Actual source code: chwirut1.c

tao-2.1-p0 2012-07-24
  1: /* 
  2:    Include "tao.h" so that we can use TAO solvers.  Note that this
  3:    file automatically includes libraries such as:
  4:      petsc.h       - base PETSc routines   petscvec.h - vectors
  5:      petscsys.h    - sysem routines        petscmat.h - matrices
  6:      petscis.h     - index sets            petscksp.h - Krylov subspace methods
  7:      petscviewer.h - viewers               petscpc.h  - preconditioners

  9: */

 11: #include "taosolver.h"


 14: /*
 15: Description:   These data are the result of a NIST study involving
 16:                ultrasonic calibration.  The response variable is
 17:                ultrasonic response, and the predictor variable is
 18:                metal distance.

 20: Reference:     Chwirut, D., NIST (197?).  
 21:                Ultrasonic Reference Block Study. 
 22: */



 26: static char help[]="Finds the nonlinear least-squares solution to the model \n\
 27:             y = exp[-b1*x]/(b2+b3*x)  +  e \n";



 31: /*T
 32:    Concepts: TAO - Solving a system of nonlinear equations, nonlinear ;east squares
 33:    Routines: TaoInitialize(); TaoFinalize(); 
 34:    Routines: TaoCreate();
 35:    Routines: TaoSetType(); 
 36:    Routines: TaoSetSeparableObjectiveRoutine();
 37:    Routines: TaoSetJacobianRoutine();
 38:    Routines: TaoSetInitialVector();
 39:    Routines: TaoSetFromOptions();
 40:    Routines: TaoSetHistory(); TaoGetHistory();
 41:    Routines: TaoSolve();
 42:    Routines: TaoView(); TaoDestroy(); 
 43:    Processors: 1
 44: T*/

 46: #define NOBSERVATIONS 214
 47: #define NPARAMETERS 3

 49: /* User-defined application context */
 50: typedef struct {
 51:   /* Working space */
 52:   PetscReal t[NOBSERVATIONS];   /* array of independent variables of observation */
 53:   PetscReal y[NOBSERVATIONS];   /* array of dependent variables */
 54:   PetscReal j[NOBSERVATIONS][NPARAMETERS]; /* dense jacobian matrix array*/
 55:   PetscInt idm[NOBSERVATIONS];  /* Matrix indices for jacobian */
 56:   PetscInt idn[NPARAMETERS];
 57: } AppCtx;

 59: /* User provided Routines */
 60: PetscErrorCode InitializeData(AppCtx *user);
 61: PetscErrorCode FormStartingPoint(Vec);
 62: PetscErrorCode EvaluateFunction(TaoSolver, Vec, Vec, void *);
 63: PetscErrorCode EvaluateJacobian(TaoSolver, Vec, Mat*, Mat*, MatStructure*,void *);


 66: /*--------------------------------------------------------------------*/
 69: int main(int argc,char **argv)
 70: {
 72:   Vec        x, f;               /* solution, function */
 73:   Mat        J;                  /* Jacobian matrix */ 
 74:   TaoSolver  tao;                /* TaoSolver solver context */
 75:   PetscInt   i;               /* iteration information */
 76:   PetscReal  hist[100],resid[100];
 77:   PetscInt   nhist;
 78:   PetscBool  printhistory;
 79:   AppCtx     user;               /* user-defined work context */

 81:    /* Initialize TAO and PETSc */
 82:   PetscInitialize(&argc,&argv,(char *)0,help);
 83:   TaoInitialize(&argc,&argv,(char *)0,help);

 85:   printhistory = PETSC_FALSE;
 86:   PetscOptionsGetBool(PETSC_NULL,"-printhistory",&printhistory,0); 
 87:   /* Allocate vectors */
 88:   VecCreateSeq(MPI_COMM_SELF,NPARAMETERS,&x); 
 89:   VecCreateSeq(MPI_COMM_SELF,NOBSERVATIONS,&f); 

 91:   /* Create the Jacobian matrix. */
 92:   MatCreateSeqDense(MPI_COMM_SELF,NOBSERVATIONS,NPARAMETERS,PETSC_NULL,&J);  
 93:   

 95:   for (i=0;i<NOBSERVATIONS;i++)
 96:     user.idm[i] = i;

 98:   for (i=0;i<NPARAMETERS;i++)
 99:     user.idn[i] = i;



103:   /* TAO code begins here */

105:   /* Create TAO solver and set desired solution method */
106:   TaoCreate(PETSC_COMM_SELF,&tao);
107:   TaoSetType(tao,"tao_pounders"); 

109:  /* Set the function and Jacobian routines. */
110:   InitializeData(&user); 
111:   FormStartingPoint(x); 
112:   TaoSetInitialVector(tao,x); 
113:   TaoSetSeparableObjectiveRoutine(tao,f,EvaluateFunction,(void*)&user); 
114:   TaoSetJacobianRoutine(tao, J, J, EvaluateJacobian, (void*)&user);  


117:   /* Check for any TAO command line arguments */
118:   TaoSetFromOptions(tao); 
119:   
120:   TaoSetHistory(tao,hist,resid,0,100,PETSC_TRUE); 
121:   /* Perform the Solve */
122:   TaoSolve(tao); 
123:   if (printhistory) {
124:     TaoGetHistory(tao,0,0,0,&nhist); 
125:     for (i=0;i<nhist;i++) {
126:       PetscPrintf(PETSC_COMM_WORLD,"%G\t%G\n",hist[i],resid[i]); 
127:     }
128:   }
129:   TaoView(tao,PETSC_VIEWER_STDOUT_SELF); 

131:   /* Free TAO data structures */
132:   TaoDestroy(&tao); 

134:    /* Free PETSc data structures */
135:   VecDestroy(&x); 
136:   VecDestroy(&f); 
137:   MatDestroy(&J); 


140:   /* Finalize TAO */
141:   TaoFinalize();
142:   PetscFinalize();
143:   
144:   return 0;     
145: }




150: /*--------------------------------------------------------------------*/
153: PetscErrorCode EvaluateFunction(TaoSolver tao, Vec X, Vec F, void *ptr)
154: {
155:   AppCtx *user = (AppCtx *)ptr;
156:   PetscInt i;
157:   PetscReal *y=user->y,*x,*f,*t=user->t;

161:   VecGetArray(X,&x); 
162:   VecGetArray(F,&f); 

164:   for (i=0;i<NOBSERVATIONS;i++) {
165:     f[i] = y[i] - PetscExpScalar(-x[0]*t[i])/(x[1] + x[2]*t[i]);
166:   }

168:   

170:   VecRestoreArray(X,&x); 
171:   VecRestoreArray(F,&f); 

173:   PetscLogFlops(6*NOBSERVATIONS);
174:   return(0);
175: }

177: /*------------------------------------------------------------*/
178: /* J[i][j] = df[i]/dt[j] */
181: PetscErrorCode EvaluateJacobian(TaoSolver tao, Vec X, Mat *J, Mat *Jpre, MatStructure*matstruct,void *ptr)
182: {
183:   AppCtx *user = (AppCtx *)ptr;
184:   PetscInt i;
185:   PetscReal *x,*t=user->t;
186:   PetscReal base;



192:   /* Get handles to the Vectors */
193:   VecGetArray(X,&x); 



197:   for (i=0;i<NOBSERVATIONS;i++) {
198:     base = PetscExpScalar(-x[0]*t[i])/(x[1] + x[2]*t[i]);

200:     user->j[i][0] = t[i]*base;
201:     user->j[i][1] = base/(x[1] + x[2]*t[i]);
202:     user->j[i][2] = base*t[i]/(x[1] + x[2]*t[i]);

204:   }

206:   /* Assemble the matrix */
207:   MatSetValues(*J,NOBSERVATIONS,user->idm, NPARAMETERS, user->idn,(PetscReal *)user->j,
208:                       INSERT_VALUES); 
209:   MatAssemblyBegin(*J,MAT_FINAL_ASSEMBLY); 
210:   MatAssemblyEnd(*J,MAT_FINAL_ASSEMBLY); 
211:   
212:   /* Return the handles */
213:   VecRestoreArray(X,&x); 

215:   PetscLogFlops(NOBSERVATIONS * 13);

217:   return(0);
218: }

220: /* ------------------------------------------------------------ */
223: PetscErrorCode FormStartingPoint(Vec X)
224: {
225:   PetscReal *x;
227:   

230:   VecGetArray(X,&x); 

232:   x[0] = 0.15;
233:   x[1] = 0.008;
234:   x[2] = 0.010;

236:   VecRestoreArray(X,&x); 

238:   return(0);
239: }


242: /* ---------------------------------------------------------------------- */
245: PetscErrorCode InitializeData(AppCtx *user)
246: {
247:   PetscReal *t=user->t,*y=user->y;
248:   PetscInt i=0;



253:   y[i] =   92.9000;   t[i++] =  0.5000;
254:   y[i] =    78.7000;  t[i++] =   0.6250;
255:   y[i] =    64.2000;  t[i++] =   0.7500;
256:   y[i] =    64.9000;  t[i++] =   0.8750;
257:   y[i] =    57.1000;  t[i++] =   1.0000;
258:   y[i] =    43.3000;  t[i++] =   1.2500;
259:   y[i] =    31.1000;   t[i++] =  1.7500;
260:   y[i] =    23.6000;   t[i++] =  2.2500;
261:   y[i] =    31.0500;   t[i++] =  1.7500;
262:   y[i] =    23.7750;   t[i++] =  2.2500;
263:   y[i] =    17.7375;   t[i++] =  2.7500;
264:   y[i] =    13.8000;   t[i++] =  3.2500;
265:   y[i] =    11.5875;   t[i++] =  3.7500;
266:   y[i] =     9.4125;   t[i++] =  4.2500;
267:   y[i] =     7.7250;   t[i++] =  4.7500;
268:   y[i] =     7.3500;   t[i++] =  5.2500;
269:   y[i] =     8.0250;   t[i++] =  5.7500;
270:   y[i] =    90.6000;   t[i++] =  0.5000;
271:   y[i] =    76.9000;   t[i++] =  0.6250;
272:   y[i] =    71.6000;   t[i++] = 0.7500;
273:   y[i] =    63.6000;   t[i++] =  0.8750;
274:   y[i] =    54.0000;   t[i++] =  1.0000;
275:   y[i] =    39.2000;   t[i++] =  1.2500;
276:   y[i] =    29.3000;   t[i++] = 1.7500;
277:   y[i] =    21.4000;   t[i++] =  2.2500;
278:   y[i] =    29.1750;   t[i++] =  1.7500;
279:   y[i] =    22.1250;   t[i++] =  2.2500;
280:   y[i] =    17.5125;   t[i++] =  2.7500;
281:   y[i] =    14.2500;   t[i++] =  3.2500;
282:   y[i] =     9.4500;   t[i++] =  3.7500;
283:   y[i] =     9.1500;   t[i++] =  4.2500;
284:   y[i] =     7.9125;   t[i++] =  4.7500;
285:   y[i] =     8.4750;   t[i++] =  5.2500;
286:   y[i] =     6.1125;   t[i++] =  5.7500;
287:   y[i] =    80.0000;   t[i++] =  0.5000;
288:   y[i] =    79.0000;   t[i++] =  0.6250;
289:   y[i] =    63.8000;   t[i++] =  0.7500;
290:   y[i] =    57.2000;   t[i++] =  0.8750;
291:   y[i] =    53.2000;   t[i++] =  1.0000;
292:   y[i] =   42.5000;   t[i++] =  1.2500;
293:   y[i] =   26.8000;   t[i++] =  1.7500;
294:   y[i] =    20.4000;   t[i++] =  2.2500;
295:   y[i] =    26.8500;  t[i++] =   1.7500;
296:   y[i] =    21.0000;  t[i++] =   2.2500;
297:   y[i] =    16.4625;  t[i++] =   2.7500;
298:   y[i] =    12.5250;  t[i++] =   3.2500;
299:   y[i] =    10.5375;  t[i++] =   3.7500;
300:   y[i] =     8.5875;  t[i++] =   4.2500;
301:   y[i] =     7.1250;  t[i++] =   4.7500;
302:   y[i] =     6.1125;  t[i++] =   5.2500;
303:   y[i] =     5.9625;  t[i++] =   5.7500;
304:   y[i] =    74.1000;  t[i++] =   0.5000;
305:   y[i] =    67.3000;  t[i++] =   0.6250;
306:   y[i] =    60.8000;  t[i++] =   0.7500;
307:   y[i] =    55.5000;  t[i++] =   0.8750;
308:   y[i] =    50.3000;  t[i++] =   1.0000;
309:   y[i] =    41.0000;  t[i++] =   1.2500;
310:   y[i] =    29.4000;  t[i++] =   1.7500;
311:   y[i] =    20.4000;  t[i++] =   2.2500;
312:   y[i] =    29.3625;  t[i++] =   1.7500;
313:   y[i] =    21.1500;  t[i++] =   2.2500;
314:   y[i] =    16.7625;  t[i++] =   2.7500;
315:   y[i] =    13.2000;  t[i++] =   3.2500;
316:   y[i] =    10.8750;  t[i++] =   3.7500;
317:   y[i] =     8.1750;  t[i++] =   4.2500;
318:   y[i] =     7.3500;  t[i++] =   4.7500;
319:   y[i] =     5.9625;  t[i++] =  5.2500;
320:   y[i] =     5.6250;  t[i++] =   5.7500;
321:   y[i] =    81.5000;  t[i++] =    .5000;
322:   y[i] =    62.4000;  t[i++] =    .7500;
323:   y[i] =    32.5000;  t[i++] =   1.5000;
324:   y[i] =    12.4100;  t[i++] =   3.0000;
325:   y[i] =    13.1200;  t[i++] =   3.0000;
326:   y[i] =    15.5600;  t[i++] =   3.0000;
327:   y[i] =     5.6300;  t[i++] =   6.0000;
328:   y[i] =    78.0000;   t[i++] =   .5000;
329:   y[i] =    59.9000;  t[i++] =    .7500;
330:   y[i] =    33.2000;  t[i++] =   1.5000;
331:   y[i] =    13.8400;  t[i++] =   3.0000;
332:   y[i] =    12.7500;  t[i++] =   3.0000;
333:   y[i] =    14.6200;  t[i++] =   3.0000;
334:   y[i] =     3.9400;  t[i++] =   6.0000;
335:   y[i] =    76.8000;  t[i++] =    .5000;
336:   y[i] =    61.0000;  t[i++] =    .7500;
337:   y[i] =    32.9000;  t[i++] =   1.5000;
338:   y[i] =   13.8700;   t[i++] = 3.0000;
339:   y[i] =    11.8100;  t[i++] =   3.0000;
340:   y[i] =    13.3100;  t[i++] =   3.0000;
341:   y[i] =     5.4400;  t[i++] =   6.0000;
342:   y[i] =    78.0000;  t[i++] =    .5000;
343:   y[i] =    63.5000;  t[i++] =    .7500;
344:   y[i] =    33.8000;  t[i++] =   1.5000;
345:   y[i] =    12.5600;  t[i++] =   3.0000;
346:   y[i] =     5.6300;  t[i++] =   6.0000;
347:   y[i] =    12.7500;  t[i++] =   3.0000;
348:   y[i] =    13.1200;  t[i++] =   3.0000;
349:   y[i] =     5.4400;  t[i++] =   6.0000;
350:   y[i] =    76.8000;  t[i++] =    .5000;
351:   y[i] =    60.0000;  t[i++] =    .7500;
352:   y[i] =    47.8000;  t[i++] =   1.0000;
353:   y[i] =    32.0000;  t[i++] =   1.5000;
354:   y[i] =    22.2000;  t[i++] =   2.0000;
355:   y[i] =    22.5700;  t[i++] =   2.0000;
356:   y[i] =    18.8200;  t[i++] =   2.5000;
357:   y[i] =    13.9500;  t[i++] =   3.0000;
358:   y[i] =    11.2500;  t[i++] =   4.0000;
359:   y[i] =     9.0000;  t[i++] =   5.0000;
360:   y[i] =     6.6700;  t[i++] =   6.0000;
361:   y[i] =    75.8000;  t[i++] =    .5000;
362:   y[i] =    62.0000;  t[i++] =    .7500;
363:   y[i] =    48.8000;  t[i++] =   1.0000;
364:   y[i] =    35.2000;  t[i++] =   1.5000;
365:   y[i] =    20.0000;  t[i++] =   2.0000;
366:   y[i] =    20.3200;  t[i++] =   2.0000;
367:   y[i] =    19.3100;  t[i++] =   2.5000;
368:   y[i] =    12.7500;  t[i++] =   3.0000;
369:   y[i] =    10.4200;  t[i++] =   4.0000;
370:   y[i] =     7.3100;  t[i++] =   5.0000;
371:   y[i] =     7.4200;  t[i++] =   6.0000;
372:   y[i] =    70.5000;  t[i++] =    .5000;
373:   y[i] =    59.5000;  t[i++] =    .7500;
374:   y[i] =    48.5000;  t[i++] =   1.0000;
375:   y[i] =    35.8000;  t[i++] =   1.5000;
376:   y[i] =    21.0000;  t[i++] =   2.0000;
377:   y[i] =    21.6700;  t[i++] =   2.0000;
378:   y[i] =    21.0000;  t[i++] =   2.5000;
379:   y[i] =    15.6400;  t[i++] =   3.0000;
380:   y[i] =     8.1700;  t[i++] =   4.0000;
381:   y[i] =     8.5500;  t[i++] =   5.0000;
382:   y[i] =    10.1200;  t[i++] =   6.0000;
383:   y[i] =    78.0000;  t[i++] =    .5000;
384:   y[i] =    66.0000;  t[i++] =    .6250;
385:   y[i] =    62.0000;  t[i++] =    .7500;
386:   y[i] =    58.0000;  t[i++] =    .8750;
387:   y[i] =    47.7000;  t[i++] =   1.0000;
388:   y[i] =    37.8000;  t[i++] =   1.2500;
389:   y[i] =    20.2000;  t[i++] =   2.2500;
390:   y[i] =    21.0700;  t[i++] =   2.2500;
391:   y[i] =    13.8700;  t[i++] =   2.7500;
392:   y[i] =     9.6700;  t[i++] =   3.2500;
393:   y[i] =     7.7600;  t[i++] =   3.7500;
394:   y[i] =    5.4400;   t[i++] =  4.2500;
395:   y[i] =    4.8700;   t[i++] =  4.7500;
396:   y[i] =     4.0100;  t[i++] =   5.2500;
397:   y[i] =     3.7500;  t[i++] =   5.7500;
398:   y[i] =    24.1900;  t[i++] =   3.0000;
399:   y[i] =    25.7600;  t[i++] =   3.0000;
400:   y[i] =    18.0700;  t[i++] =   3.0000;
401:   y[i] =    11.8100;  t[i++] =   3.0000;
402:   y[i] =    12.0700;  t[i++] =   3.0000;
403:   y[i] =    16.1200;  t[i++] =   3.0000;
404:   y[i] =    70.8000;  t[i++] =    .5000;
405:   y[i] =    54.7000;  t[i++] =    .7500;
406:   y[i] =    48.0000;  t[i++] =   1.0000;
407:   y[i] =    39.8000;  t[i++] =   1.5000;
408:   y[i] =    29.8000;  t[i++] =   2.0000;
409:   y[i] =    23.7000;  t[i++] =   2.5000;
410:   y[i] =    29.6200;  t[i++] =   2.0000;
411:   y[i] =    23.8100;  t[i++] =   2.5000;
412:   y[i] =    17.7000;  t[i++] =   3.0000;
413:   y[i] =    11.5500;  t[i++] =   4.0000;
414:   y[i] =    12.0700;  t[i++] =   5.0000;
415:   y[i] =     8.7400;  t[i++] =   6.0000;
416:   y[i] =    80.7000;  t[i++] =    .5000;
417:   y[i] =    61.3000;  t[i++] =    .7500;
418:   y[i] =    47.5000;  t[i++] =   1.0000;
419:    y[i] =   29.0000;  t[i++] =   1.5000;
420:    y[i] =   24.0000;  t[i++] =   2.0000;
421:   y[i] =    17.7000;  t[i++] =   2.5000;
422:   y[i] =    24.5600;  t[i++] =   2.0000;
423:   y[i] =    18.6700;  t[i++] =   2.5000;
424:    y[i] =   16.2400;  t[i++] =   3.0000;
425:   y[i] =     8.7400;  t[i++] =   4.0000;
426:   y[i] =     7.8700;  t[i++] =   5.0000;
427:   y[i] =     8.5100;  t[i++] =   6.0000;
428:   y[i] =    66.7000;  t[i++] =    .5000;
429:   y[i] =    59.2000;  t[i++] =    .7500;
430:   y[i] =    40.8000;  t[i++] =   1.0000;
431:   y[i] =    30.7000;  t[i++] =   1.5000;
432:   y[i] =    25.7000;  t[i++] =   2.0000;
433:   y[i] =    16.3000;  t[i++] =   2.5000;
434:   y[i] =    25.9900;  t[i++] =   2.0000;
435:   y[i] =    16.9500;  t[i++] =   2.5000;
436:   y[i] =    13.3500;  t[i++] =   3.0000;
437:   y[i] =     8.6200;  t[i++] =   4.0000;
438:   y[i] =     7.2000;  t[i++] =   5.0000;
439:   y[i] =     6.6400;  t[i++] =   6.0000;
440:   y[i] =    13.6900;  t[i++] =   3.0000;
441:   y[i] =    81.0000;  t[i++] =    .5000;
442:   y[i] =    64.5000;  t[i++] =    .7500;
443:   y[i] =    35.5000;  t[i++] =   1.5000;
444:    y[i] =   13.3100;  t[i++] =   3.0000;
445:   y[i] =     4.8700;  t[i++] =   6.0000;
446:   y[i] =    12.9400;  t[i++] =   3.0000;
447:   y[i] =     5.0600;  t[i++] =   6.0000;
448:   y[i] =    15.1900;  t[i++] =   3.0000;
449:   y[i] =    14.6200;  t[i++] =   3.0000;
450:   y[i] =    15.6400;  t[i++] =   3.0000;
451:   y[i] =    25.5000;  t[i++] =   1.7500;
452:   y[i] =    25.9500;  t[i++] =   1.7500;
453:   y[i] =    81.7000;  t[i++] =    .5000;
454:   y[i] =    61.6000;  t[i++] =    .7500;
455:   y[i] =    29.8000;  t[i++] =   1.7500;
456:   y[i] =    29.8100;  t[i++] =   1.7500;
457:   y[i] =    17.1700;  t[i++] =   2.7500;
458:   y[i] =    10.3900;  t[i++] =   3.7500;
459:   y[i] =    28.4000;  t[i++] =   1.7500;
460:   y[i] =    28.6900;  t[i++] =   1.7500;
461:   y[i] =    81.3000;  t[i++] =    .5000;
462:   y[i] =    60.9000;  t[i++] =    .7500;
463:   y[i] =    16.6500;  t[i++] =   2.7500;
464:   y[i] =    10.0500;  t[i++] =   3.7500;
465:   y[i] =    28.9000;  t[i++] =   1.7500;
466:   y[i] =    28.9500;  t[i++] =   1.7500;

468:   return(0);
469: }