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lsline.F
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Sun, Jul 6, 12:36

lsline.F
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subroutine lsline(
& simul
& , nn, ifail, lphprint
& , ifunc, nfunc
& , ff, dotdg
& , tmin, tmax, tact, epsx
& , dd, gg, xx, xdiff
& )
c ==================================================================
c SUBROUTINE lsline
c ==================================================================
c
c o line search algorithm for determining control vector update;
c After computing updated control vector from given gradient,
c a forward and adjoint model run are performed (simul.F)
c using the updated control vector.
c Tests are then applied to see whether solution has improved.
c
c o Reference: J.C. Gilbert & C. Lemarechal
c Some numerical experiments with variable-storage
c quasi-Newton algorithms
c Mathematical Programming 45 (1989), pp. 407-435
c
c o started: ??? not reproducible
c
c o changed: Patrick Heimbach, MIT/EAPS
c
c o Version: 2.0, 24-Feb-2000: Patrick Heimbach, MIT/EAPS
c - severe changes in structure including various
c shifts of variables which are only used in this
c routine
c - number of 3 control flags for error handling
c (indic, moderl, ifail) reduced to 1 (ifail)
c and homogenized with routine lsoptv
c
c o Version: 2.1.0, 02-Mar-2000: Patrick Heimbach, MIT/EAPS
c - initial computation of tact and
c xdiff = xx + tact*dd
c moved to new routine lsupdxx
c tmin, tmax, tact needed as parameters
c
c ==================================================================
c SUBROUTINE lsline
c ==================================================================
#include "blas1.h"
implicit none
c----------------------------------
c declare arguments
c----------------------------------
integer nn, ifail, ifunc, nfunc
double precision ff, dotdg, tmin, tmax, tact, epsx
double precision xx(nn), dd(nn), gg(nn), xdiff(nn)
logical lphprint
external simul
c----------------------------------
c declare local variables
c----------------------------------
double precision xpara1, xpara2
parameter( xpara1 = 0.0001, xpara2=0.9 )
double precision factor
parameter( factor = 0.2 )
double precision barmax
parameter( barmax = 0.3 )
double precision barmul
parameter( barmul = 5.0 )
double precision barmin
parameter( barmin = 0.01 )
integer i, indic
double precision tg, fg, td, ta
double precision fa, fpa, fp
double precision fnew, fdiff
double precision z, test, barr
double precision left, right, told
external DDOT
double precision DDOT
c----------------------------------
c check parameters
c----------------------------------
if ( (nn.le.0)
& .or. (dotdg.ge.0.0)
& .or. (xpara1.le.0.0) .or. (xpara1.ge.0.5)
& .or. (xpara2.le.xpara1) .or. (xpara2.ge.1.0) ) then
ifail = 9
go to 999
endif
c----------------------------------
c initialization
c----------------------------------
indic = 0
barr = barmin
fg = ff
fa = ff
fpa = dotdg
td = 0.0
tg = 0.0
ta = 0.0
c=======================================================================
c begin of simulation iter.
c=======================================================================
do ifunc = 1, nfunc
if (lphprint)
& print *, 'pathei-lsopt: ', ifunc, ' simul.'
c------------------------------------
c compute cost function and gradient
c------------------------------------
call simul( indic, nn, xdiff, fnew, gg )
fp = DDOT( nn, dd, 1, gg, 1 )
fdiff = fnew - ff
c-----------------------------------------
c apply 1st Wolfe test
c-----------------------------------------
if (fdiff .gt. tact*xpara1*dotdg) then
td = tact
ifail = 0
go to 500
endif
c-----------------------------------------
c 1st Wolfe test 1 ok, apply 2nd Wolf test
c-----------------------------------------
if (fp .gt. xpara2*dotdg) then
ifail = 0
go to 320
endif
if (ifail.eq.0) go to 350
c-----------------------------------------
c 2nd Wolfe test 2 ok, donc pas serieux, on sort
c-----------------------------------------
320 continue
ff = fnew
do i = 1, nn
xx(i) = xdiff(i)
end do
cph(
if (lphprint)
& print *, 'pathei-lsopt: no inter-/extrapolation in lsline'
cph)
go to 999
c-----------------------------------------
c extrapolation
c-----------------------------------------
350 continue
tg = tact
fg = fnew
if (td .ne. 0.0) go to 500
told = tact
left = (1.0+barmin)*tact
right = 10.0*tact
call cubic( tact, fnew, fp, ta, fa, fpa, left, right )
ta = told
if (tact.ge.tmax) then
ifail = 7
tact = tmax
endif
if (lphprint)
& print *, 'pathei-lsopt: extrapolation: ',
& 'td, tg, tact, ifail = ', td, tg, tact, ifail
go to 900
c-----------------------------------------
c interpolation
c-----------------------------------------
500 continue
test = barr*(td-tg)
left = tg+test
right = td-test
told = tact
call cubic( tact, fnew, fp, ta, fa, fpa, left, right )
ta = told
if (tact.gt.left .and. tact.lt.right) then
barr = dmax1( barmin, barr/barmul )
else
barr = dmin1( barmul*barr, barmax )
endif
if (lphprint)
& print *, 'pathei-lsopt: interpolation: ',
& 'td, tg, tact, ifail = ', td, tg, tact, ifail
c-----------------------------------------
c end of iteration loop
c - tact peut etre bloque sur tmax
c (venant de lextrapolation avec ifail=7)
c-----------------------------------------
900 continue
fa = fnew
fpa = fp
c-----------------------------------------
c --- faut-il continuer ?
c-----------------------------------------
if (td .eq. 0.0) go to 950
if (td-tg .lt. tmin) go to 920
c-----------------------------------------
c limit due to machine precision
c-----------------------------------------
do i = 1, nn
z = xx(i) + tact*dd(i)
if ((z.ne.xx(i)) .and. (z.ne.xdiff(i))) go to 950
end do
c-----------------------------------------
c arret sur dxmin ou de secours
c-----------------------------------------
920 continue
ifail = 8
c-----------------------------------------
c si tg=0, xx = xx_depart,
c sinon on prend xx=x_left qui fait decroitre f
c-----------------------------------------
if (tg .ne. 0.0) then
ff = fg
do i = 1, nn
xx(i) = xx(i) + tg*dd(i)
end do
endif
go to 999
c-----------------------------------------
c update vector for new simulation
c-----------------------------------------
950 continue
do i = 1, nn
xdiff(i) = xx(i) + tact*dd(i)
end do
c=======================================================================
c end of simulation iter.
c=======================================================================
end do
c-----------------------------------------
c too many function evaluations
c-----------------------------------------
ifail = 6
ff = fg
do i = 1, nn
xx(i) = xx(i) + tg*dd(i)
end do
c-----------------------------------------
c end of routine
c-----------------------------------------
999 continue
return
end

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