns and parallel layout as p

 33: .seealso: TSGetRHSJacobianP()
 34: @*/
 35: PetscErrorCode TSSetRHSJacobianP(TS ts,Mat Amat,PetscErrorCode (*func)(TS,PetscReal,Vec,Mat,void*),void *ctx)
 36: {


 43:   ts->rhsjacobianp    = func;
 44:   ts->rhsjacobianpctx = ctx;
 45:   if (Amat) {
 46:     PetscObjectReference((PetscObject)Amat);
 47:     MatDestroy(&ts->Jacprhs);
 48:     ts->Jacprhs = Amat;
 49:   }
 50:   return(0);
 51: }

 53: /*@C
 54:   TSGetRHSJacobianP - Gets the function that computes the Jacobian of G w.r.t. the parameters P where U_t = G(U,P,t), as well as the location to store the matrix.

 56:   Logically Collective on TS

 58:   Input Parameter:
 59: . ts - TS context obtained from TSCreate()

 61:   Output Parameters:
 62: + Amat - JacobianP matrix
 63: . func - function
 64: - ctx - [optional] user-defined function context

 66:   Calling sequence of func:
 67: $ func (TS ts,PetscReal t,Vec y,Mat A,void *ctx);
 68: +   t - current timestep
 69: .   U - input vector (current ODE solution)
 70: .   A - output matrix
 71: -   ctx - [optional] user-defined function context

 73:   Level: intermediate

 75:   Notes:
 76:     Amat has the same number of rows and the same row parallel layout as u, Amat has the same number of columns and parallel layout as p

 78: .seealso: TSSetRHSJacobianP()
 79: @*/
 80: PetscErrorCode TSGetRHSJacobianP(TS ts,Mat *Amat,PetscErrorCode (**func)(TS,PetscReal,Vec,Mat,void*),void **ctx)
 81: {
 83:   if (func) *func = ts->rhsjacobianp;
 84:   if (ctx) *ctx  = ts->rhsjacobianpctx;
 85:   if (Amat) *Amat = ts->Jacprhs;
 86:   return(0);
 87: }

 89: /*@C
 90:   TSComputeRHSJacobianP - Runs the user-defined JacobianP function.

 92:   Collective on TS

 94:   Input Parameters:
 95: . ts   - The TS context obtained from TSCreate()

 97:   Level: developer

 99: .seealso: TSSetRHSJacobianP()
100: @*/
101: PetscErrorCode TSComputeRHSJacobianP(TS ts,PetscReal t,Vec U,Mat Amat)
102: {

106:   if (!Amat) return(0);

110:   PetscStackPush("TS user JacobianP function for sensitivity analysis");
111:   (*ts->rhsjacobianp)(ts,t,U,Amat,ts->rhsjacobianpctx);
112:   PetscStackPop;
113:   return(0);
114: }

116: /*@C
117:   TSSetIJacobianP - Sets the function that computes the Jacobian of F w.r.t. the parameters P where F(Udot,U,t) = G(U,P,t), as well as the location to store the matrix.

119:   Logically Collective on TS

121:   Input Parameters:
122: + ts - TS context obtained from TSCreate()
123: . Amat - JacobianP matrix
124: . func - function
125: - ctx - [optional] user-defined function context

127:   Calling sequence of func:
128: $ func (TS ts,PetscReal t,Vec y,Mat A,void *ctx);
129: +   t - current timestep
130: .   U - input vector (current ODE solution)
131: .   Udot - time derivative of state vector
132: .   shift - shift to apply, see note below
133: .   A - output matrix
134: -   ctx - [optional] user-defined function context

136:   Level: intermediate

138:   Notes:
139:     Amat has the same number of rows and the same row parallel layout as u, Amat has the same number of columns and parallel layout as p

141: .seealso:
142: @*/
143: PetscErrorCode TSSetIJacobianP(TS ts,Mat Amat,PetscErrorCode (*func)(TS,PetscReal,Vec,Vec,PetscReal,Mat,void*),void *ctx)
144: {


151:   ts->ijacobianp    = func;
152:   ts->ijacobianpctx = ctx;
153:   if (Amat) {
154:     PetscObjectReference((PetscObject)Amat);
155:     MatDestroy(&ts->Jacp);
156:     ts->Jacp = Amat;
157:   }
158:   return(0);
159: }

161: /*@C
162:   TSComputeIJacobianP - Runs the user-defined IJacobianP function.

164:   Collective on TS

166:   Input Parameters:
167: + ts - the TS context
168: . t - current timestep
169: . U - state vector
170: . Udot - time derivative of state vector
171: . shift - shift to apply, see note below
172: - imex - flag indicates if the method is IMEX so that the RHSJacobian should be kept separate

174:   Output Parameters:
175: . A - Jacobian matrix

177:   Level: developer

179: .seealso: TSSetIJacobianP()
180: @*/
181: PetscErrorCode TSComputeIJacobianP(TS ts,PetscReal t,Vec U,Vec Udot,PetscReal shift,Mat Amat,PetscBool imex)
182: {

186:   if (!Amat) return(0);

191:   PetscLogEventBegin(TS_JacobianPEval,ts,U,Amat,0);
192:   if (ts->ijacobianp) {
193:     PetscStackPush("TS user JacobianP function for sensitivity analysis");
194:     (*ts->ijacobianp)(ts,t,U,Udot,shift,Amat,ts->ijacobianpctx);
195:     PetscStackPop;
196:   }
197:   if (imex) {
198:     if (!ts->ijacobianp) {  /* system was written as Udot = G(t,U) */
199:       PetscBool assembled;
200:       MatZeroEntries(Amat);
201:       MatAssembled(Amat,&assembled);
202:       if (!assembled) {
203:         MatAssemblyBegin(Amat,MAT_FINAL_ASSEMBLY);
204:         MatAssemblyEnd(Amat,MAT_FINAL_ASSEMBLY);
205:       }
206:     }
207:   } else {
208:     if (ts->rhsjacobianp) {
209:       TSComputeRHSJacobianP(ts,t,U,ts->Jacprhs);
210:     }
211:     if (ts->Jacprhs == Amat) { /* No IJacobian, so we only have the RHS matrix */
212:       MatScale(Amat,-1);
213:     } else if (ts->Jacprhs) { /* Both IJacobian and RHSJacobian */
214:       MatStructure axpy = DIFFERENT_NONZERO_PATTERN;
215:       if (!ts->ijacobianp) { /* No IJacobianp provided, but we have a separate RHS matrix */
216:         MatZeroEntries(Amat);
217:       }
218:       MatAXPY(Amat,-1,ts->Jacprhs,axpy);
219:     }
220:   }
221:   PetscLogEventEnd(TS_JacobianPEval,ts,U,Amat,0);
222:   return(0);
223: }

225: /*@C
226:     TSSetCostIntegrand - Sets the routine for evaluating the integral term in one or more cost functions

228:     Logically Collective on TS

230:     Input Parameters:
231: +   ts - the TS context obtained from TSCreate()
232: .   numcost - number of gradients to be computed, this is the number of cost functions
233: .   costintegral - vector that stores the integral values
234: .   rf - routine for evaluating the integrand function
235: .   drduf - function that computes the gradients of the r's with respect to u
236: .   drdpf - function that computes the gradients of the r's with respect to p, can be NULL if parametric sensitivity is not desired (mu=NULL)
237: .   fwd - flag indicating whether to evaluate cost integral in the forward run or the adjoint run
238: -   ctx - [optional] user-defined context for private data for the function evaluation routine (may be NULL)

240:     Calling sequence of rf:
241: $   PetscErrorCode rf(TS ts,PetscReal t,Vec U,Vec F,void *ctx);

243:     Calling sequence of drduf:
244: $   PetscErroCode drduf(TS ts,PetscReal t,Vec U,Vec *dRdU,void *ctx);

246:     Calling sequence of drdpf:
247: $   PetscErroCode drdpf(TS ts,PetscReal t,Vec U,Vec *dRdP,void *ctx);

249:     Level: deprecated

251:     Notes:
252:     For optimization there is usually a single cost function (numcost = 1). For sensitivities there may be multiple cost functions

254: .seealso: TSSetRHSJacobianP(), TSGetCostGradients(), TSSetCostGradients()
255: @*/
256: PetscErrorCode TSSetCostIntegrand(TS ts,PetscInt numcost,Vec costintegral,PetscErrorCode (*rf)(TS,PetscReal,Vec,Vec,void*),
257:                                                           PetscErrorCode (*drduf)(TS,PetscReal,Vec,Vec*,void*),
258:                                                           PetscErrorCode (*drdpf)(TS,PetscReal,Vec,Vec*,void*),
259:                                                           PetscBool fwd,void *ctx)
260: {

266:   if (ts->numcost && ts->numcost!=numcost) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"The number of cost functions (2nd parameter of TSSetCostIntegrand()) is inconsistent with the one set by TSSetCostGradients() or TSForwardSetIntegralGradients()");
267:   if (!ts->numcost) ts->numcost=numcost;

269:   if (costintegral) {
270:     PetscObjectReference((PetscObject)costintegral);
271:     VecDestroy(&ts->vec_costintegral);
272:     ts->vec_costintegral = costintegral;
273:   } else {
274:     if (!ts->vec_costintegral) { /* Create a seq vec if user does not provide one */
275:       VecCreateSeq(PETSC_COMM_SELF,numcost,&ts->vec_costintegral);
276:     } else {
277:       VecSet(ts->vec_costintegral,0.0);
278:     }
279:   }
280:   if (!ts->vec_costintegrand) {
281:     VecDuplicate(ts->vec_costintegral,&ts->vec_costintegrand);
282:   } else {
283:     VecSet(ts->vec_costintegrand,0.0);
284:   }
285:   ts->costintegralfwd  = fwd; /* Evaluate the cost integral in forward run if fwd is true */
286:   ts->costintegrand    = rf;
287:   ts->costintegrandctx = ctx;
288:   ts->drdufunction     = drduf;
289:   ts->drdpfunction     = drdpf;
290:   return(0);
291: }

293: /*@C
294:    TSGetCostIntegral - Returns the values of the integral term in the cost functions.
295:    It is valid to call the routine after a backward run.

297:    Not Collective

299:    Input Parameter:
300: .  ts - the TS context obtained from TSCreate()

302:    Output Parameter:
303: .  v - the vector containing the integrals for each cost function

305:    Level: intermediate

307: .seealso: TSSetCostIntegrand()

309: @*/
310: PetscErrorCode  TSGetCostIntegral(TS ts,Vec *v)
311: {
312:   TS             quadts;

318:   TSGetQuadratureTS(ts,NULL,&quadts);
319:   *v = quadts->vec_sol;
320:   return(0);
321: }

323: /*@C
324:    TSComputeCostIntegrand - Evaluates the integral function in the cost functions.

326:    Input Parameters:
327: +  ts - the TS context
328: .  t - current time
329: -  U - state vector, i.e. current solution

331:    Output Parameter:
332: .  Q - vector of size numcost to hold the outputs

334:    Notes:
335:    Most users should not need to explicitly call this routine, as it
336:    is used internally within the sensitivity analysis context.

338:    Level: deprecated

340: .seealso: TSSetCostIntegrand()
341: @*/
342: PetscErrorCode TSComputeCostIntegrand(TS ts,PetscReal t,Vec U,Vec Q)
343: {


351:   PetscLogEventBegin(TS_FunctionEval,ts,U,Q,0);
352:   if (ts->costintegrand) {
353:     PetscStackPush("TS user integrand in the cost function");
354:     (*ts->costintegrand)(ts,t,U,Q,ts->costintegrandctx);
355:     PetscStackPop;
356:   } else {
357:     VecZeroEntries(Q);
358:   }

360:   PetscLogEventEnd(TS_FunctionEval,ts,U,Q,0);
361:   return(0);
362: }

364: /*@C
365:   TSComputeDRDUFunction - Deprecated, use TSGetQuadratureTS() then TSComputeRHSJacobian()

367:   Level: deprecated

369: @*/
370: PetscErrorCode TSComputeDRDUFunction(TS ts,PetscReal t,Vec U,Vec *DRDU)
371: {

375:   if (!DRDU) return(0);

379:   PetscStackPush("TS user DRDU function for sensitivity analysis");
380:   (*ts->drdufunction)(ts,t,U,DRDU,ts->costintegrandctx);
381:   PetscStackPop;
382:   return(0);
383: }

385: /*@C
386:   TSComputeDRDPFunction - Deprecated, use TSGetQuadratureTS() then TSComputeRHSJacobianP()

388:   Level: deprecated

390: @*/
391: PetscErrorCode TSComputeDRDPFunction(TS ts,PetscReal t,Vec U,Vec *DRDP)
392: {

396:   if (!DRDP) return(0);

400:   PetscStackPush("TS user DRDP function for sensitivity analysis");
401:   (*ts->drdpfunction)(ts,t,U,DRDP,ts->costintegrandctx);
402:   PetscStackPop;
403:   return(0);
404: }

406: /*@C
407:   TSSetIHessianProduct - Sets the function that computes the vector-Hessian-vector product. The Hessian is the second-order derivative of F (IFunction) w.r.t. the state variable.

409:   Logically Collective on TS

411:   Input Parameters:
412: + ts - TS context obtained from TSCreate()
413: . ihp1 - an array of vectors storing the result of vector-Hessian-vector product for F_UU
414: . hessianproductfunc1 - vector-Hessian-vector product function for F_UU
415: . ihp2 - an array of vectors storing the result of vector-Hessian-vector product for F_UP
416: . hessianproductfunc2 - vector-Hessian-vector product function for F_UP
417: . ihp3 - an array of vectors storing the result of vector-Hessian-vector product for F_PU
418: . hessianproductfunc3 - vector-Hessian-vector product function for F_PU
419: . ihp4 - an array of vectors storing the result of vector-Hessian-vector product for F_PP
420: - hessianproductfunc4 - vector-Hessian-vector product function for F_PP

422:   Calling sequence of ihessianproductfunc:
423: $ ihessianproductfunc (TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV,void *ctx);
424: +   t - current timestep
425: .   U - input vector (current ODE solution)
426: .   Vl - an array of input vectors to be left-multiplied with the Hessian
427: .   Vr - input vector to be right-multiplied with the Hessian
428: .   VHV - an array of output vectors for vector-Hessian-vector product
429: -   ctx - [optional] user-defined function context

431:   Level: intermediate

433:   Notes:
434:   The first Hessian function and the working array are required.
435:   As an example to implement the callback functions, the second callback function calculates the vector-Hessian-vector product
436:   $ Vl_n^T*F_UP*Vr
437:   where the vector Vl_n (n-th element in the array Vl) and Vr are of size N and M respectively, and the Hessian F_UP is of size N x N x M.
438:   Each entry of F_UP corresponds to the derivative
439:   $ F_UP[i][j][k] = \frac{\partial^2 F[i]}{\partial U[j] \partial P[k]}.
440:   The result of the vector-Hessian-vector product for Vl_n needs to be stored in vector VHV_n with the j-th entry being
441:   $ VHV_n[j] = \sum_i \sum_k {Vl_n[i] * F_UP[i][j][k] * Vr[k]}
442:   If the cost function is a scalar, there will be only one vector in Vl and VHV.

444: .seealso:
445: @*/
446: PetscErrorCode TSSetIHessianProduct(TS ts,Vec *ihp1,PetscErrorCode (*ihessianproductfunc1)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
447:                                           Vec *ihp2,PetscErrorCode (*ihessianproductfunc2)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
448:                                           Vec *ihp3,PetscErrorCode (*ihessianproductfunc3)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
449:                                           Vec *ihp4,PetscErrorCode (*ihessianproductfunc4)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
450:                                     void *ctx)
451: {

456:   ts->ihessianproductctx = ctx;
457:   if (ihp1) ts->vecs_fuu = ihp1;
458:   if (ihp2) ts->vecs_fup = ihp2;
459:   if (ihp3) ts->vecs_fpu = ihp3;
460:   if (ihp4) ts->vecs_fpp = ihp4;
461:   ts->ihessianproduct_fuu = ihessianproductfunc1;
462:   ts->ihessianproduct_fup = ihessianproductfunc2;
463:   ts->ihessianproduct_fpu = ihessianproductfunc3;
464:   ts->ihessianproduct_fpp = ihessianproductfunc4;
465:   return(0);
466: }

468: /*@C
469:   TSComputeIHessianProductFunctionUU - Runs the user-defined vector-Hessian-vector product function for Fuu.

471:   Collective on TS

473:   Input Parameters:
474: . ts   - The TS context obtained from TSCreate()

476:   Notes:
477:   TSComputeIHessianProductFunctionUU() is typically used for sensitivity implementation,
478:   so most users would not generally call this routine themselves.

480:   Level: developer

482: .seealso: TSSetIHessianProduct()
483: @*/
484: PetscErrorCode TSComputeIHessianProductFunctionUU(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
485: {

489:   if (!VHV) return(0);

493:   if (ts->ihessianproduct_fuu) {
494:     PetscStackPush("TS user IHessianProduct function 1 for sensitivity analysis");
495:     (*ts->ihessianproduct_fuu)(ts,t,U,Vl,Vr,VHV,ts->ihessianproductctx);
496:     PetscStackPop;
497:   }
498:   /* does not consider IMEX for now, so either IHessian or RHSHessian will be calculated, using the same output VHV */
499:   if (ts->rhshessianproduct_guu) {
500:     PetscInt nadj;
501:     TSComputeRHSHessianProductFunctionUU(ts,t,U,Vl,Vr,VHV);
502:     for (nadj=0; nadj<ts->numcost; nadj++) {
503:       VecScale(VHV[nadj],-1);
504:     }
505:   }
506:   return(0);
507: }

509: /*@C
510:   TSComputeIHessianProductFunctionUP - Runs the user-defined vector-Hessian-vector product function for Fup.

512:   Collective on TS

514:   Input Parameters:
515: . ts   - The TS context obtained from TSCreate()

517:   Notes:
518:   TSComputeIHessianProductFunctionUP() is typically used for sensitivity implementation,
519:   so most users would not generally call this routine themselves.

521:   Level: developer

523: .seealso: TSSetIHessianProduct()
524: @*/
525: PetscErrorCode TSComputeIHessianProductFunctionUP(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
526: {

530:   if (!VHV) return(0);

534:   if (ts->ihessianproduct_fup) {
535:     PetscStackPush("TS user IHessianProduct function 2 for sensitivity analysis");
536:     (*ts->ihessianproduct_fup)(ts,t,U,Vl,Vr,VHV,ts->ihessianproductctx);
537:     PetscStackPop;
538:   }
539:   /* does not consider IMEX for now, so either IHessian or RHSHessian will be calculated, using the same output VHV */
540:   if (ts->rhshessianproduct_gup) {
541:     PetscInt nadj;
542:     TSComputeRHSHessianProductFunctionUP(ts,t,U,Vl,Vr,VHV);
543:     for (nadj=0; nadj<ts->numcost; nadj++) {
544:       VecScale(VHV[nadj],-1);
545:     }
546:   }
547:   return(0);
548: }

550: /*@C
551:   TSComputeIHessianProductFunctionPU - Runs the user-defined vector-Hessian-vector product function for Fpu.

553:   Collective on TS

555:   Input Parameters:
556: . ts   - The TS context obtained from TSCreate()

558:   Notes:
559:   TSComputeIHessianProductFunctionPU() is typically used for sensitivity implementation,
560:   so most users would not generally call this routine themselves.

562:   Level: developer

564: .seealso: TSSetIHessianProduct()
565: @*/
566: PetscErrorCode TSComputeIHessianProductFunctionPU(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
567: {

571:   if (!VHV) return(0);

575:   if (ts->ihessianproduct_fpu) {
576:     PetscStackPush("TS user IHessianProduct function 3 for sensitivity analysis");
577:     (*ts->ihessianproduct_fpu)(ts,t,U,Vl,Vr,VHV,ts->ihessianproductctx);
578:     PetscStackPop;
579:   }
580:   /* does not consider IMEX for now, so either IHessian or RHSHessian will be calculated, using the same output VHV */
581:   if (ts->rhshessianproduct_gpu) {
582:     PetscInt nadj;
583:     TSComputeRHSHessianProductFunctionPU(ts,t,U,Vl,Vr,VHV);
584:     for (nadj=0; nadj<ts->numcost; nadj++) {
585:       VecScale(VHV[nadj],-1);
586:     }
587:   }
588:   return(0);
589: }

591: /*@C
592:   TSComputeIHessianProductFunctionPP - Runs the user-defined vector-Hessian-vector product function for Fpp.

594:   Collective on TS

596:   Input Parameters:
597: . ts   - The TS context obtained from TSCreate()

599:   Notes:
600:   TSComputeIHessianProductFunctionPP() is typically used for sensitivity implementation,
601:   so most users would not generally call this routine themselves.

603:   Level: developer

605: .seealso: TSSetIHessianProduct()
606: @*/
607: PetscErrorCode TSComputeIHessianProductFunctionPP(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
608: {

612:   if (!VHV) return(0);

616:   if (ts->ihessianproduct_fpp) {
617:     PetscStackPush("TS user IHessianProduct function 3 for sensitivity analysis");
618:     (*ts->ihessianproduct_fpp)(ts,t,U,Vl,Vr,VHV,ts->ihessianproductctx);
619:     PetscStackPop;
620:   }
621:   /* does not consider IMEX for now, so either IHessian or RHSHessian will be calculated, using the same output VHV */
622:   if (ts->rhshessianproduct_gpp) {
623:     PetscInt nadj;
624:     TSComputeRHSHessianProductFunctionPP(ts,t,U,Vl,Vr,VHV);
625:     for (nadj=0; nadj<ts->numcost; nadj++) {
626:       VecScale(VHV[nadj],-1);
627:     }
628:   }
629:   return(0);
630: }

632: /*@C
633:   TSSetRHSHessianProduct - Sets the function that computes the vector-Hessian-vector product. The Hessian is the second-order derivative of G (RHSFunction) w.r.t. the state variable.

635:   Logically Collective on TS

637:   Input Parameters:
638: + ts - TS context obtained from TSCreate()
639: . rhshp1 - an array of vectors storing the result of vector-Hessian-vector product for G_UU
640: . hessianproductfunc1 - vector-Hessian-vector product function for G_UU
641: . rhshp2 - an array of vectors storing the result of vector-Hessian-vector product for G_UP
642: . hessianproductfunc2 - vector-Hessian-vector product function for G_UP
643: . rhshp3 - an array of vectors storing the result of vector-Hessian-vector product for G_PU
644: . hessianproductfunc3 - vector-Hessian-vector product function for G_PU
645: . rhshp4 - an array of vectors storing the result of vector-Hessian-vector product for G_PP
646: - hessianproductfunc4 - vector-Hessian-vector product function for G_PP

648:   Calling sequence of ihessianproductfunc:
649: $ rhshessianproductfunc (TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV,void *ctx);
650: +   t - current timestep
651: .   U - input vector (current ODE solution)
652: .   Vl - an array of input vectors to be left-multiplied with the Hessian
653: .   Vr - input vector to be right-multiplied with the Hessian
654: .   VHV - an array of output vectors for vector-Hessian-vector product
655: -   ctx - [optional] user-defined function context

657:   Level: intermediate

659:   Notes:
660:   The first Hessian function and the working array are required.
661:   As an example to implement the callback functions, the second callback function calculates the vector-Hessian-vector product
662:   $ Vl_n^T*G_UP*Vr
663:   where the vector Vl_n (n-th element in the array Vl) and Vr are of size N and M respectively, and the Hessian G_UP is of size N x N x M.
664:   Each entry of G_UP corresponds to the derivative
665:   $ G_UP[i][j][k] = \frac{\partial^2 G[i]}{\partial U[j] \partial P[k]}.
666:   The result of the vector-Hessian-vector product for Vl_n needs to be stored in vector VHV_n with j-th entry being
667:   $ VHV_n[j] = \sum_i \sum_k {Vl_n[i] * G_UP[i][j][k] * Vr[k]}
668:   If the cost function is a scalar, there will be only one vector in Vl and VHV.

670: .seealso:
671: @*/
672: PetscErrorCode TSSetRHSHessianProduct(TS ts,Vec *rhshp1,PetscErrorCode (*rhshessianproductfunc1)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
673:                                           Vec *rhshp2,PetscErrorCode (*rhshessianproductfunc2)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
674:                                           Vec *rhshp3,PetscErrorCode (*rhshessianproductfunc3)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
675:                                           Vec *rhshp4,PetscErrorCode (*rhshessianproductfunc4)(TS,PetscReal,Vec,Vec*,Vec,Vec*,void*),
676:                                     void *ctx)
677: {

682:   ts->rhshessianproductctx = ctx;
683:   if (rhshp1) ts->vecs_guu = rhshp1;
684:   if (rhshp2) ts->vecs_gup = rhshp2;
685:   if (rhshp3) ts->vecs_gpu = rhshp3;
686:   if (rhshp4) ts->vecs_gpp = rhshp4;
687:   ts->rhshessianproduct_guu = rhshessianproductfunc1;
688:   ts->rhshessianproduct_gup = rhshessianproductfunc2;
689:   ts->rhshessianproduct_gpu = rhshessianproductfunc3;
690:   ts->rhshessianproduct_gpp = rhshessianproductfunc4;
691:   return(0);
692: }

694: /*@C
695:   TSComputeRHSHessianProductFunctionUU - Runs the user-defined vector-Hessian-vector product function for Guu.

697:   Collective on TS

699:   Input Parameters:
700: . ts   - The TS context obtained from TSCreate()

702:   Notes:
703:   TSComputeRHSHessianProductFunctionUU() is typically used for sensitivity implementation,
704:   so most users would not generally call this routine themselves.

706:   Level: developer

708: .seealso: TSSetRHSHessianProduct()
709: @*/
710: PetscErrorCode TSComputeRHSHessianProductFunctionUU(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
711: {

715:   if (!VHV) return(0);

719:   PetscStackPush("TS user RHSHessianProduct function 1 for sensitivity analysis");
720:   (*ts->rhshessianproduct_guu)(ts,t,U,Vl,Vr,VHV,ts->rhshessianproductctx);
721:   PetscStackPop;
722:   return(0);
723: }

725: /*@C
726:   TSComputeRHSHessianProductFunctionUP - Runs the user-defined vector-Hessian-vector product function for Gup.

728:   Collective on TS

730:   Input Parameters:
731: . ts   - The TS context obtained from TSCreate()

733:   Notes:
734:   TSComputeRHSHessianProductFunctionUP() is typically used for sensitivity implementation,
735:   so most users would not generally call this routine themselves.

737:   Level: developer

739: .seealso: TSSetRHSHessianProduct()
740: @*/
741: PetscErrorCode TSComputeRHSHessianProductFunctionUP(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
742: {

746:   if (!VHV) return(0);

750:   PetscStackPush("TS user RHSHessianProduct function 2 for sensitivity analysis");
751:   (*ts->rhshessianproduct_gup)(ts,t,U,Vl,Vr,VHV,ts->rhshessianproductctx);
752:   PetscStackPop;
753:   return(0);
754: }

756: /*@C
757:   TSComputeRHSHessianProductFunctionPU - Runs the user-defined vector-Hessian-vector product function for Gpu.

759:   Collective on TS

761:   Input Parameters:
762: . ts   - The TS context obtained from TSCreate()

764:   Notes:
765:   TSComputeRHSHessianProductFunctionPU() is typically used for sensitivity implementation,
766:   so most users would not generally call this routine themselves.

768:   Level: developer

770: .seealso: TSSetRHSHessianProduct()
771: @*/
772: PetscErrorCode TSComputeRHSHessianProductFunctionPU(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
773: {

777:   if (!VHV) return(0);

781:   PetscStackPush("TS user RHSHessianProduct function 3 for sensitivity analysis");
782:   (*ts->rhshessianproduct_gpu)(ts,t,U,Vl,Vr,VHV,ts->rhshessianproductctx);
783:   PetscStackPop;
784:   return(0);
785: }

787: /*@C
788:   TSComputeRHSHessianProductFunctionPP - Runs the user-defined vector-Hessian-vector product function for Gpp.

790:   Collective on TS

792:   Input Parameters:
793: . ts   - The TS context obtained from TSCreate()

795:   Notes:
796:   TSComputeRHSHessianProductFunctionPP() is typically used for sensitivity implementation,
797:   so most users would not generally call this routine themselves.

799:   Level: developer

801: .seealso: TSSetRHSHessianProduct()
802: @*/
803: PetscErrorCode TSComputeRHSHessianProductFunctionPP(TS ts,PetscReal t,Vec U,Vec *Vl,Vec Vr,Vec *VHV)
804: {

808:   if (!VHV) return(0);

812:   PetscStackPush("TS user RHSHessianProduct function 3 for sensitivity analysis");
813:   (*ts->rhshessianproduct_gpp)(ts,t,U,Vl,Vr,VHV,ts->rhshessianproductctx);
814:   PetscStackPop;
815:   return(0);
816: }

818: /* --------------------------- Adjoint sensitivity ---------------------------*/

820: /*@
821:    TSSetCostGradients - Sets the initial value of the gradients of the cost function w.r.t. initial values and w.r.t. the problem parameters
822:       for use by the TSAdjoint routines.

824:    Logically Collective on TS

826:    Input Parameters:
827: +  ts - the TS context obtained from TSCreate()
828: .  numcost - number of gradients to be computed, this is the number of cost functions
829: .  lambda - gradients with respect to the initial condition variables, the dimension and parallel layout of these vectors is the same as the ODE solution vector
830: -  mu - gradients with respect to the parameters, the number of entries in these vectors is the same as the number of parameters

832:    Level: beginner

834:    Notes:
835:     the entries in these vectors must be correctly initialized with the values lamda_i = df/dy|finaltime  mu_i = df/dp|finaltime

837:    After TSAdjointSolve() is called the lamba and the mu contain the computed sensitivities

839: .seealso TSGetCostGradients()
840: @*/
841: PetscErrorCode TSSetCostGradients(TS ts,PetscInt numcost,Vec *lambda,Vec *mu)
842: {
846:   ts->vecs_sensi  = lambda;
847:   ts->vecs_sensip = mu;
848:   if (ts->numcost && ts->numcost!=numcost) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"The number of cost functions (2nd parameter of TSSetCostIntegrand()) is inconsistent with the one set by TSSetCostIntegrand");
849:   ts->numcost  = numcost;
850:   return(0);
851: }

853: /*@
854:    TSGetCostGradients - Returns the gradients from the TSAdjointSolve()

856:    Not Collective, but Vec returned is parallel if TS is parallel

858:    Input Parameter:
859: .  ts - the TS context obtained from TSCreate()

861:    Output Parameters:
862: +  numcost - size of returned arrays
863: .  lambda - vectors containing the gradients of the cost functions with respect to the ODE/DAE solution variables
864: -  mu - vectors containing the gradients of the cost functions with respect to the problem parameters

866:    Level: intermediate

868: .seealso: TSSetCostGradients()
869: @*/
870: PetscErrorCode TSGetCostGradients(TS ts,PetscInt *numcost,Vec **lambda,Vec **mu)
871: {
874:   if (numcost) *numcost = ts->numcost;
875:   if (lambda)  *lambda  = ts->vecs_sensi;
876:   if (mu)      *mu      = ts->vecs_sensip;
877:   return(0);
878: }

880: /*@
881:    TSSetCostHessianProducts - Sets the initial value of the Hessian-vector products of the cost function w.r.t. initial values and w.r.t. the problem parameters
882:       for use by the TSAdjoint routines.

884:    Logically Collective on TS

886:    Input Parameters:
887: +  ts - the TS context obtained from TSCreate()
888: .  numcost - number of cost functions
889: .  lambda2 - Hessian-vector product with respect to the initial condition variables, the dimension and parallel layout of these vectors is the same as the ODE solution vector
890: .  mu2 - Hessian-vector product with respect to the parameters, the number of entries in these vectors is the same as the number of parameters
891: -  dir - the direction vector that are multiplied with the Hessian of the cost functions

893:    Level: beginner

895:    Notes: Hessian of the cost function is completely different from Hessian of the ODE/DAE system

897:    For second-order adjoint, one needs to call this function and then TSAdjointSetForward() before TSSolve().

899:    After TSAdjointSolve() is called, the lamba2 and the mu2 will contain the computed second-order adjoint sensitivities, and can be used to produce Hessian-vector product (not the full Hessian matrix). Users must provide a direction vector; it is usually generated by an optimization solver.

901:    Passing NULL for lambda2 disables the second-order calculation.
902: .seealso: TSAdjointSetForward()
903: @*/
904: PetscErrorCode TSSetCostHessianProducts(TS ts,PetscInt numcost,Vec *lambda2,Vec *mu2,Vec dir)
905: {
908:   if (ts->numcost && ts->numcost!=numcost) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"The number of cost functions (2nd parameter of TSSetCostIntegrand()) is inconsistent with the one set by TSSetCostIntegrand");
909:   ts->numcost       = numcost;
910:   ts->vecs_sensi2   = lambda2;
911:   ts->vecs_sensi2p  = mu2;
912:   ts->vec_dir       = dir;
913:   return(0);
914: }

916: /*@
917:    TSGetCostHessianProducts - Returns the gradients from the TSAdjointSolve()

919:    Not Collective, but Vec returned is parallel if TS is parallel

921:    Input Parameter:
922: .  ts - the TS context obtained from TSCreate()

924:    Output Parameters:
925: +  numcost - number of cost functions
926: .  lambda2 - Hessian-vector product with respect to the initial condition variables, the dimension and parallel layout of these vectors is the same as the ODE solution vector
927: .  mu2 - Hessian-vector product with respect to the parameters, the number of entries in these vectors is the same as the number of parameters
928: -  dir - the direction vector that are multiplied with the Hessian of the cost functions

930:    Level: intermediate

932: .seealso: TSSetCostHessianProducts()
933: @*/
934: PetscErrorCode TSGetCostHessianProducts(TS ts,PetscInt *numcost,Vec **lambda2,Vec **mu2, Vec *dir)
935: {
938:   if (numcost) *numcost = ts->numcost;
939:   if (lambda2) *lambda2 = ts->vecs_sensi2;
940:   if (mu2)     *mu2     = ts->vecs_sensi2p;
941:   if (dir)     *dir     = ts->vec_dir;
942:   return(0);
943: }

945: /*@
946:   TSAdjointSetForward - Trigger the tangent linear solver and initialize the forward sensitivities

948:   Logically Collective on TS

950:   Input Parameters:
951: +  ts - the TS context obtained from TSCreate()
952: -  didp - the derivative of initial values w.r.t. parameters

954:   Level: intermediate

956:   Notes: When computing sensitivies w.r.t. initial condition, set didp to NULL so that the solver will take it as an identity matrix mathematically. TSAdjoint does not reset the tangent linear solver automatically, TSAdjointResetForward() should be called to reset the tangent linear solver.

958: .seealso: TSSetCostHessianProducts(), TSAdjointResetForward()
959: @*/
960: PetscErrorCode TSAdjointSetForward(TS ts,Mat didp)
961: {
962:   Mat            A;
963:   Vec            sp;
964:   PetscScalar    *xarr;
965:   PetscInt       lsize;

969:   ts->forward_solve = PETSC_TRUE; /* turn on tangent linear mode */
970:   if (!ts->vecs_sensi2) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"Must call TSSetCostHessianProducts() first");
971:   if (!ts->vec_dir) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"Directional vector is missing. Call TSSetCostHessianProducts() to set it.");
972:   /* create a single-column dense matrix */
973:   VecGetLocalSize(ts->vec_sol,&lsize);
974:   MatCreateDense(PetscObjectComm((PetscObject)ts),lsize,PETSC_DECIDE,PETSC_DECIDE,1,NULL,&A);

976:   VecDuplicate(ts->vec_sol,&sp);
977:   MatDenseGetColumn(A,0,&xarr);
978:   VecPlaceArray(sp,xarr);
979:   if (ts->vecs_sensi2p) { /* tangent linear variable initialized as 2*dIdP*dir */
980:     if (didp) {
981:       MatMult(didp,ts->vec_dir,sp);
982:       VecScale(sp,2.);
983:     } else {
984:       VecZeroEntries(sp);
985:     }
986:   } else { /* tangent linear variable initialized as dir */
987:     VecCopy(ts->vec_dir,sp);
988:   }
989:   VecResetArray(sp);
990:   MatDenseRestoreColumn(A,&xarr);
991:   VecDestroy(&sp);

993:   TSForwardSetInitialSensitivities(ts,A); /* if didp is NULL, identity matrix is assumed */

995:   MatDestroy(&A);
996:   return(0);
997: }

999: /*@
1000:   TSAdjointResetForward - Reset the tangent linear solver and destroy the tangent linear context

1002:   Logically Collective on TS

1004:   Input Parameters:
1005: .  ts - the TS context obtained from TSCreate()

1007:   Level: intermediate

1009: .seealso: TSAdjointSetForward()
1010: @*/
1011: PetscErrorCode TSAdjointResetForward(TS ts)
1012: {

1016:   ts->forward_solve = PETSC_FALSE; /* turn off tangent linear mode */
1017:   TSForwardReset(ts);
1018:   return(0);
1019: }

1021: /*@
1022:    TSAdjointSetUp - Sets up the internal data structures for the later use
1023:    of an adjoint solver

1025:    Collective on TS

1027:    Input Parameter:
1028: .  ts - the TS context obtained from TSCreate()

1030:    Level: advanced

1032: .seealso: TSCreate(), TSAdjointStep(), TSSetCostGradients()
1033: @*/
1034: PetscErrorCode TSAdjointSetUp(TS ts)
1035: {
1036:   TSTrajectory     tj;
1037:   PetscBool        match;
1038:   PetscErrorCode   ierr;

1042:   if (ts->adjointsetupcalled) return(0);
1043:   if (!ts->vecs_sensi) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_ARG_WRONGSTATE,"Must call TSSetCostGradients() first");
1044:   if (ts->vecs_sensip && !ts->Jacp && !ts->Jacprhs) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_ARG_WRONGSTATE,"Must call TSSetRHSJacobianP() or TSSetIJacobianP() first");
1045:   TSGetTrajectory(ts,&tj);
1046:   PetscObjectTypeCompare((PetscObject)tj,TSTRAJECTORYBASIC,&match);
1047:   if (match) {
1048:     PetscBool solution_only;
1049:     TSTrajectoryGetSolutionOnly(tj,&solution_only);
1050:     if (solution_only) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"TSAdjoint cannot use the solution-only mode when choosing the Basic TSTrajectory type. Turn it off with -ts_trajectory_solution_only 0");
1051:   }
1052:   TSTrajectorySetUseHistory(tj,PETSC_FALSE); /* not use TSHistory */

1054:   if (ts->quadraturets) { /* if there is integral in the cost function */
1055:     VecDuplicate(ts->vecs_sensi[0],&ts->vec_drdu_col);
1056:     if (ts->vecs_sensip) {
1057:       VecDuplicate(ts->vecs_sensip[0],&ts->vec_drdp_col);
1058:     }
1059:   }

1061:   if (ts->ops->adjointsetup) {
1062:     (*ts->ops->adjointsetup)(ts);
1063:   }
1064:   ts->adjointsetupcalled = PETSC_TRUE;
1065:   return(0);
1066: }

1068: /*@
1069:    TSAdjointReset - Resets a TSAdjoint context and removes any allocated Vecs and Mats.

1071:    Collective on TS

1073:    Input Parameter:
1074: .  ts - the TS context obtained from TSCreate()

1076:    Level: beginner

1078: .seealso: TSCreate(), TSAdjointSetUp(), TSADestroy()
1079: @*/
1080: PetscErrorCode TSAdjointReset(TS ts)
1081: {

1086:   if (ts->ops->adjointreset) {
1087:     (*ts->ops->adjointreset)(ts);
1088:   }
1089:   if (ts->quadraturets) { /* if there is integral in the cost function */
1090:     VecDestroy(&ts->vec_drdu_col);
1091:     if (ts->vecs_sensip) {
1092:       VecDestroy(&ts->vec_drdp_col);
1093:     }
1094:   }
1095:   ts->vecs_sensi         = NULL;
1096:   ts->vecs_sensip        = NULL;
1097:   ts->vecs_sensi2        = NULL;
1098:   ts->vecs_sensi2p       = NULL;
1099:   ts->vec_dir            = NULL;
1100:   ts->adjointsetupcalled = PETSC_FALSE;
1101:   return(0);
1102: }

1104: /*@
1105:    TSAdjointSetSteps - Sets the number of steps the adjoint solver should take backward in time

1107:    Logically Collective on TS

1109:    Input Parameters:
1110: +  ts - the TS context obtained from TSCreate()
1111: -  steps - number of steps to use

1113:    Level: intermediate

1115:    Notes:
1116:     Normally one does not call this and TSAdjointSolve() integrates back to the original timestep. One can call this
1117:           so as to integrate back to less than the original timestep

1119: .seealso: TSSetExactFinalTime()
1120: @*/
1121: PetscErrorCode TSAdjointSetSteps(TS ts,PetscInt steps)
1122: {
1126:   if (steps < 0) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_ARG_OUTOFRANGE,"Cannot step back a negative number of steps");
1127:   if (steps > ts->steps) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_ARG_OUTOFRANGE,"Cannot step back more than the total number of forward steps");
1128:   ts->adjoint_max_steps = steps;
1129:   return(0);
1130: }

1132: /*@C
1133:   TSAdjointSetRHSJacobian - Deprecated, use TSSetRHSJacobianP()

1135:   Level: deprecated

1137: @*/
1138: PetscErrorCode TSAdjointSetRHSJacobian(TS ts,Mat Amat,PetscErrorCode (*func)(TS,PetscReal,Vec,Mat,void*),void *ctx)
1139: {


1146:   ts->rhsjacobianp    = func;
1147:   ts->rhsjacobianpctx = ctx;
1148:   if (Amat) {
1149:     PetscObjectReference((PetscObject)Amat);
1150:     MatDestroy(&ts->Jacp);
1151:     ts->Jacp = Amat;
1152:   }
1153:   return(0);
1154: }

1156: /*@C
1157:   TSAdjointComputeRHSJacobian - Deprecated, use TSComputeRHSJacobianP()

1159:   Level: deprecated

1161: @*/
1162: PetscErrorCode TSAdjointComputeRHSJacobian(TS ts,PetscReal t,Vec U,Mat Amat)
1163: {


1171:   PetscStackPush("TS user JacobianP function for sensitivity analysis");
1172:   (*ts->rhsjacobianp)(ts,t,U,Amat,ts->rhsjacobianpctx);
1173:   PetscStackPop;
1174:   return(0);
1175: }

1177: /*@
1178:   TSAdjointComputeDRDYFunction - Deprecated, use TSGetQuadratureTS() then TSComputeRHSJacobian()

1180:   Level: deprecated

1182: @*/
1183: PetscErrorCode TSAdjointComputeDRDYFunction(TS ts,PetscReal t,Vec U,Vec *DRDU)
1184: {


1191:   PetscStackPush("TS user DRDY function for sensitivity analysis");
1192:   (*ts->drdufunction)(ts,t,U,DRDU,ts->costintegrandctx);
1193:   PetscStackPop;
1194:   return(0);
1195: }

1197: /*@
1198:   TSAdjointComputeDRDPFunction - Deprecated, use TSGetQuadratureTS() then TSComputeRHSJacobianP()

1200:   Level: deprecated

1202: @*/
1203: PetscErrorCode TSAdjointComputeDRDPFunction(TS ts,PetscReal t,Vec U,Vec *DRDP)
1204: {


1211:   PetscStackPush("TS user DRDP function for sensitivity analysis");
1212:   (*ts->drdpfunction)(ts,t,U,DRDP,ts->costintegrandctx);
1213:   PetscStackPop;
1214:   return(0);
1215: }

1217: /*@C
1218:    TSAdjointMonitorSensi - monitors the first lambda sensitivity

1220:    Level: intermediate

1222: .seealso: TSAdjointMonitorSet()
1223: @*/
1224: PetscErrorCode TSAdjointMonitorSensi(TS ts,PetscInt step,PetscReal ptime,Vec v,PetscInt numcost,Vec *lambda,Vec *mu,PetscViewerAndFormat *vf)
1225: {
1227:   PetscViewer    viewer = vf->viewer;

1231:   PetscViewerPushFormat(viewer,vf->format);
1232:   VecView(lambda[0],viewer);
1233:   PetscViewerPopFormat(viewer);
1234:   return(0);
1235: }

1237: /*@C
1238:    TSAdjointMonitorSetFromOptions - Sets a monitor function and viewer appropriate for the type indicated by the user

1240:    Collective on TS

1242:    Input Parameters:
1243: +  ts - TS object you wish to monitor
1244: .  name - the monitor type one is seeking
1245: .  help - message indicating what monitoring is done
1246: .  manual - manual page for the monitor
1247: .  monitor - the monitor function
1248: -  monitorsetup - a function that is called once ONLY if the user selected this monitor that may set additional features of the TS or PetscViewer objects

1250:    Level: developer

1252: .seealso: PetscOptionsGetViewer(), PetscOptionsGetReal(), PetscOptionsHasName(), PetscOptionsGetString(),
1253:           PetscOptionsGetIntArray(), PetscOptionsGetRealArray(), PetscOptionsBool()
1254:           PetscOptionsInt(), PetscOptionsString(), PetscOptionsReal(), PetscOptionsBool(),
1255:           PetscOptionsName(), PetscOptionsBegin(), PetscOptionsEnd(), PetscOptionsHead(),
1256:           PetscOptionsStringArray(),PetscOptionsRealArray(), PetscOptionsScalar(),
1257:           PetscOptionsBoolGroupBegin(), PetscOptionsBoolGroup(), PetscOptionsBoolGroupEnd(),
1258:           PetscOptionsFList(), PetscOptionsEList()
1259: @*/
1260: PetscErrorCode TSAdjointMonitorSetFromOptions(TS ts,const char name[],const char help[], const char manual[],PetscErrorCode (*monitor)(TS,PetscInt,PetscReal,Vec,PetscInt,Vec*,Vec*,PetscViewerAndFormat*),PetscErrorCode (*monitorsetup)(TS,PetscViewerAndFormat*))
1261: {
1262:   PetscErrorCode    ierr;
1263:   PetscViewer       viewer;
1264:   PetscViewerFormat format;
1265:   PetscBool         flg;

1268:   PetscOptionsGetViewer(PetscObjectComm((PetscObject)ts),((PetscObject) ts)->options,((PetscObject)ts)->prefix,name,&viewer,&format,&flg);
1269:   if (flg) {
1270:     PetscViewerAndFormat *vf;
1271:     PetscViewerAndFormatCreate(viewer,format,&vf);
1272:     PetscObjectDereference((PetscObject)viewer);
1273:     if (monitorsetup) {
1274:       (*monitorsetup)(ts,vf);
1275:     }
1276:     TSAdjointMonitorSet(ts,(PetscErrorCode (*)(TS,PetscInt,PetscReal,Vec,PetscInt,Vec*,Vec*,void*))monitor,vf,(PetscErrorCode (*)(void**))PetscViewerAndFormatDestroy);
1277:   }
1278:   return(0);
1279: }

1281: /*@C
1282:    TSAdjointMonitorSet - Sets an ADDITIONAL function that is to be used at every
1283:    timestep to display the iteration's  progress.

1285:    Logically Collective on TS

1287:    Input Parameters:
1288: +  ts - the TS context obtained from TSCreate()
1289: .  adjointmonitor - monitoring routine
1290: .  adjointmctx - [optional] user-defined context for private data for the
1291:              monitor routine (use NULL if no context is desired)
1292: -  adjointmonitordestroy - [optional] routine that frees monitor context
1293:           (may be NULL)

1295:    Calling sequence of monitor:
1296: $    int adjointmonitor(TS ts,PetscInt steps,PetscReal time,Vec u,PetscInt numcost,Vec *lambda, Vec *mu,void *adjointmctx)

1298: +    ts - the TS context
1299: .    steps - iteration number (after the final time step the monitor routine is called with a step of -1, this is at the final time which may have
1300:                                been interpolated to)
1301: .    time - current time
1302: .    u - current iterate
1303: .    numcost - number of cost functionos
1304: .    lambda - sensitivities to initial conditions
1305: .    mu - sensitivities to parameters
1306: -    adjointmctx - [optional] adjoint monitoring context

1308:    Notes:
1309:    This routine adds an additional monitor to the list of monitors that
1310:    already has been loaded.

1312:    Fortran Notes:
1313:     Only a single monitor function can be set for each TS object

1315:    Level: intermediate

1317: .seealso: TSAdjointMonitorCancel()
1318: @*/
1319: PetscErrorCode TSAdjointMonitorSet(TS ts,PetscErrorCode (*adjointmonitor)(TS,PetscInt,PetscReal,Vec,PetscInt,Vec*,Vec*,void*),void *adjointmctx,PetscErrorCode (*adjointmdestroy)(void**))
1320: {
1322:   PetscInt       i;
1323:   PetscBool      identical;

1327:   for (i=0; i<ts->numbermonitors;i++) {
1328:     PetscMonitorCompare((PetscErrorCode (*)(void))adjointmonitor,adjointmctx,adjointmdestroy,(PetscErrorCode (*)(void))ts->adjointmonitor[i],ts->adjointmonitorcontext[i],ts->adjointmonitordestroy[i],&identical);
1329:     if (identical) return(0);
1330:   }
1331:   if (ts->numberadjointmonitors >= MAXTSMONITORS) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Too many adjoint monitors set");
1332:   ts->adjointmonitor[ts->numberadjointmonitors]          = adjointmonitor;
1333:   ts->adjointmonitordestroy[ts->numberadjointmonitors]   = adjointmdestroy;
1334:   ts->adjointmonitorcontext[ts->numberadjointmonitors++] = (void*)adjointmctx;
1335:   return(0);
1336: }

1338: /*@C
1339:    TSAdjointMonitorCancel - Clears all the adjoint monitors that have been set on a time-step object.

1341:    Logically Collective on TS

1343:    Input Parameters:
1344: .  ts - the TS context obtained from TSCreate()

1346:    Notes:
1347:    There is no way to remove a single, specific monitor.

1349:    Level: intermediate

1351: .seealso: TSAdjointMonitorSet()
1352: @*/
1353: PetscErrorCode TSAdjointMonitorCancel(TS ts)
1354: {
1356:   PetscInt       i;

1360:   for (i=0; i<ts->numberadjointmonitors; i++) {
1361:     if (ts->adjointmonitordestroy[i]) {
1362:       (*ts->adjointmonitordestroy[i])(&ts->adjointmonitorcontext[i]);
1363:     }
1364:   }
1365:   ts->numberadjointmonitors = 0;
1366:   return(0);
1367: }

1369: /*@C
1370:    TSAdjointMonitorDefault - the default monitor of adjoint computations

1372:    Level: intermediate

1374: .seealso: TSAdjointMonitorSet()
1375: @*/
1376: PetscErrorCode TSAdjointMonitorDefault(TS ts,PetscInt step,PetscReal ptime,Vec v,PetscInt numcost,Vec *lambda,Vec *mu,PetscViewerAndFormat *vf)
1377: {
1379:   PetscViewer    viewer = vf->viewer;

1383:   PetscViewerPushFormat(viewer,vf->format);
1384:   PetscViewerASCIIAddTab(viewer,((PetscObject)ts)->tablevel);
1385:   PetscViewerASCIIPrintf(viewer,"%D TS dt %g time %g%s",step,(double)ts->time_step,(double)ptime,ts->steprollback ? " (r)\n" : "\n");
1386:   PetscViewerASCIISubtractTab(viewer,((PetscObject)ts)->tablevel);
1387:   PetscViewerPopFormat(viewer);
1388:   return(0);
1389: }

1391: /*@C
1392:    TSAdjointMonitorDrawSensi - Monitors progress of the adjoint TS solvers by calling
1393:    VecView() for the sensitivities to initial states at each timestep

1395:    Collective on TS

1397:    Input Parameters:
1398: +  ts - the TS context
1399: .  step - current time-step
1400: .  ptime - current time
1401: .  u - current state
1402: .  numcost - number of cost functions
1403: .  lambda - sensitivities to initial conditions
1404: .  mu - sensitivities to parameters
1405: -  dummy - either a viewer or NULL

1407:    Level: intermediate

1409: .seealso: TSAdjointMonitorSet(), TSAdjointMonitorDefault(), VecView()
1410: @*/
1411: PetscErrorCode TSAdjointMonitorDrawSensi(TS ts,PetscInt step,PetscReal ptime,Vec u,PetscInt numcost,Vec *lambda,Vec *mu,void *dummy)
1412: {
1413:   PetscErrorCode   ierr;
1414:   TSMonitorDrawCtx ictx = (TSMonitorDrawCtx)dummy;
1415:   PetscDraw        draw;
1416:   PetscReal        xl,yl,xr,yr,h;
1417:   char             time[32];

1420:   if (!(((ictx->howoften > 0) && (!(step % ictx->howoften))) || ((ictx->howoften == -1) && ts->reason))) return(0);

1422:   VecView(lambda[0],ictx->viewer);
1423:   PetscViewerDrawGetDraw(ictx->viewer,0,&draw);
1424:   PetscSNPrintf(time,32,"Timestep %d Time %g",(int)step,(double)ptime);
1425:   PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1426:   h    = yl + .95*(yr - yl);
1427:   PetscDrawStringCentered(draw,.5*(xl+xr),h,PETSC_DRAW_BLACK,time);
1428:   PetscDrawFlush(draw);
1429:   return(0);
1430: }

1432: /*
1433:    TSAdjointSetFromOptions - Sets various TSAdjoint parameters from user options.

1435:    Collective on TSAdjoint

1437:    Input Parameter:
1438: .  ts - the TS context

1440:    Options Database Keys:
1441: +  -ts_adjoint_solve <yes,no> After solving the ODE/DAE solve the adjoint problem (requires -ts_save_trajectory)
1442: .  -ts_adjoint_monitor - print information at each adjoint time step
1443: -  -ts_adjoint_monitor_draw_sensi - monitor the sensitivity of the first cost function wrt initial conditions (lambda[0]) graphically

1445:    Level: developer

1447:    Notes:
1448:     This is not normally called directly by users

1450: .seealso: TSSetSaveTrajectory(), TSTrajectorySetUp()
1451: */
1452: PetscErrorCode TSAdjointSetFromOptions(PetscOptionItems *PetscOptionsObject,TS ts)
1453: {
1454:   PetscBool      tflg,opt;

1459:   PetscOptionsHead(PetscOptionsObject,"TS Adjoint options");
1460:   tflg = ts->adjoint_solve ? PETSC_TRUE : PETSC_FALSE;
1461:   PetscOptionsBool("-ts_adjoint_solve","Solve the adjoint problem immediately after solving the forward problem","",tflg,&tflg,&opt);
1462:   if (opt) {
1463:     TSSetSaveTrajectory(ts);
1464:     ts->adjoint_solve = tflg;
1465:   }
1466:   TSAdjointMonitorSetFromOptions(ts,"-ts_adjoint_monitor","Monitor adjoint timestep size","TSAdjointMonitorDefault",TSAdjointMonitorDefault,NULL);
1467:   TSAdjointMonitorSetFromOptions(ts,"-ts_adjoint_monitor_sensi","Monitor sensitivity in the adjoint computation","TSAdjointMonitorSensi",TSAdjointMonitorSensi,NULL);
1468:   opt  = PETSC_FALSE;
1469:   PetscOptionsName("-ts_adjoint_monitor_draw_sensi","Monitor adjoint sensitivities (lambda only) graphically","TSAdjointMonitorDrawSensi",&opt);
1470:   if (opt) {
1471:     TSMonitorDrawCtx ctx;
1472:     PetscInt         howoften = 1;

1474:     PetscOptionsInt("-ts_adjoint_monitor_draw_sensi","Monitor adjoint sensitivities (lambda only) graphically","TSAdjointMonitorDrawSensi",howoften,&howoften,NULL);
1475:     TSMonitorDrawCtxCreate(PetscObjectComm((PetscObject)ts),NULL,NULL,PETSC_DECIDE,PETSC_DECIDE,300,300,howoften,&ctx);
1476:     TSAdjointMonitorSet(ts,TSAdjointMonitorDrawSensi,ctx,(PetscErrorCode (*)(void**))TSMonitorDrawCtxDestroy);
1477:   }
1478:   return(0);
1479: }

1481: /*@
1482:    TSAdjointStep - Steps one time step backward in the adjoint run

1484:    Collective on TS

1486:    Input Parameter:
1487: .  ts - the TS context obtained from TSCreate()

1489:    Level: intermediate

1491: .seealso: TSAdjointSetUp(), TSAdjointSolve()
1492: @*/
1493: PetscErrorCode TSAdjointStep(TS ts)
1494: {
1495:   DM               dm;
1496:   PetscErrorCode   ierr;

1500:   TSGetDM(ts,&dm);
1501:   TSAdjointSetUp(ts);
1502:   ts->steps--; /* must decrease the step index before the adjoint step is taken. */

1504:   ts->reason = TS_CONVERGED_ITERATING;
1505:   ts->ptime_prev = ts->ptime;
1506:   if (!ts->ops->adjointstep) SETERRQ1(PetscObjectComm((PetscObject)ts),PETSC_ERR_NOT_CONVERGED,"TSStep has failed because the adjoint of  %s has not been implemented, try other time stepping methods for adjoint sensitivity analysis",((PetscObject)ts)->type_name);
1507:   PetscLogEventBegin(TS_AdjointStep,ts,0,0,0);
1508:   (*ts->ops->adjointstep)(ts);
1509:   PetscLogEventEnd(TS_AdjointStep,ts,0,0,0);
1510:   ts->adjoint_steps++;

1512:   if (ts->reason < 0) {
1513:     if (ts->errorifstepfailed) SETERRQ1(PetscObjectComm((PetscObject)ts),PETSC_ERR_NOT_CONVERGED,"TSAdjointStep has failed due to %s",TSConvergedReasons[ts->reason]);
1514:   } else if (!ts->reason) {
1515:     if (ts->adjoint_steps >= ts->adjoint_max_steps) ts->reason = TS_CONVERGED_ITS;
1516:   }
1517:   return(0);
1518: }

1520: /*@
1521:    TSAdjointSolve - Solves the discrete ajoint problem for an ODE/DAE

1523:    Collective on TS

1525:    Input Parameter:
1526: .  ts - the TS context obtained from TSCreate()

1528:    Options Database:
1529: . -ts_adjoint_view_solution <viewerinfo> - views the first gradient with respect to the initial values

1531:    Level: intermediate

1533:    Notes:
1534:    This must be called after a call to TSSolve() that solves the forward problem

1536:    By default this will integrate back to the initial time, one can use TSAdjointSetSteps() to step back to a later time

1538: .seealso: TSCreate(), TSSetCostGradients(), TSSetSolution(), TSAdjointStep()
1539: @*/
1540: PetscErrorCode TSAdjointSolve(TS ts)
1541: {
1542:   static PetscBool cite = PETSC_FALSE;
1543: #if defined(TSADJOINT_STAGE)
1544:   PetscLogStage  adjoint_stage;
1545: #endif

1550:   PetscCitationsRegister("@article{tsadjointpaper,\n"
1551:                                 "  title         = {{PETSc TSAdjoint: a discrete adjoint ODE solver for first-order and second-order sensitivity analysis}},\n"
1552:                                 "  author        = {Zhang, Hong and Constantinescu, Emil M.  and Smith, Barry F.},\n"
1553:                                 "  journal       = {arXiv e-preprints},\n"
1554:                                 "  eprint        = {1912.07696},\n"
1555:                                 "  archivePrefix = {arXiv},\n"
1556:                                 "  year          = {2019}\n}\n",&cite);
1557: #if defined(TSADJOINT_STAGE)
1558:   PetscLogStageRegister("TSAdjoint",&adjoint_stage);
1559:   PetscLogStagePush(adjoint_stage);
1560: #endif
1561:   TSAdjointSetUp(ts);

1563:   /* reset time step and iteration counters */
1564:   ts->adjoint_steps     = 0;
1565:   ts->ksp_its           = 0;
1566:   ts->snes_its          = 0;
1567:   ts->num_snes_failures = 0;
1568:   ts->reject            = 0;
1569:   ts->reason            = TS_CONVERGED_ITERATING;

1571:   if (!ts->adjoint_max_steps) ts->adjoint_max_steps = ts->steps;
1572:   if (ts->adjoint_steps >= ts->adjoint_max_steps) ts->reason = TS_CONVERGED_ITS;

1574:   while (!ts->reason) {
1575:     TSTrajectoryGet(ts->trajectory,ts,ts->steps,&ts->ptime);
1576:     TSAdjointMonitor(ts,ts->steps,ts->ptime,ts->vec_sol,ts->numcost,ts->vecs_sensi,ts->vecs_sensip);
1577:     TSAdjointEventHandler(ts);
1578:     TSAdjointStep(ts);
1579:     if ((ts->vec_costintegral || ts->quadraturets) && !ts->costintegralfwd) {
1580:       TSAdjointCostIntegral(ts);
1581:     }
1582:   }
1583:   TSTrajectoryGet(ts->trajectory,ts,ts->steps,&ts->ptime);
1584:   TSAdjointMonitor(ts,ts->steps,ts->ptime,ts->vec_sol,ts->numcost,ts->vecs_sensi,ts->vecs_sensip);
1585:   ts->solvetime = ts->ptime;
1586:   TSTrajectoryViewFromOptions(ts->trajectory,NULL,"-ts_trajectory_view");
1587:   VecViewFromOptions(ts->vecs_sensi[0],(PetscObject) ts, "-ts_adjoint_view_solution");
1588:   ts->adjoint_max_steps = 0;
1589: #if defined(TSADJOINT_STAGE)
1590:   PetscLogStagePop();
1591: #endif
1592:   return(0);
1593: }

1595: /*@C
1596:    TSAdjointMonitor - Runs all user-provided adjoint monitor routines set using TSAdjointMonitorSet()

1598:    Collective on TS

1600:    Input Parameters:
1601: +  ts - time stepping context obtained from TSCreate()
1602: .  step - step number that has just completed
1603: .  ptime - model time of the state
1604: .  u - state at the current model time
1605: .  numcost - number of cost functions (dimension of lambda  or mu)
1606: .  lambda - vectors containing the gradients of the cost functions with respect to the ODE/DAE solution variables
1607: -  mu - vectors containing the gradients of the cost functions with respect to the problem parameters

1609:    Notes:
1610:    TSAdjointMonitor() is typically used automatically within the time stepping implementations.
1611:    Users would almost never call this routine directly.

1613:    Level: developer

1615: @*/
1616: PetscErrorCode TSAdjointMonitor(TS ts,PetscInt step,PetscReal ptime,Vec u,PetscInt numcost,Vec *lambda, Vec *mu)
1617: {
1619:   PetscInt       i,n = ts->numberadjointmonitors;

1624:   VecLockReadPush(u);
1625:   for (i=0; i<n; i++) {
1626:     (*ts->adjointmonitor[i])(ts,step,ptime,u,numcost,lambda,mu,ts->adjointmonitorcontext[i]);
1627:   }
1628:   VecLockReadPop(u);
1629:   return(0);
1630: }

1632: /*@
1633:  TSAdjointCostIntegral - Evaluate the cost integral in the adjoint run.

1635:  Collective on TS

1637:  Input Parameter:
1638:  .  ts - time stepping context

1640:  Level: advanced

1642:  Notes:
1643:  This function cannot be called until TSAdjointStep() has been completed.

1645:  .seealso: TSAdjointSolve(), TSAdjointStep
1646:  @*/
1647: PetscErrorCode TSAdjointCostIntegral(TS ts)
1648: {
1652:   if (!ts->ops->adjointintegral) SETERRQ1(PetscObjectComm((PetscObject)ts),PETSC_ERR_SUP,"%s does not provide integral evaluation in the adjoint run",((PetscObject)ts)->type_name);
1653:   (*ts->ops->adjointintegral)(ts);
1654:   return(0);
1655: }

1657: /* ------------------ Forward (tangent linear) sensitivity  ------------------*/

1659: /*@
1660:   TSForwardSetUp - Sets up the internal data structures for the later use
1661:   of forward sensitivity analysis

1663:   Collective on TS

1665:   Input Parameter:
1666: . ts - the TS context obtained from TSCreate()

1668:   Level: advanced

1670: .seealso: TSCreate(), TSDestroy(), TSSetUp()
1671: @*/
1672: PetscErrorCode TSForwardSetUp(TS ts)
1673: {

1678:   if (ts->forwardsetupcalled) return(0);
1679:   if (ts->ops->forwardsetup) {
1680:     (*ts->ops->forwardsetup)(ts);
1681:   }
1682:   VecDuplicate(ts->vec_sol,&ts->vec_sensip_col);
1683:   ts->forwardsetupcalled = PETSC_TRUE;
1684:   return(0);
1685: }

1687: /*@
1688:   TSForwardReset - Reset the internal data structures used by forward sensitivity analysis

1690:   Collective on TS

1692:   Input Parameter:
1693: . ts - the TS context obtained from TSCreate()

1695:   Level: advanced

1697: .seealso: TSCreate(), TSDestroy(), TSForwardSetUp()
1698: @*/
1699: PetscErrorCode TSForwardReset(TS ts)
1700: {
1701:   TS             quadts = ts->quadraturets;

1706:   if (ts->ops->forwardreset) {
1707:     (*ts->ops->forwardreset)(ts);
1708:   }
1709:   MatDestroy(&ts->mat_sensip);
1710:   if (quadts) {
1711:     MatDestroy(&quadts->mat_sensip);
1712:   }
1713:   VecDestroy(&ts->vec_sensip_col);
1714:   ts->forward_solve      = PETSC_FALSE;
1715:   ts->forwardsetupcalled = PETSC_FALSE;
1716:   return(0);
1717: }

1719: /*@
1720:   TSForwardSetIntegralGradients - Set the vectors holding forward sensitivities of the integral term.

1722:   Input Parameters:
1723: + ts- the TS context obtained from TSCreate()
1724: . numfwdint- number of integrals
1725: - vp = the vectors containing the gradients for each integral w.r.t. parameters

1727:   Level: deprecated

1729: .seealso: TSForwardGetSensitivities(), TSForwardSetIntegralGradients(), TSForwardGetIntegralGradients(), TSForwardStep()
1730: @*/
1731: PetscErrorCode TSForwardSetIntegralGradients(TS ts,PetscInt numfwdint,Vec *vp)
1732: {
1735:   if (ts->numcost && ts->numcost!=numfwdint) SETERRQ(PetscObjectComm((PetscObject)ts),PETSC_ERR_USER,"The number of cost functions (2nd parameter of TSSetCostIntegrand()) is inconsistent with the one set by TSSetCostIntegrand()");
1736:   if (!ts->numcost) ts->numcost = numfwdint;

1738:   ts->vecs_integral_sensip = vp;
1739:   return(0);
1740: }

1742: /*@
1743:   TSForwardGetIntegralGradients - Returns the forward sensitivities ofthe integral term.

1745:   Input Parameter:
1746: . ts- the TS context obtained from TSCreate()

1748:   Output Parameter:
1749: . vp = the vectors containing the gradients for each integral w.r.t. parameters

1751:   Level: deprecated

1753: .seealso: TSForwardSetSensitivities(), TSForwardSetIntegralGradients(), TSForwardGetIntegralGradients(), TSForwardStep()
1754: @*/
1755: PetscErrorCode TSForwardGetIntegralGradients(TS ts,PetscInt *numfwdint,Vec **vp)
1756: {
1760:   if (numfwdint) *numfwdint = ts->numcost;
1761:   if (vp) *vp = ts->vecs_integral_sensip;
1762:   return(0);
1763: }

1765: /*@
1766:   TSForwardStep - Compute the forward sensitivity for one time step.

1768:   Collective on TS

1770:   Input Parameter:
1771: . ts - time stepping context

1773:   Level: advanced

1775:   Notes:
1776:   This function cannot be called until TSStep() has been completed.

1778: .seealso: TSForwardSetSensitivities(), TSForwardGetSensitivities(), TSForwardSetIntegralGradients(), TSForwardGetIntegralGradients(), TSForwardSetUp()
1779: @*/
1780: PetscErrorCode TSForwardStep(TS ts)
1781: {
1785:   if (!ts->ops->forwardstep) SETERRQ1(PetscObjectComm((PetscObject)ts),PETSC_ERR_SUP,"%s does not provide forward sensitivity analysis",((PetscObject)ts)->type_name);
1786:   PetscLogEventBegin(TS_ForwardStep,ts,0,0,0);
1787:   (*ts->ops->forwardstep)(ts);
1788:   PetscLogEventEnd(TS_ForwardStep,ts,0,0,0);
1789:   if (ts->reason < 0 && ts->errorifstepfailed) SETERRQ1(PetscObjectComm((PetscObject)ts),PETSC_ERR_NOT_CONVERGED,"TSFowardStep has failed due to %s",TSConvergedReasons[ts->reason]);
1790:   return(0);
1791: }

1793: /*@
1794:   TSForwardSetSensitivities - Sets the initial value of the trajectory sensitivities of solution  w.r.t. the problem parameters and initial values.

1796:   Logically Collective on TS

1798:   Input Parameters:
1799: + ts - the TS context obtained from TSCreate()
1800: . nump - number of parameters
1801: - Smat - sensitivities with respect to the parameters, the number of entries in these vectors is the same as the number of parameters

1803:   Level: beginner

1805:   Notes:
1806:   Forward sensitivity is also called 'trajectory sensitivity' in some fields such as power systems.
1807:   This function turns on a flag to trigger TSSolve() to compute forward sensitivities automatically.
1808:   You must call this function before TSSolve().
1809:   The entries in the sensitivity matrix must be correctly initialized with the values S = dy/dp|startingtime.

1811: .seealso: TSForwardGetSensitivities(), TSForwardSetIntegralGradients(), TSForwardGetIntegralGradients(), TSForwardStep()
1812: @*/
1813: PetscErrorCode TSForwardSetSensitivities(TS ts,PetscInt nump,Mat Smat)
1814: {

1820:   ts->forward_solve  = PETSC_TRUE;
1821:   if (nump == PETSC_DEFAULT) {
1822:     MatGetSize(Smat,NULL,&ts->num_parameters);
1823:   } else ts->num_parameters = nump;
1824:   PetscObjectReference((PetscObject)Smat);
1825:   MatDestroy(&ts->mat_sensip);
1826:   ts->mat_sensip = Smat;
1827:   return(0);
1828: }

1830: /*@
1831:   TSForwardGetSensitivities - Returns the trajectory sensitivities

1833:   Not Collective, but Vec returned is parallel if TS is parallel

1835:   Output Parameters:
1836: + ts - the TS context obtained from TSCreate()
1837: . nump - number of parameters
1838: - Smat - sensitivities with respect to the parameters, the number of entries in these vectors is the same as the number of parameters

1840:   Level: intermediate

1842: .seealso: TSForwardSetSensitivities(), TSForwardSetIntegralGradients(), TSForwardGetIntegralGradients(), TSForwardStep()
1843: @*/
1844: PetscErrorCode TSForwardGetSensitivities(TS ts,PetscInt *nump,Mat *Smat)
1845: {
1848:   if (nump) *nump = ts->num_parameters;
1849:   if (Smat) *Smat = ts->mat_sensip;
1850:   return(0);
1851: }

1853: /*@
1854:    TSForwardCostIntegral - Evaluate the cost integral in the forward run.

1856:    Collective on TS

1858:    Input Parameter:
1859: .  ts - time stepping context

1861:    Level: advanced

1863:    Notes:
1864:    This function cannot be called until TSStep() has been completed.

1866: .seealso: TSSolve(), TSAdjointCostIntegral()
1867: @*/
1868: PetscErrorCode TSForwardCostIntegral(TS ts)
1869: {

1874:   if (!ts->ops->forwardintegral) SETERRQ1(PetscObjectComm((PetscObject)ts),PETSC_ERR_SUP,"%s does not provide integral evaluation in the forward run",((PetscObject)ts)->type_name);
1875:   (*ts->ops->forwardintegral)(ts);
1876:   return(0);
1877: }

1879: /*@
1880:   TSForwardSetInitialSensitivities - Set initial values for tangent linear sensitivities

1882:   Collective on TS

1884:   Input Parameters:
1885: + ts - the TS context obtained from TSCreate()
1886: - didp - parametric sensitivities of the initial condition

1888:   Level: intermediate

1890:   Notes: TSSolve() allows users to pass the initial solution directly to TS. But the tangent linear variables cannot be initialized in this way. This function is used to set initial values for tangent linear variables.

1892: .seealso: TSForwardSetSensitivities()
1893: @*/
1894: PetscErrorCode TSForwardSetInitialSensitivities(TS ts,Mat didp)
1895: {

1901:   if (!ts->mat_sensip) {
1902:     TSForwardSetSensitivities(ts,PETSC_DEFAULT,didp);
1903:   }
1904:   return(0);
1905: }

1907: /*@
1908:    TSForwardGetStages - Get the number of stages and the tangent linear sensitivities at the intermediate stages

1910:    Input Parameter:
1911: .  ts - the TS context obtained from TSCreate()

1913:    Output Parameters:
1914: +  ns - number of stages
1915: -  S - tangent linear sensitivities at the intermediate stages

1917:    Level: advanced

1919: @*/
1920: PetscErrorCode TSForwardGetStages(TS ts,PetscInt *ns,Mat **S)
1921: {


1927:   if (!ts->ops->getstages) *S=NULL;
1928:   else {
1929:     (*ts->ops->forwardgetstages)(ts,ns,S);
1930:   }
1931:   return(0);
1932: }

1934: /*@
1935:    TSCreateQuadratureTS - Create a sub-TS that evaluates integrals over time

1937:    Input Parameters:
1938: +  ts - the TS context obtained from TSCreate()
1939: -  fwd - flag indicating whether to evaluate cost integral in the forward run or the adjoint run

1941:    Output Parameters:
1942: .  quadts - the child TS context

1944:    Level: intermediate

1946: .seealso: TSGetQuadratureTS()
1947: @*/
1948: PetscErrorCode TSCreateQuadratureTS(TS ts,PetscBool fwd,TS *quadts)
1949: {
1950:   char prefix[128];

1956:   TSDestroy(&ts->quadraturets);
1957:   TSCreate(PetscObjectComm((PetscObject)ts),&ts->quadraturets);
1958:   PetscObjectIncrementTabLevel((PetscObject)ts->quadraturets,(PetscObject)ts,1);
1959:   PetscLogObjectParent((PetscObject)ts,(PetscObject)ts->quadraturets);
1960:   PetscSNPrintf(prefix,sizeof(prefix),"%squad_",((PetscObject)ts)->prefix ? ((PetscObject)ts)->prefix : "");
1961:   TSSetOptionsPrefix(ts->quadraturets,prefix);
1962:   *quadts = ts->quadraturets;

1964:   if (ts->numcost) {
1965:     VecCreateSeq(PETSC_COMM_SELF,ts->numcost,&(*quadts)->vec_sol);
1966:   } else {
1967:     VecCreateSeq(PETSC_COMM_SELF,1,&(*quadts)->vec_sol);
1968:   }
1969:   ts->costintegralfwd = fwd;
1970:   return(0);
1971: }

1973: /*@
1974:    TSGetQuadratureTS - Return the sub-TS that evaluates integrals over time

1976:    Input Parameter:
1977: .  ts - the TS context obtained from TSCreate()

1979:    Output Parameters:
1980: +  fwd - flag indicating whether to evaluate cost integral in the forward run or the adjoint run
1981: -  quadts - the child TS context

1983:    Level: intermediate

1985: .seealso: TSCreateQuadratureTS()
1986: @*/
1987: PetscErrorCode TSGetQuadratureTS(TS ts,PetscBool *fwd,TS *quadts)
1988: {
1991:   if (fwd) *fwd = ts->costintegralfwd;
1992:   if (quadts) *quadts = ts->quadraturets;
1993:   return(0);
1994: }

1996: /*@
1997:    TSComputeSNESJacobian - Compute the SNESJacobian

1999:    Input Parameters:
2000: +  ts - the TS context obtained from TSCreate()
2001: -  x - state vector

2003:    Output Parameters:
2004: +  J - Jacobian matrix
2005: -  Jpre - preconditioning matrix for J (may be same as J)

2007:    Level: developer

2009:    Notes:
2010:    Using SNES to compute the Jacobian enables finite differencing when TS Jacobian is not available.
2011: @*/
2012: PetscErrorCode TSComputeSNESJacobian(TS ts,Vec x,Mat J,Mat Jpre)
2013: {
2014:   SNES           snes = ts->snes;
2015:   PetscErrorCode (*jac)(SNES,Vec,Mat,Mat,void*) = NULL;

2019:   /*
2020:     Unlike implicit methods, explicit methods do not have SNESMatFDColoring in the snes object
2021:     because SNESSolve() has not been called yet; so querying SNESMatFDColoring does not work for
2022:     explicit methods. Instead, we check the Jacobian compute function directly to determin if FD
2023:     coloring is used.
2024:   */
2025:   SNESGetJacobian(snes,NULL,NULL,&jac,NULL);
2026:   if (jac == SNESComputeJacobianDefaultColor) {
2027:     Vec f;
2028:     SNESSetSolution(snes,x);
2029:     SNESGetFunction(snes,&f,NULL,NULL);
2030:     /* Force MatFDColoringApply to evaluate the SNES residual function for the base vector */
2031:     SNESComputeFunction(snes,x,f);
2032:   }
2033:   SNESComputeJacobian(snes,x,J,Jpre);
2034:   return(0);
2035: }