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test.dat
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PROGRAM btwolfe
C
INTEGER N,RESET,LWORK
PARAMETER( N = 2,
* RESET = 7,
* LWORK = (RESET+N+9)*RESET+4*N+10)
C
INTEGER IPRINT,MAXCOM,MAXIT,IFAIL,IOUT
DOUBLE PRECISION F,EPS,FM
C
INTEGER IWORK(RESET)
DOUBLE PRECISION X(N), G(N), WORK(LWORK)
C
EXTERNAL COMPFG
C
X(1)= 5.0D0
X(2)= 4.0D0
C
MAXCOM= 1000
MAXIT = 500
IPRINT= 2
EPS = 1D-8
FM = 0D0
C
CALL COMPFG(N,X,F,G)
C
WRITE(*,*)
WRITE(*,*) 'BT - convex'
WRITE(*,*) '==========='
WRITE(*,*)
WRITE(*,*) 'Wolfe, dimension=2'
WRITE(*,*) '=================='
WRITE(*,*)
WRITE(*,*) 'starting value:', F
WRITE(*,*)
WRITE(*,*) 'optimal value ca. -8'
WRITE(*,*)
C
IOUT=7
OPEN(IOUT,FILE='iter.out',STATUS='NEW')
CALL BT (N,X,F,G,COMPFG,FM,EPS,MAXCOM,MAXIT,RESET,
* IPRINT,IFAIL,IWORK,WORK,LWORK,IOUT)
C
CLOSE(IOUT)
WRITE(*,*)
WRITE(*,*)
* 'number of iterations : ',MAXIT
WRITE(*,*)
* 'number of function/subgradient-evaluations: ',MAXCOM
WRITE(*,*) 'ifail:',IFAIL
WRITE(*,*)
WRITE(*,'(A)') ' solution x = '
WRITE(*,'(2E16.8)') X
WRITE(*,'(A,E16.8)') ' optimal value f(x) = ', F
END
C===================================================================
SUBROUTINE COMPFG(N,X,F,G)
C Wolfe
C
INTEGER N
DOUBLE PRECISION F
DOUBLE PRECISION X(N),G(N)
C
DOUBLE PRECISION GH
C
IF (X(1) .GE. ABS(X(2))) THEN
F= 5.0D0*DSQRT(9.0D0*X(1)**2+16.0D0*X(2)**2)
ELSEIF (X(1) .GT. 0) THEN
F= 9.0D0*X(1)+16.0D0*DABS(X(2))
ELSE
F= 9.0D0*X(1)+16.0D0*DABS(X(2))-X(1)**9
ENDIF
C
IF (X(1) .GE. DABS(X(2))) THEN
GH= 2.5D0/(DSQRT(9.0D0*X(1)**2+16.0D0*X(2)**2))
G(1)= 18.0D0*X(1)*GH
G(2)= 32.0D0*X(2)*GH
ELSEIF (X(1) .GT. 0) THEN
G(1)=9.0D0
IF (X(2) .GE. 0.0D0) THEN
G(2)= 16.0D0
ELSE
G(2)=-16.0D0
ENDIF
ELSE
G(1)= 9.0D0-9.0D0*X(1)**8
IF (X(2) .GE. 0.0D0) THEN
G(2)= 16.0D0
ELSE
G(2)=-16.0D0
ENDIF
ENDIF
END
BT - convex
===========
Wolfe, dimension=2
==================
starting value: 109.65856099730654
optimal value ca. -8
BT-Algorithm
============
workspace provided is WORK( 144 )
to solve problem we need WORK( 144 )
niter ncomp f gn alpha
1 1 0.10965856E+03 0.17836460E+02 0.00000000E+00
2 6 0.56573486E+02 0.11999766E+02 0.32385779E+02
3 12 0.35548245E+02 0.60347736E+01 0.30988498E+02
4 13 -0.67327740E+01 0.74758293E-01 0.21642451E+01
5 14 -0.72060770E+01 0.53161228E-01 0.15737130E+01
6 15 -0.74595865E+01 0.16915462E-01 0.66286472E+00
7 16 -0.77890028E+01 0.40042920E-02 0.29740037E+00
Reset, nn= 3
8 20 -0.79927891E+01 0.19131175E-05 0.46905312E-01
9 21 -0.79927891E+01 0.16516088E-04 0.24940170E-01
10 22 -0.79955982E+01 0.45133547E-06 0.11065726E-01
11 23 -0.79955982E+01 0.80139442E-05 0.10029670E-01
Reset, nn= 3
12 24 -0.79999333E+01 0.41925884E-05 0.51534885E-02
13 25 -0.79999333E+01 0.20475936E-05 0.17196602E-02
14 26 -0.79999333E+01 0.10348634E-05 0.20239842E-03
15 27 -0.79999333E+01 0.10290328E-05 0.16582637E-03
Reset, nn= 3
16 28 -0.79999333E+01 0.25654830E-05 0.14768667E-03
17 29 -0.79999333E+01 0.12856837E-05 0.79562772E-04
18 30 -0.79999782E+01 0.64063313E-06 0.34142238E-04
19 31 -0.79999954E+01 0.69233448E-09 0.84872307E-05
Reset, nn= 3
20 32 -0.79999954E+01 0.32013163E-06 0.61516343E-05
21 33 -0.79999994E+01 0.16011182E-06 0.11686962E-05
22 34 -0.80000000E+01 0.22115677E-10 0.27071083E-06
23 35 -0.80000000E+01 0.80023159E-07 0.42838004E-07
Reset, nn= 3
24 36 -0.80000000E+01 0.80023159E-07 0.42838004E-07
25 37 -0.80000000E+01 0.80034685E-07 0.21422425E-07
26 38 -0.80000000E+01 0.80034685E-07 0.21422425E-07
27 39 -0.80000000E+01 0.80034685E-07 0.21422425E-07
Reset, nn= 3
28 40 -0.80000000E+01 0.16006369E-06 0.21419971E-07
29 41 -0.80000000E+01 0.16006369E-06 0.21419971E-07
30 42 -0.80000000E+01 0.16006369E-06 0.21419971E-07
31 43 -0.80000000E+01 0.16008416E-06 0.10712147E-07
Reset, nn= 3
32 44 -0.80000000E+01 0.80016913E-07 0.53558316E-08
33 45 -0.80000000E+01 0.40018073E-07 0.36683917E-08
34 46 -0.80000000E+01 0.20500125E-10 0.44958190E-08
convergence
number of iterations : 34
number of function/subgradient-evaluations: 46
ifail: 0
solution x =
-0.10000056E+01 -0.54914406E-13
optimal value f(x) = -0.80000000E+01