minpack.f

      subroutine chkder(m,n,x,fvec,fjac,ldfjac,xp,fvecp,mode,err)
      integer m,n,ldfjac,mode
      double precision x(n),fvec(m),fjac(ldfjac,n),xp(n),fvecp(m),
     *                 err(m)

c*********************************************************************72
c
cc CHKDER checks the gradients of M functions of N variables.
c
c     this subroutine checks the gradients of m nonlinear functions
c     in n variables, evaluated at a point x, for consistency with
c     the functions themselves. the user must call chkder twice,
c     first with mode = 1 and then with mode = 2.
c
c     mode = 1. on input, x must contain the point of evaluation.
c               on output, xp is set to a neighboring point.
c
c     mode = 2. on input, fvec must contain the functions and the
c                         rows of fjac must contain the gradients
c                         of the respective functions each evaluated
c                         at x, and fvecp must contain the functions
c                         evaluated at xp.
c               on output, err contains measures of correctness of
c                          the respective gradients.
c
c     the subroutine does not perform reliably if cancellation or
c     rounding errors cause a severe loss of significance in the
c     evaluation of a function. therefore, none of the components
c     of x should be unusually small (in particular, zero) or any
c     other value which may cause loss of significance.
c
c     the subroutine statement is
c
c       subroutine chkder(m,n,x,fvec,fjac,ldfjac,xp,fvecp,mode,err)
c
c     where
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables.
c
c       x is an input array of length n.
c
c       fvec is an array of length m. on input when mode = 2,
c         fvec must contain the functions evaluated at x.
c
c       fjac is an m by n array. on input when mode = 2,
c         the rows of fjac must contain the gradients of
c         the respective functions evaluated at x.
c
c       ldfjac is a positive integer input parameter not less than m
c         which specifies the leading dimension of the array fjac.
c
c       xp is an array of length n. on output when mode = 1,
c         xp is set to a neighboring point of x.
c
c       fvecp is an array of length m. on input when mode = 2,
c         fvecp must contain the functions evaluated at xp.
c
c       mode is an integer input variable set to 1 on the first call
c         and 2 on the second. other values of mode are equivalent
c         to mode = 1.
c
c       err is an array of length m. on output when mode = 2,
c         err contains measures of correctness of the respective
c         gradients. if there is no severe loss of significance,
c         then if err(i) is 1.0 the i-th gradient is correct,
c         while if err(i) is 0.0 the i-th gradient is incorrect.
c         for values of err between 0.0 and 1.0, the categorization
c         is less certain. in general, a value of err(i) greater
c         than 0.5 indicates that the i-th gradient is probably
c         correct, while a value of err(i) less than 0.5 indicates
c         that the i-th gradient is probably incorrect.
c
c     subprograms called
c
c       minpack supplied ... dpmparmp
c
c       fortran supplied ... dabs,dlog10,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j
      double precision eps,epsf,epslog,epsmch,factor,one,temp,zero
      double precision dpmparmp
      data factor,one,zero /1.0d2,1.0d0,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      eps = dsqrt(epsmch)
c
      if (mode .eq. 2) go to 20
c
c        mode = 1.
c
         do 10 j = 1, n
            temp = eps*dabs(x(j))
            if (temp .eq. zero) temp = eps
            xp(j) = x(j) + temp
   10       continue
         go to 70
   20 continue
c
c        mode = 2.
c
         epsf = factor*epsmch
         epslog = dlog10(eps)
         do 30 i = 1, m
            err(i) = zero
   30       continue
         do 50 j = 1, n
            temp = dabs(x(j))
            if (temp .eq. zero) temp = one
            do 40 i = 1, m
               err(i) = err(i) + temp*fjac(i,j)
   40          continue
   50       continue
         do 60 i = 1, m
            temp = one
            if (fvec(i) .ne. zero .and. fvecp(i) .ne. zero
     *          .and. dabs(fvecp(i)-fvec(i)) .ge. epsf*dabs(fvec(i)))
     *         temp = eps*dabs((fvecp(i)-fvec(i))/eps-err(i))
     *                /(dabs(fvec(i)) + dabs(fvecp(i)))
            err(i) = one
            if (temp .gt. epsmch .and. temp .lt. eps)
     *         err(i) = (dlog10(temp) - epslog)/epslog
            if (temp .ge. eps) err(i) = zero
   60       continue
   70 continue
c
      return
c
c     last card of subroutine chkder.
c
      end
      subroutine dogleg(n,r,lr,diag,qtb,delta,x,wa1,wa2)
      integer n,lr
      double precision delta
      double precision r(lr),diag(n),qtb(n),x(n),wa1(n),wa2(n)

c*********************************************************************72
c
cc DOGLEG finds the minimizing combination of Gauss-Newton and gradient steps.
c
c     given an m by n matrix a, an n by n nonsingular diagonal
c     matrix d, an m-vector b, and a positive number delta, the
c     problem is to determine the convex combination x of the
c     gauss-newton and scaled gradient directions that minimizes
c     (a*x - b) in the least squares sense, subject to the
c     restriction that the euclidean norm of d*x be at most delta.
c
c     this subroutine completes the solution of the problem
c     if it is provided with the necessary information from the
c     qr factorization of a. that is, if a = q*r, where q has
c     orthogonal columns and r is an upper triangular matrix,
c     then dogleg expects the full upper triangle of r and
c     the first n components of (q transpose)*b.
c
c     the subroutine statement is
c
c       subroutine dogleg(n,r,lr,diag,qtb,delta,x,wa1,wa2)
c
c     where
c
c       n is a positive integer input variable set to the order of r.
c
c       r is an input array of length lr which must contain the upper
c         triangular matrix r stored by rows.
c
c       lr is a positive integer input variable not less than
c         (n*(n+1))/2.
c
c       diag is an input array of length n which must contain the
c         diagonal elements of the matrix d.
c
c       qtb is an input array of length n which must contain the first
c         n elements of the vector (q transpose)*b.
c
c       delta is a positive input variable which specifies an upper
c         bound on the euclidean norm of d*x.
c
c       x is an output array of length n which contains the desired
c         convex combination of the gauss-newton direction and the
c         scaled gradient direction.
c
c       wa1 and wa2 are work arrays of length n.
c
c     subprograms called
c
c       minpack-supplied ... dpmparmp,enormmp
c
c       fortran-supplied ... dabs,dmax1,dmin1,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j,jj,jp1,k,l
      double precision alpha,bnorm,epsmch,gnorm,one,qnorm,sgnorm,sum,
     *                 temp,zero
      double precision dpmparmp,enormmp
      data one,zero /1.0d0,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
c     first, calculate the gauss-newton direction.
c
      jj = (n*(n + 1))/2 + 1
      do 50 k = 1, n
         j = n - k + 1
         jp1 = j + 1
         jj = jj - k
         l = jj + 1
         sum = zero
         if (n .lt. jp1) go to 20
         do 10 i = jp1, n
            sum = sum + r(l)*x(i)
            l = l + 1
   10       continue
   20    continue
         temp = r(jj)
         if (temp .ne. zero) go to 40
         l = j
         do 30 i = 1, j
            temp = dmax1(temp,dabs(r(l)))
            l = l + n - i
   30       continue
         temp = epsmch*temp
         if (temp .eq. zero) temp = epsmch
   40    continue
         x(j) = (qtb(j) - sum)/temp
   50    continue
c
c     test whether the gauss-newton direction is acceptable.
c
      do 60 j = 1, n
         wa1(j) = zero
         wa2(j) = diag(j)*x(j)
   60    continue
      qnorm = enormmp(n,wa2)
      if (qnorm .le. delta) go to 140
c
c     the gauss-newton direction is not acceptable.
c     next, calculate the scaled gradient direction.
c
      l = 1
      do 80 j = 1, n
         temp = qtb(j)
         do 70 i = j, n
            wa1(i) = wa1(i) + r(l)*temp
            l = l + 1
   70       continue
         wa1(j) = wa1(j)/diag(j)
   80    continue
c
c     calculate the norm of the scaled gradient and test for
c     the special case in which the scaled gradient is zero.
c
      gnorm = enormmp(n,wa1)
      sgnorm = zero
      alpha = delta/qnorm
      if (gnorm .eq. zero) go to 120
c
c     calculate the point along the scaled gradient
c     at which the quadratic is minimized.
c
      do 90 j = 1, n
         wa1(j) = (wa1(j)/gnorm)/diag(j)
   90    continue
      l = 1
      do 110 j = 1, n
         sum = zero
         do 100 i = j, n
            sum = sum + r(l)*wa1(i)
            l = l + 1
  100       continue
         wa2(j) = sum
  110    continue
      temp = enormmp(n,wa2)
      sgnorm = (gnorm/temp)/temp
c
c     test whether the scaled gradient direction is acceptable.
c
      alpha = zero
      if (sgnorm .ge. delta) go to 120
c
c     the scaled gradient direction is not acceptable.
c     finally, calculate the point along the dogleg
c     at which the quadratic is minimized.
c
      bnorm = enormmp(n,qtb)
      temp = (bnorm/gnorm)*(bnorm/qnorm)*(sgnorm/delta)
      temp = temp - (delta/qnorm)*(sgnorm/delta)**2
     *       + dsqrt((temp-(delta/qnorm))**2
     *               +(one-(delta/qnorm)**2)*(one-(sgnorm/delta)**2))
      alpha = ((delta/qnorm)*(one - (sgnorm/delta)**2))/temp
  120 continue
c
c     form appropriate convex combination of the gauss-newton
c     direction and the scaled gradient direction.
c
      temp = (one - alpha)*dmin1(sgnorm,delta)
      do 130 j = 1, n
         x(j) = temp*wa1(j) + alpha*x(j)
  130    continue
  140 continue
      return
c
c     last card of subroutine dogleg.
c
      end
      double precision function dpmparmp(i)
      integer i

c*********************************************************************72
c
cc DPMPAR provides double precision machine parameters.
c
c     This function provides double precision machine parameters
c     when the appropriate set of data statements is activated (by
c     removing the c from column 1) and all other data statements are
c     rendered inactive. Most of the parameter values were obtained
c     from the corresponding Bell Laboratories Port Library function.
c
c     The function statement is
c
c       double precision function dpmparmp(i)
c
c     where
c
c       i is an integer input variable set to 1, 2, or 3 which
c         selects the desired machine parameter. If the machine has
c         t base b digits and its smallest and largest exponents are
c         emin and emax, respectively, then these parameters are
c
c         dpmparmp(1) = b**(1 - t), the machine precision,
c
c         dpmparmp(2) = b**(emin - 1), the smallest magnitude,
c
c         dpmparmp(3) = b**emax*(1 - b**(-t)), the largest magnitude.
c
c     Argonne National Laboratory. MINPACK Project. November 1996.
c     Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More'
c
c     **********
      integer mcheps(4)
      integer minmag(4)
      integer maxmag(4)
      double precision dmach(3)
      equivalence (dmach(1),mcheps(1))
      equivalence (dmach(2),minmag(1))
      equivalence (dmach(3),maxmag(1))
c
c     Machine constants for the IBM 360/370 series,
c     the Amdahl 470/V6, the ICL 2900, the Itel AS/6,
c     the Xerox Sigma 5/7/9 and the Sel systems 85/86.
c
c     data mcheps(1),mcheps(2) / z34100000, z00000000 /
c     data minmag(1),minmag(2) / z00100000, z00000000 /
c     data maxmag(1),maxmag(2) / z7fffffff, zffffffff /
c
c     Machine constants for the Honeywell 600/6000 series.
c
c     data mcheps(1),mcheps(2) / o606400000000, o000000000000 /
c     data minmag(1),minmag(2) / o402400000000, o000000000000 /
c     data maxmag(1),maxmag(2) / o376777777777, o777777777777 /
c
c     Machine constants for the CDC 6000/7000 series.
c
c     data mcheps(1) / 15614000000000000000b /
c     data mcheps(2) / 15010000000000000000b /
c
c     data minmag(1) / 00604000000000000000b /
c     data minmag(2) / 00000000000000000000b /
c
c     data maxmag(1) / 37767777777777777777b /
c     data maxmag(2) / 37167777777777777777b /
c
c     Machine constants for the PDP-10 (KA processor).
c
c     data mcheps(1),mcheps(2) / "114400000000, "000000000000 /
c     data minmag(1),minmag(2) / "033400000000, "000000000000 /
c     data maxmag(1),maxmag(2) / "377777777777, "344777777777 /
c
c     Machine constants for the PDP-10 (KI processor).
c
c     data mcheps(1),mcheps(2) / "104400000000, "000000000000 /
c     data minmag(1),minmag(2) / "000400000000, "000000000000 /
c     data maxmag(1),maxmag(2) / "377777777777, "377777777777 /
c
c     Machine constants for the PDP-11. 
c
c     data mcheps(1),mcheps(2) /   9472,      0 /
c     data mcheps(3),mcheps(4) /      0,      0 /
c
c     data minmag(1),minmag(2) /    128,      0 /
c     data minmag(3),minmag(4) /      0,      0 /
c
c     data maxmag(1),maxmag(2) /  32767,     -1 /
c     data maxmag(3),maxmag(4) /     -1,     -1 /
c
c     Machine constants for the Burroughs 6700/7700 systems.
c
c     data mcheps(1) / o1451000000000000 /
c     data mcheps(2) / o0000000000000000 /
c
c     data minmag(1) / o1771000000000000 /
c     data minmag(2) / o7770000000000000 /
c
c     data maxmag(1) / o0777777777777777 /
c     data maxmag(2) / o7777777777777777 /
c
c     Machine constants for the Burroughs 5700 system.
c
c     data mcheps(1) / o1451000000000000 /
c     data mcheps(2) / o0000000000000000 /
c
c     data minmag(1) / o1771000000000000 /
c     data minmag(2) / o0000000000000000 /
c
c     data maxmag(1) / o0777777777777777 /
c     data maxmag(2) / o0007777777777777 /
c
c     Machine constants for the Burroughs 1700 system.
c
c     data mcheps(1) / zcc6800000 /
c     data mcheps(2) / z000000000 /
c
c     data minmag(1) / zc00800000 /
c     data minmag(2) / z000000000 /
c
c     data maxmag(1) / zdffffffff /
c     data maxmag(2) / zfffffffff /
c
c     Machine constants for the Univac 1100 series.
c
c     data mcheps(1),mcheps(2) / o170640000000, o000000000000 /
c     data minmag(1),minmag(2) / o000040000000, o000000000000 /
c     data maxmag(1),maxmag(2) / o377777777777, o777777777777 /
c
c     Machine constants for the Data General Eclipse S/200.
c
c     Note - it may be appropriate to include the following card -
c     static dmach(3)
c
c     data minmag/20k,3*0/,maxmag/77777k,3*177777k/
c     data mcheps/32020k,3*0/
c
c     Machine constants for the Harris 220.
c
c     data mcheps(1),mcheps(2) / '20000000, '00000334 /
c     data minmag(1),minmag(2) / '20000000, '00000201 /
c     data maxmag(1),maxmag(2) / '37777777, '37777577 /
c
c     Machine constants for the Cray-1.
c
c     data mcheps(1) / 0376424000000000000000b /
c     data mcheps(2) / 0000000000000000000000b /
c
c     data minmag(1) / 0200034000000000000000b /
c     data minmag(2) / 0000000000000000000000b /
c
c     data maxmag(1) / 0577777777777777777777b /
c     data maxmag(2) / 0000007777777777777776b /
c
c     Machine constants for the Prime 400.
c
c     data mcheps(1),mcheps(2) / :10000000000, :00000000123 /
c     data minmag(1),minmag(2) / :10000000000, :00000100000 /
c     data maxmag(1),maxmag(2) / :17777777777, :37777677776 /
c
c     Machine constants for the VAX-11.
c
c     data mcheps(1),mcheps(2) /   9472,  0 /
c     data minmag(1),minmag(2) /    128,  0 /
c     data maxmag(1),maxmag(2) / -32769, -1 /
c
c     Machine constants for IEEE machines.
c
      data dmach(1) /2.22044604926d-16/
      data dmach(2) /2.22507385852d-308/
      data dmach(3) /1.79769313485d+308/
c
      dpmparmp = dmach(i)
      return
c
c     Last card of function dpmparmp.
c
      end
      double precision function enormmp(n,x)
      integer n
      double precision x(n)

c*********************************************************************72
c
cc ENORM computes the Euclidean norm of a vector.
c
c     given an n-vector x, this function calculates the
c     euclidean norm of x.
c
c     the euclidean norm is computed by accumulating the sum of
c     squares in three different sums. the sums of squares for the
c     small and large components are scaled so that no overflows
c     occur. non-destructive underflows are permitted. underflows
c     and overflows do not occur in the computation of the unscaled
c     sum of squares for the intermediate components.
c     the definitions of small, intermediate and large components
c     depend on two constants, rdwarf and rgiant. the main
c     restrictions on these constants are that rdwarf**2 not
c     underflow and rgiant**2 not overflow. the constants
c     given here are suitable for every known computer.
c
c     the function statement is
c
c       double precision function enormmp(n,x)
c
c     where
c
c       n is a positive integer input variable.
c
c       x is an input array of length n.
c
c     subprograms called
c
c       fortran-supplied ... dabs,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i
      double precision agiant,floatn,one,rdwarf,rgiant,s1,s2,s3,xabs,
     *                 x1max,x3max,zero
      data one,zero,rdwarf,rgiant /1.0d0,0.0d0,3.834d-20,1.304d19/
      s1 = zero
      s2 = zero
      s3 = zero
      x1max = zero
      x3max = zero
      floatn = n
      agiant = rgiant/floatn
      do 90 i = 1, n
         xabs = dabs(x(i))
         if (xabs .gt. rdwarf .and. xabs .lt. agiant) go to 70
            if (xabs .le. rdwarf) go to 30
c
c              sum for large components.
c
               if (xabs .le. x1max) go to 10
                  s1 = one + s1*(x1max/xabs)**2
                  x1max = xabs
                  go to 20
   10          continue
                  s1 = s1 + (xabs/x1max)**2
   20          continue
               go to 60
   30       continue
c
c              sum for small components.
c
               if (xabs .le. x3max) go to 40
                  s3 = one + s3*(x3max/xabs)**2
                  x3max = xabs
                  go to 50
   40          continue
                  if (xabs .ne. zero) s3 = s3 + (xabs/x3max)**2
   50          continue
   60       continue
            go to 80
   70    continue
c
c           sum for intermediate components.
c
            s2 = s2 + xabs**2
   80    continue
   90    continue
c
c     calculation of norm.
c
      if (s1 .eq. zero) go to 100
         enormmp = x1max*dsqrt(s1+(s2/x1max)/x1max)
         go to 130
  100 continue
         if (s2 .eq. zero) go to 110
            if (s2 .ge. x3max)
     *         enormmp = dsqrt(s2*(one+(x3max/s2)*(x3max*s3)))
            if (s2 .lt. x3max)
     *         enormmp = dsqrt(x3max*((s2/x3max)+(x3max*s3)))
            go to 120
  110    continue
            enormmp = x3max*dsqrt(s3)
  120    continue
  130 continue
      return
c
c     last card of function enormmp.
c
      end
      subroutine fdjac1(fcn,n,x,fvec,fjac,ldfjac,iflag,ml,mu,epsfcn,
     *                  wa1,wa2)
      integer n,ldfjac,iflag,ml,mu
      double precision epsfcn
      double precision x(n),fvec(n),fjac(ldfjac,n),wa1(n),wa2(n)

c*********************************************************************72
c
cc FDJAC1 estimates an N by N jacobian matrix using forward differences.
c
c     this subroutine computes a forward-difference approximation
c     to the n by n jacobian matrix associated with a specified
c     problem of n functions in n variables. if the jacobian has
c     a banded form, then function evaluations are saved by only
c     approximating the nonzero terms.
c
c     the subroutine statement is
c
c       subroutine fdjac1(fcn,n,x,fvec,fjac,ldfjac,iflag,ml,mu,epsfcn,
c                         wa1,wa2)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions. fcn must be declared
c         in an external statement in the user calling
c         program, and should be written as follows.
c
c         subroutine fcn(n,x,fvec,iflag)
c         integer n,iflag
c         double precision x(n),fvec(n)
c         ----------
c         calculate the functions at x and
c         return this vector in fvec.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of fdjac1.
c         in this case set iflag to a negative integer.
c
c       n is a positive integer input variable set to the number
c         of functions and variables.
c
c       x is an input array of length n.
c
c       fvec is an input array of length n which must contain the
c         functions evaluated at x.
c
c       fjac is an output n by n array which contains the
c         approximation to the jacobian matrix evaluated at x.
c
c       ldfjac is a positive integer input variable not less than n
c         which specifies the leading dimension of the array fjac.
c
c       iflag is an integer variable which can be used to terminate
c         the execution of fdjac1. see description of fcn.
c
c       ml is a nonnegative integer input variable which specifies
c         the number of subdiagonals within the band of the
c         jacobian matrix. if the jacobian is not banded, set
c         ml to at least n - 1.
c
c       epsfcn is an input variable used in determining a suitable
c         step length for the forward-difference approximation. this
c         approximation assumes that the relative errors in the
c         functions are of the order of epsfcn. if epsfcn is less
c         than the machine precision, it is assumed that the relative
c         errors in the functions are of the order of the machine
c         precision.
c
c       mu is a nonnegative integer input variable which specifies
c         the number of superdiagonals within the band of the
c         jacobian matrix. if the jacobian is not banded, set
c         mu to at least n - 1.
c
c       wa1 and wa2 are work arrays of length n. if ml + mu + 1 is at
c         least n, then the jacobian is considered dense, and wa2 is
c         not referenced.
c
c     subprograms called
c
c       minpack-supplied ... dpmparmp
c
c       fortran-supplied ... dabs,dmax1,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j,k,msum
      double precision eps,epsmch,h,temp,zero
      double precision dpmparmp
      data zero /0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      eps = dsqrt(dmax1(epsfcn,epsmch))
      msum = ml + mu + 1
      if (msum .lt. n) go to 40
c
c        computation of dense approximate jacobian.
c
         do 20 j = 1, n
            temp = x(j)
            h = eps*dabs(temp)
            if (h .eq. zero) h = eps
            x(j) = temp + h
            call fcn(n,x,wa1,iflag)
            if (iflag .lt. 0) go to 30
            x(j) = temp
            do 10 i = 1, n
               fjac(i,j) = (wa1(i) - fvec(i))/h
   10          continue
   20       continue
   30    continue
         go to 110
   40 continue
c
c        computation of banded approximate jacobian.
c
         do 90 k = 1, msum
            do 60 j = k, n, msum
               wa2(j) = x(j)
               h = eps*dabs(wa2(j))
               if (h .eq. zero) h = eps
               x(j) = wa2(j) + h
   60          continue
            call fcn(n,x,wa1,iflag)
            if (iflag .lt. 0) go to 100
            do 80 j = k, n, msum
               x(j) = wa2(j)
               h = eps*dabs(wa2(j))
               if (h .eq. zero) h = eps
               do 70 i = 1, n
                  fjac(i,j) = zero
                  if (i .ge. j - mu .and. i .le. j + ml)
     *               fjac(i,j) = (wa1(i) - fvec(i))/h
   70             continue
   80          continue
   90       continue
  100    continue
  110 continue
      return
c
c     last card of subroutine fdjac1.
c
      end

      subroutine fdjac2(fcn,m,n,x,fvec,fjac,ldfjac,iflag,epsfcn,wa)
      integer m,n,ldfjac,iflag
      double precision epsfcn
      double precision x(n),fvec(m),fjac(ldfjac,n),wa(m)

c*********************************************************************72
c
cc FDJAC2 estimates an M by N jacobian matrix using forward differences.
c
c     this subroutine computes a forward-difference approximation
c     to the m by n jacobian matrix associated with a specified
c     problem of m functions in n variables.
c
c     the subroutine statement is
c
c       subroutine fdjac2(fcn,m,n,x,fvec,fjac,ldfjac,iflag,epsfcn,wa)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions. fcn must be declared
c         in an external statement in the user calling
c         program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,iflag)
c         integer m,n,iflag
c         double precision x(n),fvec(m)
c         ----------
c         calculate the functions at x and
c         return this vector in fvec.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of fdjac2.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an input array of length n.
c
c       fvec is an input array of length m which must contain the
c         functions evaluated at x.
c
c       fjac is an output m by n array which contains the
c         approximation to the jacobian matrix evaluated at x.
c
c       ldfjac is a positive integer input variable not less than m
c         which specifies the leading dimension of the array fjac.
c
c       iflag is an integer variable which can be used to terminate
c         the execution of fdjac2. see description of fcn.
c
c       epsfcn is an input variable used in determining a suitable
c         step length for the forward-difference approximation. this
c         approximation assumes that the relative errors in the
c         functions are of the order of epsfcn. if epsfcn is less
c         than the machine precision, it is assumed that the relative
c         errors in the functions are of the order of the machine
c         precision.
c
c       wa is a work array of length m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... dpmparmp
c
c       fortran-supplied ... dabs,dmax1,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j
      double precision eps,epsmch,h,temp,zero
      double precision dpmparmp
      data zero /0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      eps = dsqrt(dmax1(epsfcn,epsmch))
      do 20 j = 1, n
         temp = x(j)
         h = eps*dabs(temp)
         if (h .eq. zero) h = eps
         x(j) = temp + h
         call fcn(m,n,x,wa,iflag)
         if (iflag .lt. 0) go to 30
         x(j) = temp
         do 10 i = 1, m
            fjac(i,j) = (wa(i) - fvec(i))/h
   10       continue
   20    continue
   30 continue
      return
c
c     last card of subroutine fdjac2.
c
      end
      subroutine hybrd(fcn,n,x,fvec,xtol,maxfev,ml,mu,epsfcn,diag,
     *                 mode,factor,nprint,info,nfev,fjac,ldfjac,r,lr,
     *                 qtf,wa1,wa2,wa3,wa4)
      integer n,maxfev,ml,mu,mode,nprint,info,nfev,ldfjac,lr
      double precision xtol,epsfcn,factor
      double precision x(n),fvec(n),diag(n),fjac(ldfjac,n),r(lr),
     *                 qtf(n),wa1(n),wa2(n),wa3(n),wa4(n)
      external fcn

c*********************************************************************72
c
cc HYBRD seeks a zero of N nonlinear equations in N variables.
c
c     the purpose of hybrd is to find a zero of a system of
c     n nonlinear functions in n variables by a modification
c     of the powell hybrid method. the user must provide a
c     subroutine which calculates the functions. the jacobian is
c     then calculated by a forward-difference approximation.
c
c     the subroutine statement is
c
c       subroutine hybrd(fcn,n,x,fvec,xtol,maxfev,ml,mu,epsfcn,
c                        diag,mode,factor,nprint,info,nfev,fjac,
c                        ldfjac,r,lr,qtf,wa1,wa2,wa3,wa4)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions. fcn must be declared
c         in an external statement in the user calling
c         program, and should be written as follows.
c
c         subroutine fcn(n,x,fvec,iflag)
c         integer n,iflag
c         double precision x(n),fvec(n)
c         ----------
c         calculate the functions at x and
c         return this vector in fvec.
c         ---------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of hybrd.
c         in this case set iflag to a negative integer.
c
c       n is a positive integer input variable set to the number
c         of functions and variables.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length n which contains
c         the functions evaluated at the output x.
c
c       xtol is a nonnegative input variable. termination
c         occurs when the relative error between two consecutive
c         iterates is at most xtol.
c
c       maxfev is a positive integer input variable. termination
c         occurs when the number of calls to fcn is at least maxfev
c         by the end of an iteration.
c
c       ml is a nonnegative integer input variable which specifies
c         the number of subdiagonals within the band of the
c         jacobian matrix. if the jacobian is not banded, set
c         ml to at least n - 1.
c
c       mu is a nonnegative integer input variable which specifies
c         the number of superdiagonals within the band of the
c         jacobian matrix. if the jacobian is not banded, set
c         mu to at least n - 1.
c
c       epsfcn is an input variable used in determining a suitable
c         step length for the forward-difference approximation. this
c         approximation assumes that the relative errors in the
c         functions are of the order of epsfcn. if epsfcn is less
c         than the machine precision, it is assumed that the relative
c         errors in the functions are of the order of the machine
c         precision.
c
c       diag is an array of length n. if mode = 1 (see
c         below), diag is internally set. if mode = 2, diag
c         must contain positive entries that serve as
c         multiplicative scale factors for the variables.
c
c       mode is an integer input variable. if mode = 1, the
c         variables will be scaled internally. if mode = 2,
c         the scaling is specified by the input diag. other
c         values of mode are equivalent to mode = 1.
c
c       factor is a positive input variable used in determining the
c         initial step bound. this bound is set to the product of
c         factor and the euclidean norm of diag*x if nonzero, or else
c         to factor itself. in most cases factor should lie in the
c         interval (.1,100.). 100. is a generally recommended value.
c
c       nprint is an integer input variable that enables controlled
c         printing of iterates if it is positive. in this case,
c         fcn is called with iflag = 0 at the beginning of the first
c         iteration and every nprint iterations thereafter and
c         immediately prior to return, with x and fvec available
c         for printing. if nprint is not positive, no special calls
c         of fcn with iflag = 0 are made.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0   improper input parameters.
c
c         info = 1   relative error between two consecutive iterates
c                    is at most xtol.
c
c         info = 2   number of calls to fcn has reached or exceeded
c                    maxfev.
c
c         info = 3   xtol is too small. no further improvement in
c                    the approximate solution x is possible.
c
c         info = 4   iteration is not making good progress, as
c                    measured by the improvement from the last
c                    five jacobian evaluations.
c
c         info = 5   iteration is not making good progress, as
c                    measured by the improvement from the last
c                    ten iterations.
c
c       nfev is an integer output variable set to the number of
c         calls to fcn.
c
c       fjac is an output n by n array which contains the
c         orthogonal matrix q produced by the qr factorization
c         of the final approximate jacobian.
c
c       ldfjac is a positive integer input variable not less than n
c         which specifies the leading dimension of the array fjac.
c
c       r is an output array of length lr which contains the
c         upper triangular matrix produced by the qr factorization
c         of the final approximate jacobian, stored rowwise.
c
c       lr is a positive integer input variable not less than
c         (n*(n+1))/2.
c
c       qtf is an output array of length n which contains
c         the vector (q transpose)*fvec.
c
c       wa1, wa2, wa3, and wa4 are work arrays of length n.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... dogleg,dpmparmp,enormmp,fdjac1,
c                            qform,qrfacmp,r1mpyq,r1updt
c
c       fortran-supplied ... dabs,dmax1,dmin1,min0,mod
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,iflag,iter,j,jm1,l,msum,ncfail,ncsuc,nslow1,nslow2
      integer iwa(1)
      logical jeval,sing
      double precision actred,delta,epsmch,fnorm,fnorm1,one,pnorm,
     *                 prered,p1,p5,p001,p0001,ratio,sum,temp,xnorm,
     *                 zero
      double precision dpmparmp,enormmp
      data one,p1,p5,p001,p0001,zero
     *     /1.0d0,1.0d-1,5.0d-1,1.0d-3,1.0d-4,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      info = 0
      iflag = 0
      nfev = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. xtol .lt. zero .or. maxfev .le. 0
     *    .or. ml .lt. 0 .or. mu .lt. 0 .or. factor .le. zero
     *    .or. ldfjac .lt. n .or. lr .lt. (n*(n + 1))/2) go to 300
      if (mode .ne. 2) go to 20
      do 10 j = 1, n
         if (diag(j) .le. zero) go to 300
   10    continue
   20 continue
c
c     evaluate the function at the starting point
c     and calculate its norm.
c
      iflag = 1
      call fcn(n,x,fvec,iflag)
      nfev = 1
      if (iflag .lt. 0) go to 300
      fnorm = enormmp(n,fvec)
c
c     determine the number of calls to fcn needed to compute
c     the jacobian matrix.
c
      msum = min0(ml+mu+1,n)
c
c     initialize iteration counter and monitors.
c
      iter = 1
      ncsuc = 0
      ncfail = 0
      nslow1 = 0
      nslow2 = 0
c
c     beginning of the outer loop.
c
   30 continue
         jeval = .true.
c
c        calculate the jacobian matrix.
c
         iflag = 2
         call fdjac1(fcn,n,x,fvec,fjac,ldfjac,iflag,ml,mu,epsfcn,wa1,
     *               wa2)
         nfev = nfev + msum
         if (iflag .lt. 0) go to 300
c
c        compute the qr factorization of the jacobian.
c
         call qrfacmp(n,n,fjac,ldfjac,.false.,iwa,1,wa1,wa2,wa3)
c
c        on the first iteration and if mode is 1, scale according
c        to the norms of the columns of the initial jacobian.
c
         if (iter .ne. 1) go to 70
         if (mode .eq. 2) go to 50
         do 40 j = 1, n
            diag(j) = wa2(j)
            if (wa2(j) .eq. zero) diag(j) = one
   40       continue
   50    continue
c
c        on the first iteration, calculate the norm of the scaled x
c        and initialize the step bound delta.
c
         do 60 j = 1, n
            wa3(j) = diag(j)*x(j)
   60       continue
         xnorm = enormmp(n,wa3)
         delta = factor*xnorm
         if (delta .eq. zero) delta = factor
   70    continue
c
c        form (q transpose)*fvec and store in qtf.
c
         do 80 i = 1, n
            qtf(i) = fvec(i)
   80       continue
         do 120 j = 1, n
            if (fjac(j,j) .eq. zero) go to 110
            sum = zero
            do 90 i = j, n
               sum = sum + fjac(i,j)*qtf(i)
   90          continue
            temp = -sum/fjac(j,j)
            do 100 i = j, n
               qtf(i) = qtf(i) + fjac(i,j)*temp
  100          continue
  110       continue
  120       continue
c
c        copy the triangular factor of the qr factorization into r.
c
         sing = .false.
         do 150 j = 1, n
            l = j
            jm1 = j - 1
            if (jm1 .lt. 1) go to 140
            do 130 i = 1, jm1
               r(l) = fjac(i,j)
               l = l + n - i
  130          continue
  140       continue
            r(l) = wa1(j)
            if (wa1(j) .eq. zero) sing = .true.
  150       continue
c
c        accumulate the orthogonal factor in fjac.
c
         call qform(n,n,fjac,ldfjac,wa1)
c
c        rescale if necessary.
c
         if (mode .eq. 2) go to 170
         do 160 j = 1, n
            diag(j) = dmax1(diag(j),wa2(j))
  160       continue
  170    continue
c
c        beginning of the inner loop.
c
  180    continue
c
c           if requested, call fcn to enable printing of iterates.
c
            if (nprint .le. 0) go to 190
            iflag = 0
            if (mod(iter-1,nprint) .eq. 0) call fcn(n,x,fvec,iflag)
            if (iflag .lt. 0) go to 300
  190       continue
c
c           determine the direction p.
c
            call dogleg(n,r,lr,diag,qtf,delta,wa1,wa2,wa3)
c
c           store the direction p and x + p. calculate the norm of p.
c
            do 200 j = 1, n
               wa1(j) = -wa1(j)
               wa2(j) = x(j) + wa1(j)
               wa3(j) = diag(j)*wa1(j)
  200          continue
            pnorm = enormmp(n,wa3)
c
c           on the first iteration, adjust the initial step bound.
c
            if (iter .eq. 1) delta = dmin1(delta,pnorm)
c
c           evaluate the function at x + p and calculate its norm.
c
            iflag = 1
            call fcn(n,wa2,wa4,iflag)
            nfev = nfev + 1
            if (iflag .lt. 0) go to 300
            fnorm1 = enormmp(n,wa4)
c
c           compute the scaled actual reduction.
c
            actred = -one
            if (fnorm1 .lt. fnorm) actred = one - (fnorm1/fnorm)**2
c
c           compute the scaled predicted reduction.
c
            l = 1
            do 220 i = 1, n
               sum = zero
               do 210 j = i, n
                  sum = sum + r(l)*wa1(j)
                  l = l + 1
  210             continue
               wa3(i) = qtf(i) + sum
  220          continue
            temp = enormmp(n,wa3)
            prered = zero
            if (temp .lt. fnorm) prered = one - (temp/fnorm)**2
c
c           compute the ratio of the actual to the predicted
c           reduction.
c
            ratio = zero
            if (prered .gt. zero) ratio = actred/prered
c
c           update the step bound.
c
            if (ratio .ge. p1) go to 230
               ncsuc = 0
               ncfail = ncfail + 1
               delta = p5*delta
               go to 240
  230       continue
               ncfail = 0
               ncsuc = ncsuc + 1
               if (ratio .ge. p5 .or. ncsuc .gt. 1)
     *            delta = dmax1(delta,pnorm/p5)
               if (dabs(ratio-one) .le. p1) delta = pnorm/p5
  240       continue
c
c           test for successful iteration.
c
            if (ratio .lt. p0001) go to 260
c
c           successful iteration. update x, fvec, and their norms.
c
            do 250 j = 1, n
               x(j) = wa2(j)
               wa2(j) = diag(j)*x(j)
               fvec(j) = wa4(j)
  250          continue
            xnorm = enormmp(n,wa2)
            fnorm = fnorm1
            iter = iter + 1
  260       continue
c
c           determine the progress of the iteration.
c
            nslow1 = nslow1 + 1
            if (actred .ge. p001) nslow1 = 0
            if (jeval) nslow2 = nslow2 + 1
            if (actred .ge. p1) nslow2 = 0
c
c           test for convergence.
c
            if (delta .le. xtol*xnorm .or. fnorm .eq. zero) info = 1
            if (info .ne. 0) go to 300
c
c           tests for termination and stringent tolerances.
c
            if (nfev .ge. maxfev) info = 2
            if (p1*dmax1(p1*delta,pnorm) .le. epsmch*xnorm) info = 3
            if (nslow2 .eq. 5) info = 4
            if (nslow1 .eq. 10) info = 5
            if (info .ne. 0) go to 300
c
c           criterion for recalculating jacobian approximation
c           by forward differences.
c
            if (ncfail .eq. 2) go to 290
c
c           calculate the rank one modification to the jacobian
c           and update qtf if necessary.
c
            do 280 j = 1, n
               sum = zero
               do 270 i = 1, n
                  sum = sum + fjac(i,j)*wa4(i)
  270             continue
               wa2(j) = (sum - wa3(j))/pnorm
               wa1(j) = diag(j)*((diag(j)*wa1(j))/pnorm)
               if (ratio .ge. p0001) qtf(j) = sum
  280          continue
c
c           compute the qr factorization of the updated jacobian.
c
            call r1updt(n,n,r,lr,wa1,wa2,wa3,sing)
            call r1mpyq(n,n,fjac,ldfjac,wa2,wa3)
            call r1mpyq(1,n,qtf,1,wa2,wa3)
c
c           end of the inner loop.
c
            jeval = .false.
            go to 180
  290    continue
c
c        end of the outer loop.
c
         go to 30
  300 continue
c
c     termination, either normal or user imposed.
c
      if (iflag .lt. 0) info = iflag
      iflag = 0
      if (nprint .gt. 0) call fcn(n,x,fvec,iflag)
      return
c
c     last card of subroutine hybrd.
c
      end
      subroutine hybrd1(fcn,n,x,fvec,tol,info,wa,lwa)
      integer n,info,lwa
      double precision tol
      double precision x(n),fvec(n),wa(lwa)
      external fcn

c*********************************************************************72
c
cc HYBRD1 seeks a zero of N nonlinear equations in N variables.
c
c     the purpose of hybrd1 is to find a zero of a system of
c     n nonlinear functions in n variables by a modification
c     of the powell hybrid method. this is done by using the
c     more general nonlinear equation solver hybrd. the user
c     must provide a subroutine which calculates the functions.
c     the jacobian is then calculated by a forward-difference
c     approximation.
c
c     the subroutine statement is
c
c       subroutine hybrd1(fcn,n,x,fvec,tol,info,wa,lwa)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions. fcn must be declared
c         in an external statement in the user calling
c         program, and should be written as follows.
c
c         subroutine fcn(n,x,fvec,iflag)
c         integer n,iflag
c         double precision x(n),fvec(n)
c         ----------
c         calculate the functions at x and
c         return this vector in fvec.
c         ---------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of hybrd1.
c         in this case set iflag to a negative integer.
c
c       n is a positive integer input variable set to the number
c         of functions and variables.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length n which contains
c         the functions evaluated at the output x.
c
c       tol is a nonnegative input variable. termination occurs
c         when the algorithm estimates that the relative error
c         between x and the solution is at most tol.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0   improper input parameters.
c
c         info = 1   algorithm estimates that the relative error
c                    between x and the solution is at most tol.
c
c         info = 2   number of calls to fcn has reached or exceeded
c                    200*(n+1).
c
c         info = 3   tol is too small. no further improvement in
c                    the approximate solution x is possible.
c
c         info = 4   iteration is not making good progress.
c
c       wa is a work array of length lwa.
c
c       lwa is a positive integer input variable not less than
c         (n*(3*n+13))/2.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... hybrd
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer index,j,lr,maxfev,ml,mode,mu,nfev,nprint
      double precision epsfcn,factor,one,xtol,zero
      data factor,one,zero /1.0d2,1.0d0,0.0d0/
      info = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. tol .lt. zero .or. lwa .lt. (n*(3*n + 13))/2)
     *   go to 20
c
c     call hybrd.
c
      maxfev = 200*(n + 1)
      xtol = tol
      ml = n - 1
      mu = n - 1
      epsfcn = zero
      mode = 2
      do 10 j = 1, n
         wa(j) = one
   10    continue
      nprint = 0
      lr = (n*(n + 1))/2
      index = 6*n + lr
      call hybrd(fcn,n,x,fvec,xtol,maxfev,ml,mu,epsfcn,wa(1),mode,
     *           factor,nprint,info,nfev,wa(index+1),n,wa(6*n+1),lr,
     *           wa(n+1),wa(2*n+1),wa(3*n+1),wa(4*n+1),wa(5*n+1))
      if (info .eq. 5) info = 4
   20 continue
      return
c
c     last card of subroutine hybrd1.
c
      end
      subroutine hybrj(fcn,n,x,fvec,fjac,ldfjac,xtol,maxfev,diag,mode,
     *                 factor,nprint,info,nfev,njev,r,lr,qtf,wa1,wa2,
     *                 wa3,wa4)
      integer n,ldfjac,maxfev,mode,nprint,info,nfev,njev,lr
      double precision xtol,factor
      double precision x(n),fvec(n),fjac(ldfjac,n),diag(n),r(lr),
     *                 qtf(n),wa1(n),wa2(n),wa3(n),wa4(n)

c*********************************************************************72
c
cc HYBRJ seeks a zero of N nonlinear equations in N variables.
c
c     the purpose of hybrj is to find a zero of a system of
c     n nonlinear functions in n variables by a modification
c     of the powell hybrid method. the user must provide a
c     subroutine which calculates the functions and the jacobian.
c
c     the subroutine statement is
c
c       subroutine hybrj(fcn,n,x,fvec,fjac,ldfjac,xtol,maxfev,diag,
c                        mode,factor,nprint,info,nfev,njev,r,lr,qtf,
c                        wa1,wa2,wa3,wa4)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions and the jacobian. fcn must
c         be declared in an external statement in the user
c         calling program, and should be written as follows.
c
c         subroutine fcn(n,x,fvec,fjac,ldfjac,iflag)
c         integer n,ldfjac,iflag
c         double precision x(n),fvec(n),fjac(ldfjac,n)
c         ----------
c         if iflag = 1 calculate the functions at x and
c         return this vector in fvec. do not alter fjac.
c         if iflag = 2 calculate the jacobian at x and
c         return this matrix in fjac. do not alter fvec.
c         ---------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of hybrj.
c         in this case set iflag to a negative integer.
c
c       n is a positive integer input variable set to the number
c         of functions and variables.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length n which contains
c         the functions evaluated at the output x.
c
c       fjac is an output n by n array which contains the
c         orthogonal matrix q produced by the qr factorization
c         of the final approximate jacobian.
c
c       ldfjac is a positive integer input variable not less than n
c         which specifies the leading dimension of the array fjac.
c
c       xtol is a nonnegative input variable. termination
c         occurs when the relative error between two consecutive
c         iterates is at most xtol.
c
c       maxfev is a positive integer input variable. termination
c         occurs when the number of calls to fcn with iflag = 1
c         has reached maxfev.
c
c       diag is an array of length n. if mode = 1 (see
c         below), diag is internally set. if mode = 2, diag
c         must contain positive entries that serve as
c         multiplicative scale factors for the variables.
c
c       mode is an integer input variable. if mode = 1, the
c         variables will be scaled internally. if mode = 2,
c         the scaling is specified by the input diag. other
c         values of mode are equivalent to mode = 1.
c
c       factor is a positive input variable used in determining the
c         initial step bound. this bound is set to the product of
c         factor and the euclidean norm of diag*x if nonzero, or else
c         to factor itself. in most cases factor should lie in the
c         interval (.1,100.). 100. is a generally recommended value.
c
c       nprint is an integer input variable that enables controlled
c         printing of iterates if it is positive. in this case,
c         fcn is called with iflag = 0 at the beginning of the first
c         iteration and every nprint iterations thereafter and
c         immediately prior to return, with x and fvec available
c         for printing. fvec and fjac should not be altered.
c         if nprint is not positive, no special calls of fcn
c         with iflag = 0 are made.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0   improper input parameters.
c
c         info = 1   relative error between two consecutive iterates
c                    is at most xtol.
c
c         info = 2   number of calls to fcn with iflag = 1 has
c                    reached maxfev.
c
c         info = 3   xtol is too small. no further improvement in
c                    the approximate solution x is possible.
c
c         info = 4   iteration is not making good progress, as
c                    measured by the improvement from the last
c                    five jacobian evaluations.
c
c         info = 5   iteration is not making good progress, as
c                    measured by the improvement from the last
c                    ten iterations.
c
c       nfev is an integer output variable set to the number of
c         calls to fcn with iflag = 1.
c
c       njev is an integer output variable set to the number of
c         calls to fcn with iflag = 2.
c
c       r is an output array of length lr which contains the
c         upper triangular matrix produced by the qr factorization
c         of the final approximate jacobian, stored rowwise.
c
c       lr is a positive integer input variable not less than
c         (n*(n+1))/2.
c
c       qtf is an output array of length n which contains
c         the vector (q transpose)*fvec.
c
c       wa1, wa2, wa3, and wa4 are work arrays of length n.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... dogleg,dpmparmp,enormmp,
c                            qform,qrfacmp,r1mpyq,r1updt
c
c       fortran-supplied ... dabs,dmax1,dmin1,mod
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,iflag,iter,j,jm1,l,ncfail,ncsuc,nslow1,nslow2
      integer iwa(1)
      logical jeval,sing
      double precision actred,delta,epsmch,fnorm,fnorm1,one,pnorm,
     *                 prered,p1,p5,p001,p0001,ratio,sum,temp,xnorm,
     *                 zero
      double precision dpmparmp,enormmp
      data one,p1,p5,p001,p0001,zero
     *     /1.0d0,1.0d-1,5.0d-1,1.0d-3,1.0d-4,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      info = 0
      iflag = 0
      nfev = 0
      njev = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. ldfjac .lt. n .or. xtol .lt. zero
     *    .or. maxfev .le. 0 .or. factor .le. zero
     *    .or. lr .lt. (n*(n + 1))/2) go to 300
      if (mode .ne. 2) go to 20
      do 10 j = 1, n
         if (diag(j) .le. zero) go to 300
   10    continue
   20 continue
c
c     evaluate the function at the starting point
c     and calculate its norm.
c
      iflag = 1
      call fcn(n,x,fvec,fjac,ldfjac,iflag)
      nfev = 1
      if (iflag .lt. 0) go to 300
      fnorm = enormmp(n,fvec)
c
c     initialize iteration counter and monitors.
c
      iter = 1
      ncsuc = 0
      ncfail = 0
      nslow1 = 0
      nslow2 = 0
c
c     beginning of the outer loop.
c
   30 continue
         jeval = .true.
c
c        calculate the jacobian matrix.
c
         iflag = 2
         call fcn(n,x,fvec,fjac,ldfjac,iflag)
         njev = njev + 1
         if (iflag .lt. 0) go to 300
c
c        compute the qr factorization of the jacobian.
c
         call qrfacmp(n,n,fjac,ldfjac,.false.,iwa,1,wa1,wa2,wa3)
c
c        on the first iteration and if mode is 1, scale according
c        to the norms of the columns of the initial jacobian.
c
         if (iter .ne. 1) go to 70
         if (mode .eq. 2) go to 50
         do 40 j = 1, n
            diag(j) = wa2(j)
            if (wa2(j) .eq. zero) diag(j) = one
   40       continue
   50    continue
c
c        on the first iteration, calculate the norm of the scaled x
c        and initialize the step bound delta.
c
         do 60 j = 1, n
            wa3(j) = diag(j)*x(j)
   60       continue
         xnorm = enormmp(n,wa3)
         delta = factor*xnorm
         if (delta .eq. zero) delta = factor
   70    continue
c
c        form (q transpose)*fvec and store in qtf.
c
         do 80 i = 1, n
            qtf(i) = fvec(i)
   80       continue
         do 120 j = 1, n
            if (fjac(j,j) .eq. zero) go to 110
            sum = zero
            do 90 i = j, n
               sum = sum + fjac(i,j)*qtf(i)
   90          continue
            temp = -sum/fjac(j,j)
            do 100 i = j, n
               qtf(i) = qtf(i) + fjac(i,j)*temp
  100          continue
  110       continue
  120       continue
c
c        copy the triangular factor of the qr factorization into r.
c
         sing = .false.
         do 150 j = 1, n
            l = j
            jm1 = j - 1
            if (jm1 .lt. 1) go to 140
            do 130 i = 1, jm1
               r(l) = fjac(i,j)
               l = l + n - i
  130          continue
  140       continue
            r(l) = wa1(j)
            if (wa1(j) .eq. zero) sing = .true.
  150       continue
c
c        accumulate the orthogonal factor in fjac.
c
         call qform(n,n,fjac,ldfjac,wa1)
c
c        rescale if necessary.
c
         if (mode .eq. 2) go to 170
         do 160 j = 1, n
            diag(j) = dmax1(diag(j),wa2(j))
  160       continue
  170    continue
c
c        beginning of the inner loop.
c
  180    continue
c
c           if requested, call fcn to enable printing of iterates.
c
            if (nprint .le. 0) go to 190
            iflag = 0
            if (mod(iter-1,nprint) .eq. 0)
     *         call fcn(n,x,fvec,fjac,ldfjac,iflag)
            if (iflag .lt. 0) go to 300
  190       continue
c
c           determine the direction p.
c
            call dogleg(n,r,lr,diag,qtf,delta,wa1,wa2,wa3)
c
c           store the direction p and x + p. calculate the norm of p.
c
            do 200 j = 1, n
               wa1(j) = -wa1(j)
               wa2(j) = x(j) + wa1(j)
               wa3(j) = diag(j)*wa1(j)
  200          continue
            pnorm = enormmp(n,wa3)
c
c           on the first iteration, adjust the initial step bound.
c
            if (iter .eq. 1) delta = dmin1(delta,pnorm)
c
c           evaluate the function at x + p and calculate its norm.
c
            iflag = 1
            call fcn(n,wa2,wa4,fjac,ldfjac,iflag)
            nfev = nfev + 1
            if (iflag .lt. 0) go to 300
            fnorm1 = enormmp(n,wa4)
c
c           compute the scaled actual reduction.
c
            actred = -one
            if (fnorm1 .lt. fnorm) actred = one - (fnorm1/fnorm)**2
c
c           compute the scaled predicted reduction.
c
            l = 1
            do 220 i = 1, n
               sum = zero
               do 210 j = i, n
                  sum = sum + r(l)*wa1(j)
                  l = l + 1
  210             continue
               wa3(i) = qtf(i) + sum
  220          continue
            temp = enormmp(n,wa3)
            prered = zero
            if (temp .lt. fnorm) prered = one - (temp/fnorm)**2
c
c           compute the ratio of the actual to the predicted
c           reduction.
c
            ratio = zero
            if (prered .gt. zero) ratio = actred/prered
c
c           update the step bound.
c
            if (ratio .ge. p1) go to 230
               ncsuc = 0
               ncfail = ncfail + 1
               delta = p5*delta
               go to 240
  230       continue
               ncfail = 0
               ncsuc = ncsuc + 1
               if (ratio .ge. p5 .or. ncsuc .gt. 1)
     *            delta = dmax1(delta,pnorm/p5)
               if (dabs(ratio-one) .le. p1) delta = pnorm/p5
  240       continue
c
c           test for successful iteration.
c
            if (ratio .lt. p0001) go to 260
c
c           successful iteration. update x, fvec, and their norms.
c
            do 250 j = 1, n
               x(j) = wa2(j)
               wa2(j) = diag(j)*x(j)
               fvec(j) = wa4(j)
  250          continue
            xnorm = enormmp(n,wa2)
            fnorm = fnorm1
            iter = iter + 1
  260       continue
c
c           determine the progress of the iteration.
c
            nslow1 = nslow1 + 1
            if (actred .ge. p001) nslow1 = 0
            if (jeval) nslow2 = nslow2 + 1
            if (actred .ge. p1) nslow2 = 0
c
c           test for convergence.
c
            if (delta .le. xtol*xnorm .or. fnorm .eq. zero) info = 1
            if (info .ne. 0) go to 300
c
c           tests for termination and stringent tolerances.
c
            if (nfev .ge. maxfev) info = 2
            if (p1*dmax1(p1*delta,pnorm) .le. epsmch*xnorm) info = 3
            if (nslow2 .eq. 5) info = 4
            if (nslow1 .eq. 10) info = 5
            if (info .ne. 0) go to 300
c
c           criterion for recalculating jacobian.
c
            if (ncfail .eq. 2) go to 290
c
c           calculate the rank one modification to the jacobian
c           and update qtf if necessary.
c
            do 280 j = 1, n
               sum = zero
               do 270 i = 1, n
                  sum = sum + fjac(i,j)*wa4(i)
  270             continue
               wa2(j) = (sum - wa3(j))/pnorm
               wa1(j) = diag(j)*((diag(j)*wa1(j))/pnorm)
               if (ratio .ge. p0001) qtf(j) = sum
  280          continue
c
c           compute the qr factorization of the updated jacobian.
c
            call r1updt(n,n,r,lr,wa1,wa2,wa3,sing)
            call r1mpyq(n,n,fjac,ldfjac,wa2,wa3)
            call r1mpyq(1,n,qtf,1,wa2,wa3)
c
c           end of the inner loop.
c
            jeval = .false.
            go to 180
  290    continue
c
c        end of the outer loop.
c
         go to 30
  300 continue
c
c     termination, either normal or user imposed.
c
      if (iflag .lt. 0) info = iflag
      iflag = 0
      if (nprint .gt. 0) call fcn(n,x,fvec,fjac,ldfjac,iflag)
      return
c
c     last card of subroutine hybrj.
c
      end
      subroutine hybrj1(fcn,n,x,fvec,fjac,ldfjac,tol,info,wa,lwa)
      integer n,ldfjac,info,lwa
      double precision tol
      double precision x(n),fvec(n),fjac(ldfjac,n),wa(lwa)
      external fcn

c*********************************************************************72
c
cc HYBRJ1 seeks a zero of N nonlinear equations in N variables by Powell's method.
c
c     the purpose of hybrj1 is to find a zero of a system of
c     n nonlinear functions in n variables by a modification
c     of the powell hybrid method. this is done by using the
c     more general nonlinear equation solver hybrj. the user
c     must provide a subroutine which calculates the functions
c     and the jacobian.
c
c     the subroutine statement is
c
c       subroutine hybrj1(fcn,n,x,fvec,fjac,ldfjac,tol,info,wa,lwa)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions and the jacobian. fcn must
c         be declared in an external statement in the user
c         calling program, and should be written as follows.
c
c         subroutine fcn(n,x,fvec,fjac,ldfjac,iflag)
c         integer n,ldfjac,iflag
c         double precision x(n),fvec(n),fjac(ldfjac,n)
c         ----------
c         if iflag = 1 calculate the functions at x and
c         return this vector in fvec. do not alter fjac.
c         if iflag = 2 calculate the jacobian at x and
c         return this matrix in fjac. do not alter fvec.
c         ---------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of hybrj1.
c         in this case set iflag to a negative integer.
c
c       n is a positive integer input variable set to the number
c         of functions and variables.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length n which contains
c         the functions evaluated at the output x.
c
c       fjac is an output n by n array which contains the
c         orthogonal matrix q produced by the qr factorization
c         of the final approximate jacobian.
c
c       ldfjac is a positive integer input variable not less than n
c         which specifies the leading dimension of the array fjac.
c
c       tol is a nonnegative input variable. termination occurs
c         when the algorithm estimates that the relative error
c         between x and the solution is at most tol.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0   improper input parameters.
c
c         info = 1   algorithm estimates that the relative error
c                    between x and the solution is at most tol.
c
c         info = 2   number of calls to fcn with iflag = 1 has
c                    reached 100*(n+1).
c
c         info = 3   tol is too small. no further improvement in
c                    the approximate solution x is possible.
c
c         info = 4   iteration is not making good progress.
c
c       wa is a work array of length lwa.
c
c       lwa is a positive integer input variable not less than
c         (n*(n+13))/2.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... hybrj
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer j,lr,maxfev,mode,nfev,njev,nprint
      double precision factor,one,xtol,zero
      data factor,one,zero /1.0d2,1.0d0,0.0d0/
      info = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. ldfjac .lt. n .or. tol .lt. zero
     *    .or. lwa .lt. (n*(n + 13))/2) go to 20
c
c     call hybrj.
c
      maxfev = 100*(n + 1)
      xtol = tol
      mode = 2
      do 10 j = 1, n
         wa(j) = one
   10    continue
      nprint = 0
      lr = (n*(n + 1))/2
      call hybrj(fcn,n,x,fvec,fjac,ldfjac,xtol,maxfev,wa(1),mode,
     *           factor,nprint,info,nfev,njev,wa(6*n+1),lr,wa(n+1),
     *           wa(2*n+1),wa(3*n+1),wa(4*n+1),wa(5*n+1))
      if (info .eq. 5) info = 4
   20 continue
      return
c
c     last card of subroutine hybrj1.
c
      end
      subroutine lmdermp(fcn,m,n,x,fvec,fjac,ldfjac,ftol,xtol,gtol,
     *                 maxfev,diag,mode,factor,nprint,info,nfev,njev,
     *                 ipvt,qtf,wa1,wa2,wa3,wa4)
      integer m,n,ldfjac,maxfev,mode,nprint,info,nfev,njev
      integer ipvt(n)
      double precision ftol,xtol,gtol,factor
      double precision x(n),fvec(m),fjac(ldfjac,n),diag(n),qtf(n),
     *                 wa1(n),wa2(n),wa3(n),wa4(m)

c*********************************************************************72
c
cc LMDER minimizes M functions in N variables by the Levenberg-Marquardt method.
c
c     the purpose of lmdermp is to minimize the sum of the squares of
c     m nonlinear functions in n variables by a modification of
c     the levenberg-marquardt algorithm. the user must provide a
c     subroutine which calculates the functions and the jacobian.
c
c     the subroutine statement is
c
c       subroutine lmdermp(fcn,m,n,x,fvec,fjac,ldfjac,ftol,xtol,gtol,
c                        maxfev,diag,mode,factor,nprint,info,nfev,
c                        njev,ipvt,qtf,wa1,wa2,wa3,wa4)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions and the jacobian. fcn must
c         be declared in an external statement in the user
c         calling program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,fjac,ldfjac,iflag)
c         integer m,n,ldfjac,iflag
c         double precision x(n),fvec(m),fjac(ldfjac,n)
c         ----------
c         if iflag = 1 calculate the functions at x and
c         return this vector in fvec. do not alter fjac.
c         if iflag = 2 calculate the jacobian at x and
c         return this matrix in fjac. do not alter fvec.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of lmdermp.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length m which contains
c         the functions evaluated at the output x.
c
c       fjac is an output m by n array. the upper n by n submatrix
c         of fjac contains an upper triangular matrix r with
c         diagonal elements of nonincreasing magnitude such that
c
c                t     t           t
c               p *(jac *jac)*p = r *r,
c
c         where p is a permutation matrix and jac is the final
c         calculated jacobian. column j of p is column ipvt(j)
c         (see below) of the identity matrix. the lower trapezoidal
c         part of fjac contains information generated during
c         the computation of r.
c
c       ldfjac is a positive integer input variable not less than m
c         which specifies the leading dimension of the array fjac.
c
c       ftol is a nonnegative input variable. termination
c         occurs when both the actual and predicted relative
c         reductions in the sum of squares are at most ftol.
c         therefore, ftol measures the relative error desired
c         in the sum of squares.
c
c       xtol is a nonnegative input variable. termination
c         occurs when the relative error between two consecutive
c         iterates is at most xtol. therefore, xtol measures the
c         relative error desired in the approximate solution.
c
c       gtol is a nonnegative input variable. termination
c         occurs when the cosine of the angle between fvec and
c         any column of the jacobian is at most gtol in absolute
c         value. therefore, gtol measures the orthogonality
c         desired between the function vector and the columns
c         of the jacobian.
c
c       maxfev is a positive integer input variable. termination
c         occurs when the number of calls to fcn with iflag = 1
c         has reached maxfev.
c
c       diag is an array of length n. if mode = 1 (see
c         below), diag is internally set. if mode = 2, diag
c         must contain positive entries that serve as
c         multiplicative scale factors for the variables.
c
c       mode is an integer input variable. if mode = 1, the
c         variables will be scaled internally. if mode = 2,
c         the scaling is specified by the input diag. other
c         values of mode are equivalent to mode = 1.
c
c       factor is a positive input variable used in determining the
c         initial step bound. this bound is set to the product of
c         factor and the euclidean norm of diag*x if nonzero, or else
c         to factor itself. in most cases factor should lie in the
c         interval (.1,100.).100. is a generally recommended value.
c
c       nprint is an integer input variable that enables controlled
c         printing of iterates if it is positive. in this case,
c         fcn is called with iflag = 0 at the beginning of the first
c         iteration and every nprint iterations thereafter and
c         immediately prior to return, with x, fvec, and fjac
c         available for printing. fvec and fjac should not be
c         altered. if nprint is not positive, no special calls
c         of fcn with iflag = 0 are made.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0  improper input parameters.
c
c         info = 1  both actual and predicted relative reductions
c                   in the sum of squares are at most ftol.
c
c         info = 2  relative error between two consecutive iterates
c                   is at most xtol.
c
c         info = 3  conditions for info = 1 and info = 2 both hold.
c
c         info = 4  the cosine of the angle between fvec and any
c                   column of the jacobian is at most gtol in
c                   absolute value.
c
c         info = 5  number of calls to fcn with iflag = 1 has
c                   reached maxfev.
c
c         info = 6  ftol is too small. no further reduction in
c                   the sum of squares is possible.
c
c         info = 7  xtol is too small. no further improvement in
c                   the approximate solution x is possible.
c
c         info = 8  gtol is too small. fvec is orthogonal to the
c                   columns of the jacobian to machine precision.
c
c       nfev is an integer output variable set to the number of
c         calls to fcn with iflag = 1.
c
c       njev is an integer output variable set to the number of
c         calls to fcn with iflag = 2.
c
c       ipvt is an integer output array of length n. ipvt
c         defines a permutation matrix p such that jac*p = q*r,
c         where jac is the final calculated jacobian, q is
c         orthogonal (not stored), and r is upper triangular
c         with diagonal elements of nonincreasing magnitude.
c         column j of p is column ipvt(j) of the identity matrix.
c
c       qtf is an output array of length n which contains
c         the first n elements of the vector (q transpose)*fvec.
c
c       wa1, wa2, and wa3 are work arrays of length n.
c
c       wa4 is a work array of length m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... dpmparmp,enormmp,lmparmp,qrfacmp
c
c       fortran-supplied ... dabs,dmax1,dmin1,dsqrt,mod
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,iflag,iter,j,l
      double precision actred,delta,dirder,epsmch,fnorm,fnorm1,gnorm,
     *                 one,par,pnorm,prered,p1,p5,p25,p75,p0001,ratio,
     *                 sum,temp,temp1,temp2,xnorm,zero
      double precision dpmparmp,enormmp
      data one,p1,p5,p25,p75,p0001,zero
     *     /1.0d0,1.0d-1,5.0d-1,2.5d-1,7.5d-1,1.0d-4,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      info = 0
      iflag = 0
      nfev = 0
      njev = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. m .lt. n .or. ldfjac .lt. m
     *    .or. ftol .lt. zero .or. xtol .lt. zero .or. gtol .lt. zero
     *    .or. maxfev .le. 0 .or. factor .le. zero) go to 300
      if (mode .ne. 2) go to 20
      do 10 j = 1, n
         if (diag(j) .le. zero) go to 300
   10    continue
   20 continue
c
c     evaluate the function at the starting point
c     and calculate its norm.
c
      iflag = 1
      call fcn(m,n,x,fvec,fjac,ldfjac,iflag)
      nfev = 1
      if (iflag .lt. 0) go to 300
      fnorm = enormmp(m,fvec)
c
c     initialize levenberg-marquardt parameter and iteration counter.
c
      par = zero
      iter = 1
c
c     beginning of the outer loop.
c
   30 continue
c
c        calculate the jacobian matrix.
c
         iflag = 2
         call fcn(m,n,x,fvec,fjac,ldfjac,iflag)
         njev = njev + 1
         if (iflag .lt. 0) go to 300
c
c        if requested, call fcn to enable printing of iterates.
c
         if (nprint .le. 0) go to 40
         iflag = 0
         if (mod(iter-1,nprint) .eq. 0)
     *      call fcn(m,n,x,fvec,fjac,ldfjac,iflag)
         if (iflag .lt. 0) go to 300
   40    continue
c
c        compute the qr factorization of the jacobian.
c
         call qrfacmp(m,n,fjac,ldfjac,.true.,ipvt,n,wa1,wa2,wa3)
c
c        on the first iteration and if mode is 1, scale according
c        to the norms of the columns of the initial jacobian.
c
         if (iter .ne. 1) go to 80
         if (mode .eq. 2) go to 60
         do 50 j = 1, n
            diag(j) = wa2(j)
            if (wa2(j) .eq. zero) diag(j) = one
   50       continue
   60    continue
c
c        on the first iteration, calculate the norm of the scaled x
c        and initialize the step bound delta.
c
         do 70 j = 1, n
            wa3(j) = diag(j)*x(j)
   70       continue
         xnorm = enormmp(n,wa3)
         delta = factor*xnorm
         if (delta .eq. zero) delta = factor
   80    continue
c
c        form (q transpose)*fvec and store the first n components in
c        qtf.
c
         do 90 i = 1, m
            wa4(i) = fvec(i)
   90       continue
         do 130 j = 1, n
            if (fjac(j,j) .eq. zero) go to 120
            sum = zero
            do 100 i = j, m
               sum = sum + fjac(i,j)*wa4(i)
  100          continue
            temp = -sum/fjac(j,j)
            do 110 i = j, m
               wa4(i) = wa4(i) + fjac(i,j)*temp
  110          continue
  120       continue
            fjac(j,j) = wa1(j)
            qtf(j) = wa4(j)
  130       continue
c
c        compute the norm of the scaled gradient.
c
         gnorm = zero
         if (fnorm .eq. zero) go to 170
         do 160 j = 1, n
            l = ipvt(j)
            if (wa2(l) .eq. zero) go to 150
            sum = zero
            do 140 i = 1, j
               sum = sum + fjac(i,j)*(qtf(i)/fnorm)
  140          continue
            gnorm = dmax1(gnorm,dabs(sum/wa2(l)))
  150       continue
  160       continue
  170    continue
c
c        test for convergence of the gradient norm.
c
         if (gnorm .le. gtol) info = 4
         if (info .ne. 0) go to 300
c
c        rescale if necessary.
c
         if (mode .eq. 2) go to 190
         do 180 j = 1, n
            diag(j) = dmax1(diag(j),wa2(j))
  180       continue
  190    continue
c
c        beginning of the inner loop.
c
  200    continue
c
c           determine the levenberg-marquardt parameter.
c
            call lmparmp(n,fjac,ldfjac,ipvt,diag,qtf,delta,par,wa1,wa2,
     *                 wa3,wa4)
c
c           store the direction p and x + p. calculate the norm of p.
c
            do 210 j = 1, n
               wa1(j) = -wa1(j)
               wa2(j) = x(j) + wa1(j)
               wa3(j) = diag(j)*wa1(j)
  210          continue
            pnorm = enormmp(n,wa3)
c
c           on the first iteration, adjust the initial step bound.
c
            if (iter .eq. 1) delta = dmin1(delta,pnorm)
c
c           evaluate the function at x + p and calculate its norm.
c
            iflag = 1
            call fcn(m,n,wa2,wa4,fjac,ldfjac,iflag)
            nfev = nfev + 1
            if (iflag .lt. 0) go to 300
            fnorm1 = enormmp(m,wa4)
c
c           compute the scaled actual reduction.
c
            actred = -one
            if (p1*fnorm1 .lt. fnorm) actred = one - (fnorm1/fnorm)**2
c
c           compute the scaled predicted reduction and
c           the scaled directional derivative.
c
            do 230 j = 1, n
               wa3(j) = zero
               l = ipvt(j)
               temp = wa1(l)
               do 220 i = 1, j
                  wa3(i) = wa3(i) + fjac(i,j)*temp
  220             continue
  230          continue
            temp1 = enormmp(n,wa3)/fnorm
            temp2 = (dsqrt(par)*pnorm)/fnorm
            prered = temp1**2 + temp2**2/p5
            dirder = -(temp1**2 + temp2**2)
c
c           compute the ratio of the actual to the predicted
c           reduction.
c
            ratio = zero
            if (prered .ne. zero) ratio = actred/prered
c
c           update the step bound.
c
            if (ratio .gt. p25) go to 240
               if (actred .ge. zero) temp = p5
               if (actred .lt. zero)
     *            temp = p5*dirder/(dirder + p5*actred)
               if (p1*fnorm1 .ge. fnorm .or. temp .lt. p1) temp = p1
               delta = temp*dmin1(delta,pnorm/p1)
               par = par/temp
               go to 260
  240       continue
               if (par .ne. zero .and. ratio .lt. p75) go to 250
               delta = pnorm/p5
               par = p5*par
  250          continue
  260       continue
c
c           test for successful iteration.
c
            if (ratio .lt. p0001) go to 290
c
c           successful iteration. update x, fvec, and their norms.
c
            do 270 j = 1, n
               x(j) = wa2(j)
               wa2(j) = diag(j)*x(j)
  270          continue
            do 280 i = 1, m
               fvec(i) = wa4(i)
  280          continue
            xnorm = enormmp(n,wa2)
            fnorm = fnorm1
            iter = iter + 1
  290       continue
c
c           tests for convergence.
c
            if (dabs(actred) .le. ftol .and. prered .le. ftol
     *          .and. p5*ratio .le. one) info = 1
            if (delta .le. xtol*xnorm) info = 2
            if (dabs(actred) .le. ftol .and. prered .le. ftol
     *          .and. p5*ratio .le. one .and. info .eq. 2) info = 3
            if (info .ne. 0) go to 300
c
c           tests for termination and stringent tolerances.
c
            if (nfev .ge. maxfev) info = 5
            if (dabs(actred) .le. epsmch .and. prered .le. epsmch
     *          .and. p5*ratio .le. one) info = 6
            if (delta .le. epsmch*xnorm) info = 7
            if (gnorm .le. epsmch) info = 8
            if (info .ne. 0) go to 300
c
c           end of the inner loop. repeat if iteration unsuccessful.
c
            if (ratio .lt. p0001) go to 200
c
c        end of the outer loop.
c
         go to 30
  300 continue
c
c     termination, either normal or user imposed.
c
      if (iflag .lt. 0) info = iflag
      iflag = 0
      if (nprint .gt. 0) call fcn(m,n,x,fvec,fjac,ldfjac,iflag)
      return
c
c     last card of subroutine lmdermp.
c
      end
      subroutine lmdermp1(fcn,m,n,x,fvec,fjac,ldfjac,tol,info,ipvt,wa,
     *                  lwa)
      integer m,n,ldfjac,info,lwa
      integer ipvt(n)
      double precision tol
      double precision x(n),fvec(m),fjac(ldfjac,n),wa(lwa)
      external fcn

c*********************************************************************72
c
cc LMDER1 minimizes M functions in N variables by the Levenberg-Marquardt method.
c
c     the purpose of lmdermp1 is to minimize the sum of the squares of
c     m nonlinear functions in n variables by a modification of the
c     levenberg-marquardt algorithm. this is done by using the more
c     general least-squares solver lmdermp. the user must provide a
c     subroutine which calculates the functions and the jacobian.
c
c     the subroutine statement is
c
c       subroutine lmdermp1(fcn,m,n,x,fvec,fjac,ldfjac,tol,info,
c                         ipvt,wa,lwa)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions and the jacobian. fcn must
c         be declared in an external statement in the user
c         calling program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,fjac,ldfjac,iflag)
c         integer m,n,ldfjac,iflag
c         double precision x(n),fvec(m),fjac(ldfjac,n)
c         ----------
c         if iflag = 1 calculate the functions at x and
c         return this vector in fvec. do not alter fjac.
c         if iflag = 2 calculate the jacobian at x and
c         return this matrix in fjac. do not alter fvec.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of lmdermp1.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length m which contains
c         the functions evaluated at the output x.
c
c       fjac is an output m by n array. the upper n by n submatrix
c         of fjac contains an upper triangular matrix r with
c         diagonal elements of nonincreasing magnitude such that
c
c                t     t           t
c               p *(jac *jac)*p = r *r,
c
c         where p is a permutation matrix and jac is the final
c         calculated jacobian. column j of p is column ipvt(j)
c         (see below) of the identity matrix. the lower trapezoidal
c         part of fjac contains information generated during
c         the computation of r.
c
c       ldfjac is a positive integer input variable not less than m
c         which specifies the leading dimension of the array fjac.
c
c       tol is a nonnegative input variable. termination occurs
c         when the algorithm estimates either that the relative
c         error in the sum of squares is at most tol or that
c         the relative error between x and the solution is at
c         most tol.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0  improper input parameters.
c
c         info = 1  algorithm estimates that the relative error
c                   in the sum of squares is at most tol.
c
c         info = 2  algorithm estimates that the relative error
c                   between x and the solution is at most tol.
c
c         info = 3  conditions for info = 1 and info = 2 both hold.
c
c         info = 4  fvec is orthogonal to the columns of the
c                   jacobian to machine precision.
c
c         info = 5  number of calls to fcn with iflag = 1 has
c                   reached 100*(n+1).
c
c         info = 6  tol is too small. no further reduction in
c                   the sum of squares is possible.
c
c         info = 7  tol is too small. no further improvement in
c                   the approximate solution x is possible.
c
c       ipvt is an integer output array of length n. ipvt
c         defines a permutation matrix p such that jac*p = q*r,
c         where jac is the final calculated jacobian, q is
c         orthogonal (not stored), and r is upper triangular
c         with diagonal elements of nonincreasing magnitude.
c         column j of p is column ipvt(j) of the identity matrix.
c
c       wa is a work array of length lwa.
c
c       lwa is a positive integer input variable not less than 5*n+m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... lmdermp
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer maxfev,mode,nfev,njev,nprint
      double precision factor,ftol,gtol,xtol,zero
      data factor,zero /1.0d2,0.0d0/
      info = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. m .lt. n .or. ldfjac .lt. m .or. tol .lt. zero
     *    .or. lwa .lt. 5*n + m) go to 10
c
c     call lmdermp.
c
      maxfev = 100*(n + 1)
      ftol = tol
      xtol = tol
      gtol = zero
      mode = 1
      nprint = 0
      call lmdermp(fcn,m,n,x,fvec,fjac,ldfjac,ftol,xtol,gtol,maxfev,
     *           wa(1),mode,factor,nprint,info,nfev,njev,ipvt,wa(n+1),
     *           wa(2*n+1),wa(3*n+1),wa(4*n+1),wa(5*n+1))
      if (info .eq. 8) info = 4
   10 continue
      return
c
c     last card of subroutine lmdermp1.
c
      end
      subroutine lmdif(fcn,m,n,x,fvec,ftol,xtol,gtol,maxfev,epsfcn,
     *                 diag,mode,factor,nprint,info,nfev,fjac,ldfjac,
     *                 ipvt,qtf,wa1,wa2,wa3,wa4)
      integer m,n,maxfev,mode,nprint,info,nfev,ldfjac
      integer ipvt(n)
      double precision ftol,xtol,gtol,epsfcn,factor
      double precision x(n),fvec(m),diag(n),fjac(ldfjac,n),qtf(n),
     *                 wa1(n),wa2(n),wa3(n),wa4(m)
      external fcn

c*********************************************************************72
c
cc LMDIF minimizes M functions in N variables by the Levenberg-Marquardt method.
c
c     the purpose of lmdif is to minimize the sum of the squares of
c     m nonlinear functions in n variables by a modification of
c     the levenberg-marquardt algorithm. the user must provide a
c     subroutine which calculates the functions. the jacobian is
c     then calculated by a forward-difference approximation.
c
c     the subroutine statement is
c
c       subroutine lmdif(fcn,m,n,x,fvec,ftol,xtol,gtol,maxfev,epsfcn,
c                        diag,mode,factor,nprint,info,nfev,fjac,
c                        ldfjac,ipvt,qtf,wa1,wa2,wa3,wa4)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions. fcn must be declared
c         in an external statement in the user calling
c         program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,iflag)
c         integer m,n,iflag
c         double precision x(n),fvec(m)
c         ----------
c         calculate the functions at x and
c         return this vector in fvec.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of lmdif.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length m which contains
c         the functions evaluated at the output x.
c
c       ftol is a nonnegative input variable. termination
c         occurs when both the actual and predicted relative
c         reductions in the sum of squares are at most ftol.
c         therefore, ftol measures the relative error desired
c         in the sum of squares.
c
c       xtol is a nonnegative input variable. termination
c         occurs when the relative error between two consecutive
c         iterates is at most xtol. therefore, xtol measures the
c         relative error desired in the approximate solution.
c
c       gtol is a nonnegative input variable. termination
c         occurs when the cosine of the angle between fvec and
c         any column of the jacobian is at most gtol in absolute
c         value. therefore, gtol measures the orthogonality
c         desired between the function vector and the columns
c         of the jacobian.
c
c       maxfev is a positive integer input variable. termination
c         occurs when the number of calls to fcn is at least
c         maxfev by the end of an iteration.
c
c       epsfcn is an input variable used in determining a suitable
c         step length for the forward-difference approximation. this
c         approximation assumes that the relative errors in the
c         functions are of the order of epsfcn. if epsfcn is less
c         than the machine precision, it is assumed that the relative
c         errors in the functions are of the order of the machine
c         precision.
c
c       diag is an array of length n. if mode = 1 (see
c         below), diag is internally set. if mode = 2, diag
c         must contain positive entries that serve as
c         multiplicative scale factors for the variables.
c
c       mode is an integer input variable. if mode = 1, the
c         variables will be scaled internally. if mode = 2,
c         the scaling is specified by the input diag. other
c         values of mode are equivalent to mode = 1.
c
c       factor is a positive input variable used in determining the
c         initial step bound. this bound is set to the product of
c         factor and the euclidean norm of diag*x if nonzero, or else
c         to factor itself. in most cases factor should lie in the
c         interval (.1,100.). 100. is a generally recommended value.
c
c       nprint is an integer input variable that enables controlled
c         printing of iterates if it is positive. in this case,
c         fcn is called with iflag = 0 at the beginning of the first
c         iteration and every nprint iterations thereafter and
c         immediately prior to return, with x and fvec available
c         for printing. if nprint is not positive, no special calls
c         of fcn with iflag = 0 are made.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0  improper input parameters.
c
c         info = 1  both actual and predicted relative reductions
c                   in the sum of squares are at most ftol.
c
c         info = 2  relative error between two consecutive iterates
c                   is at most xtol.
c
c         info = 3  conditions for info = 1 and info = 2 both hold.
c
c         info = 4  the cosine of the angle between fvec and any
c                   column of the jacobian is at most gtol in
c                   absolute value.
c
c         info = 5  number of calls to fcn has reached or
c                   exceeded maxfev.
c
c         info = 6  ftol is too small. no further reduction in
c                   the sum of squares is possible.
c
c         info = 7  xtol is too small. no further improvement in
c                   the approximate solution x is possible.
c
c         info = 8  gtol is too small. fvec is orthogonal to the
c                   columns of the jacobian to machine precision.
c
c       nfev is an integer output variable set to the number of
c         calls to fcn.
c
c       fjac is an output m by n array. the upper n by n submatrix
c         of fjac contains an upper triangular matrix r with
c         diagonal elements of nonincreasing magnitude such that
c
c                t     t           t
c               p *(jac *jac)*p = r *r,
c
c         where p is a permutation matrix and jac is the final
c         calculated jacobian. column j of p is column ipvt(j)
c         (see below) of the identity matrix. the lower trapezoidal
c         part of fjac contains information generated during
c         the computation of r.
c
c       ldfjac is a positive integer input variable not less than m
c         which specifies the leading dimension of the array fjac.
c
c       ipvt is an integer output array of length n. ipvt
c         defines a permutation matrix p such that jac*p = q*r,
c         where jac is the final calculated jacobian, q is
c         orthogonal (not stored), and r is upper triangular
c         with diagonal elements of nonincreasing magnitude.
c         column j of p is column ipvt(j) of the identity matrix.
c
c       qtf is an output array of length n which contains
c         the first n elements of the vector (q transpose)*fvec.
c
c       wa1, wa2, and wa3 are work arrays of length n.
c
c       wa4 is a work array of length m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... dpmparmp,enormmp,fdjac2,lmparmp,qrfacmp
c
c       fortran-supplied ... dabs,dmax1,dmin1,dsqrt,mod
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,iflag,iter,j,l
      double precision actred,delta,dirder,epsmch,fnorm,fnorm1,gnorm,
     *                 one,par,pnorm,prered,p1,p5,p25,p75,p0001,ratio,
     *                 sum,temp,temp1,temp2,xnorm,zero
      double precision dpmparmp,enormmp
      data one,p1,p5,p25,p75,p0001,zero
     *     /1.0d0,1.0d-1,5.0d-1,2.5d-1,7.5d-1,1.0d-4,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      info = 0
      iflag = 0
      nfev = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. m .lt. n .or. ldfjac .lt. m
     *    .or. ftol .lt. zero .or. xtol .lt. zero .or. gtol .lt. zero
     *    .or. maxfev .le. 0 .or. factor .le. zero) go to 300
      if (mode .ne. 2) go to 20
      do 10 j = 1, n
         if (diag(j) .le. zero) go to 300
   10    continue
   20 continue
c
c     evaluate the function at the starting point
c     and calculate its norm.
c
      iflag = 1
      call fcn(m,n,x,fvec,iflag)
      nfev = 1
      if (iflag .lt. 0) go to 300
      fnorm = enormmp(m,fvec)
c
c     initialize levenberg-marquardt parameter and iteration counter.
c
      par = zero
      iter = 1
c
c     beginning of the outer loop.
c
   30 continue
c
c        calculate the jacobian matrix.
c
         iflag = 2
         call fdjac2(fcn,m,n,x,fvec,fjac,ldfjac,iflag,epsfcn,wa4)
         nfev = nfev + n
         if (iflag .lt. 0) go to 300
c
c        if requested, call fcn to enable printing of iterates.
c
         if (nprint .le. 0) go to 40
         iflag = 0
         if (mod(iter-1,nprint) .eq. 0) call fcn(m,n,x,fvec,iflag)
         if (iflag .lt. 0) go to 300
   40    continue
c
c        compute the qr factorization of the jacobian.
c
         call qrfacmp(m,n,fjac,ldfjac,.true.,ipvt,n,wa1,wa2,wa3)
c
c        on the first iteration and if mode is 1, scale according
c        to the norms of the columns of the initial jacobian.
c
         if (iter .ne. 1) go to 80
         if (mode .eq. 2) go to 60
         do 50 j = 1, n
            diag(j) = wa2(j)
            if (wa2(j) .eq. zero) diag(j) = one
   50       continue
   60    continue
c
c        on the first iteration, calculate the norm of the scaled x
c        and initialize the step bound delta.
c
         do 70 j = 1, n
            wa3(j) = diag(j)*x(j)
   70       continue
         xnorm = enormmp(n,wa3)
         delta = factor*xnorm
         if (delta .eq. zero) delta = factor
   80    continue
c
c        form (q transpose)*fvec and store the first n components in
c        qtf.
c
         do 90 i = 1, m
            wa4(i) = fvec(i)
   90       continue
         do 130 j = 1, n
            if (fjac(j,j) .eq. zero) go to 120
            sum = zero
            do 100 i = j, m
               sum = sum + fjac(i,j)*wa4(i)
  100          continue
            temp = -sum/fjac(j,j)
            do 110 i = j, m
               wa4(i) = wa4(i) + fjac(i,j)*temp
  110          continue
  120       continue
            fjac(j,j) = wa1(j)
            qtf(j) = wa4(j)
  130       continue
c
c        compute the norm of the scaled gradient.
c
         gnorm = zero
         if (fnorm .eq. zero) go to 170
         do 160 j = 1, n
            l = ipvt(j)
            if (wa2(l) .eq. zero) go to 150
            sum = zero
            do 140 i = 1, j
               sum = sum + fjac(i,j)*(qtf(i)/fnorm)
  140          continue
            gnorm = dmax1(gnorm,dabs(sum/wa2(l)))
  150       continue
  160       continue
  170    continue
c
c        test for convergence of the gradient norm.
c
         if (gnorm .le. gtol) info = 4
         if (info .ne. 0) go to 300
c
c        rescale if necessary.
c
         if (mode .eq. 2) go to 190
         do 180 j = 1, n
            diag(j) = dmax1(diag(j),wa2(j))
  180       continue
  190    continue
c
c        beginning of the inner loop.
c
  200    continue
c
c           determine the levenberg-marquardt parameter.
c
            call lmparmp(n,fjac,ldfjac,ipvt,diag,qtf,delta,par,wa1,wa2,
     *                 wa3,wa4)
c
c           store the direction p and x + p. calculate the norm of p.
c
            do 210 j = 1, n
               wa1(j) = -wa1(j)
               wa2(j) = x(j) + wa1(j)
               wa3(j) = diag(j)*wa1(j)
  210          continue
            pnorm = enormmp(n,wa3)
c
c           on the first iteration, adjust the initial step bound.
c
            if (iter .eq. 1) delta = dmin1(delta,pnorm)
c
c           evaluate the function at x + p and calculate its norm.
c
            iflag = 1
            call fcn(m,n,wa2,wa4,iflag)
            nfev = nfev + 1
            if (iflag .lt. 0) go to 300
            fnorm1 = enormmp(m,wa4)
c
c           compute the scaled actual reduction.
c
            actred = -one
            if (p1*fnorm1 .lt. fnorm) actred = one - (fnorm1/fnorm)**2
c
c           compute the scaled predicted reduction and
c           the scaled directional derivative.
c
            do 230 j = 1, n
               wa3(j) = zero
               l = ipvt(j)
               temp = wa1(l)
               do 220 i = 1, j
                  wa3(i) = wa3(i) + fjac(i,j)*temp
  220             continue
  230          continue
            temp1 = enormmp(n,wa3)/fnorm
            temp2 = (dsqrt(par)*pnorm)/fnorm
            prered = temp1**2 + temp2**2/p5
            dirder = -(temp1**2 + temp2**2)
c
c           compute the ratio of the actual to the predicted
c           reduction.
c
            ratio = zero
            if (prered .ne. zero) ratio = actred/prered
c
c           update the step bound.
c
            if (ratio .gt. p25) go to 240
               if (actred .ge. zero) temp = p5
               if (actred .lt. zero)
     *            temp = p5*dirder/(dirder + p5*actred)
               if (p1*fnorm1 .ge. fnorm .or. temp .lt. p1) temp = p1
               delta = temp*dmin1(delta,pnorm/p1)
               par = par/temp
               go to 260
  240       continue
               if (par .ne. zero .and. ratio .lt. p75) go to 250
               delta = pnorm/p5
               par = p5*par
  250          continue
  260       continue
c
c           test for successful iteration.
c
            if (ratio .lt. p0001) go to 290
c
c           successful iteration. update x, fvec, and their norms.
c
            do 270 j = 1, n
               x(j) = wa2(j)
               wa2(j) = diag(j)*x(j)
  270          continue
            do 280 i = 1, m
               fvec(i) = wa4(i)
  280          continue
            xnorm = enormmp(n,wa2)
            fnorm = fnorm1
            iter = iter + 1
  290       continue
c
c           tests for convergence.
c
            if (dabs(actred) .le. ftol .and. prered .le. ftol
     *          .and. p5*ratio .le. one) info = 1
            if (delta .le. xtol*xnorm) info = 2
            if (dabs(actred) .le. ftol .and. prered .le. ftol
     *          .and. p5*ratio .le. one .and. info .eq. 2) info = 3
            if (info .ne. 0) go to 300
c
c           tests for termination and stringent tolerances.
c
            if (nfev .ge. maxfev) info = 5
            if (dabs(actred) .le. epsmch .and. prered .le. epsmch
     *          .and. p5*ratio .le. one) info = 6
            if (delta .le. epsmch*xnorm) info = 7
            if (gnorm .le. epsmch) info = 8
            if (info .ne. 0) go to 300
c
c           end of the inner loop. repeat if iteration unsuccessful.
c
            if (ratio .lt. p0001) go to 200
c
c        end of the outer loop.
c
         go to 30
  300 continue
c
c     termination, either normal or user imposed.
c
      if (iflag .lt. 0) info = iflag
      iflag = 0
      if (nprint .gt. 0) call fcn(m,n,x,fvec,iflag)
      return
c
c     last card of subroutine lmdif.
c
      end
      subroutine lmdif1(fcn,m,n,x,fvec,tol,info,iwa,wa,lwa)
      integer m,n,info,lwa
      integer iwa(n)
      double precision tol
      double precision x(n),fvec(m),wa(lwa)
      external fcn

c*********************************************************************72
c
cc LMDIF1 minimizes M functions in N variables using the Levenberg-Marquardt method.
c
c     the purpose of lmdif1 is to minimize the sum of the squares of
c     m nonlinear functions in n variables by a modification of the
c     levenberg-marquardt algorithm. this is done by using the more
c     general least-squares solver lmdif. the user must provide a
c     subroutine which calculates the functions. the jacobian is
c     then calculated by a forward-difference approximation.
c
c     the subroutine statement is
c
c       subroutine lmdif1(fcn,m,n,x,fvec,tol,info,iwa,wa,lwa)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions. fcn must be declared
c         in an external statement in the user calling
c         program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,iflag)
c         integer m,n,iflag
c         double precision x(n),fvec(m)
c         ----------
c         calculate the functions at x and
c         return this vector in fvec.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of lmdif1.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length m which contains
c         the functions evaluated at the output x.
c
c       tol is a nonnegative input variable. termination occurs
c         when the algorithm estimates either that the relative
c         error in the sum of squares is at most tol or that
c         the relative error between x and the solution is at
c         most tol.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0  improper input parameters.
c
c         info = 1  algorithm estimates that the relative error
c                   in the sum of squares is at most tol.
c
c         info = 2  algorithm estimates that the relative error
c                   between x and the solution is at most tol.
c
c         info = 3  conditions for info = 1 and info = 2 both hold.
c
c         info = 4  fvec is orthogonal to the columns of the
c                   jacobian to machine precision.
c
c         info = 5  number of calls to fcn has reached or
c                   exceeded 200*(n+1).
c
c         info = 6  tol is too small. no further reduction in
c                   the sum of squares is possible.
c
c         info = 7  tol is too small. no further improvement in
c                   the approximate solution x is possible.
c
c       iwa is an integer work array of length n.
c
c       wa is a work array of length lwa.
c
c       lwa is a positive integer input variable not less than
c         m*n+5*n+m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... lmdif
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer maxfev,mode,mp5n,nfev,nprint
      double precision epsfcn,factor,ftol,gtol,xtol,zero
      data factor,zero /1.0d2,0.0d0/
      info = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. m .lt. n .or. tol .lt. zero
     *    .or. lwa .lt. m*n + 5*n + m) go to 10
c
c     call lmdif.
c
      maxfev = 200*(n + 1)
      ftol = tol
      xtol = tol
      gtol = zero
      epsfcn = zero
      mode = 1
      nprint = 0
      mp5n = m + 5*n
      call lmdif(fcn,m,n,x,fvec,ftol,xtol,gtol,maxfev,epsfcn,wa(1),
     *           mode,factor,nprint,info,nfev,wa(mp5n+1),m,iwa,
     *           wa(n+1),wa(2*n+1),wa(3*n+1),wa(4*n+1),wa(5*n+1))
      if (info .eq. 8) info = 4
   10 continue
      return
c
c     last card of subroutine lmdif1.
c
      end
      subroutine lmparmp(n,r,ldr,ipvt,diag,qtb,delta,par,x,sdiag,wa1,
     *                 wa2)
      integer n,ldr
      integer ipvt(n)
      double precision delta,par
      double precision r(ldr,n),diag(n),qtb(n),x(n),sdiag(n),wa1(n),
     *                 wa2(n)

c*********************************************************************72
c
cc LMPAR computes a parameter for the Levenberg-Marquardt method.
c
c     given an m by n matrix a, an n by n nonsingular diagonal
c     matrix d, an m-vector b, and a positive number delta,
c     the problem is to determine a value for the parameter
c     par such that if x solves the system
c
c           a*x = b ,     sqrt(par)*d*x = 0 ,
c
c     in the least squares sense, and dxnorm is the euclidean
c     norm of d*x, then either par is zero and
c
c           (dxnorm-delta) .le. 0.1*delta ,
c
c     or par is positive and
c
c           abs(dxnorm-delta) .le. 0.1*delta .
c
c     this subroutine completes the solution of the problem
c     if it is provided with the necessary information from the
c     qr factorization, with column pivoting, of a. that is, if
c     a*p = q*r, where p is a permutation matrix, q has orthogonal
c     columns, and r is an upper triangular matrix with diagonal
c     elements of nonincreasing magnitude, then lmparmp expects
c     the full upper triangle of r, the permutation matrix p,
c     and the first n components of (q transpose)*b. on output
c     lmparmp also provides an upper triangular matrix s such that
c
c            t   t                   t
c           p *(a *a + par*d*d)*p = s *s .
c
c     s is employed within lmparmp and may be of separate interest.
c
c     only a few iterations are generally needed for convergence
c     of the algorithm. if, however, the limit of 10 iterations
c     is reached, then the output par will contain the best
c     value obtained so far.
c
c     the subroutine statement is
c
c       subroutine lmparmp(n,r,ldr,ipvt,diag,qtb,delta,par,x,sdiag,
c                        wa1,wa2)
c
c     where
c
c       n is a positive integer input variable set to the order of r.
c
c       r is an n by n array. on input the full upper triangle
c         must contain the full upper triangle of the matrix r.
c         on output the full upper triangle is unaltered, and the
c         strict lower triangle contains the strict upper triangle
c         (transposed) of the upper triangular matrix s.
c
c       ldr is a positive integer input variable not less than n
c         which specifies the leading dimension of the array r.
c
c       ipvt is an integer input array of length n which defines the
c         permutation matrix p such that a*p = q*r. column j of p
c         is column ipvt(j) of the identity matrix.
c
c       diag is an input array of length n which must contain the
c         diagonal elements of the matrix d.
c
c       qtb is an input array of length n which must contain the first
c         n elements of the vector (q transpose)*b.
c
c       delta is a positive input variable which specifies an upper
c         bound on the euclidean norm of d*x.
c
c       par is a nonnegative variable. on input par contains an
c         initial estimate of the levenberg-marquardt parameter.
c         on output par contains the final estimate.
c
c       x is an output array of length n which contains the least
c         squares solution of the system a*x = b, sqrt(par)*d*x = 0,
c         for the output par.
c
c       sdiag is an output array of length n which contains the
c         diagonal elements of the upper triangular matrix s.
c
c       wa1 and wa2 are work arrays of length n.
c
c     subprograms called
c
c       minpack-supplied ... dpmparmp,enormmp,qrsolvmp
c
c       fortran-supplied ... dabs,dmax1,dmin1,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,iter,j,jm1,jp1,k,l,nsing
      double precision dxnorm,dwarf,fp,gnorm,parc,parl,paru,p1,p001,
     *                 sum,temp,zero
      double precision dpmparmp,enormmp
      data p1,p001,zero /1.0d-1,1.0d-3,0.0d0/
c
c     dwarf is the smallest positive magnitude.
c
      dwarf = dpmparmp(2)
c
c     compute and store in x the gauss-newton direction. if the
c     jacobian is rank-deficient, obtain a least squares solution.
c
      nsing = n
      do 10 j = 1, n
         wa1(j) = qtb(j)
         if (r(j,j) .eq. zero .and. nsing .eq. n) nsing = j - 1
         if (nsing .lt. n) wa1(j) = zero
   10    continue
      if (nsing .lt. 1) go to 50
      do 40 k = 1, nsing
         j = nsing - k + 1
         wa1(j) = wa1(j)/r(j,j)
         temp = wa1(j)
         jm1 = j - 1
         if (jm1 .lt. 1) go to 30
         do 20 i = 1, jm1
            wa1(i) = wa1(i) - r(i,j)*temp
   20       continue
   30    continue
   40    continue
   50 continue
      do 60 j = 1, n
         l = ipvt(j)
         x(l) = wa1(j)
   60    continue
c
c     initialize the iteration counter.
c     evaluate the function at the origin, and test
c     for acceptance of the gauss-newton direction.
c
      iter = 0
      do 70 j = 1, n
         wa2(j) = diag(j)*x(j)
   70    continue
      dxnorm = enormmp(n,wa2)
      fp = dxnorm - delta
      if (fp .le. p1*delta) go to 220
c
c     if the jacobian is not rank deficient, the newton
c     step provides a lower bound, parl, for the zero of
c     the function. otherwise set this bound to zero.
c
      parl = zero
      if (nsing .lt. n) go to 120
      do 80 j = 1, n
         l = ipvt(j)
         wa1(j) = diag(l)*(wa2(l)/dxnorm)
   80    continue
      do 110 j = 1, n
         sum = zero
         jm1 = j - 1
         if (jm1 .lt. 1) go to 100
         do 90 i = 1, jm1
            sum = sum + r(i,j)*wa1(i)
   90       continue
  100    continue
         wa1(j) = (wa1(j) - sum)/r(j,j)
  110    continue
      temp = enormmp(n,wa1)
      parl = ((fp/delta)/temp)/temp
  120 continue
c
c     calculate an upper bound, paru, for the zero of the function.
c
      do 140 j = 1, n
         sum = zero
         do 130 i = 1, j
            sum = sum + r(i,j)*qtb(i)
  130       continue
         l = ipvt(j)
         wa1(j) = sum/diag(l)
  140    continue
      gnorm = enormmp(n,wa1)
      paru = gnorm/delta
      if (paru .eq. zero) paru = dwarf/dmin1(delta,p1)
c
c     if the input par lies outside of the interval (parl,paru),
c     set par to the closer endpoint.
c
      par = dmax1(par,parl)
      par = dmin1(par,paru)
      if (par .eq. zero) par = gnorm/dxnorm
c
c     beginning of an iteration.
c
  150 continue
         iter = iter + 1
c
c        evaluate the function at the current value of par.
c
         if (par .eq. zero) par = dmax1(dwarf,p001*paru)
         temp = dsqrt(par)
         do 160 j = 1, n
            wa1(j) = temp*diag(j)
  160       continue
         call qrsolvmp(n,r,ldr,ipvt,wa1,qtb,x,sdiag,wa2)
         do 170 j = 1, n
            wa2(j) = diag(j)*x(j)
  170       continue
         dxnorm = enormmp(n,wa2)
         temp = fp
         fp = dxnorm - delta
c
c        if the function is small enough, accept the current value
c        of par. also test for the exceptional cases where parl
c        is zero or the number of iterations has reached 10.
c
         if (dabs(fp) .le. p1*delta
     *       .or. parl .eq. zero .and. fp .le. temp
     *            .and. temp .lt. zero .or. iter .eq. 10) go to 220
c
c        compute the newton correction.
c
         do 180 j = 1, n
            l = ipvt(j)
            wa1(j) = diag(l)*(wa2(l)/dxnorm)
  180       continue
         do 210 j = 1, n
            wa1(j) = wa1(j)/sdiag(j)
            temp = wa1(j)
            jp1 = j + 1
            if (n .lt. jp1) go to 200
            do 190 i = jp1, n
               wa1(i) = wa1(i) - r(i,j)*temp
  190          continue
  200       continue
  210       continue
         temp = enormmp(n,wa1)
         parc = ((fp/delta)/temp)/temp
c
c        depending on the sign of the function, update parl or paru.
c
         if (fp .gt. zero) parl = dmax1(parl,par)
         if (fp .lt. zero) paru = dmin1(paru,par)
c
c        compute an improved estimate for par.
c
         par = dmax1(parl,par+parc)
c
c        end of an iteration.
c
         go to 150
  220 continue
c
c     termination.
c
      if (iter .eq. 0) par = zero
      return
c
c     last card of subroutine lmparmp.
c
      end
      subroutine lmstr(fcn,m,n,x,fvec,fjac,ldfjac,ftol,xtol,gtol,
     *                 maxfev,diag,mode,factor,nprint,info,nfev,njev,
     *                 ipvt,qtf,wa1,wa2,wa3,wa4)
      integer m,n,ldfjac,maxfev,mode,nprint,info,nfev,njev
      integer ipvt(n)
      logical sing
      double precision ftol,xtol,gtol,factor
      double precision x(n),fvec(m),fjac(ldfjac,n),diag(n),qtf(n),
     *                 wa1(n),wa2(n),wa3(n),wa4(m)

c*********************************************************************72
c
cc LMSTR minimizes M functions in N variables using the Levenberg-Marquardt method.
c
c     the purpose of lmstr is to minimize the sum of the squares of
c     m nonlinear functions in n variables by a modification of
c     the levenberg-marquardt algorithm which uses minimal storage.
c     the user must provide a subroutine which calculates the
c     functions and the rows of the jacobian.
c
c     the subroutine statement is
c
c       subroutine lmstr(fcn,m,n,x,fvec,fjac,ldfjac,ftol,xtol,gtol,
c                        maxfev,diag,mode,factor,nprint,info,nfev,
c                        njev,ipvt,qtf,wa1,wa2,wa3,wa4)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions and the rows of the jacobian.
c         fcn must be declared in an external statement in the
c         user calling program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,fjrow,iflag)
c         integer m,n,iflag
c         double precision x(n),fvec(m),fjrow(n)
c         ----------
c         if iflag = 1 calculate the functions at x and
c         return this vector in fvec.
c         if iflag = i calculate the (i-1)-st row of the
c         jacobian at x and return this vector in fjrow.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of lmstr.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length m which contains
c         the functions evaluated at the output x.
c
c       fjac is an output n by n array. the upper triangle of fjac
c         contains an upper triangular matrix r such that
c
c                t     t           t
c               p *(jac *jac)*p = r *r,
c
c         where p is a permutation matrix and jac is the final
c         calculated jacobian. column j of p is column ipvt(j)
c         (see below) of the identity matrix. the lower triangular
c         part of fjac contains information generated during
c         the computation of r.
c
c       ldfjac is a positive integer input variable not less than n
c         which specifies the leading dimension of the array fjac.
c
c       ftol is a nonnegative input variable. termination
c         occurs when both the actual and predicted relative
c         reductions in the sum of squares are at most ftol.
c         therefore, ftol measures the relative error desired
c         in the sum of squares.
c
c       xtol is a nonnegative input variable. termination
c         occurs when the relative error between two consecutive
c         iterates is at most xtol. therefore, xtol measures the
c         relative error desired in the approximate solution.
c
c       gtol is a nonnegative input variable. termination
c         occurs when the cosine of the angle between fvec and
c         any column of the jacobian is at most gtol in absolute
c         value. therefore, gtol measures the orthogonality
c         desired between the function vector and the columns
c         of the jacobian.
c
c       maxfev is a positive integer input variable. termination
c         occurs when the number of calls to fcn with iflag = 1
c         has reached maxfev.
c
c       diag is an array of length n. if mode = 1 (see
c         below), diag is internally set. if mode = 2, diag
c         must contain positive entries that serve as
c         multiplicative scale factors for the variables.
c
c       mode is an integer input variable. if mode = 1, the
c         variables will be scaled internally. if mode = 2,
c         the scaling is specified by the input diag. other
c         values of mode are equivalent to mode = 1.
c
c       factor is a positive input variable used in determining the
c         initial step bound. this bound is set to the product of
c         factor and the euclidean norm of diag*x if nonzero, or else
c         to factor itself. in most cases factor should lie in the
c         interval (.1,100.). 100. is a generally recommended value.
c
c       nprint is an integer input variable that enables controlled
c         printing of iterates if it is positive. in this case,
c         fcn is called with iflag = 0 at the beginning of the first
c         iteration and every nprint iterations thereafter and
c         immediately prior to return, with x and fvec available
c         for printing. if nprint is not positive, no special calls
c         of fcn with iflag = 0 are made.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0  improper input parameters.
c
c         info = 1  both actual and predicted relative reductions
c                   in the sum of squares are at most ftol.
c
c         info = 2  relative error between two consecutive iterates
c                   is at most xtol.
c
c         info = 3  conditions for info = 1 and info = 2 both hold.
c
c         info = 4  the cosine of the angle between fvec and any
c                   column of the jacobian is at most gtol in
c                   absolute value.
c
c         info = 5  number of calls to fcn with iflag = 1 has
c                   reached maxfev.
c
c         info = 6  ftol is too small. no further reduction in
c                   the sum of squares is possible.
c
c         info = 7  xtol is too small. no further improvement in
c                   the approximate solution x is possible.
c
c         info = 8  gtol is too small. fvec is orthogonal to the
c                   columns of the jacobian to machine precision.
c
c       nfev is an integer output variable set to the number of
c         calls to fcn with iflag = 1.
c
c       njev is an integer output variable set to the number of
c         calls to fcn with iflag = 2.
c
c       ipvt is an integer output array of length n. ipvt
c         defines a permutation matrix p such that jac*p = q*r,
c         where jac is the final calculated jacobian, q is
c         orthogonal (not stored), and r is upper triangular.
c         column j of p is column ipvt(j) of the identity matrix.
c
c       qtf is an output array of length n which contains
c         the first n elements of the vector (q transpose)*fvec.
c
c       wa1, wa2, and wa3 are work arrays of length n.
c
c       wa4 is a work array of length m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... dpmparmp,enormmp,lmparmp,qrfacmp,rwupdt
c
c       fortran-supplied ... dabs,dmax1,dmin1,dsqrt,mod
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, dudley v. goetschel, kenneth e. hillstrom,
c     jorge j. more
c
c     **********
      integer i,iflag,iter,j,l
      double precision actred,delta,dirder,epsmch,fnorm,fnorm1,gnorm,
     *                 one,par,pnorm,prered,p1,p5,p25,p75,p0001,ratio,
     *                 sum,temp,temp1,temp2,xnorm,zero
      double precision dpmparmp,enormmp
      data one,p1,p5,p25,p75,p0001,zero
     *     /1.0d0,1.0d-1,5.0d-1,2.5d-1,7.5d-1,1.0d-4,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
      info = 0
      iflag = 0
      nfev = 0
      njev = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. m .lt. n .or. ldfjac .lt. n
     *    .or. ftol .lt. zero .or. xtol .lt. zero .or. gtol .lt. zero
     *    .or. maxfev .le. 0 .or. factor .le. zero) go to 340
      if (mode .ne. 2) go to 20
      do 10 j = 1, n
         if (diag(j) .le. zero) go to 340
   10    continue
   20 continue
c
c     evaluate the function at the starting point
c     and calculate its norm.
c
      iflag = 1
      call fcn(m,n,x,fvec,wa3,iflag)
      nfev = 1
      if (iflag .lt. 0) go to 340
      fnorm = enormmp(m,fvec)
c
c     initialize levenberg-marquardt parameter and iteration counter.
c
      par = zero
      iter = 1
c
c     beginning of the outer loop.
c
   30 continue
c
c        if requested, call fcn to enable printing of iterates.
c
         if (nprint .le. 0) go to 40
         iflag = 0
         if (mod(iter-1,nprint) .eq. 0) call fcn(m,n,x,fvec,wa3,iflag)
         if (iflag .lt. 0) go to 340
   40    continue
c
c        compute the qr factorization of the jacobian matrix
c        calculated one row at a time, while simultaneously
c        forming (q transpose)*fvec and storing the first
c        n components in qtf.
c
         do 60 j = 1, n
            qtf(j) = zero
            do 50 i = 1, n
               fjac(i,j) = zero
   50          continue
   60       continue
         iflag = 2
         do 70 i = 1, m
            call fcn(m,n,x,fvec,wa3,iflag)
            if (iflag .lt. 0) go to 340
            temp = fvec(i)
            call rwupdt(n,fjac,ldfjac,wa3,qtf,temp,wa1,wa2)
            iflag = iflag + 1
   70       continue
         njev = njev + 1
c
c        if the jacobian is rank deficient, call qrfacmp to
c        reorder its columns and update the components of qtf.
c
         sing = .false.
         do 80 j = 1, n
            if (fjac(j,j) .eq. zero) sing = .true.
            ipvt(j) = j
            wa2(j) = enormmp(j,fjac(1,j))
   80       continue
         if (.not.sing) go to 130
         call qrfacmp(n,n,fjac,ldfjac,.true.,ipvt,n,wa1,wa2,wa3)
         do 120 j = 1, n
            if (fjac(j,j) .eq. zero) go to 110
            sum = zero
            do 90 i = j, n
               sum = sum + fjac(i,j)*qtf(i)
   90          continue
            temp = -sum/fjac(j,j)
            do 100 i = j, n
               qtf(i) = qtf(i) + fjac(i,j)*temp
  100          continue
  110       continue
            fjac(j,j) = wa1(j)
  120       continue
  130    continue
c
c        on the first iteration and if mode is 1, scale according
c        to the norms of the columns of the initial jacobian.
c
         if (iter .ne. 1) go to 170
         if (mode .eq. 2) go to 150
         do 140 j = 1, n
            diag(j) = wa2(j)
            if (wa2(j) .eq. zero) diag(j) = one
  140       continue
  150    continue
c
c        on the first iteration, calculate the norm of the scaled x
c        and initialize the step bound delta.
c
         do 160 j = 1, n
            wa3(j) = diag(j)*x(j)
  160       continue
         xnorm = enormmp(n,wa3)
         delta = factor*xnorm
         if (delta .eq. zero) delta = factor
  170    continue
c
c        compute the norm of the scaled gradient.
c
         gnorm = zero
         if (fnorm .eq. zero) go to 210
         do 200 j = 1, n
            l = ipvt(j)
            if (wa2(l) .eq. zero) go to 190
            sum = zero
            do 180 i = 1, j
               sum = sum + fjac(i,j)*(qtf(i)/fnorm)
  180          continue
            gnorm = dmax1(gnorm,dabs(sum/wa2(l)))
  190       continue
  200       continue
  210    continue
c
c        test for convergence of the gradient norm.
c
         if (gnorm .le. gtol) info = 4
         if (info .ne. 0) go to 340
c
c        rescale if necessary.
c
         if (mode .eq. 2) go to 230
         do 220 j = 1, n
            diag(j) = dmax1(diag(j),wa2(j))
  220       continue
  230    continue
c
c        beginning of the inner loop.
c
  240    continue
c
c           determine the levenberg-marquardt parameter.
c
            call lmparmp(n,fjac,ldfjac,ipvt,diag,qtf,delta,par,wa1,wa2,
     *                 wa3,wa4)
c
c           store the direction p and x + p. calculate the norm of p.
c
            do 250 j = 1, n
               wa1(j) = -wa1(j)
               wa2(j) = x(j) + wa1(j)
               wa3(j) = diag(j)*wa1(j)
  250          continue
            pnorm = enormmp(n,wa3)
c
c           on the first iteration, adjust the initial step bound.
c
            if (iter .eq. 1) delta = dmin1(delta,pnorm)
c
c           evaluate the function at x + p and calculate its norm.
c
            iflag = 1
            call fcn(m,n,wa2,wa4,wa3,iflag)
            nfev = nfev + 1
            if (iflag .lt. 0) go to 340
            fnorm1 = enormmp(m,wa4)
c
c           compute the scaled actual reduction.
c
            actred = -one
            if (p1*fnorm1 .lt. fnorm) actred = one - (fnorm1/fnorm)**2
c
c           compute the scaled predicted reduction and
c           the scaled directional derivative.
c
            do 270 j = 1, n
               wa3(j) = zero
               l = ipvt(j)
               temp = wa1(l)
               do 260 i = 1, j
                  wa3(i) = wa3(i) + fjac(i,j)*temp
  260             continue
  270          continue
            temp1 = enormmp(n,wa3)/fnorm
            temp2 = (dsqrt(par)*pnorm)/fnorm
            prered = temp1**2 + temp2**2/p5
            dirder = -(temp1**2 + temp2**2)
c
c           compute the ratio of the actual to the predicted
c           reduction.
c
            ratio = zero
            if (prered .ne. zero) ratio = actred/prered
c
c           update the step bound.
c
            if (ratio .gt. p25) go to 280
               if (actred .ge. zero) temp = p5
               if (actred .lt. zero)
     *            temp = p5*dirder/(dirder + p5*actred)
               if (p1*fnorm1 .ge. fnorm .or. temp .lt. p1) temp = p1
               delta = temp*dmin1(delta,pnorm/p1)
               par = par/temp
               go to 300
  280       continue
               if (par .ne. zero .and. ratio .lt. p75) go to 290
               delta = pnorm/p5
               par = p5*par
  290          continue
  300       continue
c
c           test for successful iteration.
c
            if (ratio .lt. p0001) go to 330
c
c           successful iteration. update x, fvec, and their norms.
c
            do 310 j = 1, n
               x(j) = wa2(j)
               wa2(j) = diag(j)*x(j)
  310          continue
            do 320 i = 1, m
               fvec(i) = wa4(i)
  320          continue
            xnorm = enormmp(n,wa2)
            fnorm = fnorm1
            iter = iter + 1
  330       continue
c
c           tests for convergence.
c
            if (dabs(actred) .le. ftol .and. prered .le. ftol
     *          .and. p5*ratio .le. one) info = 1
            if (delta .le. xtol*xnorm) info = 2
            if (dabs(actred) .le. ftol .and. prered .le. ftol
     *          .and. p5*ratio .le. one .and. info .eq. 2) info = 3
            if (info .ne. 0) go to 340
c
c           tests for termination and stringent tolerances.
c
            if (nfev .ge. maxfev) info = 5
            if (dabs(actred) .le. epsmch .and. prered .le. epsmch
     *          .and. p5*ratio .le. one) info = 6
            if (delta .le. epsmch*xnorm) info = 7
            if (gnorm .le. epsmch) info = 8
            if (info .ne. 0) go to 340
c
c           end of the inner loop. repeat if iteration unsuccessful.
c
            if (ratio .lt. p0001) go to 240
c
c        end of the outer loop.
c
         go to 30
  340 continue
c
c     termination, either normal or user imposed.
c
      if (iflag .lt. 0) info = iflag
      iflag = 0
      if (nprint .gt. 0) call fcn(m,n,x,fvec,wa3,iflag)
      return
c
c     last card of subroutine lmstr.
c
      end
      subroutine lmstr1(fcn,m,n,x,fvec,fjac,ldfjac,tol,info,ipvt,wa,
     *                  lwa)
      integer m,n,ldfjac,info,lwa
      integer ipvt(n)
      double precision tol
      double precision x(n),fvec(m),fjac(ldfjac,n),wa(lwa)
      external fcn

c*********************************************************************72
c
cc LMSTR1 minimizes M functions in N variables using the Levenberg-Marquardt method.
c
c     the purpose of lmstr1 is to minimize the sum of the squares of
c     m nonlinear functions in n variables by a modification of
c     the levenberg-marquardt algorithm which uses minimal storage.
c     this is done by using the more general least-squares solver
c     lmstr. the user must provide a subroutine which calculates
c     the functions and the rows of the jacobian.
c
c     the subroutine statement is
c
c       subroutine lmstr1(fcn,m,n,x,fvec,fjac,ldfjac,tol,info,
c                         ipvt,wa,lwa)
c
c     where
c
c       fcn is the name of the user-supplied subroutine which
c         calculates the functions and the rows of the jacobian.
c         fcn must be declared in an external statement in the
c         user calling program, and should be written as follows.
c
c         subroutine fcn(m,n,x,fvec,fjrow,iflag)
c         integer m,n,iflag
c         double precision x(n),fvec(m),fjrow(n)
c         ----------
c         if iflag = 1 calculate the functions at x and
c         return this vector in fvec.
c         if iflag = i calculate the (i-1)-st row of the
c         jacobian at x and return this vector in fjrow.
c         ----------
c         return
c         end
c
c         the value of iflag should not be changed by fcn unless
c         the user wants to terminate execution of lmstr1.
c         in this case set iflag to a negative integer.
c
c       m is a positive integer input variable set to the number
c         of functions.
c
c       n is a positive integer input variable set to the number
c         of variables. n must not exceed m.
c
c       x is an array of length n. on input x must contain
c         an initial estimate of the solution vector. on output x
c         contains the final estimate of the solution vector.
c
c       fvec is an output array of length m which contains
c         the functions evaluated at the output x.
c
c       fjac is an output n by n array. the upper triangle of fjac
c         contains an upper triangular matrix r such that
c
c                t     t           t
c               p *(jac *jac)*p = r *r,
c
c         where p is a permutation matrix and jac is the final
c         calculated jacobian. column j of p is column ipvt(j)
c         (see below) of the identity matrix. the lower triangular
c         part of fjac contains information generated during
c         the computation of r.
c
c       ldfjac is a positive integer input variable not less than n
c         which specifies the leading dimension of the array fjac.
c
c       tol is a nonnegative input variable. termination occurs
c         when the algorithm estimates either that the relative
c         error in the sum of squares is at most tol or that
c         the relative error between x and the solution is at
c         most tol.
c
c       info is an integer output variable. if the user has
c         terminated execution, info is set to the (negative)
c         value of iflag. see description of fcn. otherwise,
c         info is set as follows.
c
c         info = 0  improper input parameters.
c
c         info = 1  algorithm estimates that the relative error
c                   in the sum of squares is at most tol.
c
c         info = 2  algorithm estimates that the relative error
c                   between x and the solution is at most tol.
c
c         info = 3  conditions for info = 1 and info = 2 both hold.
c
c         info = 4  fvec is orthogonal to the columns of the
c                   jacobian to machine precision.
c
c         info = 5  number of calls to fcn with iflag = 1 has
c                   reached 100*(n+1).
c
c         info = 6  tol is too small. no further reduction in
c                   the sum of squares is possible.
c
c         info = 7  tol is too small. no further improvement in
c                   the approximate solution x is possible.
c
c       ipvt is an integer output array of length n. ipvt
c         defines a permutation matrix p such that jac*p = q*r,
c         where jac is the final calculated jacobian, q is
c         orthogonal (not stored), and r is upper triangular.
c         column j of p is column ipvt(j) of the identity matrix.
c
c       wa is a work array of length lwa.
c
c       lwa is a positive integer input variable not less than 5*n+m.
c
c     subprograms called
c
c       user-supplied ...... fcn
c
c       minpack-supplied ... lmstr
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, dudley v. goetschel, kenneth e. hillstrom,
c     jorge j. more
c
c     **********
      integer maxfev,mode,nfev,njev,nprint
      double precision factor,ftol,gtol,xtol,zero
      data factor,zero /1.0d2,0.0d0/
      info = 0
c
c     check the input parameters for errors.
c
      if (n .le. 0 .or. m .lt. n .or. ldfjac .lt. n .or. tol .lt. zero
     *    .or. lwa .lt. 5*n + m) go to 10
c
c     call lmstr.
c
      maxfev = 100*(n + 1)
      ftol = tol
      xtol = tol
      gtol = zero
      mode = 1
      nprint = 0
      call lmstr(fcn,m,n,x,fvec,fjac,ldfjac,ftol,xtol,gtol,maxfev,
     *           wa(1),mode,factor,nprint,info,nfev,njev,ipvt,wa(n+1),
     *           wa(2*n+1),wa(3*n+1),wa(4*n+1),wa(5*n+1))
      if (info .eq. 8) info = 4
   10 continue
      return
c
c     last card of subroutine lmstr1.
c
      end
      subroutine qform(m,n,q,ldq,wa)
      integer m,n,ldq
      double precision q(ldq,m),wa(m)
c     **********
c
cc QFORM produces the explicit QR factorization of a matrix.
c
c     this subroutine proceeds from the computed qr factorization of
c     an m by n matrix a to accumulate the m by m orthogonal matrix
c     q from its factored form.
c
c     the subroutine statement is
c
c       subroutine qform(m,n,q,ldq,wa)
c
c     where
c
c       m is a positive integer input variable set to the number
c         of rows of a and the order of q.
c
c       n is a positive integer input variable set to the number
c         of columns of a.
c
c       q is an m by m array. on input the full lower trapezoid in
c         the first min(m,n) columns of q contains the factored form.
c         on output q has been accumulated into a square matrix.
c
c       ldq is a positive integer input variable not less than m
c         which specifies the leading dimension of the array q.
c
c       wa is a work array of length m.
c
c     subprograms called
c
c       fortran-supplied ... min0
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j,jm1,k,l,minmn,np1
      double precision one,sum,temp,zero
      data one,zero /1.0d0,0.0d0/
c
c     zero out upper triangle of q in the first min(m,n) columns.
c
      minmn = min0(m,n)
      if (minmn .lt. 2) go to 30
      do 20 j = 2, minmn
         jm1 = j - 1
         do 10 i = 1, jm1
            q(i,j) = zero
   10       continue
   20    continue
   30 continue
c
c     initialize remaining columns to those of the identity matrix.
c
      np1 = n + 1
      if (m .lt. np1) go to 60
      do 50 j = np1, m
         do 40 i = 1, m
            q(i,j) = zero
   40       continue
         q(j,j) = one
   50    continue
   60 continue
c
c     accumulate q from its factored form.
c
      do 120 l = 1, minmn
         k = minmn - l + 1
         do 70 i = k, m
            wa(i) = q(i,k)
            q(i,k) = zero
   70       continue
         q(k,k) = one
         if (wa(k) .eq. zero) go to 110
         do 100 j = k, m
            sum = zero
            do 80 i = k, m
               sum = sum + q(i,j)*wa(i)
   80          continue
            temp = sum/wa(k)
            do 90 i = k, m
               q(i,j) = q(i,j) - temp*wa(i)
   90          continue
  100       continue
  110    continue
  120    continue
      return
c
c     last card of subroutine qform.
c
      end
      subroutine qrfacmp(m,n,a,lda,pivot,ipvt,lipvt,rdiag,acnorm,wa)
      integer m,n,lda,lipvt
      integer ipvt(lipvt)
      logical pivot
      double precision a(lda,n),rdiag(n),acnorm(n),wa(n)

c*********************************************************************72
c
cc QRFAC computes a QR factorization using Householder transformations.
c
c     this subroutine uses householder transformations with column
c     pivoting (optional) to compute a qr factorization of the
c     m by n matrix a. that is, qrfacmp determines an orthogonal
c     matrix q, a permutation matrix p, and an upper trapezoidal
c     matrix r with diagonal elements of nonincreasing magnitude,
c     such that a*p = q*r. the householder transformation for
c     column k, k = 1,2,...,min(m,n), is of the form
c
c                           t
c           i - (1/u(k))*u*u
c
c     where u has zeros in the first k-1 positions. the form of
c     this transformation and the method of pivoting first
c     appeared in the corresponding linpack subroutine.
c
c     the subroutine statement is
c
c       subroutine qrfacmp(m,n,a,lda,pivot,ipvt,lipvt,rdiag,acnorm,wa)
c
c     where
c
c       m is a positive integer input variable set to the number
c         of rows of a.
c
c       n is a positive integer input variable set to the number
c         of columns of a.
c
c       a is an m by n array. on input a contains the matrix for
c         which the qr factorization is to be computed. on output
c         the strict upper trapezoidal part of a contains the strict
c         upper trapezoidal part of r, and the lower trapezoidal
c         part of a contains a factored form of q (the non-trivial
c         elements of the u vectors described above).
c
c       lda is a positive integer input variable not less than m
c         which specifies the leading dimension of the array a.
c
c       pivot is a logical input variable. if pivot is set true,
c         then column pivoting is enforced. if pivot is set false,
c         then no column pivoting is done.
c
c       ipvt is an integer output array of length lipvt. ipvt
c         defines the permutation matrix p such that a*p = q*r.
c         column j of p is column ipvt(j) of the identity matrix.
c         if pivot is false, ipvt is not referenced.
c
c       lipvt is a positive integer input variable. if pivot is false,
c         then lipvt may be as small as 1. if pivot is true, then
c         lipvt must be at least n.
c
c       rdiag is an output array of length n which contains the
c         diagonal elements of r.
c
c       acnorm is an output array of length n which contains the
c         norms of the corresponding columns of the input matrix a.
c         if this information is not needed, then acnorm can coincide
c         with rdiag.
c
c       wa is a work array of length n. if pivot is false, then wa
c         can coincide with rdiag.
c
c     subprograms called
c
c       minpack-supplied ... dpmparmp,enormmp
c
c       fortran-supplied ... dmax1,dsqrt,min0
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j,jp1,k,kmax,minmn
      double precision ajnorm,epsmch,one,p05,sum,temp,zero
      double precision dpmparmp,enormmp
      data one,p05,zero /1.0d0,5.0d-2,0.0d0/
c
c     epsmch is the machine precision.
c
      epsmch = dpmparmp(1)
c
c     compute the initial column norms and initialize several arrays.
c
      do 10 j = 1, n
         acnorm(j) = enormmp(m,a(1,j))
         rdiag(j) = acnorm(j)
         wa(j) = rdiag(j)
         if (pivot) ipvt(j) = j
   10    continue
c
c     reduce a to r with householder transformations.
c
      minmn = min0(m,n)
      do 110 j = 1, minmn
         if (.not.pivot) go to 40
c
c        bring the column of largest norm into the pivot position.
c
         kmax = j
         do 20 k = j, n
            if (rdiag(k) .gt. rdiag(kmax)) kmax = k
   20       continue
         if (kmax .eq. j) go to 40
         do 30 i = 1, m
            temp = a(i,j)
            a(i,j) = a(i,kmax)
            a(i,kmax) = temp
   30       continue
         rdiag(kmax) = rdiag(j)
         wa(kmax) = wa(j)
         k = ipvt(j)
         ipvt(j) = ipvt(kmax)
         ipvt(kmax) = k
   40    continue
c
c        compute the householder transformation to reduce the
c        j-th column of a to a multiple of the j-th unit vector.
c
         ajnorm = enormmp(m-j+1,a(j,j))
         if (ajnorm .eq. zero) go to 100
         if (a(j,j) .lt. zero) ajnorm = -ajnorm
         do 50 i = j, m
            a(i,j) = a(i,j)/ajnorm
   50       continue
         a(j,j) = a(j,j) + one
c
c        apply the transformation to the remaining columns
c        and update the norms.
c
         jp1 = j + 1
         if (n .lt. jp1) go to 100
         do 90 k = jp1, n
            sum = zero
            do 60 i = j, m
               sum = sum + a(i,j)*a(i,k)
   60          continue
            temp = sum/a(j,j)
            do 70 i = j, m
               a(i,k) = a(i,k) - temp*a(i,j)
   70          continue
            if (.not.pivot .or. rdiag(k) .eq. zero) go to 80
            temp = a(j,k)/rdiag(k)
            rdiag(k) = rdiag(k)*dsqrt(dmax1(zero,one-temp**2))
            if (p05*(rdiag(k)/wa(k))**2 .gt. epsmch) go to 80
            rdiag(k) = enormmp(m-j,a(jp1,k))
            wa(k) = rdiag(k)
   80       continue
   90       continue
  100    continue
         rdiag(j) = -ajnorm
  110    continue
      return
c
c     last card of subroutine qrfacmp.
c
      end
      subroutine qrsolvmp(n,r,ldr,ipvt,diag,qtb,x,sdiag,wa)
      integer n,ldr
      integer ipvt(n)
      double precision r(ldr,n),diag(n),qtb(n),x(n),sdiag(n),wa(n)

c*********************************************************************72
c
cc QRSOLV solves a rectangular linear system A*x=b in the least squares sense.
c
c     given an m by n matrix a, an n by n diagonal matrix d,
c     and an m-vector b, the problem is to determine an x which
c     solves the system
c
c           a*x = b ,     d*x = 0 ,
c
c     in the least squares sense.
c
c     this subroutine completes the solution of the problem
c     if it is provided with the necessary information from the
c     qr factorization, with column pivoting, of a. that is, if
c     a*p = q*r, where p is a permutation matrix, q has orthogonal
c     columns, and r is an upper triangular matrix with diagonal
c     elements of nonincreasing magnitude, then qrsolvmp expects
c     the full upper triangle of r, the permutation matrix p,
c     and the first n components of (q transpose)*b. the system
c     a*x = b, d*x = 0, is then equivalent to
c
c                  t       t
c           r*z = q *b ,  p *d*p*z = 0 ,
c
c     where x = p*z. if this system does not have full rank,
c     then a least squares solution is obtained. on output qrsolvmp
c     also provides an upper triangular matrix s such that
c
c            t   t               t
c           p *(a *a + d*d)*p = s *s .
c
c     s is computed within qrsolvmp and may be of separate interest.
c
c     the subroutine statement is
c
c       subroutine qrsolvmp(n,r,ldr,ipvt,diag,qtb,x,sdiag,wa)
c
c     where
c
c       n is a positive integer input variable set to the order of r.
c
c       r is an n by n array. on input the full upper triangle
c         must contain the full upper triangle of the matrix r.
c         on output the full upper triangle is unaltered, and the
c         strict lower triangle contains the strict upper triangle
c         (transposed) of the upper triangular matrix s.
c
c       ldr is a positive integer input variable not less than n
c         which specifies the leading dimension of the array r.
c
c       ipvt is an integer input array of length n which defines the
c         permutation matrix p such that a*p = q*r. column j of p
c         is column ipvt(j) of the identity matrix.
c
c       diag is an input array of length n which must contain the
c         diagonal elements of the matrix d.
c
c       qtb is an input array of length n which must contain the first
c         n elements of the vector (q transpose)*b.
c
c       x is an output array of length n which contains the least
c         squares solution of the system a*x = b, d*x = 0.
c
c       sdiag is an output array of length n which contains the
c         diagonal elements of the upper triangular matrix s.
c
c       wa is a work array of length n.
c
c     subprograms called
c
c       fortran-supplied ... dabs,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j,jp1,k,kp1,l,nsing
      double precision cos,cotan,p5,p25,qtbpj,sin,sum,tan,temp,zero
      data p5,p25,zero /5.0d-1,2.5d-1,0.0d0/
c
c     copy r and (q transpose)*b to preserve input and initialize s.
c     in particular, save the diagonal elements of r in x.
c
      do 20 j = 1, n
         do 10 i = j, n
            r(i,j) = r(j,i)
   10       continue
         x(j) = r(j,j)
         wa(j) = qtb(j)
   20    continue
c
c     eliminate the diagonal matrix d using a givens rotation.
c
      do 100 j = 1, n
c
c        prepare the row of d to be eliminated, locating the
c        diagonal element using p from the qr factorization.
c
         l = ipvt(j)
         if (diag(l) .eq. zero) go to 90
         do 30 k = j, n
            sdiag(k) = zero
   30       continue
         sdiag(j) = diag(l)
c
c        the transformations to eliminate the row of d
c        modify only a single element of (q transpose)*b
c        beyond the first n, which is initially zero.
c
         qtbpj = zero
         do 80 k = j, n
c
c           determine a givens rotation which eliminates the
c           appropriate element in the current row of d.
c
            if (sdiag(k) .eq. zero) go to 70
            if (dabs(r(k,k)) .ge. dabs(sdiag(k))) go to 40
               cotan = r(k,k)/sdiag(k)
               sin = p5/dsqrt(p25+p25*cotan**2)
               cos = sin*cotan
               go to 50
   40       continue
               tan = sdiag(k)/r(k,k)
               cos = p5/dsqrt(p25+p25*tan**2)
               sin = cos*tan
   50       continue
c
c           compute the modified diagonal element of r and
c           the modified element of ((q transpose)*b,0).
c
            r(k,k) = cos*r(k,k) + sin*sdiag(k)
            temp = cos*wa(k) + sin*qtbpj
            qtbpj = -sin*wa(k) + cos*qtbpj
            wa(k) = temp
c
c           accumulate the tranformation in the row of s.
c
            kp1 = k + 1
            if (n .lt. kp1) go to 70
            do 60 i = kp1, n
               temp = cos*r(i,k) + sin*sdiag(i)
               sdiag(i) = -sin*r(i,k) + cos*sdiag(i)
               r(i,k) = temp
   60          continue
   70       continue
   80       continue
   90    continue
c
c        store the diagonal element of s and restore
c        the corresponding diagonal element of r.
c
         sdiag(j) = r(j,j)
         r(j,j) = x(j)
  100    continue
c
c     solve the triangular system for z. if the system is
c     singular, then obtain a least squares solution.
c
      nsing = n
      do 110 j = 1, n
         if (sdiag(j) .eq. zero .and. nsing .eq. n) nsing = j - 1
         if (nsing .lt. n) wa(j) = zero
  110    continue
      if (nsing .lt. 1) go to 150
      do 140 k = 1, nsing
         j = nsing - k + 1
         sum = zero
         jp1 = j + 1
         if (nsing .lt. jp1) go to 130
         do 120 i = jp1, nsing
            sum = sum + r(i,j)*wa(i)
  120       continue
  130    continue
         wa(j) = (wa(j) - sum)/sdiag(j)
  140    continue
  150 continue
c
c     permute the components of z back to components of x.
c
      do 160 j = 1, n
         l = ipvt(j)
         x(l) = wa(j)
  160    continue
      return
c
c     last card of subroutine qrsolvmp.
c
      end
      subroutine r1mpyq(m,n,a,lda,v,w)
      integer m,n,lda
      double precision a(lda,n),v(n),w(n)

c*********************************************************************72
c
cc R1MPYQ computes A*Q, where Q is the product of Householder transformations.
c
c     given an m by n matrix a, this subroutine computes a*q where
c     q is the product of 2*(n - 1) transformations
c
c           gv(n-1)*...*gv(1)*gw(1)*...*gw(n-1)
c
c     and gv(i), gw(i) are givens rotations in the (i,n) plane which
c     eliminate elements in the i-th and n-th planes, respectively.
c     q itself is not given, rather the information to recover the
c     gv, gw rotations is supplied.
c
c     the subroutine statement is
c
c       subroutine r1mpyq(m,n,a,lda,v,w)
c
c     where
c
c       m is a positive integer input variable set to the number
c         of rows of a.
c
c       n is a positive integer input variable set to the number
c         of columns of a.
c
c       a is an m by n array. on input a must contain the matrix
c         to be postmultiplied by the orthogonal matrix q
c         described above. on output a*q has replaced a.
c
c       lda is a positive integer input variable not less than m
c         which specifies the leading dimension of the array a.
c
c       v is an input array of length n. v(i) must contain the
c         information necessary to recover the givens rotation gv(i)
c         described above.
c
c       w is an input array of length n. w(i) must contain the
c         information necessary to recover the givens rotation gw(i)
c         described above.
c
c     subroutines called
c
c       fortran-supplied ... dabs,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more
c
c     **********
      integer i,j,nmj,nm1
      double precision cos,one,sin,temp
      data one /1.0d0/
c
c     apply the first set of givens rotations to a.
c
      nm1 = n - 1
      if (nm1 .lt. 1) go to 50
      do 20 nmj = 1, nm1
         j = n - nmj
         if (dabs(v(j)) .gt. one) cos = one/v(j)
         if (dabs(v(j)) .gt. one) sin = dsqrt(one-cos**2)
         if (dabs(v(j)) .le. one) sin = v(j)
         if (dabs(v(j)) .le. one) cos = dsqrt(one-sin**2)
         do 10 i = 1, m
            temp = cos*a(i,j) - sin*a(i,n)
            a(i,n) = sin*a(i,j) + cos*a(i,n)
            a(i,j) = temp
   10       continue
   20    continue
c
c     apply the second set of givens rotations to a.
c
      do 40 j = 1, nm1
         if (dabs(w(j)) .gt. one) cos = one/w(j)
         if (dabs(w(j)) .gt. one) sin = dsqrt(one-cos**2)
         if (dabs(w(j)) .le. one) sin = w(j)
         if (dabs(w(j)) .le. one) cos = dsqrt(one-sin**2)
         do 30 i = 1, m
            temp = cos*a(i,j) + sin*a(i,n)
            a(i,n) = -sin*a(i,j) + cos*a(i,n)
            a(i,j) = temp
   30       continue
   40    continue
   50 continue
      return
c
c     last card of subroutine r1mpyq.
c
      end
      subroutine r1updt(m,n,s,ls,u,v,w,sing)
      integer m,n,ls
      logical sing
      double precision s(ls),u(m),v(n),w(m)

c*********************************************************************72
c
cc R1UPDT re-triangularizes a matrix after a rank one update.
c
c     given an m by n lower trapezoidal matrix s, an m-vector u,
c     and an n-vector v, the problem is to determine an
c     orthogonal matrix q such that
c
c                   t
c           (s + u*v )*q
c
c     is again lower trapezoidal.
c
c     this subroutine determines q as the product of 2*(n - 1)
c     transformations
c
c           gv(n-1)*...*gv(1)*gw(1)*...*gw(n-1)
c
c     where gv(i), gw(i) are givens rotations in the (i,n) plane
c     which eliminate elements in the i-th and n-th planes,
c     respectively. q itself is not accumulated, rather the
c     information to recover the gv, gw rotations is returned.
c
c     the subroutine statement is
c
c       subroutine r1updt(m,n,s,ls,u,v,w,sing)
c
c     where
c
c       m is a positive integer input variable set to the number
c         of rows of s.
c
c       n is a positive integer input variable set to the number
c         of columns of s. n must not exceed m.
c
c       s is an array of length ls. on input s must contain the lower
c         trapezoidal matrix s stored by columns. on output s contains
c         the lower trapezoidal matrix produced as described above.
c
c       ls is a positive integer input variable not less than
c         (n*(2*m-n+1))/2.
c
c       u is an input array of length m which must contain the
c         vector u.
c
c       v is an array of length n. on input v must contain the vector
c         v. on output v(i) contains the information necessary to
c         recover the givens rotation gv(i) described above.
c
c       w is an output array of length m. w(i) contains information
c         necessary to recover the givens rotation gw(i) described
c         above.
c
c       sing is a logical output variable. sing is set true if any
c         of the diagonal elements of the output s are zero. otherwise
c         sing is set false.
c
c     subprograms called
c
c       minpack-supplied ... dpmparmp
c
c       fortran-supplied ... dabs,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, kenneth e. hillstrom, jorge j. more,
c     john l. nazareth
c
c     **********
      integer i,j,jj,l,nmj,nm1
      double precision cos,cotan,giant,one,p5,p25,sin,tan,tau,temp,
     *                 zero
      double precision dpmparmp
      data one,p5,p25,zero /1.0d0,5.0d-1,2.5d-1,0.0d0/
c
c     giant is the largest magnitude.
c
      giant = dpmparmp(3)
c
c     initialize the diagonal element pointer.
c
      jj = (n*(2*m - n + 1))/2 - (m - n)
c
c     move the nontrivial part of the last column of s into w.
c
      l = jj
      do 10 i = n, m
         w(i) = s(l)
         l = l + 1
   10    continue
c
c     rotate the vector v into a multiple of the n-th unit vector
c     in such a way that a spike is introduced into w.
c
      nm1 = n - 1
      if (nm1 .lt. 1) go to 70
      do 60 nmj = 1, nm1
         j = n - nmj
         jj = jj - (m - j + 1)
         w(j) = zero
         if (v(j) .eq. zero) go to 50
c
c        determine a givens rotation which eliminates the
c        j-th element of v.
c
         if (dabs(v(n)) .ge. dabs(v(j))) go to 20
            cotan = v(n)/v(j)
            sin = p5/dsqrt(p25+p25*cotan**2)
            cos = sin*cotan
            tau = one
            if (dabs(cos)*giant .gt. one) tau = one/cos
            go to 30
   20    continue
            tan = v(j)/v(n)
            cos = p5/dsqrt(p25+p25*tan**2)
            sin = cos*tan
            tau = sin
   30    continue
c
c        apply the transformation to v and store the information
c        necessary to recover the givens rotation.
c
         v(n) = sin*v(j) + cos*v(n)
         v(j) = tau
c
c        apply the transformation to s and extend the spike in w.
c
         l = jj
         do 40 i = j, m
            temp = cos*s(l) - sin*w(i)
            w(i) = sin*s(l) + cos*w(i)
            s(l) = temp
            l = l + 1
   40       continue
   50    continue
   60    continue
   70 continue
c
c     add the spike from the rank 1 update to w.
c
      do 80 i = 1, m
         w(i) = w(i) + v(n)*u(i)
   80    continue
c
c     eliminate the spike.
c
      sing = .false.
      if (nm1 .lt. 1) go to 140
      do 130 j = 1, nm1
         if (w(j) .eq. zero) go to 120
c
c        determine a givens rotation which eliminates the
c        j-th element of the spike.
c
         if (dabs(s(jj)) .ge. dabs(w(j))) go to 90
            cotan = s(jj)/w(j)
            sin = p5/dsqrt(p25+p25*cotan**2)
            cos = sin*cotan
            tau = one
            if (dabs(cos)*giant .gt. one) tau = one/cos
            go to 100
   90    continue
            tan = w(j)/s(jj)
            cos = p5/dsqrt(p25+p25*tan**2)
            sin = cos*tan
            tau = sin
  100    continue
c
c        apply the transformation to s and reduce the spike in w.
c
         l = jj
         do 110 i = j, m
            temp = cos*s(l) + sin*w(i)
            w(i) = -sin*s(l) + cos*w(i)
            s(l) = temp
            l = l + 1
  110       continue
c
c        store the information necessary to recover the
c        givens rotation.
c
         w(j) = tau
  120    continue
c
c        test for zero diagonal elements in the output s.
c
         if (s(jj) .eq. zero) sing = .true.
         jj = jj + (m - j + 1)
  130    continue
  140 continue
c
c     move w back into the last column of the output s.
c
      l = jj
      do 150 i = n, m
         s(l) = w(i)
         l = l + 1
  150    continue
      if (s(jj) .eq. zero) sing = .true.
      return
c
c     last card of subroutine r1updt.
c
      end
      subroutine r8mat_print ( m, n, a, title )

c*********************************************************************72
c
cc R8MAT_PRINT prints an R8MAT.
c
c  Discussion:
c
c    An R8MAT is an array of R8's.
c
c  Licensing:
c
c    This code is distributed under the GNU LGPL license.
c
c  Modified:
c
c    20 May 2004
c
c  Author:
c
c    John Burkardt
c
c  Parameters:
c
c    Input, integer M, the number of rows in A.
c
c    Input, integer N, the number of columns in A.
c
c    Input, double precision A(M,N), the matrix.
c
c    Input, character ( len = * ) TITLE, a title to be printed.
c
      implicit none

      integer m
      integer n

      double precision a(m,n)
      character ( len = * ) title

      call r8mat_print_some ( m, n, a, 1, 1, m, n, title )

      return
      end
      subroutine r8mat_print_some ( m, n, a, ilo, jlo, ihi, jhi,
     &  title )

c*********************************************************************72
c
cc R8MAT_PRINT_SOME prints some of an R8MAT.
c
c  Discussion:
c
c    An R8MAT is an array of R8's.
c
c  Licensing:
c
c    This code is distributed under the GNU LGPL license.
c
c  Modified:
c
c    25 January 2007
c
c  Author:
c
c    John Burkardt
c
c  Parameters:
c
c    Input, integer M, N, the number of rows and columns.
c
c    Input, double precision A(M,N), an M by N matrix to be printed.
c
c    Input, integer ILO, JLO, the first row and column to print.
c
c    Input, integer IHI, JHI, the last row and column to print.
c
c    Input, character ( len = * ) TITLE, an optional title.
c
      implicit none

      integer incx
      parameter ( incx = 5 )
      integer m
      integer n

      double precision a(m,n)
      character * ( 14 ) ctemp(incx)
      integer i
      integer i2hi
      integer i2lo
      integer ihi
      integer ilo
      integer inc
      integer j
      integer j2
      integer j2hi
      integer j2lo
      integer jhi
      integer jlo
      character * ( * ) title
      integer title_length

      write ( *, '(a)' ) ' '
      write ( *, '(a)' ) trim ( title )

      do j2lo = max ( jlo, 1 ), min ( jhi, n ), incx

        j2hi = j2lo + incx - 1
        j2hi = min ( j2hi, n )
        j2hi = min ( j2hi, jhi )

        inc = j2hi + 1 - j2lo

        write ( *, '(a)' ) ' '

        do j = j2lo, j2hi
          j2 = j + 1 - j2lo
          write ( ctemp(j2), '(i7,7x)') j
        end do

        write ( *, '(''  Col   '',5a14)' ) ( ctemp(j), j = 1, inc )
        write ( *, '(a)' ) '  Row'
        write ( *, '(a)' ) ' '

        i2lo = max ( ilo, 1 )
        i2hi = min ( ihi, m )

        do i = i2lo, i2hi

          do j2 = 1, inc

            j = j2lo - 1 + j2

            write ( ctemp(j2), '(g14.6)' ) a(i,j)

          end do

          write ( *, '(i5,1x,5a14)' ) i, ( ctemp(j), j = 1, inc )

        end do

      end do

      return
      end
      subroutine r8vec_print ( n, a, title )

c*********************************************************************72
c
cc R8VEC_PRINT prints an R8VEC.
c
c  Discussion:
c
c    An R8VEC is a vector of R8's.
c
c  Licensing:
c
c    This code is distributed under the GNU LGPL license.
c
c  Modified:
c
c    12 January 2007
c
c  Author:
c
c    John Burkardt
c
c  Parameters:
c
c    Input, integer N, the number of components of the vector.
c
c    Input, double precision A(N), the vector to be printed.
c
c    Input, character * ( * ) TITLE, a title.
c
      implicit none

      integer n

      double precision a(n)
      integer i
      character ( len = * ) title

      write ( *, '(a)' ) ' '
      write ( *, '(a)' ) trim ( title )
      write ( *, '(a)' ) ' '
      do i = 1, n
        write ( *, '(2x,i8,2x,g16.8)' ) i, a(i)
      end do

      return
      end
      subroutine rwupdt(n,r,ldr,w,b,alpha,cos,sin)
      integer n,ldr
      double precision alpha
      double precision r(ldr,n),w(n),b(n),cos(n),sin(n)

c*********************************************************************72
c
cc RWUPDT computes the decomposition of a triangular matrix augmented by one row.
c
c     given an n by n upper triangular matrix r, this subroutine
c     computes the qr decomposition of the matrix formed when a row
c     is added to r. if the row is specified by the vector w, then
c     rwupdt determines an orthogonal matrix q such that when the
c     n+1 by n matrix composed of r augmented by w is premultiplied
c     by (q transpose), the resulting matrix is upper trapezoidal.
c     the matrix (q transpose) is the product of n transformations
c
c           g(n)*g(n-1)* ... *g(1)
c
c     where g(i) is a givens rotation in the (i,n+1) plane which
c     eliminates elements in the (n+1)-st plane. rwupdt also
c     computes the product (q transpose)*c where c is the
c     (n+1)-vector (b,alpha). q itself is not accumulated, rather
c     the information to recover the g rotations is supplied.
c
c     the subroutine statement is
c
c       subroutine rwupdt(n,r,ldr,w,b,alpha,cos,sin)
c
c     where
c
c       n is a positive integer input variable set to the order of r.
c
c       r is an n by n array. on input the upper triangular part of
c         r must contain the matrix to be updated. on output r
c         contains the updated triangular matrix.
c
c       ldr is a positive integer input variable not less than n
c         which specifies the leading dimension of the array r.
c
c       w is an input array of length n which must contain the row
c         vector to be added to r.
c
c       b is an array of length n. on input b must contain the
c         first n elements of the vector c. on output b contains
c         the first n elements of the vector (q transpose)*c.
c
c       alpha is a variable. on input alpha must contain the
c         (n+1)-st element of the vector c. on output alpha contains
c         the (n+1)-st element of the vector (q transpose)*c.
c
c       cos is an output array of length n which contains the
c         cosines of the transforming givens rotations.
c
c       sin is an output array of length n which contains the
c         sines of the transforming givens rotations.
c
c     subprograms called
c
c       fortran-supplied ... dabs,dsqrt
c
c     argonne national laboratory. minpack project. march 1980.
c     burton s. garbow, dudley v. goetschel, kenneth e. hillstrom,
c     jorge j. more
c
c     **********
      integer i,j,jm1
      double precision cotan,one,p5,p25,rowj,tan,temp,zero
      data one,p5,p25,zero /1.0d0,5.0d-1,2.5d-1,0.0d0/
c
      do 60 j = 1, n
         rowj = w(j)
         jm1 = j - 1
c
c        apply the previous transformations to
c        r(i,j), i=1,2,...,j-1, and to w(j).
c
         if (jm1 .lt. 1) go to 20
         do 10 i = 1, jm1
            temp = cos(i)*r(i,j) + sin(i)*rowj
            rowj = -sin(i)*r(i,j) + cos(i)*rowj
            r(i,j) = temp
   10       continue
   20    continue
c
c        determine a givens rotation which eliminates w(j).
c
         cos(j) = one
         sin(j) = zero
         if (rowj .eq. zero) go to 50
         if (dabs(r(j,j)) .ge. dabs(rowj)) go to 30
            cotan = r(j,j)/rowj
            sin(j) = p5/dsqrt(p25+p25*cotan**2)
            cos(j) = sin(j)*cotan
            go to 40
   30    continue
            tan = rowj/r(j,j)
            cos(j) = p5/dsqrt(p25+p25*tan**2)
            sin(j) = cos(j)*tan
   40    continue
c
c        apply the current transformation to r(j,j), b(j), and alpha.
c
         r(j,j) = cos(j)*r(j,j) + sin(j)*rowj
         temp = cos(j)*b(j) + sin(j)*alpha
         alpha = -sin(j)*b(j) + cos(j)*alpha
         b(j) = temp
   50    continue
   60    continue
      return
c
c     last card of subroutine rwupdt.
c
      end
      subroutine timestamp ( )

c*********************************************************************72
c
cc TIMESTAMP prints out the current YMDHMS date as a timestamp.
c
c  Licensing:
c
c    This code is distributed under the GNU LGPL license.
c
c  Modified:
c
c    12 January 2007
c
c  Author:
c
c    John Burkardt
c
c  Parameters:
c
c    None
c
      implicit none

      character * ( 8 ) ampm
      integer d
      character * ( 8 ) date
      integer h
      integer m
      integer mm
      character * ( 9 ) month(12)
      integer n
      integer s
      character * ( 10 ) time
      integer y

      save month

      data month /
     &  'January  ', 'February ', 'March    ', 'April    ', 
     &  'May      ', 'June     ', 'July     ', 'August   ', 
     &  'September', 'October  ', 'November ', 'December ' /

      call date_and_time ( date, time )

      read ( date, '(i4,i2,i2)' ) y, m, d
      read ( time, '(i2,i2,i2,1x,i3)' ) h, n, s, mm

      if ( h .lt. 12 ) then
        ampm = 'AM'
      else if ( h .eq. 12 ) then
        if ( n .eq. 0 .and. s .eq. 0 ) then
          ampm = 'Noon'
        else
          ampm = 'PM'
        end if
      else
        h = h - 12
        if ( h .lt. 12 ) then
          ampm = 'PM'
        else if ( h .eq. 12 ) then
          if ( n .eq. 0 .and. s .eq. 0 ) then
            ampm = 'Midnight'
          else
            ampm = 'AM'
          end if
        end if
      end if

      write ( *, 
     &  '(i2,1x,a,1x,i4,2x,i2,a1,i2.2,a1,i2.2,a1,i3.3,1x,a)' ) 
     &  d, month(m), y, h, ':', n, ':', s, '.', mm, ampm

      return
      end