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) real ftol,xtol,gtol,epsfcn,factor real x(n),fvec(m),diag(n),fjac(ldfjac,n),qtf(n),wa1(n),wa2(n), * wa3(n),wa4(m) external fcn c ********** c c subroutine lmdif 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 real 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 ... spmpar,enorm,fdjac2,lmpar,qrfac c c fortran-supplied ... abs,amax1,amin1,sqrt,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 real actred,delta,dirder,epsmch,fnorm,fnorm1,gnorm,one,par, * pnorm,prered,p1,p5,p25,p75,p0001,ratio,sum,temp,temp1, * temp2,xnorm,zero real spmpar,enorm data one,p1,p5,p25,p75,p0001,zero * /1.0e0,1.0e-1,5.0e-1,2.5e-1,7.5e-1,1.0e-4,0.0e0/ c c epsmch is the machine precision. c epsmch = spmpar(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 = enorm(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 qrfac(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 = enorm(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 = amax1(gnorm,abs(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) = amax1(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 lmpar(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 = enorm(n,wa3) c c on the first iteration, adjust the initial step bound. c if (iter .eq. 1) delta = amin1(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 = enorm(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 = enorm(n,wa3)/fnorm temp2 = (sqrt(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*amin1(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 = enorm(n,wa2) fnorm = fnorm1 iter = iter + 1 290 continue c c tests for convergence. c if (abs(actred) .le. ftol .and. prered .le. ftol * .and. p5*ratio .le. one) info = 1 if (delta .le. xtol*xnorm) info = 2 if (abs(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 (abs(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