Actual source code: chwirut1f.F90
petsc-3.9.4 2018-09-11
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: !
10: !!/*T
11: ! Concepts: TAO^Solving an unconstrained minimization problem
12: ! Routines: TaoCreate();
13: ! Routines: TaoSetType();
14: ! Routines: TaoSetInitialVector();
15: ! Routines: TaoSetSeparableObjectiveRoutine();
16: ! Routines: TaoSetFromOptions();
17: ! Routines: TaoSolve();
18: ! Routines: TaoDestroy();
19: ! Processors: 1
20: !T*/
23: !
24: ! ----------------------------------------------------------------------
25: !
26: #include "chwirut1f.h"
28: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
29: ! Variable declarations
30: ! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
31: !
32: ! See additional variable declarations in the file chwirut1f.h
34: PetscErrorCode ierr ! used to check for functions returning nonzeros
35: Vec x ! solution vector
36: Vec f ! vector of functions
37: Tao tao ! Tao context
38: PetscInt nhist
39: PetscMPIInt size,rank ! number of processes running
40: PetscReal zero
41: PetscReal hist(100) ! objective value history
42: PetscReal resid(100)! residual history
43: PetscReal cnorm(100)! cnorm history
44: PetscInt lits(100) ! #ksp history
45: PetscInt oh
46: TaoConvergedReason reason
48: ! Note: Any user-defined Fortran routines (such as FormGradient)
49: ! MUST be declared as external.
51: external FormFunction
53: zero = 0.0
55: ! Initialize TAO and PETSc
56: call PetscInitialize(PETSC_NULL_CHARACTER,ierr)
57: if (ierr .ne. 0) then
58: print*,'Unable to initialize PETSc'
59: stop
60: endif
62: call MPI_Comm_size(PETSC_COMM_WORLD,size,ierr)
63: call MPI_Comm_rank(PETSC_COMM_WORLD,rank,ierr)
64: if (size .ne. 1) then; SETERRA(PETSC_COMM_SELF,1,'This is a uniprocessor example only '); endif
66: ! Initialize problem parameters
67: m = 214
68: n = 3
71: ! Allocate vectors for the solution and gradient
72: call VecCreateSeq(PETSC_COMM_SELF,n,x,ierr)
73: call VecCreateSeq(PETSC_COMM_SELF,m,f,ierr)
76: ! The TAO code begins here
78: ! Create TAO solver
79: call TaoCreate(PETSC_COMM_SELF,tao,ierr);CHKERRA(ierr)
80: call TaoSetType(tao,TAOPOUNDERS,ierr);CHKERRA(ierr)
81: ! Set routines for function, gradient, and hessian evaluation
83: call TaoSetSeparableObjectiveRoutine(tao,f, &
84: & FormFunction,0,ierr)
85: CHKERRA(ierr)
87: ! Optional: Set initial guess
88: call InitializeData()
89: call FormStartingPoint(x)
90: call TaoSetInitialVector(tao, x, ierr)
91: CHKERRA(ierr)
94: ! Check for TAO command line options
95: call TaoSetFromOptions(tao,ierr)
96: CHKERRA(ierr)
97: oh = 100
98: call TaoSetConvergenceHistory(tao,hist,resid,cnorm,lits, &
99: & oh,PETSC_TRUE,ierr)
100: CHKERRA(ierr)
101: ! SOLVE THE APPLICATION
102: call TaoSolve(tao,ierr)
103: CHKERRA(ierr)
104: call TaoGetConvergenceHistory(tao,nhist,ierr)
105: CHKERRA(ierr)
106: call TaoGetConvergedReason(tao, reason, ierr)
107: if (reason .le. 0) then
108: print *,'Tao failed.'
109: print *,'Try a different TAO method, adjust some parameters,'
110: print *,'or check the function evaluation routines.'
111: endif
113: ! Free TAO data structures
114: call TaoDestroy(tao,ierr)
116: ! Free PETSc data structures
117: call VecDestroy(x,ierr)
118: call VecDestroy(f,ierr)
120: call PetscFinalize(ierr)
122: end
125: ! --------------------------------------------------------------------
126: ! FormFunction - Evaluates the function f(X) and gradient G(X)
127: !
128: ! Input Parameters:
129: ! tao - the Tao context
130: ! X - input vector
131: ! dummy - not used
132: !
133: ! Output Parameters:
134: ! f - function vector
136: subroutine FormFunction(tao, x, f, dummy, ierr)
137: #include "chwirut1f.h"
139: Tao tao
140: Vec x,f
141: PetscErrorCode ierr
142: PetscInt dummy
144: PetscInt i
146: ! PETSc's VecGetArray acts differently in Fortran than it does in C.
147: ! Calling VecGetArray((Vec) X, (PetscReal) x_array(0:1), (PetscOffset) x_index, ierr)
148: ! will return an array of doubles referenced by x_array offset by x_index.
149: ! i.e., to reference the kth element of X, use x_array(k + x_index).
150: ! Notice that by declaring the arrays with range (0:1), we are using the C 0-indexing practice.
151: PetscReal f_v(0:1),x_v(0:1)
152: PetscOffset f_i,x_i
154: 0
156: ! Get pointers to vector data
157: call VecGetArray(x,x_v,x_i,ierr)
158: CHKERRQ(ierr)
159: call VecGetArray(f,f_v,f_i,ierr)
160: CHKERRQ(ierr)
163: ! Compute F(X)
164: do i=0,m-1
165: f_v(f_i+i) = y(i) - exp(-x_v(x_i+0)*t(i))/ &
166: & (x_v(x_i+1) + x_v(x_i+2)*t(i))
168: enddo
171: ! Restore vectors
172: call VecRestoreArray(X,x_v,x_i,ierr)
173: CHKERRQ(ierr)
174: call VecRestoreArray(F,f_v,f_i,ierr)
175: CHKERRQ(ierr)
178: return
179: end
181: subroutine FormStartingPoint(x)
182: #include "chwirut1f.h"
184: Vec x
185: PetscReal x_v(0:1)
186: PetscOffset x_i
187: PetscErrorCode ierr
189: call VecGetArray(x,x_v,x_i,ierr)
190: x_v(x_i) = 0.15
191: x_v(x_i+1) = 0.008
192: x_v(x_i+2) = 0.01
193: call VecRestoreArray(x,x_v,x_i,ierr)
194: return
195: end
197: subroutine InitializeData()
198: #include "chwirut1f.h"
200: integer i
201: i=0
202: y(i) = 92.9000; t(i) = 0.5000; i=i+1
203: y(i) = 78.7000; t(i) = 0.6250; i=i+1
204: y(i) = 64.2000; t(i) = 0.7500; i=i+1
205: y(i) = 64.9000; t(i) = 0.8750; i=i+1
206: y(i) = 57.1000; t(i) = 1.0000; i=i+1
207: y(i) = 43.3000; t(i) = 1.2500; i=i+1
208: y(i) = 31.1000; t(i) = 1.7500; i=i+1
209: y(i) = 23.6000; t(i) = 2.2500; i=i+1
210: y(i) = 31.0500; t(i) = 1.7500; i=i+1
211: y(i) = 23.7750; t(i) = 2.2500; i=i+1
212: y(i) = 17.7375; t(i) = 2.7500; i=i+1
213: y(i) = 13.8000; t(i) = 3.2500; i=i+1
214: y(i) = 11.5875; t(i) = 3.7500; i=i+1
215: y(i) = 9.4125; t(i) = 4.2500; i=i+1
216: y(i) = 7.7250; t(i) = 4.7500; i=i+1
217: y(i) = 7.3500; t(i) = 5.2500; i=i+1
218: y(i) = 8.0250; t(i) = 5.7500; i=i+1
219: y(i) = 90.6000; t(i) = 0.5000; i=i+1
220: y(i) = 76.9000; t(i) = 0.6250; i=i+1
221: y(i) = 71.6000; t(i) = 0.7500; i=i+1
222: y(i) = 63.6000; t(i) = 0.8750; i=i+1
223: y(i) = 54.0000; t(i) = 1.0000; i=i+1
224: y(i) = 39.2000; t(i) = 1.2500; i=i+1
225: y(i) = 29.3000; t(i) = 1.7500; i=i+1
226: y(i) = 21.4000; t(i) = 2.2500; i=i+1
227: y(i) = 29.1750; t(i) = 1.7500; i=i+1
228: y(i) = 22.1250; t(i) = 2.2500; i=i+1
229: y(i) = 17.5125; t(i) = 2.7500; i=i+1
230: y(i) = 14.2500; t(i) = 3.2500; i=i+1
231: y(i) = 9.4500; t(i) = 3.7500; i=i+1
232: y(i) = 9.1500; t(i) = 4.2500; i=i+1
233: y(i) = 7.9125; t(i) = 4.7500; i=i+1
234: y(i) = 8.4750; t(i) = 5.2500; i=i+1
235: y(i) = 6.1125; t(i) = 5.7500; i=i+1
236: y(i) = 80.0000; t(i) = 0.5000; i=i+1
237: y(i) = 79.0000; t(i) = 0.6250; i=i+1
238: y(i) = 63.8000; t(i) = 0.7500; i=i+1
239: y(i) = 57.2000; t(i) = 0.8750; i=i+1
240: y(i) = 53.2000; t(i) = 1.0000; i=i+1
241: y(i) = 42.5000; t(i) = 1.2500; i=i+1
242: y(i) = 26.8000; t(i) = 1.7500; i=i+1
243: y(i) = 20.4000; t(i) = 2.2500; i=i+1
244: y(i) = 26.8500; t(i) = 1.7500; i=i+1
245: y(i) = 21.0000; t(i) = 2.2500; i=i+1
246: y(i) = 16.4625; t(i) = 2.7500; i=i+1
247: y(i) = 12.5250; t(i) = 3.2500; i=i+1
248: y(i) = 10.5375; t(i) = 3.7500; i=i+1
249: y(i) = 8.5875; t(i) = 4.2500; i=i+1
250: y(i) = 7.1250; t(i) = 4.7500; i=i+1
251: y(i) = 6.1125; t(i) = 5.2500; i=i+1
252: y(i) = 5.9625; t(i) = 5.7500; i=i+1
253: y(i) = 74.1000; t(i) = 0.5000; i=i+1
254: y(i) = 67.3000; t(i) = 0.6250; i=i+1
255: y(i) = 60.8000; t(i) = 0.7500; i=i+1
256: y(i) = 55.5000; t(i) = 0.8750; i=i+1
257: y(i) = 50.3000; t(i) = 1.0000; i=i+1
258: y(i) = 41.0000; t(i) = 1.2500; i=i+1
259: y(i) = 29.4000; t(i) = 1.7500; i=i+1
260: y(i) = 20.4000; t(i) = 2.2500; i=i+1
261: y(i) = 29.3625; t(i) = 1.7500; i=i+1
262: y(i) = 21.1500; t(i) = 2.2500; i=i+1
263: y(i) = 16.7625; t(i) = 2.7500; i=i+1
264: y(i) = 13.2000; t(i) = 3.2500; i=i+1
265: y(i) = 10.8750; t(i) = 3.7500; i=i+1
266: y(i) = 8.1750; t(i) = 4.2500; i=i+1
267: y(i) = 7.3500; t(i) = 4.7500; i=i+1
268: y(i) = 5.9625; t(i) = 5.2500; i=i+1
269: y(i) = 5.6250; t(i) = 5.7500; i=i+1
270: y(i) = 81.5000; t(i) = .5000; i=i+1
271: y(i) = 62.4000; t(i) = .7500; i=i+1
272: y(i) = 32.5000; t(i) = 1.5000; i=i+1
273: y(i) = 12.4100; t(i) = 3.0000; i=i+1
274: y(i) = 13.1200; t(i) = 3.0000; i=i+1
275: y(i) = 15.5600; t(i) = 3.0000; i=i+1
276: y(i) = 5.6300; t(i) = 6.0000; i=i+1
277: y(i) = 78.0000; t(i) = .5000; i=i+1
278: y(i) = 59.9000; t(i) = .7500; i=i+1
279: y(i) = 33.2000; t(i) = 1.5000; i=i+1
280: y(i) = 13.8400; t(i) = 3.0000; i=i+1
281: y(i) = 12.7500; t(i) = 3.0000; i=i+1
282: y(i) = 14.6200; t(i) = 3.0000; i=i+1
283: y(i) = 3.9400; t(i) = 6.0000; i=i+1
284: y(i) = 76.8000; t(i) = .5000; i=i+1
285: y(i) = 61.0000; t(i) = .7500; i=i+1
286: y(i) = 32.9000; t(i) = 1.5000; i=i+1
287: y(i) = 13.8700; t(i) = 3.0000; i=i+1
288: y(i) = 11.8100; t(i) = 3.0000; i=i+1
289: y(i) = 13.3100; t(i) = 3.0000; i=i+1
290: y(i) = 5.4400; t(i) = 6.0000; i=i+1
291: y(i) = 78.0000; t(i) = .5000; i=i+1
292: y(i) = 63.5000; t(i) = .7500; i=i+1
293: y(i) = 33.8000; t(i) = 1.5000; i=i+1
294: y(i) = 12.5600; t(i) = 3.0000; i=i+1
295: y(i) = 5.6300; t(i) = 6.0000; i=i+1
296: y(i) = 12.7500; t(i) = 3.0000; i=i+1
297: y(i) = 13.1200; t(i) = 3.0000; i=i+1
298: y(i) = 5.4400; t(i) = 6.0000; i=i+1
299: y(i) = 76.8000; t(i) = .5000; i=i+1
300: y(i) = 60.0000; t(i) = .7500; i=i+1
301: y(i) = 47.8000; t(i) = 1.0000; i=i+1
302: y(i) = 32.0000; t(i) = 1.5000; i=i+1
303: y(i) = 22.2000; t(i) = 2.0000; i=i+1
304: y(i) = 22.5700; t(i) = 2.0000; i=i+1
305: y(i) = 18.8200; t(i) = 2.5000; i=i+1
306: y(i) = 13.9500; t(i) = 3.0000; i=i+1
307: y(i) = 11.2500; t(i) = 4.0000; i=i+1
308: y(i) = 9.0000; t(i) = 5.0000; i=i+1
309: y(i) = 6.6700; t(i) = 6.0000; i=i+1
310: y(i) = 75.8000; t(i) = .5000; i=i+1
311: y(i) = 62.0000; t(i) = .7500; i=i+1
312: y(i) = 48.8000; t(i) = 1.0000; i=i+1
313: y(i) = 35.2000; t(i) = 1.5000; i=i+1
314: y(i) = 20.0000; t(i) = 2.0000; i=i+1
315: y(i) = 20.3200; t(i) = 2.0000; i=i+1
316: y(i) = 19.3100; t(i) = 2.5000; i=i+1
317: y(i) = 12.7500; t(i) = 3.0000; i=i+1
318: y(i) = 10.4200; t(i) = 4.0000; i=i+1
319: y(i) = 7.3100; t(i) = 5.0000; i=i+1
320: y(i) = 7.4200; t(i) = 6.0000; i=i+1
321: y(i) = 70.5000; t(i) = .5000; i=i+1
322: y(i) = 59.5000; t(i) = .7500; i=i+1
323: y(i) = 48.5000; t(i) = 1.0000; i=i+1
324: y(i) = 35.8000; t(i) = 1.5000; i=i+1
325: y(i) = 21.0000; t(i) = 2.0000; i=i+1
326: y(i) = 21.6700; t(i) = 2.0000; i=i+1
327: y(i) = 21.0000; t(i) = 2.5000; i=i+1
328: y(i) = 15.6400; t(i) = 3.0000; i=i+1
329: y(i) = 8.1700; t(i) = 4.0000; i=i+1
330: y(i) = 8.5500; t(i) = 5.0000; i=i+1
331: y(i) = 10.1200; t(i) = 6.0000; i=i+1
332: y(i) = 78.0000; t(i) = .5000; i=i+1
333: y(i) = 66.0000; t(i) = .6250; i=i+1
334: y(i) = 62.0000; t(i) = .7500; i=i+1
335: y(i) = 58.0000; t(i) = .8750; i=i+1
336: y(i) = 47.7000; t(i) = 1.0000; i=i+1
337: y(i) = 37.8000; t(i) = 1.2500; i=i+1
338: y(i) = 20.2000; t(i) = 2.2500; i=i+1
339: y(i) = 21.0700; t(i) = 2.2500; i=i+1
340: y(i) = 13.8700; t(i) = 2.7500; i=i+1
341: y(i) = 9.6700; t(i) = 3.2500; i=i+1
342: y(i) = 7.7600; t(i) = 3.7500; i=i+1
343: y(i) = 5.4400; t(i) = 4.2500; i=i+1
344: y(i) = 4.8700; t(i) = 4.7500; i=i+1
345: y(i) = 4.0100; t(i) = 5.2500; i=i+1
346: y(i) = 3.7500; t(i) = 5.7500; i=i+1
347: y(i) = 24.1900; t(i) = 3.0000; i=i+1
348: y(i) = 25.7600; t(i) = 3.0000; i=i+1
349: y(i) = 18.0700; t(i) = 3.0000; i=i+1
350: y(i) = 11.8100; t(i) = 3.0000; i=i+1
351: y(i) = 12.0700; t(i) = 3.0000; i=i+1
352: y(i) = 16.1200; t(i) = 3.0000; i=i+1
353: y(i) = 70.8000; t(i) = .5000; i=i+1
354: y(i) = 54.7000; t(i) = .7500; i=i+1
355: y(i) = 48.0000; t(i) = 1.0000; i=i+1
356: y(i) = 39.8000; t(i) = 1.5000; i=i+1
357: y(i) = 29.8000; t(i) = 2.0000; i=i+1
358: y(i) = 23.7000; t(i) = 2.5000; i=i+1
359: y(i) = 29.6200; t(i) = 2.0000; i=i+1
360: y(i) = 23.8100; t(i) = 2.5000; i=i+1
361: y(i) = 17.7000; t(i) = 3.0000; i=i+1
362: y(i) = 11.5500; t(i) = 4.0000; i=i+1
363: y(i) = 12.0700; t(i) = 5.0000; i=i+1
364: y(i) = 8.7400; t(i) = 6.0000; i=i+1
365: y(i) = 80.7000; t(i) = .5000; i=i+1
366: y(i) = 61.3000; t(i) = .7500; i=i+1
367: y(i) = 47.5000; t(i) = 1.0000; i=i+1
368: y(i) = 29.0000; t(i) = 1.5000; i=i+1
369: y(i) = 24.0000; t(i) = 2.0000; i=i+1
370: y(i) = 17.7000; t(i) = 2.5000; i=i+1
371: y(i) = 24.5600; t(i) = 2.0000; i=i+1
372: y(i) = 18.6700; t(i) = 2.5000; i=i+1
373: y(i) = 16.2400; t(i) = 3.0000; i=i+1
374: y(i) = 8.7400; t(i) = 4.0000; i=i+1
375: y(i) = 7.8700; t(i) = 5.0000; i=i+1
376: y(i) = 8.5100; t(i) = 6.0000; i=i+1
377: y(i) = 66.7000; t(i) = .5000; i=i+1
378: y(i) = 59.2000; t(i) = .7500; i=i+1
379: y(i) = 40.8000; t(i) = 1.0000; i=i+1
380: y(i) = 30.7000; t(i) = 1.5000; i=i+1
381: y(i) = 25.7000; t(i) = 2.0000; i=i+1
382: y(i) = 16.3000; t(i) = 2.5000; i=i+1
383: y(i) = 25.9900; t(i) = 2.0000; i=i+1
384: y(i) = 16.9500; t(i) = 2.5000; i=i+1
385: y(i) = 13.3500; t(i) = 3.0000; i=i+1
386: y(i) = 8.6200; t(i) = 4.0000; i=i+1
387: y(i) = 7.2000; t(i) = 5.0000; i=i+1
388: y(i) = 6.6400; t(i) = 6.0000; i=i+1
389: y(i) = 13.6900; t(i) = 3.0000; i=i+1
390: y(i) = 81.0000; t(i) = .5000; i=i+1
391: y(i) = 64.5000; t(i) = .7500; i=i+1
392: y(i) = 35.5000; t(i) = 1.5000; i=i+1
393: y(i) = 13.3100; t(i) = 3.0000; i=i+1
394: y(i) = 4.8700; t(i) = 6.0000; i=i+1
395: y(i) = 12.9400; t(i) = 3.0000; i=i+1
396: y(i) = 5.0600; t(i) = 6.0000; i=i+1
397: y(i) = 15.1900; t(i) = 3.0000; i=i+1
398: y(i) = 14.6200; t(i) = 3.0000; i=i+1
399: y(i) = 15.6400; t(i) = 3.0000; i=i+1
400: y(i) = 25.5000; t(i) = 1.7500; i=i+1
401: y(i) = 25.9500; t(i) = 1.7500; i=i+1
402: y(i) = 81.7000; t(i) = .5000; i=i+1
403: y(i) = 61.6000; t(i) = .7500; i=i+1
404: y(i) = 29.8000; t(i) = 1.7500; i=i+1
405: y(i) = 29.8100; t(i) = 1.7500; i=i+1
406: y(i) = 17.1700; t(i) = 2.7500; i=i+1
407: y(i) = 10.3900; t(i) = 3.7500; i=i+1
408: y(i) = 28.4000; t(i) = 1.7500; i=i+1
409: y(i) = 28.6900; t(i) = 1.7500; i=i+1
410: y(i) = 81.3000; t(i) = .5000; i=i+1
411: y(i) = 60.9000; t(i) = .7500; i=i+1
412: y(i) = 16.6500; t(i) = 2.7500; i=i+1
413: y(i) = 10.0500; t(i) = 3.7500; i=i+1
414: y(i) = 28.9000; t(i) = 1.7500; i=i+1
415: y(i) = 28.9500; t(i) = 1.7500; i=i+1
417: return
418: end
420: !/*TEST
421: !
422: ! build:
423: ! requires: !complex
424: !
425: ! test:
426: ! args: -tao_smonitor -tao_max_it 100 -tao_type pounders
427: ! requires: !single
428: ! TODO: too many inconsistent results across machines
429: !
430: !TEST*/