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*/