Actual source code: chwirut1f.F90

  1: !  Program usage: mpiexec -n 1 chwirut1f [-help] [all TAO options]
  2: !
  3: !  Description:  This example demonstrates use of the TAO package to solve a
  4: !  nonlinear least-squares problem on a single processor.  We minimize the
  5: !  Chwirut function:
  6: !       sum_{i=0}^{n/2-1} ( alpha*(x_{2i+1}-x_{2i}^2)^2 + (1-x_{2i})^2)
  7: !
  8: !  The C version of this code is test_chwirut1.c
  9: !

 11: !
 12: ! ----------------------------------------------------------------------
 13: !
 14: module chwirut1fmodule
 15: #include <petsc/finclude/petsctao.h>
 16:   use petsctao
 17:   implicit none

 19:   PetscReal t(0:213)
 20:   PetscReal y(0:213)
 21:   PetscInt m, n
 22: end module chwirut1fmodule

 24: program main
 25:   use chwirut1fmodule
 26: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 27: !                   Variable declarations
 28: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 29: !
 30: !  See additional variable declarations in the file chwirut1f.h

 32:   PetscErrorCode ierr    ! used to check for functions returning nonzeros
 33:   Vec x       ! solution vector
 34:   Vec f       ! vector of functions
 35:   Tao ta     ! Tao context
 36:   PetscInt nhist
 37:   PetscMPIInt size, rank    ! number of processes running
 38:   PetscReal hist(100) ! objective value history
 39:   PetscReal resid(100)! residual history
 40:   PetscReal cnorm(100)! cnorm history
 41:   PetscInt lits(100)   ! #ksp history
 42:   PetscInt oh
 43:   TaoConvergedReason reason

 45: !  Note: Any user-defined Fortran routines (such as FormGradient)
 46: !  MUST be declared as external.

 48:   external FormFunction

 50: !  Initialize TAO and PETSc
 51:   PetscCallA(PetscInitialize(ierr))

 53:   PetscCallMPIA(MPI_Comm_size(PETSC_COMM_WORLD, size, ierr))
 54:   PetscCallMPIA(MPI_Comm_rank(PETSC_COMM_WORLD, rank, ierr))
 55:   PetscCheckA(size == 1, PETSC_COMM_SELF, PETSC_ERR_WRONG_MPI_SIZE, 'This is a uniprocessor example only')

 57: !  Initialize problem parameters
 58:   m = 214
 59:   n = 3

 61: !  Allocate vectors for the solution and gradient
 62:   PetscCallA(VecCreateSeq(PETSC_COMM_SELF, n, x, ierr))
 63:   PetscCallA(VecCreateSeq(PETSC_COMM_SELF, m, f, ierr))

 65: !  The TAO code begins here

 67: !  Create TAO solver
 68:   PetscCallA(TaoCreate(PETSC_COMM_SELF, ta, ierr))
 69:   PetscCallA(TaoSetType(ta, TAOPOUNDERS, ierr))
 70: !  Set routines for function, gradient, and hessian evaluation

 72:   PetscCallA(TaoSetResidualRoutine(ta, f, FormFunction, 0, ierr))

 74: !  Optional: Set initial guess
 75:   call InitializeData()
 76:   call FormStartingPoint(x)
 77:   PetscCallA(TaoSetSolution(ta, x, ierr))

 79: !  Check for TAO command line options
 80:   PetscCallA(TaoSetFromOptions(ta, ierr))
 81:   oh = 100
 82:   PetscCallA(TaoSetConvergenceHistory(ta, hist, resid, cnorm, lits, oh, PETSC_TRUE, ierr))
 83: !  SOLVE THE APPLICATION
 84:   PetscCallA(TaoSolve(ta, ierr))
 85:   PetscCallA(TaoGetConvergenceHistory(ta, nhist, ierr))
 86:   PetscCallA(TaoGetConvergedReason(ta, reason, ierr))
 87:   if (reason%v <= 0) then
 88:     print *, 'Tao failed.'
 89:     print *, 'Try a different TAO method, adjust some parameters,'
 90:     print *, 'or check the function evaluation routines.'
 91:   end if

 93: !  Free TAO data structures
 94:   PetscCallA(TaoDestroy(ta, ierr))

 96: !  Free PETSc data structures
 97:   PetscCallA(VecDestroy(x, ierr))
 98:   PetscCallA(VecDestroy(f, ierr))

100:   PetscCallA(PetscFinalize(ierr))

102: end

104: ! --------------------------------------------------------------------
105: !  FormFunction - Evaluates the function f(X) and gradient G(X)
106: !
107: !  Input Parameters:
108: !  tao - the Tao context
109: !  X   - input vector
110: !  dummy - not used
111: !
112: !  Output Parameters:
113: !  f - function vector

115: subroutine FormFunction(ta, x, f, dummy, ierr)
116:   use chwirut1fmodule

118:   Tao ta
119:   Vec x, f
120:   PetscErrorCode ierr
121:   PetscInt dummy

123:   PetscInt i
124:   PetscScalar, pointer, dimension(:)  :: x_v, f_v

126:   ierr = 0

128: !     Get pointers to vector data
129:   PetscCall(VecGetArray(x, x_v, ierr))
130:   PetscCall(VecGetArray(f, f_v, ierr))

132: !     Compute F(X)
133:   do i = 0, m - 1
134:     f_v(i + 1) = y(i) - exp(-x_v(1)*t(i))/(x_v(2) + x_v(3)*t(i))
135:   end do

137: !     Restore vectors
138:   PetscCall(VecRestoreArray(X, x_v, ierr))
139:   PetscCall(VecRestoreArray(F, f_v, ierr))

141: end

143: subroutine FormStartingPoint(x)
144:   use chwirut1fmodule

146:   Vec x
147:   PetscScalar, pointer, dimension(:)  :: x_v
148:   PetscErrorCode ierr

150:   PetscCall(VecGetArray(x, x_v, ierr))
151:   x_v(1) = 0.15
152:   x_v(2) = 0.008
153:   x_v(3) = 0.01
154:   PetscCall(VecRestoreArray(x, x_v, ierr))
155: end

157: subroutine InitializeData()
158:   use chwirut1fmodule

160:   integer i
161:   i = 0
162:   y(i) = 92.9000; t(i) = 0.5000; i = i + 1
163:   y(i) = 78.7000; t(i) = 0.6250; i = i + 1
164:   y(i) = 64.2000; t(i) = 0.7500; i = i + 1
165:   y(i) = 64.9000; t(i) = 0.8750; i = i + 1
166:   y(i) = 57.1000; t(i) = 1.0000; i = i + 1
167:   y(i) = 43.3000; t(i) = 1.2500; i = i + 1
168:   y(i) = 31.1000; t(i) = 1.7500; i = i + 1
169:   y(i) = 23.6000; t(i) = 2.2500; i = i + 1
170:   y(i) = 31.0500; t(i) = 1.7500; i = i + 1
171:   y(i) = 23.7750; t(i) = 2.2500; i = i + 1
172:   y(i) = 17.7375; t(i) = 2.7500; i = i + 1
173:   y(i) = 13.8000; t(i) = 3.2500; i = i + 1
174:   y(i) = 11.5875; t(i) = 3.7500; i = i + 1
175:   y(i) = 9.4125; t(i) = 4.2500; i = i + 1
176:   y(i) = 7.7250; t(i) = 4.7500; i = i + 1
177:   y(i) = 7.3500; t(i) = 5.2500; i = i + 1
178:   y(i) = 8.0250; t(i) = 5.7500; i = i + 1
179:   y(i) = 90.6000; t(i) = 0.5000; i = i + 1
180:   y(i) = 76.9000; t(i) = 0.6250; i = i + 1
181:   y(i) = 71.6000; t(i) = 0.7500; i = i + 1
182:   y(i) = 63.6000; t(i) = 0.8750; i = i + 1
183:   y(i) = 54.0000; t(i) = 1.0000; i = i + 1
184:   y(i) = 39.2000; t(i) = 1.2500; i = i + 1
185:   y(i) = 29.3000; t(i) = 1.7500; i = i + 1
186:   y(i) = 21.4000; t(i) = 2.2500; i = i + 1
187:   y(i) = 29.1750; t(i) = 1.7500; i = i + 1
188:   y(i) = 22.1250; t(i) = 2.2500; i = i + 1
189:   y(i) = 17.5125; t(i) = 2.7500; i = i + 1
190:   y(i) = 14.2500; t(i) = 3.2500; i = i + 1
191:   y(i) = 9.4500; t(i) = 3.7500; i = i + 1
192:   y(i) = 9.1500; t(i) = 4.2500; i = i + 1
193:   y(i) = 7.9125; t(i) = 4.7500; i = i + 1
194:   y(i) = 8.4750; t(i) = 5.2500; i = i + 1
195:   y(i) = 6.1125; t(i) = 5.7500; i = i + 1
196:   y(i) = 80.0000; t(i) = 0.5000; i = i + 1
197:   y(i) = 79.0000; t(i) = 0.6250; i = i + 1
198:   y(i) = 63.8000; t(i) = 0.7500; i = i + 1
199:   y(i) = 57.2000; t(i) = 0.8750; i = i + 1
200:   y(i) = 53.2000; t(i) = 1.0000; i = i + 1
201:   y(i) = 42.5000; t(i) = 1.2500; i = i + 1
202:   y(i) = 26.8000; t(i) = 1.7500; i = i + 1
203:   y(i) = 20.4000; t(i) = 2.2500; i = i + 1
204:   y(i) = 26.8500; t(i) = 1.7500; i = i + 1
205:   y(i) = 21.0000; t(i) = 2.2500; i = i + 1
206:   y(i) = 16.4625; t(i) = 2.7500; i = i + 1
207:   y(i) = 12.5250; t(i) = 3.2500; i = i + 1
208:   y(i) = 10.5375; t(i) = 3.7500; i = i + 1
209:   y(i) = 8.5875; t(i) = 4.2500; i = i + 1
210:   y(i) = 7.1250; t(i) = 4.7500; i = i + 1
211:   y(i) = 6.1125; t(i) = 5.2500; i = i + 1
212:   y(i) = 5.9625; t(i) = 5.7500; i = i + 1
213:   y(i) = 74.1000; t(i) = 0.5000; i = i + 1
214:   y(i) = 67.3000; t(i) = 0.6250; i = i + 1
215:   y(i) = 60.8000; t(i) = 0.7500; i = i + 1
216:   y(i) = 55.5000; t(i) = 0.8750; i = i + 1
217:   y(i) = 50.3000; t(i) = 1.0000; i = i + 1
218:   y(i) = 41.0000; t(i) = 1.2500; i = i + 1
219:   y(i) = 29.4000; t(i) = 1.7500; i = i + 1
220:   y(i) = 20.4000; t(i) = 2.2500; i = i + 1
221:   y(i) = 29.3625; t(i) = 1.7500; i = i + 1
222:   y(i) = 21.1500; t(i) = 2.2500; i = i + 1
223:   y(i) = 16.7625; t(i) = 2.7500; i = i + 1
224:   y(i) = 13.2000; t(i) = 3.2500; i = i + 1
225:   y(i) = 10.8750; t(i) = 3.7500; i = i + 1
226:   y(i) = 8.1750; t(i) = 4.2500; i = i + 1
227:   y(i) = 7.3500; t(i) = 4.7500; i = i + 1
228:   y(i) = 5.9625; t(i) = 5.2500; i = i + 1
229:   y(i) = 5.6250; t(i) = 5.7500; i = i + 1
230:   y(i) = 81.5000; t(i) = .5000; i = i + 1
231:   y(i) = 62.4000; t(i) = .7500; i = i + 1
232:   y(i) = 32.5000; t(i) = 1.5000; i = i + 1
233:   y(i) = 12.4100; t(i) = 3.0000; i = i + 1
234:   y(i) = 13.1200; t(i) = 3.0000; i = i + 1
235:   y(i) = 15.5600; t(i) = 3.0000; i = i + 1
236:   y(i) = 5.6300; t(i) = 6.0000; i = i + 1
237:   y(i) = 78.0000; t(i) = .5000; i = i + 1
238:   y(i) = 59.9000; t(i) = .7500; i = i + 1
239:   y(i) = 33.2000; t(i) = 1.5000; i = i + 1
240:   y(i) = 13.8400; t(i) = 3.0000; i = i + 1
241:   y(i) = 12.7500; t(i) = 3.0000; i = i + 1
242:   y(i) = 14.6200; t(i) = 3.0000; i = i + 1
243:   y(i) = 3.9400; t(i) = 6.0000; i = i + 1
244:   y(i) = 76.8000; t(i) = .5000; i = i + 1
245:   y(i) = 61.0000; t(i) = .7500; i = i + 1
246:   y(i) = 32.9000; t(i) = 1.5000; i = i + 1
247:   y(i) = 13.8700; t(i) = 3.0000; i = i + 1
248:   y(i) = 11.8100; t(i) = 3.0000; i = i + 1
249:   y(i) = 13.3100; t(i) = 3.0000; i = i + 1
250:   y(i) = 5.4400; t(i) = 6.0000; i = i + 1
251:   y(i) = 78.0000; t(i) = .5000; i = i + 1
252:   y(i) = 63.5000; t(i) = .7500; i = i + 1
253:   y(i) = 33.8000; t(i) = 1.5000; i = i + 1
254:   y(i) = 12.5600; t(i) = 3.0000; i = i + 1
255:   y(i) = 5.6300; t(i) = 6.0000; i = i + 1
256:   y(i) = 12.7500; t(i) = 3.0000; i = i + 1
257:   y(i) = 13.1200; t(i) = 3.0000; i = i + 1
258:   y(i) = 5.4400; t(i) = 6.0000; i = i + 1
259:   y(i) = 76.8000; t(i) = .5000; i = i + 1
260:   y(i) = 60.0000; t(i) = .7500; i = i + 1
261:   y(i) = 47.8000; t(i) = 1.0000; i = i + 1
262:   y(i) = 32.0000; t(i) = 1.5000; i = i + 1
263:   y(i) = 22.2000; t(i) = 2.0000; i = i + 1
264:   y(i) = 22.5700; t(i) = 2.0000; i = i + 1
265:   y(i) = 18.8200; t(i) = 2.5000; i = i + 1
266:   y(i) = 13.9500; t(i) = 3.0000; i = i + 1
267:   y(i) = 11.2500; t(i) = 4.0000; i = i + 1
268:   y(i) = 9.0000; t(i) = 5.0000; i = i + 1
269:   y(i) = 6.6700; t(i) = 6.0000; i = i + 1
270:   y(i) = 75.8000; t(i) = .5000; i = i + 1
271:   y(i) = 62.0000; t(i) = .7500; i = i + 1
272:   y(i) = 48.8000; t(i) = 1.0000; i = i + 1
273:   y(i) = 35.2000; t(i) = 1.5000; i = i + 1
274:   y(i) = 20.0000; t(i) = 2.0000; i = i + 1
275:   y(i) = 20.3200; t(i) = 2.0000; i = i + 1
276:   y(i) = 19.3100; t(i) = 2.5000; i = i + 1
277:   y(i) = 12.7500; t(i) = 3.0000; i = i + 1
278:   y(i) = 10.4200; t(i) = 4.0000; i = i + 1
279:   y(i) = 7.3100; t(i) = 5.0000; i = i + 1
280:   y(i) = 7.4200; t(i) = 6.0000; i = i + 1
281:   y(i) = 70.5000; t(i) = .5000; i = i + 1
282:   y(i) = 59.5000; t(i) = .7500; i = i + 1
283:   y(i) = 48.5000; t(i) = 1.0000; i = i + 1
284:   y(i) = 35.8000; t(i) = 1.5000; i = i + 1
285:   y(i) = 21.0000; t(i) = 2.0000; i = i + 1
286:   y(i) = 21.6700; t(i) = 2.0000; i = i + 1
287:   y(i) = 21.0000; t(i) = 2.5000; i = i + 1
288:   y(i) = 15.6400; t(i) = 3.0000; i = i + 1
289:   y(i) = 8.1700; t(i) = 4.0000; i = i + 1
290:   y(i) = 8.5500; t(i) = 5.0000; i = i + 1
291:   y(i) = 10.1200; t(i) = 6.0000; i = i + 1
292:   y(i) = 78.0000; t(i) = .5000; i = i + 1
293:   y(i) = 66.0000; t(i) = .6250; i = i + 1
294:   y(i) = 62.0000; t(i) = .7500; i = i + 1
295:   y(i) = 58.0000; t(i) = .8750; i = i + 1
296:   y(i) = 47.7000; t(i) = 1.0000; i = i + 1
297:   y(i) = 37.8000; t(i) = 1.2500; i = i + 1
298:   y(i) = 20.2000; t(i) = 2.2500; i = i + 1
299:   y(i) = 21.0700; t(i) = 2.2500; i = i + 1
300:   y(i) = 13.8700; t(i) = 2.7500; i = i + 1
301:   y(i) = 9.6700; t(i) = 3.2500; i = i + 1
302:   y(i) = 7.7600; t(i) = 3.7500; i = i + 1
303:   y(i) = 5.4400; t(i) = 4.2500; i = i + 1
304:   y(i) = 4.8700; t(i) = 4.7500; i = i + 1
305:   y(i) = 4.0100; t(i) = 5.2500; i = i + 1
306:   y(i) = 3.7500; t(i) = 5.7500; i = i + 1
307:   y(i) = 24.1900; t(i) = 3.0000; i = i + 1
308:   y(i) = 25.7600; t(i) = 3.0000; i = i + 1
309:   y(i) = 18.0700; t(i) = 3.0000; i = i + 1
310:   y(i) = 11.8100; t(i) = 3.0000; i = i + 1
311:   y(i) = 12.0700; t(i) = 3.0000; i = i + 1
312:   y(i) = 16.1200; t(i) = 3.0000; i = i + 1
313:   y(i) = 70.8000; t(i) = .5000; i = i + 1
314:   y(i) = 54.7000; t(i) = .7500; i = i + 1
315:   y(i) = 48.0000; t(i) = 1.0000; i = i + 1
316:   y(i) = 39.8000; t(i) = 1.5000; i = i + 1
317:   y(i) = 29.8000; t(i) = 2.0000; i = i + 1
318:   y(i) = 23.7000; t(i) = 2.5000; i = i + 1
319:   y(i) = 29.6200; t(i) = 2.0000; i = i + 1
320:   y(i) = 23.8100; t(i) = 2.5000; i = i + 1
321:   y(i) = 17.7000; t(i) = 3.0000; i = i + 1
322:   y(i) = 11.5500; t(i) = 4.0000; i = i + 1
323:   y(i) = 12.0700; t(i) = 5.0000; i = i + 1
324:   y(i) = 8.7400; t(i) = 6.0000; i = i + 1
325:   y(i) = 80.7000; t(i) = .5000; i = i + 1
326:   y(i) = 61.3000; t(i) = .7500; i = i + 1
327:   y(i) = 47.5000; t(i) = 1.0000; i = i + 1
328:   y(i) = 29.0000; t(i) = 1.5000; i = i + 1
329:   y(i) = 24.0000; t(i) = 2.0000; i = i + 1
330:   y(i) = 17.7000; t(i) = 2.5000; i = i + 1
331:   y(i) = 24.5600; t(i) = 2.0000; i = i + 1
332:   y(i) = 18.6700; t(i) = 2.5000; i = i + 1
333:   y(i) = 16.2400; t(i) = 3.0000; i = i + 1
334:   y(i) = 8.7400; t(i) = 4.0000; i = i + 1
335:   y(i) = 7.8700; t(i) = 5.0000; i = i + 1
336:   y(i) = 8.5100; t(i) = 6.0000; i = i + 1
337:   y(i) = 66.7000; t(i) = .5000; i = i + 1
338:   y(i) = 59.2000; t(i) = .7500; i = i + 1
339:   y(i) = 40.8000; t(i) = 1.0000; i = i + 1
340:   y(i) = 30.7000; t(i) = 1.5000; i = i + 1
341:   y(i) = 25.7000; t(i) = 2.0000; i = i + 1
342:   y(i) = 16.3000; t(i) = 2.5000; i = i + 1
343:   y(i) = 25.9900; t(i) = 2.0000; i = i + 1
344:   y(i) = 16.9500; t(i) = 2.5000; i = i + 1
345:   y(i) = 13.3500; t(i) = 3.0000; i = i + 1
346:   y(i) = 8.6200; t(i) = 4.0000; i = i + 1
347:   y(i) = 7.2000; t(i) = 5.0000; i = i + 1
348:   y(i) = 6.6400; t(i) = 6.0000; i = i + 1
349:   y(i) = 13.6900; t(i) = 3.0000; i = i + 1
350:   y(i) = 81.0000; t(i) = .5000; i = i + 1
351:   y(i) = 64.5000; t(i) = .7500; i = i + 1
352:   y(i) = 35.5000; t(i) = 1.5000; i = i + 1
353:   y(i) = 13.3100; t(i) = 3.0000; i = i + 1
354:   y(i) = 4.8700; t(i) = 6.0000; i = i + 1
355:   y(i) = 12.9400; t(i) = 3.0000; i = i + 1
356:   y(i) = 5.0600; t(i) = 6.0000; i = i + 1
357:   y(i) = 15.1900; t(i) = 3.0000; i = i + 1
358:   y(i) = 14.6200; t(i) = 3.0000; i = i + 1
359:   y(i) = 15.6400; t(i) = 3.0000; i = i + 1
360:   y(i) = 25.5000; t(i) = 1.7500; i = i + 1
361:   y(i) = 25.9500; t(i) = 1.7500; i = i + 1
362:   y(i) = 81.7000; t(i) = .5000; i = i + 1
363:   y(i) = 61.6000; t(i) = .7500; i = i + 1
364:   y(i) = 29.8000; t(i) = 1.7500; i = i + 1
365:   y(i) = 29.8100; t(i) = 1.7500; i = i + 1
366:   y(i) = 17.1700; t(i) = 2.7500; i = i + 1
367:   y(i) = 10.3900; t(i) = 3.7500; i = i + 1
368:   y(i) = 28.4000; t(i) = 1.7500; i = i + 1
369:   y(i) = 28.6900; t(i) = 1.7500; i = i + 1
370:   y(i) = 81.3000; t(i) = .5000; i = i + 1
371:   y(i) = 60.9000; t(i) = .7500; i = i + 1
372:   y(i) = 16.6500; t(i) = 2.7500; i = i + 1
373:   y(i) = 10.0500; t(i) = 3.7500; i = i + 1
374:   y(i) = 28.9000; t(i) = 1.7500; i = i + 1
375:   y(i) = 28.9500; t(i) = 1.7500; i = i + 1

377: end

379: !/*TEST
380: !
381: !   build:
382: !      requires: !complex
383: !
384: !   test:
385: !      args: -tao_monitor_short -tao_max_it 100 -tao_type pounders -tao_gatol 1.e-5
386: !      requires: !single
387: !
388: !TEST*/