c                                                                                      
c  L-BFGS-B is released under the “New BSD License” (aka “Modified BSD License”        
c  or “3-clause license”)                                                              
c  Please read attached file License.txt                                               
c                                        
c                             DRIVER 1 in Fortran 77
c     --------------------------------------------------------------
c                SIMPLE DRIVER FOR L-BFGS-B (version 3.0)
c     --------------------------------------------------------------
c
c        L-BFGS-B is a code for solving large nonlinear optimization
c             problems with simple bounds on the variables.
c
c        The code can also be used for unconstrained problems and is
c        as efficient for these problems as the earlier limited memory
c                          code L-BFGS.
c
c        This is the simplest driver in the package. It uses all the
c                    default settings of the code.
c
c
c     References:
c
c        [1] R. H. Byrd, P. Lu, J. Nocedal and C. Zhu, ``A limited
c        memory algorithm for bound constrained optimization'',
c        SIAM J. Scientific Computing 16 (1995), no. 5, pp. 1190--1208.
c
c        [2] C. Zhu, R.H. Byrd, P. Lu, J. Nocedal, ``L-BFGS-B: FORTRAN
c        Subroutines for Large Scale Bound Constrained Optimization''
c        Tech. Report, NAM-11, EECS Department, Northwestern University,
c        1994.
c
c
c          (Postscript files of these papers are available via anonymous
c           ftp to eecs.nwu.edu in the directory pub/lbfgs/lbfgs_bcm.)
c
c                              *  *  *
c
c         March 2011   (latest revision)
c         Optimization Center at Northwestern University
c         Instituto Tecnologico Autonomo de Mexico
c
c         Jorge Nocedal and Jose Luis Morales, Remark on "Algorithm 778: 
c         L-BFGS-B: Fortran Subroutines for Large-Scale Bound Constrained 
c         Optimization"  (2011). To appear in  ACM Transactions on 
c         Mathematical Software,

c     --------------------------------------------------------------
c             DESCRIPTION OF THE VARIABLES IN L-BFGS-B
c     --------------------------------------------------------------
c
c     n is an INTEGER variable that must be set by the user to the
c       number of variables.  It is not altered by the routine.
c
c     m is an INTEGER variable that must be set by the user to the
c       number of corrections used in the limited memory matrix.
c       It is not altered by the routine.  Values of m < 3  are
c       not recommended, and large values of m can result in excessive
c       computing time. The range  3 <= m <= 20 is recommended. 
c
c     x is a DOUBLE PRECISION array of length n.  On initial entry
c       it must be set by the user to the values of the initial
c       estimate of the solution vector.  Upon successful exit, it
c       contains the values of the variables at the best point
c       found (usually an approximate solution).
c
c     l is a DOUBLE PRECISION array of length n that must be set by
c       the user to the values of the lower bounds on the variables. If
c       the i-th variable has no lower bound, l(i) need not be defined.
c
c     u is a DOUBLE PRECISION array of length n that must be set by
c       the user to the values of the upper bounds on the variables. If
c       the i-th variable has no upper bound, u(i) need not be defined.
c
c     nbd is an INTEGER array of dimension n that must be set by the
c       user to the type of bounds imposed on the variables:
c       nbd(i)=0 if x(i) is unbounded,
c              1 if x(i) has only a lower bound,
c              2 if x(i) has both lower and upper bounds, 
c              3 if x(i) has only an upper bound.
c
c     f is a DOUBLE PRECISION variable.  If the routine setulb returns
c       with task(1:2)= 'FG', then f must be set by the user to
c       contain the value of the function at the point x.
c
c     g is a DOUBLE PRECISION array of length n.  If the routine setulb
c       returns with taskb(1:2)= 'FG', then g must be set by the user to
c       contain the components of the gradient at the point x.
c
c     factr is a DOUBLE PRECISION variable that must be set by the user.
c       It is a tolerance in the termination test for the algorithm.
c       The iteration will stop when
c
c        (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr*epsmch
c
c       where epsmch is the machine precision which is automatically
c       generated by the code. Typical values for factr on a computer
c       with 15 digits of accuracy in double precision are:
c       factr=1.d+12 for low accuracy;
c             1.d+7  for moderate accuracy; 
c             1.d+1  for extremely high accuracy.
c       The user can suppress this termination test by setting factr=0.
c
c     pgtol is a double precision variable.
c       On entry pgtol >= 0 is specified by the user.  The iteration
c         will stop when
c
c                 max{|proj g_i | i = 1, ..., n} <= pgtol
c
c         where pg_i is the ith component of the projected gradient.
c       The user can suppress this termination test by setting pgtol=0.
c
c     wa is a DOUBLE PRECISION  array of length 
c       (2mmax + 5)nmax + 11mmax^2 + 8mmax used as workspace.
c       This array must not be altered by the user.
c
c     iwa is an INTEGER  array of length 3nmax used as
c       workspace. This array must not be altered by the user.
c
c     task is a CHARACTER string of length 60.
c       On first entry, it must be set to 'START'.
c       On a return with task(1:2)='FG', the user must evaluate the
c         function f and gradient g at the returned value of x.
c       On a return with task(1:5)='NEW_X', an iteration of the
c         algorithm has concluded, and f and g contain f(x) and g(x)
c         respectively.  The user can decide whether to continue or stop
c         the iteration. 
c       When
c         task(1:4)='CONV', the termination test in L-BFGS-B has been 
c           satisfied;
c         task(1:4)='ABNO', the routine has terminated abnormally
c           without being able to satisfy the termination conditions,
c           x contains the best approximation found,
c           f and g contain f(x) and g(x) respectively;
c         task(1:5)='ERROR', the routine has detected an error in the
c           input parameters;
c       On exit with task = 'CONV', 'ABNO' or 'ERROR', the variable task
c         contains additional information that the user can print.
c       This array should not be altered unless the user wants to
c          stop the run for some reason.  See driver2 or driver3
c          for a detailed explanation on how to stop the run 
c          by assigning task(1:4)='STOP' in the driver.
c
c     iprint is an INTEGER variable that must be set by the user.
c       It controls the frequency and type of output generated:
c        iprint<0    no output is generated;
c        iprint=0    print only one line at the last iteration;
c        0<iprint<99 print also f and |proj g| every iprint iterations;
c        iprint=99   print details of every iteration except n-vectors;
c        iprint=100  print also the changes of active set and final x;
c        iprint>100  print details of every iteration including x and g;
c       When iprint > 0, the file iterate.dat will be created to
c                        summarize the iteration.
c
c     csave  is a CHARACTER working array of length 60.
c
c     lsave is a LOGICAL working array of dimension 4.
c       On exit with task = 'NEW_X', the following information is
c         available:
c       lsave(1) = .true.  the initial x did not satisfy the bounds;
c       lsave(2) = .true.  the problem contains bounds;
c       lsave(3) = .true.  each variable has upper and lower bounds.
c
c     isave is an INTEGER working array of dimension 44.
c       On exit with task = 'NEW_X', it contains information that
c       the user may want to access:
c         isave(30) = the current iteration number;
c         isave(34) = the total number of function and gradient
c                         evaluations;
c         isave(36) = the number of function value or gradient
c                                  evaluations in the current iteration;
c         isave(38) = the number of free variables in the current
c                         iteration;
c         isave(39) = the number of active constraints at the current
c                         iteration;
c
c         see the subroutine setulb.f for a description of other 
c         information contained in isave
c
c     dsave is a DOUBLE PRECISION working array of dimension 29.
c       On exit with task = 'NEW_X', it contains information that
c         the user may want to access:
c         dsave(2) = the value of f at the previous iteration;
c         dsave(5) = the machine precision epsmch generated by the code;
c         dsave(13) = the infinity norm of the projected gradient;
c
c         see the subroutine setulb.f for a description of other 
c         information contained in dsave
c
c     --------------------------------------------------------------
c           END OF THE DESCRIPTION OF THE VARIABLES IN L-BFGS-B
c     --------------------------------------------------------------

      program driver
 
c     This simple driver demonstrates how to call the L-BFGS-B code to
c       solve a sample problem (the extended Rosenbrock function 
c       subject to bounds on the variables). The dimension n of this
c       problem is variable.
 
      integer          nmax, mmax
      parameter        (nmax=1024, mmax=17)
c        nmax is the dimension of the largest problem to be solved.
c        mmax is the maximum number of limited memory corrections.
 
c     Declare the variables needed by the code.
c       A description of all these variables is given at the end of 
c       the driver.
 
      character*60     task, csave
      logical          lsave(4)
      integer          n, m, iprint,
     +                 nbd(nmax), iwa(3*nmax), isave(44)
      double precision f, factr, pgtol, 
     +                 x(nmax), l(nmax), u(nmax), g(nmax), dsave(29), 
     +                 wa(2*mmax*nmax + 5*nmax + 11*mmax*mmax + 8*mmax)

c     Declare a few additional variables for this sample problem.

      double precision t1, t2
      integer          i
 
c     We wish to have output at every iteration.

      iprint = 1

c     We specify the tolerances in the stopping criteria.

      factr=1.0d+7
      pgtol=1.0d-5

c     We specify the dimension n of the sample problem and the number
c        m of limited memory corrections stored.  (n and m should not
c        exceed the limits nmax and mmax respectively.)
 
      n=25
      m=5
 
c     We now provide nbd which defines the bounds on the variables:
c                    l   specifies the lower bounds,
c                    u   specifies the upper bounds. 
 
c     First set bounds on the odd-numbered variables.

      do 10 i=1,n,2
         nbd(i)=2
         l(i)=1.0d0
         u(i)=1.0d2
  10  continue

c     Next set bounds on the even-numbered variables.

      do 12 i=2,n,2
         nbd(i)=2
         l(i)=-1.0d2
         u(i)=1.0d2
  12   continue

c     We now define the starting point.

      do 14 i=1,n
         x(i)=3.0d0
  14  continue
 
      write (6,16)
  16  format(/,5x, 'Solving sample problem.',
     +       /,5x, ' (f = 0.0 at the optimal solution.)',/)

c     We start the iteration by initializing task.
c 
      task = 'START'

c        ------- the beginning of the loop ----------
 
 111  continue
      
c     This is the call to the L-BFGS-B code.
 
      call setulb(n,m,x,l,u,nbd,f,g,factr,pgtol,wa,iwa,task,iprint,
     +            csave,lsave,isave,dsave)
 
      if (task(1:2) .eq. 'FG') then
c        the minimization routine has returned to request the
c        function f and gradient g values at the current x.

c        Compute function value f for the sample problem.

         f=.25d0*(x(1)-1.d0)**2
         do 20 i=2,n
            f=f+(x(i)-x(i-1)**2)**2
  20     continue
         f=4.d0*f

c        Compute gradient g for the sample problem.

         t1=x(2)-x(1)**2
         g(1)=2.d0*(x(1)-1.d0)-1.6d1*x(1)*t1
         do 22 i=2,n-1
            t2=t1
            t1=x(i+1)-x(i)**2
            g(i)=8.d0*t2-1.6d1*x(i)*t1
  22     continue
         g(n)=8.d0*t1

c          go back to the minimization routine.
         goto 111
      endif
c
      if (task(1:5) .eq. 'NEW_X')  goto 111
c        the minimization routine has returned with a new iterate,
c         and we have opted to continue the iteration.

c           ---------- the end of the loop -------------
 
c     If task is neither FG nor NEW_X we terminate execution.

      stop
 
      end

c======================= The end of driver1 ============================
